Reviewerly/exHarmony huggingface dataset:
Build and Control of a Continuous Passive Motion Device using Mobile Application and Arduino

Number of people with motion disabilities is increasing worldwide, thereby, increasing the need for rehabilitation and physiotherapy devices. Automated assistive tools and devices have proven to be very efficient and crucial in treating physical injuries and disabilities. In this paper, a knee-joint rehabilitation device was designed and built. The Continuous Passive Motion (CPM) device was modeled and analyzed using SolidWorks. Furthermore, a device control system was developed using stepper motor, Arduino UNO board and electronic circuitry. A mobile phone application was developed using App Inventor to control the CPM device. The developed mobile application was implemented to communicate wirelessly with the CPM device allowing for easy control and monitoring. Tests and experiments were performed on the developed device mechanical structure as well as its control system to validate device accuracy and repeatability.
Important Features in Java’s Enhancement Path

Welcome to Chapter 14. In this book, my focus has been on the fundamental concepts of object-oriented programming in Java only. All programs were compiled in Java 8, which was the baseline in this book. Like other popular languages, Java is continuously growing, with new features added regularly. Though detailed coverage of these features was out of the scope of this book, in this chapter, you will examine some features or enhancements from various versions of Java. You get the most value out of a new enhancement if you are familiar with the existing features. So, for this chapter, I have picked only topics with which you are already familiar.
Unique Identity Management Scheme for Distributed NFV Market Place using Ethereum

Identity Management is all about managing attributes of an entity, it can be an individual or organization. Network infrastructure and VNFs will come back from completely different suppliers, therefore it involves multi domain orchestration to run the VNFs and configuring traffic steering rules between the VNFs. Most Identity Management schemes nowadays centralized wherever one entity like a corporation owns and controls the system. For Multidomain Networking Environment like NFV requires a more sophisticated and decentralized Identity Management since it involves Life Cycle and Configuration of different entities (Hardware/Software/Firmware) from different domains or department within the same domain. So, Blockchain Technology will be right candidate for Identity Management in NFV Market place. In this paper we proposed a novel Identity Management Scheme for managing entities in a typical NFV Environment using Ethereum Blockchain.
Learning Product Automata

In this paper we give an optimization for active learning algorithms, applicable to learning Moore machines where the output comprises several observables. These machines can be decomposed themselves by projecting on each observable, resulting in smaller components. These components can then be learnt with fewer queries. This is in particular interesting for learning software, where compositional methods are important for guaranteeing scalability.
CacheQuery: Learning Replacement Policies from Hardware Caches

We show how to infer deterministic cache replacement policies using off-the-shelf automata learning and program synthesis techniques. For this, we construct and chain two abstractions that expose the cache replacement policy of any set in the cache hierarchy as a membership oracle to the learning algorithm, based on timing measurements on a silicon CPU. Our experiments demonstrate an advantage in scope and scalability over prior art and uncover 2 previously undocumented cache replacement policies.
Designing an electronic reverse dictionary based on two word association norms of English language

This work introduces the exploitation of some language resources, namely word association norms, for building lexical search engines. We used the Edinburgh Associative Thesaurus and the University of South Florida Free Association Norms for the construction of knowledge graphs that will let us execute algorithms over the nodes and edges in order to do a lexical search. The aim of the search is to perform an inverse dictionary search that, given the description of a concept as a query in natural language, will retrieve a target word. We evaluated two graph approaches, namely Betweenness Centrality and PageRank, using a corpus of human-definitions. The results are compared with the BM25 text-retrieval algorithm and also with an online reverse dictionary– OneLook Reverse Dictionary. The experiments show that our lexical search method is competitive with the IR models in our case study, even with a slight outperformance. This demonstrates that an inverse dictionary is possible to build with these kind of resources, no matter the language of the Word Association Norm.
ANFIS Based UKF-SLAM Path Planning Method

Reducing error in each step of robot motion is one of the important issue in the Simultaneous Localization and Mapping algorithms. Hence, an accurate estimation method can enhance the behavior of the SLAM algorithm in the unknown environment. A hybrid estimation method is presented in this work which is consisted of ANFIS with Unscented Kalman Filter (UKF) to estimate the estate of robots and landmarks. This estimation will be reduced the localization errors of the landmark and robot pose. So, the accuracy of the system will be improved in the planar system. The simulation results based on different environment revealed that the proposed algorithm is adaptable to the different environment with high accuracy, and efficiency in comparison with the previous works such as UKFSLAM and Neuro based UKFSLAM.
Designing Visual Guides for Casual Listeners of Live Orchestral Music

