Reviewerly/exHarmony huggingface dataset:
SOCIO-ECONOMIC DEVELOPMENT IN DEVELOPED AND DEVELOPING COUNTRIES UNDER GLOBALIZATION: ESSENTIAL DIFFERENCES

Globalization is a process, which means the interrelationship of national economies across the whole world in affairs such as labor, trade, investment and the movement of services, goods and people with their knowledge and traditions. The phenomenon of globalization progresses all the time and involves total fields of the country. It can be claimed that every matter has its own advantages and disadvantages, globalization is not an exception. Because of these reasons, the study compares economic globalization, which manifests itself differently depending on the effects of globalization. Therefore, research objective – impact of globalization on strong and weak states of economic processes. Research objective – to analyze the contact of globalization on economic growth. Continuing, empirical research has chosen to investigate the interaction of some economic factors with real GDP, which is one of the main economic indicators characterizing the country’s economic development. The main idea in the future – to include as many economic factors as possible in the study in order to achieve more accurate results.
Knowledge Representation and Reasoning — A History of DARPA Leadership

A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from the Defense Advanced Research Projects Agency and other agencies. This article highlights several decades of advances in knowledge representation and reasoning methods, paying particular attention to research on planning and on the impact of the Defense Advanced Research Projects Agency's support.
Demonstrating Wellbeing and Healthcare HCI Through Multidisciplinary Innovation and Experiential Prototyping

Multidisciplinary design research is a development trend of scientific research and design innovation. Through multi-angle and multi-dimensional design innovation, multidisciplinary teams help various fields solve complex design problems. This study demonstrates three cases that focus on the healthcare field. The age of the target user groups ranges from a five-year-old child to a seventy-year-old man. We explore different users’ needs, materialize design innovation through multidisciplinary collaboration. Three wellbeing and healthcare auxiliary demonstrations are designed and developed for busy white-collars, children who are afraid of visiting doctors, and the elderly who take long-term medication, which includes a smart wearable device, children’s diagnosis companion toy, and smart pillbox. These prototypes meet users’ health needs in different contexts through informal user testing sessions. Furthermore, they encourage a healthy lifestyle and show humanistic care to target users.
Interaction Mixed Reality Rare Animal Indonesia with Voice Input Using Microsoft Hololens

Abstract Indonesia is one country that is known for its natural wealth. Various types of flora and fauna living in this country. Examples of rich fauna that exist in Indonesia and can still be found at this point that one-horned rhino (Java rhinoceros), Sumatran elephant, Sumatran tiger, deer Bawean even dragons. However, over the times that make more and more people are not responsible for expanding residential land for private purposes and poaching that occurred in Indonesia, the animals are increasingly difficult to find because of habitats and their populations are dwindling. In this research, researchers applied a mixed reality technology with voice input to bring the Indonesian endangered species that has begun difficult to be found with the actual scale without having to disturb the habitat or environment. How it works is a mixed reality which brings together the virtual world with the real world. So that thanks to the development of today’s technological world, users can feel the direct presence of these animals without having to hunt or look for them.
Evaluation of PHP Framework Measured Using Object-Oriented Metrics with the Analytic Hierarchy Process

Abstract The existence of various PHP frameworks may become one alternative choice in developing a web application that PT XYZ wants to evaluate its Wisanggeni internal PHP framework in order to measure how far the design quality of Wisanggeni framework is working. An evaluation on PHP framework using object-oriented metric parameter and design quality is then conducted in this research. The results of measurement conducted using object-oriented metric parameters for each framework are evaluated with the Analytic Hierarchy Process (AHP) method to determine the best framework model design quality used as a reference for the development of Wisanggeni. This study also uses a combination of PHPDepend and PHPMetrics devices to measure the values of object-oriented metrics in each PHP framework. The research final result is that each framework has one value representing each framework’s quality in which the quality of Wisanggeni framework is almost to the same with the Laravel and CodeIgniter framework.
Implementation of a Holonic Multi-agent System in Mixed or Augmented Reality for Large Scale Interactions

We present a new holonic organizational meta-model that uses the multi-agent approach to design a mixed/augmented reality framework enhancing user-experience for the entertainment industry. Our applied model can control games played by groups in a mixed universe (real and virtual) and a distributed environment (city, theme park, etc.). We will particularly present a game in which visitors can interact and experience varying stories through the introduction of autonomous virtual agents. In our case, characters evolve according to their personality and to the user's inputs. Furthermore, we programmed behaviors for the characters letting their dynamics play the animation rather than programming the animation directly. Our demonstration during PAAMS will show, through a mobile application, new kind of interactions with the system leading to a personalized experience with virtual characters.
Antonyms-Synonyms Discrimination Based on Exploiting Rich Vietnamese Features

