pwc-archive/papers-with-abstracts huggingface dataset:
A Neural Architecture for Person Ontology population
Authors: Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh, Hima Patel, Berthold Reinwald
Date: 2020-01-22 00:00:00
Tasks: Classification, General Classification, Knowledge Graphs, Relation, Relation Extraction

A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention. While artificial neural networks have led to improvements in Entity Recognition, Entity Classification, and Relation Extraction, creating an ontology largely remains a manual process, because it requires a fixed set of semantic relations between concepts. In this work, we present a system for automatically populating a person ontology graph from unstructured data using neural models for Entity Classification and Relation Extraction. We introduce a new dataset for these tasks and discuss our results.
ARAACOM: ARAbic Algerian Corpus for Opinion Mining
Authors: Zitouni Abdelhafid, Hichem Rahab, Abdelhafid Zitouni, Mahieddine Djoudi
Date: 2020-01-22 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Nowadays, it is no more needed to do an enormous effort to distribute a lot of forms to thousands of people and collect them, then convert this from into electronic format to track people opinion about some subjects. A lot of web sites can today reach a large spectrum with less effort. The majority of web sites suggest to their visitors to leave backups about their feeling of the site or events. So, this makes for us a lot of data which need powerful mean to exploit. Opinion mining in the web becomes more and more an attracting task, due the increasing need for individuals and societies to track the mood of people against several subjects of daily life (sports, politics, television,...). A lot of works in opinion mining was developed in western languages especially English, such works in Arabic language still very scarce. In this paper, we propose our approach, for opinion mining in Arabic Algerian news paper. CCS CONCEPTS $\bullet$Information systems~Sentiment analysis $\bullet$ Computing methodologies~Natural language processing
Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks
Authors: Oliver Willers, Sebastian Sudholt, Shervin Raafatnia, Stephanie Abrecht
Date: 2020-01-22 00:00:00
Tasks: Deep Learning

Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for autonomous agents at large scale already are safety concerns. Deep learning approaches typically exhibit a black-box behavior which makes it hard for them to be evaluated with respect to safety-critical aspects. While there have been some work on safety in deep learning, most papers typically focus on high-level safety concerns. In this work, we seek to dive into the safety concerns of deep learning methods and present a concise enumeration on a deeply technical level. Additionally, we present extensive discussions on possible mitigation methods and give an outlook regarding what mitigation methods are still missing in order to facilitate an argumentation for the safety of a deep learning method.
On Solving Cooperative MARL Problems with a Few Good Experiences
Authors: Rajiv Ranjan Kumar, Pradeep Varakantham
Date: 2020-01-22 00:00:00
Tasks: Descriptive, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Cooperative Multi-agent Reinforcement Learning (MARL) is crucial for cooperative decentralized decision learning in many domains such as search and rescue, drone surveillance, package delivery and fire fighting problems. In these domains, a key challenge is learning with a few good experiences, i.e., positive reinforcements are obtained only in a few situations (e.g., on extinguishing a fire or tracking a crime or delivering a package) and in most other situations there is zero or negative reinforcement. Learning decisions with a few good experiences is extremely challenging in cooperative MARL problems due to three reasons. First, compared to the single agent case, exploration is harder as multiple agents have to be coordinated to receive a good experience. Second, environment is not stationary as all the agents are learning at the same time (and hence change policies). Third, scale of problem increases significantly with every additional agent. Relevant existing work is extensive and has focussed on dealing with a few good experiences in single-agent RL problems or on scalable approaches for handling non-stationarity in MARL problems. Unfortunately, neither of these approaches (or their extensions) are able to address the problem of sparse good experiences effectively. Therefore, we provide a novel fictitious self imitation approach that is able to simultaneously handle non-stationarity and sparse good experiences in a scalable manner. Finally, we provide a thorough comparison (experimental or descriptive) against relevant cooperative MARL algorithms to demonstrate the utility of our approach.
Emotion and Sentiment Lexicon Impact on Sentiment Analysis Applied to Book Reviews
Authors: Patrice Bellot, Lerch Soëlie, Bruno Emmanuel, Murisasco Elisabeth
Date: 2020-01-22 00:00:00
Tasks: Sentiment Analysis

