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2102.05229

Binjie Qin

Dongdong Hao, Song Ding, Linwei Qiu, Yisong Lv, Baowei Fei, Yueqi Zhu, Binjie Qin

Sequential vessel segmentation via deep channel attention network

14

Neural Networks, 2020

10.1016/j.neunet.2020.05.005

cs.CV physics.med-ph

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

This paper develops a novel encoder-decoder deep network architecture which exploits the several contextual frames of 2D+t sequential images in a sliding window centered at current frame to segment 2D vessel masks from the current frame. The architecture is equipped with temporal-spatial feature extraction in encoder stage, feature fusion in skip connection layers and channel attention mechanism in decoder stage. In the encoder stage, a series of 3D convolutional layers are employed to hierarchically extract temporal-spatial features. Skip connection layers subsequently fuse the temporal-spatial feature maps and deliver them to the corresponding decoder stages. To efficiently discriminate vessel features from the complex and noisy backgrounds in the XCA images, the decoder stage effectively utilizes channel attention blocks to refine the intermediate feature maps from skip connection layers for subsequently decoding the refined features in 2D ways to produce the segmented vessel masks. Furthermore, Dice loss function is implemented to train the proposed deep network in order to tackle the class imbalance problem in the XCA data due to the wide distribution of complex background artifacts. Extensive experiments by comparing our method with other state-of-the-art algorithms demonstrate the proposed method's superior performance over other methods in terms of the quantitative metrics and visual validation. The source codes are at https://github.com/Binjie-Qin/SVS-net

2021-02-11

2102.05260

Sm Zobaed

Sm Zobaed, Md Enamul Haque, Md Fazle Rabby, and Mohsen Amini Salehi

SensPick: Sense Picking for Word Sense Disambiguation

16th IEEE International Conference on Semantic Computing, ICSC'2021

cs.CL cs.IR

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

Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.

2021-02-11

2102.05263

Santiago Ontanon

Robert C. Gray, Jichen Zhu, Santiago Onta\~n\'on

Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits

8 pages

In proceedings of the 2020 IEEE Conference on Games (CoG) (pp. 312-319)

cs.LG cs.AI stat.ML

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

This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i.e., when the bandit strategy is only allowed very few interactions with the environment. This is an understudied setting in the MAB literature with many applications in the context of games, such as player modeling. Specifically, we pursue three different ideas. First, we explore the use of regression oracles, which replace the simple average used in strategies such as epsilon-greedy with linear regression models. Second, we examine different exploration patterns such as forced exploration phases. Finally, we introduce a new variant of the UCB1 strategy called UCBT that has interesting properties and no tunable parameters. We present experimental results in a domain motivated by exergames, where the goal is to maximize a player's daily steps. Our results show that the combination of epsilon-greedy or epsilon-decreasing with regression oracles outperforms all other tested strategies in the short horizon setting.

2021-02-11

2102.05264

Santiago Ontanon

Robert C. Gray, Jichen Zhu, Dannielle Arigo, Evan Forman and Santiago Onta\~n\'on

Player Modeling via Multi-Armed Bandits

In Proceedings of the International Conference on the Foundations of Digital Games (FDG 2020)

cs.AI cs.LG

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

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players' social comparison orientation (SCO) and present empirical results from both simulations and real players.

2021-02-11

2102.05424

Jintai Chen

Jintai Chen, Bohan Yu, Biwen Lei, Ruiwei Feng, Danny Z. Chen, Jian Wu

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs" @inproceedings{chen2020doctor, title={Doctor imitator: A graph-based bone age assessment framework using hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020} }

International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI-2020)

10.1007/978-3-030-59725-2_74

cs.CV cs.AI

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

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.

2023-04-25

2102.05599

Muhammad Burhan Hafez

Julien Scholz, Cornelius Weber, Muhammad Burhan Hafez and Stefan Wermter

Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

Proc. Intl. Joint Conf. Neural Networks (IJCNN), 2021, forthcoming

10.1109/IJCNN52387.2021.9534023

cs.LG cs.AI

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

Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be learned dynamically, generalizing the agent to many more tasks while at the same time achieving state-of-the-art performance. Notably, MuZero uses internal state representations derived from real environment states for its predictions. In this paper, we bind the model's predicted internal state representation to the environment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently and unsupervised, acting as constraints to stabilize the learning process. Our experiments show that this new integration of reconstruction model loss and simpler consistency loss provide a significant performance increase in OpenAI Gym environments. Our modifications also enable self-supervised pretraining for MuZero, so the algorithm can learn about environment dynamics before a goal is made available.

2022-01-19

2102.05645

Andrew Brown

Andrew Brown, Ernesto Coto, Andrew Zisserman

Automated Video Labelling: Identifying Faces by Corroborative Evidence

IEEE 4th International Conference on Multimedia Information Processing and Retrieval (IEEE MIPR 2021)

cs.CV

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

We present a method for automatically labelling all faces in video archives, such as TV broadcasts, by combining multiple evidence sources and multiple modalities (visual and audio). We target the problem of ever-growing online video archives, where an effective, scalable indexing solution cannot require a user to provide manual annotation or supervision. To this end, we make three key contributions: (1) We provide a novel, simple, method for determining if a person is famous or not using image-search engines. In turn this enables a face-identity model to be built reliably and robustly, and used for high precision automatic labelling; (2) We show that even for less-famous people, image-search engines can then be used for corroborative evidence to accurately label faces that are named in the scene or the speech; (3) Finally, we quantitatively demonstrate the benefits of our approach on different video domains and test settings, such as TV shows and news broadcasts. Our method works across three disparate datasets without any explicit domain adaptation, and sets new state-of-the-art results on all the public benchmarks.

2021-02-11

2102.05875

Kaiwen Li

Kaiwen Li, Tao Zhang, Rui Wang Yuheng Wang, and Yi Han

Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems

26 August 2021, IEEE Transactions on Cybernetics

10.1109/TCYB.2021.3103811

cs.NE cs.AI

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

This paper introduces a new deep learning approach to approximately solve the Covering Salesman Problem (CSP). In this approach, given the city locations of a CSP as input, a deep neural network model is designed to directly output the solution. It is trained using the deep reinforcement learning without supervision. Specifically, in the model, we apply the Multi-head Attention to capture the structural patterns, and design a dynamic embedding to handle the dynamic patterns of the problem. Once the model is trained, it can generalize to various types of CSP tasks (different sizes and topologies) with no need of re-training. Through controlled experiments, the proposed approach shows desirable time complexity: it runs more than 20 times faster than the traditional heuristic solvers with a tiny gap of optimality. Moreover, it significantly outperforms the current state-of-the-art deep learning approaches for combinatorial optimization in the aspect of both training and inference. In comparison with traditional solvers, this approach is highly desirable for most of the challenging tasks in practice that are usually large-scale and require quick decisions.

2021-09-15

2102.05894

Ali Nassif

Ali Bou Nassif, Ismail Shahin, Shibani Hamsa, Nawel Nemmour, Keikichi Hirose

CASA-Based Speaker Identification Using Cascaded GMM-CNN Classifier in Noisy and Emotional Talking Conditions

Published in Applied Soft Computing journal

Applied Soft Computing, Elsevier, 2021

10.1016/j.asoc.2021.107141

cs.SD cs.AI eess.AS

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

This work aims at intensifying text-independent speaker identification performance in real application situations such as noisy and emotional talking conditions. This is achieved by incorporating two different modules: a Computational Auditory Scene Analysis CASA based pre-processing module for noise reduction and cascaded Gaussian Mixture Model Convolutional Neural Network GMM-CNN classifier for speaker identification followed by emotion recognition. This research proposes and evaluates a novel algorithm to improve the accuracy of speaker identification in emotional and highly-noise susceptible conditions. Experiments demonstrate that the proposed model yields promising results in comparison with other classifiers when Speech Under Simulated and Actual Stress SUSAS database, Emirati Speech Database ESD, the Ryerson Audio-Visual Database of Emotional Speech and Song RAVDESS database and the Fluent Speech Commands database are used in a noisy environment.

2021-02-12

2102.05918

Chao Jia

Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

ICML 2021

International Conference on Machine Learning 2021

cs.CV cs.CL cs.LG

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

Pre-trained representations are becoming crucial for many NLP and perception tasks. While representation learning in NLP has transitioned to training on raw text without human annotations, visual and vision-language representations still rely heavily on curated training datasets that are expensive or require expert knowledge. For vision applications, representations are mostly learned using datasets with explicit class labels such as ImageNet or OpenImages. For vision-language, popular datasets like Conceptual Captions, MSCOCO, or CLIP all involve a non-trivial data collection (and cleaning) process. This costly curation process limits the size of datasets and hence hinders the scaling of trained models. In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset. A simple dual-encoder architecture learns to align visual and language representations of the image and text pairs using a contrastive loss. We show that the scale of our corpus can make up for its noise and leads to state-of-the-art representations even with such a simple learning scheme. Our visual representation achieves strong performance when transferred to classification tasks such as ImageNet and VTAB. The aligned visual and language representations enables zero-shot image classification and also set new state-of-the-art results on Flickr30K and MSCOCO image-text retrieval benchmarks, even when compared with more sophisticated cross-attention models. The representations also enable cross-modality search with complex text and text + image queries.

2021-06-14

2102.05954

Paramita Koley

Paramita Koley, Avirup Saha, Sourangshu Bhattacharya, Niloy Ganguly, and Abir De

Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach

25 Pages, Accepted in ACM TKDD, 2021

ACM Trans. Knowl. Discov. Data. 1, 1, Article 1 (January 2021), 25 pages

10.1145/3449361

cs.SI cs.AI cs.LG

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

The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc. Accurate demarcation of endogenous and exogenous messages offers an important cue to opinion modeling, thereby enhancing its predictive performance. In this paper, we design a suite of unsupervised classification methods based on experimental design approaches, in which, we aim to select the subsets of events which minimize different measures of mean estimation error. In more detail, we first show that these subset selection tasks are NP-Hard. Then we show that the associated objective functions are weakly submodular, which allows us to cast efficient approximation algorithms with guarantees. Finally, we validate the efficacy of our proposal on various real-world datasets crawled from Twitter as well as diverse synthetic datasets. Our experiments range from validating prediction performance on unsanitized and sanitized events to checking the effect of selecting optimal subsets of various sizes. Through various experiments, we have found that our method offers a significant improvement in accuracy in terms of opinion forecasting, against several competitors.

2021-02-12

2102.06019

Anav Mehta

Anav Mehta

Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)

Canadian Science Fair Journal Volume 4 Issue 1 https://csfjournal.com/volume-4-issue-1-1/2021/9/24/reinforcement-learning-for-constraint-satisfaction-game-agents-15-puzzle-minesweeper-2048-and-sudoku

cs.LG cs.AI

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

In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network. Deep Q-Learning has shown promising results in games such as Atari and AlphaGo. Instead of learning the entire Q-table, it learns an estimate of the Q function that determines a state's policy action. We use Q-Learning and deep Q-learning, to learn control policies of four constraint satisfaction games (15-Puzzle, Minesweeper, 2048, and Sudoku). 15-Puzzle is a sliding permutation puzzle and provides a challenge in addressing its large state space. Minesweeper and Sudoku involve partially observable states and guessing. 2048 is also a sliding puzzle but allows for easier state representation (compared to 15-Puzzle) and uses interesting reward shaping to solve the game. These games offer unique insights into the potential and limits of reinforcement learning. The Q agent is trained with no rules of the game, with only the reward corresponding to each state's action. Our unique contribution is in choosing the reward structure, state representation, and formulation of the deep neural network. For low shuffle, 15-Puzzle, achieves a 100% win rate, the medium and high shuffle achieve about 43% and 22% win rates respectively. On a standard 16x16 Minesweeper board, both low and high-density boards achieve close to 45% win rate, whereas medium density boards have a low win rate of 15%. For 2048, the 1024 win rate was achieved with significant ease (100%) with high win rates for 2048, 4096, 8192 and 16384 as 40%, 0.05%, 0.01% and 0.004% , respectively. The easy Sudoku games had a win rate of 7%, while medium and hard games had 2.1% and 1.2% win rates, respectively. This paper explores the environment complexity and behavior of a subset of constraint games using reward structures which can get us closer to understanding how humans learn.

2021-10-08

2102.06125

Dong Si

Dong Si, Andrew Nakamura, Runbang Tang, Haowen Guan, Jie Hou, Ammaar Firozi, Renzhi Cao, Kyle Hippe, Minglei Zhao

Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM

Wiley Interdisciplinary Reviews: Computational Molecular Science, e1542 (2021)

10.1002/wcms.1542

q-bio.BM cs.AI physics.bio-ph physics.comp-ph

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

Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been drastically improved to generate high-resolution three-dimensional (3D) maps that contain detailed structural information about macromolecules, the computational methods for using the data to automatically build structure models are lagging far behind. The traditional cryo-EM model building approach is template-based homology modeling. Manual de novo modeling is very time-consuming when no template model is found in the database. In recent years, de novo cryo-EM modeling using machine learning (ML) and deep learning (DL) has ranked among the top-performing methods in macromolecular structure modeling. Deep-learning-based de novo cryo-EM modeling is an important application of artificial intelligence, with impressive results and great potential for the next generation of molecular biomedicine. Accordingly, we systematically review the representative ML/DL-based de novo cryo-EM modeling methods. And their significances are discussed from both practical and methodological viewpoints. We also briefly describe the background of cryo-EM data processing workflow. Overall, this review provides an introductory guide to modern research on artificial intelligence (AI) for de novo molecular structure modeling and future directions in this emerging field.

