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2101.10964

Oren Neumann

Oren Neumann, Claudius Gros

Investment vs. reward in a competitive knapsack problem

Learning Meets Combinatorial Algorithms at NeurIPS2020 (2020)

cs.AI

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

Natural selection drives species to develop brains, with sizes that increase with the complexity of the tasks to be tackled. Our goal is to investigate the balance between the metabolic costs of larger brains compared to the advantage they provide in solving general and combinatorial problems. Defining advantage as the performance relative to competitors, a two-player game based on the knapsack problem is used. Within this framework, two opponents compete over shared resources, with the goal of collecting more resources than the opponent. Neural nets of varying sizes are trained using a variant of the AlphaGo Zero algorithm. A surprisingly simple relation, $N_A/(N_A+N_B)$, is found for the relative win rate of a net with $N_A$ neurons against one with $N_B$. Success increases linearly with investments in additional resources when the networks sizes are very different, i.e. when $N_A \ll N_B$, with returns diminishing when both networks become comparable in size.

2021-01-28

2101.10977

Lukas Brunke

Lukas Brunke, Prateek Agrawal, Nikhil George

Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison

ECCV 2020: Computer Vision - ECCV 2020 Workshops pp 120-134

10.1007/978-3-030-66415-2_8

cs.LG cs.CV

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

Input perturbation methods occlude parts of an input to a function and measure the change in the function's output. Recently, input perturbation methods have been applied to generate and evaluate saliency maps from convolutional neural networks. In practice, neutral baseline images are used for the occlusion, such that the baseline image's impact on the classification probability is minimal. However, in this paper we show that arguably neutral baseline images still impact the generated saliency maps and their evaluation with input perturbations. We also demonstrate that many choices of hyperparameters lead to the divergence of saliency maps generated by input perturbations. We experimentally reveal inconsistencies among a selection of input perturbation methods and find that they lack robustness for generating saliency maps and for evaluating saliency maps as saliency metrics.

2021-01-27

2101.11002

Evan Debenham

Evan R.M. Debenham and Roberto Solis-Oba (The University of Western Ontario, Canada)

New Algorithms for Computing Field of Vision over 2D Grids

Presented at the 6th International Conference on Computer Science, Engineering And Applications (CSEA 2020) 18 pages, 11 figures, 4 tables

6th International Conference on Computer Science, Engineering And Applications (CSEA 2020), Volume 10, Number 18, December 2020, pg. 1-18

cs.CV

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

The aim of this paper is to propose new algorithms for Field of Vision (FOV) computation which improve on existing work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a scene of a computer game. We summarize existing algorithms for FOV computation, describe their limitations, and present new algorithms which aim to address these limitations. We first present an algorithm which makes use of spatial data structures in a way which is new for FOV calculation. We then present a novel technique which updates a previously calculated FOV, rather than re-calculating an FOV from scratch. We compare our algorithms to existing FOV algorithms and show they provide substantial improvements to running time. Our algorithms provide the largest improvement over existing FOV algorithms at large grid sizes, thus allowing the possibility of the design of high resolution FOV-based video games.

2021-01-28

2101.11023

Taro Sakurai

Taro Sakurai (Chiba University)

On formal concepts of random formal contexts

7 pages, 2 figures, 1 table

Information Sciences 578 (2021) 615-620

10.1016/j.ins.2021.07.065

cs.AI cs.DS math.CO

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

In formal concept analysis, it is well-known that the number of formal concepts can be exponential in the worst case. To analyze the average case, we introduce a probabilistic model for random formal contexts and prove that the average number of formal concepts has a superpolynomial asymptotic lower bound.

2021-08-02

2101.11060

Xinwei Zhao

Xinwei Zhao and Matthew C. Stamm

Defenses Against Multi-Sticker Physical Domain Attacks on Classifiers

This paper is published on European Conference on Computer Vision 2020, page 202-219, Springer

cs.CR cs.CV

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

Recently, physical domain adversarial attacks have drawn significant attention from the machine learning community. One important attack proposed by Eykholt et al. can fool a classifier by placing black and white stickers on an object such as a road sign. While this attack may pose a significant threat to visual classifiers, there are currently no defenses designed to protect against this attack. In this paper, we propose new defenses that can protect against multi-sticker attacks. We present defensive strategies capable of operating when the defender has full, partial, and no prior information about the attack. By conducting extensive experiments, we show that our proposed defenses can outperform existing defenses against physical attacks when presented with a multi-sticker attack.

2021-01-28

2101.11081

Xinwei Zhao

Xinwei Zhao and Matthew C. Stamm

The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs

Published at Electronic Imaging, Media Watermarking, Security, and Forensics 2020, pp. 119-1-119-7(7)

cs.CV cs.CR cs.LG

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

In recent years, convolutional neural networks (CNNs) have been widely used by researchers to perform forensic tasks such as image tampering detection. At the same time, adversarial attacks have been developed that are capable of fooling CNN-based classifiers. Understanding the transferability of adversarial attacks, i.e. an attacks ability to attack a different CNN than the one it was trained against, has important implications for designing CNNs that are resistant to attacks. While attacks on object recognition CNNs are believed to be transferrable, recent work by Barni et al. has shown that attacks on forensic CNNs have difficulty transferring to other CNN architectures or CNNs trained using different datasets. In this paper, we demonstrate that adversarial attacks on forensic CNNs are even less transferrable than previously thought even between virtually identical CNN architectures! We show that several common adversarial attacks against CNNs trained to identify image manipulation fail to transfer to CNNs whose only difference is in the class definitions (i.e. the same CNN architectures trained using the same data). We note that all formulations of class definitions contain the unaltered class. This has important implications for the future design of forensic CNNs that are robust to adversarial and anti-forensic attacks.

2021-01-28

2101.11174

Weiwei Jiang

Weiwei Jiang, Jiayun Luo

Graph Neural Network for Traffic Forecasting: A Survey

Expert Systems with Applications Volume, vol. 207, 30 November 2022, 117921

10.1016/j.eswa.2022.117921

cs.LG cs.AI

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

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.

2022-07-08

2101.11183

Haipeng Li

Haipeng Li, Shuaicheng Liu, Jue Wang

DeepOIS: Gyroscope-Guided Deep Optical Image Stabilizer Compensation

IEEE Transactions on Circuits and Systems for Video Technology ( Volume: 32, Issue: 5, May 2022)

10.1109/TCSVT.2021.3103281

21690602

cs.CV

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

Mobile captured images can be aligned using their gyroscope sensors. Optical image stabilizer (OIS) terminates this possibility by adjusting the images during the capturing. In this work, we propose a deep network that compensates the motions caused by the OIS, such that the gyroscopes can be used for image alignment on the OIS cameras. To achieve this, first, we record both videos and gyroscopes with an OIS camera as training data. Then, we convert gyroscope readings into motion fields. Second, we propose a Fundamental Mixtures motion model for rolling shutter cameras, where an array of rotations within a frame are extracted as the ground-truth guidance. Third, we train a convolutional neural network with gyroscope motions as input to compensate for the OIS motion. Once finished, the compensation network can be applied for other scenes, where the image alignment is purely based on gyroscopes with no need for images contents, delivering strong robustness. Experiments show that our results are comparable with that of non-OIS cameras, and outperform image-based alignment results with a relatively large margin. Code and dataset are available at https://github.com/lhaippp/DeepOIS

2023-07-06

2101.11217

Tejas Khare

Tejas Atul Khare and Anuradha C. Phadke

Automated Crop Field Surveillance using Computer Vision

6 Pages, 10 Figures

Proceedings reference - 978-1-7281-9885-9/20/$31.00 \c{opyright}2020 IEEE

10.1109/DISCOVER50404.2020.9278072

cs.CV

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

Artificial Intelligence is everywhere today. But unfortunately, Agriculture has not been able to get that much attention from Artificial Intelligence (AI). A lack of automation persists in the agriculture industry. For over many years, farmers and crop field owners have been facing a problem of trespassing of wild animals for which no feasible solution has been provided. Installing a fence or barrier like structure is neither feasible nor efficient due to the large areas covered by the fields. Also, if the landowner can afford to build a wall or barrier, government policies for building walls are often very irksome. The paper intends to give a simple intelligible solution to the problem with Automated Crop Field Surveillance using Computer Vision. The solution will significantly reduce the cost of crops destroyed annually and completely automate the security of the field.

2021-01-28

2101.11302

Niels van der Heijden

Niels van der Heijden, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova

Multilingual and cross-lingual document classification: A meta-learning approach

11 pages, 1 figure

Association for Computational Linguistics, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 2021, 1966--1976

cs.CL

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

The great majority of languages in the world are considered under-resourced for the successful application of deep learning methods. In this work, we propose a meta-learning approach to document classification in limited-resource setting and demonstrate its effectiveness in two different settings: few-shot, cross-lingual adaptation to previously unseen languages; and multilingual joint training when limited target-language data is available during training. We conduct a systematic comparison of several meta-learning methods, investigate multiple settings in terms of data availability and show that meta-learning thrives in settings with a heterogeneous task distribution. We propose a simple, yet effective adjustment to existing meta-learning methods which allows for better and more stable learning, and set a new state of the art on several languages while performing on-par on others, using only a small amount of labeled data.

2021-04-27

2101.11431

Nicola Melluso

Silvia Fareri, Nicola Melluso, Filippo Chiarello, Gualtiero Fantoni

SkillNER: Mining and Mapping Soft Skills from any Text

Expert Systems With Applications 184 (2021) 115544

10.1016/j.eswa.2021.115544

cs.CL cs.IR

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

In today's digital world, there is an increasing focus on soft skills. On the one hand, they facilitate innovation at companies, but on the other, they are unlikely to be automated soon. Researchers struggle with accurately approaching quantitatively the study of soft skills due to the lack of data-driven methods to retrieve them. This limits the possibility for psychologists and HR managers to understand the relation between humans and digitalisation. This paper presents SkillNER, a novel data-driven method for automatically extracting soft skills from text. It is a named entity recognition (NER) system trained with a support vector machine (SVM) on a corpus of more than 5000 scientific papers. We developed this system by measuring the performance of our approach against different training models and validating the results together with a team of psychologists. Finally, SkillNER was tested in a real-world case study using the job descriptions of ESCO (European Skill/Competence Qualification and Occupation) as textual source. The system enabled the detection of communities of job profiles based on their shared soft skills and communities of soft skills based on their shared job profiles. This case study demonstrates that the tool can automatically retrieve soft skills from a large corpus in an efficient way, proving useful for firms, institutions, and workers. The tool is open and available online to foster quantitative methods for the study of soft skills.

2021-07-14

2101.11435

Yakup Kutlu

Apdullah Yayik, Yakup Kutlu

Online LDA based brain-computer interface system to aid disabled people

13 pages, 4 figures, Natural and Engineering Sciences

Natural and Engineering Sciences, 2017

cs.HC cs.AI

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

This paper aims to develop brain-computer interface system based on electroencephalography that can aid disabled people in daily life. The system relies on one of the most effective event-related potential wave, P300, which can be elicited by oddball paradigm. Developed application has a basic interaction tool that enables disabled people to convey their needs to other people selecting related objects. These objects pseudo-randomly flash in a visual interface on computer screen. The user must focus on related object to convey desired needs. The system can convey desired needs correctly by detecting P300 wave in acquired 14-channel EEG signal and classifying using linear discriminant analysis classifier just in 15 seconds. Experiments have been carried out on 19 volunteers to validate developed BCI system. As a result, accuracy rate of 90.83% is achieved in online performance

2021-01-28

2101.11436

Yakup Kutlu

Kadir Tohma, Yakup Kutlu

Challenges Encountered in Turkish Natural Language Processing Studies

8 pages, Natural and Engineering Sciences

Natural and Engineering Sciences, 2020

cs.CL cs.AI

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

Natural language processing is a branch of computer science that combines artificial intelligence with linguistics. It aims to analyze a language element such as writing or speaking with software and convert it into information. Considering that each language has its own grammatical rules and vocabulary diversity, the complexity of the studies in this field is somewhat understandable. For instance, Turkish is a very interesting language in many ways. Examples of this are agglutinative word structure, consonant/vowel harmony, a large number of productive derivational morphemes (practically infinite vocabulary), derivation and syntactic relations, a complex emphasis on vocabulary and phonological rules. In this study, the interesting features of Turkish in terms of natural language processing are mentioned. In addition, summary info about natural language processing techniques, systems and various sources developed for Turkish are given.

2021-01-28

2101.11508

Olivier Rukundo

Olivier Rukundo

Effects of Image Size on Deep Learning

22 pages, 23 figures, 5 tables

Electronics 2023, 12(4), 985

10.3390/electronics12040985

cs.CV cs.LG eess.IV

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

In this work, the best size for late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) images in the training dataset was determined to optimize deep learning training outcomes. Non-extra pixel and extra pixel interpolation algorithms were used to determine the new size of the LGE-MRI images. A novel strategy was introduced to handle interpolation masks and remove extra class labels in interpolated ground truth (GT) segmentation masks. The expectation maximization, weighted intensity, a priori information (EWA) algorithm was used for quantification of myocardial infarction (MI) in automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the sums, and sums of differences are methods used to estimate the relationship between semi-automatic or manual and fully automated quantification of myocardial infarction (MI) results. The relationship between semi-automatic and fully automated quantification of MI results was found to be closer in the case of bigger LGE MRI images (55.5% closer to manual results) than in the case of smaller LGE MRI images (22.2% closer to manual results).

2023-02-20

2101.11560

Ece Calikus

Ece Calikus, Slawomir Nowaczyk, Mohamed-Rafik Bouguelia, and Onur Dikmen

Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection

Data Mining Knowledge Discovery (2022)

10.1007/s10618-022-00868-7

cs.LG cs.AI

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

In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specified contextual features. However, identifying the right context can be very challenging in practice, especially in datasets, with a large number of attributes. Furthermore, in real-world systems, there might be multiple anomalies that occur in different contexts and, therefore, require a combination of several "useful" contexts to unveil them. In this work, we leverage active learning and ensembles to effectively detect complex contextual anomalies in situations where the true contextual and behavioral attributes are unknown. We propose a novel approach, called WisCon (Wisdom of the Contexts), that automatically creates contexts from the feature set. Our method constructs an ensemble of multiple contexts, with varying importance scores, based on the assumption that not all useful contexts are equally so. Experiments show that WisCon significantly outperforms existing baselines in different categories (i.e., active classifiers, unsupervised contextual and non-contextual anomaly detectors, and supervised classifiers) on seven datasets. Furthermore, the results support our initial hypothesis that there is no single perfect context that successfully uncovers all kinds of contextual anomalies, and leveraging the "wisdom" of multiple contexts is necessary.

