pwc-archive/papers-with-abstracts huggingface dataset:
Deep One-Class Classification via Interpolated Gaussian Descriptor
Authors: Yuanhong Chen, Yu Tian, Guansong Pang, Gustavo Carneiro
Date: 2021-01-25 00:00:00
Tasks: Anomaly Detection, Classification, One-Class Classification, Unsupervised Anomaly Detection

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their distance to the Gaussian centre and the standard deviation of these distances, offering the model a discriminability w.r.t. the given samples during training. The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples. This enables our model to learn the data description based on the representative normal samples rather than fringe or anomalous samples, resulting in significantly improved normality description. In extensive experiments on diverse popular benchmarks, including MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves better detection accuracy than current state-of-the-art models. IGD also shows better robustness in problems with small or contaminated training sets. Code is available at https://github.com/tianyu0207/IGD.
Deep Graph Convolutional Networks for Wind Speed Prediction
Authors: Tomasz Stańczyk, Siamak Mehrkanoon
Date: 2021-01-25 00:00:00
Tasks: Management, Prediction

Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph convolutional networks (GCNs). Given hourly data of several weather variables acquired from multiple weather stations, wind speed values are predicted for multiple time steps ahead. In particular, the weather stations are treated as nodes of a graph whose associated adjacency matrix is learnable. In this way, the network learns the graph spatial structure and determines the strength of relations between the weather stations based on the historical weather data. We add a self-loop connection to the learnt adjacency matrix and normalize the adjacency matrix. We examine two scenarios with the self-loop connection setting (two separate models). In the first scenario, the self-loop connection is imposed as a constant additive. In the second scenario a learnable parameter is included to enable the network to decide about the self-loop connection strength. Furthermore, we incorporate data from multiple time steps with temporal convolution, which together with spatial graph convolution constitutes spatio-temporal graph convolution. We perform experiments on real datasets collected from weather stations located in cities in Denmark and the Netherlands. The numerical experiments show that our proposed models outperform previously developed baseline models on the referenced datasets. We provide additional insights by visualizing learnt adjacency matrices from each layer of our models.
Complexity of Linear Minimization and Projection on Some Sets
Authors: Cyrille W. Combettes, Sebastian Pokutta
Date: 2021-01-25 00:00:00

The Frank-Wolfe algorithm is a method for constrained optimization that relies on linear minimizations, as opposed to projections. Therefore, a motivation put forward in a large body of work on the Frank-Wolfe algorithm is the computational advantage of solving linear minimizations instead of projections. However, the discussions supporting this advantage are often too succinct or incomplete. In this paper, we review the complexity bounds for both tasks on several sets commonly used in optimization. Projection methods onto the $\ell_p$-ball, $p\in\left]1,2\right[\cup\left]2,+\infty\right[$, and the Birkhoff polytope are also proposed.
Latent Factor Modeling of Users Subjective Perception for Stereoscopic 3D Video Recommendation
Authors: Balasubramanyam Appina, Mansi Sharma, Santosh Kumar
Date: 2021-01-25 00:00:00
Tasks: Video Quality Assessment

Numerous stereoscopic 3D movies are released every year to theaters and created large revenues. Despite the improvement in stereo capturing and 3D video post-production technology, stereoscopic artifacts which cause viewer discomfort continue to appear even in high-budget films. Existing automatic 3D video quality measurement tools can detect distortions in stereoscopic images or videos, but they fail to consider the viewer's subjective perception of those artifacts, and how these distortions affect their choices. In this paper, we introduce a novel recommendation system for stereoscopic 3D movies based on a latent factor model that meticulously analyse the viewer's subjective ratings and influence of 3D video distortions on their preferences. To the best of our knowledge, this is a first-of-its-kind model that recommends 3D movies based on stereo-film quality ratings accounting correlation between the viewer's visual discomfort and stereoscopic-artifact perception. The proposed model is trained and tested on benchmark Nama3ds1-cospad1 and LFOVIAS3DPh2 S3D video quality assessment datasets. The experiments revealed that resulting matrix-factorization based recommendation system is able to generalize considerably better for the viewer's subjective ratings.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction
Authors: Hassan Alhuzali, Sophia Ananiadou
Date: 2021-01-25 00:00:00
Proceeding: EACL 2021 2
Tasks: Author Profiling, Classification, Emotion Classification, Emotion Recognition, General Classification, Prediction, Sentence