2021-06-01

2102.06202

Anastasios Angelopoulos

Anastasios N. Angelopoulos and Stephen Bates and Tijana Zrnic and Michael I. Jordan

Private Prediction Sets

Code available at https://github.com/aangelopoulos/private_prediction_sets

Harvard Data Science Review, 4(2). 2022

10.1162/99608f92.16c71dad

cs.LG cs.AI cs.CR stat.ME stat.ML

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

In real-world settings involving consequential decision-making, the deployment of machine learning systems generally requires both reliable uncertainty quantification and protection of individuals' privacy. We present a framework that treats these two desiderata jointly. Our framework is based on conformal prediction, a methodology that augments predictive models to return prediction sets that provide uncertainty quantification -- they provably cover the true response with a user-specified probability, such as 90%. One might hope that when used with privately-trained models, conformal prediction would yield privacy guarantees for the resulting prediction sets; unfortunately, this is not the case. To remedy this key problem, we develop a method that takes any pre-trained predictive model and outputs differentially private prediction sets. Our method follows the general approach of split conformal prediction; we use holdout data to calibrate the size of the prediction sets but preserve privacy by using a privatized quantile subroutine. This subroutine compensates for the noise introduced to preserve privacy in order to guarantee correct coverage. We evaluate the method on large-scale computer vision datasets.

2024-03-05

2102.06243

Yuping Fan

Yuping Fan, Zhiling Lan, Taylor Childers, Paul Rich, William Allcock and Michael E. Papka

Deep Reinforcement Agent for Scheduling in HPC

Accepted by IPDPS 2021

35th IEEE International Parallel & Distributed Processing Symposium (2021)

cs.DC cs.AI cs.LG

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

Cluster scheduler is crucial in high-performance computing (HPC). It determines when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing complexity of computing systems and the highly dynamic nature of application workloads have placed tremendous burden on manually designed and tuned scheduling heuristics. More aggressive optimization and automation are needed for cluster scheduling in HPC. In this work, we present an automated HPC scheduling agent named DRAS (Deep Reinforcement Agent for Scheduling) by leveraging deep reinforcement learning. DRAS is built on a novel, hierarchical neural network incorporating special HPC scheduling features such as resource reservation and backfilling. A unique training strategy is presented to enable DRAS to rapidly learn the target environment. Once being provided a specific scheduling objective given by system manager, DRAS automatically learns to improve its policy through interaction with the scheduling environment and dynamically adjusts its policy as workload changes. The experiments with different production workloads demonstrate that DRAS outperforms the existing heuristic and optimization approaches by up to 45%.

2021-04-21

2102.06274

Oleg Szehr

Oleg Szehr

Hedging of Financial Derivative Contracts via Monte Carlo Tree Search

Corrected typos. Shorter Presentation. 15 pages, 5 figures

Journal of Computational Finance, Volume 27, Number 2, Pages: 47-80, 2023

10.21314/JCF.2023.009

cs.AI cs.GT cs.LG q-fin.PR

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

The construction of approximate replication strategies for pricing and hedging of derivative contracts in incomplete markets is a key problem of financial engineering. Recently Reinforcement Learning algorithms for hedging under realistic market conditions have attracted significant interest. While research in the derivatives area mostly focused on variations of $Q$-learning, in artificial intelligence Monte Carlo Tree Search is the recognized state-of-the-art method for various planning problems, such as the games of Hex, Chess, Go,... This article introduces Monte Carlo Tree Search as a method to solve the stochastic optimal control problem behind the pricing and hedging tasks. As compared to $Q$-learning it combines Reinforcement Learning with tree search techniques. As a consequence Monte Carlo Tree Search has higher sample efficiency, is less prone to over-fitting to specific market models and generally learns stronger policies faster. In our experiments we find that Monte Carlo Tree Search, being the world-champion in games like Chess and Go, is easily capable of maximizing the utility of investor's terminal wealth without setting up an auxiliary mathematical framework.

2023-11-02

2102.06320

Andriy Miranskyy

Jared Rand and Andriy Miranskyy

On Automatic Parsing of Log Records

Shortened version accepted for publication in Proceedings of the 43rd International Conference on Software Engineering: New Ideas and Emerging Results, 2021

In Proceedings 2021 IEEE/ACM 43rd International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), pp. 41-45

10.1109/ICSE-NIER52604.2021.00017

cs.SE cs.CL cs.LG

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

Software log analysis helps to maintain the health of software solutions and ensure compliance and security. Existing software systems consist of heterogeneous components emitting logs in various formats. A typical solution is to unify the logs using manually built parsers, which is laborious. Instead, we explore the possibility of automating the parsing task by employing machine translation (MT). We create a tool that generates synthetic Apache log records which we used to train recurrent-neural-network-based MT models. Models' evaluation on real-world logs shows that the models can learn Apache log format and parse individual log records. The median relative edit distance between an actual real-world log record and the MT prediction is less than or equal to 28%. Thus, we show that log parsing using an MT approach is promising.

2021-08-04

2102.06386

Shigemichi Matsuzaki

Shigemichi Matsuzaki, Jun Miura and Hiroaki Masuzawa

Multi-source Pseudo-label Learning of Semantic Segmentation for the Scene Recognition of Agricultural Mobile Robots

Published in Advanced Robotics

Advanced Robotics, 36:19, 1011-1029 (2022)

10.1080/01691864.2022.2109427

cs.CV cs.RO

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

This paper describes a novel method of training a semantic segmentation model for scene recognition of agricultural mobile robots exploiting publicly available datasets of outdoor scenes that are different from the target greenhouse environments. Semantic segmentation models require abundant labels given by tedious manual annotation. A method to work around it is unsupervised domain adaptation (UDA) that transfers knowledge from labeled source datasets to unlabeled target datasets. However, the effectiveness of existing methods is not well studied in adaptation between heterogeneous environments, such as urban scenes and greenhouses. In this paper, we propose a method to train a semantic segmentation model for greenhouse images without manually labeled datasets of greenhouse images. The core of our idea is to use multiple rich image datasets of different environments with segmentation labels to generate pseudo-labels for the target images to effectively transfer the knowledge from multiple sources and realize a precise training of semantic segmentation. Along with the pseudo-label generation, we introduce state-of-the-art methods to deal with noise in the pseudo-labels to further improve the performance. We demonstrate in experiments with multiple greenhouse datasets that our proposed method improves the performance compared to the single-source baselines and an existing approach.

2023-01-16

2102.06388

Roohallah Alizadehsani

Roohallah Alizadehsani, Danial Sharifrazi, Navid Hoseini Izadi, Javad Hassannataj Joloudari, Afshin Shoeibi, Juan M. Gorriz, Sadiq Hussain, Juan E. Arco, Zahra Alizadeh Sani, Fahime Khozeimeh, Abbas Khosravi, Saeid Nahavandi, Sheikh Mohammed Shariful Islam, U Rajendra Acharya

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 17Issue 3sOctober 2021

10.1145/3462635

eess.IV cs.CV

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

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data (SCLLD) relying on Sobel edge detection and Generative Adversarial Networks (GANs) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 +- 0.20%, 99.88 +- 0.24%, and 99.40 +- 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 +- 4.11%, 91.2 +- 6.15%, and 46.40 +- 5.21%.

2021-12-28

2102.06448

Haoran Chen

Haoran Chen, Jianmin Li, Simone Frintrop, Xiaolin Hu

The MSR-Video to Text Dataset with Clean Annotations

The paper is under consideration at Computer Vision and Image Understanding

Computer Vision and Image Understanding, 225, p.103581 (2022)

10.1016/j.cviu.2022.103581

cs.CV cs.LG

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

Video captioning automatically generates short descriptions of the video content, usually in form of a single sentence. Many methods have been proposed for solving this task. A large dataset called MSR Video to Text (MSR-VTT) is often used as the benchmark dataset for testing the performance of the methods. However, we found that the human annotations, i.e., the descriptions of video contents in the dataset are quite noisy, e.g., there are many duplicate captions and many captions contain grammatical problems. These problems may pose difficulties to video captioning models for learning underlying patterns. We cleaned the MSR-VTT annotations by removing these problems, then tested several typical video captioning models on the cleaned dataset. Experimental results showed that data cleaning boosted the performances of the models measured by popular quantitative metrics. We recruited subjects to evaluate the results of a model trained on the original and cleaned datasets. The human behavior experiment demonstrated that trained on the cleaned dataset, the model generated captions that were more coherent and more relevant to the contents of the video clips.

2024-02-27

2102.06535

Zainab Abohashima

Essam H. Houssein, Zainab Abohashima, Mohamed Elhoseny, Waleed M. Mohamed

Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

Journal of Computational Design and Engineering, Volume 9, Issue 2, April 2022,

10.1093/jcde/qwac003

eess.IV cs.CV cs.LG

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

Despite the great efforts to find an effective way for COVID-19 prediction, the virus nature and mutation represent a critical challenge to diagnose the covered cases. However, developing a model to predict COVID-19 via Chest X-Ray (CXR) images with accurate performance is necessary to help in early diagnosis. In this paper, a hybrid quantum-classical convolutional Neural Networks (HQCNN) model used the random quantum circuits (RQCs) as a base to detect COVID-19 patients with CXR images. A collection of 6952 CXR images, including 1161 COVID-19, 1575 normal, and 5216 pneumonia images, were used as a dataset in this work. The proposed HQCNN model achieved higher performance with an accuracy of 98.4\% and a sensitivity of 99.3\% on the first dataset cases. Besides, it obtained an accuracy of 99\% and a sensitivity of 99.7\% on the second dataset cases. Also, it achieved accuracy, and sensitivity of 88.6\%, and 88.7\%, respectively, on the third multi-class dataset cases. Furthermore, the HQCNN model outperforms various models in balanced accuracy, precision, F1-measure, and AUC-ROC score. The experimental results are achieved by the proposed model prove its ability in predicting positive COVID-19 cases.

2022-03-14

2102.06607

Sabrina Kirrane

Sabrina Kirrane

Intelligent Software Web Agents: A Gap Analysis

Journal of Web Semantics (2021)

10.1016/j.websem.2021.100659

cs.AI cs.MA cs.NI

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

Semantic web technologies have shown their effectiveness, especially when it comes to knowledge representation, reasoning, and data integration. However, the original semantic web vision, whereby machine readable web data could be automatically actioned upon by intelligent software web agents, has yet to be realised. In order to better understand the existing technological opportunities and challenges, in this paper we examine the status quo in terms of intelligent software web agents, guided by research with respect to requirements and architectural components, coming from the agents community. We use the identified requirements to both further elaborate on the semantic web agent motivating use case scenario, and to summarise different perspectives on the requirements from the semantic web agent literature. We subsequently propose a hybrid semantic web agent architecture, and use the various components and subcomponents in order to provide a focused discussion in relation to existing semantic web standards and community activities. Finally, we highlight open research opportunities and challenges and take a broader perspective of the research by discussing the potential for intelligent software web agents as an enabling technology for emerging domains, such as digital assistants, cloud computing, and the internet of things.

2021-09-27

2102.06793

Ernest Davis

Ernest Davis

Unanswerable Questions about Images and Texts

15 pages, 4 figures

Frontiers in Artificial Intelligence: Language and Computation. July 2020

10.3389/frai.2020.00051

cs.CV cs.AI cs.CL

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

Questions about a text or an image that cannot be answered raise distinctive issues for an AI. This note discusses the problem of unanswerable questions in VQA (visual question answering), in QA (visual question answering), and in AI generally.

2021-02-16

2102.06815

Leonid Boytsov

Leonid Boytsov, Zico Kolter

Exploring Classic and Neural Lexical Translation Models for Information Retrieval: Interpretability, Effectiveness, and Efficiency Benefits

ECIR 2021 (The 43rd European Conference on Information Retrieval)

cs.CL cs.IR

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

We study the utility of the lexical translation model (IBM Model 1) for English text retrieval, in particular, its neural variants that are trained end-to-end. We use the neural Model1 as an aggregator layer applied to context-free or contextualized query/document embeddings. This new approach to design a neural ranking system has benefits for effectiveness, efficiency, and interpretability. Specifically, we show that adding an interpretable neural Model 1 layer on top of BERT-based contextualized embeddings (1) does not decrease accuracy and/or efficiency; and (2) may overcome the limitation on the maximum sequence length of existing BERT models. The context-free neural Model 1 is less effective than a BERT-based ranking model, but it can run efficiently on a CPU (without expensive index-time precomputation or query-time operations on large tensors). Using Model 1 we produced best neural and non-neural runs on the MS MARCO document ranking leaderboard in late 2020.

2021-03-19

2102.06868

Jerome Quenum

Jerome Quenum, Kehan Wang, Avideh Zakhor

Fast, Accurate Barcode Detection in Ultra High-Resolution Images

5 pages, 4 figures, 3 tables, GitHub Link added, Initial ArXiv Submission is 13 Feb 2021, Accepted at IEEE International Conference on Image Processing, September 2021, USA

2021 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP42928.2021.9506134

pp. 1019-1023

cs.CV

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

Object detection in Ultra High-Resolution (UHR) images has long been a challenging problem in computer vision due to the varying scales of the targeted objects. When it comes to barcode detection, resizing UHR input images to smaller sizes often leads to the loss of pertinent information, while processing them directly is highly inefficient and computationally expensive. In this paper, we propose using semantic segmentation to achieve a fast and accurate detection of barcodes of various scales in UHR images. Our pipeline involves a modified Region Proposal Network (RPN) on images of size greater than 10k$\times$10k and a newly proposed Y-Net segmentation network, followed by a post-processing workflow for fitting a bounding box around each segmented barcode mask. The end-to-end system has a latency of 16 milliseconds, which is $2.5\times$ faster than YOLOv4 and $5.9\times$ faster than Mask R-CNN. In terms of accuracy, our method outperforms YOLOv4 and Mask R-CNN by a $mAP$ of 5.5% and 47.1% respectively, on a synthetic dataset. We have made available the generated synthetic barcode dataset and its code at http://www.github.com/viplabB/SBD/.

