Reviewerly/exHarmony huggingface dataset:
Effective Parallel Processing Social Media Analytics Framework

The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real-time processing of social media data to capture insights about user's sentiment polarity, user’s opinion, and current trends of the domain. In recent years lots of research were conducted and various machine learning algorithms were developed around the processing of data to achieve higher accuracy while reducing the processing time is still challenging. Big data technologies came unraveled these challenges but they have their own set of complexities along with having hardware deadweight on the system. The contribution of this research paper is to touch upon the mentioned challenges by presenting a climbable, instantaneous and fault-tolerant framework to process real-time data to extract hidden insights within it while not bearing any additional overhead of big data technologies. This framework is versatile enough to support batch processing along with real-time data streams in parallel and distributed environments. Experimental results concluded that 4-threaded parallel architecture of the framework performs at 2X speed compared to single-threaded architecture and shared URLs, embedded Images and Author's meta info it boosting the tweets prediction. Moreover, this research additionally provides a comparison of Support Vector Machines (SVM), Light GBM (LGBM) and Long Short Term Memory (LSTM) supervised machine learning techniques for sentiment analysis and concluded LGBM is the most effective model.
Comparative Study of Movie Shot Classification Based on Semantic Segmentation

The shot-type decision is a very important pre-task in movie analysis due to the vast information, such as the emotion, psychology of the characters, and space information, from the shot type chosen. In order to analyze a variety of movies, a technique that automatically classifies shot types is required. Previous shot type classification studies have classified shot types by the proportion of the face on-screen or using a convolutional neural network (CNN). Studies that have classified shot types by the proportion of the face on-screen have not classified the shot if a person is not on the screen. A CNN classifies shot types even in the absence of a person on the screen, but there are certain shots that cannot be classified because instead of semantically analyzing the image, the method classifies them only by the characteristics and patterns of the image. Therefore, additional information is needed to access the image semantically, which can be done through semantic segmentation. Consequently, in the present study, the performance of shot type classification was improved by preprocessing the semantic segmentation of the frame extracted from the movie. Semantic segmentation approaches the images semantically and distinguishes the boundary relationships among objects. The representative technologies of semantic segmentation include Mask R-CNN and Yolact. A study was conducted to compare and evaluate performance using these as pretreatments for shot type classification. As a result, the average accuracy of shot type classification using a frame preprocessed with semantic segmentation increased by 1.9%, from 93% to 94.9%, when compared with shot type classification using the frame without such preprocessing. In particular, when using ResNet-50 and Yolact, the classification of shot type showed a 3% performance improvement (to 96% accuracy from 93%).
Author Correction: Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning

Approaches to Grounded Language Learning typically focus on a single task-based final performance measure that may not depend on desirable properties of the learned hidden representations, such as their ability to predict salient attributes or to generalise to unseen situations.To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three subtasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zeroshot evaluation.We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations, in particular concerning attribute grounding.To this end, we extend the original GuessWhat?! dataset by including a semantic layer on top of the perceptual one.Specifically, we enrich the Vi-sualGenome scene graphs associated with the GuessWhat?! images with abstract and situated attributes.By using diagnostic classifiers, we show that current models learn representations that are not expressive enough to encode object attributes (average F1 of 44.27).In addition, they do not learn strategies nor representations that are robust enough to perform well when novel scenes or objects are involved in gameplay (zero-shot best accuracy 50.06%).
Your Noise, My Signal

Attacks based on power analysis have been long existing and studied, with some recent works focused on data exfiltration from victim systems without using conventional communications (e.g., WiFi). Nonetheless, prior works typically rely on intrusive direct power measurement, either by implanting meters in the power outlet or tapping into the power cable, thus jeopardizing the stealthiness of attacks. In this paper, we propose NoDE (Noise for Data Exfiltration), a new system for stealthy data exfiltration from enterprise desktop computers. Specifically, NoDE achieves data exfiltration over a building's power network by exploiting high-frequency voltage ripples (i.e., switching noises) generated by power factor correction circuits built into today's computers. Located at a distance and even from a different room, the receiver can non-intrusively measure the voltage of a power outlet to capture the high-frequency switching noises for online information decoding without supervised training/learning. To evaluate NoDE, we run experiments on seven different computers from top vendors and using top-brand power supply units. Our results show that for a single transmitter, NoDE achieves a rate of up to 28.48 bits/second with a distance of 90 feet (27.4 meters) without the line of sight, demonstrating a practically stealthy threat. Based on the orthogonality of switching noise frequencies of different computers, we also demonstrate simultaneous data exfiltration from four computers using only one receiver. Finally, we present a few possible defenses, such as installing noise filters, and discuss their limitations.
Dynamic Language Binding in Relational Visual Reasoning