2022-10-05

2101.11587

Steven Frank

Steven J. Frank

The Work of Art in an Age of Mechanical Generation

This is the author's final version; the article has been accepted for publication in Leonardo Journal

Leonardo(2022) 55(4): 378-381

10.1162/leon_a_02095

cs.CY cs.AI cs.CV

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

Can we define what it means to be "creative," and if so, can our definition drive artificial intelligence (AI) systems to feats of creativity indistinguishable from human efforts? This mixed question is considered from technological and social perspectives. Beginning with an exploration of the value we attach to authenticity in works of art, the article considers the ability of AI to detect forgeries of renowned paintings and, in so doing, somehow reveal the quiddity of a work of art. We conclude by considering whether evolving technical capability can revise traditional relationships among art, artist, and the market.

2022-08-12

2101.11717

Francois Malgouyres

Adrien Gauffriau, Fran\c{c}ois Malgouyres (IMT), M\'elanie Ducoffe

Overestimation learning with guarantees

AAAI-21, workshop on safeAI, Feb 2021, Valence (Virtual), Spain

cs.LG cs.AI cs.NE stat.ML

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

We describe a complete method that learns a neural network which is guaranteed to overestimate a reference function on a given domain. The neural network can then be used as a surrogate for the reference function. The method involves two steps. In the first step, we construct an adaptive set of Majoring Points. In the second step, we optimize a well-chosen neural network to overestimate the Majoring Points. In order to extend the guarantee on the Majoring Points to the whole domain, we necessarily have to make an assumption on the reference function. In this study, we assume that the reference function is monotonic. We provide experiments on synthetic and real problems. The experiments show that the density of the Majoring Points concentrate where the reference function varies. The learned over-estimations are both guaranteed to overestimate the reference function and are proven empirically to provide good approximations of it. Experiments on real data show that the method makes it possible to use the surrogate function in embedded systems for which an underestimation is critical; when computing the reference function requires too many resources.

2021-01-29

2101.11844

Iena Petronella Derks

Iena Petronella Derks and Alta de Waal

A Taxonomy of Explainable Bayesian Networks

In: Gerber A. (eds) Artificial Intelligence Research. SACAIR 2021. Communications in Computer and Information Science, vol 1342. Springer, Cham (2020)

10.1007/978-3-030-66151-9_14

cs.AI

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

Artificial Intelligence (AI), and in particular, the explainability thereof, has gained phenomenal attention over the last few years. Whilst we usually do not question the decision-making process of these systems in situations where only the outcome is of interest, we do however pay close attention when these systems are applied in areas where the decisions directly influence the lives of humans. It is especially noisy and uncertain observations close to the decision boundary which results in predictions which cannot necessarily be explained that may foster mistrust among end-users. This drew attention to AI methods for which the outcomes can be explained. Bayesian networks are probabilistic graphical models that can be used as a tool to manage uncertainty. The probabilistic framework of a Bayesian network allows for explainability in the model, reasoning and evidence. The use of these methods is mostly ad hoc and not as well organised as explainability methods in the wider AI research field. As such, we introduce a taxonomy of explainability in Bayesian networks. We extend the existing categorisation of explainability in the model, reasoning or evidence to include explanation of decisions. The explanations obtained from the explainability methods are illustrated by means of a simple medical diagnostic scenario. The taxonomy introduced in this paper has the potential not only to encourage end-users to efficiently communicate outcomes obtained, but also support their understanding of how and, more importantly, why certain predictions were made.

2021-01-29

2101.11978

Rodrigo Agerri

Elena Zotova, Rodrigo Agerri, German Rigau

Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter

Stance detection, multilingualism, text categorization, fake news, deep learning

Expert Systems with Applications, 170 (2021), Elsevier

10.1016/j.eswa.2020.114547

cs.CL

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

Popular social media networks provide the perfect environment to study the opinions and attitudes expressed by users. While interactions in social media such as Twitter occur in many natural languages, research on stance detection (the position or attitude expressed with respect to a specific topic) within the Natural Language Processing field has largely been done for English. Although some efforts have recently been made to develop annotated data in other languages, there is a telling lack of resources to facilitate multilingual and crosslingual research on stance detection. This is partially due to the fact that manually annotating a corpus of social media texts is a difficult, slow and costly process. Furthermore, as stance is a highly domain- and topic-specific phenomenon, the need for annotated data is specially demanding. As a result, most of the manually labeled resources are hindered by their relatively small size and skewed class distribution. This paper presents a method to obtain multilingual datasets for stance detection in Twitter. Instead of manually annotating on a per tweet basis, we leverage user-based information to semi-automatically label large amounts of tweets. Empirical monolingual and cross-lingual experimentation and qualitative analysis show that our method helps to overcome the aforementioned difficulties to build large, balanced and multilingual labeled corpora. We believe that our method can be easily adapted to easily generate labeled social media data for other Natural Language Processing tasks and domains.

2021-01-29

2101.12047

Samuel Alexander

Samuel Alexander, Bill Hibbard

Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure

25 pages

Journal of Artificial General Intelligence 12(1), 2021

10.2478/jagi-2021-0001

cs.AI

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

In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard's idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard's intelligence measure is based on the latter growth-rate-measuring method, we survey other methods for measuring function growth rates, and exhibit the resulting Hibbard-like intelligence measures and taxonomies. Of particular interest, we obtain intelligence taxonomies based on Big-O and Big-Theta notation systems, which taxonomies are novel in that they challenge conventional notions of what an intelligence measure should look like. We discuss how intelligence measurement of sequence predictors can indirectly serve as intelligence measurement for agents with Artificial General Intelligence (AGIs).

2021-01-29

2101.12072

Kashif Rasul

Kashif Rasul, Calvin Seward, Ingmar Schuster, Roland Vollgraf

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting

Proceedings of the 38th International Conference on Machine Learning, PMLR 139:8857-8868, 2021

cs.LG cs.AI

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

In this work, we propose \texttt{TimeGrad}, an autoregressive model for multivariate probabilistic time series forecasting which samples from the data distribution at each time step by estimating its gradient. To this end, we use diffusion probabilistic models, a class of latent variable models closely connected to score matching and energy-based methods. Our model learns gradients by optimizing a variational bound on the data likelihood and at inference time converts white noise into a sample of the distribution of interest through a Markov chain using Langevin sampling. We demonstrate experimentally that the proposed autoregressive denoising diffusion model is the new state-of-the-art multivariate probabilistic forecasting method on real-world data sets with thousands of correlated dimensions. We hope that this method is a useful tool for practitioners and lays the foundation for future research in this area.

2021-07-09

2101.12102

Samuel Rivera

Deborah Weeks and Samuel Rivera

Domain Adaptation by Topology Regularization

SPIE Defense + Commercial Sensing, 2021

cs.CV

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

Deep learning has become the leading approach to assisted target recognition. While these methods typically require large amounts of labeled training data, domain adaptation (DA) or transfer learning (TL) enables these algorithms to transfer knowledge from a labelled (source) data set to an unlabelled but related (target) data set of interest. DA enables networks to overcome the distribution mismatch between the source and target that leads to poor generalization in the target domain. DA techniques align these distributions by minimizing a divergence measurement between source and target, making the transfer of knowledge from source to target possible. While these algorithms have advanced significantly in recent years, most do not explicitly leverage global data manifold structure in aligning the source and target. We propose to leverage global data structure by applying a topological data analysis (TDA) technique called persistent homology to TL. In this paper, we examine the use of persistent homology in a domain adversarial (DAd) convolutional neural network (CNN) architecture. The experiments show that aligning persistence alone is insufficient for transfer, but must be considered along with the lifetimes of the topological singularities. In addition, we found that longer lifetimes indicate robust discriminative features and more favorable structure in data. We found that existing divergence minimization based approaches to DA improve the topological structure, as indicated over a baseline without these regularization techniques. We hope these experiments highlight how topological structure can be leveraged to boost performance in TL tasks.

2021-01-29

2101.12136

Ghada Sokar

Ghada Sokar, Decebal Constantin Mocanu, Mykola Pechenizkiy

Self-Attention Meta-Learner for Continual Learning

20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)

cs.LG cs.AI cs.CV

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

Continual learning aims to provide intelligent agents capable of learning multiple tasks sequentially with neural networks. One of its main challenging, catastrophic forgetting, is caused by the neural networks non-optimal ability to learn in non-stationary distributions. In most settings of the current approaches, the agent starts from randomly initialized parameters and is optimized to master the current task regardless of the usefulness of the learned representation for future tasks. Moreover, each of the future tasks uses all the previously learned knowledge although parts of this knowledge might not be helpful for its learning. These cause interference among tasks, especially when the data of previous tasks is not accessible. In this paper, we propose a new method, named Self-Attention Meta-Learner (SAM), which learns a prior knowledge for continual learning that permits learning a sequence of tasks, while avoiding catastrophic forgetting. SAM incorporates an attention mechanism that learns to select the particular relevant representation for each future task. Each task builds a specific representation branch on top of the selected knowledge, avoiding the interference between tasks. We evaluate the proposed method on the Split CIFAR-10/100 and Split MNIST benchmarks in the task agnostic inference. We empirically show that we can achieve a better performance than several state-of-the-art methods for continual learning by building on the top of selected representation learned by SAM. We also show the role of the meta-attention mechanism in boosting informative features corresponding to the input data and identifying the correct target in the task agnostic inference. Finally, we demonstrate that popular existing continual learning methods gain a performance boost when they adopt SAM as a starting point.

2021-01-29

2101.12446

Matthew Olson

Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li, Weng-Keen Wong

Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learning

Full source code available at https://github.com/mattolson93/counterfactual-state-explanations

Artificial Intelligence, 2021, 103455, ISSN 0004-3702

10.1016/j.artint.2021.103455

cs.AI cs.HC cs.LG

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

Counterfactual explanations, which deal with "why not?" scenarios, can provide insightful explanations to an AI agent's behavior. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our first user study investigates if humans can discern if the counterfactual state explanations are produced by the actual game or produced by a generative deep learning approach. Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game. Our results indicate that counterfactual state explanations have sufficient fidelity to the actual game images to enable non-experts to more effectively identify a flawed RL agent compared to the nearest neighbor baseline and to having no explanation at all.

2021-02-01

2101.12463

Hao Li

Chenghao Chen and Hao Li

Robust Representation Learning with Feedback for Single Image Deraining

IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 2021, pp.7742-7751

eess.IV cs.CV

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

A deraining network can be interpreted as a conditional generator that aims at removing rain streaks from image. Most existing image deraining methods ignore model errors caused by uncertainty that reduces embedding quality. Unlike existing image deraining methods that embed low-quality features into the model directly, we replace low-quality features by latent high-quality features. The spirit of closed-loop feedback in the automatic control field is borrowed to obtain latent high-quality features. A new method for error detection and feature compensation is proposed to address model errors. Extensive experiments on benchmark datasets as well as specific real datasets demonstrate that the proposed method outperforms recent state-of-the-art methods. Code is available at: \\ https://github.com/LI-Hao-SJTU/DerainRLNet

2021-06-22

2102.00322

Vaneet Aggarwal

Mayank Gupta and Lingjun Chen and Denny Yu and Vaneet Aggarwal

A Supervised Learning Approach for Robust Health Monitoring using Face Videos

The main part of the paper appeared in DFHS'20: Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing; while the Supplementary did not appear in the proceedings

Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing (DFHS 2020) Nov. 2020 pp. 6-10

10.1145/3427772.3429392

cs.CV cs.HC

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

Monitoring of cardiovascular activity is highly desired and can enable novel applications in diagnosing potential cardiovascular diseases and maintaining an individual's well-being. Currently, such vital signs are measured using intrusive contact devices such as an electrocardiogram (ECG), chest straps, and pulse oximeters that require the patient or the health provider to manually implement. Non-contact, device-free human sensing methods can eliminate the need for specialized heart and blood pressure monitoring equipment. Non-contact methods can have additional advantages since they are scalable with any environment where video can be captured, can be used for continuous measurements, and can be used on patients with varying levels of dexterity and independence, from people with physical impairments to infants (e.g., baby camera). In this paper, we used a non-contact method that only requires face videos recorded using commercially-available webcams. These videos were exploited to predict the health attributes like pulse rate and variance in pulse rate. The proposed approach used facial recognition to detect the face in each frame of the video using facial landmarks, followed by supervised learning using deep neural networks to train the machine learning model. The videos captured subjects performing different physical activities that result in varying cardiovascular responses. The proposed method did not require training data from every individual and thus the prediction can be obtained for the new individuals for which there is no prior data; critical in approach generalization. The approach was also evaluated on a dataset of people with different ethnicity. The proposed approach had less than a 4.6\% error in predicting the pulse rate.

2021-02-02

2102.00385

Guangsheng Bao

Guangsheng Bao and Yue Zhang

Contextualized Rewriting for Text Summarization

AAAI 2021

cs.CL

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

Extractive summarization suffers from irrelevance, redundancy and incoherence. Existing work shows that abstractive rewriting for extractive summaries can improve the conciseness and readability. These rewriting systems consider extracted summaries as the only input, which is relatively focused but can lose important background knowledge. In this paper, we investigate contextualized rewriting, which ingests the entire original document. We formalize contextualized rewriting as a seq2seq problem with group alignments, introducing group tag as a solution to model the alignments, identifying extracted summaries through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning, achieving strong improvements on ROUGE scores upon multiple extractive summarizers.

