Reviewerly/exHarmony huggingface dataset:
Toward an Understanding of the Characteristics of Secondary School Cyberhate Perpetrators

While the Internet offers many opportunities to access information, training and communication, it has created new grounds for risks, threats and harm. With the rise of populism and extremism, new forms of cyberbullying emerge, more specifically cyberhate. The Internet has become a privileged tool to disseminate hatred, based on racism, xenophobia, bigotry and islamophobia. Organized groups use the internet as a dissemination tool for their ideas, to build collective identity and to recruit young people. The presence of these groups has been facilitated worldwide thanks to technology. Yet, little attention has been granted to the way the Internet eases the activities of individuals who promote and propagate hate online. The role they play in spreading racism, xenophobia and bigotry is paramount as they regularly comment online about news and events, interacting with like-minded people with impunity because the web prevents people from being easily identified or controlled. While literature on exposure to hateful contents and cyberhate victimization is growing, little is known about who the perpetrators really are. A survey with young people aged 12-20 (N= 1,889) was completed in France and forms the basis of this article. It provides an understanding of the characteristics and associated variables of cyberhate perpetration. The Structural Equation model shows that cyberhate perpetration is heavily related to time spent online, victimization, belonging to a deviant youth group, positive attitudes towards violence and racism. Results from the SEM further suggest that people who suffered from online victimization will themselves have a greater tendency to belong to deviant youth groups. Multiple mediation analysis further suggests that trust in institutions may however prevent young people from belonging to a deviant youth group and decrease positive attitudes toward violence, thus diminishing the tendency to perform hateful aggression.
A Hybrid Feature Selection Approach for Handling a High-Dimensional Data

We proposed a Hybrid FeatureVenkatesh, B. selection method, the combinationAnuradha, J. of Mutual Information (MI) a filter method, and Recursive Feature Elimination (RFE) a wrapper method. The methodology combines the strengths of both filter and Wrapper method. Performance of the proposed method is measured on three benchmark datasets (Ionosphere, Libras Movement, and Clean) from the UCI Repository. We compared the classification accuracy of the proposed Hybrid method with MI, RFE, Original Features by using random forest classifier. The performance are compared using four classification measures i.e. 1. F1-Score 2. Recall 3. Precession 4. Accuracy. It is evidence from the result analysis that the proposed hybrid method has out performed other methods.
Composition of heterogeneous web services: A systematic review

Initial developments in Service-Oriented Computing (SOC) led to the development of Web Services using the SOAP protocol and an extensive set of tools and methods for composing new services from those existing. Subsequently, other types of services also emerged, such as event-oriented services and RESTful services. Nevertheless, all mentioned service types expose data and functionality, and users can benefit from their composition, regardless of the service type chosen for their implementation. In the Internet of Things, it is relevant to employ event-oriented services for sensing and SOAP, RESTful or lightweight web APIs for control. In the emerging field of microservices, heterogeneity is embraced as a design principle and services that are part of a single system may be implemented using heterogeneous technologies and paradigms. The research question of this review is: How heterogeneous services can be composed? There are several surveys that cover service composition with each of the existing service types, but the composition of heterogeneous services is only marginally addressed. This systematic literature review focuses explicitly on the heterogeneity of the aforementioned service types. A total of 66 documents, published from 2005 to 2018, have been surveyed, targeting all possible combinations of the three service types. In addition to summarizing existing works, the specific methods employed for supporting service type heterogeneity are grouped into archetypes and have their limitations and capabilities analyzed. Despite the large number of documents found, there are several open issues on heterogeneous service composition. The results of this review are confronted with emerging fields in service computing, namely microservices, serverless and IoT, yielding additional research directions.
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences

A robust foreground detection system is presented, which is resilient to noise in video sequences. The proposed model divides each video frame in patches that are fed to a stacked denoising autoencoder, which is responsible for the extraction of significant features from each image patch. After that, a probabilistic model that is composed of a mixture of Gaussian distributions decides whether the given feature vector describes a patch belonging to the background or the foreground. In order to test the model robustness, several trials with noise of different types and intensities have been carried out. A comparison with other ten state of the art foreground detection algorithms has been drawn. The algorithms have been ranked according to the obtained results, and our proposal appears among the first three positions in most case and its the one that best performs on average.
Deep Neural Network Models for Paraphrased Text Classification in the Arabic Language