Antonymy and Synonymy are paradigmatic relations which are in the core problems of language. Distinguishing antonyms from synonyms is a key task to achieve high performance in natural language processing systems. Distinguishing between antonyms and synonyms is a hard problem because the co-occurrence distributions of the antonyms or synonyms tend to be highly similar. On the other hand, this issue has been thoroughly studied in English. However, it has not been effectively addressed for Vietnamese. Compared to English, Vietnamese has its own word-level characteristics that indicate the synonymous or antonymous relation. In this paper, we introduce a framework which exploits exhaustively special Vietnamese features to distinguish between antonyms from synonyms. We propose a deep neural network model (ViASNet) that can utilize not only lexico-syntactic information captured from the context of word pairs in a corpus but also its word-level features, and distribution features as well. The experimental results show that the proposed method is effective. Furthermore, our method achieved high performance in comparison to several the state of the art methods.
Optical character recognition and long short-term memory neural network approach for book classification by librarians

Abstract The book is classified by librarians that use Decimal Dewey Classification (DDS) System. It is used for cataloging and indexing books. DDC has three divisions, a ten, a hundred, and a thousand. The book subject is reflected in each division. Commonly, to know the book content, librarians read the book title. Then, they identify the book index in DDC system. Nevertheless, it requires more time. To read the book title, Optical Character Recognition (OCR) aids them to get the book title efficiently that convert the image of the book cover into the text-editable. Librarians use a web camera to scan the book cover, especially the book title area. There are three steps for pre-processing, the lowercase changing, the useless word removing, and tokenizing. To detect the book categories, Long Short-Term Memory Neural Network is good implemented in this research. It is almost used for text classification. In this research, It gives high performance that achieves more than 92% accurately.
CMA-ES for one-class constraint synthesis

We propose CMA-ES for One-Class Constraint Synthesis (CMAESOCCS), a method that synthesizes Mixed-Integer Linear Programming (MILP) model from exemplary feasible solutions to this model using Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES). Given a one-class training set, CMAESOCCS adaptively detects partitions in this set, synthesizes independent Linear Programming models for all partitions and merges these models into a single MILP model. CMAESOCCS is evaluated experimentally using synthetic problems. A practical use case of CMAESOCCS is demonstrated based on a problem of synthesis of a model for a rice farm. The obtained results are competitive when compared to a state-of-the-art method.
A Cascaded Feature Fusion Residual Network for Image Super-resolution

In view of existing problem of image super-resolution (SR) algorithms based on convolutional neural network (CNN) that they ignore the feature hierarchical characteristic in deep networks and fail to take fully use of prior information, a cascaded feature fusion residual network (FFResNet) is proposed. Especially, a feature fusion residual block (FFresblock) is proposed in cascaded way to densely fuse the prior information in mid-layers. Each FFresblock not only learns the feature spatial correlation and channel correlation between features, but also adaptively decides how much of the prior information and current state should be reserved by concatenating intermediate features from previous ones which leads to a global contiguous information memory mechanism. Moreover, with the benefit of fully use of intermediate status information, FFResNet achieves favorable performance against state-of-the-art methods on the benchmark tests. The results show that the image details reconstructed by FFResNet are more visual pleasing with clearer outline.
Multi-layer local optima networks for the analysis of advanced local search-based algorithms

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improving the design process. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis. Constructing such models, extracting and analyzing their metrics are the preliminary steps into the direction of extending the study on single neighborhood operator heuristics to more sophisticated ones that use multiple operators. Therefore, in the present paper we investigate a two-layer LON obtained from instances of a combinatorial problem using bit-flip and swap operators. First, we enumerate instances of NK-landscape model and use the hill climbing heuristic to build the corresponding LONs. Then, using LON metrics, we analyze how efficiently the search might be when combining both strategies. The experiments show promising results and demonstrate the ability of multi-layer LONs to provide useful information that could be used for in metaheuristics based on multiple operators such as Variable Neighborhood Search.
Nexus Analysis: Internet of Things and Business Performance