2021-04-27

2102.00515

Fatih Uysal

Fatih Uysal, F{\i}rat Hardala\c{c}, Ozan Peker, Tolga Tolunay and Nil Tokg\"oz

Classification of Shoulder X-Ray Images with Deep Learning Ensemble Models

This paper is accepted at Applied Sciences, MDPI, 2021, 11(6), 2723. Section: "Applied Biosciences and Bioengineering". Special Issue: "Advancing Biomedical Image Retrieval and Classification for Computer Aided Diagnosis"

Applied Sciences, MDPI, 2021, 11(6), 2723. Section: "Applied Biosciences and Bioengineering". Special Issue: "Advancing Biomedical Image Retrieval and Classification for Computer Aided Diagnosis"

10.3390/app11062723

eess.IV cs.CV cs.LG

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

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from Xradiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture / non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pretrained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pretrained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pretrained models with the best performance, test accuracy was 0.8455,0.8472, Cohens kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862,0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohens kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

2021-03-23

2102.00760

Vivien Cabannes

Vivien Cabannes and Alessandro Rudi and Francis Bach

Fast rates in structured prediction

14 main pages, 3 main figures, 43 pages, 4 figures (with appendix)

Conference on Learning Theory, PMLR 134, 2021

stat.ML cs.AI cs.LG math.ST stat.TH

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

Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, between estimate and solution, by the surrogate error endows discrete problems with convergence rates already shown for continuous instances. Yet, current approaches do not leverage the fact that discrete problems are essentially predicting a discrete output when continuous problems are predicting a continuous value. In this paper, we tackle this issue for general structured prediction problems, opening the way to "super fast" rates, that is, convergence rates for the excess risk faster than $n^{-1}$, where $n$ is the number of observations, with even exponential rates with the strongest assumptions. We first illustrate it for predictors based on nearest neighbors, generalizing rates known for binary classification to any discrete problem within the framework of structured prediction. We then consider kernel ridge regression where we improve known rates in $n^{-1/4}$ to arbitrarily fast rates, depending on a parameter characterizing the hardness of the problem, thus allowing, under smoothness assumptions, to bypass the curse of dimensionality.

2021-07-16

2102.00838

Rafael Angarita

Shufan Jiang (CRESTIC, ISEP), Rafael Angarita (ISEP), Stephane Cormier (CRESTIC), Francis Rousseaux (CRESTIC)

Fine-tuning BERT-based models for Plant Health Bulletin Classification

Technology and Environment Workshop'21, Jan 2021, Montpellier, France

cs.CL

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

In the era of digitization, different actors in agriculture produce numerous data. Such data contains already latent historical knowledge in the domain. This knowledge enables us to precisely study natural hazards within global or local aspects, and then improve the risk prevention tasks and augment the yield, which helps to tackle the challenge of growing population and changing alimentary habits. In particular, French Plants Health Bulletins (BSV, for its name in French Bulletin de Sant{\'e} du V{\'e}g{\'e}tal) give information about the development stages of phytosanitary risks in agricultural production. However, they are written in natural language, thus, machines and human cannot exploit them as efficiently as it could be. Natural language processing (NLP) technologies aim to automatically process and analyze large amounts of natural language data. Since the 2010s, with the increases in computational power and parallelization, representation learning and deep learning methods became widespread in NLP. Recent advancements Bidirectional Encoder Representations from Transformers (BERT) inspire us to rethink of knowledge representation and natural language understanding in plant health management domain. The goal in this work is to propose a BERT-based approach to automatically classify the BSV to make their data easily indexable. We sampled 200 BSV to finetune the pretrained BERT language models and classify them as pest or/and disease and we show preliminary results.

2021-02-02

2102.00841

Alexander Sagel

Alexander Sagel, Julian W\"ormann, Hao Shen

Dynamic Texture Recognition via Nuclear Distances on Kernelized Scattering Histogram Spaces

\c{opyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

10.1109/ICASSP39728.2021.9414783

cs.CV

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

Distance-based dynamic texture recognition is an important research field in multimedia processing with applications ranging from retrieval to segmentation of video data. Based on the conjecture that the most distinctive characteristic of a dynamic texture is the appearance of its individual frames, this work proposes to describe dynamic textures as kernelized spaces of frame-wise feature vectors computed using the Scattering transform. By combining these spaces with a basis-invariant metric, we get a framework that produces competitive results for nearest neighbor classification and state-of-the-art results for nearest class center classification.

2021-05-17

2102.00881

G\"ul\c{s}en Eryi\u{g}it

G\"ul\c{s}en Eryi\u{g}it, Ali \c{S}enta\c{s}, Johanna Monti

Gamified Crowdsourcing for Idiom Corpora Construction

25 pages, 8 figures, 6 tables

Natural Language Engineering, Cambridge Press, 2022

10.1017/S1351324921000401

cs.CL

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

Learning idiomatic expressions is seen as one of the most challenging stages in second language learning because of their unpredictable meaning. A similar situation holds for their identification within natural language processing applications such as machine translation and parsing. The lack of high-quality usage samples exacerbates this challenge not only for humans but also for artificial intelligence systems. This article introduces a gamified crowdsourcing approach for collecting language learning materials for idiomatic expressions; a messaging bot is designed as an asynchronous multiplayer game for native speakers who compete with each other while providing idiomatic and nonidiomatic usage examples and rating other players' entries. As opposed to classical crowdprocessing annotation efforts in the field, for the first time in the literature, a crowdcreating & crowdrating approach is implemented and tested for idiom corpora construction. The approach is language independent and evaluated on two languages in comparison to traditional data preparation techniques in the field. The reaction of the crowd is monitored under different motivational means (namely, gamification affordances and monetary rewards). The results reveal that the proposed approach is powerful in collecting the targeted materials, and although being an explicit crowdsourcing approach, it is found entertaining and useful by the crowd. The approach has been shown to have the potential to speed up the construction of idiom corpora for different natural languages to be used as second language learning material, training data for supervised idiom identification systems, or samples for lexicographic studies.

2022-01-21

2102.00898

Mohit Sewak

Mohit Sewak and Sanjay K. Sahay and Hemant Rathore

DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware

Defence Science Journal, 71(1), 55-65

10.14429/dsj.71.15780

cs.CR cs.AI cs.LG

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

In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep Reinforcement Learning based De-Obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against 'zero-day' attacks from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system to date that uses advanced DRL to intelligently and automatically normalize obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS' classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardized dataset that contains multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise un-detectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.

2021-02-02

2102.00997

Gorka Azkune

Aitzol Elu, Gorka Azkune, Oier Lopez de Lacalle, Ignacio Arganda-Carreras, Aitor Soroa, Eneko Agirre

Inferring spatial relations from textual descriptions of images

Accepted in Pattern Recognition

Pattern Recognition, Volume 113, 2021, 107847

10.1016/j.patcog.2021.107847

cs.AI

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

Generating an image from its textual description requires both a certain level of language understanding and common sense knowledge about the spatial relations of the physical entities being described. In this work, we focus on inferring the spatial relation between entities, a key step in the process of composing scenes based on text. More specifically, given a caption containing a mention to a subject and the location and size of the bounding box of that subject, our goal is to predict the location and size of an object mentioned in the caption. Previous work did not use the caption text information, but a manually provided relation holding between the subject and the object. In fact, the used evaluation datasets contain manually annotated ontological triplets but no captions, making the exercise unrealistic: a manual step was required; and systems did not leverage the richer information in captions. Here we present a system that uses the full caption, and Relations in Captions (REC-COCO), a dataset derived from MS-COCO which allows to evaluate spatial relation inference from captions directly. Our experiments show that: (1) it is possible to infer the size and location of an object with respect to a given subject directly from the caption; (2) the use of full text allows to place the object better than using a manually annotated relation. Our work paves the way for systems that, given a caption, decide which entities need to be depicted and their respective location and sizes, in order to then generate the final image.

2021-02-03

2102.01013

Valentin Pelloin

Valentin Pelloin, Nathalie Camelin, Antoine Laurent, Renato De Mori, Antoine Caubri\`ere, Yannick Est\`eve, Sylvain Meignier

End2End Acoustic to Semantic Transduction

Accepted at IEEE ICASSP 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

10.1109/ICASSP39728.2021.9413581

cs.CL cs.SD eess.AS

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

In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism. It reliably selects contextual acoustic features in order to hypothesize semantic contents. An initial architecture capable of extracting all pronounced words and concepts from acoustic spans is designed and tested. With a shallow fusion language model, this system reaches a 13.6 concept error rate (CER) and an 18.5 concept value error rate (CVER) on the French MEDIA corpus, achieving an absolute 2.8 points reduction compared to the state-of-the-art. Then, an original model is proposed for hypothesizing concepts and their values. This transduction reaches a 15.4 CER and a 21.6 CVER without any new type of context.

2021-05-20

2102.01149

Devorah Kletenik

Lisa Hellerstein, Devorah Kletenik and Srinivasan Parthasarathy

A Tight Bound for Stochastic Submodular Cover

This work extends the result of Srinivasan Parthasarathy in his paper arXiv:1803.07639 from the problem of Stochastic Set Cover to that of Stochastic Submodular Cover

Journal of Artificial Intelligence Research 71(2021) 347 - 370

10.1613/jair.1.12368

cs.DS cs.AI

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

We show that the Adaptive Greedy algorithm of Golovin and Krause (2011) achieves an approximation bound of $(\ln (Q/\eta)+1)$ for Stochastic Submodular Cover: here $Q$ is the "goal value" and $\eta$ is the smallest non-zero marginal increase in utility deliverable by an item. (For integer-valued utility functions, we show a bound of $H(Q)$, where $H(Q)$ is the $Q^{th}$ Harmonic number.) Although this bound was claimed by Golovin and Krause in the original version of their paper, the proof was later shown to be incorrect by Nan and Saligrama (2017). The subsequent corrected proof of Golovin and Krause (2017) gives a quadratic bound of $(\ln(Q/\eta) + 1)^2$. Other previous bounds for the problem are $56(\ln(Q/\eta) + 1)$, implied by work of Im et al. (2016) on a related problem, and $k(\ln (Q/\eta)+1)$, due to Deshpande et al. (2016) and Hellerstein and Kletenik (2018), where $k$ is the number of states. Our bound generalizes the well-known $(\ln~m + 1)$ approximation bound on the greedy algorithm for the classical Set Cover problem, where $m$ is the size of the ground set.

2021-08-03

2102.01260

Xiong Liu

Xiong Liu, Craig E. Thomas, Christian C. Felder

The impact of external innovation on new drug approvals: A retrospective analysis

International Journal of Pharmaceutics, Volume 563, Pages 273-281, 2019

10.1016/j.ijpharm.2018.12.093

PMID: 30664998

cs.CL cs.CY q-bio.QM

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

Pharmaceutical companies are relying more often on external sources of innovation to boost their discovery research productivity. However, more in-depth knowledge about how external innovation may translate to successful product launches is still required in order to better understand how to best leverage the innovation ecosystem. We analyzed the pre-approval publication histories for FDA-approved new molecular entities (NMEs) and new biologic entities (NBEs) launched by 13 top research pharma companies during the last decade (2006-2016). We found that academic institutions contributed the majority of pre-approval publications and that publication subject matter is closely aligned with the strengths of the respective innovator. We found this to also be true for candidate drugs terminated in Phase 3, but the volume of literature on these molecules is substantially less than for approved drugs. This may suggest that approved drugs are often associated with a more robust dataset provided by a large number of institutes. Collectively, the results of our analysis support the hypothesis that a collaborative research innovation environment spanning across academia, industry and government is highly conducive to successful drug approvals.

2021-02-03

2102.01284

Peng Yao

Peng Yao, Shuwei Shen, Mengjuan Xu, Peng Liu, Fan Zhang, Jinyu Xing, Pengfei Shao, Benjamin Kaffenberger, and Ronald X. Xu

Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification

IEEE TRANSACTIONS ON MEDICAL IMAGING, 2021

10.1109/TMI.2021.3136682

cs.CV cs.LG

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

Deep convolutional neural network (DCNN) models have been widely explored for skin disease diagnosis and some of them have achieved the diagnostic outcomes comparable or even superior to those of dermatologists. However, broad implementation of DCNN in skin disease detection is hindered by small size and data imbalance of the publically accessible skin lesion datasets. This paper proposes a novel single-model based strategy for classification of skin lesions on small and imbalanced datasets. First, various DCNNs are trained on different small and imbalanced datasets to verify that the models with moderate complexity outperform the larger models. Second, regularization DropOut and DropBlock are added to reduce overfitting and a Modified RandAugment augmentation strategy is proposed to deal with the defects of sample underrepresentation in the small dataset. Finally, a novel Multi-Weighted New Loss (MWNL) function and an end-to-end cumulative learning strategy (CLS) are introduced to overcome the challenge of uneven sample size and classification difficulty and to reduce the impact of abnormal samples on training. By combining Modified RandAugment, MWNL and CLS, our single DCNN model method achieved the classification accuracy comparable or superior to those of multiple ensembling models on different dermoscopic image datasets. Our study shows that this method is able to achieve a high classification performance at a low cost of computational resources and inference time, potentially suitable to implement in mobile devices for automated screening of skin lesions and many other malignancies in low resource settings.

2022-02-14

2102.01295

Heecheol Kim

Heecheol Kim, Yoshiyuki Ohmura, and Yasuo Kuniyoshi

Gaze-based dual resolution deep imitation learning for high-precision dexterous robot manipulation

8 pages. The supplementary video can be found at: https://www.youtube.com/watch?v=ytpChcFqD5g Published in IEEE Robotics and Automation Letters. Replaced to add video url in the manuscript

IEEE Robotics and Automation Letters, Vol. 6, No. 2, 2021

10.1109/LRA.2021.3059619

cs.RO cs.AI

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

A high-precision manipulation task, such as needle threading, is challenging. Physiological studies have proposed connecting low-resolution peripheral vision and fast movement to transport the hand into the vicinity of an object, and using high-resolution foveated vision to achieve the accurate homing of the hand to the object. The results of this study demonstrate that a deep imitation learning based method, inspired by the gaze-based dual resolution visuomotor control system in humans, can solve the needle threading task. First, we recorded the gaze movements of a human operator who was teleoperating a robot. Then, we used only a high-resolution image around the gaze to precisely control the thread position when it was close to the target. We used a low-resolution peripheral image to reach the vicinity of the target. The experimental results obtained in this study demonstrate that the proposed method enables precise manipulation tasks using a general-purpose robot manipulator and improves computational efficiency.