2021-10-18

2102.07271

Yongwan Lim

Yongwan Lim, Shrikanth S. Narayanan, Krishna S. Nayak

Attention-gated convolutional neural networks for off-resonance correction of spiral real-time MRI

8 pages, 4 figures, 1 table

28th Int. Soc. Magn. Reson. Med. (ISMRM) Scientific Sessions, 2020, p.1005

eess.IV cs.CV

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

Spiral acquisitions are preferred in real-time MRI because of their efficiency, which has made it possible to capture vocal tract dynamics during natural speech. A fundamental limitation of spirals is blurring and signal loss due to off-resonance, which degrades image quality at air-tissue boundaries. Here, we present a new CNN-based off-resonance correction method that incorporates an attention-gate mechanism. This leverages spatial and channel relationships of filtered outputs and improves the expressiveness of the networks. We demonstrate improved performance with the attention-gate, on 1.5 Tesla spiral speech RT-MRI, compared to existing off-resonance correction methods.

2021-02-16

2102.07280

Sina Mohammadi

Sina Mohammadi, Mariana Belgiu, Alfred Stein

3D Fully Convolutional Neural Networks with Intersection Over Union Loss for Crop Mapping from Multi-Temporal Satellite Images

Accepted by IGARSS 2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 5834-5837

10.1109/IGARSS47720.2021.9554573

cs.CV

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

Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generating this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images. In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images. In addition, we propose the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data. The proposed method was applied to identify soybean and corn from a study area situated in the US corn belt using multi-temporal Landsat images. The study shows that our method outperforms related methods, obtaining a Kappa coefficient of 91.8%. We conclude that using the IOU loss function provides a superior choice to learn individual crop types.

2021-12-01

2102.07304

Mingu Kang

Mingu Kang, Trung Quang Tran, Seungju Cho, Daeyoung Kim

CAP-GAN: Towards Adversarial Robustness with Cycle-consistent Attentional Purification

IJCNN 2021

10.1109/IJCNN52387.2021.9533322

cs.LG cs.CR cs.CV

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

Adversarial attack is aimed at fooling the target classifier with imperceptible perturbation. Adversarial examples, which are carefully crafted with a malicious purpose, can lead to erroneous predictions, resulting in catastrophic accidents. To mitigate the effects of adversarial attacks, we propose a novel purification model called CAP-GAN. CAP-GAN takes account of the idea of pixel-level and feature-level consistency to achieve reasonable purification under cycle-consistent learning. Specifically, we utilize the guided attention module and knowledge distillation to convey meaningful information to the purification model. Once a model is fully trained, inputs would be projected into the purification model and transformed into clean-like images. We vary the capacity of the adversary to argue the robustness against various types of attack strategies. On the CIFAR-10 dataset, CAP-GAN outperforms other pre-processing based defenses under both black-box and white-box settings.

2021-11-19

2102.07507

Sijie Ji

Sijie Ji, Mo Li

CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback

IEEE Wireless Communications Letters, 2021

10.1109/LWC.2021.3100493

cs.IT cs.AI eess.SP math.IT

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

Unleashing the full potential of massive MIMO in FDD mode by reducing the overhead of CSI feedback has recently garnered attention. Numerous deep learning for massive MIMO CSI feedback approaches have demonstrated their efficiency and potential. However, most existing methods improve accuracy at the cost of computational complexity and the accuracy decreases significantly as the CSI compression rate increases. This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. CLNet proposes a forge complex-valued input layer to process signals and utilizes attention mechanism to enhance the performance of the network. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41\% in both outdoor and indoor scenarios with average 24.1\% less computational overhead. Codes for deep learning-based CSI feedback CLNet are available at GitHub.

2023-05-01

2102.07545

Keisuke Fujii

Keisuke Fujii

Data-driven Analysis for Understanding Team Sports Behaviors

9 pages, 2 figures. This is the first draft and the final version will be published in the Journal of Robotics and Mechatronics

J. Robot. Mechatron., Vol.33, No.3, pp. 505-514, 2021

10.20965/jrm.2021.p0505

cs.AI

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

Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., data-driven approaches such as machine learning, provides an effective way for the analysis of such behaviors. Although most data-driven models have non-linear structures and high prediction performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of invasion team sports behaviors such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world.

2021-06-22

2102.07617

Yingxu Wang Prof. PhD FIEEE

Yingxu Wang, Fakhri Karray, Sam Kwong, Konstantinos N. Plataniotis, Henry Leung, Ming Hou, Edward Tunstel, Imre J. Rudas, Ljiljana Trajkovic, Okyay Kaynak, Janusz Kacprzyk, Mengchu Zhou, Michael H. Smith, Philip Chen and Shushma Patel

On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)

Accepted by Phil. Trans. Royal Society (A): Math, Phys & Engg Sci., 379(219x), 2021, Oxford, UK

Phil. Trans. Royal Society (A): Math, Phys & Engg Sci., 379(219x), 2021, Oxford, UK

10.1098/rsta.2020.0362

cs.AI

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

Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general AI technologies functioning without human intervention or hybrid symbiotic systems synergizing humans and intelligent machines into coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviors. This paper explores their cognitive and mathematical foundations. The challenge to seamless human-machine interactions in a hybrid environment is addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, autonomous computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via an autonomous knowledge learning system that symbiotically works between humans and cognitive robots.

2021-09-15

2102.07716

Eric Langlois

Eric D. Langlois and Tom Everitt

How RL Agents Behave When Their Actions Are Modified

10 pages (+6 appendix); 7 figures. Published in the AAAI 2021 Conference on AI. Code is available at https://github.com/edlanglois/mamdp

Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 11586-11594 (2021)

cs.AI

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

Reinforcement learning in complex environments may require supervision to prevent the agent from attempting dangerous actions. As a result of supervisor intervention, the executed action may differ from the action specified by the policy. How does this affect learning? We present the Modified-Action Markov Decision Process, an extension of the MDP model that allows actions to differ from the policy. We analyze the asymptotic behaviours of common reinforcement learning algorithms in this setting and show that they adapt in different ways: some completely ignore modifications while others go to various lengths in trying to avoid action modifications that decrease reward. By choosing the right algorithm, developers can prevent their agents from learning to circumvent interruptions or constraints, and better control agent responses to other kinds of action modification, like self-damage.

2021-07-01

2102.07737

Burhaneddin Yaman

Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Ak\c{c}akaya

Zero-Shot Self-Supervised Learning for MRI Reconstruction

International Conference on Learning Representations (ICLR), 2022

eess.IV cs.CV cs.LG eess.SP physics.med-ph

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

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but often necessitates a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or translational acquisitions in development. Moreover, recent studies show that database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology to enable subject-specific DL MRI reconstruction without external training datasets, since it is clinically imperative to provide high-quality reconstructions that can be used to identify lesions/disease for \emph{every individual}. In this work, we propose a zero-shot self-supervised learning approach to perform subject-specific accelerated DL MRI reconstruction to tackle these issues. The proposed approach partitions the available measurements from a single scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training for self-supervision, while the last set serves to self-validate, establishing an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning for faster convergence time and reduced computational complexity. The code is available at \url{https://github.com/byaman14/ZS-SSL}.

2023-11-30

2102.07849

Sara Abdali

Sara Abdali, Rutuja Gurav, Siddharth Menon, Daniel Fonseca, Negin Entezari, Neil Shah, Evangelos E. Papalexakis

Identifying Misinformation from Website Screenshots

The International AAAI Conference on Web and Social Media (ICWSM) 2021

cs.LG cs.AI cs.CY cs.SI

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

Can the look and the feel of a website give information about the trustworthiness of an article? In this paper, we propose to use a promising, yet neglected aspect in detecting the misinformativeness: the overall look of the domain webpage. To capture this overall look, we take screenshots of news articles served by either misinformative or trustworthy web domains and leverage a tensor decomposition based semi-supervised classification technique. The proposed approach i.e., VizFake is insensitive to a number of image transformations such as converting the image to grayscale, vectorizing the image and losing some parts of the screenshots. VizFake leverages a very small amount of known labels, mirroring realistic and practical scenarios, where labels (especially for known misinformative articles), are scarce and quickly become dated. The F1 score of VizFake on a dataset of 50k screenshots of news articles spanning more than 500 domains is roughly 85% using only 5% of ground truth labels. Furthermore, tensor representations of VizFake, obtained in an unsupervised manner, allow for exploratory analysis of the data that provides valuable insights into the problem. Finally, we compare VizFake with deep transfer learning, since it is a very popular black-box approach for image classification and also well-known text text-based methods. VizFake achieves competitive accuracy with deep transfer learning models while being two orders of magnitude faster and not requiring laborious hyper-parameter tuning.

2021-06-07

2102.07857

Sara Abdali

Sara Abdali, Neil Shah, Evangelos E. Papalexakis

KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection

Second International TrueFact Workshop 2020: Making a Credible Web for Tomorrow

cs.LG cs.AI cs.CY

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

Graphs are one of the most efficacious structures for representing datapoints and their relations, and they have been largely exploited for different applications. Previously, the higher-order relations between the nodes have been modeled by a generalization of graphs known as hypergraphs. In hypergraphs, the edges are defined by a set of nodes i.e., hyperedges to demonstrate the higher order relationships between the data. However, there is no explicit higher-order generalization for nodes themselves. In this work, we introduce a novel generalization of graphs i.e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes. In fact, in KNH, nodes are hyperplanes or more precisely m-flats instead of datapoints. We experimentally evaluate the KNH graph on two multi-aspect datasets for misinformation detection. The experimental results suggest that multi-view modeling of articles using KNH graph outperforms the classic KNN graph in terms of classification performance.

2021-02-17

2102.07899

Fanwei Kong

Fanwei Kong, Nathan Wilson, Shawn C. Shadden

A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction

Medical Image Analysis, 2021, 102222, ISSN 1361-8415

10.1016/j.media.2021.102222

eess.IV cs.CV

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

Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating whole heart reconstructions with as good or better accuracy than prior deep-learning-based methods on both CT and MR data. Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data. Our method was also able to produce temporally consistent surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics. Our code and pre-trained networks are available at https://github.com/fkong7/MeshDeformNet

2021-09-15

2102.07943

Zhao Kang

Zhao Kang, Zhiping Lin, Xiaofeng Zhu, Wenbo Xu

Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view

IEEE Transactions on Cybernetics 2021

cs.LG cs.AI cs.CV

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

Graph-based subspace clustering methods have exhibited promising performance. However, they still suffer some of these drawbacks: encounter the expensive time overhead, fail in exploring the explicit clusters, and cannot generalize to unseen data points. In this work, we propose a scalable graph learning framework, seeking to address the above three challenges simultaneously. Specifically, it is based on the ideas of anchor points and bipartite graph. Rather than building a $n\times n$ graph, where $n$ is the number of samples, we construct a bipartite graph to depict the relationship between samples and anchor points. Meanwhile, a connectivity constraint is employed to ensure that the connected components indicate clusters directly. We further establish the connection between our method and the K-means clustering. Moreover, a model to process multi-view data is also proposed, which is linear scaled with respect to $n$. Extensive experiments demonstrate the efficiency and effectiveness of our approach with respect to many state-of-the-art clustering methods.

2021-02-23

2102.08009

Abhinav Valada

Kshitij Sirohi, Rohit Mohan, Daniel B\"uscher, Wolfram Burgard, Abhinav Valada

EfficientLPS: Efficient LiDAR Panoptic Segmentation

Ranked #1 on SemanticKITTI and nuScenes panoptic segmentation benchmarks

IEEE Transactions on Robotics (T-RO), 2021

cs.CV cs.LG cs.RO

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

Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existing top-down approaches tackle this problem by either combining independent task-specific networks or translating methods from the image domain ignoring the intricacies of LiDAR data and thus often resulting in sub-optimal performance. In this paper, we present the novel top-down Efficient LiDAR Panoptic Segmentation (EfficientLPS) architecture that addresses multiple challenges in segmenting LiDAR point clouds including distance-dependent sparsity, severe occlusions, large scale-variations, and re-projection errors. EfficientLPS comprises of a novel shared backbone that encodes with strengthened geometric transformation modeling capacity and aggregates semantically rich range-aware multi-scale features. It incorporates new scale-invariant semantic and instance segmentation heads along with the panoptic fusion module which is supervised by our proposed panoptic periphery loss function. Additionally, we formulate a regularized pseudo labeling framework to further improve the performance of EfficientLPS by training on unlabelled data. We benchmark our proposed model on two large-scale LiDAR datasets: nuScenes, for which we also provide ground truth annotations, and SemanticKITTI. Notably, EfficientLPS sets the new state-of-the-art on both these datasets.

