Reviewerly/exHarmony huggingface dataset:
Hybrid Evolutionary Methods

The limitations of single algorithm approaches lead to an attempt to hybridize or fuse multiple algorithms in the hope of removing the underlying limitations. In this chapter, the authors study the evolutionary algorithms for problem solving and try to use them in a unique manner so as to get a better performance. In the first approach, they use an evolutionary algorithm for solving the problem of motion planning in a static environment. An additional factor called momentum is introduced that controls the granularity with which a robotic path is traversed to compute its fitness. By varying the momentum, the map may be treated finer or coarser. The path evolves along the generations, with each generation adding to the maximum possible complexity of the path. Along with complexity (number of turns), the authors optimize the total path length as well as the minimum distance from the obstacle in the robotic path. The requirement of evolutionary parameter individuals as well as the maximum complexity is less at the start and more at the later stages of the algorithm. Momentum is made to decrease as the algorithm proceeds. This makes the exploration vague at the start and detailed at the later stages. As an extension to the same work, in the second approach of the chapter, the authors show the manner in which a hybrid algorithm may be used in place of simple genetic algorithm for solving the problem with momentum. A Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) algorithm, which is a hybrid of a genetic algorithm and particle swarm optimization, is used in the same modeling scenario. In the third and last approach, the authors present a hierarchical evolutionary algorithm that operates in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced for computational speed. The finer hierarchy takes a section of the map and computes the path for both static and dynamic environments. Both these hierarchies carry optimization as the robot travels in the map. The static environment path gets more and more optimized along with generations. Hence, an extra setup cost is not required like other evolutionary approaches. The finer hierarchy makes the robot easily escape from the moving obstacle, almost following the path shown by the coarser hierarchy. This hierarchy extrapolates the movements of the various objects by assuming them to be moving with same speed and direction.
Enhanced Fireworks Algorithm with an Improved Gaussian Sparks Operator

As a population-based intelligence algorithm, fireworks algorithm simulates the firework’s explosion process to solve optimization problem. A comprehensive study on Gaussian spark operator in enhanced fireworks algorithm (EFWA) reveals that the search trajectory is limited by the difference vector and the diversity of swarm is not effectively increased by new sparks adding. An improved version of EFWA (IEFWA) is proposed to overcome these limitations. In IEFWA, a new Gaussian spark operator utilizes the location information of the best firework and randomly selected firework to calculate the center position and explosion amplitude, which enhance the search for potential region. Experiments on 20 well-known benchmark functions are conducted to illustrate the performance of IEFWA. The results turn out IEFWA outperforms EFWA and dynFWA on most testing functions.
A Robust Sampling Technique to Reduce Classification Time for Human Activity Recognition

Objectives: This study is an endeavor to provide quick, on-the-go classification of a human activity dataset with an aim to improve on the classification time of a machine learning algorithm for Human Activity Recognition (HAR) datasets. Methods/Statistical analysis: It proposes the use of a customized sampler called the Normal On-The-Go (Normal OTG) sampler to reduce the classification time. Concocted using a combination of stratified, random and normal sampling, the Normal OTG sampler was tested on HAR datasets and was found to significantly reduce the training time of the most commonly used machine learning algorithms. Three datasets, ShoaibSA, ShoaibPA and USC-HAD were used to conduct the experiments. Findings: It was found that using as little as 5% samples from the training dataset sampled by the Normal OTG sampler, sufficiently reliable accuracy was obtained from most of the 9 classifiers that were used. The results indicated that almost 96% of time was saved in the training process in the case of USC-HAD, and 62% and 83% time was saved in the case of ShoaibPA and ShoaibSA respectively. It was also found that the results were consistent among the three datasets. Application/Improvements: The study helps training of data in human activity recognition a faster process and thereof, making algorithm selection a less tedious procedure Keywords: Classification Time, Human Activity Recognition, Robust, Sampling Technique
Speed invariant algebraic combinations of mass, length, and time

Three simple relations obtained by algebraically pairing the relativistic expressions for mass, length, and time—such that each pair is speed-independent—are presented. The presentation includes a pedagogical application of the said relations.
Context Analysis for Computer-Assisted Near-Synonym Learning

