OpenRaiser/Intern-Atlas huggingface dataset:
The Adoption of ICT Powered Healthcare Technologies towards Managing Global Pandemics
Venue: arXiv cs.CY 2020
Year: 2020

Pandemic is an outbreak that happens over a large geographic area affecting a greater portion of the population as new pathogens appear for which people have less immune and no vaccines are available. It can spread from person to person in a very short time, and in fact, the health workers are at greater risk of infection because of the patients who carry the disease. In the 21st century, where everyone is connected through digital technologies, Information and Communication Technology (ICT) plays a critical role in improving health care for individuals and larger communities. ICT has currently been severed in a variety of application domains which signifies its importance as a major technological paradigm, and it has drawn higher attention for its potential to alleviate the burden on healthcare systems caused by a rise in chronic diseases, aging and increased population and pandemic situations. This paper surveys and offers substantial knowledge about how effective ICT Healthcare strategy can be used to manage global pandemics by presenting a four-phased framework, which can be deployed to alleviate the strain on healthcare during a pandemic. In addition, we discuss how ICT powered technologies can be used towards managing a pandemic during the transformation of simple disease outbreak into a global pandemic.
Syntax Role for Neural Semantic Role Labeling
Venue: arXiv cs.CL 2020
Year: 2020

Semantic role labeling (SRL) is dedicated to recognizing the semantic predicate-argument structure of a sentence. Previous studies in terms of traditional models have shown syntactic information can make remarkable contributions to SRL performance; however, the necessity of syntactic information was challenged by a few recent neural SRL studies that demonstrate impressive performance without syntactic backbones and suggest that syntax information becomes much less important for neural semantic role labeling, especially when paired with recent deep neural network and large-scale pre-trained language models. Despite this notion, the neural SRL field still lacks a systematic and full investigation on the relevance of syntactic information in SRL, for both dependency and both monolingual and multilingual settings. This paper intends to quantify the importance of syntactic information for neural SRL in the deep learning framework. We introduce three typical SRL frameworks (baselines), sequence-based, tree-based, and graph-based, which are accompanied by two categories of exploiting syntactic information: syntax pruning-based and syntax feature-based. Experiments are conducted on the CoNLL-2005, 2009, and 2012 benchmarks for all languages available, and results show that neural SRL models can still benefit from syntactic information under certain conditions. Furthermore, we show the quantitative significance of syntax to neural SRL models together with a thorough empirical survey using existing models.
Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering
Venue: arXiv cs.CV 2020
Year: 2020

Clustering techniques attempt to group objects with similar properties into a cluster. Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention. Graph convolutional networks (GCNs) represent an effective approach for integrating the two complementary factors of node attributes and structural information for attributed graph clustering. However, oversmoothing of GCNs produces indistinguishable representations of nodes, such that the nodes in a graph tend to be grouped into fewer clusters, and poses a challenge due to the resulting performance drop. In this study, we propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions, which senses the smoothness of a graph and adaptively terminates the current convolution once the smoothness is saturated to prevent oversmoothing. Furthermore, as an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed, in which smoothness is computed in accordance with the neighborhood conditions of a given node at a certain order of graph convolution. In addition, a self-supervision criterion is designed considering both the tightness within clusters and the separation between clusters to guide the whole neural network training process. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics across four benchmark datasets. In addition, an extensive study reveals the reasons for their effectiveness and efficiency.
Terminating cases of flooding
Venue: arXiv cs.DC 2020
Year: 2020

