Reviewerly/exHarmony huggingface dataset:
Sexual-Orientated Online Chat Conversations—Characteristics and Testing Pathways of Online Perpetrators

Children all over the world are constantly at risk of becoming a victim of webcam child sex tourism (hereafter WCST). Law enforcement agencies are struggling with tackling the problem. Little is known about the behaviour of online perpetrators who persuade minors to engage in webcam sex. In order to gain more information about the behaviour of this group, the ten-year-old Philippine avatar Sweetie was deployed as a tool. In this first preliminary study 200 chat conversations were analysed. Based on the first findings, almost all online perpetrators were male with an average reported age of 29 years old living in Europe, North America and Asia. Two types of perpetrators were found: the Clint-type, who had clear intentions to talk about sex related topics or about starting a webcam conversation to persuade the minor to engage in (online) sexual activities; and the Small talk type, who used small talk to persuade the minor to engage in (online) sexual activities. This study shows that online perpetrators can be classified into perpetrators who display avoiding and approaching behaviour, which is comparable to the offline offending pathways of Ward and Hudson.
Technical and Economic analysis of a Regulatory Call for PV Plants with Energy Storage in French Islands

European incentive policies led to a faster development of renewable energy sources.This energy transition is bringing new challenges, in particular for grid stability.Photovoltaic energy combined with electrical storage system is one of the most promising technology to enable a massive insertion of green energy into the grid.The task of this paper is to study a call for tender launched by the French government in French insular regions, to develop PV plants equipped with electrical storage systems.The technical sizing will be carried on, using the software PVsyst.Then, the energy flows between the PV panels, the battery and the grid will be optimized thanks to an algorithm.The purpose is also to study the benefits and costs of such a system, and so different scenario will be considered, each one leading to a different economic interest.More broadly, this paper considers the role of batteries today, and how it should be rewarded or compensated for service delivery.
Three-dimensional Face Recognition based on Feature Points Compression and Expansion

Many researchers have attempted to recognize three-dimensional faces using feature points extracted from two-dimensional facial photographs.However, due to the limit of flat photographs, it is very difficult to recognize faces rotated more than 15 degrees from original feature points extracted from the photographs.As such, it is difficult to create an algorithm to recognize faces in multiple angles.In this paper, it is proposed a new algorithm to recognize three-dimensional face recognition based on feature points extracted from a flat photograph.This method divides into six feature point vector zones on the face.Then, the vector value is compressed and expanded according to the rotation angle of the face to recognize the feature points of the face in a three-dimensional form.For this purpose, the average of the compressibility and the expansion rate of the face data of 100 persons by angle and face zone were obtained, and the face angle was estimated by calculating the distance between the middle of the forehead and the tail of the eye.As a result, very improved recognition performance was obtained at 30 degrees of rotated face angle.
A Proposal of Emotion Estimation Method for Words and Construction of Word-emotion Dictionary

In this paper, we propose an emotion estimation method for words and its application to construct a word-emotion dictionary. Since the estimation of more delicate emotions in various natural language processing tasks is a crucial issue, we aim to estimate various emotions to a word. In order to realize it, we employ the distributional hypothesis and assume that an emotional word in a sentence influences the surrounding words. First, we collect more than 2,000 words expressing emotions from an emotion expression dictionary. By using these emotional words and a neural model based on Continuous Bag-of- Words (CBOW), we propose an automatic system to estimate the emotions of many ordinary words. As a result, emotion vectors for 20,000 words could be obtained. We carried out experiments to examine the accuracy of the vectors. It is confirmed that the generated emotion vectors reflect the emotion image for words that humans have.
Do You Know Your Music Students’ Information Needs and Library Usage Behaviors? Interview with the Music Librarian of the Hong Kong Baptist University

The Hong Kong Baptist University (HKBU) is one of the tertiary institutions in Hong Kong that offers degree programs in Music. Supporting the teaching, learning and research needs of the Music Depa...
Twinning iterative design with community cultural wealth

Drawing upon critical justice studies and critical ethnographies in two community-centered makerspaces, we build an argument for how designing for expanded iterations that repeatedly draw from community cultural wealth, supported youth-makers and communities in co-creating an expansive, locally-grounded maker culture. Two related-foci are unpacked: First, we examine how youth engage in an "expanded" iterative process across the making cycle -- what this expanded iterative process is, and how it takes shape as youth move from collaborative ideation through to the afterlife of a maker project. Second, by delving into "moments of expanded iterations" we examine how youth articulate ownership of their making: what that means, how and why, and the subsequent generative spaces that resulted.
Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation

This work presents an empirical approach to quantifying the loss of lexical richness in Machine Translation (MT) systems compared to Human Translation (HT). Our experiments show how current MT systems indeed fail to render the lexical diversity of human generated or translated text. The inability of MT systems to generate diverse outputs and its tendency to exacerbate already frequent patterns while ignoring less frequent ones, might be the underlying cause for, among others, the currently heavily debated issues related to gender biased output. Can we indeed, aside from biased data, talk about an algorithm that exacerbates seen biases?
Characterizing the Stages of Complex Tasks

Stage is an essential facet of task. At different stages of search, users' search strategies are often influenced by different search intentions, encountered problems, as well as knowledge states. In previous studies, information seeking and interactive IR researchers have developed and validated some frameworks for describing various task facets and features. However, few studies have explored how to depict and differentiate different stages or states of complex search tasks in a comprehensive, multidimensional manner. The existing theoretical models of search process offer limited contributions to search path evaluation and the design of system recommendations for users at different states. To address this issue at both theoretical and empirical levels, my dissertation aims to construct an explainable framework that can characterize the stages or states of complex search tasks over multiple dimensions and to apply the framework in proactive search path evaluation and recommendation.
Optimization of software requirement process: An integrated conceptual model of lean six sigma and requirement planning

Providing quality requirements in Software Engineering is vital to ensure the product developed is able to deploy and function to meet the operational objectives. Software Requirement Engineering is the most complex process because it involves the integration of human, logics and process. Extracting or capturing what customers need and want is called Requirement Elicitation (RE) and it is the most crucial process in requirement engineering. If handled poorly, the cost of the failures would be very expensive. Most of the software projects that failed were due to poor requirements which occurred at RE phase. Thus, enhancing and optimizing the RE methods have been subject to a long research debate to ensure quality requirements are captured. Recently, Lean Six Sigma (LSS) had emerged as part of a continuous improvement in Software Development Life Cycles (SDLC). LSS is known for a systematic and structure business improvement successfully deployed in many fields of industry that contributes a significant gain not only in quality of products and services but also in operational costs and delivery. The objective of the research is to develop an integrated conceptual framework of LSS principles with Software Requirement Engineering methodology to optimize RE process. The article will produce conceptual framework as the comprehensive guidelines to capture quality software requirements.
A Particle Induced X-Ray Emission (PIXE) Analysis of Heavy Metals in Soil and Plantain (&amp;lt;i&amp;gt;Musa paradisiaca&amp;lt;/i&amp;gt;) Leaves at an Artisanal Gold Mining Settlement in Southwestern Nigeria

The study examined the contamination levels of the soil and plantain leaves in three communities in Atakunmosa west local government area of Osun State in southwest Nigeria, where gold mining activities have recently become intensive.Plantain is a major food crop, whose leaves are used to wrap food items for household consumption.The objectives were to examine the heavy metal concentrations in the soil and plantain leaves around the gold mine site and compare them with standard recommended safe limits for the environment.Soil and plantain leaves were sampled at different locations around the sites and at a control site from a neighboring local government area with no history of gold mining activities between March 2015 and February 2016.Both soil and leave samples were processed and analyzed for selected heavy metals (Cd, As, Cu, Zn, Cr, Mn and Fe) using the Particle Induced X-ray Emission (PIXE) in the laboratory.The study showed higher concentrations than the World Health Organization's recommended safe limits of the heavy metals in the soils and plantain leaves.Dry season concentrations of the variables were also higher than the wet season and the heavy metal concentrations at the control station were significantly (p < 0.05) lower than those of the mining environment.The study concluded that the ecosystems in the artisanal gold mining region are vulnerable to bioaccumulation of heavy metals and the leaves from the sites are sources of heavy metal contamination if consumed or used to wrap food items.
High-dimensional star-shaped distributions

Stochastic representations of star-shaped distributed random vectors having heavy or light tail density generating function g are studied for increasing dimensions along with corresponding geometric measure representations. Intervals are considered where star radius variables take values with high probability, and the derivation of values of distribution functions of g-robust statistics is proved to be based upon considering random events whose probability is asymptotically negligible if the dimension of the sample vector is approaching infinity. Moreover, a principal component representation of p-generalized elliptically contoured p-generalized Gaussian distributions is discussed.
Preventing Forgeries by Securing Healthcare Data Using Blockchain Technology