2021-11-05

2102.08019

Kevin Bello

Kevin Bello, Chuyang Ke and Jean Honorio

A Thorough View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy

17 pages, 5 figures

Artificial Intelligence and Statistics (AISTATS), 2022

cs.LG cs.AI stat.ML

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

Performing inference in graphs is a common task within several machine learning problems, e.g., image segmentation, community detection, among others. For a given undirected connected graph, we tackle the statistical problem of exactly recovering an unknown ground-truth binary labeling of the nodes from a single corrupted observation of each edge. Such problem can be formulated as a quadratic combinatorial optimization problem over the boolean hypercube, where it has been shown before that one can (with high probability and in polynomial time) exactly recover the ground-truth labeling of graphs that have an isoperimetric number that grows with respect to the number of nodes (e.g., complete graphs, regular expanders). In this work, we apply a powerful hierarchy of relaxations, known as the sum-of-squares (SoS) hierarchy, to the combinatorial problem. Motivated by empirical evidence on the improvement in exact recoverability, we center our attention on the degree-4 SoS relaxation and set out to understand the origin of such improvement from a graph theoretical perspective. We show that the solution of the dual of the relaxed problem is related to finding edge weights of the Johnson and Kneser graphs, where the weights fulfill the SoS constraints and intuitively allow the input graph to increase its algebraic connectivity. Finally, as byproduct of our analysis, we derive a novel Cheeger-type lower bound for the algebraic connectivity of graphs with signed edge weights.

2022-09-12

2102.08138

Nan Wu

Nan Wu, Yuan Xie, Cong Hao

IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning

GLSVLSI 2021

10.1145/3453688.3461495

cs.AR cs.CL cs.LG

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

Despite the great success of High-Level Synthesis (HLS) tools, we observe several unresolved challenges: 1) the high-level abstraction of programming styles in HLS sometimes conceals optimization opportunities; 2) existing HLS tools do not provide flexible trade-off (Pareto) solutions among different objectives and constraints; 3) the actual quality of the resulting RTL designs is hard to predict. To address these challenges, we propose an end-to-end framework, namelyIronMan. The primary goal is to enable a flexible and automated design space exploration (DSE), to provide either optimal solutions under user-specified constraints, or various trade-offs among different objectives (such as different types of resources, area, and latency). Such DSE either requires tedious manual efforts or is not achievable to attain these goals through existing HLS tools. There are three components in IronMan: 1) GPP, a highly accurate graph-neural-network-based performance and resource predictor; 2) RLMD, a reinforcement-learning-based multi-objective DSE engine that explores the optimal resource allocation strategy, to provide Pareto solutions between different objectives; 3) CT, a code transformer to assist RLMD and GPP, which extracts the data flow graph from original HLS C/C++ and automatically generates synthesizable code with HLS directives. The experimental results show that: 1) GPP achieves high prediction accuracy, reducing prediction errors of HLS tools by 10.9x in resource utilization and 5.7x in timing; 2) RLMD obtains optimal or Pareto solutions that outperform the genetic algorithm and simulated annealing by 12.7% and 12.9%, respectively; 3) IronMan is able to find optimized solutions perfectly matching various DSP constraints, with 2.54x fewer DSPs and up to 6x shorter latency than those of HLS tools while being up to 400x faster than the heuristic algorithms and HLS tools.

2021-12-10

2102.08145

Florian Tschopp

Florian Tschopp, Cornelius von Einem, Andrei Cramariuc, David Hug, Andrew William Palmer, Roland Siegwart, Margarita Chli, Juan Nieto

Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping

Florian Tschopp, Cornelius von Einem, and Andrei Cramariuc contributed equally to this work

IEEE Robotics and Automation Letters ( Volume: 6, Issue: 2, April 2021)

10.1109/LRA.2021.3061404

cs.RO cs.CV

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

To cope with the growing demand for transportation on the railway system, accurate, robust, and high-frequency positioning is required to enable a safe and efficient utilization of the existing railway infrastructure. As a basis for a localization system we propose a complete on-board mapping pipeline able to map robust meaningful landmarks, such as poles from power lines, in the vicinity of the vehicle. Such poles are good candidates for reliable and long term landmarks even through difficult weather conditions or seasonal changes. To address the challenges of motion blur and illumination changes in railway scenarios we employ a Dynamic Vision Sensor, a novel event-based camera. Using a sideways oriented on-board camera, poles appear as vertical lines. To map such lines in a real-time event stream, we introduce Hough2Map, a novel consecutive iterative event-based Hough transform framework capable of detecting, tracking, and triangulating close-by structures. We demonstrate the mapping reliability and accuracy of Hough2Map on real-world data in typical usage scenarios and evaluate using surveyed infrastructure ground truth maps. Hough2Map achieves a detection reliability of up to 92% and a mapping root mean square error accuracy of 1.1518m.

2021-03-30

2102.08146

Manfred Schmidt-Schauss

Manfred Schmidt-Schau{\ss} and Temur Kutsia and Jordi Levy and Mateu Villaret and Yunus Kutz

Nominal Unification and Matching of Higher Order Expressions with Recursive Let

37 pages, 9 figures, This paper is an extended version of the conference publication: Manfred Schmidt-Schau{\ss} and Temur Kutsia and Jordi Levy and Mateu Villaret and Yunus Kutz, Nominal Unification of Higher Order Expressions with Recursive Let, LOPSTR-16, Lecture Notes in Computer Science 10184, Springer, p 328 -344, 2016. arXiv admin note: text overlap with arXiv:1608.03771

Fundamenta Informaticae, Volume 185, Issue 3 (May 6, 2022) fi:7191

10.3233/FI-222110

cs.LO cs.AI

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

A sound and complete algorithm for nominal unification of higher-order expressions with a recursive let is described, and shown to run in nondeterministic polynomial time. We also explore specializations like nominal letrec-matching for expressions, for DAGs, and for garbage-free expressions and determine their complexity. We also provide a nominal unification algorithm for higher-order expressions with recursive let and atom-variables, where we show that it also runs in nondeterministic polynomial time. In addition we prove that there is a guessing strategy for nominal unification with letrec and atom-variable that is a trade-off between exponential growth and non-determinism. Nominal matching with variables representing partial letrec-environments is also shown to be in NP.

2023-06-22

2102.08148

Jintai Chen

Jintai Chen, Hongyun Yu, Ruiwei Feng, Danny Z. Chen, Jian Wu

Flow-Mixup: Classifying Multi-labeled Medical Images with Corrupted Labels

2020 IEEE International Conference on Bioinformatics and Biomedicine

10.1109/BIBM49941.2020.9313408

cs.CV cs.LG eess.IV

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

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expert-level performance on medical image interpretation, which can be attributed partially to large amounts of accurate annotations. However, manually annotating massive amounts of medical images is impractical, while automatic annotation is fast but imprecise (possibly introducing corrupted labels). In this work, we propose a new regularization approach, called Flow-Mixup, for multi-labeled medical image classification with corrupted labels. Flow-Mixup guides the models to capture robust features for each abnormality, thus helping handle corrupted labels effectively and making it possible to apply automatic annotation. Specifically, Flow-Mixup decouples the extracted features by adding constraints to the hidden states of the models. Also, Flow-Mixup is more stable and effective comparing to other known regularization methods, as shown by theoretical and empirical analyses. Experiments on two electrocardiogram datasets and a chest X-ray dataset containing corrupted labels verify that Flow-Mixup is effective and insensitive to corrupted labels.

2021-02-17

2102.08168

Jian Jin

Jian Jin, Xingxing Zhang, Xin Fu, Huan Zhang, Weisi Lin, Jian Lou, Yao Zhao

Just Noticeable Difference for Deep Machine Vision

IEEE Transactions on Circuits and Systems for Video Technology, 2021

10.1109/TCSVT.2021.3113572

cs.CV eess.IV

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

As an important perceptual characteristic of the Human Visual System (HVS), the Just Noticeable Difference (JND) has been studied for decades with image and video processing (e.g., perceptual visual signal compression). However, there is little exploration on the existence of JND for the Deep Machine Vision (DMV), although the DMV has made great strides in many machine vision tasks. In this paper, we take an initial attempt, and demonstrate that the DMV has the JND, termed as the DMV-JND. We then propose a JND model for the image classification task in the DMV. It has been discovered that the DMV can tolerate distorted images with average PSNR of only 9.56dB (the lower the better), by generating JND via unsupervised learning with the proposed DMV-JND-NET. In particular, a semantic-guided redundancy assessment strategy is designed to restrain the magnitude and spatial distribution of the DMV-JND. Experimental results on image classification demonstrate that we successfully find the JND for deep machine vision. Our DMV-JND facilitates a possible direction for DMV-oriented image and video compression, watermarking, quality assessment, deep neural network security, and so on.

2022-01-10

2102.08259

Bo Zhao

Bo Zhao, Hakan Bilen

Dataset Condensation with Differentiable Siamese Augmentation

International Conference on Machine Learning 2021

cs.LG cs.CV

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

In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large training sets into significantly smaller synthetic sets which can be used to train deep neural networks from scratch with minimum drop in performance. Inspired from the recent training set synthesis methods, we propose Differentiable Siamese Augmentation that enables effective use of data augmentation to synthesize more informative synthetic images and thus achieves better performance when training networks with augmentations. Experiments on multiple image classification benchmarks demonstrate that the proposed method obtains substantial gains over the state-of-the-art, 7% improvements on CIFAR10 and CIFAR100 datasets. We show with only less than 1% data that our method achieves 99.6%, 94.9%, 88.5%, 71.5% relative performance on MNIST, FashionMNIST, SVHN, CIFAR10 respectively. We also explore the use of our method in continual learning and neural architecture search, and show promising results.

2021-06-11

2102.08445

Nancy Xin Ru Wang

Nancy Xin Ru Wang, Douglas Burdick, Yunyao Li

TableLab: An Interactive Table Extraction System with Adaptive Deep Learning

Accepted at IUI'21

26th International Conference on Intelligent User Interfaces 2021

10.1145/3397482.3450718

cs.HC cs.AI

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

Table extraction from PDF and image documents is a ubiquitous task in the real-world. Perfect extraction quality is difficult to achieve with one single out-of-box model due to (1) the wide variety of table styles, (2) the lack of training data representing this variety and (3) the inherent ambiguity and subjectivity of table definitions between end-users. Meanwhile, building customized models from scratch can be difficult due to the expensive nature of annotating table data. We attempt to solve these challenges with TableLab by providing a system where users and models seamlessly work together to quickly customize high-quality extraction models with a few labelled examples for the user's document collection, which contains pages with tables. Given an input document collection, TableLab first detects tables with similar structures (templates) by clustering embeddings from the extraction model. Document collections often contain tables created with a limited set of templates or similar structures. It then selects a few representative table examples already extracted with a pre-trained base deep learning model. Via an easy-to-use user interface, users provide feedback to these selections without necessarily having to identify every single error. TableLab then applies such feedback to finetune the pre-trained model and returns the results of the finetuned model back to the user. The user can choose to repeat this process iteratively until obtaining a customized model with satisfactory performance.

2021-02-18

2102.08581

Byungchan Ko

Byungchan Ko and Jungseul Ok

Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning

Neurips 2022

cs.LG cs.AI

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

In deep reinforcement learning (RL), data augmentation is widely considered as a tool to induce a set of useful priors about semantic consistency and improve sample efficiency and generalization performance. However, even when the prior is useful for generalization, distilling it to RL agent often interferes with RL training and degenerates sample efficiency. Meanwhile, the agent is forgetful of the prior due to the non-stationary nature of RL. These observations suggest two extreme schedules of distillation: (i) over the entire training; or (ii) only at the end. Hence, we devise a stand-alone network distillation method to inject the consistency prior at any time (even after RL), and a simple yet efficient framework to automatically schedule the distillation. Specifically, the proposed framework first focuses on mastering train environments regardless of generalization by adaptively deciding which {\it or no} augmentation to be used for the training. After this, we add the distillation to extract the remaining benefits for generalization from all the augmentations, which requires no additional new samples. In our experiments, we demonstrate the utility of the proposed framework, in particular, that considers postponing the augmentation to the end of RL training.

2022-10-21

2102.08628

Essam Rashed

Essam A. Rashed, Sachiko Kodera, Hidenobu Shirakami, Ryotetsu Kawaguchi, Kazuhiro Watanabe, Akimasa Hirata

Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan

15 pages, 12 figures, 2 tables

Journal of Biomedical Informatics, 2021

10.1016/j.jbi.2021.103743

cs.AI eess.SP

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

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.

2021-03-23

2102.08655

Nora Hollenstein

Nora Hollenstein, Cedric Renggli, Benjamin Glaus, Maria Barrett, Marius Troendle, Nicolas Langer, Ce Zhang

Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

Frontiers of Human Neuroscience 2021

10.3389/fnhum.2021.659410

cs.CL

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

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity to this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, further research is needed. Finally, EEG data shows to be particularly promising when limited training data is available.

2021-08-11

2102.08665

Nicolas Guigui

Nicolas Guigui (UCA, EPIONE), Pamela Moceri (URRIS UR2CA), Maxime Sermesant (UCA, EPIONE), Xavier Pennec (UCA, EPIONE)

Cardiac Motion Modeling with Parallel Transport and Shape Splines

International Symposium on Biological Imaging, Apr 2021, Nice, France

cs.CV math.DG

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

In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations.In this work, we use the LDDMM framework and parallel transport to estimate and reorient deformations of the right ventricle. We then propose a normalization procedure for the amplitude of the deformation, and a second-order spline model to represent the full cardiac contraction. The method is applied to 3D meshes of the right ventricle extracted from echocardiographic sequences of 314 patients divided into three disease categories and a control group. We find significant differences between pathologies in the model parameters, revealing insights into the dynamics of each disease.

