Reviewerly/exHarmony huggingface dataset:
IMPROVING EFFICIENCY OF AWEB-BASEDDISTRIBUTED EVOLUTIONARY COMPUTING SYSTEM

The paper presents a study on performance improvement of an evolutionary computing system built on the basis of JavaScript technology.The system consists of web browsers performing computational tasks and a web server based on Go programming language (Golang technology) that controls the computing environment.The newest web technologies such as HTML5 Web Workres and JavaScript WebAssembly are used to maximize the utilization of the resources provided by web clients.Computational experiments are performed on large instances of popular NP-hard problems: travelling salesman problem (TSP), flowshop scheduling problem (FSP) and recourse constrained project scheduling problem (RCPSP).
Rancang Bangun Aplikasi Face Tracking dan Filter Berdasarkan Raut Wajah Menggunakan Algoritma Fisher-Yates Berbasis iOS

Expressions of facial expressions in addition to providing important emotional indicators, are very important objects in our daily lives too. Real-time video processing on mobile devices is a hot topic and has a very broad application. Photos that have used the filter have 21% more possibilities to be seen and 45% more likely to be commented on by photo consumers. The use of the Fisher-Yates algorithm is used as a filter scrambler for each facial expression emotion. The application is made for the iOS operating system with the Swift programming language that utilizes the Core ML and Vision framework. Custom Vision is used as a tool for creating and training models. In making a model, this study uses a dataset from Cohn-Kanade AU-Coded Facial Expression Database and Karolinska Directed Emotional Faces. Custom Vision can provide performance result training and provide precision and recall values ​​for data that has been trained. The facial expression match with the model is determined by the confidence level value. The results of trials with Hedonic Motivation System Adoption Model method produce a percentage of pleasure in using the application (joy) of 79.39% of the users agree that the application provides joy. &#x0D;
The centered ternary balance scheme: A technique to visualize surfaces of unbalanced three-part compositions

Background: The ternary balance scheme is a visualization technique that encodes three-part compositions as a mixture of three primary colors. The technique works best if the compositional data are well spread out across the domain but fails to show structure in very unbalanced data. Objective: I extend the ternary balance scheme such that it can be utilized to show variation in unbalanced compositional surfaces. Methods: By reprojecting an unbalanced compositional data set around its center of location and visualizing the transformed data with a standard ternary balance scheme, the internal variation of the data becomes visible. An appropriate centering operation has been deﬁned within the ﬁeld of compositional data analysis. Results: Using Europe’s regional workforce structure by economic sector as an example, I have demonstrated the utility of the centered ternary balance scheme in visualizing variation across unbalanced compositional surfaces. Contribution: I have proposed a technique to visualize the internal variation in surfaces of highly unbalanced compositional data and implemented it in the tricolore R package.
A multi-agent system approach based on cryptographic algorithm for securing communications and protecting stored data in the cloud-computing environment

The cloud computing has been envisioned as the next generation architecture of information technology enterprise. The Internet is indispensable in present days to provide and maintain the data. Such applications can be used by the end-user via the cloud communications without any configuration. Moreover, the user's data files can be accessed from any other computer using the Internet services. The security goal is to save data from threat and vulnerability which is handled by various approaches. The data should be encrypted prior to be transmitted and stored. We aim to propose a solution such that we will encrypt the whole data along with the cryptographic key. This contribution provides a new architecture that combines, obfuscation technique, hybrid encryption algorithms and the homomorphic encryption approaches. The proposed security architecture based on multi-agent system for cloud computing communications and storage environment that takes into account the most known security gaps.
A Cloud Environment: A First Demo

This last chapter has the goal of demonstrating the proposal made in Chaps. 6 and 7. The demonstration version presented here has been developed in an Amazon Linux 2 AMI instance, and Shiny and RStudio were used for its development. An operational version that can be used for academic and/or professional purposes will be available at the URLs www.meta-foresight.org and www.lametaprospective.org by the end of next winter 2019–2020.
Self-directed Learning Activities in Problem-based Learning: Focused on the Applying to Primary Social Studies Class