Consumers are used to consulting posted reviews on the Internet before buying a product. But it's difficult to know the global opinion considering the important number of those reviews. Sentiment analysis afford detecting polarity (positive, negative, neutral) in a expressed opinion and therefore classifying those reviews. Our purpose is to determine the influence of emotions on the polarity of books reviews. We define "bag-of-words" representation models of reviews which use a lexicon containing emotional (anticipation, sadness, fear, anger, joy, surprise, trust, disgust) and sentimental (positive, negative) words. This lexicon afford measuring felt emotions types by readers. The implemented supervised learning used is a Random Forest type. The application concerns Amazon platform's reviews. Mots-cl{\'e}s : Analyse de sentiments, Analyse d'{\'e}motions (texte), Classification de polarit{\'e} de sentiments
Machine Learning for Network Slicing Resource Management: A Comprehensive Survey
Authors: Bin Han, Hans D. Schotten
Date: 2020-01-22 00:00:00
Tasks: BIG-bench Machine Learning, Management, Survey

The emerging technology of multi-tenancy network slicing is considered as an essential feature of 5G cellular networks. It provides network slices as a new type of public cloud services, and therewith increases the service flexibility and enhances the network resource efficiency. Meanwhile, it raises new challenges of network resource management. A number of various methods have been proposed over the recent past years, in which machine learning and artificial intelligence techniques are widely deployed. In this article, we provide a survey to existing approaches of network slicing resource management, with a highlight on the roles played by machine learning in them.
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning
Authors: Ameya Pore, Gerardo Aragon-Camarasa
Date: 2020-01-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a behaviour-based reinforcement learning approach, inspired by Brook's subsumption architecture, in which simple fully connected networks are trained as reactive behaviours. Our working assumption is that a pick and place robotic task can be simplified by leveraging domain knowledge of a robotics developer to decompose and train such reactive behaviours; namely, approach, grasp, and retract. Then the robot autonomously learns how to combine them via an Actor-Critic architecture. The Actor-Critic policy is to determine the activation and inhibition mechanisms of the reactive behaviours in a particular temporal sequence. We validate our approach in a simulated robot environment where the task is picking a block and taking it to a target position while orienting the gripper from a top grasp. The latter represents an extra degree-of-freedom of which current end-to-end reinforcement learning fail to generalise. Our findings suggest that robotic learning can be more effective if each behaviour is learnt in isolation and then combined them to accomplish the task. That is, our approach learns the pick and place task in 8,000 episodes, which represents a drastic reduction in the number of training episodes required by an end-to-end approach and the existing state-of-the-art algorithms.
ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data
Authors: Di Qi, Lin Su, Jia Song, Edward Cui, Taroon Bharti, Arun Sacheti
Date: 2020-01-22 00:00:00
Tasks: Image Retrieval, Image-text matching, Language Modeling, Language Modelling, Masked Language Modeling, Retrieval, Text Matching, Text Retrieval, Zero-Shot Cross-Modal Retrieval

In this paper, we introduce a new vision-language pre-trained model -- ImageBERT -- for image-text joint embedding. Our model is a Transformer-based model, which takes different modalities as input and models the relationship between them. The model is pre-trained on four tasks simultaneously: Masked Language Modeling (MLM), Masked Object Classification (MOC), Masked Region Feature Regression (MRFR), and Image Text Matching (ITM). To further enhance the pre-training quality, we have collected a Large-scale weAk-supervised Image-Text (LAIT) dataset from Web. We first pre-train the model on this dataset, then conduct a second stage pre-training on Conceptual Captions and SBU Captions. Our experiments show that multi-stage pre-training strategy outperforms single-stage pre-training. We also fine-tune and evaluate our pre-trained ImageBERT model on image retrieval and text retrieval tasks, and achieve new state-of-the-art results on both MSCOCO and Flickr30k datasets.
A Fixation-based 360° Benchmark Dataset for Salient Object Detection
Authors: Yi Zhang, Lu Zhang, Wassim Hamidouche, Olivier Deforges
Date: 2020-01-22 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Fixation prediction (FP) in panoramic contents has been widely investigated along with the booming trend of virtual reality (VR) applications. However, another issue within the field of visual saliency, salient object detection (SOD), has been seldom explored in 360{\deg} (or omnidirectional) images due to the lack of datasets representative of real scenes with pixel-level annotations. Toward this end, we collect 107 equirectangular panoramas with challenging scenes and multiple object classes. Based on the consistency between FP and explicit saliency judgements, we further manually annotate 1,165 salient objects over the collected images with precise masks under the guidance of real human eye fixation maps. Six state-of-the-art SOD models are then benchmarked on the proposed fixation-based 360{\deg} image dataset (F-360iSOD), by applying a multiple cubic projection-based fine-tuning method. Experimental results show a limitation of the current methods when used for SOD in panoramic images, which indicates the proposed dataset is challenging. Key issues for 360{\deg} SOD is also discussed. The proposed dataset is available at https://github.com/PanoAsh/F-360iSOD.
Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions
Authors: Karsten Schweikert
Date: 2020-01-22 00:00:00
Tasks: Model Selection

