pwc-archive/papers-with-abstracts huggingface dataset:
AUSN: Approximately Uniform Quantization by Adaptively Superimposing Non-uniform Distribution for Deep Neural Networks
Authors: Liu Fangxin, Zhao Wenbo, Wang Yanzhi, Dai Changzhi, Jiang Li
Date: 2020-07-08 00:00:00
Tasks: image-classification, Image Classification, Quantization

Quantization is essential to simplify DNN inference in edge applications. Existing uniform and non-uniform quantization methods, however, exhibit an inherent conflict between the representing range and representing resolution, and thereby result in either underutilized bit-width or significant accuracy drop. Moreover, these methods encounter three drawbacks: i) the absence of a quantitative metric for in-depth analysis of the source of the quantization errors; ii) the limited focus on the image classification tasks based on CNNs; iii) the unawareness of the real hardware and energy consumption reduced by lowering the bit-width. In this paper, we first define two quantitative metrics, i.e., the Clipping Error and rounding error, to analyze the quantization error distribution. We observe that the boundary- and rounding- errors vary significantly across layers, models and tasks. Consequently, we propose a novel quantization method to quantize the weight and activation. The key idea is to Approximate the Uniform quantization by Adaptively Superposing multiple Non-uniform quantized values, namely AUSN. AUSN is consist of a decoder-free coding scheme that efficiently exploits the bit-width to its extreme, a superposition quantization algorithm that can adapt the coding scheme to different DNN layers, models and tasks without extra hardware design effort, and a rounding scheme that can eliminate the well-known bit-width overflow and re-quantization issues. Theoretical analysis~(see Appendix A) and accuracy evaluation on various DNN models of different tasks show the effectiveness and generalization of AUSN. The synthesis~(see Appendix B) results on FPGA show $2\times$ reduction of the energy consumption, and $2\times$ to $4\times$ reduction of the hardware resource.
Designing and Training of A Dual CNN for Image Denoising
Authors: Chunwei Tian, Yong Xu, WangMeng Zuo, Bo Du, Chia-Wen Lin, David Zhang
Date: 2020-07-08 00:00:00
Tasks: Denoising, Image Denoising

Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a complex task, such as real noisy images. In this paper, we propsoed a Dual denoising Network (DudeNet) to recover a clean image. Specifically, DudeNet consists of four modules: a feature extraction block, an enhancement block, a compression block, and a reconstruction block. The feature extraction block with a sparse machanism extracts global and local features via two sub-networks. The enhancement block gathers and fuses the global and local features to provide complementary information for the latter network. The compression block refines the extracted information and compresses the network. Finally, the reconstruction block is utilized to reconstruct a denoised image. The DudeNet has the following advantages: (1) The dual networks with a parse mechanism can extract complementary features to enhance the generalized ability of denoiser. (2) Fusing global and local features can extract salient features to recover fine details for complex noisy images. (3) A Small-size filter is used to reduce the complexity of denoiser. Extensive experiments demonstrate the superiority of DudeNet over existing current state-of-the-art denoising methods.
Guidestar-free image-guided wavefront-shaping
Authors: Tomer Yeminy, Ori Katz
Date: 2020-07-08 00:00:00

Optical imaging through scattering media is a fundamental challenge in many applications. Recently, substantial breakthroughs such as imaging through biological tissues and looking around corners have been obtained by the use of wavefront-shaping approaches. However, these require an implanted guide-star for determining the wavefront correction, controlled coherent illumination, and most often raster scanning of the shaped focus. Alternative novel computational approaches that exploit speckle correlations, avoid guide-stars and wavefront control but are limited to small two-dimensional objects contained within the memory-effect correlations range. Here, we present a new concept, image-guided wavefront-shaping, allowing non-invasive, guidestar-free, widefield, incoherent imaging through highly scattering layers, without illumination control. Most importantly, the wavefront-correction is found even for objects that are larger than the memory-effect range, by blindly optimizing image-quality metrics. We demonstrate imaging of extended objects through highly-scattering layers and multi-core fibers, paving the way for non-invasive imaging in various applications, from microscopy to endoscopy.
Labelling imaging datasets on the basis of neuroradiology reports: a validation study
Authors: David A. Wood, Sina Kafiabadi, Aisha Al Busaidi, Emily Guilhem, Jeremy Lynch, Matthew Townend, Antanas Montvila, Juveria Siddiqui, Naveen Gadapa, Matthew Benger, Gareth Barker, Sebastian Ourselin, James H. Cole, Thomas C. Booth
Date: 2020-07-08 00:00:00

