pwc-archive/papers-with-abstracts huggingface dataset:
A Panoramic Survey of Natural Language Processing in the Arab World
Authors: Kareem Darwish, Nizar Habash, Mourad Abbas, Hend Al-Khalifa, Huseein T. Al-Natsheh, Samhaa R. El-Beltagy, Houda Bouamor, Karim Bouzoubaa, Violetta Cavalli-Sforza, Wassim El-Hajj, Mustafa Jarrar, Hamdy Mubarak
Date: 2020-11-25 00:00:00
Tasks: Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), Question Answering, Sentiment Analysis, speech-recognition, Speech Recognition, Translation

The term natural language refers to any system of symbolic communication (spoken, signed or written) without intentional human planning and design. This distinguishes natural languages such as Arabic and Japanese from artificially constructed languages such as Esperanto or Python. Natural language processing (NLP) is the sub-field of artificial intelligence (AI) focused on modeling natural languages to build applications such as speech recognition and synthesis, machine translation, optical character recognition (OCR), sentiment analysis (SA), question answering, dialogue systems, etc. NLP is a highly interdisciplinary field with connections to computer science, linguistics, cognitive science, psychology, mathematics and others. Some of the earliest AI applications were in NLP (e.g., machine translation); and the last decade (2010-2020) in particular has witnessed an incredible increase in quality, matched with a rise in public awareness, use, and expectations of what may have seemed like science fiction in the past. NLP researchers pride themselves on developing language independent models and tools that can be applied to all human languages, e.g. machine translation systems can be built for a variety of languages using the same basic mechanisms and models. However, the reality is that some languages do get more attention (e.g., English and Chinese) than others (e.g., Hindi and Swahili). Arabic, the primary language of the Arab world and the religious language of millions of non-Arab Muslims is somewhere in the middle of this continuum. Though Arabic NLP has many challenges, it has seen many successes and developments. Next we discuss Arabic's main challenges as a necessary background, and we present a brief history of Arabic NLP. We then survey a number of its research areas, and close with a critical discussion of the future of Arabic NLP.
An Integration of UTAUT and Task-Technology Fit Frameworks for Assessing the Acceptance of Clinical Decision Support Systems in the Context of a Developing Country
Authors: Soliman Aljarboa, Shah J. Miah
Date: 2020-11-25 00:00:00

This paper is to create a basis of theoretical contribution for a new PhD thesis in the area of Clinical Decision Support Systems (CDSS) acceptance. Over the past three years, we conducted qualitative research into three distinctive phases to develop an extended Task-Technology Fit (TTF) Framework. These phases are for initiating requirement generation of the framework, discovering the factors of the framework through perspectives, and evaluating the new proposed framework. The new condition is related to developing country in which various sectors such as healthcare is mostly under attention. We conduct a new inspective for assisting decisions to support technology and its usefulness in this sector to integrate with other frameworks for assisting the value, use, and how can be better accepted in the context of healthcare professionals.
Recent Progress in Appearance-based Action Recognition
Authors: Jack Humphreys, Zhe Chen, DaCheng Tao
Date: 2020-11-25 00:00:00
Tasks: Action Recognition

Action recognition, which is formulated as a task to identify various human actions in a video, has attracted increasing interest from computer vision researchers due to its importance in various applications. Recently, appearance-based methods have achieved promising progress towards accurate action recognition. In general, these methods mainly fulfill the task by applying various schemes to model spatial and temporal visual information effectively. To better understand the current progress of appearance-based action recognition, we provide a comprehensive review of recent achievements in this area. In particular, we summarise and discuss several dozens of related research papers, which can be roughly divided into four categories according to different appearance modelling strategies. The obtained categories include 2D convolutional methods, 3D convolutional methods, motion representation-based methods, and context representation-based methods. We analyse and discuss representative methods from each category, comprehensively. Empirical results are also summarised to better illustrate cutting-edge algorithms. We conclude by identifying important areas for future research gleaned from our categorisation.
StackMix: A complementary Mix algorithm
Authors: John Chen, Samarth Sinha, Anastasios Kyrillidis
Date: 2020-11-25 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Image Classification