We present Language-binding Object Graph Network, the first neural reasoning method with dynamic relational structures across both visual and textual domains with applications in visual question answering. Relaxing the common assumption made by current models that the object predicates pre-exist and stay static, passive to the reasoning process, we propose that these dynamic predicates expand across the domain borders to include pair-wise visual-linguistic object binding. In our method, these contextualized object links are actively found within each recurrent reasoning step without relying on external predicative priors. These dynamic structures reflect the conditional dual-domain object dependency given the evolving context of the reasoning through co-attention. Such discovered dynamic graphs facilitate multi-step knowledge combination and refinements that iteratively deduce the compact representation of the final answer. The effectiveness of this model is demonstrated on image question answering demonstrating favorable performance on major VQA datasets. Our method outperforms other methods in sophisticated question-answering tasks wherein multiple object relations are involved. The graph structure effectively assists the progress of training, and therefore the network learns efficiently compared to other reasoning models.
Suboptimal Kalman Filtering in Triplet Markov Models Using Model Order Reduction

When the state space dimension increases, the computational burden can become a major challenge for optimal Kalman filtering in Gaussian triplet Markov models (TMMs). In this paper, we introduce a new model order reduction technique applicable to linear time-homogeneous Gaussian TMMs. Taking advantage of the lower state dimension of the resulting approximate model, a low-complexity suboptimal Kalman filter is obtained. The proposed estimator provides complexity reduction without significant accuracy loss and is shown to outperform two classical methods in the case of Markovian process noise.
Adversarial Attacks on Copyright Detection Systems

It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks. These vulnerabilities are especially apparent for neural network based systems. As a proof of concept, we describe a well-known music identification method, and implement this system in the form of a neural net. We then attack this system using simple gradient methods. Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system. Our goal is to raise awareness of the threats posed by adversarial examples in this space, and to highlight the importance of hardening copyright detection systems to attacks.
Empowerment, HRM practices and organizational performance: a case study of Jordanian commercial banks

This study aimed to identify the role of empowerment and Human Resource Management (HRM) practices on Organizational Performance (OP) amongst 13 Jordanian commercial banks. The research adopted a descriptive and analytical approach to highlight the concepts of the study, and analyzed and extracted the results through a random sample of 180 managers and vice-managers. To achieve the objectives of the study a questionnaire was developed to gather the optimum information from the study sample, and based on that, the data was collected, analyzed and tested and the hypotheses were selected by using the smart PLS. The response rate was 74 percent. The results indicated that of all independent variables assessed, empowerment and HRM practices, have positive and significant influence on OP.
RLgraph: Modular Computation Graphs for Deep Reinforcement Learning

Reinforcement learning (RL) tasks are challenging to implement, execute and test due to algorithmic instability, hyper-parameter sensitivity, and heterogeneous distributed communication patterns. We argue for the separation of logical component composition, backend graph definition, and distributed execution. To this end, we introduce RLgraph, a library for designing and executing reinforcement learning tasks in both static graph and define-by-run paradigms. The resulting implementations are robust, incrementally testable, and yield high performance across different deep learning frameworks and distributed backends.
Deep Learning of Warping Functions for Shape Analysis

Rate-invariant or reparameterization-invariant matching between functions and shapes of curves, respectively, is an important problem in computer vision and medical imaging. Often, the computational cost of matching using approaches such as dynamic time warping or dynamic programming is prohibitive for large datasets. Here, we propose a deep neural-network-based approach for learning the warping functions from training data consisting of a large number of optimal matches, and use it to predict optimal diffeomorphic warping functions. Results show prediction performance on a synthetic dataset of bump functions and two-dimensional curves from the ETH-80 dataset as well as a significant reduction in computational cost.
Benchmarking Approach to Compare Web Applications Static Analysis Tools Detecting OWASP Top Ten Security Vulnerabilities

