Reviewerly/exHarmony huggingface dataset:
Mean Square Error-Based Approach for the Detection of Focal Position of a Lens

Determination of focal length of a lens is a common problem encountered in optical instrumentation; however, there exist very few solutions which are simple, inexpensive and yield automated output. In this paper, we present the results of investigation undertaken towards determining the focal length a test lens with high accuracy and precision by undertaking digital processing of self-images obtained at two Talbot planes using single linear grating. Light from a collimating lens is incident onto a test lens and mirror combination; the back-reflected light is incident onto a beam splitter to draw the beam for interferometric evaluation. Single CCD camera is used to capture self-images at the first and highest feasible Talbot plane for different positions of a given test lens. Self-images have been processed digitally to find mean square error (MSE) corresponding to each position of the lens. MSE is lowest when the lens is positioned at its focal length, for all other positions of lens MSE remains higher. Hence, focal position can be exactly differentiated from all other positions of lens. Also, it has been observed that MSE increases as the distance between Talbot planes increases. Hence, if the MSE is calculated for the first and the farthest Talbot plane, sensitivity is improved. To achieve high sensitivity, Talbot self-images at the first and till the eight Talbot planes (due to practical limitations) are analysed.
Identification Pests and Diseases of the Borneo Black Sweet in Tropical Rainforest

Abstract The Borneo black sweet Coelogyne Pandurata is one of the mascots of flora in East Kalimantan Tropical Rainforest. This orchid species only grows on the Borneo. The uniqueness of The Borneo black orchid Coelogyne Pandurata, has a black tongue (labellum) with a few green and hairy stripes. This research is to develop an expert system for identifying pests and diseases of black orchids, as a support for the conservation of biodiversity in the East Kalimantan tropical rainforests. The expert system is equipped with a knowledge base in diagnosing pests, symptoms, and diseases of black orchid using Bayes theorem. In this research 4 pests disturbed black orchids, namely bare snail pests, caterpillars pests, grasshoppers pests, and ant pests. The diseases that attacked black orchids were obtained, namely brown spot disease, fusarium wilt disease, soft rot disease, and root rot disease. The result of the calculation of the Bayes Theorem based on 5 symptoms chosen by the user that black orchid has a 40% chance of experiencing root rot disease.
Impact of E-HRM Strategies on Organizational Innovation by Knowledge Repository as Mediating Role

The current study is intended to provide the analysis of the direct and indirect relationships between (E-HRM) strategies and organizational innovation through a knowledge repository as a mediation construct. From various previous studies, five (E-HRM) strategies constructs were adopted for this study. These include e-recruitment, e-selection, e-training, e-performance appraisal, and e-compensations, and used the mediating role of a knowledge repository for these constructs to improve organizational innovation. The study applied the Smart Partial Least Square (PLS) software to analyze the underlying relationships amongst the most relevant constructs between (E-HRM) strategies, knowledge repositories, and organizational innovation with a total of seven constructs. As a result of analysis of the findings from middle managers (n = 94) working for the Royal Jordanian airline. It was found that there was a strong and statistically positive significant relationship between several (E-HRM) strategies and organizational innovation by using the knowledge repository as a mediation construct.
Blocking Mobile Based Games and Nullifying the Search String Containing Inappropriate Words

Nowadays, game addiction is a major issue in society. The individuals in the age group of 5–20 years are addicted to games. This affects the mental and physical health of the individual. Teenagers due to changes in hormones, in the adolescent age, get attracted to various words like sex, suicide, porn, etc. Browsing and viewing those contents in the browser affects thinking of individual at this age due to lack of maturity. Young and bright minds are the assets of the nation. So, there is a need to overcome these problems in earlier stages itself. To solve the above-mentioned problems in the society, an android application with Natural Language Processing (NLP) is designed. In this application, the parent can add games or other applications that need to be blocked. Parent is provided with an option to set timer, which determines the amount of time the child can access the application. When the child uses browser, if the search string contains any inappropriate words or its respective synonyms, this application detects them with the help of NLP and makes the search string null. This way a child cannot search about inappropriate topics and if any such activity is detected an alert is sent to the parent regarding the activity of the child.
Exploring the Role of Local Financial Markets in the Portfolio Investment-Growth Nexus

