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2402.05158

AKM Shahariar Azad Rabby

AKM Shahariar Azad Rabby, Hasmot Ali, Md. Majedul Islam, Sheikh Abujar, Fuad Rahman

Enhancement of Bengali OCR by Specialized Models and Advanced Techniques for Diverse Document Types

8 pages, 7 figures, 4 table Link of the paper https://openaccess.thecvf.com/content/WACV2024W/WVLL/html/Rabby_Enhancement_of_Bengali_OCR_by_Specialized_Models_and_Advanced_Techniques_WACVW_2024_paper.html

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 1102-1109

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

This research paper presents a unique Bengali OCR system with some capabilities. The system excels in reconstructing document layouts while preserving structure, alignment, and images. It incorporates advanced image and signature detection for accurate extraction. Specialized models for word segmentation cater to diverse document types, including computer-composed, letterpress, typewriter, and handwritten documents. The system handles static and dynamic handwritten inputs, recognizing various writing styles. Furthermore, it has the ability to recognize compound characters in Bengali. Extensive data collection efforts provide a diverse corpus, while advanced technical components optimize character and word recognition. Additional contributions include image, logo, signature and table recognition, perspective correction, layout reconstruction, and a queuing module for efficient and scalable processing. The system demonstrates outstanding performance in efficient and accurate text extraction and analysis.

2024-02-09

2402.05248

David Gonz\'alez Ortega

David Gonz\'alez-Ortega, Francisco Javier D\'iaz-Perna, Mario Mart\'inez-Zarzuela and M\'iriam Ant\'on-Rodr\'iguez

Comparative Analysis of Kinect-Based and Oculus-Based Gaze Region Estimation Methods in a Driving Simulator

25 pages

Sensors 2021, 21, 26

10.3390/s21010026

cs.CV

http://creativecommons.org/licenses/by/4.0/

Driver's gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers' gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in a driving simulator. One uses the 3D Kinect device and another uses the virtual reality Oculus Rift device. The modules are able to detect the region, out of seven in which the driving scene was divided, where a driver is gazing at in every route processed frame. Four methods were implemented and compared for gaze estimation, which learn the relation between gaze displacement and head movement. Two are simpler and based on points that try to capture this relation and two are based on classifiers such as MLP and SVM. Experiments were carried out with 12 users that drove on the same scenario twice, each one with a different visualization display, first with a big screen and later with Oculus Rift. On the whole, Oculus Rift outperformed Kinect as the best hardware for gaze estimation. The Oculus-based gaze region estimation method with the highest performance achieved an accuracy of 97.94%. The information provided by the Oculus Rift module enriches the driving simulator data and makes it possible a multimodal driving performance analysis apart from the immersion and realism obtained with the virtual reality experience provided by Oculus.

2024-02-09

2402.05519

Mike Thelwall Prof

Mike Thelwall

Can ChatGPT evaluate research quality?

Journal of Data and Information Science, 9(2), 1-21

10.2478/jdis-2024-0013

cs.DL cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Purpose: Assess whether ChatGPT 4.0 is accurate enough to perform research evaluations on journal articles to automate this time-consuming task. Design/methodology/approach: Test the extent to which ChatGPT-4 can assess the quality of journal articles using a case study of the published scoring guidelines of the UK Research Excellence Framework (REF) 2021 to create a research evaluation ChatGPT. This was applied to 51 of my own articles and compared against my own quality judgements. Findings: ChatGPT-4 can produce plausible document summaries and quality evaluation rationales that match the REF criteria. Its overall scores have weak correlations with my self-evaluation scores of the same documents (averaging r=0.281 over 15 iterations, with 8 being statistically significantly different from 0). In contrast, the average scores from the 15 iterations produced a statistically significant positive correlation of 0.509. Thus, averaging scores from multiple ChatGPT-4 rounds seems more effective than individual scores. The positive correlation may be due to ChatGPT being able to extract the author's significance, rigour, and originality claims from inside each paper. If my weakest articles are removed, then the correlation with average scores (r=0.200) falls below statistical significance, suggesting that ChatGPT struggles to make fine-grained evaluations. Research limitations: The data is self-evaluations of a convenience sample of articles from one academic in one field. Practical implications: Overall, ChatGPT does not yet seem to be accurate enough to be trusted for any formal or informal research quality evaluation tasks. Research evaluators, including journal editors, should therefore take steps to control its use. Originality/value: This is the first published attempt at post-publication expert review accuracy testing for ChatGPT.

2024-05-01

2402.05536

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Mar\'ia Teresa Garc\'ia-Ord\'as, Mayra Russo, Ahmad Sakor, Luis Daniel Fernandes Rotger and Maria-Esther Vidal

Empowering machine learning models with contextual knowledge for enhancing the detection of eating disorders in social media posts

Semantic Web, Volume 4, Issue 5, pp. 873-892, 2023

10.3233/SW-223269

cs.LG cs.CL

http://creativecommons.org/licenses/by-nc-nd/4.0/

Social networks are vital for information sharing, especially in the health sector for discussing diseases and treatments. These platforms, however, often feature posts as brief texts, posing challenges for Artificial Intelligence (AI) in understanding context. We introduce a novel hybrid approach combining community-maintained knowledge graphs (like Wikidata) with deep learning to enhance the categorization of social media posts. This method uses advanced entity recognizers and linkers (like Falcon 2.0) to connect short post entities to knowledge graphs. Knowledge graph embeddings (KGEs) and contextualized word embeddings (like BERT) are then employed to create rich, context-based representations of these posts. Our focus is on the health domain, particularly in identifying posts related to eating disorders (e.g., anorexia, bulimia) to aid healthcare providers in early diagnosis. We tested our approach on a dataset of 2,000 tweets about eating disorders, finding that merging word embeddings with knowledge graph information enhances the predictive models' reliability. This methodology aims to assist health experts in spotting patterns indicative of mental disorders, thereby improving early detection and accurate diagnosis for personalized medicine.

2024-02-09

2402.05554

Jiajun Zeng

Jiayu Peng, Jiajun Zeng, Manlin Lai, Ruobing Huang, Dong Ni, Zhenzhou Li

One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning

Accepted by Ultrasound in Medicine & Biology

Ultrasound in Medicine & Biology, Volume 50, Issue 2, February 2024, Pages 304-314

10.1016/j.ultrasmedbio.2023.10.009

eess.IV cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Objective: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and diagnosing CTS depends heavily on the expertise of examiners. To alleviate this problem, we aimed to develop a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluate its effectiveness as a computer-aided diagnostic tool. Methods: We combined real-time MN delineation, accurate biometric measurements, and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. Results: The proposed model showed better segmentation and measurement performance than competing methods, reporting that HD95 score of 7.21px, ASSD score of 2.64px, Dice score of 85.78%, and IoU score of 76.00%, respectively. In the reader study, it demonstrated comparable performance with the average performance of the experienced in classifying the CTS, while outperformed that of the inexperienced radiologists in terms of classification metrics (e.g., accuracy score of 3.59% higher and F1 score of 5.85% higher). Conclusion: The OSA-CTSD demonstrated promising diagnostic performance with the advantages of real-time, automation, and clinical interpretability. The application of such a tool can not only reduce reliance on the expertise of examiners, but also can help to promote the future standardization of the CTS diagnosis process, benefiting both patients and radiologists.

2024-02-09

2402.05571

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Jos\'e-Manuel Alija-P\'erez, Maria-Esther Vidal, Rafael Pastor-Vargas and Mar\'ia Teresa Garc\'ia-Ord\'as

Traditional Machine Learning Models and Bidirectional Encoder Representations From Transformer (BERT)-Based Automatic Classification of Tweets About Eating Disorders: Algorithm Development and Validation Study

JMIR Medical Informatics, Volume 10, Issue 2, 2022, ID e34492

10.2196/34492

cs.CL cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

Background: Eating disorders are increasingly prevalent, and social networks offer valuable information. Objective: Our goal was to identify efficient machine learning models for categorizing tweets related to eating disorders. Methods: Over three months, we collected tweets about eating disorders. A 2,000-tweet subset was labeled for: (1) being written by individuals with eating disorders, (2) promoting eating disorders, (3) informativeness, and (4) scientific content. Both traditional machine learning and deep learning models were employed for classification, assessing accuracy, F1 score, and computational time. Results: From 1,058,957 collected tweets, transformer-based bidirectional encoder representations achieved the highest F1 scores (71.1%-86.4%) across all four categories. Conclusions: Transformer-based models outperform traditional techniques in classifying eating disorder-related tweets, though they require more computational resources.

2024-02-09

2402.05593

Thomas P\"ollabauer

Thomas P\"ollabauer, Julius K\"uhn

A Concept for Reconstructing Stucco Statues from historic Sketches using synthetic Data only

Eurographics Workshop on Graphics and Cultural Heritage 2022

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In medieval times, stuccoworkers used a red color, called sinopia, to first create a sketch of the to-be-made statue on the wall. Today, many of these statues are destroyed, but using the original drawings, deriving from the red color also called sinopia, we can reconstruct how the final statue might have looked.We propose a fully-automated approach to reconstruct a point cloud and show preliminary results by generating a color-image, a depth-map, as well as surface normals requiring only a single sketch, and without requiring a collection of other, similar samples. Our proposed solution allows real-time reconstruction on-site, for instance, within an exhibition, or to generate a useful starting point for an expert, trying to manually reconstruct the statue, all while using only synthetic data for training.

2024-02-09

2402.05782

Qizhen Zhang

Kitty Fung, Qizhen Zhang, Chris Lu, Jia Wan, Timon Willi, Jakob Foerster

Analysing the Sample Complexity of Opponent Shaping

AAMAS 2024

cs.LG cs.AI cs.GT cs.MA

http://creativecommons.org/licenses/by/4.0/

Learning in general-sum games often yields collectively sub-optimal results. Addressing this, opponent shaping (OS) methods actively guide the learning processes of other agents, empirically leading to improved individual and group performances in many settings. Early OS methods use higher-order derivatives to shape the learning of co-players, making them unsuitable for shaping multiple learning steps. Follow-up work, Model-free Opponent Shaping (M-FOS), addresses these by reframing the OS problem as a meta-game. In contrast to early OS methods, there is little theoretical understanding of the M-FOS framework. Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging. In this work, we present R-FOS, a tabular version of M-FOS that is more suitable for theoretical analysis. R-FOS discretises the continuous meta-game MDP into a tabular MDP. Within this discretised MDP, we adapt the $R_{max}$ algorithm, most prominently used to derive PAC-bounds for MDPs, as the meta-learner in the R-FOS algorithm. We derive a sample complexity bound that is exponential in the cardinality of the inner state and action space and the number of agents. Our bound guarantees that, with high probability, the final policy learned by an R-FOS agent is close to the optimal policy, apart from a constant factor. Finally, we investigate how R-FOS's sample complexity scales in the size of state-action space. Our theoretical results on scaling are supported empirically in the Matching Pennies environment.

2024-02-09

2402.05958

David Gonz\'alez Ortega

Mario Mart\'inez-Zarzuela, David Gonz\'alez-Ortega, M\'iriam Ant\'on-Rodr\'iguez, Francisco Javier D\'iaz-Pernas, Henning M\"uller, Cristina Sim\'on-Mart\'inez

A comparative study on wearables and single-camera video for upper-limb out-of-thelab activity recognition with different deep learning architectures

Gait & Posture (2023) 106, p. 119-120

10.1016/j.gaitpost.2023.07.149

cs.CV cs.LG eess.SP

http://creativecommons.org/licenses/by/4.0/

The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial.

2024-02-12

2402.05975

David Gonz\'alez Ortega

Francisco Javier D\'iaz-Pernas, Mario Mart\'inez-Zarzuela, M\'iriam Ant\'on-Rodr\'iguez, and David Gonz\'alez-Ortega

A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

14 pages

Healthcare 2021, 9, 153

10.3390/healthcare9020153

eess.IV cs.AI cs.CV cs.LG

http://creativecommons.org/licenses/by/4.0/

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is inspired in the inherent operation of the Human Visual System. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance. The performance of our method on a publicly available MRI image dataset of 3064 slices from 233 patients is compared with previously classical machine learning and deep learning published methods. In the comparison, our method remarkably obtained a tumor classification accuracy of 0.973, higher than the other approaches using the same database.

2024-02-12

2402.06075

Scotty Black

Scotty Black, Christian Darken

Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making

NATO STO-MP-MSG-207 2023

10.14339/STO-MP-MSG-207-23-PDF

STO-MP-MSG-207-23

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In this unprecedented era of technology-driven transformation, it becomes more critical than ever that we aggressively invest in developing robust artificial intelligence (AI) for wargaming in support of decision-making. By advancing AI-enabled systems and pairing these with human judgment, we will be able to enhance all-domain awareness, improve the speed and quality of our decision cycles, offer recommendations for novel courses of action, and more rapidly counter our adversary's actions. It therefore becomes imperative that we accelerate the development of AI to help us better address the complexity of modern challenges and dilemmas that currently requires human intelligence and, if possible, attempt to surpass human intelligence--not to replace humans, but to augment and better inform human decision-making at machine speed. Although deep reinforcement learning continues to show promising results in intelligent agent behavior development for the long-horizon, complex tasks typically found in combat modeling and simulation, further research is needed to enable the scaling of AI to deal with these intricate and expansive state-spaces characteristic of wargaming for either concept development, education, or analysis. To help address this challenge, in our research, we are developing and implementing a hierarchical reinforcement learning framework that includes a multi-model approach and dimension-invariant observation abstractions.

2024-02-12

2402.06078

Ruben Martinez-Cantin

Pedro Os\'orio, Alexandre Bernardino, Ruben Martinez-Cantin, Jos\'e Santos-Victor

Gaussian Mixture Models for Affordance Learning using Bayesian Networks

IEEE/RSJ International Conference on Intelligent Robots and Systems 2010

Published on the Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems 2010

10.1109/IROS.2010.5650297

cs.RO cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Affordances are fundamental descriptors of relationships between actions, objects and effects. They provide the means whereby a robot can predict effects, recognize actions, select objects and plan its behavior according to desired goals. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences. Models exist for learning the structure and the parameters of a Bayesian Network encoding this knowledge. Although Bayesian Networks are capable of dealing with uncertainty and redundancy, previous work considered complete observability of the discrete sensory data, which may lead to hard errors in the presence of noise. In this paper we consider a probabilistic representation of the sensors by Gaussian Mixture Models (GMMs) and explicitly taking into account the probability distribution contained in each discrete affordance concept, which can lead to a more correct learning.