2021-02-18

2102.08689

Zhe Chen

Zhe Chen, Daniel Harabor, Jiaoyang Li, Peter J. Stuckey

Symmetry Breaking for k-Robust Multi-Agent Path Finding

8 pages. Accepted by Thirty-Fifth AAAI Conference on Artificial Intelligence

Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), 12267-12274 (2021)

cs.AI

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

During Multi-Agent Path Finding (MAPF) problems, agents can be delayed by unexpected events. To address such situations recent work describes k-Robust Conflict-BasedSearch (k-CBS): an algorithm that produces coordinated and collision-free plan that is robust for up to k delays. In this work we introducing a variety of pairwise symmetry breaking constraints, specific to k-robust planning, that can efficiently find compatible and optimal paths for pairs of conflicting agents. We give a thorough description of the new constraints and report large improvements to success rate ina range of domains including: (i) classic MAPF benchmarks;(ii) automated warehouse domains and; (iii) on maps from the 2019 Flatland Challenge, a recently introduced railway domain where k-robust planning can be fruitfully applied to schedule trains.

2021-10-29

2102.08742

Denis Coquenet

Denis Coquenet, Cl\'ement Chatelain, Thierry Paquet

SPAN: a Simple Predict & Align Network for Handwritten Paragraph Recognition

Document Analysis and Recognition - ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science, vol 12823

10.1007/978-3-030-86334-0_5

cs.CV

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

Unconstrained handwriting recognition is an essential task in document analysis. It is usually carried out in two steps. First, the document is segmented into text lines. Second, an Optical Character Recognition model is applied on these line images. We propose the Simple Predict & Align Network: an end-to-end recurrence-free Fully Convolutional Network performing OCR at paragraph level without any prior segmentation stage. The framework is as simple as the one used for the recognition of isolated lines and we achieve competitive results on three popular datasets: RIMES, IAM and READ 2016. The proposed model does not require any dataset adaptation, it can be trained from scratch, without segmentation labels, and it does not require line breaks in the transcription labels. Our code and trained model weights are available at https://github.com/FactoDeepLearning/SPAN.

2021-09-13

2102.08755

Kristina Gligoric

Kristina Gligori\'c, Ryen W. White, Emre K{\i}c{\i}man, Eric Horvitz, Arnaud Chiolero, Robert West

Formation of Social Ties Influences Food Choice: A Campus-Wide Longitudinal Study

Proc. ACM Hum.-Comput. Interact.5, CSCW1, Article 184 (April 2021)

10.1145/34492971

cs.SI cs.AI

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

Nutrition is a key determinant of long-term health, and social influence has long been theorized to be a key determinant of nutrition. It has been difficult to quantify the postulated role of social influence on nutrition using traditional methods such as surveys, due to the typically small scale and short duration of studies. To overcome these limitations, we leverage a novel source of data: logs of 38 million food purchases made over an 8-year period on the Ecole Polytechnique Federale de Lausanne (EPFL) university campus, linked to anonymized individuals via the smartcards used to make on-campus purchases. In a longitudinal observational study, we ask: How is a person's food choice affected by eating with someone else whose own food choice is healthy vs. unhealthy? To estimate causal effects from the passively observed log data, we control confounds in a matched quasi-experimental design: we identify focal users who at first do not have any regular eating partners but then start eating with a fixed partner regularly, and we match focal users into comparison pairs such that paired users are nearly identical with respect to covariates measured before acquiring the partner, where the two focal users' new eating partners diverge in the healthiness of their respective food choice. A difference-in-differences analysis of the paired data yields clear evidence of social influence: focal users acquiring a healthy-eating partner change their habits significantly more toward healthy foods than focal users acquiring an unhealthy-eating partner. We further identify foods whose purchase frequency is impacted significantly by the eating partner's healthiness of food choice. Beyond the main results, the work demonstrates the utility of passively sensed food purchase logs for deriving insights, with the potential of informing the design of public health interventions and food offerings.

2021-02-18

2102.08773

Matthew Shardlow

Matthew Shardlow, Richard Evans and Marcos Zampieri

Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset

Lang Resources and Evaluation 56, 1153-1194 (2022)

10.1007/s10579-022-09588-2

cs.CL

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

Identifying words which may cause difficulty for a reader is an essential step in most lexical text simplification systems prior to lexical substitution and can also be used for assessing the readability of a text. This task is commonly referred to as Complex Word Identification (CWI) and is often modelled as a supervised classification problem. For training such systems, annotated datasets in which words and sometimes multi-word expressions are labelled regarding complexity are required. In this paper we analyze previous work carried out in this task and investigate the properties of CWI datasets for English. We develop a protocol for the annotation of lexical complexity and use this to annotate a new dataset, CompLex 2.0. We present experiments using both new and old datasets to investigate the nature of lexical complexity. We found that a Likert-scale annotation protocol provides an objective setting that is superior for identifying the complexity of words compared to a binary annotation protocol. We release a new dataset using our new protocol to promote the task of Lexical Complexity Prediction.

2022-11-04

2102.08892

Rudolf Rosa

Rudolf Rosa and Tom\'a\v{s} Musil and Ond\v{r}ej Du\v{s}ek and Dominik Jurko and Patr\'icia Schmidtov\'a and David Mare\v{c}ek and Ond\v{r}ej Bojar and Tom Kocmi and Daniel Hrbek and David Ko\v{s}\v{t}\'ak and Martina Kinsk\'a and Marie Nov\'akov\'a and Josef Dole\v{z}al and Kl\'ara Voseck\'a and Tom\'a\v{s} Studen\'ik and Petr \v{Z}abka

THEaiTRE 1.0: Interactive generation of theatre play scripts

Submitted to Text2Story workshop 2021

Proc. Text2Story (2021) 71-76

cs.CL cs.HC

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

We present the first version of a system for interactive generation of theatre play scripts. The system is based on a vanilla GPT-2 model with several adjustments, targeting specific issues we encountered in practice. We also list other issues we encountered but plan to only solve in a future version of the system. The presented system was used to generate a theatre play script planned for premiere in February 2021.

2021-10-26

2102.09024

Mohita Chaudhary

Mohita Chaudhary, Mohamed Sadok Gastli, Lobna Nassar, Fakhri Karray

Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters

Paper Accepted in Association for the Advancement of Artificial Intelligence (AAAI) Spring Symposium on 21st Jan, 2021

AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)

cs.LG cs.AI

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

Computational tools for forecasting yields and prices for fresh produce have been based on traditional machine learning approaches or time series modelling. We propose here an alternate approach based on deep learning algorithms for forecasting strawberry yields and prices in Santa Barbara county, California. Building the proposed forecasting model comprises three stages: first, the station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are trained and tested using the station-based soil temperature and moisture data of SantaBarbara as input and the corresponding strawberry yields or prices as output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is trained and tested using satellite images of the same county as input mapped to the same yields and prices as output. These two ensembles forecast strawberry yields and prices with minimal forecasting errors and highest model correlation for five weeks ahead forecasts.Finally, the forecasts of these two models are ensembled to have a final forecasted value for yields and prices by introducing a voting ensemble. Based on an aggregated performance measure (AGM), it is found that this voting ensemble not only enhances the forecasting performance by 5% compared to its best performing component model but also outperforms the Deep Learning (DL) ensemble model found in literature by 33% for forecasting yields and 21% for forecasting prices

2021-02-19

2102.09099

Mohamed Amgad

Mohamed Amgad (1), Lamees A. Atteya (2), Hagar Hussein (3), Kareem Hosny Mohammed (4), Ehab Hafiz (5), Maha A.T. Elsebaie (6), Ahmed M. Alhusseiny (7), Mohamed Atef AlMoslemany (8), Abdelmagid M. Elmatboly (9), Philip A. Pappalardo (10), Rokia Adel Sakr (11), Pooya Mobadersany (1), Ahmad Rachid (12), Anas M. Saad (13), Ahmad M. Alkashash (14), Inas A. Ruhban (15), Anas Alrefai (12), Nada M. Elgazar (16), Ali Abdulkarim (17), Abo-Alela Farag (12), Amira Etman (8), Ahmed G. Elsaeed (16), Yahya Alagha (17), Yomna A. Amer (8), Ahmed M. Raslan (18), Menatalla K. Nadim (19), Mai A.T. Elsebaie (12), Ahmed Ayad (20), Liza E. Hanna (3), Ahmed Gadallah (12), Mohamed Elkady (21), Bradley Drumheller (22), David Jaye (22), David Manthey (23), David A. Gutman (24), Habiba Elfandy (25, 26), Lee A.D. Cooper (1, 27, 28) ((1) Department of Pathology, Northwestern University, Chicago, IL, USA, (2) Cairo Health Care Administration, Egyptian Ministry of Health, Cairo, Egypt, (3) Department of Pathology, Nasser institute for research and treatment, Cairo, Egypt, (4) Department of Pathology and Laboratory Medicine, University of Pennsylvania, PA, USA, (5) Department of Clinical Laboratory Research, Theodor Bilharz Research Institute, Giza, Egypt, (6) Department of Medicine, Cook County Hospital, Chicago, IL, USA, (7) Department of Pathology, Baystate Medical Center, University of Massachusetts, Springfield, MA, USA, (8) Faculty of Medicine, Menoufia University, Menoufia, Egypt, (9) Faculty of Medicine, Al-Azhar University, Cairo, Egypt, (10) Consultant for The Center for Applied Proteomics and Molecular Medicine (CAPMM), George Mason University, Manassas, VA, USA, (11) Department of Pathology, National Liver Institute, Menoufia University, Menoufia, Egypt, (12) Faculty of Medicine, Ain Shams University, Cairo, Egypt, (13) Cleveland Clinic Foundation, Cleveland, OH, USA, (14) Department of Pathology, Indiana University, Indianapolis, IN, USA, (15) Faculty of Medicine, Damascus University, Damascus, Syria, (16) Faculty of Medicine, Mansoura University, Mansoura, Egypt, (17) Faculty of Medicine, Cairo University, Cairo, Egypt, (18) Department of Anaesthesia and Critical Care, Menoufia University Hospital, Menoufia, Egypt, (19) Department of Clinical Pathology, Ain Shams University, Cairo, Egypt, (20) Research Department, Oncology Consultants, PA, Houston, TX, USA, (21) Siparadigm Diagnostic Informatics, Pine Brook, NJ, USA, (22) Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA, (23) Kitware Inc., Clifton Park, NY, USA, (24) Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA, (25) Department of Pathology, National Cancer Institute, Cairo, Egypt, (26) Department of Pathology, Children's Cancer Hospital Egypt CCHE 57357, Cairo, Egypt, (27) Lurie Cancer Center, Northwestern University, Chicago, IL, USA, (28) Center for Computational Imaging and Signal Analytics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA)

NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation

GigaScience, 11 (2022)

10.1093/gigascience/giac037

eess.IV cs.CV cs.LG q-bio.QM

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

High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology. Deep learning algorithms can provide accurate mappings given large numbers of labeled instances for training and validation. Generating adequate volume of quality labels has emerged as a critical barrier in computational pathology given the time and effort required from pathologists. In this paper we describe an approach for engaging crowds of medical students and pathologists that was used to produce a dataset of over 220,000 annotations of cell nuclei in breast cancers. We show how suggested annotations generated by a weak algorithm can improve the accuracy of annotations generated by non-experts and can yield useful data for training segmentation algorithms without laborious manual tracing. We systematically examine interrater agreement and describe modifications to the MaskRCNN model to improve cell mapping. We also describe a technique we call Decision Tree Approximation of Learned Embeddings (DTALE) that leverages nucleus segmentations and morphologic features to improve the transparency of nucleus classification models. The annotation data produced in this study are freely available for algorithm development and benchmarking at: https://sites.google.com/view/nucls.

2022-07-25

2102.09260

Milan Straka

Milan Straka, Lucia Piatrikov\'a, Peter van Bokhoven, \v{L}ubo\v{s} Buzna

A matrix approach to detect temporal behavioral patterns at electric vehicle charging stations

8 pages, 5 figures, conference paper

Transportation Research Procedia (2021)

10.1016/j.trpro.2021.07.186

cs.LG cs.AI cs.CE stat.ML

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

Based on the electric vehicle (EV) arrival times and the duration of EV connection to the charging station, we identify charging patterns and derive groups of charging stations with similar charging patterns applying two approaches. The ruled based approach derives the charging patterns by specifying a set of time intervals and a threshold value. In the second approach, we combine the modified l-p norm (as a matrix dissimilarity measure) with hierarchical clustering and apply them to automatically identify charging patterns and groups of charging stations associated with such patterns. A dataset collected in a large network of public charging stations is used to test both approaches. Using both methods, we derived charging patterns. The first, rule-based approach, performed well at deriving predefined patterns and the latter, hierarchical clustering, showed the capability of delivering unexpected charging patterns.

2022-08-03

2102.09320

Daniel Gehrig

Daniel Gehrig, Michelle R\"uegg, Mathias Gehrig, Javier Hidalgo Carrio, Davide Scaramuzza

Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction

IEEE Robotics and Automation Letters (RA-L), 2021

cs.CV

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

Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras due to their high temporal resolution, high dynamic range and lack of motion blur. However, events only measure the varying component of the visual signal, which limits their ability to encode scene context. By contrast, standard cameras measure absolute intensity frames, which capture a much richer representation of the scene. Both sensors are thus complementary. However, due to the asynchronous nature of events, combining them with synchronous images remains challenging, especially for learning-based methods. This is because traditional recurrent neural networks (RNNs) are not designed for asynchronous and irregular data from additional sensors. To address this challenge, we introduce Recurrent Asynchronous Multimodal (RAM) networks, which generalize traditional RNNs to handle asynchronous and irregular data from multiple sensors. Inspired by traditional RNNs, RAM networks maintain a hidden state that is updated asynchronously and can be queried at any time to generate a prediction. We apply this novel architecture to monocular depth estimation with events and frames where we show an improvement over state-of-the-art methods by up to 30% in terms of mean absolute depth error. To enable further research on multimodal learning with events, we release EventScape, a new dataset with events, intensity frames, semantic labels, and depth maps recorded in the CARLA simulator.