To detect security vulnerabilities in a web application, the security analyst must choose the best performance Security Analysis Static Tool (SAST) in terms of discovering the greatest number of security vulnerabilities as possible. To compare static analysis tools for web applications, an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project (OWASP) Top Ten project is required. The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and implementation of those tools has different effectiveness rates in terms of security performance. Given the significant cost of commercial tools, this paper studies the performance of seven static tools using a new methodology proposal and a new benchmark designed for vulnerability categories included in the known standard OWASP Top Ten project. Thus, the practitioners will have more precise information to select the best tool using a benchmark adapted to the last versions of OWASP Top Ten project. The results of this work have been obtaining using widely acceptable metrics to classify them according to three different degree of web application criticality.
A Continuous Illusion of Having a Sixth Finger

Our body is central to our sense of self and personal identity, yet it can be manipulated in the laboratory in surprisingly easy ways. Several multisensory illusions have shown the flexibility of the mental representation of our bodies by inducing the illusion of owning an artificial body part or having a body part with altered features. Recently, new studies showed we can embody additional body parts such as a supernumerary finger. Newport et al. recently reported a novel six-finger illusion using conflicting visual and tactile signals induced with the mirror box to create the illusory perception of having a sixth finger for a brief moment. In this study, we aimed to replicate this result and to investigate whether the experience of embodiment of a sixth finger could be prolonged for an extended duration by applying continuous visual–tactile stimulation. Results showed that a continuous illusion of having a sixth finger can be clearly induced. This shows that the six-finger illusion does not reflect merely a momentary confusion due to conflicting multisensory signals but can reflect an enduring representation of a supernumerary finger.
A Taxonomy of Quality Metrics for Cloud Services

A large number of metrics with which to assess the quality of cloud services have been proposed over the last years. However, this knowledge is still dispersed, and stakeholders have little or no guidance when choosing metrics that will be suitable to evaluate their cloud services. The objective of this paper is, therefore, to systematically identify, taxonomically classify, and compare existing quality of service (QoS) metrics in the cloud computing domain. We conducted a systematic literature review of 84 studies selected from a set of 4333 studies that were published from 2006 to November 2018. We specifically identified 470 metric operationalizations that were then classified using a taxonomy, which is also introduced in this paper. The data extracted from the metrics were subsequently analyzed using thematic analysis. The findings indicated that most metrics evaluate quality attributes related to performance efficiency (64%) and that there is a need for metrics that evaluate other characteristics, such as security and compatibility. The majority of the metrics are used during the Operation phase of the cloud services and are applied to the running service. Our results also revealed that metrics for cloud services are still in the early stages of maturity - only 10% of the metrics had been empirically validated. The proposed taxonomy can be used by practitioners as a guideline when specifying service level objectives or deciding which metric is best suited to the evaluation of their cloud services, and by researchers as a comprehensive quality framework in which to evaluate their approaches.
Security, usability, and biometric authentication scheme for electronic voting using multiple keys

We propose electronic voting authentication scheme, which is a key management mechanism for electronic voting system intended to limit the number of attacks on a polling station and strengthen the security control. The motivation is to diversify security requirements of messages exchanged between polling stations. There are different types of messages exchanged between polling stations and each type of message has different security needs. A security mechanism developed on the basis of a single key is not enough to ensure the diverse security needs of voting network. In electronic voting authentication scheme, every polling station is responsible to support three different types of keys. These are global key, pairwise key, and individual key. The global keys are public keys shared with all polling stations in the voting network. The pairwise key can be used for communication with polling stations. Individual keys will be used for communication with the server. To ensure authentication of local broadcast, electronic voting authentication scheme uses one-way key chains in a well-organized way. The support of source authentication is a visible advantage of this scheme. We examine the authentication of electronic voting authentication scheme on numerous attack models. The measurement demonstrates that electronic voting authentication scheme is very operative in protecting against numerous elegant attacks such as wormhole attack, Sybil attack, and HELLO Flood attack. The proposed system is evaluated and the results demonstrate that the proposed system is practical and secure as compared to the direct recording electronic and manual systems.
Performance comparison of wavelet neural network and adaptive neuro-fuzzy inference system with small data sets