This paper analyses the influence of the Internet of Things (IoT) on business performance guided by the following objectives; assessing the influence of the Internet of things positively on sales and marketing strategies of business; to determine the influence of the Internet of things on resources management in modern businesses and to analyze the influence of the Internet of things on business profitability. The paper conducted exploratory research to study the Impact of IoT data on Business performance was conducted. Through the literature review process recently published papers on IoT and business performance including sales and marketing strategies, resource management profitability was gathered. Research papers, Journals, Internet Sites, and books were used to collate the relevant content on the subject. The analysis conducted by this study indicated that most published studies showed that IoT has huge potential for businesses across many sectors. The data collected through the implementation of IoT provide business with opportunities of increasing efficiency which improves sales and marketing, resource management, growth potential, and profitability. This study recommends that despite the challenges in the development of IoT technologies, it’s an implementation in businesses is inevitable as they seek to increase the performance.
Enhancing Deep Learning Capabilities with Genetic Algorithm for Detecting Software Defects

Regardless of existing and well-defined processes, some defects are inevitable, resulting in software performance degradation. The use of traditional machine learning techniques can automate the prediction of software defects. This automated approach significantly improves the quality of the finished product and reduces the cost incurred during development and maintenance stages. The accuracy of artificial neural networks for the automatic prediction of software bugs, can be further enhanced with the use of metaheuristics algorithms. We propose a hybrid approach which combines Genetic Algorithm (GA) and Deep Neural Network (DNN) to better classify software defects. GA is used as a pre-learning phase to automatically optimize the input features for the DNN, as irrelevant variables have a substantial negative impact on the prediction accuracy. Results from experiments using the PROMISE dataset, demonstrates that a DNN consuming optimized features yields better results.
An Overview of Theory of Combinatorial Games

組合せ論的ゲーム理論の礎とも目されるE.R. Berlekamp, J.H. Conway, R.K. GuyによるWinning Ways for Mathematical Playsという今や古典的な名著の翻訳を昨年の秋に完了した．その機会に合わせてSITA2019霧島のワークショップにおいて講演したので，そのときの話題を中心に組合せ論的ゲームを解説する．組合せ論的ゲームというのは，2人のプレーヤが交互に着手し，ゲームの規則，勝敗の判定法など必要となる情報はお互いに完全に分かり合っており，偶然性は入らないゲームをいう．そのようなゲームは子供のお遊びのチック・タック・トウ（マルとバツ），点と箱などから，囲碁，将棋，チェスなどという高度の戦略を必要とするゲームまで広範囲にわたっている．ゲームの勝敗を知るためには，局面の値を決定することが重要である．そのような値は単に数だけでは表現できず，Conwayの発案になる実数を拡張した超限実数という概念も顔を見せてくる．
Emotional Effect of Multimodal Sense Interaction in a Virtual Reality Space Using Wearable Technology

The virtual reality (VR) market has been expanding fast with the extraordinary progress of relevant hardware and software. Particularly, with the commercialization of standalone head-mounted display (HMD) devices in the recent years, there has been an increasing amount of interest in interfaces providing more expanded and sensual information to users. To intensify user immersion in a VR space, it is necessary to provide physical sense information similar to a real world. However, the sense information provided to users is limited to mostly simple vibration or force feedback, and the emotional responses of users have not yet been investigated with respect this information. Therefore, this study presents the approach of providing multimodal sense feedback to a user in a VR space through the “Emoract” interface that can be worn on both hands and also investigates the resulting emotional responses by measuring the user’s biological response signals. All the experiments were conducted using an interactive VR animation “Lonely Noah”. In the VR animation, because the user’s emotional response to the user’s sensory response changes depending on the story and situation, there is an effective interaction with vibration sensations such as the existing VR interface, and there is an effective iteration with context-based multi-modal sensory feedback rather than vibration.
DEVELOPMENT OF CRITERIA FOR THE SYSTEM ASSESSING EFFECTIVENESS OF STUDENT PARTICIPATION IN RESEARCH WORK

The article considers criteria for assessing the effectiveness of student participation in research work. Points for calculating indicators taking into account the types and levels of events are given.
A Design of Building Inventory for Risk Analysis Using Open Data

This study introduces a design of building inventory for risk analysis using open data in Korea. Recently, disasters of various sizes and types are occurring globally. Risk analysis tools are used to estimate the losses due to disasters. However, there is a limitation in the application of disaster statistical data in Korea because these data are based on the country where the tool is produced. To improve the accuracy of loss assessment, it is necessary to utilize data in consideration of each country’s environment. However, the data opened at the national level are distributed among various organizations. Thus, considerable time is required to build infrastructure. Therefore, we collect the open data distributed among various institutions and ensure data compatibility and interoperability through the standardization of the data in various formats. In addition to a disaster analysis system, a design method for a database system that can support various analysis systems through the data is proposed considering scalability.
Mobile Phones in Laboratory: Effects on Laboratory User Performances