Basic synchronous flooding proceeds in rounds. Given a finite undirected (network) graph $G$, a set of sources $I \subseteq G$ initiate flooding in the first round by every node in $I$ sending the same message to all of its neighbours. In each subsequent round, nodes send the message to all of their neighbours from which they did not receive the message in the previous round. Flooding terminates when no node in $G$ sends a message in a round. The question of termination has not been settled - rather, non-termination is implicitly assumed to be possible. We show that flooding terminates on every finite graph. In the case of a single source $g_0$, flooding terminates in $e$ rounds if $G$ is bipartite and $j$ rounds with $e < j \leq e+d+1$ otherwise, where $e$ and $d$ are the eccentricity of $g_0$ and diameter of $G$ respectively. For communication/broadcast to all nodes, this is asymptotically time optimal and obviates the need for construction and maintenance of spanning structures. We extend to dynamic flooding initiated in multiple rounds with possibly multiple messages. The cases where a node only sends a message to neighbours from which it did not receive {\it any} message in the previous round, and where a node sends some highest ranked message to all neighbours from which it did not receive {\it that} message in the previous round, both terminate. All these cases also hold if the network graph loses edges over time. Non-terminating cases include asynchronous flooding, flooding where messages have fixed delays at edges, cases of multiple-message flooding and cases where the network graph acquires edges over time.
COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalised medicine
Venue: arXiv cs.CY 2020
Year: 2020

A comprehensive bibliographic review with R statistical methods of the COVID pandemic in PubMed literature and Web of Science Core Collection, supported with Google Scholar search. In addition, a case study review of emerging new approaches in different regions, using medical literature, academic literature, news articles and other reliable data sources. Public responses of mistrust about privacy data misuse differ across countries, depending on the chosen public communication strategy.
Learning Daily Calorie Intake Standard using a Mobile Game
Venue: arXiv cs.HC 2020
Year: 2020

Mobile games can contribute to learning at greater success. In this paper, we have developed and evaluated a novel educational game, named FoodCalorie, to learn the food calorie intake standards. Our game is aimed to learn the calorie values of various traditional Bangladeshi foods and the calorie intake standard that varies with age and gender. Our study confirms the finding of existing studies that game-based learning can enhance the learning experience.
Country Image in COVID-19 Pandemic: A Case Study of China
Venue: arXiv cs.CY 2020
Year: 2020

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This study provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.
Guided Policy Search Based Control of a High Dimensional Advanced Manufacturing Process
Venue: arXiv cs.LG 2020
Year: 2020

In this paper we apply guided policy search (GPS) based reinforcement learning framework for a high dimensional optimal control problem arising in an additive manufacturing process. The problem comprises of controlling the process parameters so that layer-wise deposition of material leads to desired geometric characteristics of the resulting part surface while minimizing the material deposited. A realistic simulation model of the deposition process along with carefully selected set of guiding distributions generated based on iterative Linear Quadratic Regulator is used to train a neural network policy using GPS. A closed loop control based on the trained policy and in-situ measurement of the deposition profile is tested experimentally, and shows promising performance.
Memory-Efficient Fixpoint Computation
Venue: arXiv cs.PL 2020
Year: 2020

Practical adoption of static analysis often requires trading precision for performance. This paper focuses on improving the memory efficiency of abstract interpretation without sacrificing precision or time efficiency. Computationally, abstract interpretation reduces the problem of inferring program invariants to computing a fixpoint of a set of equations. This paper presents a method to minimize the memory footprint in Bourdoncle's iteration strategy, a widely-used technique for fixpoint computation. Our technique is agnostic to the abstract domain used. We prove that our technique is optimal (i.e., it results in minimum memory footprint) for Bourdoncle's iteration strategy while computing the same result. We evaluate the efficacy of our technique by implementing it in a tool called MIKOS, which extends the state-of-the-art abstract interpreter IKOS. When verifying user-provided assertions, MIKOS shows a decrease in peak-memory usage to 4.07% (24.57x) on average compared to IKOS. When performing interprocedural buffer-overflow analysis, MIKOS shows a decrease in peak-memory usage to 43.7% (2.29x) on average compared to IKOS.
MPC-Based Hierarchical Task Space Control of Underactuated and Constrained Robots for Execution of Multiple Tasks
Venue: arXiv cs.RO 2020
Year: 2020