Decentralization has gained a lot of attention due to its application in diverse fields. It is pioneered largely by bitcoin, a blockchain technology, and a financial application of decentralization, which has impacted a lot on how financial transactions happen in a secure manner. The advantage of using this technology is that there is no central authority to rely on. Thus, a decentralized storage of medical records would allow forgeries on the records to be reduced. We propose a solution to avoid forgery in healthcare sector using blockchain. The blockchain network in the proposed system will time-stamp and store healthcare management data and its associated files in the network storage. The network is decentralized; thus, the data is inherently secure. Yet this approach may create a storage exploitation and may lead to breakdown of the system. However, a machine learning-based classification model is used to decide upon which records that get into the blockchain to reduce the required storage. Hence, a system to securely store healthcare data using blockchain technology can be implemented or created.
Implementation of a Real Time Analog Secure Image Communication System via a Chaotic Circuit

A new secure image communication system has been designed and implemented based on a synchronized chaotic circuit. The new chaotic circuit, which has recently proposed by one of the authors contains a resistor, two inductors, and two diodes (i.e. R2L2D). The synchronization part operates with a master – slave configuration. In order to achieve the real-time communication, initially the analog master circuit has been implemented to generate chaotic sequences of numbers. Then, that analog tool has been transmitted to a computer via an analog/digital converter and a hidden image has been obtained. Besides, the slave circuit has also received a copy of that chaotic sequences in order to use it in subtraction process of image gray levels. The digital chaotic image has been transmitted to the slave circuit via a digital/analog converter with an efficient method and the decrypted image has been obtained in real-time. The advantage of that technique over the conversional ones is that it does not require any saved copy of the digital data, even for sending them in gray images to the receiver, since it is real-time. The conventional techniques use the stored and processed data and that can cause security problems in today’s advanced web media. Thus to send the encrypted image in a real-time device by using an analog chaotic equipment has certain security superiority. The analyses on the preliminary decrypted images proves that the process is efficient in time and secure.
The Impact of Systematic Edits in History Slicing

While extracting a subset of a commit history, specifying the necessary portion is a time-consuming task for developers. Several commit-based history slicing techniques have been proposed to identify dependencies between commits and to extract a related set of commits using a specific commit as a slicing criterion. However, the resulting subset of commits become large if commits for systematic edits whose changes do not depend on each other exist. We empirically investigated the impact of systematic edits on history slicing. In this study, commits in which systematic edits were detected are split between each file so that unnecessary dependencies between commits are eliminated. In several histories of open source systems, the size of history slices was reduced by 13.3-57.2% on average after splitting the commits for systematic edits.
The Transition into Virtual Reality

Abstract In “The Virtual and the Real,” David Chalmers argues that there is an epistemic and ontological parity between VR and ordinary reality. My argument here is that, whatever the plausibility of these claims, they provide no basis for supposing that there is a similar parity of value . Careful reflection upon certain aspects of the transition that individuals make from interacting with real-world, physical environments to interacting with VR provides a basis for thinking that, to the extent that there are good reasons to deny the reality of virtual objects, there are also reasons to place a correspondingly higher value upon the experience of interacting with a VR environment. Chalmers’ assumption to the contrary arises from a subtle misrepresentation of how the phenomenon of cognitive penetration works in the perception of virtual objects, and from an unwillingness to acknowledge how our attitudes toward virtual environments are conditioned by the values we adopt when engaged in gameplay.
Offside Detection System Using an Infrared Camera Tracking System

This paper describes an experimental offside detection system that will be capable of detecting offside passes during a game of soccer.Soccer is the world's most popular and most televised sport.In recent years, FIFA has implemented goal line technology in order to end controversial goals/missed goals during high profile competitive matches.The most contentious aspect of the sport is the offside rule and its many controversial calls or lack of calls.Sometimes the linesmen cannot see the passage of playing fast enough to make a correct decision.Being similar to goal line technology, people have requested offside technology to help the linesmen and to reduce the number of incorrect offside calls in a game.This paper describes a working offside detection system that can accurately detect offside passes.Positional data was exported from a VICON infrared motion tracking camera system and a MATLAB script was written so that it can analyze the positions of the players and the ball and determine if a pass was offside.
English in Saudi Arabia: Status and Challenges in The Light of Prince Mohammad Bin Salman’s Vision 2030