The experience of attending live orchestra performances is rich in cultural heritage and can be emotionally moving; however, for those unfamiliar with classical music, it can be intimidating. In this work, we explore the use of visual listening guides to supplement live performances with information that supports the casual listener's increased engagement. We employ human-centred design practices to evaluate a currently implemented guide with users, from which we extracted design requirements. We then identify dimensions of a music piece that may be visualized and created sample guide designs. Finally, we presented these designs to experts of visualization and music theory. Feedback from the two evaluations informs design implications to consider when creating visual guides of classical music for casual listeners.
Detection and Analysis of Attacks Against Web Services by the SQL Injection Method

SQL injection is a security vulnerability resulting from unauthorized access to the victim database and services by attackers. Harmful codes can be injected into the victim database, all information in the database can be accessed or the database can be deleted completely and rendered unusable by using the SQL injection method. SQL injection attacks are a simple and effective method, used frequently in hacking attacks against Web services. Despite all measures taken, SQL injection attacks continue to increase. The academic studies in this field are usually theoretical, having limited practical aspects. In this study, an SQL injection attack case against a web service was examined in detail. The study aims to evaluate the SQL injection attacks in practice.
Synthesizing the control system of the robotic center scraper for cleaning trunk pipelines

Residue forming on the walls of trunk pipelines presents a problem for the oil industry. The residue consists of resins, paraffine, and asphalt. The presence of residue also decreases throughput of the oil pipeline and leads to faster equipment deterioration. At present mechanical pipeline pigs are used to perform cleaning. Lack of control over these machines leads to their being stuck in the process of cleaning. Also, time of cleaning is long, as it requires several such pistons. The study is aimed at synthesizing the control system of the front scraper of a robotic center with allowance made to random influences. We suggest solving the above problem by developing the robotic center capable of cleaning a pipeline in one pass while minimizing the risk of accidents. In order to achieve the desired characteristics the robotic center uses movable mechanisms driven by electric motors. As a result, the center is equipped with four types of drives: front scraper drive, back scraper drive, drive module, and clamping drive. Frequency method is used for synthesizing the automatic control system. The desired system properties are achieved through embedding parallel and sequential correcting devices including a tachometer, potentiometer, current sensor, proportional-integral-derivative (PID) controller, proportional- integral controller. The paper suggests the automatic device design for cleaning pipelines. Optimal characteristics of correcting devices were selected by synthesizing the front scraper of a robotic centerResidue forming on the walls of trunk pipelines presents a problem for the oil industry. The residue consists of resins, paraffine, and asphalt. The presence of residue also decreases throughput of the oil pipeline and leads to faster equipment deterioration. At present mechanical pipeline pigs are used to perform cleaning. Lack of control over these machines leads to their being stuck in the process of cleaning. Also, time of cleaning is long, as it requires several such pistons. The study is aimed at synthesizing the control system of the front scraper of a robotic center with allowance made to random influences. We suggest solving the above problem by developing the robotic center capable of cleaning a pipeline in one pass while minimizing the risk of accidents. In order to achieve the desired characteristics the robotic center uses movable mechanisms driven by electric motors. As a result, the center is equipped with four types of drives: front scraper drive, back scraper drive, drive module, and clam...
What Is ETL?

ETL is an acronym for Extract/Transform and Load. ETL is the engine that drives your visual analytics. Without ETL you would be limited to the shape (existing layout) of the data you start with. In many cases this is OK, but as you progress in visual analytics, you will find yourself wanting to combine data sources, combine fields within data sources, or maybe even break some individual fields out into multiple fields. You might want to replace some abbreviations or codes out into the values they represent. You will certainly want to know the “shape” of your data, so you can make judgments about its trustworthiness. These are all parts of ETL, and they are all things that the Tableau Data Prep tool excels at.
A Group-Theoretic Framework for Knowledge Graph Embedding

We demonstrated the existence of a group algebraic structure hidden in relational knowledge embedding problems, which suggests that a group-based embedding framework is essential for designing embedding models. Our theoretical analysis explores merely the intrinsic property of the embedding problem itself hence is model-independent. Motivated by the theoretical analysis, we have proposed a group theory-based knowledge graph embedding framework, in which relations are embedded as group elements, and entities are represented by vectors in group action spaces. We provide a generic recipe to construct embedding models associated with two instantiating examples: SO3E and SU2E, both of which apply a continuous non-Abelian group as the relation embedding. Empirical experiments using these two exampling models have shown state-of-the-art results on benchmark datasets.
A Statistical Estimation of Solar Power for Energy Mix in Bangladesh