2024-02-12

2402.06107

Feng Xia

Yemeng Liu, Jing Ren, Jianshuo Xu, Xiaomei Bai, Roopdeep Kaur, Feng Xia

Multiple Instance Learning for Cheating Detection and Localization in Online Examinations

12 pages, 7 figures

IEEE Transactions on Cognitive and Developmental Systems 2024

10.1109/TCDS.2024.3349705

cs.CV cs.AI cs.CY cs.LG

http://creativecommons.org/licenses/by/4.0/

The spread of the Coronavirus disease-2019 epidemic has caused many courses and exams to be conducted online. The cheating behavior detection model in examination invigilation systems plays a pivotal role in guaranteeing the equality of long-distance examinations. However, cheating behavior is rare, and most researchers do not comprehensively take into account features such as head posture, gaze angle, body posture, and background information in the task of cheating behavior detection. In this paper, we develop and present CHEESE, a CHEating detection framework via multiplE inStancE learning. The framework consists of a label generator that implements weak supervision and a feature encoder to learn discriminative features. In addition, the framework combines body posture and background features extracted by 3D convolution with eye gaze, head posture and facial features captured by OpenFace 2.0. These features are fed into the spatio-temporal graph module by stitching to analyze the spatio-temporal changes in video clips to detect the cheating behaviors. Our experiments on three datasets, UCF-Crime, ShanghaiTech and Online Exam Proctoring (OEP), prove the effectiveness of our method as compared to the state-of-the-art approaches, and obtain the frame-level AUC score of 87.58% on the OEP dataset.

2024-02-12

2402.06563

Neslihan Suzen

Neslihan Suzen, Evgeny M. Mirkes, Damian Roland, Jeremy Levesley, Alexander N. Gorban, Tim J. Coats

What is Hiding in Medicine's Dark Matter? Learning with Missing Data in Medical Practices

8 pages

2023 IEEE International Conference on Big Data (BigData), 4979-4986

10.1109/BigData59044.2023.10386194

cs.LG cs.AI cs.CL cs.HC cs.IT math.IT

http://creativecommons.org/licenses/by/4.0/

Electronic patient records (EPRs) produce a wealth of data but contain significant missing information. Understanding and handling this missing data is an important part of clinical data analysis and if left unaddressed could result in bias in analysis and distortion in critical conclusions. Missing data may be linked to health care professional practice patterns and imputation of missing data can increase the validity of clinical decisions. This study focuses on statistical approaches for understanding and interpreting the missing data and machine learning based clinical data imputation using a single centre's paediatric emergency data and the data from UK's largest clinical audit for traumatic injury database (TARN). In the study of 56,961 data points related to initial vital signs and observations taken on children presenting to an Emergency Department, we have shown that missing data are likely to be non-random and how these are linked to health care professional practice patterns. We have then examined 79 TARN fields with missing values for 5,791 trauma cases. Singular Value Decomposition (SVD) and k-Nearest Neighbour (kNN) based missing data imputation methods are used and imputation results against the original dataset are compared and statistically tested. We have concluded that the 1NN imputer is the best imputation which indicates a usual pattern of clinical decision making: find the most similar patients and take their attributes as imputation.

2024-02-12

2402.06640

Ishir Rao

Ishir Rao

Modeling and Optimization of Epidemiological Control Policies Through Reinforcement Learning

22 pages, 8 figures

J. Emerging Investigators Article (2023) Vol. 6

10.59720/22-157

cs.AI q-bio.PE

http://creativecommons.org/licenses/by/4.0/

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. The impact of a pandemic can be minimized by enforcing certain restrictions on a community. However, while minimizing infection and death rates, these restrictions can also lead to economic crises. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. However, designing manual control strategies while considering disease spread and economic status is non-trivial. Optimal strategies can be designed through multi-objective reinforcement learning (MORL) models, which demonstrate how restrictions can be used to optimize the outcome of a pandemic. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model: a compartmental model for virtually simulating a pandemic day by day. We combined the SEIRD model with a deep double recurrent Q-network to train a reinforcement learning agent to enforce the optimal restriction on the SEIRD simulation based on a reward function. We tested two agents with unique reward functions and pandemic goals to obtain two strategies. The first agent placed long lockdowns to reduce the initial spread of the disease, followed by cyclical and shorter lockdowns to mitigate the resurgence of the disease. The second agent provided similar infection rates but an improved economy by implementing a 10-day lockdown and 20-day no-restriction cycle. This use of reinforcement learning and epidemiological modeling allowed for both economic and infection mitigation in multiple pandemic scenarios.

2024-02-13

2402.06694

Scotty Black

Scotty Black, Christian Darken

Scaling Intelligent Agents in Combat Simulations for Wargaming

arXiv admin note: text overlap with arXiv:2402.06075

I/ITSEC Conference Proceedings 2023

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Remaining competitive in future conflicts with technologically-advanced competitors requires us to accelerate our research and development in artificial intelligence (AI) for wargaming. More importantly, leveraging machine learning for intelligent combat behavior development will be key to one day achieving superhuman performance in this domain--elevating the quality and accelerating the speed of our decisions in future wars. Although deep reinforcement learning (RL) continues to show promising results in intelligent agent behavior development in games, it has yet to perform at or above the human level in the long-horizon, complex tasks typically found in combat modeling and simulation. Capitalizing on the proven potential of RL and recent successes of hierarchical reinforcement learning (HRL), our research is investigating and extending the use of HRL to create intelligent agents capable of performing effectively in these large and complex simulation environments. Our ultimate goal is to develop an agent capable of superhuman performance that could then serve as an AI advisor to military planners and decision-makers. This papers covers our ongoing approach and the first three of our five research areas aimed at managing the exponential growth of computations that have thus far limited the use of AI in combat simulations: (1) developing an HRL training framework and agent architecture for combat units; (2) developing a multi-model framework for agent decision-making; (3) developing dimension-invariant observation abstractions of the state space to manage the exponential growth of computations; (4) developing an intrinsic rewards engine to enable long-term planning; and (5) implementing this framework into a higher-fidelity combat simulation.

2024-02-13

2402.06733

Satvik Golechha

Pragya Srivastava, Satvik Golechha, Amit Deshpande, Amit Sharma

NICE: To Optimize In-Context Examples or Not?

Accepted as a full paper (9 pages) at ACL 2024 (Main)

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 2024 (Volume 1: Long Papers)

cs.CL cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

Recent work shows that in-context learning and optimization of in-context examples (ICE) can significantly improve the accuracy of large language models (LLMs) on a wide range of tasks, leading to an apparent consensus that ICE optimization is crucial for better performance. However, most of these studies assume a fixed or no instruction provided in the prompt. We challenge this consensus by investigating the necessity of optimizing ICE when task-specific instructions are provided and find that there are many tasks for which it yields diminishing returns. In particular, using a diverse set of tasks and a systematically created instruction set with gradually added details, we find that as the prompt instruction becomes more detailed, the returns on ICE optimization diminish. To characterize this behavior, we introduce a task-specific metric called Normalized Invariability to Choice of Examples (NICE) that quantifies the learnability of tasks from a given instruction, and provides a heuristic to help decide whether to optimize instructions or ICE for a new task. Given a task, the proposed metric can reliably predict the utility of optimizing ICE compared to using random ICE. Our code is available at https://github.com/microsoft/nice-icl.

2024-06-07

2402.06784

Stefano Martina PhD

Matteo Paiano, Stefano Martina, Carlotta Giannelli, Filippo Caruso

Transfer learning with generative models for object detection on limited datasets

28 pages, 16 figures, 1 table

2024 Mach. Learn.: Sci. Technol. 5 035041

10.1088/2632-2153/ad65b5

cs.CV cond-mat.dis-nn cs.AI cs.LG cs.NA math.NA

http://creativecommons.org/licenses/by/4.0/

The availability of data is limited in some fields, especially for object detection tasks, where it is necessary to have correctly labeled bounding boxes around each object. A notable example of such data scarcity is found in the domain of marine biology, where it is useful to develop methods to automatically detect submarine species for environmental monitoring. To address this data limitation, the state-of-the-art machine learning strategies employ two main approaches. The first involves pretraining models on existing datasets before generalizing to the specific domain of interest. The second strategy is to create synthetic datasets specifically tailored to the target domain using methods like copy-paste techniques or ad-hoc simulators. The first strategy often faces a significant domain shift, while the second demands custom solutions crafted for the specific task. In response to these challenges, here we propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an object detector in a few-real data regime. This is achieved through a diffusion-based generative model that was pretrained on large generic datasets. With respect to the state-of-the-art, we find that it is not necessary to fine tune the generative model on the specific domain of interest. We believe that this is an important advance because it mitigates the labor-intensive task of manual labeling the images in object detection tasks. We validate our approach focusing on fishes in an underwater environment, and on the more common domain of cars in an urban setting. Our method achieves detection performance comparable to models trained on thousands of images, using only a few hundreds of input data. Our results pave the way for new generative AI-based protocols for machine learning applications in various domains.

2024-09-11

2402.07043

Yunzhen Feng

Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton and Julia Kempe

A Tale of Tails: Model Collapse as a Change of Scaling Laws

ICML 2024

cs.LG cs.AI cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natural) training data. Yet, the widespread use of popular models means that the ecosystem of online data and text will co-evolve to progressively contain increased amounts of synthesized data. In this paper we ask: How will the scaling laws change in the inevitable regime where synthetic data makes its way into the training corpus? Will future models, still improve, or be doomed to degenerate up to total (model) collapse? We develop a theoretical framework of model collapse through the lens of scaling laws. We discover a wide range of decay phenomena, analyzing loss of scaling, shifted scaling with number of generations, the ''un-learning" of skills, and grokking when mixing human and synthesized data. Our theory is validated by large-scale experiments with a transformer on an arithmetic task and text generation using the large language model Llama2.

2024-06-03

2402.07085

Kenichi Fujita

Kenichi Fujita, Atsushi Ando, Yusuke Ijima

Speech Rhythm-Based Speaker Embeddings Extraction from Phonemes and Phoneme Duration for Multi-Speaker Speech Synthesis

11 pages,9 figures, Accepted to IEICE TRANSACTIONS on Information and Systems

IEICE TRANSACTIONS on Information and Systems 107.1 (2024): 93-104

10.1587/transinf.2023EDP7039

cs.SD cs.CL cs.LG eess.AS

http://creativecommons.org/licenses/by-sa/4.0/

This paper proposes a speech rhythm-based method for speaker embeddings to model phoneme duration using a few utterances by the target speaker. Speech rhythm is one of the essential factors among speaker characteristics, along with acoustic features such as F0, for reproducing individual utterances in speech synthesis. A novel feature of the proposed method is the rhythm-based embeddings extracted from phonemes and their durations, which are known to be related to speaking rhythm. They are extracted with a speaker identification model similar to the conventional spectral feature-based one. We conducted three experiments, speaker embeddings generation, speech synthesis with generated embeddings, and embedding space analysis, to evaluate the performance. The proposed method demonstrated a moderate speaker identification performance (15.2% EER), even with only phonemes and their duration information. The objective and subjective evaluation results demonstrated that the proposed method can synthesize speech with speech rhythm closer to the target speaker than the conventional method. We also visualized the embeddings to evaluate the relationship between the distance of the embeddings and the perceptual similarity. The visualization of the embedding space and the relation analysis between the closeness indicated that the distribution of embeddings reflects the subjective and objective similarity.

2024-02-13

2402.07244

Junhao Song

Junhao Song, Yingfang Yuan, Wei Pang

SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm

Proceedings of the Genetic and Evolutionary Computation Conf. Companion, GECCO '24, 2024, pp. 2115-2118

10.1145/3638530.3664188

cs.NE cs.AI

http://creativecommons.org/licenses/by/4.0/

We propose a novel type of Artificial Immune System (AIS): Symbiotic Artificial Immune Systems (SAIS), drawing inspiration from symbiotic relationships in biology. SAIS parallels the three key stages (i.e., mutualism, commensalism and parasitism) of population updating from the Symbiotic Organisms Search (SOS) algorithm. This parallel approach effectively addresses the challenges of large population size and enhances population diversity in AIS, which traditional AIS and SOS struggle to resolve efficiently. We conducted a series of experiments, which demonstrated that our SAIS achieved comparable performance to the state-of-the-art approach SOS and outperformed other popular AIS approaches and evolutionary algorithms across 26 benchmark problems. Furthermore, we investigated the problem of parameter selection and found that SAIS performs better in handling larger population sizes while requiring fewer generations. Finally, we believe SAIS, as a novel bio-inspired and immune-inspired algorithm, paves the way for innovation in bio-inspired computing with the symbiotic paradigm.

2024-09-24

2402.07301

Atharva Pandey

Atharva Pandey, Vishal Yadav, Rajendra Nagar, Santanu Chaudhury

LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions

AAAI 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. In the literature, various 3D shape representations have been developed, differing in memory efficiency and shape retrieval effectiveness, such as volumetric, parametric, and implicit surfaces. Radial basis functions provide memory-efficient parameterization of the implicit surface. However, we show that training a neural network using the mean squared error between the ground-truth implicit surface and the linear basis-based implicit surfaces does not converge to the global solution. In this work, we propose locally supported compact radial basis functions for a linear representation of the implicit surface. This representation enables us to generate 3D shapes with arbitrary topologies at any resolution due to their continuous nature. We then propose a neural network architecture for learning the linear implicit shape representation of the 3D surface of an object. We learn linear implicit shapes within a supervised learning framework using ground truth Signed-Distance Field (SDF) data for guidance. The classical strategies face difficulties in finding linear implicit shapes from a given 3D point cloud due to numerical issues (requires solving inverse of a large matrix) in basis and query point selection. The proposed approach achieves better Chamfer distance and comparable F-score than the state-of-the-art approach on the benchmark dataset. We also show the effectiveness of the proposed approach by using it for the 3D shape completion task.

2024-02-13

2402.07386

Qingkai Zeng

Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng, Zhenwen Liang, Zhihan Zhang, Meng Jiang

Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples

Published in CIKM 2024

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making automatic taxonomy construction highly desirable. In this work, we introduce Chain-of-Layer which is an in-context learning framework designed to induct taxonomies from a given set of entities. Chain-of-Layer breaks down the task into selecting relevant candidate entities in each layer and gradually building the taxonomy from top to bottom. To minimize errors, we introduce the Ensemble-based Ranking Filter to reduce the hallucinated content generated at each iteration. Through extensive experiments, we demonstrate that Chain-of-Layer achieves state-of-the-art performance on four real-world benchmarks.