Paraphrase is the act of reusing original texts without proper citation of the source. Different obfuscation operations can be employed such as addition/deletion of words, synonym substitutions, lexical changes, active to passive switching, etc. This phenomenon dramatically increased because of the progressive advancement of the web and the automatic text editing tools. Recently, deep leaning methods have gained competitive results than traditional methods for Natural Language Processing (NLP). In this context, we consider the problem of Arabic paraphrase detection. We present different deep neural networks like Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Our aim is to study the effective of each one in extracting the proper features of sentences without the knowledge of semantic and syntactic structure of Arabic language. For the experiments, we propose an automatic corpus construction seeing the lack of Arabic resources publicly available. Evaluations reveal that LSTM model achieved the higher rate of semantic similarity and outperformed significantly other state-of-the-art methods.
"Co-riding With My eBike to Get Green Lights"

Researchers are increasingly exploring interactive technology supporting human-system partnership in an exertion context, such as cycling. So far, most investigations have supported the rider cognitively, by the system "sensing and presenting" information to assist the rider to make informed decisions. In contrast, we propose systems that promote user-system co-operation, by "sensing and acting" on information to assist the rider, not only "cognitively" but also "physically", with the aim of facilitating user-system co-operation in an exertion context. Our prototype, "Ari", is a novel augmented eBike designed to facilitate user-system co-operation, where the information that each party can sense is used in regulating the speed to cross all traffic lights on green. A study with 20 bike riders resulted in five themes and six design tactics to further the design of interactive systems at the intersection of human-computer integration in an exertion context, thereby facilitating user-system co-operation to augment the exertion experience.
Leveraging Blockchain Technology in AEC Industry during Design Development Phase

Information exchange among architects, contractors, and clients is found to be limited and not completely digital, which creates difficulty in tracking and analyzing the generated unstructured data. Design drawings are developed during the design phase, but not transparently shared among contractors, due to the fear of uncertain changes in design without knowing who changed the designed element and what element is been modified, which can lead to disputes. Blockchain technology enables the transaction of digital information, and revolutionizing different industries like banking, crypto exchanges, insurance, etc., due to its decentralized, encrypted, and secured functionality. This paper proposes the potential application of blockchain technology in design development phase of architecture, engineering, and construction (AEC) processes by developing a blockchain framework. In this research, the design data generated from design development software and its log files are categorized, and flow of data encryption is determined by using blockchain concepts in order to have a secure ledger, which has the potential to provide a validation to an argument of disputes. The expected contribution of this proposed research is to create a framework to track and register the design commands and events in a secured, transparent, and collaborative platform. The anticipated benefits of this research include the record of proof of work and the mitigation of disputes and litigations related to design defects which includes design error and omissions and construction defects which includes either replacement or modifications in design, resulting to change orders and monetary losses.
Automated Summarization Evaluation (ASE) Using Natural Language Processing Tools

Summarization is an effective strategy to promote and enhance learning and deep comprehension of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation of students’ summaries requires time and effort. This problem has led to the development of automated models of summarization quality. However, these models often rely on features derived from expert ratings of student summarizations of specific source texts and are therefore not generalizable to summarizations of new texts. Further, many of the models rely of proprietary tools that are not freely or publicly available, rendering replications difficult. In this study, we introduce an automated summarization evaluation (ASE) model that depends strictly on features of the source text or the summary, allowing for a purely text-based model of quality. This model effectively classifies summaries as either low or high quality with an accuracy above 80%. Importantly, the model was developed on a large number of source texts allowing for generalizability across texts. Further, the features used in this study are freely and publicly available affording replication.
Speech-recognition cloud harvesting for improving the navigation of cyber-physical wheelchairs for disabled persons

A cyber-physical system for a speech-controlled wheelchair has been proposed. It is based on cloud-harvesting principles, which result in significant improvement in command error rate (CER). The overall methodology and developed cloud-harvesting algorithm have been presented, discussed, and tested. The combination of IBM Watson and Google Cloud Speech APIs gave significantly better results than the use of solitary speech recognition APIs. The proposed approach shows the potential for and usability of speech-controlled wheelchairs, as well as in similar applications.
A survey on semantic-based methods for the understanding of human movements

