Reviewerly/exHarmony huggingface dataset:
Maximal Prevalent Co-location Patterns

A novel maximal co-location pattern mining framework based on maximal cliques and hash tables (MCHT) is developed in this chapter. First, all maximal cliques that can compactly represent neighbor relationships between instances of a spatial data set are enumerated. The advantages of bit string operations are fully utilized to speed up the process of enumerating maximal cliques. Next, a participating instance hash table structure is constructed for these maximal cliques. Then information about maximal patterns can be queried and collected efficiently from the hash table. After that, by calculating participation indexes of these patterns to measure their prevalence, maximal prevalent co-location patterns can be filtered efficiently. Finally, a series of experiments are conducted on both synthetic and real-facility data sets to demonstrate that the proposed algorithm can efficiently reduce both the computational time and memory consumption compared with the existing algorithms.
Exploration of Artificial Intelligence Security in Multi Unmanned System Cooperation

Abstract In modern science and technology, artificial intelligence has become one of the most important and promising technologies in today’s society and plays a very important role in people’s life. In artificial intelligence, cooperation is a very important research direction, which includes the cooperation between sensors, coordinated man-machine interface and actuators on multiple UAVs. Therefore, based on the exploration of artificial intelligence security, this paper studies artificial intelligence in multi unmanned system cooperation. Firstly, this paper expounds the development of cooperative system, and then describes the purpose of multi unmanned system cooperation. Then, this paper studies the intelligent algorithm applied to the cooperation of multiple unmanned systems in the field of artificial intelligence. Finally, aiming at the existing security problems of artificial intelligence, this paper tests the functions of multiple unmanned systems. The test results show that when multiple unmanned systems work together, the accuracy of artificial intelligence in dealing with things is basically more than 90%. At the same time, it can be nearly 100% scientific, and can budget a variety of treatment schemes. This shows that in the multi unmanned system cooperation, artificial intelligence can almost meet its needs, but it still needs to be further improved.
Understanding and Predicting Characteristics of Test Collections in Information Retrieval

Research community evaluations in information retrieval, such as NIST’s Text REtrieval Conference (TREC), build reusable test collections by pooling document rankings submitted by many teams. Naturally, the quality of the resulting test collection thus greatly depends on the number of participating teams and the quality of their submitted runs. In this work, we investigate: i) how the number of participants, coupled with other factors, affects the quality of a test collection; and ii) whether the quality of a test collection can be inferred prior to collecting relevance judgments from human assessors. Experiments conducted on six TREC collections illustrate how the number of teams interacts with various other factors to influence the resulting quality of test collections. We also show that the reusability of a test collection can be predicted with high accuracy when the same document collection is used for successive years in an evaluation campaign, as is common in TREC.
MQTT Protocol Extension for Real Time Location based Service

This paper introduces an extension of the MQTT protocol to reduce communication overhead in real-time location-based service system where mobile MQTT clients move and change their administrative district location frequently. The MQTT protocol was extended to handle UNSUBSCRIBE and SUBSCRIBE processing with a single step and the LBS server was revised to handle the extended protocol. Instead of directly processing SUBSCRIBE and UNSUBSCRIBE, mobile clients need to send their GPS location to the LBS server. LBS Server communicate with MQTT Broker to using extended protocol on behalf of each client. In an environment where large number of clients frequently move, the extended MQTT protocol can drastically reduce the number of messages between MQTT Broker and clients. This is mainly because clients do not need to issue SUBSCRIBE and UNSUBSCRIBE request to MQTT Broker. In addition, in contrast to our previous study where clients receive new administrative area name after sending its GPS location to LBS server, in this research, using extended MQTT protocol, mobile clients do not need to receive new administrative name even though they move across different district areas and as a result, the number of message exchanges can also be reduced. The LBS system applying the extended MQTT protocol can be used as a real-time location-based information service for large-scale mobile devices, such as real-time pedestrian population and vehicle traffic analysis, and location-based message delivery.
Implementation of a Security System in IaaS Cloud Server through an Encrypted Blockchain

Infrastructure as a Service (IaaS) is a kind of cloud sharing service allocated to different category of application at the same time. The shared cloud services are provided through internet for computing, networking, and data storage applications. The utilization of IaaS reduces the maintenance and installation cost of physical hardware modules at the base station. The cloud service providers configure their available cloud components with respect to the suitability of the user requirements. The security protocol available in the IaaS servers are usually better than the traditional local servers. However, the IaaS servers are also open to attacks when the modules encounter misconfiguration and vulnerabilities. The work enforces an encrypted blockchain model for enhancing the quality of service in the IaaS systems on handling image data.
Knowledge engine for a Hindi text-to-scene generation system