Despite their similar meanings, near-synonyms may have different usages in different contexts. For second-language learners, such differences are not easily grasped in practical use. This chapter introduces several context analysis techniques such as pointwise mutual information (PMI), n-gram language model, latent semantic analysis (LSA), and independent component analysis (ICA) to verify whether near-synonyms do match the given contexts. Applications can benefit from such techniques to provide useful contextual information for learners, making it easier for them to understand different usages of various near-synonyms. Based on these context analysis techniques, we build a prototype computer-assisted near-synonym learning system. In experiments, we evaluate the context analysis methods on both Chinese and English sentences, and compared its performance to several previously proposed supervised and unsupervised methods. Experimental results show that training on the independent components that contain useful contextual features with minimized term dependence can improve the classifiers’ ability to discriminate among near-synonyms, thus yielding better performance.
Vocal Performance Through Electrical Flows: Making Current Kin

What do we hear in a human voice that vibrates through electrical flows? In this paper I argue for listening (and vocalizing) beyond the human in performances with audio media. I propose understanding such performance practice as engaging with what I call plasmatic voice, a phenomenon distinct from the merely additive, prosthetic conception of voice + electricity. Instead, plasmatic voice functions as instances of queer assemblage stretching to reach the radically Other that constitutes ourselves—facilitating the sense of what Alaimo (2010) terms transcorporeality, an understanding of human embodiment as “intermeshed with the more-than-human world” (2). The vibrations of plasmatic voice—as an example of Eidsheim’s (2015) intermaterial vibrational practice—loosen (post)human social constructs of race and gender and reverberate with nonhuman ecosystems, as I illustrate through analysis of musical examples.
Modeling and simulation of transients in electric power systems using hybrid system theory

Transients in electric power systems are of great interest to power engineers when designing a new or maintaining an existing system. The paper deals with using hybrid system theory for modeling and simulation of an electric power system with controllers. The presented technique is rather convenient and recommended as mathematical models of transients in electric power systems with controllers in general contain both continuous and discrete components. The modeling and simulation were carried out in the modeling and simulation environment ISMA, which is briefly presented in the paper.
Visualizing the Daily Physical Activities and Nutrition Information of High School Athletes

Tracking food intake, sleep, and physical activity, then generating consolidated peer visual reports for participants in large-scale health studies, can be challenging. This paper presents an integrated health study dashboard that enables scientists to conduct large-scale field studies involving hundreds of subjects organized into dozens of treatment groups, including the collection and analysis of data from devices and surveys. The system also provides a feature for the scientist to perform bulk processing and editing of subject demographic information, as well as to generate health reports. The system was used in six rounds of reports for 750 high school students. A preliminary qualitative evaluation was conducted with the user responsible for generating those reports for all the students, as well as five other graduate students who could use the system for other field studies in health science. Their answers revealed that the system was considered both usable and effective.
Characteristics of the Provocative Motion Stimuli

A considerable amount of work has been performed in an attempt to identify the characteristics of motion that provoke motion sickness. As you will see, this has ranged from studies in the laboratory to others that have taken place in environments more akin to the real world. Apart from giving us a better understanding of the mechanism of motion sickness, these data provide valuable design criteria to reduce provocative vehicular responses; in addition, however, we must not forget the operator’s personality and cognition. This is an example of the value of recognising the need to design with the human operator or traveller in mind from day one.
Wall-Following Control of Multi-robot Based on Moving Target Tracking and Obstacle Avoidance

Studies on wall-following problem mostly focus on an single robot only. This study proposes a wall-following method of multi-robot, which is based on moving target tracking. The leader robot moves along walls by using position information include angle and distance between robots and walls. The follower robot moves along the walls, tracks its leader and avoids collision at convex corner of walls. Two E-puck robots are used in experiment in this study. The results of experiment verify the feasibility of this method.
The Flip Side of the Coin: The Social and Ethical Problems of Introducing Digital Technologies

The article based on the generalization of data from foreign and domestic studies highlighted social and ethical problems and risks caused by the introduction of digital technologies and their manifestations in the field of human resource management. The main problems are the following: structural changes in the labor market and the attendant risks of reducing the level of professionalism and the educational system; “Digital inequality”, the emergence of new types of discrimination, including when working with big data and making decisions based on a proactive approach, and their impact on the growth of social inequality and social tension; changing confidentiality in professional and private spheres; problems associated with the development of the practice of monitoring and monitoring the effectiveness of personnel activities, incl. their impact on labor relations; responsibility issues and ethical boundaries of autonomous and intelligent technologies.
Pedagogical Model of Education of Primary Schoolchildren in the Framework of the project “Digital School”

The article discusses the features and priorities of the education of younger schoolchildren in the conditions of informatization of modern society. We are talking about the changes that followed the large-scale implementation of information and communication technologies in all spheres of life in modern society, including the primary school education process. Changes in the approach to the education of younger students are presented based on the results of the study.
Hardware-Accelerated Secured Naïve Bayesian Filter Based on Partially Homomorphic Encryption

