pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Neural Networks for Automatic Detection of Artifacts from Independent Components Represented in Scalp Topographies of EEG Signals
Authors: Giuseppe Placidi, Luigi Cinque, Matteo Polsinelli
Date: 2020-09-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Electroencephalography (EEG) measures the electrical brain activity in real-time by using sensors placed on the scalp. Artifacts, due to eye movements and blink, muscular/cardiac activity and generic electrical disturbances, have to be recognized and eliminated to allow a correct interpretation of the useful brain signals (UBS) of EEG. Independent Component Analysis (ICA) is effective to split the signal into independent components (ICs) whose re-projections on 2D scalp topographies (images), also called topoplots, allow to recognize/separate artifacts and by UBS. Until now, IC topoplot analysis, a gold standard in EEG, has been carried on visually by human experts and, hence, not usable in automatic, fast-response EEG. We present a completely automatic and effective framework for EEG artifact recognition by IC topoplots, based on 2D Convolutional Neural Networks (CNNs), capable to divide topoplots in 4 classes: 3 types of artifacts and UBS. The framework setup is described and results are presented, discussed and compared with those obtained by other competitive strategies. Experiments, carried on public EEG datasets, have shown an overall accuracy of above 98%, employing 1.4 sec on a standard PC to classify 32 topoplots, that is to drive an EEG system of 32 sensors. Though not real-time, the proposed framework is efficient enough to be used in fast-response EEG-based Brain-Computer Interfaces (BCI) and faster than other automatic methods based on ICs.
Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification
Authors: Samuel Louvan, Bernardo Magnini
Date: 2020-09-08 00:00:00
Proceeding: PACLIC 2020 10
Tasks: Data Augmentation, General Classification, intent-classification, Intent Classification, Sentence, slot-filling, Slot Filling

Neural-based models have achieved outstanding performance on slot filling and intent classification, when fairly large in-domain training data are available. However, as new domains are frequently added, creating sizeable data is expensive. We show that lightweight augmentation, a set of augmentation methods involving word span and sentence level operations, alleviates data scarcity problems. Our experiments on limited data settings show that lightweight augmentation yields significant performance improvement on slot filling on the ATIS and SNIPS datasets, and achieves competitive performance with respect to more complex, state-of-the-art, augmentation approaches. Furthermore, lightweight augmentation is also beneficial when combined with pre-trained LM-based models, as it improves BERT-based joint intent and slot filling models.
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone
Authors: Maxime De Bois, Hamdi Amroun, Mehdi Ammi
Date: 2020-09-08 00:00:00
Tasks: Activity Recognition, Reinforcement Learning (RL)

This paper presents a 3-step system that estimates the real-time energy expenditure of an individual in a non-intrusive way. First, using the user's smart-phone's sensors, we build a Decision Tree model to recognize his physical activity (\textit{running}, \textit{standing}, ...). Then, we use the detected physical activity, the time and the user's speed to infer his daily activity (\textit{watching TV}, \textit{going to the bathroom}, ...) through the use of a reinforcement learning environment, the Partially Observable Markov Decision Process framework. Once the daily activities are recognized, we translate this information into energy expenditure using the compendium of physical activities. By successfully detecting 8 physical activities at 90\%, we reached an overall accuracy of 80\% in recognizing 17 different daily activities. This result leads us to estimate the energy expenditure of the user with a mean error of 26\% of the expected estimation.
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification
Authors: Jiaxiang Liu, Xuyi Chen, Shikun Feng, Shuohuan Wang, Xuan Ouyang, Yu Sun, Zhengjie Huang, Weiyue Su
Date: 2020-09-08 00:00:00
Proceeding: SEMEVAL 2020
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification, Transfer Learning

Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language. With the increasing communication between groups with different languages, this phenomenon is more and more popular. However, there are little research and data in this area, especially in code-mixing sentiment classification. In this work, the domain transfer learning from state-of-the-art uni-language model ERNIE is tested on the code-mixing dataset, and surprisingly, a strong baseline is achieved. Furthermore, the adversarial training with a multi-lingual model is used to achieve 1st place of SemEval-2020 Task 9 Hindi-English sentiment classification competition.
IAI MovieBot: A Conversational Movie Recommender System
Authors: Javeria Habib, Shuo Zhang, Krisztian Balog
Date: 2020-09-08 00:00:00
Tasks: Recommendation Systems

Conversational recommender systems support users in accomplishing recommendation-related goals via multi-turn conversations. To better model dynamically changing user preferences and provide the community with a reusable development framework, we introduce IAI MovieBot, a conversational recommender system for movies. It features a task-specific dialogue flow, a multi-modal chat interface, and an effective way to deal with dynamically changing user preferences. The system is made available open source and is operated as a channel on Telegram.
Computational Design of Cold Bent Glass Façades
Authors: Konstantinos Gavriil, Ruslan Guseinov, Jesús Pérez, Davide Pellis, Paul Henderson, Florian Rist, Helmut Pottmann, Bernd Bickel
Date: 2020-09-08 00:00:00
Tasks: Fairness, Navigate

Cold bent glass is a promising and cost-efficient method for realizing doubly curved glass fa\c{c}ades. They are produced by attaching planar glass sheets to curved frames and require keeping the occurring stress within safe limits. However, it is very challenging to navigate the design space of cold bent glass panels due to the fragility of the material, which impedes the form-finding for practically feasible and aesthetically pleasing cold bent glass fa\c{c}ades. We propose an interactive, data-driven approach for designing cold bent glass fa\c{c}ades that can be seamlessly integrated into a typical architectural design pipeline. Our method allows non-expert users to interactively edit a parametric surface while providing real-time feedback on the deformed shape and maximum stress of cold bent glass panels. Designs are automatically refined to minimize several fairness criteria while maximal stresses are kept within glass limits. We achieve interactive frame rates by using a differentiable Mixture Density Network trained from more than a million simulations. Given a curved boundary, our regression model is capable of handling multistable configurations and accurately predicting the equilibrium shape of the panel and its corresponding maximal stress. We show predictions are highly accurate and validate our results with a physical realization of a cold bent glass surface.
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting
Authors: Rodrigo Rivera-Castro, Aleksandr Pletnev, Polina Pilyugina, Grecia Diaz, Ivan Nazarov, Wanyi Zhu, Evgeny Burnaev
Date: 2020-09-08 00:00:00
Tasks: Cloud Computing, Clustering, Demand Forecasting, regression, Time Series, Time Series Analysis, Topological Data Analysis

Topological Data Analysis (TDA) is a recent approach to analyze data sets from the perspective of their topological structure. Its use for time series data has been limited. In this work, a system developed for a leading provider of cloud computing combining both user segmentation and demand forecasting is presented. It consists of a TDA-based clustering method for time series inspired by a popular managerial framework for customer segmentation and extended to the case of clusterwise regression using matrix factorization methods to forecast demand. Increasing customer loyalty and producing accurate forecasts remain active topics of discussion both for researchers and managers. Using a public and a novel proprietary data set of commercial data, this research shows that the proposed system enables analysts to both cluster their user base and plan demand at a granular level with significantly higher accuracy than a state of the art baseline. This work thus seeks to introduce TDA-based clustering of time series and clusterwise regression with matrix factorization methods as viable tools for the practitioner.
Learning more expressive joint distributions in multimodal variational methods
Authors: Sasho Nedelkoski, Mihail Bogojeski, Odej Kao
Date: 2020-09-08 00:00:00
Tasks: Colorization, Variational Inference, Weakly-supervised Learning