We have developed Preksha - a text-to-scene generation system for the Hindi language. Hindi is a morphologically rich free word order language. Preksha has three major engines: a language engine, a knowledge engine, and a scene engine. The input text is processed by the language engine using a rule-based approach with a layered contextual pre-parser and a language parser based on the tree-adjoining grammar formalism. The language engine transforms the input text into parsed derived trees represented in a language-independent Extensible Markup Language format. After a review of our earlier version of the language engine, this chapter mainly focuses on the knowledge engine of Preksha that takes these parsed trees as its input and generates output for further processing by the scene engine. The knowledge engine consists of a knowledge extraction module and a knowledge representation module. We introduce object visualization features which support these knowledge engine processes. An example of a scene having multiple objects with spatial relations generated using the knowledge engine is given.
Automated Modulated Parameter Implementation Using Neural Network for Enhancement of Paint Spray

Artificial neural networks (ANNs) were introduced to be implemented for the paint spray process. ANNs are biologically inspired computational networks. It is a technology based on studies of the brain and nervous system. ANN models simulate the electrical activities of the brain and nervous system. ANN models include three layers (an input layer, hidden layer and output layer). ANN can be simulated in Matlab to predict the optimum conditions for maximum production. Surrounding conditions are the inputs for simulation. Data are collected and tabulated as a database for the model to generate the result. The result will be evaluated, analysed in the end. The prediction was trained using 70% of the data, and in the validation process, 30% of data was used. The optimum number of neurons is determined by training the network using a different number of neurons, and the performance of each network is compared. The statistical performance measures consisting of root mean square error (RMSE) and the square root of the coefficient of determination (R). A high predictive accuracy ANN model will possess the value of R close to one and low RMSE. The results of the training set were not high, R = 68.466% validation set with R = 63.173%.
The Dual Function of Explanations: Why Computing Explanations is of Value

The increasing dependence of decision-making on some level of automation has naturally led to discussions about the trustworthiness of such automation, calls for transparent automated decision-making and the emergence of 'explainable Artificial Intelligence' (XAI).Although XAI research has produced a number of taxonomies for the explanation of Artificial Intelligence (AI) and Machine Learning (ML) models, the legal debate has so far been mainly focused on whether a 'right to explanation' exists in the GDPR.Lately, a growing body of interdisciplinary literature is concentrating on the goals and substance of explanations produced for automated decision-making, with a view to clarify their role and improve their value against unfairss, discrimination and opacity for the purposes of ensuring compliance with Article 22 of the GDPR.At the same time, several researchers have warned that transparency of the algorithmic processes in itself is not enough and tools for better and easier assessment and review of the whole socio-technical system that includes automated decision-making are needed.In this paper, we suggest that generating computed explanations would be useful for most of the obligations set forth by the GDPR and can assist towards a holistic compliance strategy when used as detective controls.Computing explanations to support the detection of data protection breaches facilitates the monitoring and auditing of automated decision-making pipelines.Carefully constructed explanations can empower both the data controller and external recipients such as data subjects and regulators and should be seen as key controls in order to meet accountability and data protection-by-design obligations.To illustrate this claim, this paper presents the work undertaken by the PLEAD project towards 'explainable-by-design' socio-technical systems.PLEAD acknowledges the dual function of explanations as internal detective controls (to benefit data controllers) and external detective controls (to benefit data subjects) and leverages provenance-based technology to compute explanations and support the deployment of systematic compliance strategies.
Learner Model for AI Convergence Education in Technology Subject

This study devises a model of learner for individualized learning of AI convergence in technological problem solving on technology subjects. It uses the Kolb's research method, which derives four learning styles by synthesizing the correlation between two types of information perception methods and two types of information processing methods. In this study, a creativity-execution model was devised in consideration of the ‘adaptation-innovation type’ of the creativity level and the ‘reflection-action type’ of the execution level. In addition, an interactive-attitude model was devised in which the 'independent-cooperative type' in the interaction dimension and the 'avoidance-participation type' in the attitude dimension act dynamically. Furthermore, a three-factor learner model was developed considering the dimensions of cognitive judgment, creativity, and execution. Also, a five-factor learner model for technological problem-solving was devised and presented, with all five dimensions of cognition-judgment, creativity, execution, interaction, and attitude of the technological problem-solving learning style as factors. The learner characteristics defined through the model design suggested the direction of AI convergence education considering individual characteristics of learners and provided basic data for it. In the future, it is suggested that specific individualized teaching and learning development research be conducted in AI convergence education, considering the characteristics of learners according to the learning style of technological problem-solving.
Exploration of Research-Based Classroom Teaching Model in the Teaching of Biochemistry and Molecular Biology