2024-02-27

2102.01301

Yi-Jun Cao

Yi-Jun Cao, Chuan Lin, and Yong-Jie Li

Learning Crisp Boundaries Using Deep Refinement Network and Adaptive Weighting Loss

11 pages, 7 figures

IEEE Transactions on Multimedia, vol. 23, pp. 761-771, 2021

10.1109/TED.2020.3041567

cs.CV

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

Significant progress has been made in boundary detection with the help of convolutional neural networks. Recent boundary detection models not only focus on real object boundary detection but also "crisp" boundaries (precisely localized along the object's contour). There are two methods to evaluate crisp boundary performance. One uses more strict tolerance to measure the distance between the ground truth and the detected contour. The other focuses on evaluating the contour map without any postprocessing. In this study, we analyze both methods and conclude that both methods are two aspects of crisp contour evaluation. Accordingly, we propose a novel network named deep refinement network (DRNet) that stacks multiple refinement modules to achieve richer feature representation and a novel loss function, which combines cross-entropy and dice loss through effective adaptive fusion. Experimental results demonstrated that we achieve state-of-the-art performance for several available datasets.

2021-03-10

2102.01380

Zhong Meng

Zhong Meng, Naoyuki Kanda, Yashesh Gaur, Sarangarajan Parthasarathy, Eric Sun, Liang Lu, Xie Chen, Jinyu Li, Yifan Gong

Internal Language Model Training for Domain-Adaptive End-to-End Speech Recognition

5 pages, ICASSP 2021

2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Canada

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

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

The efficacy of external language model (LM) integration with existing end-to-end (E2E) automatic speech recognition (ASR) systems can be improved significantly using the internal language model estimation (ILME) method. In this method, the internal LM score is subtracted from the score obtained by interpolating the E2E score with the external LM score, during inference. To improve the ILME-based inference, we propose an internal LM training (ILMT) method to minimize an additional internal LM loss by updating only the E2E model components that affect the internal LM estimation. ILMT encourages the E2E model to form a standalone LM inside its existing components, without sacrificing ASR accuracy. After ILMT, the more modular E2E model with matched training and inference criteria enables a more thorough elimination of the source-domain internal LM, and therefore leads to a more effective integration of the target-domain external LM. Experimented with 30K-hour trained recurrent neural network transducer and attention-based encoder-decoder models, ILMT with ILME-based inference achieves up to 31.5% and 11.4% relative word error rate reductions from standard E2E training with Shallow Fusion on out-of-domain LibriSpeech and in-domain Microsoft production test sets, respectively.

2021-04-26

2102.01405

Ruben Tolosana

Ruben Tolosana, Juan Carlos Ruiz-Garcia, Ruben Vera-Rodriguez, Jaime Herreros-Rodriguez, Sergio Romero-Tapiador, Aythami Morales, Julian Fierrez

Child-Computer Interaction with Mobile Devices: Recent Works, New Dataset, and Age Detection

IEEE Transactions on Emerging Topics in Computing, 2022

10.1109/TETC.2022.3150836

cs.HC cs.CV

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

This article provides an overview of recent research in Child-Computer Interaction with mobile devices and describe our framework ChildCI intended for: i) overcoming the lack of large-scale publicly available databases in the area, ii) generating a better understanding of the cognitive and neuromotor development of children along time, contrary to most previous studies in the literature focused on a single-session acquisition, and iii) enabling new applications in e-Learning and e-Health through the acquisition of additional information such as the school grades and children's disorders, among others. Our framework includes a new mobile application, specific data acquisition protocols, and a first release of the ChildCI dataset (ChildCIdb v1), which is planned to be extended yearly to enable longitudinal studies. In our framework children interact with a tablet device, using both a pen stylus and the finger, performing different tasks that require different levels of neuromotor and cognitive skills. ChildCIdb is the first database in the literature that comprises more than 400 children from 18 months to 8 years old, considering therefore the first three development stages of the Piaget's theory. In addition, and as a demonstration of the potential of the ChildCI framework, we include experimental results for one of the many applications enabled by ChildCIdb: children age detection based on device interaction.

2022-02-23

2102.01460

Alberto Pretto

Alessandro Saviolo, Matteo Bonotto, Daniele Evangelista, Marco Imperoli, Jacopo Lazzaro, Emanuele Menegatti and Alberto Pretto

Learning to Segment Human Body Parts with Synthetically Trained Deep Convolutional Networks

This paper has been published in: Proceedings of the 16th International Conference on Intelligent Autonomous Systems (IAS 2021)

Proceedings of the 16th International Conference on Intelligent Autonomous Systems (IAS 2021)

10.1007/978-3-030-95892-3_52

cs.CV

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

This paper presents a new framework for human body part segmentation based on Deep Convolutional Neural Networks trained using only synthetic data. The proposed approach achieves cutting-edge results without the need of training the models with real annotated data of human body parts. Our contributions include a data generation pipeline, that exploits a game engine for the creation of the synthetic data used for training the network, and a novel pre-processing module, that combines edge response maps and adaptive histogram equalization to guide the network to learn the shape of the human body parts ensuring robustness to changes in the illumination conditions. For selecting the best candidate architecture, we perform exhaustive tests on manually annotated images of real human body limbs. We further compare our method against several high-end commercial segmentation tools on the body parts segmentation task. The results show that our method outperforms the other models by a significant margin. Finally, we present an ablation study to validate our pre-processing module. With this paper, we release an implementation of the proposed approach along with the acquired datasets.

2022-06-08

2102.01486

Cheng Ma

Cheng Ma, Jiwen Lu, Jie Zhou

Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search

IEEE Transactions on Multimedia, 2020

10.1109/TMM.2020.3034534

cs.CV cs.MM

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

As hashing becomes an increasingly appealing technique for large-scale image retrieval, multi-label hashing is also attracting more attention for the ability to exploit multi-level semantic contents. In this paper, we propose a novel deep hashing method for scalable multi-label image search. Unlike existing approaches with conventional objectives such as contrast and triplet losses, we employ a rank list, rather than pairs or triplets, to provide sufficient global supervision information for all the samples. Specifically, a new rank-consistency objective is applied to align the similarity orders from two spaces, the original space and the hamming space. A powerful loss function is designed to penalize the samples whose semantic similarity and hamming distance are mismatched in two spaces. Besides, a multi-label softmax cross-entropy loss is presented to enhance the discriminative power with a concise formulation of the derivative function. In order to manipulate the neighborhood structure of the samples with different labels, we design a multi-label clustering loss to cluster the hashing vectors of the samples with the same labels by reducing the distances between the samples and their multiple corresponding class centers. The state-of-the-art experimental results achieved on three public multi-label datasets, MIRFLICKR-25K, IAPRTC12 and NUS-WIDE, demonstrate the effectiveness of the proposed method.

2021-02-03

2102.01498

Iuliana Marin

Andrei Vasilateanu, Nicolae Goga, Elena-Alice Tanase, Iuliana Marin

Enterprise domain ontology learning from web-based corpus

2015 6th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

10.1109/ICCCNT.2015.7395227

cs.AI cs.SE

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

Enterprise knowledge is a key asset in the competing and fast-changing corporate landscape. The ability to learn, store and distribute implicit and explicit knowledge can be the difference between success and failure. While enterprise knowledge management is a well-defined research domain, current implementations lack orientation towards small and medium enterprise. We propose a semantic search engine for relevant documents in an enterprise, based on automatic generated domain ontologies. In this paper we focus on the component for ontology learning and population.

2021-02-16

2102.01502

Satyapriya Krishna

Satyapriya Krishna, Rahul Gupta, Christophe Dupuy

ADePT: Auto-encoder based Differentially Private Text Transformation

The 16th conference of the European Chapter of the Association for Computational Linguistics (EACL), 2021

cs.CR cs.AI cs.CL cs.LG

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

Privacy is an important concern when building statistical models on data containing personal information. Differential privacy offers a strong definition of privacy and can be used to solve several privacy concerns (Dwork et al., 2014). Multiple solutions have been proposed for the differentially-private transformation of datasets containing sensitive information. However, such transformation algorithms offer poor utility in Natural Language Processing (NLP) tasks due to noise added in the process. In this paper, we address this issue by providing a utility-preserving differentially private text transformation algorithm using auto-encoders. Our algorithm transforms text to offer robustness against attacks and produces transformations with high semantic quality that perform well on downstream NLP tasks. We prove the theoretical privacy guarantee of our algorithm and assess its privacy leakage under Membership Inference Attacks(MIA) (Shokri et al., 2017) on models trained with transformed data. Our results show that the proposed model performs better against MIA attacks while offering lower to no degradation in the utility of the underlying transformation process compared to existing baselines.

2021-02-03

2102.01565

Juan Pedro Dominguez-Morales

Luis J. Mu\~noz-Molina, Ignacio Cazorla-Pi\~nar, Juan P. Dominguez-Morales, Fernando Perez-Pe\~na

Real-time detection of uncalibrated sensors using Neural Networks

Neural Comput & Applic (2022)

10.1007/s00521-021-06865-z

cs.LG cs.AI

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

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data.

2022-01-26

2102.01578

Marco Gaido

Marco Gaido, Mauro Cettolo, Matteo Negri, Marco Turchi

CTC-based Compression for Direct Speech Translation

Accepted at EACL2021

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (2021), 690-696

cs.CL

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

Previous studies demonstrated that a dynamic phone-informed compression of the input audio is beneficial for speech translation (ST). However, they required a dedicated model for phone recognition and did not test this solution for direct ST, in which a single model translates the input audio into the target language without intermediate representations. In this work, we propose the first method able to perform a dynamic compression of the input indirect ST models. In particular, we exploit the Connectionist Temporal Classification (CTC) to compress the input sequence according to its phonetic characteristics. Our experiments demonstrate that our solution brings a 1.3-1.5 BLEU improvement over a strong baseline on two language pairs (English-Italian and English-German), contextually reducing the memory footprint by more than 10%.

2021-10-15

2102.01579

Xiangyu Xu

Xiangyu Xu, Yongrui Ma, Wenxiu Sun, Ming-Hsuan Yang

Exploiting Raw Images for Real-Scene Super-Resolution

A larger version with higher-resolution figures is available at: https://sites.google.com/view/xiangyuxu. arXiv admin note: text overlap with arXiv:1905.12156

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020

cs.CV cs.AI

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

Super-resolution is a fundamental problem in computer vision which aims to overcome the spatial limitation of camera sensors. While significant progress has been made in single image super-resolution, most algorithms only perform well on synthetic data, which limits their applications in real scenarios. In this paper, we study the problem of real-scene single image super-resolution to bridge the gap between synthetic data and real captured images. We focus on two issues of existing super-resolution algorithms: lack of realistic training data and insufficient utilization of visual information obtained from cameras. To address the first issue, we propose a method to generate more realistic training data by mimicking the imaging process of digital cameras. For the second issue, we develop a two-branch convolutional neural network to exploit the radiance information originally-recorded in raw images. In addition, we propose a dense channel-attention block for better image restoration as well as a learning-based guided filter network for effective color correction. Our model is able to generalize to different cameras without deliberately training on images from specific camera types. Extensive experiments demonstrate that the proposed algorithm can recover fine details and clear structures, and achieve high-quality results for single image super-resolution in real scenes.

2021-02-03

2102.01582

Mats Richter

Mats L. Richter, Wolf Byttner, Ulf Krumnack, Ludwdig Schallner, Justin Shenk

Size Matters

Preprint

Artificial Neural Networks and Machine Learning ICANN 2021 133-144

10.1007/978-3-030-86340-1_11

cs.LG cs.AI cs.CV

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

Fully convolutional neural networks can process input of arbitrary size by applying a combination of downsampling and pooling. However, we find that fully convolutional image classifiers are not agnostic to the input size but rather show significant differences in performance: presenting the same image at different scales can result in different outcomes. A closer look reveals that there is no simple relationship between input size and model performance (no `bigger is better'), but that each each network has a preferred input size, for which it shows best results. We investigate this phenomenon by applying different methods, including spectral analysis of layer activations and probe classifiers, showing that there are characteristic features depending on the network architecture. From this we find that the size of discriminatory features is critically influencing how the inference process is distributed among the layers.

2021-10-13

2102.01645

Federico Galatolo

Federico A. Galatolo and Mario G.C.A. Cimino and Gigliola Vaglini

Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search

IMPROVE, ISBN 978-989-758-511-1, pages 166-174 (2021)

10.5220/0010503701660174

cs.NE cs.AI cs.LG

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

In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption (or an image) as an input, and generates the image (or the caption) whose CLIP embedding is the most similar to the input one. This optimal image (or caption) is produced via a generative network, after an exploration by a genetic algorithm. Promising results are shown, based on the experimentation of the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2

2021-10-04

2102.01767

Jorge Miguel Ferreira Da Silva

Jorge Miguel Silva, Diogo Pratas, Rui Antunes, S\'ergio Matos, and Armando J. Pinho

Automatic analysis of artistic paintings using information-based measures

Website: http://panther.web.ua.pt 24 Pages; 19 pages article; 5 pages supplementary material

Pattern Recognition (2021) 107864

10.1016/j.patcog.2021.107864

cs.CV cs.IT cs.LG math.IT

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

The artistic community is increasingly relying on automatic computational analysis for authentication and classification of artistic paintings. In this paper, we identify hidden patterns and relationships present in artistic paintings by analysing their complexity, a measure that quantifies the sum of characteristics of an object. Specifically, we apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors and examine the potential of these information-based measures as descriptors of artistic paintings. Both measures consistently described the equivalent types of paintings, authors, and artistic movements. Moreover, combining the NC with a measure of the roughness of the paintings creates an efficient stylistic descriptor. Furthermore, by quantifying the local information of each painting, we define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques. More fundamentally, this information describes how each author typically composes and distributes the elements across the canvas and, therefore, how their work is perceived. Finally, we demonstrate that regional complexity and two-point height difference correlation function are useful auxiliary features that improve current methodologies in style and author classification of artistic paintings. The whole study is supported by an extensive website (http://panther.web.ua.pt) for fast author characterization and authentication.