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process.
Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment
Authors: Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor, Youngjin Yoo, Eli Gibson, R. S. Vishwanath, Abishek Balachandran, James M. Balter, Yue Cao, Ramandeep Singh, Subba R. Digumarthy, Mannudeep K. Kalra, Sasa Grbic, Dorin Comaniciu
Date: 2020-07-08 00:00:00
Tasks: Anatomy, Classification, General Classification

The interpretation of medical images is a challenging task, often complicated by the presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest radiography, where there is a high inter-rater variability in the detection and classification of abnormalities. This is largely due to inconclusive evidence in the data or subjective definitions of disease appearance. An additional example is the classification of anatomical views based on 2D Ultrasound images. Often, the anatomical context captured in a frame is not sufficient to recognize the underlying anatomy. Current machine learning solutions for these problems are typically limited to providing probabilistic predictions, relying on the capacity of underlying models to adapt to limited information and the high degree of label noise. In practice, however, this leads to overconfident systems with poor generalization on unseen data. To account for this, we propose a system that learns not only the probabilistic estimate for classification, but also an explicit uncertainty measure which captures the confidence of the system in the predicted output. We argue that this approach is essential to account for the inherent ambiguity characteristic of medical images from different radiologic exams including computed radiography, ultrasonography and magnetic resonance imaging. In our experiments we demonstrate that sample rejection based on the predicted uncertainty can significantly improve the ROC-AUC for various tasks, e.g., by 8% to 0.91 with an expected rejection rate of under 25% for the classification of different abnormalities in chest radiographs. In addition, we show that using uncertainty-driven bootstrapping to filter the training data, one can achieve a significant increase in robustness and accuracy.
The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets
Authors: Ikechukwu Onyenwe, Samuel Nwagbo, Njideka Mbeledogu, Ebele Onyedinma
Date: 2020-07-07 00:00:00
Tasks: Sentiment Analysis

This work investigates empirically the impact of political party control over its candidates or vice versa on winning an election using a natural language processing technique called sentiment analysis (SA). To do this, a set of 7430 tweets bearing or related to #AnambraDecides2017 was streamed during the November 18, 2017, Anambra State gubernatorial election. These are Twitter discussions on the top five political parties and their candidates termed political actors in this paper. We conduct polarity and subjectivity sentiment analyses on all the tweets considering time as a useful dimension of SA. Furthermore, we use the word frequency to find words most associated with the political actors in a given time. We find most talked about topics using a topic modeling algorithm and how the computed sentiments and most frequent words are related to the topics per political actor. Among other things, we deduced from the experimental results that even though a political party serves as a platform that sales the personality of a candidate, the acceptance of the candidate/party adds to the winning of an election. For example, we found the winner of the election Willie Obiano benefiting from the values his party share among the people of the State. Associating his name with his party, All Progressive Grand Alliance (APGA) displays more positive sentiments and the subjective sentiment analysis indicates that Twitter users mentioning APGA are less emotionally subjective in their tweets than the other parties.
Agile Approach for IT Forensics Management
Authors: Matthias Schopp, Peter Hillmann
Date: 2020-07-08 00:00:00
Tasks: Management