Our research group practiced a research-based teaching model in the teaching of biochemistry and molecular biology.The influence of this teaching model on students' final exam score was analyzed.The results showed that in the final exam, the average score of the students taught using the research-based classroom teaching mode was 64.3±10.5 (Mean ± Std) points, which was similar to the score of 64.3±12.2(Mean ± Std) points of the students taught using the traditional teaching mode (P>0.05).However, there are significant differences in the score of essay questions (P<0.05).The essay questions mainly examine the students' scientific research literacy, the ability to utilize professional knowledge to analyze and solve problems, the ability to develop logical reasoning , and so on.Therefore, our results indiacate that the research-based classroom teaching model can cultivate and improve students' scientific research literacy mentioned above and is weathy of promotion and application
The Use of Ontologies in Software elicitation: Review

Research in recent years has probed the integration of research in the fields of Software Engineering and Semantic Web technologies in several domains.Semantic approaches in general have allowed a better understanding of domains that are known for their ambiguities and incompleteness.We have seen artificial intelligence in general being used to improve many stages of the software development process.Ontologies are being used successfully in the requirements engineering phase.This paper reviews some of the research related to the use of ontologies in requirements engineering (RE).specifically review some approaches, methods, and tools.The main objective of this work is to show how ontologies are used in requirement elicitation which is then first step of (RE).Requirement elicitation is a crucial phase of (RE) and the use of ontologies to manage and facilitate the activities undertaken during this phase is of great importance.In summary, the main findings of this work are:(1) there are empirical evidences of the benefits of using ontology's in (RE) in several domains for reducing ambiguity, inconsistency and incompleteness of requirements;(2) several research works have developed novel approaches for ontology usage in (RE); (3) some of which developed algorithms that improved and enhanced the (RE) process; (4) most studies addressed only functional requirements; (5) some studies focused on the use of ontologies in requirement modeling.Finally, we conclude this work by suggesting several areas of ontology in (RE) search opportunities in ontology and some issues that need to be addressed.
Adaptive Computing (and Agents) for Enhanced Collaboration (ACEC 2021)

The 19 th edition of the "Adaptive Computing (and Agents) for Enhanced Collaboration" (ACEC) track at WETICE 2021 targets the relevant topic of adaptation in relation to tools, methods and techniques for the enterprise.The aim of the track is to bring together researches and practitioners, working on agentbased software systems and/or adaptive computing, to present results and discuss innovative ideas.This report outlines the content of the papers accepted for presentation at the track.
Visual media compression

Chapter Contents: 15.1 3D video compression 15.1.1 Image and video compression 15.2 MPEG standardization and compression with 2D video codecs 15.2.1 Cubemap video 15.2.2 Multiview video and depth compression (3D-HEVC) 15.2.3 Dense light field compression 15.3 Future challenges in 2D video compression 15.4 MPEG codecs for 3D immersion 15.4.1 Point cloud coding with 2D video codecs 15.4.2 MPEG immersive video compression 15.4.3 Visual volumetric video coding 15.4.4 Compression for light field displays References
Phenomenon of the university lecture in A.P. Chekhov’s novella “A Dreary Story” and the critical reflection of teaching higher mathematics

The relevance of A.P. Chekhov’s novella “A Dreary Story” with the subtitle “From the Notebooks of an Old Man” is revealed in why the famous professor and a very successful lecturer fades from natural old age and illness, loses ties with the outside world and falls into the abyss and emptiness of indifference. The article analyzes the problem of lecturer skill and addresses the philosophical reflection of individual features of higher mathematics teachers. It defines the role of critical reflection in the aesthetic phenomenon of a good university lecture on mathematics in the formation of student’s critical thinking.
FACULTY PERFECTIONISM IN THE CONTEXT OF UNIVERSITY ORGANIZATIONAL AND CORPORATE CULTURE