In recent times, foreign portfolio investment inflows have been considered pivotal to sub-Saharan Africa's growth (SSA) as they help enhance liquidity and make a substantial fund available for investment. However, some scholars have stressed that the sustainable inflows of portfolio investments and their impact on growth depend on the extent to which the recipient country can develop its local financial markets. As a result, this chapter aims to determine the moderating role of local financial markets in facilitating the effects of portfolio investments on economic growth in 28 SSA between the period 1995-2018. The study employed the system generalized method of moments (SGMM) and found that portfolio investments positively and significantly impact economic growth. However, the study observes that the interaction between portfolio investments and financial market development is negative and significant, presupposing that the relationship between portfolio investment and economic growth is not contingent on local financial markets.
Blockchain-centric Resource Management System for Disaster Response and Relief

Disaster response and relief efforts require effective handling of resources and timely impact of the goods at the disaster affected areas with least processing time. Time required for goods to travel from origin to affected region has a few components but the one which can be reduced most is the time taken to handle the legalities and paperwork. We propose a resource management system to tackle the issue of extensive paperwork for deployment of relief material through a blockchain based web/mobile application using Hyperledger Fabric. This acts as a communication channel for a consortium of relief enterprises and organisations, a gateway for the populous to donate goods and for volunteers to make their services available to the various non-profit organisations on the platform.
Training System for Autistic Children Based on Virtual Reality

Autism or autism spectrum disorder is coming under an inescapable issue leading to weakness in hearing, talking, feeling, thinking and social interaction. Thus, children experiencing autism disorder need to follow special training sets which will increase their capacity to learn and adapt new skill sets and knowledge. This key feature of this model is an adaptation of conventional training patterns with first-person virtual reality (VR) interactions to train the children suffering from autism disorder. The new training system combines the features of a smart phone and virtual reality (VR) to help the individual learn, through real-world interactions. The hardware set-up consists of a Google cardboard device and a smart phone as the computational and interactive device. The virtual reality platform can reduce the burden on the user in terms of training, memorization, and blend of the real and virtual worlds. The system framework is divided into three units: training, testing and real-world exposure. This model makes use of keyword or audio-visual matching sentence that helps teaching children new pictures, colours or objects and promising the individual’s participation.The training stage is followed by a testing stage, to check whether the person is able to recognize the object she/he has been trained for. The final stage will be a real-world exposure, based on the knowledge the individual has acquired from the previous training, the child will be able to cope up with the real-world situation. Finally, the feedbacks from the application can be effectively monitored and accessed by instructors, psychologists and researchers at any point of time.
Using of live response technologies during the blocking of the attacks and investigation of incidents in real time

Abstract Acquisition of a clear idea of the events and artifacts occurring eventually is a difficult task which needs to be solved in digital criminalistics. Recovery of chronology of events and artifacts, allows experts to understand chronology of digital crimes and to interpret a conclusion in the form of digital proofs with use of live-response technologies.
Zero-Shot Machine Learning Technique for Classification of Multi-User Big Data Workloads

During the last decade machine learning has revolutionized computer science applications. Supervised machine learning algorithms have become especially successful in many industries including health, legal, security, finance, travel, and others. Training supervised learning algorithms, however, is expensive because the real world contains a very large number of different classes that need to be covered by the training set. This is especially true for the highly variable, multi-user workload data produced by the strategically vital big data and cloud software stacks. It is very important, however, to be able to accurately classify these complex workloads in order to enable autonomic management and optimization. Zero-Shot Learning (ZSL) is an advanced machine learning approach that enables classification of objects without having to explicitly train on examples of those objects. In this paper we present a new ZSL technique intended to reduce the expense of assembling workload training sets for big data analytic workloads. We demonstrate that multi-user big data workloads can be treated as hybrids of simpler, single-user workload classes, and classified accurately without having to explicitly train on example instances of multi-user workloads. Our technique is able to accurately classify both unseen multi-user workloads, and seen single-user workloads using the same classifier. We demonstrate 83% classification accuracy for the unseen multi-user workloads, and 92% classification accuracy for the seen, single-user workload classes.
An Integrated Approach of Proposed Pruning Based Feature Selection Technique (PBFST) for Phishing E-mail Detection