문제중심학습은 학습자의 자기주도적 학습효과를 구현할 수 있다는 장점을 갖고 있는 모형으로 인정받고 있다. 그래서 문제중심학습은 2015개정교육과정에서도 긴요하게 적용되기를 요구받고 있는 교수학습 모형이다. 특히, 사회과교육은 민주시민성 함양을 교육의 목표로 삼고 있기 때문에 자기주도적 학습력 함양 교육의 필요성을 더 많이 요구받고 있다. 이에, 본 연구에서는 특별히, 자기주도적 학습력 향상에 초점을 두고, 이를 이끌어낼 수 있는 문제중심학습의 방안을 제시해 보고자 하였다. 이를 위해 연구자는 자기주도적 학습 및 문제중심학습과 관련된 선행연구들을 검토하여 자기주도적 학습력 향상을 위한 학습전략을 분석하였고, 문제중심학습의 일반적인 교수-학습 과정에 적용할 수 있는 방안을 탐색하였다. 자기주도적 학습력 향상을 위해 학습동기, 메타 인지전략 활용, 상호작용 활성화, 자기성찰 등 4가지 학습전략을 추출하였다. 이러한 전략적 학습요소들은 문제중심학습의 각 단계에 특화되어 적용된다기 보다는 전 단계에서 유기적으로 연계 적용되어야 함을 제시하였다. 또한, 문제발견 및 문제정의 단계에서는 학습자주도적 문제발견과 문제정의가 이뤄져서 이후 학습에 대한 학습동기가 촉발되도록 하는 것과 문제해결 계획 및 탐색 활동 단계에서 학습자가 주도적으로 자신의 학습자원을 검토, 관리하면서 자신의 경험 및 지식과 연계하여 조직화된 전략을 설계하도록 하는 과정이 자기주도적 문제중심학습을 수행하는 데 중요함을 제시하였다.Problem-based learning is recognized as a model that has the advantage of being able to implement learner-centered education and education linked to the learner s real life. Therefore, problem-based learning leads us to expect the improvement of the learner s thinking power and the learner s self-directed learning ability. This is why problem-based learning has received much attention as a teaching-learning model today. Especially, in the social studies education, the purpose of education is to cultivate the democratic citizenship and the inquiry learning is applied as the main learning method. So, it is necessary to cultivate self-directed learning ability. This study focuses on the improvement of self-directed learning ability and suggests a problem-based learning approach that can lead to self-directed learning ability. To do this, I analyzed the learning strategies for improving self-directed learning ability by reviewing previous studies related to self-directed learning and explored the general teaching-learning process of problem-based learning by reviewing previous problem-based learning studies. I suggested a problem-based learning approach for self-directed learning by applying four strategies, learning motivation, meta-cognition, interaction, and self-reflection.
JSS special issue program debugging and repair

Nowadays, the continuously evolving open-source community and the increasing demands of end users are forming a new software development paradigm; developers rely more on reusing components from online sources to minimize the time and cost of software development. An important challenge in this context is to evaluate the degree to which a software component is suitable for reuse, i.e. its reusability. Contemporary approaches assess reusability using static analysis metrics by relying on the help of experts, who usually set metric thresholds or provide ground truth values so that estimation models are built. However, even when expert help is available, it may still be subjective or case-specific. In this work, we refrain from expert-based solutions and employ the actual reuse rate of source code components as ground truth for building a reusability estimation model. We initially build a benchmark dataset, harnessing the power of online repositories to determine the number of reuse occurrences for each component in the dataset. Subsequently, we build a model based on static analysis metrics to assess reusability from five different properties: complexity, cohesion, coupling, inheritance, documentation and size. The evaluation of our methodology indicates that our system can effectively assess reusability as perceived by developers.
Avoiding Pitfalls and Extracting Value: Lessons for Induced Seismicity Monitoring.