2021-02-19

2102.09390

Al-Akhir Nayan

Al-Akhir Nayan, Ahamad Nokib Mozumder, Joyeta Saha, Khan Raqib Mahmud, Abul Kalam Al Azad, Muhammad Golam Kibria

A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality

TRENDS IN SCIENCES 2021; 18(21): 351

10.48048/tis.2021.351

cs.LG cs.AI

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

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps to mitigate the potential outbreak and thus to avert financial losses with apparent negative implications to the national economy. Typically, fish diseases are caused by viruses and bacteria; according to biochemical studies, the presence of certain bacteria and viruses may affect the level of pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, and NH3-N in water, resulting in the death of fishes. Besides, natural processes, e.g., photosynthesis, respiration, and decomposition, also contribute to the alteration of water quality that adversely affects fish health. Being motivated by the recent successes of machine learning techniques, a state-of-art machine learning algorithm has been adopted in this paper to detect and predict the degradation of water quality timely and accurately. Thus, it helps to take preemptive steps against potential fish diseases. The experimental results show high accuracy in detecting fish diseases specific to water quality based on the algorithm with real datasets.

2021-11-12

2102.09470

Lovedeep Singh

Lovedeep Singh

Fake News Detection: a comparison between available Deep Learning techniques in vector space

for citiation purpose, use details available on official IEEE Xplore page: https://doi.org/10.1109/CICT51604.2020.9312099

2020 IEEE 4th Conference on Information & Communication Technology (CICT)

10.1109/CICT51604.2020.9312099

cs.CL cs.AI

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

Fake News Detection is an essential problem in the field of Natural Language Processing. The benefits of an effective solution in this area are manifold for the goodwill of society. On a surface level, it broadly matches with the general problem of text classification. Researchers have proposed various approaches to tackle fake news using simple as well as some complex techniques. In this paper, we try to make a comparison between the present Deep Learning techniques by representing the news instances in some vector space using a combination of common mathematical operations with available vector space representations. We do a number of experiments using various combinations and permutations. Finally, we conclude with a sound analysis of the results and evaluate the reasons for such results.

2021-02-19

2102.09553

Arlene Casey J

Arlene Casey, Emma Davidson, Michael Poon, Hang Dong, Daniel Duma, Andreas Grivas, Claire Grover, V\'ictor Su\'arez-Paniagua, Richard Tobin, William Whiteley, Honghan Wu, Beatrice Alex

A Systematic Review of Natural Language Processing Applied to Radiology Reports

BMC Medical Informatics and Decision Making 2021

10.1186/s12911-021-01533-7

cs.CL

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

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication.

2021-06-10

2102.09635

Bibek Paudel

Bibek Paudel, Abraham Bernstein

Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

Web Conference 2021 (WWW '21)

Proceedings of the Web Conference 2021 (WWW '21), April 19--23, 2021, Ljubljana, Slovenia

10.1145/3442381.3449970

cs.SI cs.AI cs.LG

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

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.

2021-02-26

2102.09680

Mario Campos Soberanis

Diego Campos-Sobrino, Mario Campos-Soberanis, Iv\'an Mart\'inez-Chin, V\'ictor Uc-Cetina

Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics

13 pages, 4 figures. This article is a translation of the paper "Correcci\'on de errores del reconocedor de voz de Google usando m\'etricas de distancia fon\'etica" presented in COMIA 2018

Research in Computing Science 148(1), 2019, pp. 57-70. ISSN 1870-4069

cs.CL eess.AS

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

Speech recognition systems for the Spanish language, such as Google's, produce errors quite frequently when used in applications of a specific domain. These errors mostly occur when recognizing words new to the recognizer's language model or ad hoc to the domain. This article presents an algorithm that uses Levenshtein distance on phonemes to reduce the speech recognizer's errors. The preliminary results show that it is possible to correct the recognizer's errors significantly by using this metric and using a dictionary of specific phrases from the domain of the application. Despite being designed for particular domains, the algorithm proposed here is of general application. The phrases that must be recognized can be explicitly defined for each application, without the algorithm having to be modified. It is enough to indicate to the algorithm the set of sentences on which it must work. The algorithm's complexity is $O(tn)$, where $t$ is the number of words in the transcript to be corrected, and $n$ is the number of phrases specific to the domain.

2021-02-22

2102.09761

Tom Hope

Tom Hope, Ronen Tamari, Hyeonsu Kang, Daniel Hershcovich, Joel Chan, Aniket Kittur, Dafna Shahaf

Scaling Creative Inspiration with Fine-Grained Functional Aspects of Ideas

To appear in CHI 2022

CHI 2022

cs.HC cs.AI cs.CL

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

Large repositories of products, patents and scientific papers offer an opportunity for building systems that scour millions of ideas and help users discover inspirations. However, idea descriptions are typically in the form of unstructured text, lacking key structure that is required for supporting creative innovation interactions. Prior work has explored idea representations that were either limited in expressivity, required significant manual effort from users, or dependent on curated knowledge bases with poor coverage. We explore a novel representation that automatically breaks up products into fine-grained functional aspects capturing the purposes and mechanisms of ideas, and use it to support important creative innovation interactions: functional search for ideas, and exploration of the design space around a focal problem by viewing related problem perspectives pooled from across many products. In user studies, our approach boosts the quality of creative search and inspirations, substantially outperforming strong baselines by 50-60%.

2022-02-18

2102.09854

Sao Mai Nguyen

Nicolas Duminy (Lab-STICC), Sao Mai Nguyen (U2IS), Junshuai Zhu (IMT Atlantique), Dominique Duhaut (UBS), Jerome Kerdreux (Lab-STICC)

Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy

Applied Sciences, MDPI, 2021, 11 (3), pp.975

10.3390/app11030975

cs.AI cs.LG stat.ML

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

In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment. Combining both goal-babbling with imitation learning, and active learning with transfer of knowledge based on intrinsic motivation, our algorithm self-organises its learning process. It chooses at any given time a task to focus on; and what, how, when and from whom to transfer knowledge. We show with a simulation and a real industrial robot arm, in cross-task and cross-learner transfer settings, that task composition is key to tackle highly complex tasks. Task decomposition is also efficiently transferred across different embodied learners and by active imitation, where the robot requests just a small amount of demonstrations and the adequate type of information. The robot learns and exploits task dependencies so as to learn tasks of every complexity.

2021-02-22

2102.09965

Mahieddine Djoudi

Hichem Rahab, Abdelhafid Zitouni, Mahieddine Djoudi (TECHN\'E - EA 6316)

An Enhanced Corpus for Arabic Newspapers Comments

arXiv admin note: substantial text overlap with arXiv:2006.00459

International Arab Journal of Information Technology, Colleges of Computing and Information Society (CCIS), 2020, 17 (5), pp.789-798

10.34028/iajit/17/5/12

cs.IR cs.CL cs.MA

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

In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, na{\"i}ve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones.

2021-02-22

2102.10015

Daniel Larsson

Daniel T. Larsson, Dipankar Maity, Panagiotis Tsiotras

Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming

2021 Proceedings of the Workshop on Computation-Aware Algorithmic Design for Cyber-Physical Systems

10.1145/3457335.3461704

cs.RO cs.AI cs.IT math.IT stat.AP

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

In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented. The formulation leverages concepts from information-theoretic signal compression, specifically the information bottleneck (IB) method, to pose a graph abstraction problem as an optimal encoder search over the space of multi-resolution trees. The abstractions emerge in a task-relevant manner as a function of agent information-processing constraints, and are not provided to the system a priori. We detail our formulation and show how the problem can be realized as an integer linear program. A non-trivial numerical example is presented to demonstrate the utility in employing our approach to obtain hierarchical tree abstractions for resource-limited agents.

2021-07-01

2102.10033

Ting-Yao Hu

Ting-Yao Hu, Alexander G. Hauptmann

Pose Guided Person Image Generation with Hidden p-Norm Regression

ICIP 2021

cs.CV

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

In this paper, we propose a novel approach to solve the pose guided person image generation task. We assume that the relation between pose and appearance information can be described by a simple matrix operation in hidden space. Based on this assumption, our method estimates a pose-invariant feature matrix for each identity, and uses it to predict the target appearance conditioned on the target pose. The estimation process is formulated as a p-norm regression problem in hidden space. By utilizing the differentiation of the solution of this regression problem, the parameters of the whole framework can be trained in an end-to-end manner. While most previous works are only applicable to the supervised training and single-shot generation scenario, our method can be easily adapted to unsupervised training and multi-shot generation. Extensive experiments on the challenging Market-1501 dataset show that our method yields competitive performance in all the aforementioned variant scenarios.

2021-05-27

2102.10050

David Leslie

David Leslie

The Arc of the Data Scientific Universe

43 pages

Harvard Data Science Review (Winter 2021)

10.1162/99608f92.938a18d7

physics.hist-ph cs.AI cs.LG stat.AP

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

In this paper I explore the scaffolding of normative assumptions that supports Sabina Leonelli's implicit appeal to the values of epistemic integrity and the global public good that conjointly animate the ethos of responsible and sustainable data work in the context of COVID-19. Drawing primarily on the writings of sociologist Robert K. Merton, the thinkers of the Vienna Circle, and Charles Sanders Peirce, I make some of these assumptions explicit by telling a longer story about the evolution of social thinking about the normative structure of science from Merton's articulation of his well-known norms (those of universalism, communism, organized skepticism, and disinterestedness) to the present. I show that while Merton's norms and his intertwinement of these with the underlying mechanisms of democratic order provide us with an especially good starting point to explore and clarify the commitments and values of science, Leonelli's broader, more context-responsive, and more holistic vision of the epistemic integrity of data scientific understanding, and her discernment of the global and biospheric scope of its moral-practical reach, move beyond Merton's schema in ways that effectively draw upon important critiques. Stepping past Merton, I argue that a combination of situated universalism, methodological pluralism, strong objectivity, and unbounded communalism must guide the responsible and sustainable data work of the future.

2021-02-22

2102.10055

Jindong Gu

Jindong Gu, Baoyuan Wu, Volker Tresp

Effective and Efficient Vote Attack on Capsule Networks

International Conference on Learning Representations (ICLR), 2021

cs.CV

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

Standard Convolutional Neural Networks (CNNs) can be easily fooled by images with small quasi-imperceptible artificial perturbations. As alternatives to CNNs, the recently proposed Capsule Networks (CapsNets) are shown to be more robust to white-box attacks than CNNs under popular attack protocols. Besides, the class-conditional reconstruction part of CapsNets is also used to detect adversarial examples. In this work, we investigate the adversarial robustness of CapsNets, especially how the inner workings of CapsNets change when the output capsules are attacked. The first observation is that adversarial examples misled CapsNets by manipulating the votes from primary capsules. Another observation is the high computational cost, when we directly apply multi-step attack methods designed for CNNs to attack CapsNets, due to the computationally expensive routing mechanism. Motivated by these two observations, we propose a novel vote attack where we attack votes of CapsNets directly. Our vote attack is not only effective but also efficient by circumventing the routing process. Furthermore, we integrate our vote attack into the detection-aware attack paradigm, which can successfully bypass the class-conditional reconstruction based detection method. Extensive experiments demonstrate the superior attack performance of our vote attack on CapsNets.

2021-02-22

2102.10062

Stephen Bonner

Stephen Bonner and Ian P Barrett and Cheng Ye and Rowan Swiers and Ola Engkvist and Andreas Bender and Charles Tapley Hoyt and William L Hamilton

A Review of Biomedical Datasets Relating to Drug Discovery: A Knowledge Graph Perspective

Briefings in Bioinformatics, 2022

10.1093/bib/bbac404

cs.AI

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

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritisation. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, whilst relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data is required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorised according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and a evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, whilst also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

2022-09-27

2102.10243

Thuy Vu

Thuy Vu and Alessandro Moschitti

Machine Translation Customization via Automatic Training Data Selection from the Web

ECIR 2021

cs.CL

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

Machine translation (MT) systems, especially when designed for an industrial setting, are trained with general parallel data derived from the Web. Thus, their style is typically driven by word/structure distribution coming from the average of many domains. In contrast, MT customers want translations to be specialized to their domain, for which they are typically able to provide text samples. We describe an approach for customizing MT systems on specific domains by selecting data similar to the target customer data to train neural translation models. We build document classifiers using monolingual target data, e.g., provided by the customers to select parallel training data from Web crawled data. Finally, we train MT models on our automatically selected data, obtaining a system specialized to the target domain. We tested our approach on the benchmark from WMT-18 Translation Task for News domains enabling comparisons with state-of-the-art MT systems. The results show that our models outperform the top systems while using less data and smaller models.