2024-07-26

2402.07422

Chufeng Jiang

Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang, Weisheng Chen

News Recommendation with Attention Mechanism

7 pages, Journal of Industrial Engineering and Applied Science

Journal of Industrial Engineering and Applied Science 2024

10.5281/zenodo.10635481

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms.

2024-02-21

2402.07429

Chufeng Jiang

Tianrui Liu, Changxin Xu, Yuxin Qiao, Chufeng Jiang, Jiqiang Yu

Particle Filter SLAM for Vehicle Localization

6 pages, Journal of Industrial Engineering and Applied Science

Journal of Industrial Engineering and Applied Science 2024

10.5281/zenodo.10635489

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Simultaneous Localization and Mapping (SLAM) presents a formidable challenge in robotics, involving the dynamic construction of a map while concurrently determining the precise location of the robotic agent within an unfamiliar environment. This intricate task is further compounded by the inherent "chicken-and-egg" dilemma, where accurate mapping relies on a dependable estimation of the robot's location, and vice versa. Moreover, the computational intensity of SLAM adds an additional layer of complexity, making it a crucial yet demanding topic in the field. In our research, we address the challenges of SLAM by adopting the Particle Filter SLAM method. Our approach leverages encoded data and fiber optic gyro (FOG) information to enable precise estimation of vehicle motion, while lidar technology contributes to environmental perception by providing detailed insights into surrounding obstacles. The integration of these data streams culminates in the establishment of a Particle Filter SLAM framework, representing a key endeavor in this paper to effectively navigate and overcome the complexities associated with simultaneous localization and mapping in robotic systems.

2024-02-21

2402.07526

Gilles Bertrand

Gilles Bertrand (LIGM)

Morse sequences

International Conference on Discrete Geometry and Mathematical Morphology (DGMM), Apr 2024, Florence, Italy

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We introduce the notion of a Morse sequence, which provides a simple and effective approach to discrete Morse theory. A Morse sequence is a sequence composed solely of two elementary operations, that is, expansions (the inverse of a collapse), and fillings (the inverse of a perforation). We show that a Morse sequence may be seen as an alternative way to represent the gradient vector field of an arbitrary discrete Morse function. We also show that it is possible, in a straightforward manner, to make a link between Morse sequences and different kinds of Morse functions. At last, we introduce maximal Morse sequences, which formalize two basic schemes for building a Morse sequence from an arbitrary simplicial complex.

2024-02-13

2402.07547

Stefania Costantini

Stefania Costantini

Ensuring trustworthy and ethical behaviour in intelligent logical agents

Journal of Logic and Computation, Volume 32, Issue 2, March 2022, Pages 443-478

10.1093/logcom/exab091

cs.MA cs.AI cs.LO cs.SC

http://creativecommons.org/licenses/by/4.0/

Autonomous Intelligent Agents are employed in many applications upon which the life and welfare of living beings and vital social functions may depend. Therefore, agents should be trustworthy. A priori certification techniques (i.e., techniques applied prior to system's deployment) can be useful, but are not sufficient for agents that evolve, and thus modify their epistemic and belief state, and for open Multi-Agent Systems, where heterogeneous agents can join or leave the system at any stage of its operation. In this paper, we propose/refine/extend dynamic (runtime) logic-based self-checking techniques, devised in order to be able to ensure agents' trustworthy and ethical behaviour.

2024-02-13

2402.07633

Zecheng Li

Zecheng Li, Zening Zeng, Yuqi Liang, Jin-Gang Yu

Complete Instances Mining for Weakly Supervised Instance Segmentation

7 pages

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence(IJCAI 2023). Main Track. Pages 1142-1150

10.24963/ijcai.2023/127

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However, with the advancement of deep neural networks (DNNs), WSIS has garnered significant attention. Following a proposal-based paradigm, we encounter a redundant segmentation problem resulting from a single instance being represented by multiple proposals. For example, we feed a picture of a dog and proposals into the network and expect to output only one proposal containing a dog, but the network outputs multiple proposals. To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels. Our approach allows the network to become aware of multiple instances and complete instances, and we further improve its robustness through the incorporation of an Anti-noise strategy. Empirical evaluations on the PASCAL VOC 2012 and MS COCO datasets demonstrate that our method achieves state-of-the-art performance with a notable margin. Our implementation will be made available at https://github.com/ZechengLi19/CIM.

2024-02-13

2402.07680

Tanmoy Dam

Tanmoy Dam, Sanjay Bhargav Dharavath, Sameer Alam, Nimrod Lilith, Supriyo Chakraborty and Mir Feroskhan

AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer

This paper has been accepted for ICRA 2024, and copyright will automatically transfer to IEEE upon its availability on the IEEE portal

2024 IEEE International Conference on Robotics and Automation (ICRA)

10.1109/ICRA57147.2024.10610908

cs.CV cs.AI cs.RO

http://creativecommons.org/licenses/by-sa/4.0/

Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR's sparse data and the dense resolution of cameras. Besides, discrepancies in the two data representations further complicate fusion methods. We introduce AYDIV, a novel framework integrating a tri-phase alignment process specifically designed to enhance long-distance detection even amidst data discrepancies. AYDIV consists of the Global Contextual Fusion Alignment Transformer (GCFAT), which improves the extraction of camera features and provides a deeper understanding of large-scale patterns; the Sparse Fused Feature Attention (SFFA), which fine-tunes the fusion of LiDAR and camera details; and the Volumetric Grid Attention (VGA) for a comprehensive spatial data fusion. AYDIV's performance on the Waymo Open Dataset (WOD) with an improvement of 1.24% in mAPH value(L2 difficulty) and the Argoverse2 Dataset with a performance improvement of 7.40% in AP value demonstrates its efficacy in comparison to other existing fusion-based methods. Our code is publicly available at https://github.com/sanjay-810/AYDIV2

2024-09-04

2402.07682

Marie Candito

Marie Candito

Auxiliary Tasks to Boost Biaffine Semantic Dependency Parsing

Findings of the Association for Computational Linguistics: ACL 2022, pp. 2422-2429

10.18653/v1/2022.findings-acl.190

cs.CL

http://creativecommons.org/licenses/by-sa/4.0/

The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens). To circumvent such an independence of decision, while retaining the O(n^2) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs. Experiments on the three English acyclic datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic performance gains on a near state-of-the-art baseline using transformer-based contextualized representations. This provides a simple and robust method to boost SDP performance.

2024-02-13

2402.07956

Cristobal Romero

C. Romero, S. Ventura

Educational data mining and learning analytics: An updated survey

Wiley interdisciplinary reviews: Data mining and knowledge discovery;2020; 10(3):e1355

10.1002/widm.1355

cs.HC cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide range of related terms are now used in the bibliography such as Academic Analytics, Institutional Analytics, Teaching Analytics, Data-Driven Education, Data-Driven Decision-Making in Education, Big Data in Education, and Educational Data Science. This paper provides the current state of the art by reviewing the main publications, the key milestones, the knowledge discovery cycle, the main educational environments, the specific tools, the free available datasets, the most used methods, the main objectives, and the future trends in this research area.

2024-02-14

2402.08145

Rushang Karia

Rushang Karia, Pulkit Verma, Alberto Speranzon, Siddharth Srivastava

Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings

To appear at ICAPS-24

Proceedings of the International Conference on Automated Planning and Scheduling, 34(1), 310-318, 2024

10.1609/icaps.v34i1.31489

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This paper introduces a new approach for continual planning and model learning in relational, non-stationary stochastic environments. Such capabilities are essential for the deployment of sequential decision-making systems in the uncertain and constantly evolving real world. Working in such practical settings with unknown (and non-stationary) transition systems and changing tasks, the proposed framework models gaps in the agent's current state of knowledge and uses them to conduct focused, investigative explorations. Data collected using these explorations is used for learning generalizable probabilistic models for solving the current task despite continual changes in the environment dynamics. Empirical evaluations on several non-stationary benchmark domains show that this approach significantly outperforms planning and RL baselines in terms of sample complexity. Theoretical results show that the system exhibits desirable convergence properties when stationarity holds.

2024-07-24

2402.08310

Thomas P\"ollabauer

Thomas P\"ollabauer, Julius K\"uhn, Jiayi Li, Arjan Kuijper

One-to-many Reconstruction of 3D Geometry of cultural Artifacts using a synthetically trained Generative Model

21st Eurographics Workshop on Graphics and Cultural Heritage (GCH 2023)

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Estimating the 3D shape of an object using a single image is a difficult problem. Modern approaches achieve good results for general objects, based on real photographs, but worse results on less expressive representations such as historic sketches. Our automated approach generates a variety of detailed 3D representation from a single sketch, depicting a medieval statue, and can be guided by multi-modal inputs, such as text prompts. It relies solely on synthetic data for training, making it adoptable even in cases of only small numbers of training examples. Our solution allows domain experts such as a curators to interactively reconstruct potential appearances of lost artifacts.

2024-02-14

2402.08318

Martin Ruskov

Alba Morollon Diaz-Faes, Carla Sofia Ribeiro Murteira, Martin Ruskov

Values That Are Explicitly Present in Fairy Tales: Comparing Samples from German, Italian and Portuguese Traditions

In Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages

Journal of Data Mining & Digital Humanities, NLP4DH (June 4, 2024) jdmdh:13120

10.46298/jdmdh.13120

cs.CL cs.CY

http://creativecommons.org/licenses/by/4.0/

Looking at how social values are represented in fairy tales can give insights about the variations in communication of values across cultures. We study how values are communicated in fairy tales from Portugal, Italy and Germany using a technique called word embedding with a compass to quantify vocabulary differences and commonalities. We study how these three national traditions differ in their explicit references to values. To do this, we specify a list of value-charged tokens, consider their word stems and analyse the distance between these in a bespoke pre-trained Word2Vec model. We triangulate and critically discuss the validity of the resulting hypotheses emerging from this quantitative model. Our claim is that this is a reusable and reproducible method for the study of the values explicitly referenced in historical corpora. Finally, our preliminary findings hint at a shared cultural understanding and the expression of values such as Benevolence, Conformity, and Universalism across the studied cultures, suggesting the potential existence of a pan-European cultural memory.

2024-08-07

2402.08345

Ufuk Can Bi\c{c}ici

Ufuk Can Bicici, Tuna Han Salih Meral, Lale Akarun

Conditional Information Gain Trellis

Accepted by Pattern Recognition Letters

Conditional Information Gain Trellis, Pattern Recognition Letters, Volume 184, 2024, Pages 212-218, ISSN 0167-8655

10.1016/j.patrec.2024.06.018

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Conditional computing processes an input using only part of the neural network's computational units. Learning to execute parts of a deep convolutional network by routing individual samples has several advantages: Reducing the computational burden is an obvious advantage. Furthermore, if similar classes are routed to the same path, that part of the network learns to discriminate between finer differences and better classification accuracies can be attained with fewer parameters. Recently, several papers have exploited this idea to take a particular child of a node in a tree-shaped network or to skip parts of a network. In this work, we follow a Trellis-based approach for generating specific execution paths in a deep convolutional neural network. We have designed routing mechanisms that use differentiable information gain-based cost functions to determine which subset of features in a convolutional layer will be executed. We call our method Conditional Information Gain Trellis (CIGT). We show that our conditional execution mechanism achieves comparable or better model performance compared to unconditional baselines, using only a fraction of the computational resources.

2024-07-09

2402.08400

Alaa Anani

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing

International Conference on Machine Learning (ICML), 2024

cs.LG cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Certification for machine learning is proving that no adversarial sample can evade a model within a range under certain conditions, a necessity for safety-critical domains. Common certification methods for segmentation use a flat set of fine-grained classes, leading to high abstain rates due to model uncertainty across many classes. We propose a novel, more practical setting, which certifies pixels within a multi-level hierarchy, and adaptively relaxes the certification to a coarser level for unstable components classic methods would abstain from, effectively lowering the abstain rate whilst providing more certified semantically meaningful information. We mathematically formulate the problem setup, introduce an adaptive hierarchical certification algorithm and prove the correctness of its guarantees. Since certified accuracy does not take the loss of information into account for coarser classes, we introduce the Certified Information Gain ($\mathrm{CIG}$) metric, which is proportional to the class granularity level. Our extensive experiments on the datasets Cityscapes, PASCAL-Context, ACDC and COCO-Stuff demonstrate that our adaptive algorithm achieves a higher $\mathrm{CIG}$ and lower abstain rate compared to the current state-of-the-art certification method. Our code can be found here: https://github.com/AlaaAnani/adaptive-certify.

2024-06-05

2402.08430

Oliviero Riganelli

Ionut Daniel Fagadau, Leonardo Mariani, Daniela Micucci and Oliviero Riganelli

Analyzing Prompt Influence on Automated Method Generation: An Empirical Study with Copilot

Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension (ICPC 2024)

10.1145/3643916.3644409

cs.SE cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Generative AI is changing the way developers interact with software systems, providing services that can produce and deliver new content, crafted to satisfy the actual needs of developers. For instance, developers can ask for new code directly from within their IDEs by writing natural language prompts, and integrated services based on generative AI, such as Copilot, immediately respond to prompts by providing ready-to-use code snippets. Formulating the prompt appropriately, and incorporating the useful information while avoiding any information overload, can be an important factor in obtaining the right piece of code. The task of designing good prompts is known as prompt engineering. In this paper, we systematically investigate the influence of eight prompt features on the style and the content of prompts, on the level of correctness, complexity, size, and similarity to the developers' code of the generated code. We specifically consider the task of using Copilot with 124,800 prompts obtained by systematically combining the eight considered prompt features to generate the implementation of 200 Java methods. Results show how some prompt features, such as the presence of examples and the summary of the purpose of the method, can significantly influence the quality of the result.