Summary We present case studies from microseismic monitoring including the Groningen gas field and the Decatur CCS site. We highlight often overlooked monitoring considerations including choice of location algorithm and waveform complexity, and demonstrate how modelling can avoid or mitigate the negative effects. Interpretation of microseismicity can lead to valuable insight into reservoir processes. However, to extract the most value, analyses must go beyond standard processing techniques. We show how combining waveform cross-correlation, full-waveform modelling, travel-time and ray-path analysis, and relative relocation methods can provide the necessary constraints to deliver improved interpretations from microseismic data sets.
Statistical mechanics of maximally informative representations

We show that representations that are maximally informative on the generating process of complex data, such as those produced by deep learning machines, are characterized by an exponential distribution of energy levels. This corresponds to the fact that finite samples drawn from Maximally Informative Representations (MIR) exhibit statistical criticality. In particular, Zipf's law arises at the threshold between lossy and lossless compression. The reason why statistical criticality requires no parameter fine tuning in maximally informative samples is that, MIR satisfy a principle of minimal entropy, that is conjugate of the maximum entropy principle in statistical mechanics. Indeed, energy corresponds to a coding cost in MIR and it's relation to the probability of states is fixed by information theory. The Boltzmann entropy, instead, measures the residual degeneracy between states that a representation cannot untangle. Hence MIR minimize the entropy at a fixed average energy, over the distribution of energy levels. Furthermore, we study an ensemble of optimisation problems over random functions of many variables, part of which describe a sub-system and the rest account for its interaction with the environment. We show that this problem reproduces the statistics of MIR and of physical systems, like the Random Energy Model, thereby highlighting the distinctive features of MIR. We show, in particular, that MIR sit at the boundary between learnable systems with a smooth energy landscape and unlearnable ones with a rough energy landscape. In addition, we show that MIR are invariant under the addition of irrelevant details.
Evaluating User Perception of Multi-Factor Authentication: A Systematic Review.

Security vulnerabilities of traditional single factor authentication has become a major concern for security practitioners and researchers. To mitigate single point failures, new and technologically advanced Multi-Factor Authentication (MFA) tools have been developed as security solutions. However, the usability and adoption of such tools have raised concerns. An obvious solution can be viewed as conducting user studies to create more user-friendly MFA tools. To learn more, we performed a systematic literature review of recently published academic papers (N = 623) that primarily focused on MFA technologies. While majority of these papers (m = 300) proposed new MFA tools, only 9.1% of papers performed any user evaluation research. Our meta-analysis of user focused studies (n = 57) showed that researchers found lower adoption rate to be inevitable for MFAs, while avoidance was pervasive among mandatory use. Furthermore, we noted several reporting and methodological discrepancies in the user focused studies. We identified trends in participant recruitment that is indicative of demographic biases.
Experimentation and Analysis of Ensemble Deep Learning in IoT Applications

This paper presents an experimental study of Ensemble Deep Learning (DL) techniques for the analysis of time series data on IoT devices. We have shown in our earlier work that DL demonstrates superior performance compared to traditional machine learning techniques on fall detection applications due to the fact that important features in time series data can be learned and need not be determined manually by the domain expert. However, DL networks generally require large datasets for training. In the health care domain, such as the real-time smartwatch-based fall detection, there are no publicly available large annotated datasets that can be used for training, due to the nature of the problem (i.e. a fall is not a common event). Moreover, fall data is also inherently noisy since motions generated by the wrist-worn smartwatch can be mistaken for a fall. This paper explores combing DL (Recurrent Neural Network) with ensemble techniques (Stacking and AdaBoosting) using a fall detection application as a case study. We conducted a series of experiments using two different datasets of simulated falls for training various ensemble models. Our results show that an ensemble of deep learning models combined by the stacking ensemble technique, outperforms a single deep learning model trained on the same data samples, and thus, may be better suited for small-size datasets.
Seismic Fault Damage Zone Characterisation for Reservoir Modelling Using Advanced Attribute Analysis

Summary Faults play a key role in reservoir connectivity by enhancing or restricting fluid flow. While seismic data is one the main ways of subsurface investigation, faults are still interpreted as surfaces as their internal structure is often at the limit of seismic resolution. In order to populate reservoir models of fault volumes using fault facies techniques, we introduce an innovative seismic attribute analysis to characterise fault core and damage zone from undamaged surrounding rocks in siliclastic settings. We use a total horizontal derivative attribute in addition to the standard seismic attributes applied to fault analyses, and use statistical methods to establish the outer limits of the damage zone. We apply our workflow in porous silisclastic normal faulting in the Thebe Gas Field, Exmouth Plateau of the Canarvon basin, offshore Northwest Australia. Based on this analysis, we are able to define seismic facies calibrated with well and analogue data that can be used for spatial conditioning of fault-facies based reservoir modelling. Our work provide a visual and quantitative tool to define the internal structure of the fault damage zone in order to model fluid flow more accurately in reservoir grids.
Development of University Students’ Motivation with the Help of E-learning