Techniques combining multiple images as input/output have proven to be effective data augmentations for training convolutional neural networks. In this paper, we present StackMix: Each input is presented as a concatenation of two images, and the label is the mean of the two one-hot labels. On its own, StackMix rivals other widely used methods in the "Mix" line of work. More importantly, unlike previous work, significant gains across a variety of benchmarks are achieved by combining StackMix with existing Mix augmentation, effectively mixing more than two images. E.g., by combining StackMix with CutMix, test error in the supervised setting is improved across a variety of settings over CutMix, including 0.8\% on ImageNet, 3\% on Tiny ImageNet, 2\% on CIFAR-100, 0.5\% on CIFAR-10, and 1.5\% on STL-10. Similar results are achieved with Mixup.We further show that gains hold for robustness to common input corruptions and perturbations at varying severities with a 0.7\% improvement on CIFAR-100-C, by combining StackMix with AugMix over AugMix. On its own, improvements with StackMix hold across different number of labeled samples on CIFAR-100, maintaining approximately a 2\% gap in test accuracy -- down to using only 5\% of the whole dataset -- and is effective in the semi-supervised setting with a 2\% improvement with the standard benchmark $\Pi$-model. Finally, we perform an extensive ablation study to better understand the proposed algorithm.
Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings
Authors: Marco Toldo, Umberto Michieli, Pietro Zanuttigh
Date: 2020-11-25 00:00:00
Tasks: Clustering, Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones. In this paper we propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution and groups features of the same class into tight and well-separated clusters. Furthermore, we introduce two novel learning objectives to enhance the discriminative clustering performance: an orthogonality loss forces spaced out individual representations to be orthogonal, while a sparsity loss reduces class-wise the number of active feature channels. The joint effect of these modules is to regularize the structure of the feature space. Extensive evaluations in the synthetic-to-real scenario show that we achieve state-of-the-art performance.
Automatic Identification of MHD Modes in Magnetic Fluctuations Spectrograms using Deep Learning Techniques
Authors: A. Bustos, E. Ascasibar, A. Cappa, R. Mayo-Garcia
Date: 2020-11-25 00:00:00

The control and mitigation of MHD oscillations modes is an open problem in fusion science because they can contribute to the outward particle/energy flux and can drive the device away from ignition conditions. It is then of general interest to extract the mode information from large experimental databases in a fast and reliable way. We present a software tool based on Deep Learning that can identify these oscillations modes taking Mirnov coil spectrograms as input data. It uses Convolutional Neural Networks that we trained with manually annotated spectrograms from the TJ-II stellarator database. We have tested several detector architectures, resultingin a detector AUC score of 0.99 on the test set. Finally, it is applied to find MHD modes in our spectrograms to show how this new software tool can be used to mine other databases.
Rank-One Network: An Effective Framework for Image Restoration
Authors: Shangqi Gao, Xiahai Zhuang
Date: 2020-11-25 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising, Image Restoration, Image Super-Resolution, Super-Resolution

The principal rank-one (RO) components of an image represent the self-similarity of the image, which is an important property for image restoration. However, the RO components of a corrupted image could be decimated by the procedure of image denoising. We suggest that the RO property should be utilized and the decimation should be avoided in image restoration. To achieve this, we propose a new framework comprised of two modules, i.e., the RO decomposition and RO reconstruction. The RO decomposition is developed to decompose a corrupted image into the RO components and residual. This is achieved by successively applying RO projections to the image or its residuals to extract the RO components. The RO projections, based on neural networks, extract the closest RO component of an image. The RO reconstruction is aimed to reconstruct the important information, respectively from the RO components and residual, as well as to restore the image from this reconstructed information. Experimental results on four tasks, i.e., noise-free image super-resolution (SR), realistic image SR, gray-scale image denoising, and color image denoising, show that the method is effective and efficient for image restoration, and it delivers superior performance for realistic image SR and color image denoising.
The Landscape of Ontology Reuse Approaches
Authors: Valentina Anita Carriero, Marilena Daquino, Aldo Gangemi, Andrea Giovanni Nuzzolese, Silvio Peroni, Valentina Presutti, Francesca Tomasi
Date: 2020-11-25 00:00:00
Tasks: Decision Making