2024-02-15

2402.08509

Philipp Seifer

Philipp Seifer, Daniel Hern\'andez, Ralf L\"ammel, Steffen Staab

From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries (Extended Version)

19 pages, 5 figures

WWW '24: Proceedings of the ACM Web Conference 2024. ACM, 2024, pp. 2064-2074

10.1145/3589334.3645550

cs.DB cs.AI cs.LO

http://creativecommons.org/licenses/by/4.0/

SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of a data processing pipeline without knowing the particular input data: Shape constraints on the input graph may affect the output graph, but may no longer apply literally, and new shapes may be imposed by the query template. In this paper, we study the derivation of shape constraints that hold on all possible output graphs of a given SPARQL CONSTRUCT query. We assume that the SPARQL CONSTRUCT query is fixed, e.g., being part of a program, whereas the input graphs adhere to input shape constraints but may otherwise vary over time and, thus, are mostly unknown. We study a fragment of SPARQL CONSTRUCT queries (SCCQ) and a fragment of SHACL (Simple SHACL). We formally define the problem of deriving the most restrictive set of Simple SHACL shapes that constrain the results from evaluating a SCCQ over any input graph restricted by a given set of Simple SHACL shapes. We propose and implement an algorithm that statically analyses input SHACL shapes and CONSTRUCT queries and prove its soundness and complexity.

2024-05-22

2402.08702

Yongchao Chen

Yongchao Chen, Jacob Arkin, Yilun Hao, Yang Zhang, Nicholas Roy, Chuchu Fan

PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling

62 pages, 14 figures, Published in EMNLP 2024 Main

EMNLP 2024 Main (The 2024 Conference on Empirical Methods on Natural Language Processing )

cs.CL cs.AI cs.HC cs.RO

http://creativecommons.org/publicdomain/zero/1.0/

Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. LLMs have been successfully used to help find and improve prompt candidates for single-step tasks. However, realistic tasks for agents are multi-step and introduce new challenges: (1) Prompt content is likely to be more extensive and complex, making it more difficult for LLMs to analyze errors, (2) the impact of an individual step is difficult to evaluate, and (3) different people may have varied preferences about task execution. While humans struggle to optimize prompts, they are good at providing feedback about LLM outputs; we therefore introduce a new LLM-driven discrete prompt optimization framework PRompt Optimization in Multi-Step Tasks (PROMST) that incorporates human-designed feedback rules to automatically offer direct suggestions for improvement. We also use an extra learned heuristic model that predicts prompt performance to efficiently sample from prompt candidates. This approach significantly outperforms both human-engineered prompts and several other prompt optimization methods across 11 representative multi-step tasks (an average 10.6\%-29.3\% improvement to current best methods on five LLMs respectively). We believe our work can serve as a benchmark for automatic prompt optimization for LLM-driven multi-step tasks. Datasets and Codes are available at https://github.com/yongchao98/PROMST. Project Page is available at https://yongchao98.github.io/MIT-REALM-PROMST.

2024-10-04

2402.08957

Yinya Huang

Yinya Huang, Xiaohan Lin, Zhengying Liu, Qingxing Cao, Huajian Xin, Haiming Wang, Zhenguo Li, Linqi Song, Xiaodan Liang

MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data

ICLR 2024 spotlight

cs.AI cs.CL cs.FL cs.LG cs.PL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict and formal multi-step inference, they are appealing domains for exploring the reasoning ability of LLMs but still face important challenges. Previous studies such as Chain-of-Thought (CoT) have revealed the effectiveness of intermediate steps guidance. However, such step-wise annotation requires heavy labor, leading to insufficient training steps for current benchmarks. To fill this gap, this work introduces MUSTARD, a data generation framework that masters uniform synthesis of theorem and proof data of high quality and diversity. MUSTARD synthesizes data in three stages: (1) It samples a few mathematical concept seeds as the problem category. (2) Then, it prompts a generative language model with the sampled concepts to obtain both the problems and their step-wise formal solutions. (3) Lastly, the framework utilizes a proof assistant (e.g., Lean Prover) to filter the valid proofs. With the proposed MUSTARD, we present a theorem-and-proof benchmark MUSTARDSAUCE with 5,866 valid data points. Each data point contains an informal statement, an informal proof, and a translated formal proof that passes the prover validation. We perform extensive analysis and demonstrate that MUSTARD generates validated high-quality step-by-step data. We further apply the MUSTARDSAUCE for fine-tuning smaller language models. The fine-tuned Llama 2-7B achieves a 15.41% average relative performance gain in automated theorem proving, and 8.18% in math word problems. Codes and data are available at https://github.com/Eleanor-H/MUSTARD.

2024-05-24

2402.09056

Mira J\"urgens

Mira J\"urgens, Nis Meinert, Viktor Bengs, Eyke H\"ullermeier, Willem Waegeman

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

Proceedings of the 41st International Conference on Machine Learning (ICML), 2024, pp. 22624--22642

cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

Trustworthy ML systems should not only return accurate predictions, but also a reliable representation of their uncertainty. Bayesian methods are commonly used to quantify both aleatoric and epistemic uncertainty, but alternative approaches, such as evidential deep learning methods, have become popular in recent years. The latter group of methods in essence extends empirical risk minimization (ERM) for predicting second-order probability distributions over outcomes, from which measures of epistemic (and aleatoric) uncertainty can be extracted. This paper presents novel theoretical insights of evidential deep learning, highlighting the difficulties in optimizing second-order loss functions and interpreting the resulting epistemic uncertainty measures. With a systematic setup that covers a wide range of approaches for classification, regression and counts, it provides novel insights into issues of identifiability and convergence in second-order loss minimization, and the relative (rather than absolute) nature of epistemic uncertainty measures.

2024-09-11

2402.09066

Luca Morandini

Piero Fraternali, Luca Morandini and Sergio Luis Herrera Gonz\'alez

Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey

Waste Management 189 (2024) 88-102

10.1016/j.wasman.2024.08.003

cs.CV cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-nd/4.0/

The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive. Remote sensing is a cost-effective solution for the identification and monitoring of solid waste disposal sites that enables broad coverage and repeated acquisitions over time. Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades. Researchers proposed specialized techniques that leverage remote sensing imagery to perform a range of tasks such as waste site detection, dumping site monitoring, and assessment of suitable locations for new landfills. This review aims to provide a detailed illustration of the most relevant proposals for the detection and monitoring of solid waste sites by describing and comparing the approaches, the implemented techniques, and the employed data. Furthermore, since the data sources are of the utmost importance for developing an effective solid waste detection model, a comprehensive overview of the satellites and publicly available data sets is presented. Finally, this paper identifies the open issues in the state-of-the-art and discusses the relevant research directions for reducing the costs and improving the effectiveness of novel solid waste detection methods.

2024-08-29

2402.09085

Oliver Broadrick

Oliver Broadrick, Honghua Zhang, Guy Van den Broeck

Polynomial Semantics of Tractable Probabilistic Circuits

In Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence (UAI), 2024

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Probabilistic circuits compute multilinear polynomials that represent multivariate probability distributions. They are tractable models that support efficient marginal inference. However, various polynomial semantics have been considered in the literature (e.g., network polynomials, likelihood polynomials, generating functions, and Fourier transforms). The relationships between circuit representations of these polynomial encodings of distributions is largely unknown. In this paper, we prove that for distributions over binary variables, each of these probabilistic circuit models is equivalent in the sense that any circuit for one of them can be transformed into a circuit for any of the others with only a polynomial increase in size. They are therefore all tractable for marginal inference on the same class of distributions. Finally, we explore the natural extension of one such polynomial semantics, called probabilistic generating circuits, to categorical random variables, and establish that inference becomes #P-hard.

2024-08-09

2402.09091

Zhiyuan Chang

Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues

13 pages, 6 figures

The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)

cs.CR cs.AI cs.HC

http://creativecommons.org/licenses/by/4.0/

With the development of LLMs, the security threats of LLMs are getting more and more attention. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks primarily utilize scenario camouflage techniques. However their explicitly mention of malicious intent will be easily recognized and defended by LLMs. In this paper, we propose an indirect jailbreak attack approach, Puzzler, which can bypass the LLM's defense strategy and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. In addition, inspired by the wisdom of "When unable to attack, defend" from Sun Tzu's Art of War, we adopt a defensive stance to gather clues about the original malicious query through LLMs. Extensive experimental results show that Puzzler achieves a query success rate of 96.6% on closed-source LLMs, which is 57.9%-82.7% higher than baselines. Furthermore, when tested against the state-of-the-art jailbreak detection approaches, Puzzler proves to be more effective at evading detection compared to baselines.

2024-08-22

2402.09100

Fatemeh Ghorbani Lohesara

Fatemeh Ghorbani Lohesara, Karen Egiazarian, Sebastian Knorr

Towards Realistic Landmark-Guided Facial Video Inpainting Based on GANs

Accepted in Electronic Imaging 2024

Electronic Imaging 2024

10.2352/EI.2024.36.10.IPAS-246

Volume: 36 | Article ID: IPAS-246

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Facial video inpainting plays a crucial role in a wide range of applications, including but not limited to the removal of obstructions in video conferencing and telemedicine, enhancement of facial expression analysis, privacy protection, integration of graphical overlays, and virtual makeup. This domain presents serious challenges due to the intricate nature of facial features and the inherent human familiarity with faces, heightening the need for accurate and persuasive completions. In addressing challenges specifically related to occlusion removal in this context, our focus is on the progressive task of generating complete images from facial data covered by masks, ensuring both spatial and temporal coherence. Our study introduces a network designed for expression-based video inpainting, employing generative adversarial networks (GANs) to handle static and moving occlusions across all frames. By utilizing facial landmarks and an occlusion-free reference image, our model maintains the user's identity consistently across frames. We further enhance emotional preservation through a customized facial expression recognition (FER) loss function, ensuring detailed inpainted outputs. Our proposed framework exhibits proficiency in eliminating occlusions from facial videos in an adaptive form, whether appearing static or dynamic on the frames, while providing realistic and coherent results.

2024-07-12

2402.09137

Ayodeji Ijishakin

Ayodeji Ijishakin, Sophie Martin, Florence Townend, Federica Agosta, Edoardo Gioele Spinelli, Silvia Basaia, Paride Schito, Yuri Falzone, Massimo Filippi, James Cole, Andrea Malaspina

Semi-Supervised Diffusion Model for Brain Age Prediction

Deep Generative Models for Health Workshop, NeurIPS 2023

eess.IV cs.CV

http://creativecommons.org/licenses/by-nc-sa/4.0/

Brain age prediction models have succeeded in predicting clinical outcomes in neurodegenerative diseases, but can struggle with tasks involving faster progressing diseases and low quality data. To enhance their performance, we employ a semi-supervised diffusion model, obtaining a 0.83(p<0.01) correlation between chronological and predicted age on low quality T1w MR images. This was competitive with state-of-the-art non-generative methods. Furthermore, the predictions produced by our model were significantly associated with survival length (r=0.24, p<0.05) in Amyotrophic Lateral Sclerosis. Thus, our approach demonstrates the value of diffusion-based architectures for the task of brain age prediction.

2024-02-15

2402.09161

Igor Ivkic

Rita Stampfl, Igor Ivki\'c and Barbara Geyer

Role-Playing Simulation Games using ChatGPT

Link to online article: https://ercim-news.ercim.eu/en136/special/role-playing-simulation-games-using-chatgpt

ERCIM News Special Theme: Large Language Models 2024

cs.AI cs.HC

http://creativecommons.org/licenses/by/4.0/

Since the COVID-19 pandemic, educational institutions have embarked on digital transformation projects. The success of these projects depends on integrating new technologies and understanding the needs of digitally literate students. The "learning by doing" approach suggests that real success in learning new skills is achieved when students can try out and practise these skills. In this article, we demonstrate how Large Language Models (LLMs) can enhance the quality of teaching by using ChatGPT in a role-playing simulation game scenario to promote active learning. Moreover, we discuss how LLMs can boost students' interest in learning by allowing them to practice real-life scenarios using ChatGPT.

2024-02-15

2402.09199

Qiang Sheng

Yuhui Shi, Qiang Sheng, Juan Cao, Hao Mi, Beizhe Hu, Danding Wang

Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling

13 pages, 6 figures, 7 tables

IJCAI 2024

10.24963/ijcai.2024/55

cs.CL cs.AI cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

With the rapidly increasing application of large language models (LLMs), their abuse has caused many undesirable societal problems such as fake news, academic dishonesty, and information pollution. This makes AI-generated text (AIGT) detection of great importance. Among existing methods, white-box methods are generally superior to black-box methods in terms of performance and generalizability, but they require access to LLMs' internal states and are not applicable to black-box settings. In this paper, we propose to estimate word generation probabilities as pseudo white-box features via multiple re-sampling to help improve AIGT detection under the black-box setting. Specifically, we design POGER, a proxy-guided efficient re-sampling method, which selects a small subset of representative words (e.g., 10 words) for performing multiple re-sampling in black-box AIGT detection. Experiments on datasets containing texts from humans and seven LLMs show that POGER outperforms all baselines in macro F1 under black-box, partial white-box, and out-of-distribution settings and maintains lower re-sampling costs than its existing counterparts.

2024-08-29

2402.09204

Jiexin Wang

Jiexin Wang, Jiahao Chen, Bing Su

Domain-adaptive and Subgroup-specific Cascaded Temperature Regression for Out-of-distribution Calibration

2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seoul, Korea

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Although deep neural networks yield high classification accuracy given sufficient training data, their predictions are typically overconfident or under-confident, i.e., the prediction confidences cannot truly reflect the accuracy. Post-hoc calibration tackles this problem by calibrating the prediction confidences without re-training the classification model. However, current approaches assume congruence between test and validation data distributions, limiting their applicability to out-of-distribution scenarios. To this end, we propose a novel meta-set-based cascaded temperature regression method for post-hoc calibration. Our method tailors fine-grained scaling functions to distinct test sets by simulating various domain shifts through data augmentation on the validation set. We partition each meta-set into subgroups based on predicted category and confidence level, capturing diverse uncertainties. A regression network is then trained to derive category-specific and confidence-level-specific scaling, achieving calibration across meta-sets. Extensive experimental results on MNIST, CIFAR-10, and TinyImageNet demonstrate the effectiveness of the proposed method.