In this paper, we propose an adaptive group lasso procedure to efficiently estimate structural breaks in cointegrating regressions. It is well-known that the group lasso estimator is not simultaneously estimation consistent and model selection consistent in structural break settings. Hence, we use a first step group lasso estimation of a diverging number of breakpoint candidates to produce weights for a second adaptive group lasso estimation. We prove that parameter changes are estimated consistently by group lasso and show that the number of estimated breaks is greater than the true number but still sufficiently close to it. Then, we use these results and prove that the adaptive group lasso has oracle properties if weights are obtained from our first step estimation. Simulation results show that the proposed estimator delivers the expected results. An economic application to the long-run US money demand function demonstrates the practical importance of this methodology.
Machine Learning assisted Handover and Resource Management for Cellular Connected Drones
Authors: Amin Azari, Fayezeh Ghavimi, Mustafa Ozger, Riku Jantti, Cicek Cavdar
Date: 2020-01-22 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Management, Reinforcement Learning

Enabling cellular connectivity for drones introduces a wide set of challenges and opportunities. Communication of cellular-connected drones is influenced by 3-dimensional mobility and line-of-sight channel characteristics which results in higher number of handovers with increasing altitude. Our cell planning simulations in coexistence of aerial and terrestrial users indicate that the severe interference from drones to base stations is a major challenge for uplink communications of terrestrial users. Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm. Then, we derive analytical models for the key performance indicators (KPIs), including communications delay and interference over cellular networks, and formulate the handover and radio resource management (H-RRM) optimization problem. Afterwards, we transform this problem into a machine learning problem, and propose a deep reinforcement learning solution to solve H-RRM problem. Finally, using simulation results, we present how the speed and altitude of drones, and the tolerable level of interference, shape the optimal H-RRM policy in the network. Especially, the heat-maps of handover decisions in different drone's altitudes/speeds have been presented, which promote a revision of the legacy handover schemes and redefining the boundaries of cells in the sky.
CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking
Authors: Grigori Fursin, Herve Guillou, Nicolas Essayan
Date: 2020-01-22 00:00:00
Tasks: Benchmarking, object-detection, Object Detection

We present CodeReef - an open platform to share all the components necessary to enable cross-platform MLOps (MLSysOps), i.e. automating the deployment of ML models across diverse systems in the most efficient way. We also introduce the CodeReef solution - a way to package and share models as non-virtualized, portable, customizable and reproducible archive files. Such ML packages include JSON meta description of models with all dependencies, Python APIs, CLI actions and portable workflows necessary to automatically build, benchmark, test and customize models across diverse platforms, AI frameworks, libraries, compilers and datasets. We demonstrate several CodeReef solutions to automatically build, run and measure object detection based on SSD-Mobilenets, TensorFlow and COCO dataset from the latest MLPerf inference benchmark across a wide range of platforms from Raspberry Pi, Android phones and IoT devices to data centers. Our long-term goal is to help researchers share their new techniques as production-ready packages along with research papers to participate in collaborative and reproducible benchmarking, compare the different ML/software/hardware stacks and select the most efficient ones on a Pareto frontier using online CodeReef dashboards.
Adversarial Attack on Community Detection by Hiding Individuals
Authors: Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng, Junzhou Huang
Date: 2020-01-22 00:00:00
Tasks: Adversarial Attack, Community Detection, Graph Classification