Ontology reuse aims to foster interoperability and facilitate knowledge reuse. Several approaches are typically evaluated by ontology engineers when bootstrapping a new project. However, current practices are often motivated by subjective, case-by-case decisions, which hamper the definition of a recommended behaviour. In this chapter we argue that to date there are no effective solutions for supporting developers' decision-making process when deciding on an ontology reuse strategy. The objective is twofold: (i) to survey current approaches to ontology reuse, presenting motivations, strategies, benefits and limits, and (ii) to analyse two representative approaches and discuss their merits.
Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques
Authors: Devinder Kaur, Shama Naz Islam, Md. Apel Mahmud, Md. Enamul Haque, ZhaoYang Dong
Date: 2020-11-25 00:00:00
Tasks: Management, Probabilistic Deep Learning

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE).
MTCRNN: A multi-scale RNN for directed audio texture synthesis
Authors: M. Huzaifah, L. Wyse
Date: 2020-11-25 00:00:00
Tasks: Texture Synthesis

Audio textures are a subset of environmental sounds, often defined as having stable statistical characteristics within an adequately large window of time but may be unstructured locally. They include common everyday sounds such as from rain, wind, and engines. Given that these complex sounds contain patterns on multiple timescales, they are a challenge to model with traditional methods. We introduce a novel modelling approach for textures, combining recurrent neural networks trained at different levels of abstraction with a conditioning strategy that allows for user-directed synthesis. We demonstrate the model's performance on a variety of datasets, examine its performance on various metrics, and discuss some potential applications.
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
Authors: Jaskirat Singh, Liang Zheng
Date: 2020-11-25 00:00:00
Proceeding: CVPR 2021 1
Tasks: Deep Reinforcement Learning, Model-based Reinforcement Learning, Object, Position, reinforcement-learning, Reinforcement Learning (RL)

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds and Stanford Cars-196 datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI dataset. Source code and models are available at https://github.com/1jsingh/semantic-guidance.
RRCN: A Reinforced Random Convolutional Network based Reciprocal Recommendation Approach for Online Dating
Authors: Linhao Luo, Liqi Yang, Ju Xin, Yixiang Fang, Xiaofeng Zhang, Xiaofei Yang, Kai Chen, Zhiyuan Zhang, Kai Liu
Date: 2020-11-25 00:00:00

Recently, the reciprocal recommendation, especially for online dating applications, has attracted more and more research attention. Different from conventional recommendation problems, the reciprocal recommendation aims to simultaneously best match users' mutual preferences. Intuitively, the mutual preferences might be affected by a few key attributes that users like or dislike. Meanwhile, the interactions between users' attributes and their key attributes are also important for key attributes selection. Motivated by these observations, in this paper we propose a novel reinforced random convolutional network (RRCN) approach for the reciprocal recommendation task. In particular, we technically propose a novel random CNN component that can randomly convolute non-adjacent features to capture their interaction information and learn feature embeddings of key attributes to make the final recommendation. Moreover, we design a reinforcement learning based strategy to integrate with the random CNN component to select salient attributes to form the candidate set of key attributes. We evaluate the proposed RRCN against a number of both baselines and the state-of-the-art approaches on two real-world datasets, and the promising results have demonstrated the superiority of RRCN against the compared approaches in terms of a number of evaluation criteria.
Supervised Learning Achieves Human-Level Performance in MOBA Games: A Case Study of Honor of Kings
Authors: Deheng Ye, Guibin Chen, Peilin Zhao, Fuhao Qiu, Bo Yuan, Wen Zhang, Sheng Chen, Mingfei Sun, Xiaoqian Li, Siqin Li, Jing Liang, Zhenjie Lian, Bei Shi, Liang Wang, Tengfei Shi, Qiang Fu, Wei Yang, Lanxiao Huang
Date: 2020-11-25 00:00:00