The forensic investigation of cyber attacks and IT incidents is becoming increasingly difficult due to increasing complexity and intensify networking. Especially with Advanced Attacks (AT) like the increasing Advanced Persistent Threats an agile approach is indispensable. Several systems are involved in an attack (multi-host attacks). Current forensic models and procedures show considerable deficits in the process of analyzing such attacks. For this purpose, this paper presents the novel flower model, which uses agile methods and forms a new forensic management approach. In this way, the growing challenges of ATs are met. In the forensic investigation of such attacks, big data problems have to be solved due to the amount of data that needs to be analyzed. The proposed model meets this requirement by precisely defining the questions that need to be answered in an early state and collecting only the evidence usable in court proceedings that is needed to answer these questions. Additionally, the novel flower model for AT is presented that meets the different phases of an investigation process.
TripMD: Driving patterns investigation via Motif Analysis
Authors: Maria Inês Silva, Roberto Henriques
Date: 2020-07-07 00:00:00

Processing driving data and investigating driving behavior has been receiving an increasing interest in the last decades, with applications ranging from car insurance pricing to policy making. A common strategy to analyze driving behavior is to study the maneuvers being performance by the driver. In this paper, we propose TripMD, a system that extracts the most relevant driving patterns from sensor recordings (such as acceleration) and provides a visualization that allows for an easy investigation. Additionally, we test our system using the UAH-DriveSet dataset, a publicly available naturalistic driving dataset. We show that (1) our system can extract a rich number of driving patterns from a single driver that are meaningful to understand driving behaviors and (2) our system can be used to identify the driving behavior of an unknown driver from a set of drivers whose behavior we know.
Deep Reinforcement Learning and its Neuroscientific Implications
Authors: Matthew Botvinick, Jane. X. Wang, Will Dabney, Kevin J. Miller, Zeb Kurth-Nelson
Date: 2020-07-07 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, image-classification, Image Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The emergence of powerful artificial intelligence is defining new research directions in neuroscience. To date, this research has focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. However, there is another area of recent AI work which has so far received less attention from neuroscientists, but which may have profound neuroscientific implications: deep reinforcement learning. Deep RL offers a comprehensive framework for studying the interplay among learning, representation and decision-making, offering to the brain sciences a new set of research tools and a wide range of novel hypotheses. In the present review, we provide a high-level introduction to deep RL, discuss some of its initial applications to neuroscience, and survey its wider implications for research on brain and behavior, concluding with a list of opportunities for next-stage research.
Reconciling Causality and Statistics
Authors: Pirmin Lemberger, Denis Oblin
Date: 2020-07-08 00:00:00

Statisticians have warned us since the early days of their discipline that experimental correlation between two observations by no means implies the existence of a causal relation. The question about what clues exist in observational data that could informs us about the existence of such causal relations is nevertheless more that legitimate. It lies actually at the root of any scientific endeavor. For decades however the only accepted method among statisticians to elucidate causal relationships was the so called Randomized Controlled Trial. Besides this notorious exception causality questions remained largely taboo for many. One reason for this state of affairs was the lack of an appropriate mathematical framework to formulate such questions in an unambiguous way. Fortunately thinks have changed these last years with the advent of the so called Causality Revolution initiated by Judea Pearl and coworkers. The aim of this pedagogical paper is to present their ideas and methods in a compact and self-contained fashion with concrete business examples as illustrations.
Dung's semantics satisfy attack removal monotonicity
Authors: Leila Amgoud, Srdjan Vesic
Date: 2020-07-08 00:00:00

We show that preferred, stable, complete, and grounded semantics satisfy attack removal monotonicity. This means that if an attack from b to a is removed, the status of a cannot worsen, e.g. if a was skeptically accepted, it cannot become rejected.
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads
Authors: Siyu Wang, Yi Rong, Shiqing Fan, Zhen Zheng, Lansong Diao, Guoping Long, Jun Yang, Xiaoyong Liu, Wei. Lin
Date: 2020-07-08 00:00:00
Tasks: Deep Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) parallelize training tasks onto multiple devices. However, these approaches always rely on specific deep learning frameworks and requires elaborate manual design, which make it difficult to maintain and share between different type of models. In this paper, we propose Auto-MAP, a framework for exploring distributed execution plans for DNN workloads, which can automatically discovering fast parallelization strategies through reinforcement learning on IR level of deep learning models. Efficient exploration remains a major challenge for reinforcement learning. We leverage DQN with task-specific pruning strategies to help efficiently explore the search space including optimized strategies. Our evaluation shows that Auto-MAP can find the optimal solution in two hours, while achieving better throughput on several NLP and convolution models.
Modeling both context- and speaker-sensitive dependence for emotion detection in multi-speaker conversations
Authors: Dong Zhang, Liangqing Wu, Changlong Sun, Shoushan Li, Qiaoming Zhu, Guodong Zhou
Date: 2019-07-01 00:00:00
Proceeding: IJCAI 2019 7
Tasks: Emotion Recognition in Conversation