In this work, we provide the first practical secure email filtering scheme based on homomorphic encryption. Specifically, we construct a secure naïve Bayesian filter (SNBF) using the Paillier scheme, a partially homomorphic encryption (PHE) scheme. We first show that SNBF can be implemented with only the additive homomorphism, thus eliminating the need to employ expensive fully homomorphic schemes. In addition, the design space for specialized hardware architecture realizing SNBF is explored. We utilize a recursive Karatsuba Montgomery structure to accelerate the homomorphic operations, where multiplication of 2048-bit integers are carried out. Through the experiment, both software and hardware versions of the SNBF are implemented. On software, 104-105x runtime and 103x storage reduction are achieved by SNBF, when compared to existing fully homomorphic approaches. By instantiating the designed hardware for SNBF, a further 33x runtime and 1919x power reduction are achieved. The proposed hardware implementation classifies an average-length email in under 0.5s, which is much more practical than existing solutions.
Information Technology for the Coordinated Control of Unmanned Aerial Vehicle Teams Based on the Scenario-Case Approach

The authors present a dynamic scenario-case approach to coordinated control of heterogeneous ensembles of unmanned aerial vehicles, which use coordination patterns of activity in similar situations described as spatial configurations affected by observed events. The method of obtaining deviations for approximate spatial configurations, which allows obtaining elements of the safe vehicle's trajectories. The method of qualitative safety assessment is presented. It uses a soft level topology to obtaining blurred boundaries of dynamic safety domains using fuzzy soft level sets and allows finding suitable compensations of vehicles' activity scenarios that can both keep the spatial configuration and satisfy all safety restrictions. The authors demonstrate that the proposed approach significantly reduces the computational complexity of problem solving and provides the acceptable performance.
KONUŞMACI DOĞRULAMA İÇİN EĞİTİM SETİ OPTİMİZASYONLU İKİ AŞAMALI KARAR VERME ALGORİTMASI

In this paper, a two-stage decision making algorithm is proposed for the task of speaker verification. This two-stage algorithm aims to eliminate the first-stage qualifying impostors by the help of impostor-resistant structure in the second stage. First, a baseline system is formed using mel-frequency cepstral coefficients (MFCC) as features and, a radial basis function (RBF) neural network for speaker modelling. Then, the investigations have been realized for optimizing the training set by means of two issues: (1) the ratio of impostor features to genuine speaker features, (2) the ratio of same gender features to opposite gender features (in respect of the genuine speaker) within the impostor speakers’ set. Last, the two-stage decision making algorithm is presented, and the performance enhancement provided by the two-stage system is given with the test results.
Greek-Chinese Interlinear of the New Testament Gospels and its Applications

This article describes a Greek-Chinese interlinear glossed text consisting of the four gospels in the New Testament. Each word in the original Koine Greek text is annotated with several layers of linguistic information, including its Chinese gloss; word alignments to its Chinese counterparts in the Revised Chinese Union Version, a modern Chinese translation of the Bible; and its morphological and syntactic information, adapted from the PROIEL project. We evaluate the precision and consistency of the Chinese gloss. We report a pilot study on using this interlinear to teach Greek to beginner students, illustrating how it can facilitate data-driven learning for aspects of grammar and lexical usage.
APIs and Your Privacy

Application programming interfaces, or APIs, have been the topic of much recent discussion. Newsworthy events, including those involving Facebook’s API and Cambridge Analytica obtaining information about millions of Facebook users, have highlighted the technical capabilities of APIs for prominent websites and mobile applications. At the same time, media coverage of ways that APIs have been misused has sparked concern for potential privacy invasions and other issues of public policy. This paper seeks to educate consumers on how APIs work and how they are used within popular websites and mobile apps to gather, share, and utilize data.APIs are used in mobile games, search engines, social media platforms, news and shopping websites, video and music streaming services, dating apps, and mobile payment systems. If a third-party company, like an app developer or advertiser, would like to gain access to your information through a website you visit or a mobile app or online service you use, what data might they obtain about you through APIs and how? This report analyzes 11 prominent online services to observe general trends and provide you an overview of the role APIs play in collecting and distributing information about consumers. For example, how might your data be gathered and shared when using your Facebook account login to sign up for Venmo or to access the Tinder dating app? How might advertisers use Pandora’s API when you are streaming music?After explaining what APIs are and how they work, this report categorizes and characterizes different kinds of APIs that companies offer to web and app developers. Services may offer content-focused APIs, feature APIs, unofficial APIs, and analytics APIs that developers of other apps and websites may access and use in different ways. Likewise, advertisers can use APIs to target a desired subset of a service’s users and possibly extract user data. This report explains how websites and apps can create user profiles based on your online behavior and generate revenue from advertiser-access to their APIs. The report concludes with observations on how various companies and platforms connecting through APIs may be able to learn information about you and aggregate it with your personal data from other sources when you are browsing the internet or using different apps on your smartphone or tablet. While the paper does not make policy recommendations, it demonstrates the importance of approaching consumer privacy from a broad perspective that includes first parties and third parties, and that considers the integral role of APIs in today’s online ecosystem.
An Evolutionary Game-Based Mechanism for Unwanted Traffic Control