solar radiation (SR) is a significant parameter for producing solar power for the electric energy mix. Sun radiation data is hard to be accumulated at particular geographical location of a country due to measurement instruments is limited especially in developing country like Bangladesh. Hence, estimation of SR for a meteorological location is an important research to learn solar energy potentiality. In this regards, this paper presents a statistical inference model for the estimation of bright sunshine hour which is important metric for solar irradiance. Normal distribution model is inferred from the historical data of some important locations of Bangladesh. Mean and standard deviation are the parameters at 95% confidence interval which control the behavior of such distribution. In this statistical model, bright sunshine hour is estimated that expedite to find the potential of solar energy at particular location. The estimation model can be used as a decision making support to design photovoltaic power system for deployment of energy mix policy.
Stem-Education in Modern School Within the Framework Of Design Activity in Natural Scientific Disciplines

The issue of STEM education in a modern school and methodological approaches to its implementation on the subjects of the natural science cycle as part of project activities are considered. An example of the stages of work on a project, a breakdown into subject areas, is given. Since STEM education involves not only gaining knowledge in individual subjects, but also putting them into practice, the key point in working on a project is practical application. Within the framework of the subject area of mathematics and computer science, this involves making calculations and presenting the final results using modern technical means. Thus, the subject of mathematics moves from the framework of academic knowledge to the framework of practical skills. In particular, the article provides an example of the formation of a student’s financial literacy as part of a project. STEM-training allows you to combine scientific methods, mathematical modeling, technological applications and engineering design. Thus, innovative critical thinking is formed, the opportunity and need for integrated training on topics within the framework of which active communication of students occurs and a new educational space is formed.
Generative Histogram-Based Model Using Unsupervised Learning

This paper presents a new generative unsupervised learning algorithm based on a representation of the clusters distribution by histograms. The main idea is to reduce the model complexity through cluster-defined projections of the data on independent axes. The results show that the proposed approach performs efficiently compared with other algorithms. In addition, it is more efficient to generate new instances with the same distribution than the training data.
The Effect of School Life Adjustment And Major Satisfaction of University Students Majoring Dance on Career Decision-Making Self-Efficacy

The purpose of this study was to analyze the effect of dance major students'' school life adjustment and major satisfaction on career decision- making self-efficacy. Detailed results are presented below. 1. The results showed that satisfaction with major had a significant positive correlation with career decision-making self-efficacy. 2. The four sub-factors of satisfaction with major had a significant positive influence on career decision-making self-efficacy. 3. Among four sub-factors of adjustment to school life, only school’s environment adjustment had influence on career decision-making selfefficacy. 4. Overall score of major’s satisfaction and school life adjustment had influence on career dcision-making self-efficacy.
Some Initial Remarks on Non-Prepositional Genitives in the Apulian Variety of San Marco in Lamis

This work aims at an initial description of prepositionless genitives in the Romance variety of San Marco in Lamis, spoken in the Southern Italian region of Apulia. The construction will be compared with other Romance, Semitic, Albanian, and Iranian varieties whereby the expression of possession is connected to the presence of D elements, or to morphology stemming from them. The paper deals, in particular, with the behaviour of the construction with elements such as definite and indefinite articles, demonstratives, proper names, and with how pre-nominal adjectival modification of genitives and post-nominal adjectival modification of heads can only occur in the prepositional kind of the construction. This is also the case with demonstratives preceding heads and genitives in the form raised nominals. It will be seen that genitives are only interpreted as such when they are non-raised, i.e. when they are articled. The pre-genitival article is thus understood to be a pivotal element in the interpretation of the second DP as genitival.
Web-based Editor for Signal Interpretation Models

A web-based editor for Signal Interpretation Models (SIM) is presented in this paper. SIM is a modeling formalism specifically created to specify the occurrence of events based on signals variation. This formalism goal is not only to specify but also to support the validation and implementation of signals interpreters. These signals can be system input signals (environment signals) or system internal signals. The created web-based editor uses Asynchronous JavaScript and XML (AJAX) principles and runs at standard browsers. It supports the creation/edition of graphical SIM models, which are saved in XML files. The XML format and the well defined execution semantics of the SIM formalism, enable the integration of SIM with other tools. This means that the created models can be used as inputs in tools, such as automatic code generators, to create simulation or execution code (namely JavaScript, C, and VHDL). To illustrate the application of the developed editor, the model of a system that detects temperature sensor faults is presented. The created edition tool prototype is currently available at http://gres.uninova.pt/SIM-Tools/.
Discriminative structure learning of sum–product networks for data stream classification