The use of digital devices has grown exponentially over the years. Particularly, mobile phone usage has grown and now plays essential roles in human life. The use of mobile phones during lectures and laboratories has become popular among students. This paper outlines the pros and cons of mobile phone usage during lab sessions. Two different laboratories were used for the study; hands-on and computer based. Thirty-nine students participated in the study, 37 males and 2 females. During the computer lab-based sessions, student performances were better with mobile phones allowed and it was better when mobile phones were disallowed during the hands-on lab session. This study concluded that mobile phone usage cannot be totally ruled out during the lab session and its usage should be based on the type of activity involved and the nature of the lab. Findings from this study could help regarding the phone use during lab sessions.
Using Baroque music to improve reading comprehension skills

This article aims to improve students’ reading comprehension skills using classroom action research with a collaboration between the researcher and the teacher. This research was conducted in three cycles, each consisting of planning, implementation, and reflection. In language learning, students still find it difficult to find main ideas, to make an inference, or to determine the elements of a story. Therefore, the researcher together with the teachers tried to improve students' reading comprehension skills with baroque music-based learning, which was evidenced by the results of the pretest with the Completed category of only 40%; then, it increased to 60% in the first cycle, 73.33% in the second cycle, and 90.00% in the third cycle. The teacher and students were very enthusiastic about baroque music during learning because it can help the students focus, make them more confident, and make the teacher more enthusiastic in teaching.
Ontological Description of the Skills of Colonoscopy

Colonoscope insertion can be difficult due to the complicated anatomy of the large intestine. However, clinical training in colonoscope insertion remains hands-on, and introducing Artificial Intelligence (AI) to training and skill certification in colonoscopy will require ontological and semantic descriptions of the clinical procedure. Therefore, the total colonoscopy procedure was described ontologically to develop a strategic education and simulation program. We propose an ontological analysis method comprising colonoscopy procedures and techniques and investigate the feasibility of an ontological approach to the training and evaluation of clinical colonoscopy skills.
A Framework for the Development of Computing-Assisted Health Surveillance among Drug Dependents in Davao City

With the ongoing efforts of the Philippine Government in addressing drug-related problems in the society, issues like lack of healthcare service providers and medical facilities to offer rehabilitation programs has been detrimental to the success of these efforts. In this paper, we present a framework for the development of a health surveillance which takes advantage of the potential of inertial sensors in smartphones as well as other wearable devices in capturing physical and physiological data. This data is then analyzed in order to assess the rehabilitation progress and the overall rehabilitation outcome of drug-dependent individuals. The framework is composed of three main parts. First is sensing which covers the collection as well as pre-processing of data. Second is analysis which implements an ANN-based approach for the classification of physical activities. Lastly is results generation which provides healthcare service providers a means to better understand the data of individuals and assess their rehabilitation outcomes. Future works include the use of fewer bodily-sensors while collecting the same amount of data as well as providing visualization of the data analysis results.
Classification Approach to Management of Polythematic Knowledge

In this paper a brief description of the developed system of informational support for innovative activity in the Republic of Kazakhstan is given. The system is being developed as information portal, which provides navigation within national and global information space by expression of metadata about information resources, which are relevant to the user’s task. Using the expressed data, the user improves his professional competencies, obtains new knowledge and he is able to use it for decision making process. The base of the system is used for generation of new knowledge which are supposed to enrich the corporative knowledge of the enterprise. Therefore, the corporative system of the knowledge management is proposed to consider as final element of information support infrastructure. During the development of such systems for data expression, classical ontology model is traditionally used. The core of the model is descriptor dictionary providing coverage of lexis of specific monothematical subject field. The problem is considered that in practice there is polythematical knowledge leading to difficulty of development and following integration of complex of thematical thesauruses and matching of local ontologies. In order to solve this problem, the approach for polythematical knowledge management based on the use of classification type languages is proposed. In this case, a thematic rubricator is introduced into the ontology model instead of a thesaurus, where each category (rubric) has its own code, name and set of keywords, characterizing its thematic content. The proposed joint use of existing Russian thematic rubricators allows to increase the accuracy of knowledge presentation, as well as take advantage of establishing the associative relations between different classification systems. Additionally, there the possibility of verbal knowledge description in terms of keywords, characterizing the content of subject entries and words from the rubric’s titles, is maintained.