Reviewerly/exHarmony huggingface dataset:
CyberDect. A Novel Approach for Cyberbullying Detection on Twitter

Bullying is the deliberate physical and psychological abuse that a child receives from other children. The term cyberbullying has recently emerged to denote a new type of bullying that takes place over digital platforms, where the stalkers can perform their crimes on the vulnerable victims. In severe cases, the harassment has lead the victims to the extreme causing irreparable damage or leading them to suicide. In order to stop cyberbullying, the scientific community is developing effective tools capable of detecting the harassment as soon as possible; however, these detection systems are still in an early stage and must be improved. Our contribution is CyberDect, an online-tool that seeks on Social Networks indications of harassment. In a nutshell, our proposal combines Open Source Intelligence tools with Natural Language Processing techniques to analyse posts seeking for abusive language towards the victim. The evaluation of our proposal has been performed with a case-study that consisted in monitor two real high school accounts from Spain.
Sentiment Analysis Analysis of E-Wallet Sentiments on Google Play Using the Naive Bayes Algorithm Based on Particle Swarm Optimization

Increasingly sophisticated technology brings various conveniences both in transportation, information, education to the convenience of transactions in shopping, such as the development of E-wallet can now be easily done using a smartphone. From a number of e-wallet products, researchers took a case study, which is OVO product, which is currently being discussed by many groups, especially in the capital of Jakarta today. Customers or clients who are not satisfied with the services or products offered by a company will usually write their complaints on social media or reviews on Google play. However, monitoring and organizing opinions from the public is also not easy. For this reason, we need a special method or technique that is able to categorize these reviews automatically, whether positive or negative. The algorithm used in this study is Naive Bayes Classifier (NB), with the optimization of the use of Particle Swarm Optimization Feature Selection (FS). The results of cross validation NB without FS are 82.30% for accuracy and 0.780 for AUC. Whereas for NB with FS is 83.60% for accuracy and 0.801 for AUC. Very significant improvement with the use of Feature Selection (FS) Particle Swarm Optimization.&#x0D;
Impact of Small Files on Hadoop Performance: Literature Survey and Open Points

Hadoop is an open-source framework written by java and used for bigdata processing. It consists of two main components: HadoopDistributed File System (HDFS) and MapReduce. HDFS is used tostore data while MapReduce is used to distribute and process anapplication tasks in a distributed processing form. Recently, severalresearchers employ Hadoop for processing big data. The resultsindicate that Hadoop performs well with Large Files (files larger thanData Node block size). Nevertheless, Hadoop performance decreaseswith small files that are less than its block size. This is because, smallfiles consume the memory of both the DataNode and the NameNode,and increases the execution time of the applications (i.e. decreasesMapReduce performance). In this paper, the problem of the small filesin Hadoop is defined and the existing approaches to solve this problemare classified and discussed. In addition, some open points that mustbe considered when thinking of a better approach to improve theHadoop performance when processing the small files.
A Meta-Analysis on the Effect of Flipped Instruction on K–12 Students' Academic Achievement

This proposal examines the effect of the flipped instruction on K-12 students' academic achievement.Based on the 25 empirical studies that accord with the inclusion/exclusion criteria, this study reveals that the weighted effect size of FI model is more effective (SE=.56,SE=.0782, 95% CI= [0.40, 0.71] in the random-effect model), although there are multiple evidences for publication bias.Meanwhile, this meta-analysis conducts the effect-size homogeneity and finds that there is high variation among the effect sizes.The mixed-effects ANOVA-like moderator analysis shows that different subject areas, contexts, and publication types significantly moderate the effect of flipped instruction.Finally, implications for enhancing the effect of flipped instruction is discussed in this proposal.*References marked with an asterisk indicate studies included in the review.
Autonomous Navigation of Unmanned Aerial Vehicles based on Android Smartphone

In the past few years, the adoption of drone technology across industries has increased dramatically as more businesses started to recognize its potential, uses, and scale of global reach.This paper proposes a design solution of a smart GPS quadcopter aircraft navigation system, discusses its hardware and software implementation process and eventually, analyses and reports the final test results.The flight path of the quadrotor is remotely manipulated via an Android based Graphical User Interface.This outdoor handheld application allows the operator to select a point of interest through Google map satellite view; consequently, the quadrotor takes off then hovers and ultimately lands on the destination location.Instructions in conjunction with coordinates are sent and received throughout a web server which serves the communication operation between the smartphone and the quadrotor.Experimental results yield fruitful data communication and successful autonomous flight control with smooth and stable maneuvering.
3D Global and Multi-View Local Features Combination Based Qualitative Action Recognition for Volleyball Game Analysis