Data often are formed of multiple modalities, which jointly describe the observed phenomena. Modeling the joint distribution of multimodal data requires larger expressive power to capture high-level concepts and provide better data representations. However, multimodal generative models based on variational inference are limited due to the lack of flexibility of the approximate posterior, which is obtained by searching within a known parametric family of distributions. We introduce a method that improves the representational capacity of multimodal variational methods using normalizing flows. It approximates the joint posterior with a simple parametric distribution and subsequently transforms into a more complex one. Through several experiments, we demonstrate that the model improves on state-of-the-art multimodal methods based on variational inference on various computer vision tasks such as colorization, edge and mask detection, and weakly supervised learning. We also show that learning more powerful approximate joint distributions improves the quality of the generated samples. The code of our model is publicly available at https://github.com/SashoNedelkoski/BPFDMVM.
Adapted Pepper
Authors: Maxime Caniot, Vincent Bonnet, Maxime Busy, Thierry Labaye, Michel Besombes, Sebastien Courtois, Edouard Lagrue
Date: 2020-09-08 00:00:00
Tasks: GPU, object-detection, Object Detection

One of the main issue in robotics is the lack of embedded computational power. Recently, state of the art algorithms providing a better understanding of the surroundings (Object detection, skeleton tracking, etc.) are requiring more and more computational power. The lack of embedded computational power is more significant in mass-produced robots because of the difficulties to follow the increasing computational requirements of state of the art algorithms. The integration of an additional GPU allows to overcome this lack of embedded computational power. We introduce in this paper a prototype of Pepper with an embedded GPU, but also with an additional 3D camera on the head of the robot and plugged to the late GPU. This prototype, called Adapted Pepper, was built for the European project called MuMMER (MultiModal Mall Entertainment Robot) in order to embed algorithms like OpenPose, YOLO or to process sensors information and, in all cases, avoid network dependency for deported computation.
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System
Authors: Mohammad Sadegh Sadeghi Garmaroodi, Faezeh Farivar, Mohammad Sayad Haghighi, Mahdi Aliyari Shoorehdeli, Alireza Jolfaei
Date: 2020-09-08 00:00:00
Tasks: Anomaly Detection, Fault Detection

Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the help of sensors. This article is about industrial pharmaceutical systems and more specifically, water purification systems. Purified water which has certain conductivity is an important ingredient in many pharmaceutical products. Almost every pharmaceutical company has a water purifying unit as a part of its interdependent systems. Early detection of faults right at the edge can significantly decrease maintenance costs and improve safety and output quality, and as a result, lead to the production of better medicines. In this paper, with the help of a few sensors and data mining approaches, an anomaly detection system is built for CHRIST Osmotron water purifier. This is a practical research with real-world data collected from SinaDarou Labs Co. Data collection was done by using six sensors over two-week intervals before and after system overhaul. This gave us normal and faulty operation samples. Given the data, we propose two anomaly detection approaches to build up our edge fault detection system. The first approach is based on supervised learning and data mining e.g. by support vector machines. However, since we cannot collect all possible faults data, an anomaly detection approach is proposed based on normal system identification which models the system components by artificial neural networks. Extensive experiments are conducted with the dataset generated in this study to show the accuracy of the data-driven and model-based anomaly detection methods.
Imbalanced Continual Learning with Partitioning Reservoir Sampling
Authors: Chris Dongjoo Kim, Jinseo Jeong, Gunhee Kim
Date: 2020-09-08 00:00:00
Proceeding: ECCV 2020 8
Tasks: Continual Learning, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Continual learning from a sequential stream of data is a crucial challenge for machine learning research. Most studies have been conducted on this topic under the single-label classification setting along with an assumption of balanced label distribution. This work expands this research horizon towards multi-label classification. In doing so, we identify unanticipated adversity innately existent in many multi-label datasets, the long-tailed distribution. We jointly address the two independently solved problems, Catastropic Forgetting and the long-tailed label distribution by first empirically showing a new challenge of destructive forgetting of the minority concepts on the tail. Then, we curate two benchmark datasets, COCOseq and NUS-WIDEseq, that allow the study of both intra- and inter-task imbalances. Lastly, we propose a new sampling strategy for replay-based approach named Partitioning Reservoir Sampling (PRS), which allows the model to maintain a balanced knowledge of both head and tail classes. We publicly release the dataset and the code in our project page.
Unsupervised Change Detection in Satellite Images with Generative Adversarial Network
Authors: Caijun Ren, Xiangyu Wang, Jian Gao, Huanhuan Chen
Date: 2020-09-08 00:00:00
Tasks: Change Detection, Generative Adversarial Network

Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with very high spatial resolution (VHR) but made it challenging to apply image coregistration, and many change detection methods are dependent on its accuracy.Two images of the same scene taken at different time or from different angle would introduce unregistered objects and the existence of both unregistered areas and actual changed areas would lower the performance of many change detection algorithms in unsupervised condition.To alleviate the effect of unregistered objects in the paired images, we propose a novel change detection framework utilizing a special neural network architecture -- Generative Adversarial Network (GAN) to generate many better coregistered images. In this paper, we show that GAN model can be trained upon a pair of images through using the proposed expanding strategy to create a training set and optimizing designed objective functions. The optimized GAN model would produce better coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly.Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure.Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.
Deep Active Inference for Partially Observable MDPs
Authors: Otto van der Himst, Pablo Lanillos
Date: 2020-09-08 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference model that can learn successful policies directly from high-dimensional sensory inputs. The deep learning architecture optimizes a variant of the expected free energy and encodes the continuous state representation by means of a variational autoencoder. We show, in the OpenAI benchmark, that our approach has comparable or better performance than deep Q-learning, a state-of-the-art deep reinforcement learning algorithm.
Multivariable times series classification through an interpretable representation
Authors: Francisco J. Baldán, José M. Benítez
Date: 2020-09-08 00:00:00
Tasks: Classification, Descriptive, General Classification, Time Series, Time Series Analysis, Time Series Classification

Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Authors: Hu Zhang, Peng Yang, Yanglong Yu, Mingjia Li, Ke Tang
Date: 2020-09-08 00:00:00
Tasks: Atari Games, Evolutionary Algorithms, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectively. Considering that the commonly adopted neural policies usually involves millions of parameters to be optimized, the direct application of NCS to RL may face a great challenge of the large-scale search space. To address this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC) framework to scale-up NCS while largely preserving its parallel exploration search behavior. The issue of traditional CC that can deteriorate NCS is also discussed. Empirical studies on 10 popular Atari games show that the proposed method can significantly outperform three state-of-the-art deep RL methods with 50% less computational time by effectively exploring a 1.7 million-dimensional search space.
TaBooN -- Boolean Network Synthesis Based on Tabu Search
Authors: Sara Sadat Aghamiri, Franck Delaplace
Date: 2020-09-08 00:00:00

Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of Boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the taboon work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. taboon is an automated method for Boolean Network inference from experimental data that can also assist to evaluate and optimize the dynamic behaviour of the biological networks providing a reliable platform for further modelling and predictions.
Hyperparameter Optimization via Sequential Uniform Designs
Authors: Zebin Yang, Aijun Zhang
Date: 2020-09-08 00:00:00
Tasks: AutoML, global-optimization, Hyperparameter Optimization

Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML). It is a challenging task as the response surfaces of hyperparameters are generally unknown, hence essentially a global optimization problem. This paper reformulates HPO as a computer experiment and proposes a novel sequential uniform design (SeqUD) strategy with three-fold advantages: a) the hyperparameter space is adaptively explored with evenly spread design points, without the need of expensive meta-modeling and acquisition optimization; b) the batch-by-batch design points are sequentially generated with parallel processing support; c) a new augmented uniform design algorithm is developed for the efficient real-time generation of follow-up design points. Extensive experiments are conducted on both global optimization tasks and HPO applications. The numerical results show that the proposed SeqUD strategy outperforms benchmark HPO methods, and it can be therefore a promising and competitive alternative to existing AutoML tools.
Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning
Authors: Manuel Camargo, Marlon Dumas, Oscar Gonzalez-Rojas
Date: 2020-09-08 00:00:00
Tasks: Deep Learning

A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation technique with multiple deep learning techniques, which construct models are capable of generating execution traces with timestamped events. The study sheds light into the relative strengths of both approaches and raises the prospect of developing hybrid approaches that combine these strengths.