2024-02-15

2402.09251

Yang Zhong

Yang Zhong, Hongyu Yu, Jihui Yang, Xingyu Guo, Hongjun Xiang, and Xingao Gong

Universal Machine Learning Kohn-Sham Hamiltonian for Materials

20 pages, 9 figures

Chin. Phys. Lett. 41, 077103 (2024)

10.1088/0256-307X/41/7/077103

physics.comp-ph cond-mat.mtrl-sci cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

While density functional theory (DFT) serves as a prevalent computational approach in electronic structure calculations, its computational demands and scalability limitations persist. Recently, leveraging neural networks to parameterize the Kohn-Sham DFT Hamiltonian has emerged as a promising avenue for accelerating electronic structure computations. Despite advancements, challenges such as the necessity for computing extensive DFT training data to explore each new system and the complexity of establishing accurate ML models for multi-elemental materials still exist. Addressing these hurdles, this study introduces a universal electronic Hamiltonian model trained on Hamiltonian matrices obtained from first-principles DFT calculations of nearly all crystal structures on the Materials Project. We demonstrate its generality in predicting electronic structures across the whole periodic table, including complex multi-elemental systems, solid-state electrolytes, Moir\'e twisted bilayer heterostructure, and metal-organic frameworks (MOFs). Moreover, we utilize the universal model to conduct high-throughput calculations of electronic structures for crystals in GeNOME datasets, identifying 3,940 crystals with direct band gaps and 5,109 crystals with flat bands. By offering a reliable efficient framework for computing electronic properties, this universal Hamiltonian model lays the groundwork for advancements in diverse fields, such as easily providing a huge data set of electronic structures and also making the materials design across the whole periodic table possible.

2024-06-18

2402.09266

Andres Molares-Ulloa

Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos and Daniel Rivero

Machine Learning in management of precautionary closures caused by lipophilic biotoxins

Computers and Electronics in Agriculture, 197, 106956. (2022)

10.1016/j.compag.2022.106956

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer of cultivated mussels, the opening and closing of the production areas is controlled by a monitoring program. In addition to the closures resulting from the presence of toxicity exceeding the legal threshold, in the absence of a confirmatory sampling and the existence of risk factors, precautionary closures may be applied. These decisions are made by experts without the support or formalisation of the experience on which they are based. Therefore, this work proposes a predictive model capable of supporting the application of precautionary closures. Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and 0.75 respectively, the kNN algorithm has provided the best results. This allows the creation of a system capable of helping in complex situations where forecast errors are more common.

2024-02-15

2402.09267

Xiaoying Zhang

Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Lifeng Jin, Linfeng Song, Haitao Mi, Helen Meng

Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

20 pages

ACL2024 Main

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Despite showing increasingly human-like abilities, large language models (LLMs) often struggle with factual inaccuracies, i.e. "hallucinations", even when they hold relevant knowledge. To address these hallucinations, current approaches typically necessitate high-quality human factuality annotations. In this work, we explore Self-Alignment for Factuality, where we leverage the self-evaluation capability of an LLM to provide training signals that steer the model towards factuality. Specifically, we incorporate Self-Eval, a self-evaluation component, to prompt an LLM to validate the factuality of its own generated responses solely based on its internal knowledge. Additionally, we design Self-Knowledge Tuning (SK-Tuning) to augment the LLM's self-evaluation ability by improving the model's confidence estimation and calibration. We then utilize these self-annotated responses to fine-tune the model via Direct Preference Optimization algorithm. We show that the proposed self-alignment approach substantially enhances factual accuracy over Llama family models across three key knowledge-intensive tasks on TruthfulQA and BioGEN.

2024-06-12

2402.09424

Chang Gao

Qinyu Chen, Congyi Sun, Chang Gao, Shih-Chii Liu

Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer

To be published at the 2024 IEEE International Symposium on Circuits and Systems (ISCAS), Singapore

2024 IEEE International Symposium on Circuits and Systems (ISCAS)

10.1109/ISCAS58744.2024.10558341

eess.SP cs.CV cs.LG cs.NE

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the Spiking Conformer significantly reduces the classification computational cost compared to the non-spiking model. Additionally, we introduce an approximate spiking neuron layer to further reduce spike-triggered neuron updates by nearly 38% without sacrificing accuracy. Using raw EEG data as input, the proposed Spiking Conformer achieved an average sensitivity rate of 94.9% and a specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for the seizure prediction task, and needs >10x fewer operations compared to the non-spiking equivalent model.

2024-10-01

2402.09459

David Gonz\'alez Ortega

Javier Gonz\'alez-Alonso, David Oviedo-Pastor, H\'ector J. Aguado, Francisco J. D\'iaz-Pernas, David Gonz\'alez-Ortega, and Mario Mart\'inez-Zarzuela

Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar

25 pages

Sensors 2021, 21, 6642

10.3390/s21196642

eess.SP cs.CV cs.LG cs.NI

http://creativecommons.org/licenses/by/4.0/

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, trying to fill this gap, but still present some limitations that need to be overcome. At the same time, there is a growing number of scientific papers using not commercial, but custom do-it-yourself IMU-based systems in medical and sports applications. Even though these solutions can help to popularize the use of this technology, they have more limited features and the description on how to design and build them from scratch is yet too scarce in the literature. The aim of this work is two-fold: (1) Proving the feasibility of building an affordable custom solution aimed at simultaneous multiple body parts orientation tracking; while providing a detailed bottom-up description of the required hardware, tools, and mathematical operations to estimate and represent 3D movement in real-time. (2) Showing how the introduction of a custom 2.4 GHz communication protocol including a channel hopping strategy can address some of the current communication limitations of entry-level commercial solutions. The proposed system can be used for wireless real-time human body parts orientation tracking with up to 10 custom sensors, at least at 50 Hz. In addition, it provides a more reliable motion data acquisition in Bluetooth and Wi-Fi crowded environments, where the use of entry-level commercial solutions might be unfeasible. This system can be used as a groundwork for developing affordable human motion analysis solutions that do not require an accurate kinematic analysis.

2024-02-17

2402.09466

Felix Ott

Felix Ott, Lucas Heublein, Nisha Lakshmana Raichur, Tobias Feigl, Jonathan Hansen, Alexander R\"ugamer, Christopher Mutschler

Few-Shot Learning with Uncertainty-based Quadruplet Selection for Interference Classification in GNSS Data

IEEE 2024 International Conference on Localization and GNSS (ICL-GNSS)

10.1109/ICL-GNSS60721.2024.10578525

eess.SP cs.CV eess.IV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. In this paper, we propose a few-shot learning (FSL) approach to adapt to new interference classes. Our method employs quadruplet selection for the model to learn representations using various positive and negative interference classes. Furthermore, our quadruplet variant selects pairs based on the aleatoric and epistemic uncertainty to differentiate between similar classes. We recorded a dataset at a motorway with eight interference classes on which our FSL method with quadruplet loss outperforms other FSL techniques in jammer classification accuracy with 97.66%. Dataset available at: https://gitlab.cc-asp.fraunhofer.de/darcy_gnss/FIOT_highway

2024-10-08

2402.09476

Bardia Yousefi

Mahtab Darvish, Ryan Trask, Patrick Tallon, M\'elina Khansari, Lei Ren, Michelle Hershman, Bardia Yousefi

AI-Enabled Lung Cancer Prognosis

This is the author's version of a book chapter entitled: "Cancer Research: An Interdisciplinary Approach", Springer

Springer book chapter "Cancer Research: An Interdisciplinary Approach" 2024

q-bio.QM cs.AI eess.IV

http://creativecommons.org/licenses/by/4.0/

Lung cancer is the primary cause of cancer-related mortality, claiming approximately 1.79 million lives globally in 2020, with an estimated 2.21 million new cases diagnosed within the same period. Among these, Non-Small Cell Lung Cancer (NSCLC) is the predominant subtype, characterized by a notably bleak prognosis and low overall survival rate of approximately 25% over five years across all disease stages. However, survival outcomes vary considerably based on the stage at diagnosis and the therapeutic interventions administered. Recent advancements in artificial intelligence (AI) have revolutionized the landscape of lung cancer prognosis. AI-driven methodologies, including machine learning and deep learning algorithms, have shown promise in enhancing survival prediction accuracy by efficiently analyzing complex multi-omics data and integrating diverse clinical variables. By leveraging AI techniques, clinicians can harness comprehensive prognostic insights to tailor personalized treatment strategies, ultimately improving patient outcomes in NSCLC. Overviewing AI-driven data processing can significantly help bolster the understanding and provide better directions for using such systems.

2024-02-16

2402.09498

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Mar\'ia Teresa Garc\'ia-Ord\'as, Mar\'ia \'Alvarez-Gonz\'alez, Raquel Leir\'os-Rodr\'iguez and Ana F L\'opez Rodr\'iguez

Detection of the most influential variables for preventing postpartum urinary incontinence using machine learning techniques

Digital Health, Volume 8, 2022, 20552076221111289

10.1177/20552076221111289

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Background: Postpartum urinary incontinence (PUI) is a common issue among postnatal women. Previous studies identified potential related variables, but lacked analysis on certain intrinsic and extrinsic patient variables during pregnancy. Objective: The study aims to evaluate the most influential variables in PUI using machine learning, focusing on intrinsic, extrinsic, and combined variable groups. Methods: Data from 93 pregnant women were analyzed using machine learning and oversampling techniques. Four key variables were predicted: occurrence, frequency, intensity of urinary incontinence, and stress urinary incontinence. Results: Models using extrinsic variables were most accurate, with 70% accuracy for urinary incontinence, 77% for frequency, 71% for intensity, and 93% for stress urinary incontinence. Conclusions: The study highlights extrinsic variables as significant predictors of PUI issues. This suggests that PUI prevention might be achievable through healthy habits during pregnancy, although further research is needed for confirmation.

2024-02-16

2402.09553

Dilli Sharma

Dilli Prasad Sharma, Nasim Beigi-Mohammadi, Hongxiang Geng, Dawn Dixon, Rob Madro, Phil Emmenegger, Carlos Tobar, Jeff Li, Alberto Leon-Garcia

Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events

IEEE Access 12(2024) 56880-56909

10.1109/ACCESS.2024.3390089

cs.AI cs.LG stat.ML

http://creativecommons.org/licenses/by/4.0/

Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We present methods for (i) data collection and dataset development; (ii) descriptive analysis of each event type and its characteristics at different spatiotemporal levels; (iii) feature analysis and selection based on correlation coefficient analysis and feature importance analysis; and (iv) development of prediction models for the likelihood of occurrence of each event type at different temporal and spatial resolutions. We analyze the association of event types with socioeconomic and demographic data at the neighborhood level, identify a set of predictors for each event type, and develop predictive models with negative binomial regression. We conduct evaluations at neighborhood and fire station service area levels. Our results show that the models perform well for most of the event types with acceptable prediction errors for weekly and monthly periods. The evaluation shows that the prediction accuracy is consistent at the level of the fire station, so the predictions can be used in management by fire rescue service departments for planning resource allocation for these time periods. We also examine the impact of the COVID-19 pandemic on the occurrence of events and on the accuracy of event predictor models. Our findings show that COVID-19 had a significant impact on the performance of the event prediction models.

2024-04-29

2402.09592

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Jos\'e Emilio Labra, Enedina Quiroga, Vicente Mart\'in, Isa\'ias Garc\'ia, Pilar Marqu\'es-S\'anchez and Carmen Benavides

A Web-Based Tool for Automatic Data Collection, Curation, and Visualization of Complex Healthcare Survey Studies including Social Network Analysis

Computation and Mathematical Methods in Medicine, Volume 2017, Article ID 2579848

10.1155/2017/2579848

cs.AI cs.HC

http://creativecommons.org/licenses/by-nc-nd/4.0/

There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it is possible to gain insight into the current situation of a given individual regarding his/her consumption behavior. But this analysis, in order to be achieved, requires the use of tools that can ease the process of questionnaire creation, data gathering, curation and representation, and later analysis and visualization to the user. This research presents the design and construction of a web-based platform able to facilitate each of the mentioned processes by integrating the different phases into an intuitive system with a graphical user interface that hides the complexity underlying each of the questionnaires and techniques used and presenting the results in a flexible and visual way, avoiding any manual handling of data during the process. Advantages of this approach are shown and compared to the previous situation where some of the tasks were accomplished by time consuming and error prone manipulations of data.

2024-02-16

2402.09683

Zeya Chen

Zeya Chen, Ruth Schmidt

Exploring a Behavioral Model of "Positive Friction" in Human-AI Interaction

This preprint has not undergone peer review or any post-submission corrections. The Version of Record of this contribution will be published in Springer Nature Computer Science book series in Volume HCI International 2024

DESIGN, USER EXPERIENCE AND USABILITY. HCII 2024

cs.HC cs.AI cs.CY

http://creativecommons.org/licenses/by-sa/4.0/

Designing seamless, frictionless user experiences has long been a dominant trend in both applied behavioral science and artificial intelligence (AI), in which the goal of making desirable actions easy and efficient informs efforts to minimize friction in user experiences. However, in some settings, friction can be genuinely beneficial, such as the insertion of deliberate delays to increase reflection, preventing individuals from resorting to automatic or biased behaviors, and enhancing opportunities for unexpected discoveries. More recently, the popularization and availability of AI on a widespread scale has only increased the need to examine how friction can help or hinder users of AI; it also suggests a need to consider how positive friction can benefit AI practitioners, both during development processes (e.g., working with diverse teams) and to inform how AI is designed into offerings. This paper first proposes a "positive friction" model that can help characterize how friction is currently beneficial in user and developer experiences with AI, diagnose the potential need for friction where it may not yet exist in these contexts, and inform how positive friction can be used to generate solutions, especially as advances in AI continue to be progress and new opportunities emerge. It then explores this model in the context of AI users and developers by proposing the value of taking a hybrid "AI+human" lens, and concludes by suggesting questions for further exploration.

2024-02-16

2402.09766

Alexey Zaytsev

Valeriy Shevchenko, Nikita Belousov, Alexey Vasilev, Vladimir Zholobov, Artyom Sosedka, Natalia Semenova, Anna Volodkevich, Andrey Savchenko, Alexey Zaytsev

From Variability to Stability: Advancing RecSys Benchmarking Practices

8 pages with 11 figures

KDD 2024: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

10.1145/3637528.3671655

cs.IR cs.AI cs.LG

http://creativecommons.org/licenses/by/4.0/

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to holistically reflect their effectiveness due to the significant impact of dataset characteristics on algorithm performance. Addressing this deficiency, this paper introduces a novel benchmarking methodology to facilitate a fair and robust comparison of RecSys algorithms, thereby advancing evaluation practices. By utilizing a diverse set of $30$ open datasets, including two introduced in this work, and evaluating $11$ collaborative filtering algorithms across $9$ metrics, we critically examine the influence of dataset characteristics on algorithm performance. We further investigate the feasibility of aggregating outcomes from multiple datasets into a unified ranking. Through rigorous experimental analysis, we validate the reliability of our methodology under the variability of datasets, offering a benchmarking strategy that balances quality and computational demands. This methodology enables a fair yet effective means of evaluating RecSys algorithms, providing valuable guidance for future research endeavors.