In this work, performance of wavelet neural network (WNN) and adaptive neuro-fuzzy inference system (ANFIS) models were compared with small data sets by different criteria such as second order corrected Akaike information criterion (AICc), Bayesian information criterion (BIC), root mean squared error (RMSE), mean absolute relative error (MARE), coefficient of determination (R2), external Q2 function (QF32) and concordance correlation coefficient (CCC). Another criterion was the over-fitting. Ten data sets were selected from literature and their data were divided into training, test, and validation sets. Network parameters were optimized for WNN and ANFIS models and the best architectures with the lowest errors were selected for each data set. A precise survey of the number of permitted adjustable parameters (NPAP) and the total number of adjustable parameters (TNAP) in WNN and ANFIS models was shown that 60% of the ANFIS models and 30% of the WNN models had over-fitting. As a rule of thumb, to avoid over-fitting it is suggested that the ratio of the number of observations in training set to the number of input neurons must be greater than 10 and 20 for WNN and ANFIS, respectively. The smaller ratio required in WNN indicates its flexibility vs. ANFIS that relates to differences in structure and connections in the both networks.
Dependency-aware software requirements selection using fuzzy graphs and integer programming

One of the critical activities in software development is Requirements Selection, which is to find an optimal subset of the software requirements (features) with the highest value for a given budget. The values of the requirements, however, may depend on one another. Such Value Dependencies have not been considered by the existing requirements selection methods, leading to user dissatisfaction and loss of value and reputation in software projects. To mitigate this, we propose Dependency-Aware Requirements Selection (DARS) as an expert system, which explicitly accounts for value dependencies in software projects. At the heart of DARS is an Integer Linear Programming (ILP) model that reduces the risk of value loss by considering value dependencies among the requirements. These value dependencies are identified from the preferences of the users for the requirements. The validly of DARS is verified by studying a real-world software project as well as carrying out simulations. Our results demonstrate a significant reduction in value loss when DARS is employed. Also, the ILP model of DARS proved scalable to large requirement sets (experimented for up to 3000). The results of our study can be extrapolated to a wide range of expert systems that concern selecting value-dependent items.
Horse Optimization Algorithm: A Novel Bio-Inspired Algorithm for Solving Global Optimization Problems

We introduce a novel bio-inspired algorithm named Horse Optimization Algorithm (HOA) which has as primary source of inspiration the hierarchical organization of the horse herds. The article presents the main principles of the proposed algorithm, the pseudo-code version of the algorithm and a modified version named Discrete Binary Horse Optimization Algorithm (DBHOA). HOA is evaluated and validated using six objective functions and is compared with Chicken Swarm Optimization (CSO) and Cat Swarm Optimization Algorithm (CSOA). Finally, the article presents the application of DBHOA in features selection for data generated in a smart grid for the classification of the stability of the system and it considers as experimental support the Electrical Grid Stability Simulated Dataset.
SFINGE 3D: A novel benchmark for online detection and recognition of heterogeneous hand gestures from 3D fingers’ trajectories

In recent years gesture recognition has become an increasingly interesting topic for both research and industry. While interaction with a device through a gestural interface is a promising idea in several applications especially in the industrial field, some of the issues related to the task are still considered a challenge. In the scientific literature, a relevant amount of work has been recently presented on the problem of detecting and classifying gestures from 3D hands’ joints trajectories that can be captured by cheap devices installed on head-mounted displays and desktop computers. The methods proposed so far can achieve very good results on benchmarks requiring the offline supervised classification of segmented gestures of a particular kind but are not usually tested on the more realistic task of finding gestures execution within a continuous hand tracking session. In this paper, we present a novel benchmark, SFINGE 3D, aimed at evaluating online gesture detection and recognition. The dataset is composed of a dictionary of 13 segmented gestures used as a training set and 72 trajectories each containing 3–5 of the 13 gestures, performed in continuous tracking, padded with random hand movements acting as noise. The presented dataset, captured with a head-mounted Leap Motion device, is particularly suitable to evaluate gesture detection methods in a realistic use-case scenario, as it allows the analysis of online detection performance on heterogeneous gestures, characterized by static hand pose, global hand motions, and finger articulation. We exploited SFINGE 3D to compare two different approaches for the online detection and classification, one based on visual rendering and Convolutional Neural Networks and the other based on geometry-based handcrafted features and dissimilarity-based classifiers. We discuss the results, analyzing strengths and weaknesses of the methods, and deriving useful hints for their improvement.