Reviewerly/exHarmony huggingface dataset:
Robust Deep Reinforcement Learning for Interference Avoidance in Wideband Spectrum

This paper presents a design of a cognitive engine for interference and jamming resilience based on deep reinforcement learning (DRL). The proposed scheme is aimed at finding the spectrum opportunities in a heterogeneous wideband spectrum. In this paper we discuss a specific DRL mechanism based on double deep Q-learning (DDQN) with a convolutional neural network (CNN) to successfully learn such interference avoidance operation over a wideband partially observable environment. It is shown, through simulations, that the proposed technique has a low computational complexity and significantly outperforms other techniques in the literature, including other DRL-based approaches.
Koinonia

Despite years of research, many existing e-voting systems do not adequately protect voting privacy. In most cases, such systems only achieve "immediate privacy", that is, they only protect voting privacy against today's adversaries, but not against a future adversary, who may possess better attack technologies like new cryptanalysis algorithms and/or quantum computers. Previous attempts at providing long-term voting privacy (dubbed "everlasting privacy" in the literature) often require additional trusts in parties that do not need to be trusted for immediate privacy.
Finding Informative Comments for Video Viewing

Of all the information-sharing methods on the Web, video is a factor with increasing importance and will continue to influence the future Web environment. Various services such as YouTube, Vimeo, and Liveleak are information-sharing platforms that support uploading UGC (user-generated content) to the Web. Users tend to seek related information while or after watching an informative video when they are using these Web services. In this situation, the best way of satisfying information needs of this kind is to find and read the comments on Web services. However, existing services only support sorting by recentness (newest one) or rating (high LIKES score). Consequently, the search for related information is limited unless the users read all the comments. Therefore, we suggest a novel method to find informative comments by considering original content and its relevance. We developed a set of methods composed of measuring informativeness priority, which we define as the level of information provided by online users, classifying the intention of the information posted online, and clustering to eliminate duplicate themes. The first method of measuring informativeness priority calculates the extent to which the comments cover all the topics in the original contents. After the informativeness priority calculation, the second method classifies the intention of information posted in comments. Then, the next method picks the most informative comments by applying clustering methods to eliminate duplicate themes using rules. Experiments based on 20 sampled videos with 1000 comments and analysis of 1861 TED talk videos and 380,619 comments show that the suggested methods can find more informative comments compared to existing methods such as sorting by high LIKES score.
Socially-Accepted Path Planning for Robot Navigation Based on Social Interaction Spaces

Path planning is one of the most widely studied problems in robot navigation. It deals with estimating an optimal set of waypoints from an initial to a target coordinate. New generations of assistive robots should be able to compute these paths considering not only obstacles but also social conventions. This ability is commonly referred to as social navigation. This paper describes a new socially-acceptable path-planning framework where robots avoid entering areas corresponding to the personal spaces of people, but most importantly, areas related to human-human and human-object interaction. To estimate the social cost of invading personal spaces we use the concept of proxemics. To model the social cost of invading areas where interaction is happening we include the concept of object interaction space. The framework uses Dijkstra’s algorithm on a uniform graph of free space where edges are weighed according to the social traversal cost of their outbound node. Experimental results demonstrate the validity of the proposal to plan socially-accepted paths.
Survey of Memory Management Techniques for HPC and Cloud Computing

The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail.
Detection of Image Manipulations Using Siamese Convolutional Neural Networks

The processing history of an image can reveal the application of different types of image editing/manipulation operations applied to images and also can expose forgeries. This paper proposes a novel deep learning-based manipulation detection method using a siamese neural network. The advantage of the proposed method is that it can even detect manipulations not present in the training stage. The network is first trained to differentiate between different types of image editing operations. Once the network learns feature that can discriminate different image editing operations present in the training stage, the unknown manipulations are detected using the one-shot classification strategy. We show that the network can also check whether an image is downloaded from a social media platform or not. The experimental results validate the efficacy of the proposed method.
Effects of Light Stemming on Feature Extraction and Selection for Arabic Documents Classification