2021-02-23

2102.10246

Thuy Vu

Thuy Vu and Alessandro Moschitti

CDA: a Cost Efficient Content-based Multilingual Web Document Aligner

EACL 2021

cs.CL cs.AI cs.IR

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

We introduce a Content-based Document Alignment approach (CDA), an efficient method to align multilingual web documents based on content in creating parallel training data for machine translation (MT) systems operating at the industrial level. CDA works in two steps: (i) projecting documents of a web domain to a shared multilingual space; then (ii) aligning them based on the similarity of their representations in such space. We leverage lexical translation models to build vector representations using TF-IDF. CDA achieves performance comparable with state-of-the-art systems in the WMT-16 Bilingual Document Alignment Shared Task benchmark while operating in multilingual space. Besides, we created two web-scale datasets to examine the robustness of CDA in an industrial setting involving up to 28 languages and millions of documents. The experiments show that CDA is robust, cost-effective, and is significantly superior in (i) processing large and noisy web data and (ii) scaling to new and low-resourced languages.

2021-02-23

2102.10274

Deng-Ping Fan

Deng-Ping Fan, Ge-Peng Ji, Ming-Ming Cheng, Ling Shao

Concealed Object Detection

17 pages, 27 figures, Code: https://github.com/GewelsJI/SINet-V2

IEEE transactions on pattern analysis and machine intelligence, 2022, 44(10): 6024-6042

10.1109/TPAMI.2021.3085766

cs.CV

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

We present the first systematic study on concealed object detection (COD), which aims to identify objects that are "perfectly" embedded in their background. The high intrinsic similarities between the concealed objects and their background make COD far more challenging than traditional object detection/segmentation. To better understand this task, we collect a large-scale dataset, called COD10K, which consists of 10,000 images covering concealed objects in diverse real-world scenarios from 78 object categories. Further, we provide rich annotations including object categories, object boundaries, challenging attributes, object-level labels, and instance-level annotations. Our COD10K is the largest COD dataset to date, with the richest annotations, which enables comprehensive concealed object understanding and can even be used to help progress several other vision tasks, such as detection, segmentation, classification, etc. Motivated by how animals hunt in the wild, we also design a simple but strong baseline for COD, termed the Search Identification Network (SINet). Without any bells and whistles, SINet outperforms 12 cutting-edge baselines on all datasets tested, making them robust, general architectures that could serve as catalysts for future research in COD. Finally, we provide some interesting findings and highlight several potential applications and future directions. To spark research in this new field, our code, dataset, and online demo are available on our project page: http://mmcheng.net/cod.

2024-02-21

2102.10290

Luca Lugini

Luca Lugini, Diane Litman

Contextual Argument Component Classification for Class Discussions

In Proceedings of the 28th International Conference on Computational Linguistics, pp. 1475-1480. 2020

10.18653/v1/2020.coling-main.128

cs.CL

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

Argument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, prior work has not carefully analyzed the utility of different contextual properties in context-aware models. In this work, we show how two different types of contextual information, local discourse context and speaker context, can be incorporated into a computational model for classifying argument components in multi-party classroom discussions. We find that both context types can improve performance, although the improvements are dependent on context size and position.

2021-02-23

2102.10293

Luca Lugini

Luca Lugini, Christopher Olshefski, Ravneet Singh, Diane Litman, Amanda Godley

Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms

"Discussion Tracker: Supporting Teacher Learning about Students' Collaborative Argumentation in High School Classrooms." In Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations, 2020

10.18653/v1/2020.coling-demos.10

cs.CL

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

Teaching collaborative argumentation is an advanced skill that many K-12 teachers struggle to develop. To address this, we have developed Discussion Tracker, a classroom discussion analytics system based on novel algorithms for classifying argument moves, specificity, and collaboration. Results from a classroom deployment indicate that teachers found the analytics useful, and that the underlying classifiers perform with moderate to substantial agreement with humans.

2021-02-23

2102.10296

Seyedali Meghdadi Mr

Seyedali Meghdadi, Guido Tack, Ariel Liebman, Nicolas Langren\'e, Christoph Bergmeir

Versatile and Robust Transient Stability Assessment via Instance Transfer Learning

Accepted at the 2021 IEEE PES General Meeting, July 25-29 2020, Washington, DC, USA

2021 IEEE Power & Energy Society General Meeting (PESGM) 1-5

10.1109/PESGM46819.2021.9638195

eess.SY cs.AI cs.SY

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

To support N-1 pre-fault transient stability assessment, this paper introduces a new data collection method in a data-driven algorithm incorporating the knowledge of power system dynamics. The domain knowledge on how the disturbance effect will propagate from the fault location to the rest of the network is leveraged to recognise the dominant conditions that determine the stability of a system. Accordingly, we introduce a new concept called Fault-Affected Area, which provides crucial information regarding the unstable region of operation. This information is embedded in an augmented dataset to train an ensemble model using an instance transfer learning framework. The test results on the IEEE 39-bus system verify that this model can accurately predict the stability of previously unseen operational scenarios while reducing the risk of false prediction of unstable instances compared to standard approaches.

2022-03-08

2102.10338

Haimin Zhang

Haimin Zhang, Min Xu, Guoqiang Zhang, and Kenta Niwa

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

IEEE Transactions on Neural Networks and Learning Systems, 2022

10.1109/TNNLS.2022.3188888

cs.LG cs.CV

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

Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information. Repeatedly applying graph convolutions can cause the oversmoothing issue, i.e., node features at deep layers converge to similar values. Previous studies have suggested that oversmoothing is one of the major issues that restrict the performance of graph convolutional networks. In this paper, we propose a stochastic regularization method to tackle the oversmoothing problem. In the proposed method, we stochastically scale features and gradients (SSFG) by a factor sampled from a probability distribution in the training procedure. By explicitly applying a scaling factor to break feature convergence, the oversmoothing issue is alleviated. We show that applying stochastic scaling at the gradient level is complementary to that applied at the feature level to improve the overall performance. Our method does not increase the number of trainable parameters. When used together with ReLU, our SSFG can be seen as a stochastic ReLU activation function. We experimentally validate our SSFG regularization method on three commonly used types of graph networks. Extensive experimental results on seven benchmark datasets for four graph-based tasks demonstrate that our SSFG regularization is effective in improving the overall performance of the baseline graph networks.

2022-07-11

2102.10446

Andrei Iantsen

Andrei Iantsen, Dimitris Visvikis, Mathieu Hatt

Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

7 pages, 2 figures, 2 tables

In: Andrearczyk V., Oreiller V., Depeursinge A. (eds) Head and Neck Tumor Segmentation. HECKTOR 2020. Lecture Notes in Computer Science, vol 12603. Springer, Cham

10.1007/978-3-030-67194-5_4

eess.IV cs.CV

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

Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on different centers, as well as on the test set (DSC 0.759, precision 0.833, recall 0.740) that allowed us to win first prize in the HECKTOR challenge among 21 participating teams. The full implementation based on PyTorch and the trained models are available at https://github.com/iantsen/hecktor

2021-02-23

2102.10447

M\'onika Farsang

M\'onika Farsang and Luca Szegletes

Importance of Environment Design in Reinforcement Learning: A Study of a Robotic Environment

Proceedings of the Automation and Applied Computer Science Workshop 2021

cs.LG cs.AI cs.RO

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

An in-depth understanding of the particular environment is crucial in reinforcement learning (RL). To address this challenge, the decision-making process of a mobile collaborative robotic assistant modeled by the Markov decision process (MDP) framework is studied in this paper. The optimal state-action combinations of the MDP are calculated with the non-linear Bellman optimality equations. This system of equations can be solved with relative ease by the computational power of Wolfram Mathematica, where the obtained optimal action-values point to the optimal policy. Unlike other RL algorithms, this methodology does not approximate the optimal behavior, it gives the exact, explicit solution, which provides a strong foundation for our study. With this, we offer new insights into understanding the action selection mechanisms in RL by presenting various small modifications on the very same schema that lead to different optimal policies.

2021-06-29

2102.10456

M\'onika Farsang

M\'onika Farsang and Luca Szegletes

Decaying Clipping Range in Proximal Policy Optimization

2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI), 2021, pp. 000521-000526

10.1109/SACI51354.2021.9465602

cs.LG cs.AI cs.RO

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

Proximal Policy Optimization (PPO) is among the most widely used algorithms in reinforcement learning, which achieves state-of-the-art performance in many challenging problems. The keys to its success are the reliable policy updates through the clipping mechanism and the multiple epochs of minibatch updates. The aim of this research is to give new simple but effective alternatives to the former. For this, we propose linearly and exponentially decaying clipping range approaches throughout the training. With these, we would like to provide higher exploration at the beginning and stronger restrictions at the end of the learning phase. We investigate their performance in several classical control and locomotive robotic environments. During the analysis, we found that they influence the achieved rewards and are effective alternatives to the constant clipping method in many reinforcement learning tasks.

2021-07-02

2102.10461

Konik Kothari

Konik Kothari, AmirEhsan Khorashadizadeh, Maarten de Hoop, Ivan Dokmani\'c

Trumpets: Injective Flows for Inference and Inverse Problems

16 pages

Uncertainty in Artificial Intelligence (UAI 2021)

cs.LG cs.AI eess.SP

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

We propose injective generative models called Trumpets that generalize invertible normalizing flows. The proposed generators progressively increase dimension from a low-dimensional latent space. We demonstrate that Trumpets can be trained orders of magnitudes faster than standard flows while yielding samples of comparable or better quality. They retain many of the advantages of the standard flows such as training based on maximum likelihood and a fast, exact inverse of the generator. Since Trumpets are injective and have fast inverses, they can be effectively used for downstream Bayesian inference. To wit, we use Trumpet priors for maximum a posteriori estimation in the context of image reconstruction from compressive measurements, outperforming competitive baselines in terms of reconstruction quality and speed. We then propose an efficient method for posterior characterization and uncertainty quantification with Trumpets by taking advantage of the low-dimensional latent space.

2023-07-25

2102.10485

Massimiliano Lupo Pasini Dr.

Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait

Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data

Journal of Supercomputing, 2021

10.1007/s11227-021-03808-2

cs.CV cs.AI

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

We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models. Our method reduces the imbalance between generator and discriminator by partitioning the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label. Performance is assessed in terms of inception score and image quality on MNIST, CIFAR10, CIFAR100, and ImageNet1k datasets, showing a significant improvement in comparison to state-of-the-art techniques to training DC-CGANs. Weak scaling is attained on all the four datasets using up to 1,000 processes and 2,000 NVIDIA V100 GPUs on the OLCF supercomputer Summit.

2021-04-30

2102.10530

Kotaro Furuya

Kotaro Furuya and Jun Ohkubo

Semi-supervised learning combining backpropagation and STDP: STDP enhances learning by backpropagation with a small amount of labeled data in a spiking neural network

9 pages, 12 figures

J. Phys. Soc. Jpn. 90, 074802 (2021)

10.7566/JPSJ.90.074802

cs.NE cs.AI

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

A semi-supervised learning method for spiking neural networks is proposed. The proposed method consists of supervised learning by backpropagation and subsequent unsupervised learning by spike-timing-dependent plasticity (STDP), which is a biologically plausible learning rule. Numerical experiments show that the proposed method improves the accuracy without additional labeling when a small amount of labeled data is used. This feature has not been achieved by existing semi-supervised learning methods of discriminative models. It is possible to implement the proposed learning method for event-driven systems. Hence, it would be highly efficient in real-time problems if it were implemented on neuromorphic hardware. The results suggest that STDP plays an important role other than self-organization when applied after supervised learning, which differs from the previous method of using STDP as pre-training interpreted as self-organization.

2021-06-23

2102.10532

Michael Maher

Michael J. Maher

Relative Expressiveness of Defeasible Logics II

Includes extensive appendix

Theory and Practice of Logic Programming 13 (2013) 579-592

10.1017/S1471068413000367

cs.LO cs.AI

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

(Maher 2012) introduced an approach for relative expressiveness of defeasible logics, and two notions of relative expressiveness were investigated. Using the first of these definitions of relative expressiveness, we show that all the defeasible logics in the DL framework are equally expressive under this formulation of relative expressiveness. The second formulation of relative expressiveness is stronger than the first. However, we show that logics incorporating individual defeat are equally expressive as the corresponding logics with team defeat. Thus the only differences in expressiveness of logics in DL arise from differences in how ambiguity is handled. This completes the study of relative expressiveness in DL begun in \cite{Maher12}.

2024-05-15

2102.10557

Nam Nguyen

Nam Nguyen and J. Morris Chang

Contrastive Self-supervised Neural Architecture Search

IEEE Transactions on Artificial Intelligence 2 (2021) 1-16

10.1109/TAI.2021.3121663

cs.CV cs.AI

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

This paper proposes a novel cell-based neural architecture search algorithm (NAS), which completely alleviates the expensive costs of data labeling inherited from supervised learning. Our algorithm capitalizes on the effectiveness of self-supervised learning for image representations, which is an increasingly crucial topic of computer vision. First, using only a small amount of unlabeled train data under contrastive self-supervised learning allow us to search on a more extensive search space, discovering better neural architectures without surging the computational resources. Second, we entirely relieve the cost for labeled data (by contrastive loss) in the search stage without compromising architectures' final performance in the evaluation phase. Finally, we tackle the inherent discrete search space of the NAS problem by sequential model-based optimization via the tree-parzen estimator (SMBO-TPE), enabling us to reduce the computational expense response surface significantly. An extensive number of experiments empirically show that our search algorithm can achieve state-of-the-art results with better efficiency in data labeling cost, searching time, and accuracy in final validation.