2024-08-28

2402.09781

Sandeep Kumar

Vivek Tetarwal, Sandeep Kumar

A Comprehensive Review on Computer Vision Analysis of Aerial Data

112 pages

IEEE 2024

cs.CV cs.IT math.IT

http://creativecommons.org/licenses/by-nc-nd/4.0/

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.

2024-02-16

2402.09782

Zihong Luo

Zihong Luo, Zheng Tao, Yuxuan Huang, Kexin He, Chengzhi Liu

MC-DBN: A Deep Belief Network-Based Model for Modality Completion

International Conference on Computer Supported Cooperative Work in Design 2024

cs.LG cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recent advancements in multi-modal artificial intelligence (AI) have revolutionized the fields of stock market forecasting and heart rate monitoring. Utilizing diverse data sources can substantially improve prediction accuracy. Nonetheless, additional data may not always align with the original dataset. Interpolation methods are commonly utilized for handling missing values in modal data, though they may exhibit limitations in the context of sparse information. Addressing this challenge, we propose a Modality Completion Deep Belief Network-Based Model (MC-DBN). This approach utilizes implicit features of complete data to compensate for gaps between itself and additional incomplete data. It ensures that the enhanced multi-modal data closely aligns with the dynamic nature of the real world to enhance the effectiveness of the model. We conduct evaluations of the MC-DBN model in two datasets from the stock market forecasting and heart rate monitoring domains. Comprehensive experiments showcase the model's capacity to bridge the semantic divide present in multi-modal data, subsequently enhancing its performance. The source code is available at: https://github.com/logan-0623/DBN-generate

2024-03-21

2402.09795

Sakib Anwar Rieyan

Sakib Anwar Rieyan, Md. Raisul Kabir News, A.B.M. Muntasir Rahman, Sadia Afrin Khan, Sultan Tasneem Jawad Zaarif, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Michele Ianni, Giancarlo Fortino

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Information Fusion, 102, 102004 (2024)

10.1016/j.inffus.2023.102004

cs.CR cs.AI cs.DB

http://creativecommons.org/licenses/by/4.0/

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.

2024-02-16

2402.09844

Quentin Gallou\'edec

Quentin Gallou\'edec and Edward Beeching and Cl\'ement Romac and Emmanuel Dellandr\'ea

Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent

38th Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET 2024)

cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research. The prevailing methodology in Reinforcement Learning (RL) typically limits models to a single task within a unimodal framework, a limitation that contrasts with the broader vision of a versatile, multi-domain model. In this paper, we present Jack of All Trades (JAT), a transformer-based model with a unique design optimized for handling sequential decision-making tasks and multi-modal data types. The JAT model demonstrates its robust capabilities and versatility by achieving strong performance on very different RL benchmarks, along with promising results on Computer Vision (CV) and Natural Language Processing (NLP) tasks, all using a single set of weights. The JAT model marks a significant step towards more general, cross-domain AI model design, and notably, it is the first model of its kind to be fully open-sourced at https://huggingface.co/jat-project/jat, including a pioneering general-purpose dataset.

2024-07-11

2402.09934

Ritwik Banerjee

Khiem Phi, Noushin Salek Faramarzi, Chenlu Wang, Ritwik Banerjee

Paying Attention to Deflections: Mining Pragmatic Nuances for Whataboutism Detection in Online Discourse

14 pages, 5 figures

Findings of the Association for Computational Linguistics ACL. (2024) 12628-12643. https://aclanthology.org/2024.findings-acl.750

cs.CL cs.AI

http://creativecommons.org/licenses/by/4.0/

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the `what about' lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively.

2024-09-24

2402.09949

Leonidas Gee

Leonidas Gee, Leonardo Rigutini, Marco Ernandes, Andrea Zugarini

Multi-word Tokenization for Sequence Compression

The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

10.18653/v1/2023.emnlp-industry.58

cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Large Language Models have proven highly successful at modelling a variety of tasks. However, this comes at a steep computational cost that hinders wider industrial uptake. In this paper, we present MWT: a Multi-Word Tokenizer that goes beyond word boundaries by representing frequent multi-word expressions as single tokens. MWTs produce a more compact and efficient tokenization that yields two benefits: (1) Increase in performance due to a greater coverage of input data given a fixed sequence length budget; (2) Faster and lighter inference due to the ability to reduce the sequence length with negligible drops in performance. Our results show that MWT is more robust across shorter sequence lengths, thus allowing for major speedups via early sequence truncation.

2024-04-08

2402.09977

Leonardo Rigutini

Leonidas Gee and Andrea Zugarini and Leonardo Rigutini and Paolo Torroni

Fast Vocabulary Transfer for Language Model Compression

The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022)

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022): Industry Track

10.18653/v1/2022.emnlp-industry.41

cs.CL cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer. We evaluate the method on various vertical domains and downstream tasks. Our results indicate that vocabulary transfer can be effectively used in combination with other compression techniques, yielding a significant reduction in model size and inference time while marginally compromising on performance.

2024-02-16

2402.09982

Leonardo Rigutini

Enrico Randellini and Leonardo Rigutini and Claudio Sacca'

Data Augmentation and Transfer Learning Approaches Applied to Facial Expressions Recognition

The 11th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2021)

Proceeding of the 11th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2021)

10.5121/csit.2021.111912

cs.CV cs.AI cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The face expression is the first thing we pay attention to when we want to understand a person's state of mind. Thus, the ability to recognize facial expressions in an automatic way is a very interesting research field. In this paper, because the small size of available training datasets, we propose a novel data augmentation technique that improves the performances in the recognition task. We apply geometrical transformations and build from scratch GAN models able to generate new synthetic images for each emotion type. Thus, on the augmented datasets we fine tune pretrained convolutional neural networks with different architectures. To measure the generalization ability of the models, we apply extra-database protocol approach, namely we train models on the augmented versions of training dataset and test them on two different databases. The combination of these techniques allows to reach average accuracy values of the order of 85\% for the InceptionResNetV2 model.

2024-02-16

2402.10002

Hai-Tao Yu

Hai-Tao Yu, Mofei Song

MM-Point: Multi-View Information-Enhanced Multi-Modal Self-Supervised 3D Point Cloud Understanding

Accepted by AAAI 2024

AAAI 2024

cs.CV cs.AI cs.MM

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

In perception, multiple sensory information is integrated to map visual information from 2D views onto 3D objects, which is beneficial for understanding in 3D environments. But in terms of a single 2D view rendered from different angles, only limited partial information can be provided.The richness and value of Multi-view 2D information can provide superior self-supervised signals for 3D objects. In this paper, we propose a novel self-supervised point cloud representation learning method, MM-Point, which is driven by intra-modal and inter-modal similarity objectives. The core of MM-Point lies in the Multi-modal interaction and transmission between 3D objects and multiple 2D views at the same time. In order to more effectively simultaneously perform the consistent cross-modal objective of 2D multi-view information based on contrastive learning, we further propose Multi-MLP and Multi-level Augmentation strategies. Through carefully designed transformation strategies, we further learn Multi-level invariance in 2D Multi-views. MM-Point demonstrates state-of-the-art (SOTA) performance in various downstream tasks. For instance, it achieves a peak accuracy of 92.4% on the synthetic dataset ModelNet40, and a top accuracy of 87.8% on the real-world dataset ScanObjectNN, comparable to fully supervised methods. Additionally, we demonstrate its effectiveness in tasks such as few-shot classification, 3D part segmentation and 3D semantic segmentation.

2024-03-11

2402.10061

Wieland Morgenstern

Wieland Morgenstern, Niklas Gard, Simon Baumann, Anna Hilsmann, Peter Eisert

X-maps: Direct Depth Lookup for Event-based Structured Light Systems

Accepted at the CVPR 2023 Workshop on Event-based Vision: https://tub-rip.github.io/eventvision2023/

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada, 2023, pp. 4007-4015

10.1109/CVPRW59228.2023.00418

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras. These dynamic vision sensors are a great fit to be paired with laser projectors for depth estimation in a structured light approach. Our key contributions involve a conversion of the projector time map into a rectified X-map, capturing x-axis correspondences for incoming events and enabling direct disparity lookup without any additional search. Compared to previous implementations, this significantly simplifies depth estimation, making it more efficient, while the accuracy is similar to the time map-based process. Moreover, we compensate non-linear temporal behavior of cheap laser projectors by a simple time map calibration, resulting in improved performance and increased depth estimation accuracy. Since depth estimation is executed by two lookups only, it can be executed almost instantly (less than 3 ms per frame with a Python implementation) for incoming events. This allows for real-time interactivity and responsiveness, which makes our approach especially suitable for SAR experiences where low latency, high frame rates and direct feedback are crucial. We present valuable insights gained into data transformed into X-maps and evaluate our depth from disparity estimation against the state of the art time map-based results. Additional results and code are available on our project page: https://fraunhoferhhi.github.io/X-maps/

2024-02-16

2402.10067

Kristina Dzeparoska

Kristina Dzeparoska, Jieyu Lin, Ali Tizghadam, Alberto Leon-Garcia

LLM-based policy generation for intent-based management of applications

This article has been accepted for publication in 2023 19th International Conference on Network and Service Management (CNSM), 3rd International Workshop on Analytics for Service and Application Management (AnServApp 2023)

2023 19th International Conference on Network and Service Management (CNSM), 2023, pp. 1-7

10.23919/CNSM59352.2023.10327837

cs.DC cs.AI cs.FL cs.HC cs.LG

http://creativecommons.org/licenses/by-nc-sa/4.0/

Automated management requires decomposing high-level user requests, such as intents, to an abstraction that the system can understand and execute. This is challenging because even a simple intent requires performing a number of ordered steps. And the task of identifying and adapting these steps (as conditions change) requires a decomposition approach that cannot be exactly pre-defined beforehand. To tackle these challenges and support automated intent decomposition and execution, we explore the few-shot capability of Large Language Models (LLMs). We propose a pipeline that progressively decomposes intents by generating the required actions using a policy-based abstraction. This allows us to automate the policy execution by creating a closed control loop for the intent deployment. To do so, we generate and map the policies to APIs and form application management loops that perform the necessary monitoring, analysis, planning and execution. We evaluate our proposal with a use-case to fulfill and assure an application service chain of virtual network functions. Using our approach, we can generalize and generate the necessary steps to realize intents, thereby enabling intent automation for application management.

2024-02-16

2402.10135

Irina Ar\'evalo

Jose L. Salmeron, Irina Ar\'evalo, Antonio Ruiz-Celma

Benchmarking federated strategies in Peer-to-Peer Federated learning for biomedical data

Heliyon 9 (2023) e16925

cs.LG cs.AI cs.DC

http://creativecommons.org/licenses/by/4.0/

The increasing requirements for data protection and privacy has attracted a huge research interest on distributed artificial intelligence and specifically on federated learning, an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. In the initial proposal of federated learning the architecture was centralised and the aggregation was done with federated averaging, meaning that a central server will orchestrate the federation using the most straightforward averaging strategy. This research is focused on testing different federated strategies in a peer-to-peer environment. The authors propose various aggregation strategies for federated learning, including weighted averaging aggregation, using different factors and strategies based on participant contribution. The strategies are tested with varying data sizes to identify the most robust ones. This research tests the strategies with several biomedical datasets and the results of the experiments show that the accuracy-based weighted average outperforms the classical federated averaging method.

2024-02-16

2402.10365

Robert Kosk

Robert Kosk, Richard Southern, Lihua You, Shaojun Bian, Willem Kokke, Greg Maguire

Deep Spectral Meshes: Multi-Frequency Facial Mesh Processing with Graph Neural Networks

26 pages, 10 figures, journal article

Electronics. 2024; 13(4):720

10.3390/electronics13040720

cs.CV cs.CG cs.GR

http://creativecommons.org/licenses/by/4.0/

With the rising popularity of virtual worlds, the importance of data-driven parametric models of 3D meshes has grown rapidly. Numerous applications, such as computer vision, procedural generation, and mesh editing, vastly rely on these models. However, current approaches do not allow for independent editing of deformations at different frequency levels. They also do not benefit from representing deformations at different frequencies with dedicated representations, which would better expose their properties and improve the generated meshes' geometric and perceptual quality. In this work, spectral meshes are introduced as a method to decompose mesh deformations into low-frequency and high-frequency deformations. These features of low- and high-frequency deformations are used for representation learning with graph convolutional networks. A parametric model for 3D facial mesh synthesis is built upon the proposed framework, exposing user parameters that control disentangled high- and low-frequency deformations. Independent control of deformations at different frequencies and generation of plausible synthetic examples are mutually exclusive objectives. A Conditioning Factor is introduced to leverage these objectives. Our model takes further advantage of spectral partitioning by representing different frequency levels with disparate, more suitable representations. Low frequencies are represented with standardised Euclidean coordinates, and high frequencies with a normalised deformation representation (DR). This paper investigates applications of our proposed approach in mesh reconstruction, mesh interpolation, and multi-frequency editing. It is demonstrated that our method improves the overall quality of generated meshes on most datasets when considering both the $L_1$ norm and perceptual Dihedral Angle Mesh Error (DAME) metrics.

2024-02-19

2402.10373

Yanis Labrak

Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, Richard Dufour

BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains

Accepted at ACL 2024 - Proceedings of the 62st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Proceedings of the 62st Annual Meeting of the Association for Computational Linguistics - Volume 1: Long Papers (ACL 2024)

cs.CL cs.AI cs.LG

http://creativecommons.org/publicdomain/zero/1.0/

Large Language Models (LLMs) have demonstrated remarkable versatility in recent years, offering potential applications across specialized domains such as healthcare and medicine. Despite the availability of various open-source LLMs tailored for health contexts, adapting general-purpose LLMs to the medical domain presents significant challenges. In this paper, we introduce BioMistral, an open-source LLM tailored for the biomedical domain, utilizing Mistral as its foundation model and further pre-trained on PubMed Central. We conduct a comprehensive evaluation of BioMistral on a benchmark comprising 10 established medical question-answering (QA) tasks in English. We also explore lightweight models obtained through quantization and model merging approaches. Our results demonstrate BioMistral's superior performance compared to existing open-source medical models and its competitive edge against proprietary counterparts. Finally, to address the limited availability of data beyond English and to assess the multilingual generalization of medical LLMs, we automatically translated and evaluated this benchmark into 7 other languages. This marks the first large-scale multilingual evaluation of LLMs in the medical domain. Datasets, multilingual evaluation benchmarks, scripts, and all the models obtained during our experiments are freely released.