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overlook potential ambiguities, in which multiple emotions overlap. We propose a new model "SpanEmo" casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence. Furthermore, we introduce a loss function focused on modelling multiple co-existing emotions in the input sentence. Experiments performed on the SemEval2018 multi-label emotion data over three language sets (i.e., English, Arabic and Spanish) demonstrate our method's effectiveness. Finally, we present different analyses that illustrate the benefits of our method in terms of improving the model performance and learning meaningful associations between emotion classes and words in the sentence.
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series
Authors: Kevin Styp-Rekowski, Florian Schmidt, Odej Kao
Date: 2021-01-25 00:00:00
Tasks: Time Series, Time Series Analysis

Forecasting of time series in continuous systems becomes an increasingly relevant task due to recent developments in IoT and 5G. The popular forecasting model ARIMA is applied to a large variety of applications for decades. An online variant of ARIMA applies the Online Newton Step in order to learn the underlying process of the time series. This optimization method has pitfalls concerning the computational complexity and convergence. Thus, this work focuses on the computational less expensive Online Gradient Descent optimization method, which became popular for learning of neural networks in recent years. For the iterative training of such models, we propose a new approach combining different Online Gradient Descent learners (such as Adam, AMSGrad, Adagrad, Nesterov) to achieve fast convergence. The evaluation on synthetic data and experimental datasets show that the proposed approach outperforms the existing methods resulting in an overall lower prediction error.
Facilitating Terminology Translation with Target Lemma Annotations
Authors: Toms Bergmanis, Mārcis Pinnis
Date: 2021-01-25 00:00:00
Proceeding: EACL 2021 2
Tasks: Data Augmentation, LEMMA, Machine Translation, Sentence, Translation

Most of the recent work on terminology integration in machine translation has assumed that terminology translations are given already inflected in forms that are suitable for the target language sentence. In day-to-day work of professional translators, however, it is seldom the case as translators work with bilingual glossaries where terms are given in their dictionary forms; finding the right target language form is part of the translation process. We argue that the requirement for apriori specified target language forms is unrealistic and impedes the practical applicability of previous work. In this work, we propose to train machine translation systems using a source-side data augmentation method that annotates randomly selected source language words with their target language lemmas. We show that systems trained on such augmented data are readily usable for terminology integration in real-life translation scenarios. Our experiments on terminology translation into the morphologically complex Baltic and Uralic languages show an improvement of up to 7 BLEU points over baseline systems with no means for terminology integration and an average improvement of 4 BLEU points over the previous work. Results of the human evaluation indicate a 47.7% absolute improvement over the previous work in term translation accuracy when translating into Latvian.
Embedding-based Instance Segmentation in Microscopy
Authors: Manan Lalit, Pavel Tomancak, Florian Jug
Date: 2021-01-25 00:00:00
Tasks: GPU, Instance Segmentation, Segmentation, Semantic Segmentation

Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications. In the natural image domain, spatial embedding-based instance segmentation methods are known to yield high-quality results, but their utility for segmenting microscopy data is currently little researched. Here we introduce EmbedSeg, an embedding-based instance segmentation method which outperforms existing state-of-the-art baselines on 2D as well as 3D microscopy datasets. Additionally, we show that EmbedSeg has a GPU memory footprint small enough to train even on laptop GPUs, making it accessible to virtually everyone. Finally, we introduce four new 3D microscopy datasets, which we make publicly available alongside ground truth training labels. Our open-source implementation is available at https://github.com/juglab/EmbedSeg.
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Authors: Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
Date: 2021-01-25 00:00:00
Proceeding: ICCV 2021 10
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Contrastive Learning, Multiple Instance Learning, Video Anomaly Detection, Weakly-supervised Video Anomaly Detection

Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, we introduce a novel and theoretically sound method, named Robust Temporal Feature Magnitude learning (RTFM), which trains a feature magnitude learning function to effectively recognise the positive instances, substantially improving the robustness of the MIL approach to the negative instances from abnormal videos. RTFM also adapts dilated convolutions and self-attention mechanisms to capture long- and short-range temporal dependencies to learn the feature magnitude more faithfully. Extensive experiments show that the RTFM-enabled MIL model (i) outperforms several state-of-the-art methods by a large margin on four benchmark data sets (ShanghaiTech, UCF-Crime, XD-Violence and UCSD-Peds) and (ii) achieves significantly improved subtle anomaly discriminability and sample efficiency. Code is available at https://github.com/tianyu0207/RTFM.
Understanding and Achieving Efficient Robustness with Adversarial Supervised Contrastive Learning
Authors: Anh Bui, Trung Le, He Zhao, Paul Montague, Seyit Camtepe, Dinh Phung
Date: 2021-01-25 00:00:00
Tasks: Contrastive Learning

Contrastive learning (CL) has recently emerged as an effective approach to learning representation in a range of downstream tasks. Central to this approach is the selection of positive (similar) and negative (dissimilar) sets to provide the model the opportunity to `contrast' between data and class representation in the latent space. In this paper, we investigate CL for improving model robustness using adversarial samples. We first designed and performed a comprehensive study to understand how adversarial vulnerability behaves in the latent space. Based on this empirical evidence, we propose an effective and efficient supervised contrastive learning to achieve model robustness against adversarial attacks. Moreover, we propose a new sample selection strategy that optimizes the positive/negative sets by removing redundancy and improving correlation with the anchor. Extensive experiments show that our Adversarial Supervised Contrastive Learning (ASCL) approach achieves comparable performance with the state-of-the-art defenses while significantly outperforms other CL-based defense methods by using only $42.8\%$ positives and $6.3\%$ negatives.
A Review of Graph Neural Networks and Their Applications in Power Systems
Authors: Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang
Date: 2021-01-25 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity
Authors: Jichen Zhu, Diane H. Dallal, Robert C. Gray, Jennifer Villareale, Santiago Ontañón, Evan M. Forman, Danielle Arigo
Date: 2021-01-25 00:00:00
Tasks: Multi-Armed Bandits

Social comparison-based features are widely used in social computing apps. However, most existing apps are not grounded in social comparison theories and do not consider individual differences in social comparison preferences and reactions. This paper is among the first to automatically personalize social comparison targets. In the context of an m-health app for physical activity, we use artificial intelligence (AI) techniques of multi-armed bandits. Results from our user study (n=53) indicate that there is some evidence that motivation can be increased using the AI-based personalization of social comparison. The detected effects achieved small-to-moderate effect sizes, illustrating the real-world implications of the intervention for enhancing motivation and physical activity. In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change. Additionally, we propose research directions to mitigate this Personalization Paradox.
AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence
Authors: Yunhe Wang, Mingqiang Huang, Kai Han, Hanting Chen, Wei zhang, Chunjing Xu, DaCheng Tao
Date: 2021-01-25 00:00:00
Tasks: Quantization