This paper proposes an MPC-based controller to efficiently execute multiple hierarchical tasks for underactuated and constrained robotic systems. Existing task-space controllers or whole-body controllers solve instantaneous optimization problems given task trajectories and the robot plant dynamics. However, the task-space control method we propose here relies on the prediction of future state trajectories and the corresponding costs-to-go terms over a finite time-horizon for computing control commands. We employ acceleration energy error as the performance index for the optimization problem and extend it over the finite-time horizon of our MPC. Our approach employs quadratically constrained quadratic programming, which includes quadratic constraints to handle multiple hierarchical tasks, and is computationally more efficient than nonlinear MPC-based approaches that rely on nonlinear programming. We validate our approach using numerical simulations of a new type of robot manipulator system, which contains underactuated and constrained mechanical structures.
Resolving Resource Incompatibilities in Intelligent Agents
Venue: arXiv cs.AI 2020
Year: 2020

An intelligent agent may in general pursue multiple procedural goals simultaneously, which may lead to arise some conflicts (incompatibilities) among them. In this paper, we focus on the incompatibilities that emerge due to resources limitations. Thus, the contribution of this article is twofold. On one hand, we give an algorithm for identifying resource incompatibilities from a set of pursued goals and, on the other hand, we propose two ways for selecting those goals that will continue to be pursued: (i) the first is based on abstract argumentation theory, and (ii) the second based on two algorithms developed by us. We illustrate our proposal using examples throughout the article.
That looks interesting! Personalizing Communication and Segmentation with Random Forest Node Embeddings
Venue: arXiv cs.LG 2020
Year: 2020

Communicating effectively with customers is a challenge for many marketers, but especially in a context that is both pivotal to individual long-term financial well-being and difficult to understand: pensions. Around the world, participants are reluctant to consider their pension in advance, it leads to a lack of preparation of their pension retirement [1], [2]. In order to engage participants to obtain information on their expected pension benefits, personalizing the pension providers' email communication is a first and crucial step. We describe a machine learning approach to model email newsletters to fit participants' interests. The data for the modeling and analysis is collected from newsletters sent by a large Dutch pension provider of the Netherlands and is divided into two parts. The first part comprises 2,228,000 customers whereas the second part comprises the data of a pilot study, which took place in July 2018 with 465,711 participants. In both cases, our algorithm extracts features from continuous and categorical data using random forests, and then calculates node embeddings of the decision boundaries of the random forest. We illustrate the algorithm's effectiveness for the classification task, and how it can be used to perform data mining tasks. In order to confirm that the result is valid for more than one data set, we also illustrate the properties of our algorithm in benchmark data sets concerning churning. In the data sets considered, the proposed modeling demonstrates competitive performance with respect to other state of the art approaches based on random forests, achieving the best Area Under the Curve (AUC) in the pension data set (0.948). For the descriptive part, the algorithm can identify customer segmentations that can be used by marketing departments to better target their communication towards their customers.
Combining Word and Character Vector Representation on Neural Machine Translation
Venue: arXiv cs.CL 2020
Year: 2020

This paper describes combinations of word vector representation and character vector representation in English-Indonesian neural machine translation (NMT). Six configurations of NMT models were built with different input vector representations: word-based, combination of word and character representation using bidirectional LSTM(bi-LSTM), combination of word and character representation using CNN, combination of word and character representation by combining bi-LSTM and CNN by three different vector operations: addition, pointwise multiplication, and averaging. The experiment results showed that NMT models with concatenation of word and character representation obtained BLEU score higher than baseline model, ranging from 9.14 points to 11.65 points, for all models that combining both word and character representation, except the model that combining word and character representation using both bi-LSTM and CNN by addition operation. The highest BLEU score achieved was 42.48 compared to the 30.83 of the baseline model.
PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
Venue: arXiv cs.CV 2020
Year: 2020

Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method, which removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via Markov random field (MRF). Specifically, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR datasets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.
COMONet: Community Mobile Network
Venue: arXiv cs.NI 2020
Year: 2020