2024-07-18

2402.10404

Ji-Hoon Park

Ji-Hoon Park, Yeong-Joon Ju, and Seong-Whan Lee

Explaining generative diffusion models via visual analysis for interpretable decision-making process

22 pages, published in Expert Systems with Applications

Expert Systems with Applications 248 (2024) 123231

10.1016/j.eswa.2024.123231

cs.CV cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Diffusion models have demonstrated remarkable performance in generation tasks. Nevertheless, explaining the diffusion process remains challenging due to it being a sequence of denoising noisy images that are difficult for experts to interpret. To address this issue, we propose the three research questions to interpret the diffusion process from the perspective of the visual concepts generated by the model and the region where the model attends in each time step. We devise tools for visualizing the diffusion process and answering the aforementioned research questions to render the diffusion process human-understandable. We show how the output is progressively generated in the diffusion process by explaining the level of denoising and highlighting relationships to foundational visual concepts at each time step through the results of experiments with various visual analyses using the tools. Throughout the training of the diffusion model, the model learns diverse visual concepts corresponding to each time-step, enabling the model to predict varying levels of visual concepts at different stages. We substantiate our tools using Area Under Cover (AUC) score, correlation quantification, and cross-attention mapping. Our findings provide insights into the diffusion process and pave the way for further research into explainable diffusion mechanisms.

2024-02-19

2402.10515

Sagnik Bhattacharya

Sagnik Bhattacharya, Junyoung Choi, Joohyun Lee

Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA

IEEE GLOBECOM 2023

eess.SP cs.AI

http://creativecommons.org/licenses/by/4.0/

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research.

2024-02-19

2402.10553

Leonardo Rigutini

Andrea Pazienza and Nicola Macchiarulo and Felice Vitulano and Antonio Fiorentini and Marco Cammisa and Leonardo Rigutini and Ernesto Di Iorio and Achille Globo and Antonio Trevisi

A novel integrated industrial approach with cobots in the age of industry 4.0 through conversational interaction and computer vision

Proceedings of the 6th Italian Conference on Computational Linguistics (CLiC-it 2019)

cs.RO cs.CL cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

From robots that replace workers to robots that serve as helpful colleagues, the field of robotic automation is experiencing a new trend that represents a huge challenge for component manufacturers. The contribution starts from an innovative vision that sees an ever closer collaboration between Cobot, able to do a specific physical job with precision, the AI world, able to analyze information and support the decision-making process, and the man able to have a strategic vision of the future.

2024-02-19

2402.10558

Leonardo Rigutini

Achille Globo and Antonio Trevisi and Andrea Zugarini and Leonardo Rigutini and Marco Maggini and Stefano Melacci

Neural paraphrasing by automatically crawled and aligned sentence pairs

The 6th International Conference on Social Networks Analysis, Management and Security (SNAMS 2019)

Proceedings of The 6th International Conference on Social Networks Analysis, Management and Security (SNAMS 2019)

10.1109/SNAMS.2019.8931824

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphrasing to make the conversation more natural, e.g., talking about a certain topic using different paraphrases in different time instants. Recently, the task of automatically generating paraphrases has been approached in the context of Natural Language Generation (NLG). While many existing systems simply consist in rule-based models, the recent success of the Deep Neural Networks in several NLG tasks naturally suggests the possibility of exploiting such networks for generating paraphrases. However, the main obstacle toward neural-network-based paraphrasing is the lack of large datasets with aligned pairs of sentences and paraphrases, that are needed to efficiently train the neural models. In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles. We propose a similarity search procedure with linguistic constraints that, given a reference sentence, is able to locate the most similar candidate paraphrases out from millions of indexed sentences. The data generation process is evaluated in the case of the Italian language, performing experiments using pointer-based deep neural architectures.

2024-02-19

2402.10717

Raktim Kumar Mondol

Raktim Kumar Mondol, Ewan K.A. Millar, Arcot Sowmya, Erik Meijering

BioFusionNet: Deep Learning-Based Survival Risk Stratification in ER+ Breast Cancer Through Multifeature and Multimodal Data Fusion

Keywords: Multimodal Fusion, Breast Cancer, Whole Slide Images, Deep Neural Network, Survival Prediction

JBHI, 24 June 2024

10.1109/JBHI.2024.3418341

cs.CV cs.AI

http://creativecommons.org/licenses/by/4.0/

Breast cancer is a significant health concern affecting millions of women worldwide. Accurate survival risk stratification plays a crucial role in guiding personalised treatment decisions and improving patient outcomes. Here we present BioFusionNet, a deep learning framework that fuses image-derived features with genetic and clinical data to obtain a holistic profile and achieve survival risk stratification of ER+ breast cancer patients. We employ multiple self-supervised feature extractors (DINO and MoCoV3) pretrained on histopathological patches to capture detailed image features. These features are then fused by a variational autoencoder and fed to a self-attention network generating patient-level features. A co-dual-cross-attention mechanism combines the histopathological features with genetic data, enabling the model to capture the interplay between them. Additionally, clinical data is incorporated using a feed-forward network, further enhancing predictive performance and achieving comprehensive multimodal feature integration. Furthermore, we introduce a weighted Cox loss function, specifically designed to handle imbalanced survival data, which is a common challenge. Our model achieves a mean concordance index of 0.77 and a time-dependent area under the curve of 0.84, outperforming state-of-the-art methods. It predicts risk (high versus low) with prognostic significance for overall survival in univariate analysis (HR=2.99, 95% CI: 1.88--4.78, p<0.005), and maintains independent significance in multivariate analysis incorporating standard clinicopathological variables (HR=2.91, 95\% CI: 1.80--4.68, p<0.005).

2024-07-02

2402.10753

Junjie Ye

Junjie Ye, Sixian Li, Guanyu Li, Caishuang Huang, Songyang Gao, Yilong Wu, Qi Zhang, Tao Gui, Xuanjing Huang

ToolSword: Unveiling Safety Issues of Large Language Models in Tool Learning Across Three Stages

Accepted by ACL 2024 Main Conference

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 2024 (Volume 1: Long Papers)

cs.CL cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Tool learning is widely acknowledged as a foundational approach or deploying large language models (LLMs) in real-world scenarios. While current research primarily emphasizes leveraging tools to augment LLMs, it frequently neglects emerging safety considerations tied to their application. To fill this gap, we present *ToolSword*, a comprehensive framework dedicated to meticulously investigating safety issues linked to LLMs in tool learning. Specifically, ToolSword delineates six safety scenarios for LLMs in tool learning, encompassing **malicious queries** and **jailbreak attacks** in the input stage, **noisy misdirection** and **risky cues** in the execution stage, and **harmful feedback** and **error conflicts** in the output stage. Experiments conducted on 11 open-source and closed-source LLMs reveal enduring safety challenges in tool learning, such as handling harmful queries, employing risky tools, and delivering detrimental feedback, which even GPT-4 is susceptible to. Moreover, we conduct further studies with the aim of fostering research on tool learning safety. The data is released in https://github.com/Junjie-Ye/ToolSword.

2024-08-19

2402.10776

Eduardo Juarez

H. Fabelo, S. Ortega, A. Szolna, D. Bulters, J.F. Pineiro, S. Kabwama, A. Shanahan, H. Bulstrode, S. Bisshopp, B.R. Kiran, D. Ravi, R. Lazcano, D. Madronal, C. Sosa, C. Espino, M. Marquez, M. De la Luz Plaza, R. Camacho, D. Carrera, M. Hernandez, G.M. Callico, J. Morera, B. Stanciulescu, G.Z. Yang, R. Salvador, E. Juarez, C. Sanz and R. Sarmiento

In-Vivo Hyperspectral Human Brain Image Database for Brain Cancer Detection

19 pages, 12 figures

IEEE Access, 2019, 7, pp. 39098 39116

10.1109/ACCESS.2019.2904788

eess.IV cs.CV

http://creativecommons.org/licenses/by/4.0/

The use of hyperspectral imaging for medical applications is becoming more common in recent years. One of the main obstacles that researchers find when developing hyperspectral algorithms for medical applications is the lack of specific, publicly available, and hyperspectral medical data. The work described in this paper was developed within the framework of the European project HELICoiD (HypErspectraL Imaging Cancer Detection), which had as a main goal the application of hyperspectral imaging to the delineation of brain tumors in real-time during neurosurgical operations. In this paper, the methodology followed to generate the first hyperspectral database of in-vivo human brain tissues is presented. Data was acquired employing a customized hyperspectral acquisition system capable of capturing information in the Visual and Near InfraRed (VNIR) range from 400 to 1000 nm. Repeatability was assessed for the cases where two images of the same scene were captured consecutively. The analysis reveals that the system works more efficiently in the spectral range between 450 and 900 nm. A total of 36 hyperspectral images from 22 different patients were obtained. From these data, more than 300 000 spectral signatures were labeled employing a semi-automatic methodology based on the spectral angle mapper algorithm. Four different classes were defined: normal tissue, tumor tissue, blood vessel, and background elements. All the hyperspectral data has been made available in a public repository.

2024-02-19

2402.10828

Jianhao Yuan

Jianhao Yuan, Shuyang Sun, Daniel Omeiza, Bo Zhao, Paul Newman, Lars Kunze, Matthew Gadd

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model

14 pages, 6 figures

Robotics: Science and Systems (RSS) 2024

cs.RO cs.AI

http://creativecommons.org/licenses/by/4.0/

We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in complex autonomous driving. Recent advancements in Multi-Modal Large Language models (MLLMs) have shown promising potential in enhancing the explainability as a driving agent by producing control predictions along with natural language explanations. However, severe data scarcity due to expensive annotation costs and significant domain gaps between different datasets makes the development of a robust and generalisable system an extremely challenging task. Moreover, the prohibitively expensive training requirements of MLLM and the unsolved problem of catastrophic forgetting further limit their generalisability post-deployment. To address these challenges, we present RAG-Driver, a novel retrieval-augmented multi-modal large language model that leverages in-context learning for high-performance, explainable, and generalisable autonomous driving. By grounding in retrieved expert demonstration, we empirically validate that RAG-Driver achieves state-of-the-art performance in producing driving action explanations, justifications, and control signal prediction. More importantly, it exhibits exceptional zero-shot generalisation capabilities to unseen environments without further training endeavours.

2024-05-30

2402.10847

Ekta Gavas

Ekta Gavas, Kaustubh Olpadkar, Anoop Namboodiri

Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning

8 pages, 4 figures, Accepted at 19th VISIGRAPP 2024: VISAPP conference

Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 2: VISAPP, ISBN 978-989-758-679-8, ISSN 2184-4321, pages 821-828

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancement, our method employs a specialized encoder to derive representations from fingerprint images in a self-supervised manner. We further refine these representations, aiming to enhance the verification capabilities. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance against established self-supervised training techniques. Our findings not only highlight the effectiveness of our method but also pave the way for potential advancements. Crucially, our research indicates that it is feasible to extract meaningful fingerprint representations from degraded images without relying on enhanced samples.

2024-02-19

2402.10943

Benjamin Kiessling

Benjamin Kiessling (PSL), Gennady Kurin, Matthew Thomas Miller, Kader Smail

Advances and Limitations in Open Source Arabic-Script OCR: A Case Study

Digital Studies / Le champ num{\'e}rique, 2021, 11 (1)

10.16995/dscn.8094

cs.CL cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This work presents an accuracy study of the open source OCR engine, Kraken, on the leading Arabic scholarly journal, al-Abhath. In contrast with other commercially available OCR engines, Kraken is shown to be capable of producing highly accurate Arabic-script OCR. The study also assesses the relative accuracy of typeface-specific and generalized models on the al-Abhath data and provides a microanalysis of the ``error instances'' and the contextual features that may have contributed to OCR misrecognition. Building on this analysis, the paper argues that Arabic-script OCR can be significantly improved through (1) a more systematic approach to training data production, and (2) the development of key technological components, especially multi-language models and improved line segmentation and layout analysis. Cet article pr{\'e}sente une {\'e}tude d'exactitude du moteur ROC open source, Krakan, sur la revue acad{\'e}mique arabe de premier rang, al-Abhath. Contrairement {\`a} d'autres moteurs ROC disponibles sur le march{\'e}, Kraken se r{\'e}v{\`e}le {\^e}tre capable de produire de la ROC extr{\^e}mement exacte de l'{\'e}criture arabe. L'{\'e}tude {\'e}value aussi l'exactitude relative des mod{\`e}les sp{\'e}cifiquement configur{\'e}s {\`a} des polices et celle des mod{\`e}les g{\'e}n{\'e}ralis{\'e}s sur les donn{\'e}es d'al-Abhath et fournit une microanalyse des "occurrences d'erreurs", ainsi qu'une microanalyse des {\'e}l{\'e}ments contextuels qui pourraient avoir contribu{\'e} {\`a} la m{\'e}reconnaissance ROC. S'appuyant sur cette analyse, cet article fait valoir que la ROC de l'{\'e}criture arabe peut {\^e}tre consid{\'e}rablement am{\'e}lior{\'e}e gr{\^a}ce {\`a} (1) une approche plus syst{\'e}matique d'entra{\^i}nement de la production de donn{\'e}es et (2) gr{\^a}ce au d{\'e}veloppement de composants technologiques fondamentaux, notammentl'am{\'e}lioration des mod{\`e}les multilingues, de la segmentation de ligne et de l'analyse de la mise en page.

2024-02-20

2402.10948

Wenyu Li

Wenyu Li, Yinuo Zhu, Xin Lin, Ming Li, Ziyue Jiang, Ziqian Zeng

Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales

4 pages,2 figures

The Web Conference (WWW) 2024, Short Paper

10.1145/3589335.3651584.

cs.CL cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data. The generative approaches, such as those based on large language models (LLMs), have the potential to get rid of heavy annotations and provide explanations but their capabilities still fall short compared to discriminative approaches, and their explanations may be unreliable due to the fact that the generation of explanation is a black-box process. Inspired by the psychological assessment practice of using scales to evaluate mental states, our method which is called Mental Analysis by Incorporating Mental Scales (MAIMS), incorporates two procedures via LLMs. First, the patient completes mental scales, and second, the psychologist interprets the collected information from the mental scales and makes informed decisions. Experimental results show that MAIMS outperforms other zero-shot methods. MAIMS can generate more rigorous explanation based on the outputs of mental scales

2024-04-23

2402.10967

Jos\'e Alberto Ben\'itez-Andrades Ph.D.