Convolutional neural networks (CNN) have been widely used for boosting the performance of many machine intelligence tasks. However, the CNN models are usually computationally intensive and energy consuming, since they are often designed with numerous multiply-operations and considerable parameters for the accuracy reason. Thus, it is difficult to directly apply them in the resource-constrained environments such as 'Internet of Things' (IoT) devices and smart phones. To reduce the computational complexity and energy burden, here we present a novel minimalist hardware architecture using adder convolutional neural network (AdderNet), in which the original convolution is replaced by adder kernel using only additions. To maximally excavate the potential energy consumption, we explore the low-bit quantization algorithm for AdderNet with shared-scaling-factor method, and we design both specific and general-purpose hardware accelerators for AdderNet. Experimental results show that the adder kernel with int8/int16 quantization also exhibits high performance, meanwhile consuming much less resources (theoretically ~81% off). In addition, we deploy the quantized AdderNet on FPGA (Field Programmable Gate Array) platform. The whole AdderNet can practically achieve 16% enhancement in speed, 67.6%-71.4% decrease in logic resource utilization and 47.85%-77.9% decrease in power consumption compared to CNN under the same circuit architecture. With a comprehensive comparison on the performance, power consumption, hardware resource consumption and network generalization capability, we conclude the AdderNet is able to surpass all the other competitors including the classical CNN, novel memristor-network, XNOR-Net and the shift-kernel based network, indicating its great potential in future high performance and energy-efficient artificial intelligence applications.
A Simple Disaster-Related Knowledge Base for Intelligent Agents
Authors: Clark Emmanuel Paulo, Arvin Ken Ramirez, David Clarence Reducindo, Rannie Mark Mateo, Joseph Marvin Imperial
Date: 2021-01-25 00:00:00
Proceeding: PACLIC 2020 10
Tasks: Articles, Word Embeddings

In this paper, we describe our efforts in establishing a simple knowledge base by building a semantic network composed of concepts and word relationships in the context of disasters in the Philippines. Our primary source of data is a collection of news articles scraped from various Philippine news websites. Using word embeddings, we extract semantically similar and co-occurring words from an initial seed words list. We arrive at an expanded ontology with a total of 450 word assertions. We let experts from the fields of linguistics, disasters, and weather science evaluate our knowledge base and arrived at an agreeability rate of 64%. We then perform a time-based analysis of the assertions to identify important semantic changes captured by the knowledge base such as the (a) trend of roles played by human entities, (b) memberships of human entities, and (c) common association of disaster-related words. The context-specific knowledge base developed from this study can be adapted by intelligent agents such as chat bots integrated in platforms such as Facebook Messenger for answering disaster-related queries.
They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors
Authors: Sebastian Köhler, Giulio Lovisotto, Simon Birnbach, Richard Baker, Ivan Martinovic
Date: 2021-01-25 00:00:00
Tasks: object-detection, Object Detection

In this paper, we describe how the electronic rolling shutter in CMOS image sensors can be exploited using a bright, modulated light source (e.g., an inexpensive, off-the-shelf laser), to inject fine-grained image disruptions. We demonstrate the attack on seven different CMOS cameras, ranging from cheap IoT to semi-professional surveillance cameras, to highlight the wide applicability of the rolling shutter attack. We model the fundamental factors affecting a rolling shutter attack in an uncontrolled setting. We then perform an exhaustive evaluation of the attack's effect on the task of object detection, investigating the effect of attack parameters. We validate our model against empirical data collected on two separate cameras, showing that by simply using information from the camera's datasheet the adversary can accurately predict the injected distortion size and optimize their attack accordingly. We find that an adversary can hide up to 75% of objects perceived by state-of-the-art detectors by selecting appropriate attack parameters. We also investigate the stealthiness of the attack in comparison to a na\"{i}ve camera blinding attack, showing that common image distortion metrics can not detect the attack presence. Therefore, we present a new, accurate and lightweight enhancement to the backbone network of an object detector to recognize rolling shutter attacks. Overall, our results indicate that rolling shutter attacks can substantially reduce the performance and reliability of vision-based intelligent systems.
Adaptive Scheduling for Machine Learning Tasks over Networks
Authors: Konstantinos Gatsis
Date: 2021-01-25 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning, Informativeness, regression, Scheduling

A key functionality of emerging connected autonomous systems such as smart transportation systems, smart cities, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations. This is increasingly attracting attention under the terms of distributed learning and federated learning. However, in this setup data transfer takes place over communication resources that are shared among many users and tasks or subject to capacity constraints. This paper examines algorithms for efficiently allocating resources to linear regression tasks by exploiting the informativeness of the data. The algorithms developed enable adaptive scheduling of learning tasks with reliable performance guarantees.
Diverse Adversaries for Mitigating Bias in Training
Authors: Xudong Han, Timothy Baldwin, Trevor Cohn
Date: 2021-01-25 00:00:00
Proceeding: EACL 2021 2

Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and the stability of training.