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization
Authors: Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh
Date: 2020-11-25 00:00:00
Tasks: Continual Learning

This paper studies a new design of the optimization algorithm for training deep learning models with a fixed architecture of the classification network in a continual learning framework. The training data is non-stationary and the non-stationarity is imposed by a sequence of distinct tasks. We first analyze a deep model trained on only one learning task in isolation and identify a region in network parameter space, where the model performance is close to the recovered optimum. We provide empirical evidence that this region resembles a cone that expands along the convergence direction. We study the principal directions of the trajectory of the optimizer after convergence and show that traveling along a few top principal directions can quickly bring the parameters outside the cone but this is not the case for the remaining directions. We argue that catastrophic forgetting in a continual learning setting can be alleviated when the parameters are constrained to stay within the intersection of the plausible cones of individual tasks that were so far encountered during training. Based on this observation we present our direction-constrained optimization (DCO) method, where for each task we introduce a linear autoencoder to approximate its corresponding top forbidden principal directions. They are then incorporated into the loss function in the form of a regularization term for the purpose of learning the coming tasks without forgetting. Furthermore, in order to control the memory growth as the number of tasks increases, we propose a memory-efficient version of our algorithm called compressed DCO (DCO-COMP) that allocates a memory of fixed size for storing all autoencoders. We empirically demonstrate that our algorithm performs favorably compared to other state-of-art regularization-based continual learning methods.
Enhanced Scene Specificity with Sparse Dynamic Value Estimation
Authors: Jaskirat Singh, Liang Zheng
Date: 2020-11-25 00:00:00
Tasks: Specificity

Multi-scene reinforcement learning involves training the RL agent across multiple scenes / levels from the same task, and has become essential for many generalization applications. However, the inclusion of multiple scenes leads to an increase in sample variance for policy gradient computations, often resulting in suboptimal performance with the direct application of traditional methods (e.g. PPO, A3C). One strategy for variance reduction is to consider each scene as a distinct Markov decision process (MDP) and learn a joint value function dependent on both state (s) and MDP (M). However, this is non-trivial as the agent is usually unaware of the underlying level at train / test times in multi-scene RL. Recently, Singh et al. [1] tried to address this by proposing a dynamic value estimation approach that models the true joint value function distribution as a Gaussian mixture model (GMM). In this paper, we argue that the error between the true scene-specific value function and the predicted dynamic estimate can be further reduced by progressively enforcing sparse cluster assignments once the agent has explored most of the state space. The resulting agents not only show significant improvements in the final reward score across a range of OpenAI ProcGen environments, but also exhibit increased navigation efficiency while completing a game level.
ColdGAN: Resolving Cold Start User Recommendation by using Generative Adversarial Networks
Authors: Po-Lin Lai, Chih-Yun Chen, Liang-Wei Lo, Chien-Chin Chen
Date: 2020-11-25 00:00:00
Tasks: Decision Making

Mitigating the new user cold-start problem has been critical in the recommendation system for online service providers to influence user experience in decision making which can ultimately affect the intention of users to use a particular service. Previous studies leveraged various side information from users and items; however, it may be impractical due to privacy concerns. In this paper, we present ColdGAN, an end-to-end GAN based model with no use of side information to resolve this problem. The main idea of the proposed model is to train a network that learns the rating distributions of experienced users given their cold-start distributions. We further design a time-based function to restore the preferences of users to cold-start states. With extensive experiments on two real-world datasets, the results show that our proposed method achieves significantly improved performance compared with the state-of-the-art recommenders.