2021-02-10

2102.01780

Daniel Severin Dr.

Mauro Lucci, Daniel Sever\'in, Paula Zabala

A metaheuristic for crew scheduling in a pickup-and-delivery problem with time windows

Intl. Trans. in Op. Res., vol. 30, 2023, pp. 970-1001

10.1111/itor.13096

cs.AI cs.DM

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

A vehicle routing and crew scheduling problem (VRCSP) consists of simultaneously planning the routes of a fleet of vehicles and scheduling the crews, where the vehicle-crew correspondence is not fixed through time. This allows a greater planning flexibility and a more efficient use of the fleet, but in counterpart, a high synchronisation is demanded. In this work, we present a VRCSP where pickup-and-delivery requests with time windows have to be fulfilled over a given planning horizon by using trucks and drivers. Crews can be composed of 1 or 2 drivers and any of them can be relieved in a given set of locations. Moreover, they are allowed to travel among locations with non-company shuttles, at an additional cost that is minimised. As our problem considers distinct routes for trucks and drivers, we have an additional flexibility not contemplated in other previous VRCSP given in the literature where a crew is handled as an indivisible unit. We tackle this problem with a two-stage sequential approach: a set of truck routes is computed in the first stage and a set of driver routes consistent with the truck routes is obtained in the second one. We design and evaluate the performance of a metaheuristic based algorithm for the latter stage. Our algorithm is mainly a GRASP with a perturbation procedure that allows reusing solutions already found in case the search for new solutions becomes difficult. This procedure together with other to repair infeasible solutions allow us to find high-quality solutions on instances of 100 requests spread across 15 cities with a fleet of 12-32 trucks (depending on the planning horizon) in less than an hour. We also conclude that the possibility of carrying an additional driver leads to a decrease of the cost of external shuttles by about 60% on average with respect to individual crews and, in some cases, to remove this cost completely.

2024-07-11

2102.01826

Zhewei Sun

Zhewei Sun, Richard Zemel, Yang Xu

A Computational Framework for Slang Generation

Accepted for publication in TACL 2021. Author's final version

Transactions of the Association for Computational Linguistics 2021; 9 462-478

10.1162/tacl_a_00378

cs.CL

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

Slang is a common type of informal language, but its flexible nature and paucity of data resources present challenges for existing natural language systems. We take an initial step toward machine generation of slang by developing a framework that models the speaker's word choice in slang context. Our framework encodes novel slang meaning by relating the conventional and slang senses of a word while incorporating syntactic and contextual knowledge in slang usage. We construct the framework using a combination of probabilistic inference and neural contrastive learning. We perform rigorous evaluations on three slang dictionaries and show that our approach not only outperforms state-of-the-art language models, but also better predicts the historical emergence of slang word usages from 1960s to 2000s. We interpret the proposed models and find that the contrastively learned semantic space is sensitive to the similarities between slang and conventional senses of words. Our work creates opportunities for the automated generation and interpretation of informal language.

2021-05-25

2102.01850

Ru Li

Ru Li, Chuan Wang, Jue Wang, Guanghui Liu, Heng-Yu Zhang, Bing Zeng, Shuaicheng Liu

UPHDR-GAN: Generative Adversarial Network for High Dynamic Range Imaging with Unpaired Data

Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)

IEEE Transactions on Circuits and Systems for Video Technology, 2022

10.1109/TCSVT.2022.3190057

eess.IV cs.CV

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

The paper proposes a method to effectively fuse multi-exposure inputs and generate high-quality high dynamic range (HDR) images with unpaired datasets. Deep learning-based HDR image generation methods rely heavily on paired datasets. The ground truth images play a leading role in generating reasonable HDR images. Datasets without ground truth are hard to be applied to train deep neural networks. Recently, Generative Adversarial Networks (GAN) have demonstrated their potentials of translating images from source domain X to target domain Y in the absence of paired examples. In this paper, we propose a GAN-based network for solving such problems while generating enjoyable HDR results, named UPHDR-GAN. The proposed method relaxes the constraint of the paired dataset and learns the mapping from the LDR domain to the HDR domain. Although the pair data are missing, UPHDR-GAN can properly handle the ghosting artifacts caused by moving objects or misalignments with the help of the modified GAN loss, the improved discriminator network and the useful initialization phase. The proposed method preserves the details of important regions and improves the total image perceptual quality. Qualitative and quantitative comparisons against the representative methods demonstrate the superiority of the proposed UPHDR-GAN.

2022-07-18

2102.01906

Vinod Kumar Kurmi

Vinod K Kurmi, Badri N. Patro, Venkatesh K. Subramanian, Vinay P. Namboodiri

Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting

Accepted WACV 2021

WACV 2021

cs.LG cs.CV

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

One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. There have been several approaches proposed to tackle the problem of incremental learning. Most of these methods are based on knowledge distillation and do not adequately utilize the information provided by older task models, such as uncertainty estimation in predictions. The predictive uncertainty provides the distributional information can be applied to mitigate catastrophic forgetting in a deep learning framework. In the proposed work, we consider a Bayesian formulation to obtain the data and model uncertainties. We also incorporate self-attention framework to address the incremental learning problem. We define distillation losses in terms of aleatoric uncertainty and self-attention. In the proposed work, we investigate different ablation analyses on these losses. Furthermore, we are able to obtain better results in terms of accuracy on standard benchmarks.

2021-02-04

2102.01968

Claire Theobald

Claire Theobald (LORIA), Fr\'ed\'eric Pennerath (LORIA), Brieuc Conan-Guez (LORIA), Miguel Couceiro (LORIA), Amedeo Napoli (LORIA)

A Bayesian Neural Network based on Dropout Regulation

Workshop on Uncertainty in Machine Learning (WUML) at ECML-PKDD 2020 Conference, Eyke H{\"u}llermeier; S{\'e}bastien Destercke, 2020, N.A. (online), France

cs.LG cs.AI cs.NE

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

Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving...BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction.Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout.Several attempts to optimize the dropout rate exist, e.g. using a variational approach.In this paper, we present a new method called "Dropout Regulation" (DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation.DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.

2021-02-04

2102.02189

Young-Suk Lee Dr.

Janaki Sheth and Young-Suk Lee and Ramon Fernandez Astudillo and Tahira Naseem and Radu Florian and Salim Roukos and Todd Ward

Bootstrapping Multilingual AMR with Contextual Word Alignments

EACL 2021

cs.CL cs.AI

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

We develop high performance multilingualAbstract Meaning Representation (AMR) sys-tems by projecting English AMR annotationsto other languages with weak supervision. Weachieve this goal by bootstrapping transformer-based multilingual word embeddings, in partic-ular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique forforeign-text-to-English AMR alignment, usingthe contextual word alignment between En-glish and foreign language tokens. This wordalignment is weakly supervised and relies onthe contextualized XLM-R word embeddings.We achieve a highly competitive performancethat surpasses the best published results forGerman, Italian, Spanish and Chinese.

2022-05-09

2102.02304

Panayiotis Danassis

Panayiotis Danassis, Zeki Doruk Erden, Boi Faltings

Improved Cooperation by Exploiting a Common Signal

Accepted to the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)

An extended version of this paper has been published in the Autonomous Agents and Multi-Agent Systems (2022)

10.1007/s10458-021-09541-7

cs.MA cs.AI

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

Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, real-world problems are inherently large-scale and of low observability. One key concept that facilitates human coordination in such settings is the use of conventions. Inspired by human behavior, we investigate the learning dynamics and emergence of temporal conventions, focusing on common-pool resources. Extra emphasis was given in designing a realistic evaluation setting: (a) environment dynamics are modeled on real-world fisheries, (b) we assume decentralized learning, where agents can observe only their own history, and (c) we run large-scale simulations (up to 64 agents). Uncoupled policies and low observability make cooperation hard to achieve; as the number of agents grow, the probability of taking a correct gradient direction decreases exponentially. By introducing an arbitrary common signal (e.g., date, time, or any periodic set of numbers) as a means to couple the learning process, we show that temporal conventions can emerge and agents reach sustainable harvesting strategies. The introduction of the signal consistently improves the social welfare (by 258% on average, up to 3306%), the range of environmental parameters where sustainability can be achieved (by 46% on average, up to 300%), and the convergence speed in low abundance settings (by 13% on average, up to 53%).

2022-03-29

2102.02585

V\'it Novotn\'y

V\'it Novotn\'y (1) and Eniafe Festus Ayetiran (1) and Dalibor Ba\v{c}ovsk\'y (1) and D\'avid Lupt\'ak (1) and Michal \v{S}tef\'anik (1) and Petr Sojka (1) ((1) Faculty of Informatics Masaryk University)

One Size Does Not Fit All: Finding the Optimal Subword Sizes for FastText Models across Languages

RANLP (2021) 1072-1078

10.26615/978-954-452-072-4_121

cs.CL

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

Unsupervised representation learning of words from large multilingual corpora is useful for downstream tasks such as word sense disambiguation, semantic text similarity, and information retrieval. The representation precision of log-bilinear fastText models is mostly due to their use of subword information. In previous work, the optimization of fastText's subword sizes has not been fully explored, and non-English fastText models were trained using subword sizes optimized for English and German word analogy tasks. In our work, we find the optimal subword sizes on the English, German, Czech, Italian, Spanish, French, Hindi, Turkish, and Russian word analogy tasks. We then propose a simple n-gram coverage model and we show that it predicts better-than-default subword sizes on the Spanish, French, Hindi, Turkish, and Russian word analogy tasks. We show that the optimization of fastText's subword sizes matters and results in a 14% improvement on the Czech word analogy task. We also show that expensive parameter optimization can be replaced by a simple n-gram coverage model that consistently improves the accuracy of fastText models on the word analogy tasks by up to 3% compared to the default subword sizes, and that it is within 1% accuracy of the optimal subword sizes.

2021-09-21

2102.02636

Hendri Murfi

Hendri Murfi, Natasha Rosaline, Nora Hariadi

Deep Autoencoder-based Fuzzy C-Means for Topic Detection

18 pages

Array 13 (2022)

10.1016/j.array.2021.100124

cs.IR cs.CL cs.LG

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

Topic detection is a process for determining topics from a collection of textual data. One of the topic detection methods is a clustering-based method, which assumes that the centroids are topics. The clustering method has the advantage that it can process data with negative representations. Therefore, the clustering method allows a combination with a broader representation learning method. In this paper, we adopt deep learning for topic detection by using a deep autoencoder and fuzzy c-means called deep autoencoder-based fuzzy c-means (DFCM). The encoder of the autoencoder performs a lower-dimensional representation learning. Fuzzy c-means groups the lower-dimensional representation to identify the centroids. The autoencoder's decoder transforms back the centroids into the original representation to be interpreted as the topics. Our simulation shows that DFCM improves the coherence score of eigenspace-based fuzzy c-means (EFCM) and is comparable to the leading standard methods, i.e., nonnegative matrix factorization (NMF) or latent Dirichlet allocation (LDA).

2021-12-28

2102.02711

Soumick Chatterjee

Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Georg Rose, Andreas N\"urnberger and Oliver Speck

Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge

Artificial Intelligence in Medicine (2021) 102196

10.1016/j.artmed.2021.102196

eess.IV cs.CV

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

Dynamic imaging is a beneficial tool for interventions to assess physiological changes. Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised. To overcome this spatio-temporal trade-off, this research presents a super-resolution (SR) MRI reconstruction with prior knowledge based fine-tuning to maximise spatial information while reducing the required scan-time for dynamic MRIs. An U-Net based network with perceptual loss is trained on a benchmark dataset and fine-tuned using one subject-specific static high resolution MRI as prior knowledge to obtain high resolution dynamic images during the inference stage. 3D dynamic data for three subjects were acquired with different parameters to test the generalisation capabilities of the network. The method was tested for different levels of in-plane undersampling for dynamic MRI. The reconstructed dynamic SR results after fine-tuning showed higher similarity with the high resolution ground-truth, while quantitatively achieving statistically significant improvement. The average SSIM of the lowest resolution experimented during this research (6.25~\% of the k-space) before and after fine-tuning were 0.939 $\pm$ 0.008 and 0.957 $\pm$ 0.006 respectively. This could theoretically result in an acceleration factor of 16, which can potentially be acquired in less than half a second. The proposed approach shows that the super-resolution MRI reconstruction with prior-information can alleviate the spatio-temporal trade-off in dynamic MRI, even for high acceleration factors.

2021-10-26

2102.02771

Jun Wang

Jun Wang, Xiaohan Yu, Yongsheng Gao

Mask Guided Attention For Fine-Grained Patchy Image Classification

Accepted to ICIP2021

2021 IEEE International Conference on Image Processing (ICIP), 2021, pp. 1044-1048

10.1109/ICIP42928.2021.9506424

cs.CV

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

In this work, we present a novel mask guided attention (MGA) method for fine-grained patchy image classification. The key challenge of fine-grained patchy image classification lies in two folds, ultra-fine-grained inter-category variances among objects and very few data available for training. This motivates us to consider employing more useful supervision signal to train a discriminative model within limited training samples. Specifically, the proposed MGA integrates a pre-trained semantic segmentation model that produces auxiliary supervision signal, i.e., patchy attention mask, enabling a discriminative representation learning. The patchy attention mask drives the classifier to filter out the insignificant parts of images (e.g., common features between different categories), which enhances the robustness of MGA for the fine-grained patchy image classification. We verify the effectiveness of our method on three publicly available patchy image datasets. Experimental results demonstrate that our MGA method achieves superior performance on three datasets compared with the state-of-the-art methods. In addition, our ablation study shows that MGA improves the accuracy by 2.25% and 2% on the SoyCultivarVein and BtfPIS datasets, indicating its practicality towards solving the fine-grained patchy image classification.