Introduction: The entire world is shifting towards electronic communication through Email for fast and secure communication. Millions of people, including organization, government, and others, are using Email services. This growing number of Email users are facing problems; therefore, detecting phishing Email is a challenging task, especially for non-IT users. Automatic detection of phishing Email is essential to deploy along with Email software. Various authors have worked in the field of phishing Email classification with different feature selection and optimization techniques for better performance. Objective: This paper attempts to build a model for the detection of phishing Email using data mining techniques. This paper's significant contribution is to develop and apply Feature Selection Technique (FST) to reduce features from the phishing Email benchmark data set. Methods: The proposed Pruning Based Feature Selection Technique (PBFST) is used to determine the rank of feature based on the level of the tree where feature exists. The proposed algorithm is integrated with already developed Bucket Based Feature Selection Technique (BBFST). BBFST is used as an internal part to rank features in a particular level of the tree. Results : Experimental work was carried out with open source WEKA data mining software using a 10-fold cross-validation technique. The proposed FST was compared with other ranking based FSTs to check the performance of C4.5 classifier with Phishing Email data set. Conclusion: The proposed FST reduces 33 features out of 47 features which exist in phishing Email dataset and C4.5 algorithm produces remarkable accuracy of 99.06% with only 11 features and it has been found to be better than other existing FSTs.
Blockchain Technology in Energy Field: Opportunities and Challenges

Recently, blockchains technique keeps attracting considerable attention from Finance and other fields due to its strong commercial viability. In accordance with substantial examples resulting from this context, it was identified as having the potential to create remarkable profit and innovation. So what could be the effect of the combination of blockchain and energy industry? First, we should analyse the current status and the trend of decentralization of the energy sector. Then, as a decentralized and data strong consistency technology, blockchain solutions could be adopted to address the existing pain spots in energy application. To our knowledge, the pivotal enabler of blockchains committed to security, trustworthiness and retroactivity for distributed renewable energy might be judged as decentralized consensus algorithm underpinning blockchain technologies. Consequently, a lot of consensus mechanisms may be proposed to optimize the energy transaction processing speed. Additionally, it becomes increasingly recognized that a successful energy trading market must be consumer-centric after going through the California power crisis. Therefore, the improvement of the participation of prosumers and the flexibility of the renewable energy sources transactive marketplace would be quite essential. To this end, leveraging blockchain, the reliability of information, distributed management, and carbon emission control can be accomplished. After elaborating the application scenarios of blockchain technology in the energy industry, the article ends with a discussion of its challenges to be solved in the future development, passing over the hype phase in the energy sector.
Dynamics of the mathematical model of phase-locked systems with delay

In the article, the conditions for the existence of limit cycles of the first kind are obtained for self-tuning systems with delay, which, in turn, determine the conditions for the occurrence of hidden synchronization modes in such systems. The principle of the proof is based on constructing a positively invariant toroidal set using two cylindrical surfaces, whose boundaries are determined by the limit cycles of a system of the second-order differential equations. Using the results obtained in the article for limit cycles, the possibility of using the curvature of the cycle for a comparative analysis of the proximity of the cycles of phase and non-phase systems, as well as for determining the mode of hidden synchronization, is shown.
The Development of Giftedness as a Necessary Condition for the Formation of the Subject of Socio-economic Systems of Our Time

Purpose: the article consists in the analysis of the problem of diagnosis and development of giftedness as the most important condition for the growth of the intellectual and creative potential of modern Russian society and the search for the most effective methods for the development of giftedness as a necessary condition for the formation of the subject of socio-economic systems of our time. Methodology: retrospective analysis of the main approaches to the study of giftedness, its models developed by foreign and domestic scientists; system-synergistic methodology as the most effective approach to the development of a gifted person. The article describes the practical activities of the employees of the Center for the Identification and Support of Gifted Children “Talent and Success” of Karachay-Cherkess State University named after U.D. Aliyev. The work is carried out within the framework of the functioning of schools of intensive intellectual development for children of primary school age and adolescents. The main forms of organizing work along with classrooms are training, intellectual and business games, competitions. Result: it has been shown that the use of a systemic synergistic approach allows us to re-prioritize educational activities, in which the ability of a gifted person to self-work and self-development becomes the main one. The results of the work carried out with schoolchildren indicate the effectiveness of the chosen approach. Students acquired skills in communication and professional self-determination, presentation of the results of scientific research; learned to define the goals and objectives of their activities, comparing them with a system of values. The results of diagnostics according to the methodology of P. Torrens before and after studying at the school for the development of creative thinking showed significant changes in the level of creativity of students.
ConSenses: Disambiguating content word groups based on knowledge base and definition embedding