Volleyball video analysis plays important roles in providing data for TV contents and developing strategies. Among all the topics of volleyball analysis, qualitative player action recognition is essential because it potentially provides not only the action that being performed but also the quality, which means how well the action is performed. However, most action recognition researches focus on the discrimination between different actions. The quality of an action, which is helpful for evaluation and training of the player skill, has only received little attention so far. The vital problems in qualitative action recognition include occlusion, small inter-class difference and various kinds of appearance caused by the player change. This paper proposes a 3D global and multi-view local features combination based recognition framework with global team formation feature, ball state feature and abrupt pose features. The above problems are solved by the combination of 3D global features (which hide the unstable and incomplete 2D motion feature caused by occlusion) and the multi-view local features (which get detailed local motion features of body parts in multiple viewpoints). Firstly, the team formation extracts the 3D trajectories from the whole team members rather than a single target player. This proposal focuses more on the entire feature while eliminating the personal effect. Secondly, the ball motion state feature extracts features from the 3D ball trajectory. The ball motion is not affected by the personal appearance, so this proposal ignores the influence of the players appearance and makes it more robust to target player change. At last, the abrupt pose feature consists of two parts: the abrupt hit frame pose (which extracts the contour shape of the player's pose at the hit time) and abrupt pose variation (which extracts the pose variation between the preparation pose and ending pose during the action). These two features make difference of each action quality more distinguishable by focusing on the motion standard and stability between different quality actions. Experiments are conducted on game videos from the Semifinal and Final Game of 2014 Japan Inter High School Games of Men's Volleyball in Tokyo Metropolitan Gymnasium. The experimental results show the accuracy achieves 97.26%, improving 11.33% for action discrimination and 91.76%, and improving 13.72% for action quality evaluation.
PPDP-PCAO: An Efficient High-Dimensional Data Releasing Method With Differential Privacy Protection

Privacy protection in data publishing is an extremely important issue that has been the focus of extensive research in recent years. However, the existing methods have a host of limitations, especially for high-dimensional data publishing. Aiming at the problem of poor availability of publishing results caused by “the curse of dimensionality” in high-dimensional data publishing, we present PPDP-PCAO (Privacy Preserving Data Publishing with Principal Component Analysis Optimization) method, which can better address the problem of the lower utility of release results because of the high noise introduced by the curse of dimensionality. PPDP-PCAO improves the Principal Component Analysis (PCA) algorithm by employing the attribute importance, and reduces the dimension of the data with the improved PCA, which reduces the time and space cost. PPDP-PCAO introduces the evaluation mechanism based on mutual-information into data release, which evaluates the data generated by setting the different quantities of principal components to determine the optimal quantities. PPDP-PCAO considers the existence of multi-sensitive attributes in high-dimensional data, while the traditional methods of allocating privacy budgets cannot satisfy the personalized privacy protection. PPDP-PCAO introduces the sensitivity preference, combines the optimal matching theory, and designs the sensitive attribute hierarchical protection strategy. Extensive experimental results on different real datasets demonstrate that PPDP-PCAO not only guarantees the privacy of published dataset, but also significantly improves the accuracy and data utility than other high-dimensional data publishing methods.
Towards Human Values Traceability in Software: A Goal Modeling Approach

Software has become an integral part of human life and while Software Engineering (SE) is aware of the values of security or privacy that relate to software quality, to date it is unable to account for the majority of the other human values (e.g., social justice, trust, freedom). Engineering human values into software is now emerging as an area of study but requires theoretical and methodological support from SE research and practice. A part of the challenge of integrating human values into software is related to tracing human values throughout the Software Development Life Cycle (SDLC). However, the number of studies in the domain of Human Values and SE is very limited. Moreover, there has been no previous study or practice performed on Human Values Traceability in SE. Therefore, this research study examines existing techniques and their applicability to trace human values in software. First, we explore the existing techniques that have the potential to trace human values in software. This exploration is extended from SE to other related domains such as Requirement Engineering, Software Design, Values in Design and Software Traceability. Identified techniques are then evaluated to identify the extent to which they can be used to trace human values in different phases of the SDLC. Our preliminary results found goal modeling and goal-centric traceability as promising techniques to initiate human values traceability in software. Therefore, we apply goal modeling to a case study to explore the ways this technique can be used and potentially improved to trace human values. Further, the improved technique will be evaluated through another case study.
Replay Attack and Defense of Electric Vehicle Charging on GB/T 27930-2015 Communication Protocol