2021-09-23

2102.02789

Vivien Cabannes

Vivien Cabannes, Francis Bach, Alessandro Rudi

Disambiguation of weak supervision with exponential convergence rates

22 pages; 6 figures

Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021

cs.LG cs.AI stat.ML

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

Machine learning approached through supervised learning requires expensive annotation of data. This motivates weakly supervised learning, where data are annotated with incomplete yet discriminative information. In this paper, we focus on partial labelling, an instance of weak supervision where, from a given input, we are given a set of potential targets. We review a disambiguation principle to recover full supervision from weak supervision, and propose an empirical disambiguation algorithm. We prove exponential convergence rates of our algorithm under classical learnability assumptions, and we illustrate the usefulness of our method on practical examples.

2021-07-16

2102.02887

Shiwei Liu

Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training

16 pages; 10 figures; Published in Proceedings of the 38th International Conference on Machine Learning. Code can be found https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization

Proceedings of the 38th International Conference on Machine Learning (2021)

cs.LG cs.AI cs.CV

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

In this paper, we introduce a new perspective on training deep neural networks capable of state-of-the-art performance without the need for the expensive over-parameterization by proposing the concept of In-Time Over-Parameterization (ITOP) in sparse training. By starting from a random sparse network and continuously exploring sparse connectivities during training, we can perform an Over-Parameterization in the space-time manifold, closing the gap in the expressibility between sparse training and dense training. We further use ITOP to understand the underlying mechanism of Dynamic Sparse Training (DST) and indicate that the benefits of DST come from its ability to consider across time all possible parameters when searching for the optimal sparse connectivity. As long as there are sufficient parameters that have been reliably explored during training, DST can outperform the dense neural network by a large margin. We present a series of experiments to support our conjecture and achieve the state-of-the-art sparse training performance with ResNet-50 on ImageNet. More impressively, our method achieves dominant performance over the overparameterization-based sparse methods at extreme sparsity levels. When trained on CIFAR-100, our method can match the performance of the dense model even at an extreme sparsity (98%). Code can be found https://github.com/Shiweiliuiiiiiii/In-Time-Over-Parameterization.

2021-06-16

2102.02917

Allison Lahnala

Allison Lahnala, Gauri Kambhatla, Jiajun Peng, Matthew Whitehead, Gillian Minnehan, Eric Guldan, Jonathan K. Kummerfeld, An{\i}l \c{C}amc{\i}, Rada Mihalcea

Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction

16 pages, accepted to EvoMUSART

Computational Intelligence in Music, Sound, Art and Design, 10th International Conference, EvoMUSART 2021

cs.SD cs.AI cs.CL

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

Natural language processing methods have been applied in a variety of music studies, drawing the connection between music and language. In this paper, we expand those approaches by investigating \textit{chord embeddings}, which we apply in two case studies to address two key questions: (1) what musical information do chord embeddings capture?; and (2) how might musical applications benefit from them? In our analysis, we show that they capture similarities between chords that adhere to important relationships described in music theory. In the first case study, we demonstrate that using chord embeddings in a next chord prediction task yields predictions that more closely match those by experienced musicians. In the second case study, we show the potential benefits of using the representations in tasks related to musical stylometrics.

2021-02-08

2102.03022

Tim Miller

Zhengshang Liu, Yue Yang, Tim Miller, and Peta Masters

Deceptive Reinforcement Learning for Privacy-Preserving Planning

Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)

cs.LG cs.AI cs.MA

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

In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour. A key ingredient in reinforcement learning is a reward function, which determines how much reward (negative or positive) is given and when. However, in some situations, we may want to keep a reward function private; that is, to make it difficult for an observer to determine the reward function used. We define the problem of privacy-preserving reinforcement learning, and present two models for solving it. These models are based on dissimulation -- a form of deception that `hides the truth'. We evaluate our models both computationally and via human behavioural experiments. Results show that the resulting policies are indeed deceptive, and that participants can determine the true reward function less reliably than that of an honest agent.

2021-02-08

2102.03049

Shang Ran Huang

Fu-Shun Hsu, Shang-Ran Huang, Chien-Wen Huang, Chao-Jung Huang, Yuan-Ren Cheng, Chun-Chieh Chen, Jack Hsiao, Chung-Wei Chen, Li-Chin Chen, Yen-Chun Lai, Bi-Fang Hsu, Nian-Jhen Lin, Wan-Lin Tsai, Yi-Lin Wu, Tzu-Ling Tseng, Ching-Ting Tseng, Yi-Tsun Chen, Feipei Lai

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1

48 pages, 8 figures. Accepted by PLoS One

PLoS ONE, 2021, 16(7): e0254134

10.1371/journal.pone.0254134

cs.SD cs.AI cs.LG eess.AS

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

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

2022-07-13

2102.03277

Llu\'is Alemany-Puig

Llu\'is Alemany-Puig, Juan Luis Esteban, Ramon Ferrer-i-Cancho

Minimum projective linearizations of trees in linear time

Here we have corrected a mistake we made in the previous version. In particular, line 7 of Algorithm 3.2 used to say: "For i = 1 to |C_v| ..."; it should be "For i = 2 to |C_v| ..." (notice the change from 'i=1' to 'i=2')

Information Processing Letters, 174:106204 (2022)

10.1016/j.ipl.2021.106204

cs.DS cs.CL cs.DM

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

The Minimum Linear Arrangement problem (MLA) consists of finding a mapping $\pi$ from vertices of a graph to distinct integers that minimizes $\sum_{\{u,v\}\in E}|\pi(u) - \pi(v)|$. In that setting, vertices are often assumed to lie on a horizontal line and edges are drawn as semicircles above said line. For trees, various algorithms are available to solve the problem in polynomial time in $n=|V|$. There exist variants of the MLA in which the arrangements are constrained. Iordanskii, and later Hochberg and Stallmann (HS), put forward $O(n)$-time algorithms that solve the problem when arrangements are constrained to be planar (also known as one-page book embeddings). We also consider linear arrangements of rooted trees that are constrained to be projective (planar embeddings where the root is not covered by any edge). Gildea and Temperley (GT) sketched an algorithm for projective arrangements which they claimed runs in $O(n)$ but did not provide any justification of its cost. In contrast, Park and Levy claimed that GT's algorithm runs in $O(n \log d_{max})$ where $d_{max}$ is the maximum degree but did not provide sufficient detail. Here we correct an error in HS's algorithm for the planar case, show its relationship with the projective case, and derive simple algorithms for the projective and planar cases that run without a doubt in $O(n)$ time.

2024-09-13

2102.03310

Michal Ciszewski

Micha{\l} Ciszewski, Jakob S\"ohl, Geurt Jongbloed

Improving state estimation through projection post-processing for activity recognition with application to football

This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this article is published in Statistical Methods & Applications, and is available online at https://doi.org/10.1007/s10260-023-00696-z

Stat Methods Appl (2023)

10.1007/s10260-023-00696-z

cs.CV

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

The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method for activity recognition. It improves the accuracy of the classification methods by correcting for unrealistic short activities in the estimate. We also propose a new performance measure, the Locally Time-Shifted Measure (LTS measure), which addresses uncertainty in the times of state changes. The effectiveness of the post-processing method is evaluated, using the novel LTS measure, on the basis of a simulated dataset and a real application on sensor data from football. The simulation study is also used to discuss the choice of the parameters of the post-processing method and the LTS measure.

2023-05-04

2102.03380

Manuel L\'opez-Ib\'a\~nez

Manuel L\'opez-Ib\'a\~nez (University of M\'alaga, Spain), Juergen Branke (University of Warwick, UK), Lu\'is Paquete (University of Coimbra, Portugal)

Reproducibility in Evolutionary Computation

ACM Transactions on Evolutionary Learning and Optimization, 2021

10.1145/3466624

cs.AI cs.NE math.OC

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

Experimental studies are prevalent in Evolutionary Computation (EC), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery (ACM) adopted by ACM Transactions on Evolutionary Learning and Optimization (https://dlnext.acm.org/journal/telo). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.

2022-03-30

2102.03382

Tu Le

Tu Le, Danny Yuxing Huang, Noah Apthorpe, Yuan Tian

SkillBot: Identifying Risky Content for Children in Alexa Skills

ACM Transactions on Internet Technology, Volume 22, Issue 3, August 2022, Article 79, pp 1-31

10.1145/3539609

cs.MA cs.CL cs.CR cs.HC

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

Many households include children who use voice personal assistants (VPA) such as Amazon Alexa. Children benefit from the rich functionalities of VPAs and third-party apps but are also exposed to new risks in the VPA ecosystem. In this paper, we first investigate "risky" child-directed voice apps that contain inappropriate content or ask for personal information through voice interactions. We build SkillBot - a natural language processing (NLP)-based system to automatically interact with VPA apps and analyze the resulting conversations. We find 28 risky child-directed apps and maintain a growing dataset of 31,966 non-overlapping app behaviors collected from 3,434 Alexa apps. Our findings suggest that although child-directed VPA apps are subject to stricter policy requirements and more intensive vetting, children remain vulnerable to inappropriate content and privacy violations. We then conduct a user study showing that parents are concerned about the identified risky apps. Many parents do not believe that these apps are available and designed for families/kids, although these apps are actually published in Amazon's "Kids" product category. We also find that parents often neglect basic precautions such as enabling parental controls on Alexa devices. Finally, we identify a novel risk in the VPA ecosystem: confounding utterances, or voice commands shared by multiple apps that may cause a user to interact with a different app than intended. We identify 4,487 confounding utterances, including 581 shared by child-directed and non-child-directed apps. We find that 27% of these confounding utterances prioritize invoking a non-child-directed app over a child-directed app. This indicates that children are at real risk of accidentally invoking non-child-directed apps due to confounding utterances.

2022-10-13

2102.03419

Dora Jambor

Dora Jambor, Komal Teru, Joelle Pineau, William L. Hamilton

Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs

code available at https://github.com/dorajam/few-shot-link-prediction-paper

European Chapter of the ACL (EACL), 2021

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

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

Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods. These methods perform link prediction for a set of new relations, unseen during training, given only a few example facts of each relation at test time. In this work, we perform a systematic study on a spectrum of models derived by generalizing the current state of the art for few-shot link prediction, with the goal of probing the limits of learning in this few-shot setting. We find that a simple zero-shot baseline - which ignores any relation-specific information - achieves surprisingly strong performance. Moreover, experiments on carefully crafted synthetic datasets show that having only a few examples of a relation fundamentally limits models from using fine-grained structural information and only allows for exploiting the coarse-grained positional information of entities. Together, our findings challenge the implicit assumptions and inductive biases of prior work and highlight new directions for research in this area.

2021-02-09

2102.03444

Dominik Drees

Dominik Drees, Aaron Scherzinger, Ren\'e H\"agerling, Friedemann Kiefer, Xiaoyi Jiang

Scalable Robust Graph and Feature Extraction for Arbitrary Vessel Networks in Large Volumetric Datasets

BMC Bioinformatics 22 (2021) 346

10.1186/s12859-021-04262-w

cs.CV

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

Recent advances in 3D imaging technologies provide novel insights to researchers and reveal finer and more detail of examined specimen, especially in the biomedical domain, but also impose huge challenges regarding scalability for automated analysis algorithms due to rapidly increasing dataset sizes. In particular, existing research towards automated vessel network analysis does not consider memory requirements of proposed algorithms and often generates a large number of spurious branches for structures consisting of many voxels. Additionally, very often these algorithms have further restrictions such as the limitation to tree topologies or relying on the properties of specific image modalities. We present a scalable pipeline (in terms of computational cost, required main memory and robustness) that extracts an annotated abstract graph representation from the foreground segmentation of vessel networks of arbitrary topology and vessel shape. Only a single, dimensionless, a-priori determinable parameter is required. By careful engineering of individual pipeline stages and a novel iterative refinement scheme we are, for the first time, able to analyze the topology of volumes of roughly 1TB on commodity hardware. An implementation of the presented pipeline is publicly available in version 5.1 of the volume rendering and processing engine Voreen (https://www.uni-muenster.de/Voreen/).

2021-06-30

2102.03502

Zhenhan Huang

Zhenhan Huang, Fumihide Tanaka

MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management

PLoS ONE 17(2): e0263689 (2022)

10.1371/journal.pone.0263689

q-fin.PM cs.AI cs.LG q-fin.CP

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

Financial portfolio management (PM) is one of the most applicable problems in reinforcement learning (RL) owing to its sequential decision-making nature. However, existing RL-based approaches rarely focus on scalability or reusability to adapt to the ever-changing markets. These approaches are rigid and unscalable to accommodate the varying number of assets of portfolios and increasing need for heterogeneous data. Also, RL agents in the existing systems are ad-hoc trained and hardly reusable for different portfolios. To confront the above problems, a modular design is desired for the systems to be compatible with reusable asset-dedicated agents. In this paper, we propose a multi-agent RL-based system for PM (MSPM). MSPM involves two types of asynchronously-updated modules: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). An EAM is an information-generating module with a DQN agent, and it receives heterogeneous data and generates signal-comprised information for a particular asset. An SAM is a decision-making module with a PPO agent for portfolio optimization, and it connects to EAMs to reallocate the assets in a portfolio. Trained EAMs can be connected to any SAM at will. With its modularized architecture, the multi-step condensation of volatile market information, and the reusable design of EAM, MSPM simultaneously addresses the two challenges in RL-based PM: scalability and reusability. Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profit accumulation by its outperformance over five baselines in terms of accumulated rate of return (ARR), daily rate of return, and Sortino ratio. MSPM improves ARR by at least 186.5% compared to CRP, a widely-used PM strategy. To validate the indispensability of EAM, we back-test and compare MSPMs on four portfolios. EAM-enabled MSPMs improve ARR by at least 1341.8% compared to EAM-disabled MSPMs.