This chapter aims to study the effects of the light stemming technique on feature extraction where Bag of Words (BoW) and Term frequency- Inverse Documents (TF-IDF) are employed for Arabic document classification. Moreover, feature selection methods such as Chi-square (Chi2), Information gain (IG), and singular value decomposition (SVD) are used to select the most relevant features. K-nearest Neighbor (kNN), Logistic Regression (LR), and Support Vector Machine (SVM) classifiers are used to build the classification model. Experiment are conducted using a public data collected from Arab websites, namely, BBC Arabic dataset. Experiment results show that SVM outperforms LR and KNN. Furthermore, BoW outperforms TF-IDF without using a stemming technique. Using a Robust Arabic Light Stemmer (ARLStem) as our main light stemmer shows a positive effect when combined with TF-IDF over the baseline. In the experiment where Chi2 is used as the feature selection technique, SVM resulted in 0.9568% F1-micro using BoW to extract the features from the dataset where 5000 relevant features were selected. In the experiment where IG is used as the feature selection method, SVM achieved 0.9588% F1-micro with BoW and 4000 selected features. Finally in the experiment where SVD is used as the feature selection technique, SVM reached 0.9569% F1-micro when using BoW and 5000 relevant feature were selected. The aforementioned experiments report the best results achieved where stemming is not employed.
Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database

The cloud computing model has become very popular among users, as it has proven to be a cost-effective solution to store and process data, thanks to recent advancements in virtualization and distributed computing. Nevertheless, in the cloud environment, the user entrusts the safekeeping of its data entirely to the provider, which introduces the problem of how secure such data is and whether its integrity has been maintained. This paper proposes an approach to the data security in cloud by utilizing a random pattern fragmentation algorithm and combining it with a distributed NoSQL database. This not only increases the security of the data by storing it in different nodes and scramble all the bytes, but also allows the user to implement an alternative method of securing data. The performance of the approach is compared to other approaches, along with AES 256 encryption. Results indicate a significant performance improvement over encryption, highlighting the capabilities of this method for cloud stored data, as it creates a layer of protection without additional overhead.
Shapeshifter: Intelligence-driven data plane randomization resilient to data-oriented programming attacks

Non-control data attacks are becoming an increasingly major threat to cyber security. Specifically, data-oriented programming (DOP) attacks manipulate the non-control data in the target program to achieve malicious goals without violating control-flow integrity (CFI). Pioneering research has shown that such attacks can be equally as powerful and effective as control-flow attacks. However, these threats have not been adequately addressed because most previous defence mechanisms focus on preventing control-flow attacks. To this end, we propose Shapeshifter, an intelligence-driven data plane randomization technique that is resilient to non-control data attacks. We define and identify the security-critical data objects that need to be randomized through strategic behaviour analysis for DOP attacks. Driven by the threat intelligence from DOP attacks, we construct a reasonable whitelist for randomization and design a runtime randomization strategy. Shapeshifter adaptively randomizes the memory representation of both the data structure instances and the variables on the whitelist at runtime, thereby dynamically changing the attack surface and increasing the difficulty of launching DOP attacks. We implement Shapeshifter on top of the LLVM compiler and conduct an evaluation. The evaluation results show the effectiveness of Shapeshifter in mitigating non-control data attacks with a 20.1% runtime overhead on average.
Segmentation and Classification Techniques of Leukemia Using Image Processing: an Overview

Identification of blood disorders are mainly done by hematologists or experts, by examining the images of blood cells obtained with the help of microscopes. It helps to classify several diseases related to blood cells such as blood cancer, sickle cell diseases, polycythemia, etc. This paper is an overview of various studies conducted in the field of blood cancer, specifically to detect and classify different types of leukemia. This study focuses on the techniques used to segment and detect the type of leukemia by analyzing different features of the digital images of the white blood cells. Variations in these features are used as the classifier inputs which give information about different types of leukemia. At the end, comparisons of different techniques used for segmentation and classifications are also given to understand their relative merits and demerits.
Speed Planning for an Autonomous Vehicle with Conflict Moving Objects