The GB/T 27930-2015 protocol is the communication protocol between the non-vehicle-mounted charger and the battery management system (BMS) stipulated by the state.However, as the protocol adopts the way of broadcast communication and plaintext to transmit data, the data frame does not contain the source address and the destination address, making the Electric Vehicle (EV) vulnerable to replay attack in the charging process.In order to verify the security problems of the protocol, this paper uses 27,655 message data in the complete charging process provided by Shanghai Thaisen electric company, and analyzes these actual data frames one by one with the program written by C++.In order to enhance the security of the protocol, Rivest-Shamir-Adleman (RSA) digital signature and adding random numbers are proposed to resist replay attack.Under the experimental environment of Eclipse, the normal charging of electric vehicles, RSA digital signature and random number defense are simulated.Experimental results show that RSA digital signature cannot resist replay attack, and adding random numbers can effectively enhance the ability of EV to resist replay attack during charging.
A Clickthrough Rate Prediction Algorithm Based on Users’ Behaviors

Besides the ID class features, the advertisement click log file contains many significant features, which make the study of the advertisement clickthrough rate prediction more difficult. In this study, we convert original features into numerical meaningful ones, which reduce the sparsity and redundancy. In order to solve the problem of class imbalance, we propose a downsampling algorithm based on K-means model to classify large samples, then divide them into some sensible and rational features by the heuristic methods. To further improve the feature representation, we finally select and combine features by the Gradient Boosting Decision Tree model and process high-dimensional features by the logistic regression method. We conducted experiments on the dataset of Tencent SOSO and demonstrated that our approach outperforms the state-of-the-art baseline methods by 0.05% on average in terms of R2 and by 50.5% on average in terms of RMSE.
The Role of the iPad Tablet in Higher Education Science Teaching

At the University of Applied Health Sciences, the iPad tablets are used at the undergraduate professional study programs Environmental Health Engineering and Laboratory Medical Diagnostics as part of the practical classes in Biology and Chemistry. Four applications are used within the framework of the unit titled “Preparing Solutions”, “Periodic Table”, “Molarity”, “Chemist” and “Beaker”. Within the class on Biology, tablet computer applications are used to cover subject matter dealing with Genetics and Cell Structure (application “Cytogenetics”, “Diseases and disorders”, “Gene Screen”, “Population Genetics” and “Cell World”). Though e-teaching certainly cannot replace the classic course teaching, it is greatly beneficial for easier acquiring of the course subject matter.
Landau–Zener–Stückelberg Interference in Nonlinear Regime<sup>*</sup>

Landau–Zener–Stückelberg (LZS) interference has drawn renewed attention to quantum information processing research because it is not only an effective tool for characterizing two-level quantum systems but also a powerful approach to manipulate quantum states. Superconducting quantum circuits, due to their versatile tunability and degrees of control, are ideal platforms for studying LZS interference phenomena. We use a superconducting Xmon qubit to study LZS interference by parametrically modulating the qubit transition frequency nonlinearly. For dc flux biasing of the qubit slightly far away from the optimal flux point, the qubit excited state population shows an interference pattern that is very similar to the standard LZS interference in linear regime, except that all bands shift towards lower frequencies when increasing the rf modulation amplitude. For dc flux biasing close to the optimal flux point, the negative sidebands and the positive sidebands behave differently, resulting in an asymmetric interference pattern. The experimental results are also in good agreement with our analytical and numerical simulations .
Modeling the Structure of Intellectual Means of Decision-Making Using a System-Oriented NFO Approach

This article developed the structure of an intelligent decision-making system based on the constructed NFO-models, which demonstrated an increase in the effectiveness of decisions made in the medical diagnosis of some cardiovascular diseases.The presented architecture leads to the adoption of reliable decisions through the use of the verbal method of presenting data.This system uses existing approaches to formalization and diagnostics, conducts data mining, and provides the user with complete information for making a decision.Intelligent decision-making system can be used both in clinical practice by novice specialists, and in training students and doctors in continuing education institutions.
Computerized Item Modeling Practices using Computer Adaptive Formative Assessment Automatic Item Generation System: A Tutorial