2022-02-22

2102.03752

Yusheng Su

Yusheng Su, Xu Han, Yankai Lin, Zhengyan Zhang, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun

CSS-LM: A Contrastive Framework for Semi-supervised Fine-tuning of Pre-trained Language Models

IEEE/ACM Transactions on Audio, Speech, and Language Processing 2021

10.1109/TASLP.2021.3105013

2329-9290

cs.CL cs.LG

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

Fine-tuning pre-trained language models (PLMs) has demonstrated its effectiveness on various downstream NLP tasks recently. However, in many low-resource scenarios, the conventional fine-tuning strategies cannot sufficiently capture the important semantic features for downstream tasks. To address this issue, we introduce a novel framework (named "CSS-LM") to improve the fine-tuning phase of PLMs via contrastive semi-supervised learning. Specifically, given a specific task, we retrieve positive and negative instances from large-scale unlabeled corpora according to their domain-level and class-level semantic relatedness to the task. We then perform contrastive semi-supervised learning on both the retrieved unlabeled and original labeled instances to help PLMs capture crucial task-related semantic features. The experimental results show that CSS-LM achieves better results than the conventional fine-tuning strategy on a series of downstream tasks with few-shot settings, and outperforms the latest supervised contrastive fine-tuning strategies. Our datasets and source code will be available to provide more details.

2021-11-15

2102.03814

Theerawit Wilaiprasitporn

Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan and Theerawit Wilaiprasitporn

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

IEEE Transactions on Biomedical Engineering 2021

10.1109/TBME.2021.3137184

eess.SP cs.AI cs.CV cs.LG

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

Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs) allow control of several applications by decoding neurophysiological phenomena, which are usually recorded by electroencephalography (EEG) using a non-invasive technique. Despite great advances in MI-based BCI, EEG rhythms are specific to a subject and various changes over time. These issues point to significant challenges to enhance the classification performance, especially in a subject-independent manner. To overcome these challenges, we propose MIN2Net, a novel end-to-end multi-task learning to tackle this task. We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously. This approach reduces the complexity in pre-processing, results in significant performance improvement on EEG classification. Experimental results in a subject-independent manner show that MIN2Net outperforms the state-of-the-art techniques, achieving an F1-score improvement of 6.72%, and 2.23% on the SMR-BCI, and OpenBMI datasets, respectively. We demonstrate that MIN2Net improves discriminative information in the latent representation. This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without the need for calibration.

2022-01-10

2102.03858

Zaharah A. Bukhsh

Zaharah A. Bukhsh, Nils Jansen, Aaqib Saeed

Damage detection using in-domain and cross-domain transfer learning

16 pages, 8 figures, 7 tables

Neural Comput & Applic (2021)

10.1007/s00521-021-06279-x

cs.CV cs.LG

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

We investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained IMAGENET models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of IMAGENET representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.

2021-10-06

2102.03896

Simon Zhuang

Simon Zhuang, Dylan Hadfield-Menell

Consequences of Misaligned AI

NeurIPS 2020

cs.AI

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

AI systems often rely on two key components: a specified goal or reward function and an optimization algorithm to compute the optimal behavior for that goal. This approach is intended to provide value for a principal: the user on whose behalf the agent acts. The objectives given to these agents often refer to a partial specification of the principal's goals. We consider the cost of this incompleteness by analyzing a model of a principal and an agent in a resource constrained world where the $L$ attributes of the state correspond to different sources of utility for the principal. We assume that the reward function given to the agent only has support on $J < L$ attributes. The contributions of our paper are as follows: 1) we propose a novel model of an incomplete principal-agent problem from artificial intelligence; 2) we provide necessary and sufficient conditions under which indefinitely optimizing for any incomplete proxy objective leads to arbitrarily low overall utility; and 3) we show how modifying the setup to allow reward functions that reference the full state or allowing the principal to update the proxy objective over time can lead to higher utility solutions. The results in this paper argue that we should view the design of reward functions as an interactive and dynamic process and identifies a theoretical scenario where some degree of interactivity is desirable.

2021-02-09

2102.03897

Chetan Srinidhi L

Chetan L. Srinidhi, Seung Wook Kim, Fu-Der Chen, Anne L. Martel

Self-supervised driven consistency training for annotation efficient histopathology image analysis

Medical Image Analysis, Volume 75, January 2022

10.1016/j.media.2021.102256

cs.CV

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

Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and Intra-observer variability. While recent self-supervised and semi-supervised methods can alleviate this need by learn-ing unsupervised feature representations, they still struggle to generalize well to downstream tasks when the number of labeled instances is small. In this work, we overcome this challenge by leveraging both task-agnostic and task-specific unlabeled data based on two novel strategies: i) a self-supervised pretext task that harnesses the underlying multi-resolution contextual cues in histology whole-slide images to learn a powerful supervisory signal for unsupervised representation learning; ii) a new teacher-student semi-supervised consistency paradigm that learns to effectively transfer the pretrained representations to downstream tasks based on prediction consistency with the task-specific un-labeled data. We carry out extensive validation experiments on three histopathology benchmark datasets across two classification and one regression-based tasks, i.e., tumor metastasis detection, tissue type classification, and tumor cellularity quantification. Under limited-label data, the proposed method yields tangible improvements, which is close or even outperforming other state-of-the-art self-supervised and supervised baselines. Furthermore, we empirically show that the idea of bootstrapping the self-supervised pretrained features is an effective way to improve the task-specific semi-supervised learning on standard benchmarks. Code and pretrained models will be made available at: https://github.com/srinidhiPY/SSL_CR_Histo

2021-11-03

2102.03932

Fazael Ayatollahi

Fazael Ayatollahi (1 and 2), Shahriar B. Shokouhi (1), Ritse M. Mann (2), Jonas Teuwen (2 and 3) ((1) Electrical Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran, (2) Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands, (3) Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands)

Automatic Breast Lesion Detection in Ultrafast DCE-MRI Using Deep Learning

Medical physics vol. 48,10 (2021): 5897-5907

10.1002/mp.15156

eess.IV cs.CV cs.LG

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

Purpose: We propose a deep learning-based computer-aided detection (CADe) method to detect breast lesions in ultrafast DCE-MRI sequences. This method uses both the three-dimensional spatial information and temporal information obtained from the early-phase of the dynamic acquisition. Methods: The proposed CADe method, based on a modified 3D RetinaNet model, operates on ultrafast T1 weighted sequences, which are preprocessed for motion compensation, temporal normalization, and are cropped before passing into the model. The model is optimized to enable the detection of relatively small breast lesions in a screening setting, focusing on detection of lesions that are harder to differentiate from confounding structures inside the breast. Results: The method was developed based on a dataset consisting of 489 ultrafast MRI studies obtained from 462 patients containing a total of 572 lesions (365 malignant, 207 benign) and achieved a detection rate, sensitivity, and detection rate of benign lesions of 0.90 (0.876-0.934), 0.95 (0.934-0.980), and 0.81 (0.751-0.871) at 4 false positives per normal breast with 10-fold cross-testing, respectively. Conclusions: The deep learning architecture used for the proposed CADe application can efficiently detect benign and malignant lesions on ultrafast DCE-MRI. Furthermore, utilizing the less visible hard-to detect-lesions in training improves the learning process and, subsequently, detection of malignant breast lesions.

2021-11-12

2102.04034

Andrew Palmer

Andrew W. Palmer, Albi Sema, Wolfram Martens, Peter Rudolph and Wolfgang Waizenegger

The Autonomous Siemens Tram

6 pages, presented at the 2020 International Conference on Intelligent Transportation Systems (ITSC)

A. W. Palmer, A. Sema, W. Martens, P. Rudolph and W. Waizenegger, "The Autonomous Siemens Tram," 2020 IEEE 23rd ITSC, 2020, pp. 1-6

10.1109/ITSC45102.2020.9294699

cs.RO cs.CV

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

This paper presents the Autonomous Siemens Tram that was publicly demonstrated in Potsdam, Germany during the InnoTrans 2018 exhibition. The system was built on a Siemens Combino tram and used a multi-modal sensor suite to localize the vehicle, and to detect and respond to traffic signals and obstacles. An overview of the hardware and the developed localization, signal handling, and obstacle handling components is presented, along with a summary of their performance.

2021-02-09

2102.04060

Maxime Ferrera

Maxime Ferrera, Alexandre Eudes, Julien Moras, Martial Sanfourche, Guy Le Besnerais

OV$^{2}$SLAM : A Fully Online and Versatile Visual SLAM for Real-Time Applications

Accepted for publication in IEEE Robotics and Automation Letters (RA-L). Code is available at : \url{https://github.com/ov2slam/ov2slam}

IEEE Robotics and Automation Letters, IEEE 2021

cs.CV cs.RO

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

Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV$^{2}$SLAM, a fully online algorithm, handling both monocular and stereo camera setups, various map scales and frame-rates ranging from a few Hertz up to several hundreds. It combines numerous recent contributions in visual localization within an efficient multi-threaded architecture. Extensive comparisons with competing algorithms shows the state-of-the-art accuracy and real-time performance of the resulting algorithm. For the benefit of the community, we release the source code: \url{https://github.com/ov2slam/ov2slam}.

2021-02-09

2102.04201

Jennifer Cobbe Dr

Jennifer Cobbe, Michelle Seng Ah Lee, Jatinder Singh

Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems

ACM Conference on Fairness, Accountability, and Transparency (FAccT 21), March 2021, Virtual Event, Canada

cs.CY cs.AI

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

This paper introduces reviewability as a framework for improving the accountability of automated and algorithmic decision-making (ADM) involving machine learning. We draw on an understanding of ADM as a socio-technical process involving both human and technical elements, beginning before a decision is made and extending beyond the decision itself. While explanations and other model-centric mechanisms may assist some accountability concerns, they often provide insufficient information of these broader ADM processes for regulatory oversight and assessments of legal compliance. Reviewability involves breaking down the ADM process into technical and organisational elements to provide a systematic framework for determining the contextually appropriate record-keeping mechanisms to facilitate meaningful review - both of individual decisions and of the process as a whole. We argue that a reviewability framework, drawing on administrative law's approach to reviewing human decision-making, offers a practical way forward towards more a more holistic and legally-relevant form of accountability for ADM.

2021-02-11

2102.04202

Shoffan Saifullah

Shoffan Saifullah

Segmentasi Citra Menggunakan Metode Watershed Transform Berdasarkan Image Enhancement Dalam Mendeteksi Embrio Telur

8 pages, in Indonesian language, 6 figures

Systemic: Information System and Informatics Journal, 5(2), (2019), 53-60

10.29080/systemic.v5i2.798

eess.IV cs.CV

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

Image processing can be applied in the detection of egg embryos. The egg embryos detection is processed using a segmentation process. The segmentation divides the image according to the area that is divided. This process requires improvement of the image that is processed to obtain optimal results. This study will analyze the detection of egg embryos based on image processing with image enhancement and the concept of segmentation using the watershed method. Image enhancement in preprocessing in image improvement uses a combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Histogram Equalization (HE) methods. The grayscale egg image is corrected using the CLAHE method, and the results are reprocessed using HE. The image improvement results show that the CLAHE-HE combination method gives a clear picture of the object area of the egg image that has an embryo. The segmentation process using image conversion to black and white image and watershed segmentation can clearly show the object of a chicken egg that has an embryo. The results of segmentation can divide the area of the egg having embryos in a real and accurate way with a percentage \approx 98\%.

2021-02-14

2102.04216

Anusha Bompelli

Anusha Bompelli, Yanshan Wang, Ruyuan Wan, Esha Singh, Yuqi Zhou, Lin Xu, David Oniani, Bhavani Singh Agnikula Kshatriya, Joyce (Joy) E. Balls-Berry, and Rui Zhang

Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

32 pages, 5 figures

Health Data Science. 2021 Aug 24;2021:9759016

10.34133/2021/9759016

Article ID 9759016

cs.CY cs.AI

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

Background: There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been little research into how to make the most of SBDH information from EHRs. Methods: A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results: Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (NLP) technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion: Despite known associations between SBDH and disease, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive Model

2021-10-12

2102.04341

Jonathan Kelly

Justin Tomasi, Brandon Wagstaff, Steven L. Waslander, Jonathan Kelly

Learned Camera Gain and Exposure Control for Improved Visual Feature Detection and Matching

In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Robotics and Automation (ICRA'21), Xi'an, China, May 30-Jun. 5, 2021

IEEE Robotics and Automation Letters (RA-L), Vol. 6, No. 2, pp. 2028-2035, Apr. 2021

10.1109/LRA.2021.3058909

cs.RO cs.CV

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

Successful visual navigation depends upon capturing images that contain sufficient useful information. In this letter, we explore a data-driven approach to account for environmental lighting changes, improving the quality of images for use in visual odometry (VO) or visual simultaneous localization and mapping (SLAM). We train a deep convolutional neural network model to predictively adjust camera gain and exposure time parameters such that consecutive images contain a maximal number of matchable features. The training process is fully self-supervised: our training signal is derived from an underlying VO or SLAM pipeline and, as a result, the model is optimized to perform well with that specific pipeline. We demonstrate through extensive real-world experiments that our network can anticipate and compensate for dramatic lighting changes (e.g., transitions into and out of road tunnels), maintaining a substantially higher number of inlier feature matches than competing camera parameter control algorithms.

2022-07-12

2102.04366

Lucas Prado Osco

Mauro dos Santos de Arruda, Lucas Prado Osco, Plabiany Rodrigo Acosta, Diogo Nunes Gon\c{c}alves, Jos\'e Marcato Junior, Ana Paula Marques Ramos, Edson Takashi Matsubara, Zhipeng Luo, Jonathan Li, Jonathan de Andrade Silva, Wesley Nunes Gon\c{c}alves

Counting and Locating High-Density Objects Using Convolutional Neural Network

15 pages, 10 figures, 8 tables

Expert Systems with Applications, 2022

10.1016/j.eswa.2022.116555

cs.CV

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

This paper presents a Convolutional Neural Network (CNN) approach for counting and locating objects in high-density imagery. To the best of our knowledge, this is the first object counting and locating method based on a feature map enhancement and a Multi-Stage Refinement of the confidence map. The proposed method was evaluated in two counting datasets: tree and car. For the tree dataset, our method returned a mean absolute error (MAE) of 2.05, a root-mean-squared error (RMSE) of 2.87 and a coefficient of determination (R$^2$) of 0.986. For the car dataset (CARPK and PUCPR+), our method was superior to state-of-the-art methods. In the these datasets, our approach achieved an MAE of 4.45 and 3.16, an RMSE of 6.18 and 4.39, and an R$^2$ of 0.975 and 0.999, respectively. The proposed method is suitable for dealing with high object-density, returning a state-of-the-art performance for counting and locating objects.