We introduce a method for disambiguating a given group of semantically related words with respect to a certain sense inventory, such as WordNet or Cambridge English Dictionary. In our approach, every member word is converted into a set of senses to be disambiguated. The method involves clustering relevant senses and filtering out irrelevant senses, and determining the intended senses for each word in the group based on pairwise sense similarity. A preliminary evaluation of our method on several datasets shows that the method extends and outperforms the previous work that only deals with noun groups [1]. Our method is more generally applicable, allowing nouns, verbs, and adjectives groups, and can be used to aligning two anthologies to combine knowledge resources, as well as to generate training data for word sense disambiguation tasks.
Perspective of hybrid cloud computing in higher education

Cloud computing along with virtualization, offers an excellent tool in IT education to support presence learning. However, what is the most common in today's situation, they excellently support online distance teaching. Cloud offers simplified access to computing resources, services and applications that support modern approach to learning. Therefore, several universities in Slovakia have built their own cloud solutions using their local computing resources. However, their employees also use the services of public cloud providers in their daily teaching practice. This situation thus opens up and supports the transition from the use of isolated private and public CC services to the construction and use of hybrid-based solutions. The article deals with an initial analysis of issues of implementation CC models with an emphasis on hybrid (multi) CC deployment solutions with a view to further exploring and developing HCC deployments for the Slovak sector of higher education institutions.
Effective UI Design to Explore Poetry Literature Using Multiple Scripts for Ordinary End Users

India is a multilingual, multi-script country having more than one natural languages, castes, and religions. Each religion has their saint’s, who had written literature for advancing humanity for being human either in the form prose or poetry. This literature is available on the Internet in the form of poems, abhangas, verses, and shlokas in Indian languages especially in Hindi and Marathi languages along with English. To search the literature through websites and mobile applications, require the knowledge of local languages along with English. It is noticed that there are no helpful UI provided by websites to explore these literature as compared to commercial websites for ordinary end users. Therefore, this work focuses to conduct the study of available websites to look over their UI, global and local search facility for Indian saints and poets literature between twelfth and twentieth centuries from ordinary end users point of view. After knowing the key requirements of ordinary end users, this study further extended from web based to online and offline mobile applications with searching interface to access the literature written in Devanagari script supporting Hindi and Marathi languages as by default search is always in Roman script. The outcome of study provides data with subjective satisfaction to user.
Ranking on Network of Heterogeneous Information Networks

Ranking on networks plays an important role in many high-impact applications, including recommender systems, social network analysis, bioinformatics and many more. In the age of big data, a recent trend is to address the variety aspect of network ranking. Among others, two representative lines of research include (1) heterogeneous information network with different types of nodes and edges, and (2) network of networks with edges at different resolutions. In this paper, we propose a new network model named Network of Heterogeneous Information Networks (NeoHIN for short) that is capable of simultaneously modeling both different types of nodes/edges, and different edge resolutions. We further propose two new ranking algorithms on NeoHIN based on the cross-domain consistency principle. Experiments on synthetic and real-world networks show that our proposed algorithms are (1) effective, which outperform other existing methods, and (2) efficient, without additional time cost per iteration to their counterparts.
An Automatic Classification of the Primary and the Corresponding Authors in Research Articles

Researchers often rely on the byline order in a publication to estimate relative contributions made by its authors, an assumption on which existing author contribution measures are based. This byline-based approach is, however, incompatible with the alphabetical author ordering, a practice still employed by many research fields. Manually requesting authors to state their contributions can overcome the limitation of the existing methods. Such approaches, however, require resource-intensive data acquisition and preprocessing, rendering them ungeneralizable to existing bodies of bibliographic records. The present paper proposed a possibility of order-independent automatic author contribution measure by focusing on distinguishing the main contributors from the rest of the authors using machine learning algorithms, bypassing the limitation of both the byline-based numerical author contribution methods and ungeneralizable manual approaches. The experiment validated the proposed approach by successfully classifying both the primary and the corresponding authors shown as the first and the last author without utilizing byline orders. The Random Forest classifier showed the best performances, successfully classifying the first author, the last author, and both with the accuracy of 0.90, 0.89, and 0.76 respectively.
CLARIN-D: An IT-Based Research Infrastructure for the Humanities and Social Sciences

Abstract The paper discusses the idea of bridging the gap between computer sciences and the humanities by referring to an e-humanities infrastructure that provides tools and services for well-defined and frequently encountered tasks. The main goal of this infrastructure is to enable researchers in the humanities and social sciences to better exploit their potential by reusing available digital resources, and thus to increase the efficiency of e-humanities projects. CLARIN-D is an example of such a research infrastructure. The paper provides a brief overview of the basic principles and services of the CLARIN-D infrastructure, such as metadata harvesting, federated content search, and chaining Web services.