One challenging problem about autonomous vehicles (AV) is to optimize vehicle trajectories with considering the conflict moving objects (e.g. left and right turns, car following). This paper investigates an AV speed planning problem where the AV follows a given spatial path and the possible conflict regions of the moving objects are known. This problem converts the two-dimensional (2D) trajectory planning problem to a one-dimensional (1D) speed planning problem that decides the optimal speeds and accelerations of the AV along the spatial path. This paper first analyzes the feasibility region of the AV movements for a general conflict avoidance application (e.g., following a car, or passing an intersection). This analysis reveals the upper and lower bounds for a feasible trajectory. With that, a customized dynamic-programming- (DP)-based algorithm is developed to solve the optimal trajectory. In the end, a numerical experiment is conducted to demonstrate the performance of the customized DP-based algorithm. The result shows that the proposed model and algorithm can solve the studied speed planning problem effectively.
Robust Cloth Warping via Multi-Scale Patch Adversarial Loss for Virtual Try-On Framework

With the rapid growth of online commerce, image-based virtual try-on systems for fitting new in-shop garments onto a person image presents an exciting opportunity to deliver interactive customer experience. Current state-of-the-art methods achieve this in a two-stage pipeline, where the first stage transforms the in-shop cloth into fitting the body shape of the target person and the second stage employs an image composition module to seamlessly integrate the transformed in-shop cloth onto the target person image. In the present work, we introduce a multi-scale patch adversarial loss for training the warping module of a state-of-the-art virtual try-on network. We show that the proposed loss produces robust transformation of clothes to fit the body shape while preserving texture details, which in turn improves image composition in the second stage. We perform extensive evaluations of the proposed loss on the try-on performance and show significant performance improvement over the existing state-of-the-art method.
OpenPose’s Evaluation in The Video Traditional Martial Arts Presentation

Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps individuals preserve national culture, exercise and self-defense for people. However, traditional martial arts have many different postures and activities of the body and body parts. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, and so forth. Especially is 3-D human pose estimation. In this paper, we propose using Convolutional Neural Network (CNN) for estimating key points and joints of actions in traditional martial postures in the 2-D space and then projecting the results to the 3-D space and apply the measurements (length of joints, deviation angle of joints, deviation distance of key points) for evaluating pose estimation. The CNN model that we use is CPM (Convolutional Pose Machine), since then there is a comparative study proposed when CPM is trained on the classic MSCOCO Keypoints Challenge dataset [1] and Human3.6m [2], the results were evaluated on the Martial Arts, Dancing and Sports dataset of Zhang et al. [3]. The quantitatively results evaluated and published.
Reusing Bugged Source Code to Support Novice Programmers in Debugging Tasks

Novice programmers often encounter difficulties performing debugging tasks effectively. Even if modern development environments (IDEs) provide high-level support for navigating through code elements and for identifying the right conditions leading to the bug, debugging still requires considerable human effort. Programmers usually have to make hypotheses that are based on both program state evolution and their past debugging experiences. To mitigate this effort and allow novice programmers to gain debugging experience quickly, we propose an approach based on the reuse of existing bugs of open source systems to provide informed guidance from the failure site to the fault position. The goal is to help novices in reasoning on the most promising paths to follow and conditions to define. We implemented this approach as a tool that exploits the knowledge about fault and bug position in the system, as long as any bug of the system is known. The effectiveness of the proposed approach is validated through a quasi-experiment that qualitatively and quantitatively evaluates how the debugging performances of the students change when they are trained using the tool.
A Platform of Services to Support Community-Dwelling Older Adults Integrating FHIR and Complex Security Mechanisms

The paper presents the core services (i.e., information persistence, authentication, authorization, logging and auditing) of the Social Cooperation for Integrated Assisted Living (SOCIAL) platform, a platform aiming to support community-dwelling older adults. In addition to the description of the services’ implementation, using state of the art technological solutions (e.g., Fast Healthcare Interoperability Resources, eXtensible Access Control Markup Language or blockchain), the paper also presents the assessment of their performance considering an application scenario related to local authorities.
Iuml: Inception U-Net Based Multi-Task Learning For Density Level Classification And Crowd Density Estimation