Sum-product network (SPN) is a deep probabilistic representation that allows for exact and tractable inference. There has been a trend of online SPN structure learning from massive and continuous data streams. However, online structure learning of SPNs has been introduced only for the generative settings so far. In this paper, we present an online discriminative approach for SPNs for learning both the structure and parameters. The basic idea is to keep track of informative and representative examples to capture the trend of time-changing class distributions. Specifically, by estimating the goodness of model fitting of data points and dynamically maintaining a certain amount of informative examples over time, we generate new sub-SPNs in a recursive and top-down manner. Meanwhile, an outlier-robust margin-based log-likelihood loss is applied locally to each data point and the parameters of SPN are updated continuously using most probable explanation (MPE) inference. This leads to a fast yet powerful optimization procedure and improved discrimination capability between the genuine class and rival classes. Empirical results show that the proposed approach achieves better prediction performance than the state-of-the-art online structure learner for SPNs, while promising order-of-magnitude speedup. Comparison with state-of-the-art stream classifiers further proves the superiority of our approach.
A Nuance on Lateral Movement

To a threat actor, lateral movement means all the difference between compromising a single resource and potentially navigating throughout an organization to establish a persistent presence. Their goal is to remain undetected and ultimately conduct their nefarious mission even if some defenses manage to track their infiltration. While the hacker might succeed based on an opportunistic phishing attack or a targeted attack based on stolen credentials or an exploit, lateral movement is the means to find data of value, compromise additional assets, execute malware, and ultimately own accounts and identities to continue their attack. Lateral movement, by the most traditional definition, is the ability to pivot from one resource to another and to navigate among other resources in any environment. The key word for our conversation today, and why we need to talk about lateral movement, is not about assets however; it is about “resources” since they can be so much more than just computers and applications.
A Parallel and Distributed Radial Basis Function Network for Big Data Analytics

Radial Basis Function Network (RBFN) is one of the versatile neural network architectures, in that it can be used to solve regression as well as classification tasks. The proposed work implements RBFN in a parallel and distributed environment of Apache Spark. The new version of RBFN is referred to as PRBFN. The main contribution in this work is embedding of K-Means|| (a parallel distributed clustering method) in between input and hidden layer and implementation of parallel least square estimation employing outer product of matrices. The effectiveness of the PRBFN was tested on HEPMASS, HIGGS, ccFraud, and Amazon Movie Review (for sentiment classification) datasets under 10 Fold Cross Validation (10-FCV) setup. It is emphasized that the chief objective is to present a distributed and parallel version of the RBFN to handle big datasets.
FinDo: A Foreign Language Vocabulary Learning System Based on Location-Context

Recently, it has become more and more important to learn foreign languages for business or personal interests. However, it is not easy to increase the vocabulary of foreign languages, as it requires a lot of time to learn many new words. In this paper, we propose a smartphone application named FinDo which presents a vocabulary list related to the place that the user is located at. By utilizing short periods of time during the user's daily activities, FinDo allows users to comprehend the meaning of unfamiliar words in the most efficient manner with the context related to their own location. For example, when a user is in a supermarket, he/she can learn all the related words of surrounding objects there. We held an experimental evaluation to compare the proposed system with a system without the location-context content with two groups. The experimental results show that the group using FinDo were able to improve, although no significant difference can be seen between the two groups. However, the group using FinDo found it more entertaining and appealing.
THE MODEL OF PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT FOR WORKING STUDENTS WITH THE EMOTIONAL BURNOUT SYNDROME

THE MODEL OF PSYCHOLOGICAL AND PEDAGOGICAL SUPPORT FOR WORKING STUDENTS WITH THE EMOTIONAL BURNOUT SYNDROME
How to think about…INFORMATION

Information is a tricky concept to define. But get it right, and many physicists believe it could hold the secret to life, the universe and, yes, everything
Exploratory Data Analysis Towards Terrorist Activity in Indonesia Using Machine Learning Techniques

Terrorism Activity is the subject of the talks in various countries, especially in Indonesia. Theactivities of terrorism are carried out in various ways using suicide bombs, violent action thataimed to demoralize by creating fear to the society and national security. In Indonesia,according to Kompas news website recorded there were 10 suicide bombings occurred in thepast 6 years and took many casualties in every event. With this, it certainly gives a threat tothe people in Indonesia in terms of physical, moral and even in terms of national security. Toovercome this problem, it is necessary to increase the national security so that terrorism canbe prevented, and it will not happen again. This study is aimed to conduct an exploratory dataanalysis and predict terrorist activity in Indonesia using K-Nearest Neighbor (KNN), and ¬kfold cross-validation. In this research, data selection, data cleaning, data reduction wascarried out and feature selection process which aimed to find out the most influential dataattributes. Based on the result of the analysis to predict the terrorist activity, the result of theaccuracy was obtained with a value of k = 8 at 88.86%.