This paper presents semantic-based methods for the understanding of human movements in robotic applications. To understand human movements, robots need to first, recognize the observed or demonstrated human activities, and secondly, learn different parameters to execute an action or robot behavior. In order to achieve that, several challenges need to be addressed such as the automatic segmentation of human activities, identification of important features of actions, determine the correct sequencing between activities, and obtain the correct mapping between the continuous data and the symbolic and semantic interpretations of the human movements. This paper aims to present state-of-the-art semantic-based approaches, especially the new emerging approaches that tackle the challenges of finding generic and compact semantic models for the robotics domain. Finally, we will highlight potential breakthroughs and challenges for the next years such as achieving scalability, better generalization, compact and flexible models, and higher system accuracy.
Meta-learning for resampling recommendation systems

One possible approach to tackle the class imbalance in classification tasks is to resample a training dataset, i.e., to drop some of its elements or to synthesize new ones. There exist several widely-used resampling methods. Recent research showed that the choice of resampling method significantly affects the quality of classification, which raises the resampling selection problem. Exhaustive search for optimal resampling is time-consuming and hence it is of limited use. In this paper, we describe an alternative approach to the resampling selection. We follow the meta-learning concept to build resampling recommendation systems, i.e., algorithms recommending resampling for datasets on the basis of their properties.
Full-stack optimization for accelerating CNNs using powers-of-two weights with FPGA validation

We present a full-stack optimization framework for accelerating inference of CNNs (Convolutional Neural Networks) and validate the approach with a field-programmable gate array (FPGA) implementation. By jointly optimizing CNN models, computing architectures, and hardware implementations, our full-stack approach achieves unprecedented performance in the trade-off space characterized by inference latency, energy efficiency, hardware utilization, and inference accuracy. An FPGA implementation is used as the validation vehicle for our design, achieving a 2.28ms inference latency for the ImageNet benchmark. Our implementation shines in that it has 9x higher energy efficiency compared to other implementations while achieving comparable latency. A highlight of our approach which contributes to the achieved high energy efficiency is an efficient Selector-Accumulator (SAC) architecture for implementing CNNs with powers-of-two weights. Compared to an FPGA implementation for a traditional 8-bit MAC, SAC substantially reduces required hardware resources (4.85x fewer lookup tables) and power consumption (2.48x).
Personal Knowledge Base Construction from Text-based Lifelogs

Previous work on lifelogging focuses on life event extraction from image, audio, and video data via wearable sensors. In contrast to wearing an extra camera to record daily life, people are used to log their life on social media platforms. In this paper, we aim to extract life events from textual data shared on Twitter and construct personal knowledge bases of individuals. The issues to be tackled include (1) not all text descriptions are related to life events, (2) life events in a text description can be expressed explicitly or implicitly, (3) the predicates in the implicit events are often absent, and (4) the mapping from natural language predicates to knowledge base relations may be ambiguous. A joint learning approach is proposed to detect life events in tweets and extract event components including subjects, predicates, objects, and time expressions. Finally, the extracted information is transformed to knowledge base facts. The evaluation is performed on a collection of lifelogs from 18 Twitter users. Experimental results show our proposed system is effective in life event extraction, and the constructed personal knowledge bases are expected to be useful to memory recall applications.
The fifth archetype of electricity market: the blockchain marketplace

Abstract Enabling and empowering the diverse energy resources to have active yet efficient participation in the smart grid and energy market is an unrivaled challenge for the energy industry. This research expands the four dominant archetypes of business models in the energy and electricity market, creating a fifth archetype, the “blockchain marketplace”. The contributions of the study are to identify the extant electricity market designs and architectures as centralized and pseudo-decentralized while proposing a fully decentralized architecture enabled by the blockchain. The research contributes to the literature of smart grids and demand-side management and introduces the value configuration/architecture approach for the energy market and business model domains.
Voronoi-based Objective Quality Metrics for Omnidirectional Video