It is essential that educational institutions prepare students for the workforce especially when they are teaching English. In most Saudi Universities English Departments have been established in the Faculties of Arts, Languages, Education and Translation. However, recognition of the need for English in the Saudi educational system has not always been matched by acceptable educational outcomes. This is indicated by the inadequate number of well-trained and highly qualified teachers of English. Lack of recognition has hindered progress towards reaching the Kingdom Vision of 2030 that focuses on empowering citizens through reshaping the educational system and turning learners into skillful, educated and independent individuals. Therefore, this study examines the extent to which the KSA Vision 2030, in terms of teaching English as a foreign language in universities, is being implemented. A questionnaire was given to first year students at the Northern Border University, in Saudi Arabia. The questionnaire had two main sections, the first contained six general questions and the second section had 39 items covering very specific elements such as, Content &amp;amp; Teaching Methods, Evaluation &amp;amp; Assignments and Training &amp;amp; Professional Development. Analysing the data from the questionnaire was done using SPSS software.
Application of fuzzy logic to earthquake damage predictions

In this paper, a damage predicting form is developed using fuzzy logic techniques. An earthquake damages the building severely. In order to avoid and relieve devastation, it is essential to frame a disaster attentiveness plan according to the results of damage prediction. It is more coherent to deal with earthquake-related damage forecasting problems using fuzzy logic, as there be plenty of unclear and fuzzy concepts in damage predicting problem and relations involving concerning factors have confusing nature. The model is formulated by various parts “IF… THEN” approximate reasoning.
The Thermodynamic Variational Objective

We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variational inference and thermodynamic integration that results in a tighter lower bound to the log marginal likelihood than the standard variational variational evidence lower bound (ELBO) while remaining as broadly applicable. We provide a computationally efficient gradient estimator for the TVO that applies to continuous, discrete, and non-reparameterizable distributions and show that the objective functions used in variational inference, variational autoencoders, wake sleep, and inference compilation are all special cases of the TVO. We use the TVO to learn both discrete and continuous deep generative models and empirically demonstrate state of the art model and inference network learning.
Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time

A key requirement in sequence to sequence processing is the modeling of long range dependencies. To this end, a vast majority of the state-of-the-art models use attention mechanism which is of O($n^2$) complexity that leads to slow execution for long sequences. We introduce a new Shuffle-Exchange neural network model for sequence to sequence tasks which have O(log n) depth and O(n log n) total complexity. We show that this model is powerful enough to infer efficient algorithms for common algorithmic benchmarks including sorting, addition and multiplication. We evaluate our architecture on the challenging LAMBADA question answering dataset and compare it with the state-of-the-art models which use attention. Our model achieves competitive accuracy and scales to sequences with more than a hundred thousand of elements. We are confident that the proposed model has the potential for building more efficient architectures for processing large interrelated data in language modeling, music generation and other application domains.
Research on Curriculum Reform of Industrial Training Center Based on PDCA Cycle

Because of the rapid development of the modern society, the demand for the high educated students is becoming more and more urgent.At the same time, these graduates are also requested to have higher levels to suit the working conditions.Industrial Training is one of the most important series courses and play an irreplaceable role in cultivating compound talents.This paper use PDCA to discuss the course system of the Industrial Training Center in NUAA.With the use of PDCA, we redesign a new system for the whole center.When designing the new system, SOP and BSC are also used to full the whole system.It will provide a new way to the development of the Industrial Training Center.
Hybrid Algorithm for Rough Multi-level Multi-objective Decision Making Problems

The purpose of this paper is to generate compromise solutions for the multi-level multiobjective decision making (MLMODM) problems with rough parameters in the objective functions (RMLMODM) based on TOPSIS method and "Lower & Upper" approximations method.We introduce a computational hybrid algorithm for solving RMLMODM problems.Also, we solved illustrative numerical example and compared the solution of the proposed algorithm with the solution of Global Criterion (GC) method.The engineers and the scientists can apply the introduced hybrid algorithm to various practical RMLMODM problems to obtain numerical solutions.
Identifikasi Karakteristik Teknik Elisitasi pada Rekayasa Kebutuhan Perangkat Lunak: Sebuah Review Sistematis

Accuracy of elicitation techniques selection will affect the quality of software requirements.However, mostly the selection was based on familiarity only.Therefore, it is necessary to identify the characteristics of each technique and its development.The identification was initialized through paper review on requirements elicitation topics published between 2001-2016.The results show that interviews are most frequently studied techniques.In addition, interviews, brainstorming, and document analysis considered as developed techniques.The developments are related to the use of processing tools from interviews such as tool ladder, electronic communication software and analytical techniques, namely social network analysis and collaborative filtering.The use of web application tools and social network sites marked the development of brainstorming technique.While in document analysis, the involvement of business process models and organizational models characterized the development.Interviews, brainstorming, and document analysis are most frequently used combinations techniques.Document analysis is used to find basic information, while interview is used to engaged with core users, and brainstorming is used to determine the list of requirements.