relevance of the study is due to the orientation of the modern model of education to the formation of a competitive creative personality with the need of continual self-improvement and self-development. It is possible due to the high level of the learning motivation of students and the motivation to acquire a future profession. It should be emphasized that e-learning has a considerable potential in this aspect. In this regard, this article is aimed to reveal the specifics of the use of e-learning means in the development of university students motivation. article presents a series of lessons designed to develop students’ motivation, implemented by e-learning means. We elaborated a series of webinars (“I want to be an engineer – I want to be taught!”; Information crystals (effective information processing techniques), Time Management Secrets, Kaleidoscope of discoveries, Jolly mathematics); chats (The engineer of the third millennium - the future engineer, who is he?, Physics, Chemistry for Engineers: Myth and Reality); the virtual round table - The heritage of the past (about great scientists, inventions and discoveries) and others. article presents the ways of applying e-learning in the development of university students’ motivation. On one hand, it is the methodological basis of the training system; on the other hand, it provides the usage of scientific knowledge in the study of professional disciplines.
SCAH!RF: A Novel Wearable as a Subconscious Approach for Mitigating Anxiety Symptoms

Mobile and wearable interfaces have long been developed to improve mental health issues, including anxiety disorders, which represent a significant public health problem affecting more than 250 million people. However, most of the current approaches still operate in the so-called “reflective mind”, which hampers results since reflecting on your own health data can induce even more stress and anxiety. In this poster we introduce an alternative approach towards mitigating anxiety symptoms through the use of “subtle” wearable interfaces. Capitalizing on the subconscious processes of the mind is particularly attractive for anxiety disorders. We present a smart wearable in the form of a scarf that implements a subconscious, less-invasive approach in the design of assistive technologies for mental health. Preliminary results bring important implications for interaction design: combining psychological conditioning therapy (via a mobile app) with our smart scarf provides a solution that can be worn anytime anywhere to fight anxiety symptoms. But this work also raises many privacy and ethical concerns which should be discussed by the HCI community: how can designers balance the opaqueness of subconscious approaches with the necessary ethical transparency? And how can mental health technologies be conceived in such a way they do not instigate societal stigma in users?
Bargaining Compatible Explanations

Within the framework of ensemble methods, we investigate on a compatible learning scheme, denoted as learning by gossip with the aim of assessing its feasibility when facing a rather complex target function. Compatibility is in terms of probability that the learned function could be actually at the basis of the observed training set, hence an explanation of it. Feasibility is in terms of the related MSE on test sets. We base or conclusions on both theoretical and numerical arguments that are tossed on a well known benchmark.
Analysis of Aggregate Gradations Using Image Analysis Technique through Bailey Method

Aggregate gradation plays a vital role in the performance of an asphalt mixture as the stress distribution in the mix depends upon the aggregate gradation. The grain to grain contact depends upon the gradation and could be analyzed by examining the structure using image analysis technique. Bailey method evaluates a gradation and optimizes the gradation to improve its grain-to-grain contact and facilitate anti-rutting properties. The current study focuses on the relation between the Bailey method’s gradation parameters and the permanent deformation characteristics through internal aggregate structure characterization using image analysis technique. Image processing techniques using MATLAB software was used to determine the contact points (CP) and contact length (CL) of the gradations. Rut resistance increased with increase in coarse aggregate (CA) ratio and decrease in coarser portion of fine aggregate (FAc) and finer portion of fine aggregate (FAf) ratios. The mixes which have higher rut depths were found to have lower contact points and contact length. Through this study, it was concluded that Bailey method of gradation would be useful in selecting certain gradations which are resistant to permanent deformation. Correlation matrix revealed CA ratio significantly affects contact points and contact length.
Cyber literacy of bank customers-A study in Udupi and D.K District