With the development of Internet technology and the pervasive use of internet service providers (ISPs), internet users have reached an unprecedented volume. However, the existence of some malicious users seriously undermine the environment of the network by distributing a large amount of unwanted traffic, such as spam, pop-up, and malwares, which can be identified with the cooperation of individual users by installing anti-virus toolkits. In our paper, we propose an evolutionary game theoretic incentive mechanism to promote the cooperation of individual users to curb the expansion of unwanted traffic. Considering the hierarchical nature of real-world management, we model our framework as hierarchical incentive mechanism and combine reward with punishment mechanism to further incentivize cooperative behavior. Meanwhile, the acceptance condition of our framework is analyzed and we carry out a number of simulations to analyze the acceptance conditions of our framework.
Psychological Mechanisms That Exacerbate Motion Sickness

From the beginning of my research into motion sickness in the military, I have believed that there must be a psychological component in the aetiology of this malady. I have based this opinion on the notion that individuals whose careers are in jeopardy are most likely to have an arousal overlay when confronted with their provocative motion environment. This is also likely to happen to the majority of people outside the military if previous motion experiences have been uncomfortable. That does not mean to infer that motion sickness is entirely “psychological.” It merely suggests that the psychological component, based on memories of previous motion discomfort and/or the effect that motion sickness may have on future aspirations, contribute to an individual’s inability to adapt to provocative motion. In addition, I also believe that the situation is made worse for high achievers, as we shall see later.
Learning dynamics in limited-control repeated games

In imperfect-information games, a common assumption is that players can perfectly model the strategic interaction and always maintain control over their decision points. We relax this assumption by introducing the notion of limited-control repeated games. In this setting, two players repeatedly pla y a zero-sum extensive-form game and, at each iteration, a player may lose control over portions of her game tree. Intuitively, this can be seen as the chance player hijacking the interaction and taking control of certain decision points. What subsequently happens is no longer controllable–or even known–by the original players. We introduce pruned fictitious play, a variation of fictitious play that can be employed by the players to reach an equilibrium in limited-control repeated games. We motivate this technique with the notion of limited best response, which is the key step of the learning rule we employ. We provide a general result on the probabilistic guarantees of a limited best response with respect to the original game model. Then, we experimentally evaluate our technique and show that pruned fictitious play has good convergence properties.
An Empirical Study on Assessing the Quality of Use Case Metrics

Use cases are generally meant to describe the functional requirements of a software system. However, the use of some Natural Language (NL) text may inherently introduce language and interpretation related issues. Several tools and techniques have been proposed and available to assess the quality of use case specification, however, often performed manually. The precise and automated way of analyzing the quality of use cases in different aspects is a need due to volatile functionalities and rapid change in requirements. In this paper, we report the results of two separate experimental studies conducted, a replication of one another, to evaluate the significance and relevance of the use case quality assessment metrics. Our results revealed redundancies among the parameters associated with the quality measures and suggested modifications on the formulation of use case metrics which in turn make them complete, correct and consistent. Subsequently, we develop a tool support to automatically analyze the quality of use case specification on the basis of experimentally validated metrics.
Media Competence Inequality in Regular and Flexible/Distance Education Systems

One of the goals of the Ecuadorian government is to reduce inequality in its society. With this goal in mind, it has established several public policies aimed at reducing the «digital divide» and at improving media literacy among the population. The aim of this study was to compare the level of media competence of students who attended flexible and distance programs with those who studied in the standard education system. We employed a questionnaire based on six dimensions to measure students’ media competence. Results obtained from 1,003 students show that competence levels were higher in five of the six dimensions in the standard education system. Male students showed higher media competence. Those over 18 demonstrated lower media competence. The study suggests the need of more research and programs along this line, with the aim of reducing the digital divide.