Recently, emotion detection in conversations becomes a hot research topic in the Natural Language Processing community. In this paper, we focus on emotion detection in multi-speaker conversations instead of traditional two-speaker conversations in existing studies. Different from non-conversation text, emotion detection in conversation text has one specific challenge in modeling the context-sensitive dependence. Besides, emotion detection in multi-speaker conversations endorses another specific challenge in modeling the speaker-sensitive dependence. To address above two challenges, we propose a conversational graph-based convolutional neural network. On the one hand, our approach represents each utterance and each speaker as a node. On the other hand, the context-sensitive dependence is represented by an undirected edge between two utterances nodes from the same conversation and the speaker-sensitive dependence is represented by an undirected edge between an utterance node and its speaker node. In this way, the entire conversational corpus can be symbolized as a large heterogeneous graph and the emotion detection task can be recast as a classification problem of the utterance nodes in the graph. The experimental results on a multi-modal and multi-speaker conversation corpus demonstrate the great effectiveness of the proposed approach.
A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction
Authors: Sixing Wu, Fang Chen, Fangzhao Wu, Yongfeng Huang and Xing Li
Date: 2020-08-01 00:00:00
Proceeding: ECAI 2020 8
Tasks: Emotion-Cause Pair Extraction, Multi-Task Learning

In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model.
Le VOT des \'ejectives : le cas du maya yucat\`eque (The VOT of ejective stops in Maya Yucatec)
Authors: Emre Bayraktar, Rachid Ridouane
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cet article pr{\'e}sente une {\'e}tude acoustique des occlusives {\'e}jectives du maya yucat{\`e}que. S{'}int{\'e}ressant sp{\'e}cifiquement au voice onset time (VOT), l{'}{\'e}tude examine d{'}une part si le VOT est un corr{\'e}lat acoustique fiable de l{'}{\'e}jectivit{\'e} dans cette langue et d{'}autre part si le VOT varie selon le lieu d{'}articulation et la hauteur vocalique. Les r{\'e}sultats, obtenus {\`a} partir des productions de deux locuteurs natifs, montrent que les {\'e}jectives ont un VOT plus long compar{\'e}es {\`a} leurs contreparties pulmonaires. Parmi les {\'e}jectives, le VOT varie en fonction du lieu d{'}articulation, les v{\'e}laires pr{\'e}sentant le VOT le plus long. De m{\^e}me une tendance pour un VOT plus court devant les voyelles hautes a {\'e}t{\'e} observ{\'e}e. Ces r{\'e}sultats soul{\`e}vent un ensemble de questions concernant les m{\'e}canismes qui sous-tendent les variations du VOT, notamment en lien avec les contraintes a{\'e}rodynamiques en jeu lors de la production des occlusives {\'e}jectives.
Accommodation temporelle chez l'enfant dans une t\^ache de parole altern\'ee (Children's temporal accommodation in an alternated naming task)
Authors: C{\'e}line Hidalgo, Simone Falk, Daniele Sch{\"o}n
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