The alarming note for India is, around 80% of the cybercrime will never get reported. Online shopping and online banking are enhancing their space in the economic activities. Use of smartphone for the same is common phenomena. Everything is getting connected, but we are missing the lines of the safety and security in economic interactions. To redress this, knowing the ground realities and problems of the parties concerned is worthy endeavored. The present study focusing on Cyber literacy among bank customers is an attempt to contribute a snapshot of reality
Methodological Issues and Writing Strategies of Autoethnography

이 글의 목적은 저자의 자문화기술지 논문 지도 경험에서 비롯된 방법적 이슈와 그에 따른 글쓰기 전략을 학술적으로 이야기함으로써 자문화기술지의 고유한 방법적 특성을 이해하고, 그것의 엄격성을 강화하는 데 있다. 연구결과를 간략하게 제시하면 다음과 같다. 첫째, 나는 자문화기술자의 상황성과 위치성, 의식과 반영성, 그리고 윤리적 이슈에 대한 글쓰기 전략으로써 저자의 제한적인 상황과 위치를 적극적으로 드러내는 ‘연약한 저술’을 강조하였다. 둘째, 나는 원 자료의 출처와 신빙성, 질적 자료 분석, 분석과 감성의 혼재라는 방법적 이슈에 직면하였고, 자료 및 냉철한 분석에 기초하여 감성을 함께 서술하는 글쓰기 전략을 제시하였다. 셋째, 개인적 이야기와 철학 및 사회이론과의 관련, 개인적 이야기와 사회문화적 맥락 및 조건의 연결에 대한 글쓰기 전략으로는 저자의 개인적 이야기에서 공유된 의미를 도출하는 글쓰기를 요구하였다. 넷째, 나는 연구 설계의 평가준거 충족도, 타당도 평가준거, 자문화기술지의 세부 유형과 재현방식, APA 논문작성 논리 및 지침에 대응하는 글쓰기 전략을 추천하였다. 이 연구는 자문화지술지의 방법적 엄격성을 강화하고, 그것의 질을 가늠하기 위한 평가준거를 개발하는 데 도움이 될 수 있을 것이다.The aim of this paper is to understand methodological characteristics of autoethnography, and to reinforce methodical rigor by discussing methodical issues and writing strategies, which is derived from an author’s experience of evaluation and feedback on autoethnographic manuscripts during 6 years. The specific results and conclusions are as follows. Firstly, I as a methodologist had emphasized ‘vulnerable authorship’ to reveal restricted situation and position of an author as a strategy to deal with situatedness and positionality, self-consciousness, reflexivity, and ethical issue. Secondly, I as a journal reviewer and supervisor had suggested a moderate (mixture of analysis and emotion) writing style, which is rooted in raw data and rigorous data analysis to acquire validity and trustworthiness. Thirdly, a good autoethnography required shared meaning and understanding from an author’s personal narrative. Therefore, I called for autoethnography to draw shared meaning from personal narrative as a strategy to write about linking personal narrative with philosophy, social theory, sociocultural context and condition. Fourthly, I as a qualitative methodologist had recommended four writing strategies to correspond evaluative criteria in research design, validity criteria, various types and representation modes of autoethnography, and APA’s writing guideline. The results of this paper are expected to be a cornerstone to reinforce methodical rigor and develop evaluative criteria of emerging autoethnography.
PercevalCam a Smart Camera for Computer Vision and Deep Learning for Robotics

AI and more particularly the Deep Learning is becoming pervasive when perception is involved. Indeed, in the recent years, DNNs have succeeded to enhance the state-of-the-art with a quality that was not even expected. This revolution can be partially granted to the introduction of large GPUs. This technology allowed to compute, very quickly and in parallel, the training phase and the inference phase of deep learning algorithms. It can be stated that GPU technology is the foundation that allowed the maturity of DNN. However, in situations where these large GPUs are not available, which is mainly occurring on the edge, it is currently not possible to obtain real-time results that are not a trade-off relatively to the quality. This point is even more sensitive for the industrial sector where real-time means having a solution in only few milliseconds. Indeed, many industrial systems like robots have to answer reliably in a very short period of time. Usually, these systems cannot embed voluminous computer, can be limited in term of battery life, or simply want to limit the global cost.