This tutorial provides a user-friendly technical document to assist those interested in developing item models using Computer Adaptive Formative Assessment (CAFA) Automatic Item Generation (AIG), which is a specialized web-based system for AIG.The first section of the tutorial introduces AIG theory and background, clarifying its definition, listing its potential advantages, and explaining the underlying philosophy/theory.CAFA AIG system, a web-based system specialized in implementing AIG, is introduced in the second section.The tutorial concludes by providing three computerized item modeling examples to enable readers to understand how AIG can be applied in their own research.
Advanced analysis of self-potential data in ore deposits of the South Caucasus

Self-potential (SP) method is one of the most non-expensive and unsophisticated geophysical methods.However, its application limits absence of reliable interpreting methodology, first for the complex geological-environmental conditions.The typical disturbances appearing in the SP method and ways for their removing (elimination) are discussed.Some brief review of the available interpretation methods indicates their insufficient effectivity, especially for complex environments.For the magnetic method of geophysical prospecting have been recently developed special quantitative procedures applicable under complicated environments (oblique polarization, rugged relief and unknown level of the normal field).Performed analysis allowed to revealing some essential common peculiarities of magnetic and SP fields.These common aspects make it possible to apply the procedures developed in magnetic prospecting to SP method.Besides the reliable determination of the depth of anomalous target, these methodologies enable to introduce corrections for the polarization effect and non-horizontal SP observations.For classification of SPanomalies is supposed to employ a new parameter -'self-potential moment'.These procedures (improved modifications of characteristic point and tangent techniques) have been successfully tested both on SP models and in real situations in ore deposits in Turkey and Russia.Finally, interpretation procedures have been effectively applied at several ore deposits in the South Caucasus (Filizchai and Katsdag in Azerbaijan and Uchambo in Georgia).An effectiveness of multimodel approach with application of gravity, magnetic and SP methods is demonstrated on the generalized physico-geological model of ore body of Filizchai type.The obtained results indicate the high practical importance of the developed methodology.
Predicting Risk of Hospital Readmission for Comorbidity Patients through a Novel Deep Learning Framework

Hospital readmission is widely recognized as indicator of inpatient quality of care which has significant impact on healthcare cost.Thus, early recognition of readmission risk has been of growing interest in various hospitals.Additionally, there has been growing attention to provide better care to patients with more complications, whose care would impact the quality of care in multiple directions.To this regard, this research specifically targets comorbidity patients i.e., the patients with chronic disease.This research proposes a novel deep learningframework termed SDAE-GAN.The presented approach consists of three phases.Firstly, various groups of variables from heterogeneous sources are collated.These variables mainly include demographic, socioeconomic, some statistics about patient's frequent admissions and their diagnosis codes.Then, more processing applies dealing missing values, digitization and data balancing.Afterwards, stacked denoising auto-encoders function to learn underlying representation; and technically to forms a latent space.The latent variables then are used by a Generative Adversarial Neural Networks to evaluate the risk of 30day readmission.The model is fine-tuned and being compared with state-of-the-arts.Experimental results exhibit competitive performance with higher sensitivity.
Dynamic Hand Gesture to Text using Leap Motion

This paper presents a prototype for converting dynamic hand gestures to text by using a device called Leap Motion.It is one of the motion tracking technologies, which could be used for recognising hand gestures without the need of wearing any external devices or capturing any images and videos.In this study, five custom dynamic hand gestures of American Sign Language were created with Leap Motion to measure the recognition accuracy of the proposed prototype using the Geometric Template Matching, Artificial Neural Network, and Cross-Correlation algorithms.The experimental results showed that the prototype achieved recognition accuracy of more than 90% in the training phase and about 60% in the testing phase.
EFL Students’ Voices: The Role of YouTube in Developing English Competences

YouTube is one of the most visited video-sharing platforms in the world. On YouTube, students can watch many kinds of videos. This research explored the types of videos often watched by English as A Foreign Language (EFL) students in a private university in Yogyakarta. Additionally, the aspects of students’ English competence got improvement by watching YouTube videos. The researcher employed a descriptive qualitative research design in this study. Four seniors of an English Language Education Department (ELED) in one private Islamic university in Yogyakarta participated in this study. The findings of the study indicated that there were three categories of YouTube videos often watched by students, such as art and humanities, vlogs, and social sciences. Art and humanities videos consist of a music video or lyric video, film, talks, sitcom, and talk show. Vlogs include food vlog, review video, beauty vlog, haul vlog, and DIY videos. Social sciences contain simulation and educational videos. To summarize, the students watch YouTube based on their interests. The result also revealed the aspects of English improved through watching YouTube videos. They are speaking skills, listening skills, pronunciation, vocabulary, and grammar.