2021-11-09

2102.10558

L\'aszl\'o Csat\'o

Kolos Csaba \'Agoston and L\'aszl\'o Csat\'o

Inconsistency thresholds for incomplete pairwise comparison matrices

16 pages, 3 figures, 4 tables

Omega, 108: 102576, 2022

10.1016/j.omega.2021.102576

math.ST cs.AI math.OC stat.AP stat.TH

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

Pairwise comparison matrices are increasingly used in settings where some pairs are missing. However, there exist few inconsistency indices for similar incomplete data sets and no reasonable measure has an associated threshold. This paper generalises the famous rule of thumb for the acceptable level of inconsistency, proposed by Saaty, to incomplete pairwise comparison matrices. The extension is based on choosing the missing elements such that the maximal eigenvalue of the incomplete matrix is minimised. Consequently, the well-established values of the random index cannot be adopted: the inconsistency of random matrices is found to be the function of matrix size and the number of missing elements, with a nearly linear dependence in the case of the latter variable. Our results can be directly built into decision-making software and used by practitioners as a statistical criterion for accepting or rejecting an incomplete pairwise comparison matrix.

2022-02-03

2102.10590

Zahidul Islam

Zahidul Islam, Mohammad Rukonuzzaman, Raiyan Ahmed, Md. Hasanul Kabir, Moshiur Farazi

Efficient Two-Stream Network for Violence Detection Using Separable Convolutional LSTM

Accepted by the 2021 International Joint Conference on Neural Networks (IJCNN 2021)

2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1-8

10.1109/IJCNN52387.2021.9534280

cs.CV cs.LG

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

Automatically detecting violence from surveillance footage is a subset of activity recognition that deserves special attention because of its wide applicability in unmanned security monitoring systems, internet video filtration, etc. In this work, we propose an efficient two-stream deep learning architecture leveraging Separable Convolutional LSTM (SepConvLSTM) and pre-trained MobileNet where one stream takes in background suppressed frames as inputs and other stream processes difference of adjacent frames. We employed simple and fast input pre-processing techniques that highlight the moving objects in the frames by suppressing non-moving backgrounds and capture the motion in-between frames. As violent actions are mostly characterized by body movements these inputs help produce discriminative features. SepConvLSTM is constructed by replacing convolution operation at each gate of ConvLSTM with a depthwise separable convolution that enables producing robust long-range Spatio-temporal features while using substantially fewer parameters. We experimented with three fusion methods to combine the output feature maps of the two streams. Evaluation of the proposed methods was done on three standard public datasets. Our model outperforms the accuracy on the larger and more challenging RWF-2000 dataset by more than a 2% margin while matching state-of-the-art results on the smaller datasets. Our experiments lead us to conclude, the proposed models are superior in terms of both computational efficiency and detection accuracy.

2021-10-08

2102.10707

HanQin Cai

HanQin Cai, Yuchen Lou, Daniel McKenzie, Wotao Yin

A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization

Accepted to ICML 2021

Proceedings of the 38th International Conference on Machine Learning, PMLR 139:1193-1203, 2021

math.OC cs.AI cs.LG stat.ML

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

We consider the zeroth-order optimization problem in the huge-scale setting, where the dimension of the problem is so large that performing even basic vector operations on the decision variables is infeasible. In this paper, we propose a novel algorithm, coined ZO-BCD, that exhibits favorable overall query complexity and has a much smaller per-iteration computational complexity. In addition, we discuss how the memory footprint of ZO-BCD can be reduced even further by the clever use of circulant measurement matrices. As an application of our new method, we propose the idea of crafting adversarial attacks on neural network based classifiers in a wavelet domain, which can result in problem dimensions of over 1.7 million. In particular, we show that crafting adversarial examples to audio classifiers in a wavelet domain can achieve the state-of-the-art attack success rate of 97.9%.

2021-08-17

2102.10731

Sarvesh Kumar Singh Mr.

Sarvesh Kumar Singh, Bikram Pratap Banerjee and Simit Raval

Three dimensional unique identifier based automated georeferencing and coregistration of point clouds in underground environment

26 pages, 10 figures

Remote Sensing. 2021; 13(16):3145

10.3390/rs13163145

cs.CV

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

Spatially and geometrically accurate laser scans are essential in modelling infrastructure for applications in civil, mining and transportation. Monitoring of underground or indoor environments such as mines or tunnels is challenging due to unavailability of a sensor positioning framework, complicated structurally symmetric layouts, repetitive features and occlusions. Current practices largely include a manual selection of discernable reference points for georeferencing and coregistration purpose. This study aims at overcoming these practical challenges in underground or indoor laser scanning. The developed approach involves automatically and uniquely identifiable three dimensional unique identifiers (3DUIDs) in laser scans, and a 3D registration (3DReG) workflow. Field testing of the method in an underground tunnel has been found accurate, effective and efficient. Additionally, a method for automatically extracting roadway tunnel profile has been exhibited. The developed 3DUID can be used in roadway profile extraction, guided automation, sensor calibration, reference targets for routine survey and deformation monitoring.

2021-09-01

2102.10777

Tejas Khare

Tejas Khare, Vaibhav Bahel and Anuradha C. Phadke

PCB-Fire: Automated Classification and Fault Detection in PCB

6 Pages, 9 Figures, Conference

Proceeding Reference - 978-0-7381-4335-4/20/$31.00 \c{opyright}2020 IEEE

10.1109/MPCIT51588.2020.9350324

cs.CV eess.IV

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

Printed Circuit Boards are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.

2021-02-23

2102.10820

Dennis Stumpf

Dennis Stumpf, Stephan Krau\ss, Gerd Reis, Oliver Wasenm\"uller, Didier Stricker

SALT: A Semi-automatic Labeling Tool for RGB-D Video Sequences

VISAPP 2021 full paper (9 pages, 6 figures), published by SciTePress: https://www.scitepress.org/PublicationsDetail.aspx?ID=ywQZ3GZrka8=&t=1

Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4 VISAPP: VISAPP (2021) 595-603

10.5220/0010303005950603

cs.CV cs.LG cs.RO

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

Large labeled data sets are one of the essential basics of modern deep learning techniques. Therefore, there is an increasing need for tools that allow to label large amounts of data as intuitively as possible. In this paper, we introduce SALT, a tool to semi-automatically annotate RGB-D video sequences to generate 3D bounding boxes for full six Degrees of Freedom (DoF) object poses, as well as pixel-level instance segmentation masks for both RGB and depth. Besides bounding box propagation through various interpolation techniques, as well as algorithmically guided instance segmentation, our pipeline also provides built-in pre-processing functionalities to facilitate the data set creation process. By making full use of SALT, annotation time can be reduced by a factor of up to 33.95 for bounding box creation and 8.55 for RGB segmentation without compromising the quality of the automatically generated ground truth.

2021-02-23

2102.10837

Subho Sankar Banerjee

Subho S. Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, Ravishankar K. Iyer

BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics

Proceedings of the Twenty-Sixth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 21), 2021

10.1145/3445814.3446739

cs.DC cs.AI cs.AR cs.PF

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

Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system. However, HPC measurements are error-prone due to non determinism (e.g., undercounting due to event multiplexing, or OS interrupt-handling behaviors). In this paper, we present BayesPerf, a system for quantifying uncertainty in HPC measurements by using a domain-driven Bayesian model that captures microarchitectural relationships between HPCs to jointly infer their values as probability distributions. We provide the design and implementation of an accelerator that allows for low-latency and low-power inference of the BayesPerf model for x86 and ppc64 CPUs. BayesPerf reduces the average error in HPC measurements from 40.1% to 7.6% when events are being multiplexed. The value of BayesPerf in real-time decision-making is illustrated with a simple example of scheduling of PCIe transfers.

2021-02-23

2102.10848

Judit Acs

Judit \'Acs and D\'aniel L\'evai and D\'avid M\'ark Nemeskey and Andr\'as Kornai

Evaluating Contextualized Language Models for Hungarian

Hungarian NLP Conference (MSZNY2021)

cs.CL

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

We present an extended comparison of contextualized language models for Hungarian. We compare huBERT, a Hungarian model against 4 multilingual models including the multilingual BERT model. We evaluate these models through three tasks, morphological probing, POS tagging and NER. We find that huBERT works better than the other models, often by a large margin, particularly near the global optimum (typically at the middle layers). We also find that huBERT tends to generate fewer subwords for one word and that using the last subword for token-level tasks is generally a better choice than using the first one.

2021-02-23

2102.10864

Judit Acs

Judit \'Acs and \'Akos K\'ad\'ar and Andr\'as Kornai

Subword Pooling Makes a Difference

EACL2021

cs.CL

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

Contextual word-representations became a standard in modern natural language processing systems. These models use subword tokenization to handle large vocabularies and unknown words. Word-level usage of such systems requires a way of pooling multiple subwords that correspond to a single word. In this paper we investigate how the choice of subword pooling affects the downstream performance on three tasks: morphological probing, POS tagging and NER, in 9 typologically diverse languages. We compare these in two massively multilingual models, mBERT and XLM-RoBERTa. For morphological tasks, the widely used `choose the first subword' is the worst strategy and the best results are obtained by using attention over the subwords. For POS tagging both of these strategies perform poorly and the best choice is to use a small LSTM over the subwords. The same strategy works best for NER and we show that mBERT is better than XLM-RoBERTa in all 9 languages. We publicly release all code, data and the full result tables at \url{https://github.com/juditacs/subword-choice}.

2021-03-30

2102.10935

Yazhou Yao

Tao Chen, Guosen Xie, Yazhou Yao, Qiong Wang, Fumin Shen, Zhenmin Tang, and Jian Zhang

Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

IEEE Transactions on Multimedia, 2021

cs.CV cs.MM

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

One-shot semantic image segmentation aims to segment the object regions for the novel class with only one annotated image. Recent works adopt the episodic training strategy to mimic the expected situation at testing time. However, these existing approaches simulate the test conditions too strictly during the training process, and thus cannot make full use of the given label information. Besides, these approaches mainly focus on the foreground-background target class segmentation setting. They only utilize binary mask labels for training. In this paper, we propose to leverage the multi-class label information during the episodic training. It will encourage the network to generate more semantically meaningful features for each category. After integrating the target class cues into the query features, we then propose a pyramid feature fusion module to mine the fused features for the final classifier. Furthermore, to take more advantage of the support image-mask pair, we propose a self-prototype guidance branch to support image segmentation. It can constrain the network for generating more compact features and a robust prototype for each semantic class. For inference, we propose a fused prototype guidance branch for the segmentation of the query image. Specifically, we leverage the prediction of the query image to extract the pseudo-prototype and combine it with the initial prototype. Then we utilize the fused prototype to guide the final segmentation of the query image. Extensive experiments demonstrate the superiority of our proposed approach.

2021-02-23

2102.10951

Alexander Hepburn

Alexander Hepburn, Raul Santos-Rodriguez

Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception

2021 IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA

cs.CV stat.ML

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

Explaining the decisions of models is becoming pervasive in the image processing domain, whether it is by using post-hoc methods or by creating inherently interpretable models. While the widespread use of surrogate explainers is a welcome addition to inspect and understand black-box models, assessing the robustness and reliability of the explanations is key for their success. Additionally, whilst existing work in the explainability field proposes various strategies to address this problem, the challenges of working with data in the wild is often overlooked. For instance, in image classification, distortions to images can not only affect the predictions assigned by the model, but also the explanation. Given a clean and a distorted version of an image, even if the prediction probabilities are similar, the explanation may still be different. In this paper we propose a methodology to evaluate the effect of distortions in explanations by embedding perceptual distances that tailor the neighbourhoods used to training surrogate explainers. We also show that by operating in this way, we can make the explanations more robust to distortions. We generate explanations for images in the Imagenet-C dataset and demonstrate how using a perceptual distances in the surrogate explainer creates more coherent explanations for the distorted and reference images.

2021-06-17

2102.11025

Emiliano Lorini

Emiliano Lorini

A Qualitative Theory of Cognitive Attitudes and their Change

Under consideration in Theory and Practice of Logic Programming (TPLP)

Theory and Practice of Logic Programming 21 (2021) 428-458

10.1017/S1471068421000053

cs.AI cs.LO

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

We present a general logical framework for reasoning about agents' cognitive attitudes of both epistemic type and motivational type. We show that it allows us to express a variety of relevant concepts for qualitative decision theory including the concepts of knowledge, belief, strong belief, conditional belief, desire, conditional desire, strong desire and preference. We also present two extensions of the logic, one by the notion of choice and the other by dynamic operators for belief change and desire change, and we apply the former to the analysis of single-stage games under incomplete information. We provide sound and complete axiomatizations for the basic logic and for its two extensions. The paper is under consideration in Theory and Practice of Logic Programming (TPLP).

2023-06-22

2102.11032

Ozlem Uzuner

Nicholas Dobbins, David Wayne, Kahyun Lee, \"Ozlem Uzuner, Meliha Yetisgen

Performance of Automatic De-identification Across Different Note Types

AMIA Virtual Summits 2021

cs.CL

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

Free-text clinical notes detail all aspects of patient care and have great potential to facilitate quality improvement and assurance initiatives as well as advance clinical research. However, concerns about patient privacy and confidentiality limit the use of clinical notes for research. As a result, the information documented in these notes remains unavailable for most researchers. De-identification (de-id), i.e., locating and removing personally identifying protected health information (PHI), is one way of improving access to clinical narratives. However, there are limited off-the-shelf de-identification systems able to consistently detect PHI across different data sources and medical specialties. In this abstract, we present the performance of a state-of-the art de-id system called NeuroNER1 on a diverse set of notes from University of Washington (UW) when the models are trained on data from an external institution (Partners Healthcare) vs. from the same institution (UW). We present results at the level of PHI and note types.

2021-02-23