2022-05-31

2102.04394

Fabio Gonzalez

Fabio A. Gonz\'alez, Alejandro Gallego, Santiago Toledo-Cort\'es, Vladimir Vargas-Calder\'on

Learning with Density Matrices and Random Features

Final version published in Quantum Mach. Intell. 4, 23 (2022)

Quantum Mach. Intell. 4, 23 (2022)

10.1007/s42484-022-00079-9

cs.LG cs.AI quant-ph

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

A density matrix describes the statistical state of a quantum system. It is a powerful formalism to represent both the quantum and classical uncertainty of quantum systems and to express different statistical operations such as measurement, system combination and expectations as linear algebra operations. This paper explores how density matrices can be used as a building block for machine learning models exploiting their ability to straightforwardly combine linear algebra and probability. One of the main results of the paper is to show that density matrices coupled with random Fourier features could approximate arbitrary probability distributions over $\mathbb{R}^n$. Based on this finding the paper builds different models for density estimation, classification and regression. These models are differentiable, so it is possible to integrate them with other differentiable components, such as deep learning architectures and to learn their parameters using gradient-based optimization. In addition, the paper presents optimization-less training strategies based on estimation and model averaging. The models are evaluated in benchmark tasks and the results are reported and discussed.

2024-05-01

2102.04402

Xueguang Lyu

Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato

Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning

Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 2021

cs.LG cs.AI

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

Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community. In particular, actor-critic methods with a centralized critic and decentralized actors are a common instance of this idea. However, the implications of using a centralized critic in this context are not fully discussed and understood even though it is the standard choice of many algorithms. We therefore formally analyze centralized and decentralized critic approaches, providing a deeper understanding of the implications of critic choice. Because our theory makes unrealistic assumptions, we also empirically compare the centralized and decentralized critic methods over a wide set of environments to validate our theories and to provide practical advice. We show that there exist misconceptions regarding centralized critics in the current literature and show that the centralized critic design is not strictly beneficial, but rather both centralized and decentralized critics have different pros and cons that should be taken into account by algorithm designers.

2021-12-06

2102.04566

Lucas Prado Osco

Patrik Ol\~a Bressan, Jos\'e Marcato Junior, Jos\'e Augusto Correa Martins, Diogo Nunes Gon\c{c}alves, Daniel Matte Freitas, Lucas Prado Osco, Jonathan de Andrade Silva, Zhipeng Luo, Jonathan Li, Raymundo Cordero Garcia, Wesley Nunes Gon\c{c}alves

Semantic Segmentation with Labeling Uncertainty and Class Imbalance

15 pages, 9 figures, 3 tables

International Journal of Applied Earth Observation and Geoinformation, 2022

10.1016/j.jag.2022.102690

cs.CV

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

Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks. However, challenges such as the class imbalance and the uncertainty in the pixel-labeling process are not completely addressed. As such, we present a new approach that calculates a weight for each pixel considering its class and uncertainty during the labeling process. The pixel-wise weights are used during training to increase or decrease the importance of the pixels. Experimental results show that the proposed approach leads to significant improvements in three challenging segmentation tasks in comparison to baseline methods. It was also proved to be more invariant to noise. The approach presented here may be used within a wide range of semantic segmentation methods to improve their robustness.

2022-05-31

2102.04652

Xiangzeng Zhou

Xiangzeng Zhou and Pan Pan and Yun Zheng and Yinghui Xu and Rong Jin

Large Scale Long-tailed Product Recognition System at Alibaba

Acccepted by CIKM 2020

In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM20), 3353-3356 (2020)

10.1145/3340531.3417445

cs.CV

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

A practical large scale product recognition system suffers from the phenomenon of long-tailed imbalanced training data under the E-commercial circumstance at Alibaba. Besides product images at Alibaba, plenty of image related side information (e.g. title, tags) reveal rich semantic information about images. Prior works mainly focus on addressing the long tail problem in visual perspective only, but lack of consideration of leveraging the side information. In this paper, we present a novel side information based large scale visual recognition co-training~(SICoT) system to deal with the long tail problem by leveraging the image related side information. In the proposed co-training system, we firstly introduce a bilinear word attention module aiming to construct a semantic embedding over the noisy side information. A visual feature and semantic embedding co-training scheme is then designed to transfer knowledge from classes with abundant training data (head classes) to classes with few training data (tail classes) in an end-to-end fashion. Extensive experiments on four challenging large scale datasets, whose numbers of classes range from one thousand to one million, demonstrate the scalable effectiveness of the proposed SICoT system in alleviating the long tail problem. In the visual search platform Pailitao\footnote{http://www.pailitao.com} at Alibaba, we settle a practical large scale product recognition application driven by the proposed SICoT system, and achieve a significant gain of unique visitor~(UV) conversion rate.

2021-02-10

2102.04667

Yanhao Zhang

Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Jianmin Wu, Yinghui Xu, Rong Jin

Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance

accepted by CIKM 2019

CIKM 2019: 2489-2497

10.1145/3357384.3357800

cs.CV

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

Visual search plays an essential role for E-commerce. To meet the search demands of users and promote shopping experience at Alibaba, visual search relevance of real-shot images is becoming the bottleneck. Traditional visual search paradigm is usually based upon supervised learning with labeled data. However, large-scale categorical labels are required with expensive human annotations, which limits its applicability and also usually fails in distinguishing the real-shot images. In this paper, we propose to discover Virtual ID from user click behavior to improve visual search relevance at Alibaba. As a totally click-data driven approach, we collect various types of click data for training deep networks without any human annotations at all. In particular, Virtual ID are learned as classification supervision with co-click embedding, which explores image relationship from user co-click behaviors to guide category prediction and feature learning. Concretely, we deploy Virtual ID Category Network by integrating first-clicks and switch-clicks as regularizer. Incorporating triplets and list constraints, Virtual ID Feature Network is trained in a joint classification and ranking manner. Benefiting from exploration of user click data, our networks are more effective to encode richer supervision and better distinguish real-shot images in terms of category and feature. To validate our method for visual search relevance, we conduct an extensive set of offline and online experiments on the collected real-shot images. We consistently achieve better experimental results across all components, compared with alternative and state-of-the-art methods.

2021-02-10

2102.04674

Yanhao Zhang

Yanhao Zhang, Pan Pan, Yun Zheng, Kang Zhao, Yingya Zhang, Xiaofeng Ren, Rong Jin

Visual Search at Alibaba

accepted by KDD 2018

KDD 2018: 993-1001

10.1145/3219819.3219820

cs.CV

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

This paper introduces the large scale visual search algorithm and system infrastructure at Alibaba. The following challenges are discussed under the E-commercial circumstance at Alibaba (a) how to handle heterogeneous image data and bridge the gap between real-shot images from user query and the online images. (b) how to deal with large scale indexing for massive updating data. (c) how to train deep models for effective feature representation without huge human annotations. (d) how to improve the user engagement by considering the quality of the content. We take advantage of large image collection of Alibaba and state-of-the-art deep learning techniques to perform visual search at scale. We present solutions and implementation details to overcome those problems and also share our learnings from building such a large scale commercial visual search engine. Specifically, model and search-based fusion approach is introduced to effectively predict categories. Also, we propose a deep CNN model for joint detection and feature learning by mining user click behavior. The binary index engine is designed to scale up indexing without compromising recall and precision. Finally, we apply all the stages into an end-to-end system architecture, which can simultaneously achieve highly efficient and scalable performance adapting to real-shot images. Extensive experiments demonstrate the advancement of each module in our system. We hope visual search at Alibaba becomes more widely incorporated into today's commercial applications.

2021-02-10

2102.04780

Sutharsan Mahendren Mr

Sutharsan Mahendren, Chamira Edussooriya, Ranga Rodrigo

Diverse Single Image Generation with Controllable Global Structure

Published in the Neurocomputing Journal

Neurocomputing 528(2023)97-112

10.1016/j.neucom.2023.01.011

cs.CV eess.IV

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

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for mainly capturing the patch statistics and, thereby, not being able to capture global statistics very well. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. Our results are visually better than the state-of-the-art particularly in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.

2023-01-26

2102.04816

Abdelrahman Abdallah

Daniyar Nurseitov, Kairat Bostanbekov, Maksat Kanatov, Anel Alimova, Abdelrahman Abdallah, Galymzhan Abdimanap

Classification of Handwritten Names of Cities and Handwritten Text Recognition using Various Deep Learning Models

Advances in Science, Technology and Engineering Systems. 5, 934-943 (2020)

10.25046/aj0505114

cs.CV

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

This article discusses the problem of handwriting recognition in Kazakh and Russian languages. This area is poorly studied since in the literature there are almost no works in this direction. We have tried to describe various approaches and achievements of recent years in the development of handwritten recognition models in relation to Cyrillic graphics. The first model uses deep convolutional neural networks (CNNs) for feature extraction and a fully connected multilayer perceptron neural network (MLP) for word classification. The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN) layers to extract information from images. We also proposed the Bluechet and Puchserver models to compare the results. Due to the lack of available open datasets in Russian and Kazakh languages, we carried out work to collect data that included handwritten names of countries and cities from 42 different Cyrillic words, written more than 500 times in different handwriting. We also used a handwritten database of Kazakh and Russian languages (HKR). This is a new database of Cyrillic words (not only countries and cities) for the Russian and Kazakh languages, created by the authors of this work.

2021-02-10

2102.04916

Pierre Aumjaud

Pierre Aumjaud, David McAuliffe, Francisco Javier Rodr\'iguez Lera, Philip Cardiff

rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

7 pages, 5 figures

Software Impacts. 8 (2021) 100061

10.1016/j.simpa.2021.100061

cs.LG cs.AI cs.RO

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

Training reinforcement learning agents at solving a given task is highly dependent on identifying optimal sets of hyperparameters and selecting suitable environment input / output configurations. This tedious process could be eased with a straightforward toolbox allowing its user to quickly compare different training parameter sets. We present rl_reach, a self-contained, open-source and easy-to-use software package designed to run reproducible reinforcement learning experiments for customisable robotic reaching tasks. rl_reach packs together training environments, agents, hyperparameter optimisation tools and policy evaluation scripts, allowing its users to quickly investigate and identify optimal training configurations. rl_reach is publicly available at this URL: https://github.com/PierreExeter/rl_reach.

2021-03-03

2102.04993

Marc G\'orriz Blanch

Marc G\'orriz, Saverio Blasi, Alan F. Smeaton, Noel E. O'Connor, Marta Mrak

Attention-Based Neural Networks for Chroma Intra Prediction in Video Coding

IEEE Journal of Selected Topics in Signal Processing, 2020

10.1109/JSTSP.2020.3044482

eess.IV cs.CC cs.CV cs.LG cs.MM

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

Neural networks can be successfully used to improve several modules of advanced video coding schemes. In particular, compression of colour components was shown to greatly benefit from usage of machine learning models, thanks to the design of appropriate attention-based architectures that allow the prediction to exploit specific samples in the reference region. However, such architectures tend to be complex and computationally intense, and may be difficult to deploy in a practical video coding pipeline. This work focuses on reducing the complexity of such methodologies, to design a set of simplified and cost-effective attention-based architectures for chroma intra-prediction. A novel size-agnostic multi-model approach is proposed to reduce the complexity of the inference process. The resulting simplified architecture is still capable of outperforming state-of-the-art methods. Moreover, a collection of simplifications is presented in this paper, to further reduce the complexity overhead of the proposed prediction architecture. Thanks to these simplifications, a reduction in the number of parameters of around 90% is achieved with respect to the original attention-based methodologies. Simplifications include a framework for reducing the overhead of the convolutional operations, a simplified cross-component processing model integrated into the original architecture, and a methodology to perform integer-precision approximations with the aim to obtain fast and hardware-aware implementations. The proposed schemes are integrated into the Versatile Video Coding (VVC) prediction pipeline, retaining compression efficiency of state-of-the-art chroma intra-prediction methods based on neural networks, while offering different directions for significantly reducing coding complexity.

2021-02-10

2102.05067

Silvia Cascianelli PhD

Silvia Cascianelli, Gabriele Costante, Alessandro Devo, Thomas A. Ciarfuglia, Paolo Valigi, Mario L. Fravolini

The Role of the Input in Natural Language Video Description

In IEEE Transactions on Multimedia

IEEE Transactions on Multimedia, 22(1), 271-283 (2019)

cs.CV cs.CL cs.MM

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

Natural Language Video Description (NLVD) has recently received strong interest in the Computer Vision, Natural Language Processing (NLP), Multimedia, and Autonomous Robotics communities. The State-of-the-Art (SotA) approaches obtained remarkable results when tested on the benchmark datasets. However, those approaches poorly generalize to new datasets. In addition, none of the existing works focus on the processing of the input to the NLVD systems, which is both visual and textual. In this work, it is presented an extensive study dealing with the role of the visual input, evaluated with respect to the overall NLP performance. This is achieved performing data augmentation of the visual component, applying common transformations to model camera distortions, noise, lighting, and camera positioning, that are typical in real-world operative scenarios. A t-SNE based analysis is proposed to evaluate the effects of the considered transformations on the overall visual data distribution. For this study, it is considered the English subset of Microsoft Research Video Description (MSVD) dataset, which is used commonly for NLVD. It was observed that this dataset contains a relevant amount of syntactic and semantic errors. These errors have been amended manually, and the new version of the dataset (called MSVD-v2) is used in the experimentation. The MSVD-v2 dataset is released to help to gain insight into the NLVD problem.

2021-02-11

2102.05126

Jon\'a\v{s} Kulh\'anek

Jon\'a\v{s} Kulh\'anek and Vojt\v{e}ch Hude\v{c}ek and Tom\'a\v{s} Nekvinda and Ond\v{r}ej Du\v{s}ek

AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models

Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI (2021), 198-210

10.18653/v1/2021.nlp4convai-1.19

cs.CL cs.AI cs.LG

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

Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.

2022-01-17