Jos\'e Alberto Ben\'itez-Andrades, Isa\'ias Garc\'ia-Rodr\'iguez, Carmen Benavides, H\'ector Alaiz-Moret\'on and Alejandro Rodr\'iguez-Gonz\'alez

Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies

Future Generation Computer Systems, Volume 106, May 2020, Pages 154-170

10.1016/j.future.2020.01.002

cs.AI

http://creativecommons.org/licenses/by-nc-nd/4.0/

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.

2024-02-20

2402.10977

Fengqi You

Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar, Fengqi You

Generative AI and Process Systems Engineering: The Next Frontier

Computers & Chemical Engineering, Volume 187, August 2024, 108723

10.1016/j.compchemeng.2024.108723

cs.LG cs.AI cs.SY eess.SY math.OC

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.

2024-06-18

2402.11161

Zongxia Li

Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Lee Boyd-Graber

PEDANTS: Cheap but Effective and Interpretable Answer Equivalence

Efficient PEDANTS Classifier for short-form QA in github: https://github.com/zli12321/qa_metrics. arXiv admin note: text overlap with arXiv:2401.13170

Empirical Methods in Natural Language Processing 2024

cs.CL cs.AI

http://creativecommons.org/licenses/by/4.0/

Question answering (QA) can only make progress if we know if an answer is correct, but current answer correctness (AC) metrics struggle with verbose, free-form answers from large language models (LLMs). There are two challenges with current short-form QA evaluations: a lack of diverse styles of evaluation data and an over-reliance on expensive and slow LLMs. LLM-based scorers correlate better with humans, but this expensive task has only been tested on limited QA datasets. We rectify these issues by providing rubrics and datasets for evaluating machine QA adopted from the Trivia community. We also propose an efficient, and interpretable QA evaluation that is more stable than an exact match and neural methods(BERTScore).

2024-10-15

2402.11175

Yuxia Wang

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su, Artem Shelmanov, Akim Tsvigun, Osama Mohanned Afzal, Tarek Mahmoud, Giovanni Puccetti, Thomas Arnold, Alham Fikri Aji, Nizar Habash, Iryna Gurevych, Preslav Nakov

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

29 pages

ACL 2024 main

cs.CL

http://creativecommons.org/licenses/by/4.0/

The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels. This raises legitimate concerns about its potential misuse and societal implications. The need to identify and differentiate such content from genuine human-generated text is critical in combating disinformation, preserving the integrity of education and scientific fields, and maintaining trust in communication. In this work, we address this problem by introducing a new benchmark based on a multilingual, multi-domain, and multi-generator corpus of MGTs -- M4GT-Bench. The benchmark is compiled of three tasks: (1) mono-lingual and multi-lingual binary MGT detection; (2) multi-way detection where one need to identify, which particular model generated the text; and (3) mixed human-machine text detection, where a word boundary delimiting MGT from human-written content should be determined. On the developed benchmark, we have tested several MGT detection baselines and also conducted an evaluation of human performance. We see that obtaining good performance in MGT detection usually requires an access to the training data from the same domain and generators. The benchmark is available at https://github.com/mbzuai-nlp/M4GT-Bench.

2024-06-28

2402.11203

Yizheng Huang

Yizheng Huang and Jimmy Huang

Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges

Survey Paper

Web Intelligence, vol. 22, no. 1, pp. 31-44, 2024

10.3233/WEB-230363

cs.IR cs.AI cs.CL cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments.

2024-04-18

2402.11273

Yifei Chen

Yifei Chen, Chenyan Zhang, Yifan Ke, Yiyu Huang, Xuezhou Dai, Feiwei Qin, Yongquan Zhang, Xiaodong Zhang, Changmiao Wang

Semi-supervised Medical Image Segmentation Method Based on Cross-pseudo Labeling Leveraging Strong and Weak Data Augmentation Strategies

5 pages, 2 figures, accept ISBI2024

ISBI 2024

cs.CV cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Traditional supervised learning methods have historically encountered certain constraints in medical image segmentation due to the challenging collection process, high labeling cost, low signal-to-noise ratio, and complex features characterizing biomedical images. This paper proposes a semi-supervised model, DFCPS, which innovatively incorporates the Fixmatch concept. This significantly enhances the model's performance and generalizability through data augmentation processing, employing varied strategies for unlabeled data. Concurrently, the model design gives appropriate emphasis to the generation, filtration, and refinement processes of pseudo-labels. The novel concept of cross-pseudo-supervision is introduced, integrating consistency learning with self-training. This enables the model to fully leverage pseudo-labels from multiple perspectives, thereby enhancing training diversity. The DFCPS model is compared with both baseline and advanced models using the publicly accessible Kvasir-SEG dataset. Across all four subdivisions containing different proportions of unlabeled data, our model consistently exhibits superior performance. Our source code is available at https://github.com/JustlfC03/DFCPS.

2024-02-20

2402.11274

Yifei Chen

Chenyan Zhang, Yifei Chen, Zhenxiong Fan, Yiyu Huang, Wenchao Weng, Ruiquan Ge, Dong Zeng, Changmiao Wang

TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method

5 pages, 2 figures, accept ISBI2024

ISBI 2024

eess.IV cs.CV cs.LG

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Recently, diffusion models have gained significant attention as a novel set of deep learning-based generative methods. These models attempt to sample data from a Gaussian distribution that adheres to a target distribution, and have been successfully adapted to the reconstruction of MRI data. However, as an unconditional generative model, the diffusion model typically disrupts image coordination because of the consistent projection of data introduced by conditional bootstrap. This often results in image fragmentation and incoherence. Furthermore, the inherent limitations of the diffusion model often lead to excessive smoothing of the generated images. In the same vein, some deep learning-based models often suffer from poor generalization performance, meaning their effectiveness is greatly affected by different acceleration factors. To address these challenges, we propose a novel diffusion model-based MRI reconstruction method, named TC-DiffRecon, which does not rely on a specific acceleration factor for training. We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model while mitigating the over-smoothing issue to a certain extent. During the image generation sampling process, we employ a novel TCKG module and a Coarse-to-Fine sampling scheme. These additions aim to harmonize image texture, expedite the sampling process, while achieving data consistency. Our source code is available at https://github.com/JustlfC03/TC-DiffRecon.

2024-02-20

2402.11287

Tomas Jelinek

Tom\'a\v{s} Jel\'inek, Jon\'a\v{s} \v{S}er\'ych, Ji\v{r}\'i Matas

Dense Matchers for Dense Tracking

Proceedings of the 27th Computer Vision Winter Workshop. Ljubljana: Slovenian Pattern Recognition Society, 2024. p. 18-28

cs.CV

http://creativecommons.org/licenses/by/4.0/

Optical flow is a useful input for various applications, including 3D reconstruction, pose estimation, tracking, and structure-from-motion. Despite its utility, the field of dense long-term tracking, especially over wide baselines, has not been extensively explored. This paper extends the concept of combining multiple optical flows over logarithmically spaced intervals as proposed by MFT. We demonstrate the compatibility of MFT with different optical flow networks, yielding results that surpass their individual performance. Moreover, we present a simple yet effective combination of these networks within the MFT framework. This approach proves to be competitive with more sophisticated, non-causal methods in terms of position prediction accuracy, highlighting the potential of MFT in enhancing long-term tracking applications.

2024-02-22

2402.11305

Juliette Marrie

Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus

On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models

Published in Transactions on Machine Learning Research (TMLR), 2024

cs.CV

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Large pretrained visual models exhibit remarkable generalization across diverse recognition tasks. Yet, real-world applications often demand compact models tailored to specific problems. Variants of knowledge distillation have been devised for such a purpose, enabling task-specific compact models (the students) to learn from a generic large pretrained one (the teacher). In this paper, we show that the excellent robustness and versatility of recent pretrained models challenge common practices established in the literature, calling for a new set of optimal guidelines for task-specific distillation. To address the lack of samples in downstream tasks, we also show that a variant of Mixup based on stable diffusion complements standard data augmentation. This strategy eliminates the need for engineered text prompts and improves distillation of generic models into streamlined specialized networks.

2024-05-08

2402.11319

Junhyun Park

Junhyun Park, Seonghyeok Jang, Hyojae Park, Seongjun Bae, Minho Hwang

Hysteresis Compensation of Flexible Continuum Manipulator using RGBD Sensing and Temporal Convolutional Network

8 pages, 11 figures, 5 tables

IEEE Robotics and Automation Letters, Volume 9, Issue 7, 6091 - 6098, 2024

10.1109/LRA.2024.3398501

cs.RO cs.AI

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Flexible continuum manipulators are valued for minimally invasive surgery, offering access to confined spaces through nonlinear paths. However, cable-driven manipulators face control difficulties due to hysteresis from cabling effects such as friction, elongation, and coupling. These effects are difficult to model due to nonlinearity and the difficulties become even more evident when dealing with long and coupled, multi-segmented manipulator. This paper proposes a data-driven approach based on Deep Neural Networks (DNN) to capture these nonlinear and previous states-dependent characteristics of cable actuation. We collect physical joint configurations according to command joint configurations using RGBD sensing and 7 fiducial markers to model the hysteresis of the proposed manipulator. Result on a study comparing the estimation performance of four DNN models show that the Temporal Convolution Network (TCN) demonstrates the highest predictive capability. Leveraging trained TCNs, we build a control algorithm to compensate for hysteresis. Tracking tests in task space using unseen trajectories show that the proposed control algorithm reduces the average position and orientation error by 61.39% (from 13.7mm to 5.29 mm) and 64.04% (from 31.17{\deg} to 11.21{\deg}), respectively. This result implies that the proposed calibrated controller effectively reaches the desired configurations by estimating the hysteresis of the manipulator. Applying this method in real surgical scenarios has the potential to enhance control precision and improve surgical performance.

2024-06-25

2402.11353

Young-Ho Kim

Eunkyung Jo, Yuin Jeong, SoHyun Park, Daniel A. Epstein, Young-Ho Kim

Understanding the Impact of Long-Term Memory on Self-Disclosure with Large Language Model-Driven Chatbots for Public Health Intervention

Accepted to ACM CHI 2024 as a full paper

In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11-16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA

10.1145/3613904.3642420

cs.HC cs.AI cs.CL

http://creativecommons.org/licenses/by-nc-sa/4.0/

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, but we lack an understanding of how LTM impacts people's interaction with LLM-driven chatbots in public health interventions. We examine the case of CareCall -- an LLM-driven voice chatbot with LTM -- through the analysis of 1,252 call logs and interviews with nine users. We found that LTM enhanced health disclosure and fostered positive perceptions of the chatbot by offering familiarity. However, we also observed challenges in promoting self-disclosure through LTM, particularly around addressing chronic health conditions and privacy concerns. We discuss considerations for LTM integration in LLM-driven chatbots for public health monitoring, including carefully deciding what topics need to be remembered in light of public health goals.

2024-02-20

2402.11457

Shiyu Ni

Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng

When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation

Findings of ACL2024

cs.CL

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA all the time. A straightforward idea is to only conduct retrieval when LLMs are uncertain about a question. This motivates us to enhance the LLMs' ability to perceive their knowledge boundaries to help RA. In this paper, we first quantitatively measure LLMs' such ability and confirm their overconfidence. Then, we study how LLMs' certainty about a question correlates with their dependence on external retrieved information. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence. Additionally, equipped with these methods, LLMs can achieve comparable or even better performance of RA with much fewer retrieval calls.

2024-06-12

2402.11523

Peijie Sun

Peijie Sun, Le Wu, Kun Zhang, Xiangzhi Chen, and Meng Wang

Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering

IEEE TKDE, 2023

10.1109/TKDE.2023.3317068

cs.IR cs.AI

http://creativecommons.org/licenses/by/4.0/

While effective in recommendation tasks, collaborative filtering (CF) techniques face the challenge of data sparsity. Researchers have begun leveraging contrastive learning to introduce additional self-supervised signals to address this. However, this approach often unintentionally distances the target user/item from their collaborative neighbors, limiting its efficacy. In response, we propose a solution that treats the collaborative neighbors of the anchor node as positive samples within the final objective loss function. This paper focuses on developing two unique supervised contrastive loss functions that effectively combine supervision signals with contrastive loss. We analyze our proposed loss functions through the gradient lens, demonstrating that different positive samples simultaneously influence updating the anchor node's embeddings. These samples' impact depends on their similarities to the anchor node and the negative samples. Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model. Our proposed Neighborhood-Enhanced Supervised Contrastive Loss (NESCL) model substitutes the contrastive loss function in SGL with our novel loss function, showing marked performance improvement. On three real-world datasets, Yelp2018, Gowalla, and Amazon-Book, our model surpasses the original SGL by 10.09%, 7.09%, and 35.36% on NDCG@20, respectively.

2024-02-20

2402.11569

Eric Nichols

Matou\v{s} Jel\'inek and Eric Nichols and Randy Gomez

Developing Autonomous Robot-Mediated Behavior Coaching Sessions with Haru

Accepted as Late Breaking Report (LBR) at the 19th Annual ACM/IEEE International Conference on Human Robot Interaction (HRI '24)

HRI '24 Companion, March 11-14, 2024, Boulder, CO, USA

10.1145/3610978.3640583

cs.RO cs.AI cs.CL

http://creativecommons.org/licenses/by/4.0/

This study presents an empirical investigation into the design and impact of autonomous dialogues in human-robot interaction for behavior change coaching. We focus on the use of Haru, a tabletop social robot, and explore the implementation of the Tiny Habits method for fostering positive behavior change. The core of our study lies in developing a fully autonomous dialogue system that maximizes Haru's emotional expressiveness and unique personality. Our methodology involved iterative design and extensive testing of the dialogue system, ensuring it effectively embodied the principles of the Tiny Habits method while also incorporating strategies for trust-raising and trust-dampening. The effectiveness of the final version of the dialogue was evaluated in an experimental study with human participants (N=12). The results indicated a significant improvement in perceptions of Haru's liveliness, interactivity, and neutrality. Additionally, our study contributes to the broader understanding of dialogue design in social robotics, offering practical insights for future developments in the field.

2024-02-20