Nowadays, image-based people counting is an essential technique for public safety management. However, this work is still extremely challenging due to many kinds of scale issues caused by different congested scenes, different viewing points, different image sizes, and different density levels. In this paper, we proposed a CNNs-based framework for people counting and crowd density map estimation with the consideration of the scale problems. First, we introduced an encoder-decoder architecture, which is composed of Inception modules to learn the multi-scale feature representations. Besides, to be adaptive to image resolution, a multi-loss setting over different resolutions of density maps is designed for network training. Second, we apply multi-task learning to learn the joint features for the density map estimation task and the density level classification task. This helps to enhance the feature generality under different scenes. Finally, by adopting the U-net architecture, the encoder and decoder features are then fused to generate high-resolution density maps. The efficacy of the proposed method is evaluated in the extensive experiments by quantifying the counting performance through multiple evaluation criteria.
Analyzing Patient Decision Making in Online Health Communities

In recent years, many users join online health communities (OHC) to obtain information and seek social support. One of the most important type of support that a patient needs is to obtain suggestions and information when they make decisions in their diagnosis and treatments. To understand patient decision making processes, we propose to identify the threads on OHC discussion forum that are about patient decision making, and then analyze the questions that patients have. We use deep learning based model to identify such threads. Experiment results show that the proposed methods achieve good performance in precision, recall, F1 score, accuracy, and AUC. Then we leverage topic modeling techniques to analyze the questions that patients expressed in those threads to get a better understanding of patient decision making.
Ontology-Mediated Probabilistic Model Checking

Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of dynamic systems. Description logics (DLs) provide a well-suited formalism to describe and reason about terminological knowledge, used in many areas to specify background knowledge on the domain. We investigate how such knowledge can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose a formalism that links ontologies to dynamic behaviors specified by guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Further, we present and implement a technique for their analysis relying on existing DL-reasoning and PMC tools. This way, we enable the application of standard PMC techniques to analyze knowledge-intensive systems. Our approach is implemented and evaluated on a multi-server system case study, where different DL-ontologies are used to provide specifications of different server platforms and situations the system is executed in.
Implementation of User Centered Design Method in Designing Android-based Journal Reminder Application

Abstract The main objective of this study was to implement UCD in designing an Android-based journal reminder application to determine the user’s needs for functionality and application interface. This study uses Data Collection Method and Application Design using UCD. To find out the result of the implementation UCD, measurements are used at the level of acceptance and support of prospective users by making a data collection instrument in the form of a questionnaire according to the usability testing framework using the Likert scale to calculate the variables. The results of testing of prospective users indicate that 84% of prospective users state that the functionality and interface design of the android-based journal reminder application can be accepted and can be developed in the next stage. This is concluded from the results of functional and interface design evaluations through questionnaires using a Linkert scale which shows that the design built is at a GOOD interval, which means that the functional design and interface of the journal reminder application can be accepted and can proceed to the next stage. However, in order to improve the accuracy of the development of the journal reminder applications, it is necessary to make a propototype based on the results of UCD modeling and design.
Proportional data modeling via selection and estimation of a finite mixture of scaled Dirichlet distributions

Abstract This paper proposes an unsupervised algorithm for learning a finite mixture of scaled Dirichlet distributions. Parameters estimation is based on the maximum likelihood approach, and the minimum message length (MML) criterion is proposed for selecting the optimal number of components. This research work is motivated by the flexibility issues of the Dirichlet distribution, the widely used model for multivariate proportional data, which has prompted a number of scholars to search for generalizations of the Dirichlet. By introducing the extra parameters of the scaled Dirichlet, several useful statistical models could be obtained. Experimental results are presented using both synthetic and real datasets. Moreover, challenging real‐world applications are empirically investigated to evaluate the efficiency of our proposed statistical framework.