It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.
Optimized Generic Feature Learning for Few-shot Classification across Domains
Authors: Tonmoy Saikia, Thomas Brox, Cordelia Schmid
Date: 2020-01-22 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Few-Shot Image Classification, General Classification, image-classification, Image Classification, Meta-Learning

To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning. In this paper, we propose to use cross-domain, cross-task data as validation objective for hyper-parameter optimization (HPO) to improve on this goal. Given a rich enough search space, optimization of hyper-parameters learn features that maximize validation performance and, due to the objective, generalize across tasks and domains. We demonstrate the effectiveness of this strategy on few-shot image classification within and across domains. The learned features outperform all previous few-shot and meta-learning approaches.
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification
Authors: Jiawei Zhang
Date: 2020-01-22 00:00:00
Tasks: General Classification, Graph Learning, Graph Neural Network, Graph Representation Learning, Node Classification, Representation Learning

Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep. Meanwhile, for some graph learning scenarios, e.g., nodes with text/image attributes or graphs with long-distance node correlations, deep graph neural networks will be necessary for effective graph representation learning. In this paper, we propose a new graph neural network, namely DIFNET (Graph Diffusive Neural Network), for graph representation learning and node classification. DIFNET utilizes both neural gates and graph residual learning for node hidden state modeling, and includes an attention mechanism for node neighborhood information diffusion. Extensive experiments will be done in this paper to compare DIFNET against several state-of-the-art graph neural network models. The experimental results can illustrate both the learning performance advantages and effectiveness of DIFNET, especially in addressing the "suspended animation problem".
Journalists' information needs, seeking behavior, and its determinants on social media
Authors: Omid Aghili, Mark Sanderson
Date: 2017-05-24 00:00:00

We describe the results of a qualitative study on journalists' information seeking behavior on social media. Based on interviews with eleven journalists along with a study of a set of university level journalism modules, we determined the categories of information need types that lead journalists to social media. We also determined the ways that social media is exploited as a tool to satisfy information needs and to define influential factors, which impacted on journalists' information seeking behavior. We find that not only is social media used as an information source, but it can also be a supplier of stories found serendipitously. We find seven information need types that expand the types found in previous work. We also find five categories of influential factors that affect the way journalists seek information.
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication
Authors: Hamza Khan, Anis Elgabli, Sumudu Samarakoon, Mehdi Bennis, Choong Seon Hong
Date: 2020-01-22 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases. This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. The aim is to maximize the time average rate per vehicular user (VUE) while ensuring a target minimum rate for all VUEs with low signaling overhead. We first formulate the user (vehicle) association problem as a discrete non-convex optimization problem. Then, by leveraging tools from machine learning, specifically distributed deep reinforcement learning (DDRL) and the asynchronous actor critic algorithm (A3C), we propose a low complexity algorithm that approximates the solution of the proposed optimization problem. The proposed DDRL-based algorithm endows every road side unit (RSU) with a local RL agent that selects a local action based on the observed input state. Actions of different RSUs are forwarded to a central entity, that computes a global reward which is then fed back to RSUs. It is shown that each independently trained RL performs the vehicle-RSU association action with low control overhead and less computational complexity compared to running an online complex algorithm to solve the non-convex optimization problem. Finally, simulation results show that the proposed solution achieves up to 15\% gains in terms of sum rate and 20\% reduction in VUE outages compared to several baseline designs.
From abstract items to latent spaces to observed data and back: Compositional Variational Auto-Encoder
Authors: Victor Berger, Michèle Sebag
Date: 2020-01-22 00:00:00

Conditional Generative Models are now acknowledged an essential tool in Machine Learning. This paper focuses on their control. While many approaches aim at disentangling the data through the coordinate-wise control of their latent representations, another direction is explored in this paper. The proposed CompVAE handles data with a natural multi-ensemblist structure (i.e. that can naturally be decomposed into elements). Derived from Bayesian variational principles, CompVAE learns a latent representation leveraging both observational and symbolic information. A first contribution of the approach is that this latent representation supports a compositional generative model, amenable to multi-ensemblist operations (addition or subtraction of elements in the composition). This compositional ability is enabled by the invariance and generality of the whole framework w.r.t. respectively, the order and number of the elements. The second contribution of the paper is a proof of concept on synthetic 1D and 2D problems, demonstrating the efficiency of the proposed approach.