L{'}accommodation temporelle entre deux interlocuteurs est un ph{\'e}nom{\`e}ne qui {\'e}merge lors d{'}une interaction et qui jouerait un r{\^o}le important dans la fluidit{\'e} des {\'e}changes. Cette {\'e}tude examine cette capacit{\'e} temporelle chez l{'}enfant {\^a}g{\'e} de 5 {\`a} 6 ans gr{\^a}ce au d{\'e}veloppement d{'}une nouvelle t{\^a}che de d{\'e}nomination en alternance avec un partenaire virtuel. Les variables temporelles analys{\'e}es sont le tempo de l{'}alternance (lent versus rapide) et la rythmicit{\'e} des mots {\'e}chang{\'e}s (constante versus al{\'e}atoire). Les enfants sont plus pr{\'e}cis dans la condition de tempo rapide et plus r{\'e}guliers lorsque la rythmicit{\'e} des listes de mots est maintenue constante. Ces r{\'e}sultats montrent 1) que la d{\'e}nomination en alternance est un paradigme permettant de mesurer les capacit{\'e}s d{'}accommodation temporelle des enfants et que 2) d{\`e}s 5 ans, les enfants peuvent ajuster leur parole {\`a} celle d{'}un agent. Ces donn{\'e}es constituent une base pour mesurer les capacit{\'e}s linguistiques d{'}accommodation temporelle chez des populations cliniques.
Acc\`es lexical et reconnaissance du voisement en voix chuchot\'ee (Lexical acces and recognition of voicing in whisper)
Authors: Yohann Meynadier, Sophie Dufour
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7
Tasks: es-en

La reconnaissance du trait de voisement de consonnes obstruantes chuchot{\'e}es en fran{\c{c}}ais a {\'e}t{\'e} examin{\'e}e via un paradigme d{'}amor{\c{c}}age s{\'e}mantique auditif-visuel. Un effet d{'}amor{\c{c}}age d{'}amplitude similaire {\`a} celui mesur{\'e} en voix modale a {\'e}t{\'e} observ{\'e} uniquement lorsque l{'}obstruante du mot amorce chuchot{\'e} est sourde (dessert-CHOCOLAT). Aucun effet d{'}amor{\c{c}}age n{'}a {\'e}t{\'e} not{\'e} quand l{'}obstruante du mot amorce est vois{\'e}e (d{\'e}sert) que ce soit sur le mot cible SABLE associ{\'e} s{\'e}mantique de d{\'e}sert ou sur le mot cible CHOCOLAT associ{\'e} s{\'e}mantique de dessert. Ainsi, m{\^e}me si certaines travaux ont mis en {\'e}vidence qu{'}en voix chuchot{\'e}e les consonnes obstruantes vois{\'e}es maintiennent des traces phon{\'e}tiques de leur identit{\'e} sous-jacente, notre {\'e}tude montre que ces consonnes sont ambig{\"u}es pour l{'}auditeur et que leur reconnaissance n{'}est pas imm{\'e}diate.
Acquisition et reconnaissance automatique d'expressions et d'appels vocaux dans un habitat. (Acquisition and recognition of expressions and vocal calls in a smart home)
Authors: Michel Vacher, Benjamin Lecouteux, Fr{\'e}d{\'e}ric Aman, Fran{\c{c}}ois Portet, Solange Rossato
Date: 2016-07-01 00:00:00
Proceeding: JEPTALNRECITAL 2016 7

Cet article pr{\'e}sente un syst{\`e}me capable de reconna{\^\i}tre les appels {\`a} l{'}aide de personnes {\^a}g{\'e}es vivant {\`a} domicile afin de leur fournir une assistance. Le syst{\`e}me utilise une technologie de Reconnaissance Automatique de la Parole (RAP) qui doit fonctionner en conditions de parole distante et avec de la parole expressive. Pour garantir l{'}intimit{\'e}, le syst{\`e}me s{'}ex{\'e}cute localement et ne reconna{\^\i}t que des phrases pr{\'e}d{\'e}finies. Le syst{\`e}me a {\'e}t{\'e} {\'e}valu{\'e} par 17 participants jouant des sc{\'e}narios incluant des chutes dans un Living lab reproduisant un salon. Le taux d{'}erreur de d{\'e}tection obtenu, 29{\%}, est encourageant et souligne les d{\'e}fis {\`a} surmonter pour cette t{\^a}che.