Omnidirectional video (ODV) represents one of the latest and most promising trends in immersive media. The success of ODV depends on the ability to deliver high-quality ODV to the viewers. For this reason, new methods are needed to measure ODV quality that takes into account the interactive look around nature and the spherical representation of ODV. In this paper, we study full-reference objective quality metrics for ODV based on typical encoding distortions in adaptive streaming systems, namely, scaling and compression. The contribution of this paper is three-fold. First, we propose new objective metrics that take into account the unique aspects of ODV. The proposed metrics are based on the subdivision of a given ODV into multiple patches using the spherical Voronoi diagram. Second, we introduce a new dataset of 75 impaired ODVs with different resolutions and compression levels, together with the subjective quality scores gathered during an experiment with 21 participants. Third, we evaluate the proposed Voronoi-based objective metrics using our dataset. The evaluation of the proposed objective metrics and the comparison with existing metrics show that the proposed metrics achieve a better correlation with the subjective scores. The ODV dataset together with the subjective quality scores and the code of the proposed quality metrics are available with this paper.
Flexible depth segmentation method using phase-shifted wrapped phase sequences

Depth segmentation has attracted much attention in fields ranging from industrial monitoring to image understanding. However, it remains difficult to segment objects positioned at different depths with high speed and high accuracy. This paper proposes a novel depth segmentation scheme based on the phase-shift invariance of the segmentation lines and a simple optimization framework that exploits simple image processing algorithms to enhance its performance in noisy environments. Without the need for precalibration or complementary cues, this approach performs depth segmentation effectively by changing the pattern sequence selectively during the step of postprocessing. First, the structural configuration and special properties of the phase-shifting algorithm are described, following which an optimization framework is established to allow data matting and labeling. Simulation and experimental results show that this approach can perform depth segmentation efficiently despite the influence of environmental noise. Moreover, the processing is of low cost, only needing to detect the intersections of discontinuities among three different wrapped phase maps. Most significantly, this method is robust to variations in environmental noise, camera exposure, and object color and texture.
Blockchain-Based Software Engineering

Blockchain technology has found a great number of applications, from banking to the Internet of Things (IoT). However, it has not yet been envisioned whether and which problems in Software Engineering (SE) Blockchain technology could solve. In this paper, we coin this field "Blockchain-based Software Engineering" and exemplify how Blockchain technology could solve two core SE problems: Continuous Integration (CI) Services such as Travis CI and Package Managers such as apt-get. We believe that Blockchain technology could help (1) democratize and professionalize Software Engineering infrastructure that currently relies on free work done by few volunteers, (2) improve the quality of artifacts and services, and (3) increase trust in ubiquitously used systems like GitHub or Travis CI.
A Case Study: Visualizing Coulomb Forces With the Aid of Augmented Reality

In this work, we describe an educational approach based on augmented reality (AR) and mobile technology that has been developed with the aim to engage students in a learning procedure regarding the electric forces as well as Coulomb’s Law. Initially, we present the AR learning environment and we explain its design principles with respect to the educational goals we aim to accomplish. The idea behind the present approach has been to present students with a novel learning environment that aligns well with their familiarization with mobile technology and the pleasure they take in making use of this technology, in order to stimulate their intrinsic motivation and learning performance. In addition, we demonstrate the early results of a classroom study performed in order to estimate students’ reaction to the AR approach and get an indication of their motivation levels and ease of use, with respect to the AR tool. Based on the study results, we conclude that students have demonstrated positive attitudes toward the AR learning approach and we claim that mobile AR may be incorporated for educational purposes in physics curricula.
Multi-view factorization machines for mobile app recommendation based on hierarchical attention

Mobile app recommendation has been an effective solution to overcoming the information overload in mobile app markets. Recent studies have demonstrated the power of neural network in recommendation tasks which is however rarely exploited for mobile apps. As one of the development of neural network, attention-based models have shown promising results for recommendation because of its capability of filtering out uninformative features from raw inputs. In this paper, to effectively predict users’ preferences for apps, we propose a hierarchical neural network model called MV-AFM for app recommendation which models the interactions of features from different views (view interactions for short) through the attention mechanism. Specifically, the novelty of MV-AFM is the introduction of view segmentation for feature interactions and the construction of two level attention networks: the feature-level attention, starting from the feature embeddings within each view, which intends to select the representative features for the view, and the view-level attention, which learns the importance of interactions between any two views. Extensive experiments on two real-world mobile app datasets demonstrate the effectiveness of MV-AFM.