The density of mobile phones has increased rapidly in recent years. One drawback of the current mobile telephone technology is that it forces all the calls to go through cellular base stations even if the caller and the callee are within the radio range of each other. Hybrid cellular networks and Unlicensed Mobile Access (UMA) have been proposed as solutions that enable mobile phone users to bypass cellular base stations. However, these technologies either require special hardware or in some cases have to rely on the service providers. We identified that most of the Commodity-off-the-Shelf mobile phones are Wi-Fi (and Bluetooth) enabled. We propose a Community Mobile Network (COMONet) which utilizes Wi-Fi (and Bluetooth) to build ad hoc network among mobile phone users to bypass GSM base stations whenever possible. COMONet does not depend on special noncommodity hardware and it is a software based solution. COMONet monitors all the available paths over the ad hoc network and it transparently switches to the regular path over the service provider's GSM base station if a path is not available over the ad hoc network. In COMONet the caller and the callee do not have to be within the Wi-Fi or Bluetooth range of each other to make a call since the COMONet is capable of routing calls through the other mobile nodes that are participating in the COMONet.
FLaPS: Federated Learning and Privately Scaling
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this way, sensitive data does not leave the user devices. FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity. Some issues with the FL are, 1) no scalability, as the model is iteratively trained over all the devices, which amplifies with device drops; 2) security and privacy trade-off of the learning process still not robust enough and 3) overall communication efficiency and the cost are higher. To mitigate these challenges we present Federated Learning and Privately Scaling (FLaPS) architecture, which improves scalability as well as the security and privacy of the system. The devices are grouped into clusters which further gives better privacy scaled turn around time to finish a round of training. Therefore, even if a device gets dropped in the middle of training, the whole process can be started again after a definite amount of time. The data and model both are communicated using differentially private reports with iterative shuffling which provides a better privacy-utility trade-off. We evaluated FLaPS on MNIST, CIFAR10, and TINY-IMAGENET-200 dataset using various CNN models. Experimental results prove FLaPS to be an improved, time and privacy scaled environment having better and comparable after-learning-parameters with respect to the central and FL models.
Proximity Tracing in an Ecosystem of Surveillance Capitalism
Venue: arXiv cs.CR 2020
Year: 2020

Proximity tracing apps have been proposed as an aide in dealing with the COVID-19 crisis. Some of those apps leverage attenuation of Bluetooth beacons from mobile devices to build a record of proximate encounters between a pair of device owners. The underlying protocols are known to suffer from false positive and re-identification attacks. We present evidence that the attacker's difficulty in mounting such attacks has been overestimated. Indeed, an attacker leveraging a moderately successful app or SDK with Bluetooth and location access can eavesdrop and interfere with these proximity tracing systems at no hardware cost and perform these attacks against users who do not have this app or SDK installed. We describe concrete examples of actors who would be in a good position to execute such attacks. We further present a novel attack, which we call a biosurveillance attack, which allows the attacker to monitor the exposure risk of a smartphone user who installs their app or SDK but who does not use any contact tracing system and may falsely believe that they have opted out of the system. Through traffic auditing with an instrumented testbed, we characterize precisely the behaviour of one such SDK that we found in a handful of apps---but installed on more than one hundred million mobile devices. Its behaviour is functionally indistinguishable from a re-identification or biosurveillance attack and capable of executing a false positive attack with minimal effort. We also discuss how easily an attacker could acquire a position conducive to such attacks, by leveraging the lax logic for granting permissions to apps in the Android framework: any app with some geolocation permission could acquire the necessary Bluetooth permission through an upgrade, without any additional user prompt. Finally we discuss motives for conducting such attacks.
Model-based analysis support for dependable complex systems in CHESS
Venue: arXiv cs.SE 2020
Year: 2020

The challenges related to dependable complex systems are heterogeneous and involve different aspects of the system. On one hand, the decision-making processes need to take into account many options. On the other hand, the design of the system's logical architecture must consider various dependability concerns such as safety, reliability, and security. Moreover, in case of high-assurance systems, the analysis of such concerns must be performed with rigorous methods. In this paper, we present the new development of CHESS, a cross-domain, model-driven, component-based, and open-source tool for the development of high-integrity systems. We focus on the new recently distributed version of CHESS, which supports extended model-based development and analyses for safety and security concerns. Finally, we present contributions of CHESS to several international research projects.