The study aims to identify and analyze the reasons for the development of positive and unhealthy perfectionism of a university teacher in the socio-economic conditions of the labor activity. The relevance of the study is due to the uncertainty of the requirements for teachers in relation to research and scientific-methodical work. The novelty of the research lies in the identification of the teacher’s focus on maximum results, which does not contribute to maintaining a balance between life goals and a healthy lifestyle. As the main research method, the author applies the analysis of modern trends in labor activity, key performance indicators (KPIs), and the system of teachers’ remuneration. The educational environment provides the basis for self-improvement not only for students but also for teachers. The key objective of the faculty is to constantly strive to find the most effective teaching methods, as well as create new knowledge. Research work as an integral part of pedagogical activity in the field of higher education defines the teacher as the owner of the social intellectual capital, capable of developing a positive attitude and motivation among young people towards their future profession. The organizational culture of the university creates social conditions for the continuous improvement of pedagogical and scientific competencies. Nevertheless, corporate culture does not completely clarify the correlation between key performance indicators, achievements, and faculty pay as subjects of economic relations. Academic workload increases and the necessity to create electronic courses results in reduced time for conducting research. Meanwhile, key performance indicators are primarily aimed at scientific research. Unhealthy perfectionism develops as a result of the mismatch between the completed work, achieved results, and remuneration. Teachers’ all-or-nothing thinking comes into contradiction with sanitary standards, including those of working with computers. Validation of requirements in accordance with the academic labor organization and sanitary standards can become the subject of government authorities and business analysis. The practical value of the paper lies in the attempt to identify the necessity for determining the scope of extracurricular work of a teacher in terms of time, specific results, and remuneration. As a result of the analysis, the author proposes to increase the transparency of the remuneration system and use key performance indicators for teacher’s personal development.
Energy-efficient computing for future IoT applications

Chapter Contents: Abstract 10.1 Introduction 10.2 Recent work related to energy-efficient computing in IoT 10.2.1 Energy-efficient task offloading 10.2.2 Energy-efficient device control 10.2.3 Energy-efficient caching 10.2.4 Energy efficient RIS 10.3 Future opportunities and challenges 10.3.1 Integrated energy solution 10.3.2 Federated learning 10.3.3 Energy harvesting 10.4 Conclusion List of acronyms References
Ethical Perception of Leadership and Responsible Behavior: An Exploratory Study of Leaders of Public Organizations in Iraq

The study aimed to identify the effect of the ethical perception of a sample of managers in public organizations on responsible behavior in light of the rapid changes taking place in the external environment. To achieve this, the researcher followed the descriptive analytical approach by applying a questionnaire of two parts. The first part dealt with the ethical perception according to the scale of Johnson (2015), which consisted of (22) items. The second part dealt with measuring responsible behavior, which consisted of (20) items based on the scale of Development of Ethical Behavior (Narvaez, 2006) for a sample of (125) respondents randomly chosen. The results showed that the estimation degree of managers in public governmental organizations of the level of ethical perception was average with arithmetic mean (3.26) and standard deviation (1.44). Moreover, the level of responsible behavior was average with arithmetic mean (3.19) and standard deviation (1.24). The results revealed a direct statistically significant relationship between the estimation degree of managers of the level of ethical perception and that for the level of responsible behavior, as the correlation coefficient reached (0.413). They also demonstrated statistically significant differences between the average scores of managers' estimation of the level of ethical perception attributable to the personal (demographic) variables. The study recommended that the priorities of the general agenda should focus on developing ethical perceptions of leadership in public organizations, which contributes to building and promoting responsible behavior in various directions.
Augmented Reality

The main objective of this chapter is to focus on giving immense knowledge about emerged technology Augmented Reality, and it is essential to have a base knowledge about the technology before plunging into the field of experiencing. The chapter has five sections to give exclusive understanding and exposure to the world of Augmented Reality. It provides a 360-degree view of the world of AR and explains with real-time examples the variety of aspects that can be developed through Augmented Reality technology. It will provide a solid theoretical grip on the AR and a brief overview of the world of AR for underlying developments. The chapter also provides a holistic approach and concise overview of the realm of AR and how the world is being adopted by it day in and day out. AR cannot be categorized as a simple technology, but rather a combination of set of technological innovations.
Implementation and Comparison of File Security Using AES, DES and RSA and Anomaly Detection in Videos Using Convolutional Auto Encoder

Security is the vital part which helps in trying to exchange huge amount of data in the form of valid data from source to destination. Nowadays technology is modifying very fast based on the view on secure information. Every individual who are interacting with social network wishes their communication to be confidential and secured from the access of the unauthenticated users over the internet. Symmetric and Asymmetric Cryptography Techniques play a vital role as the arms of security. Cryptography is the skill of properly modifying the readable plain text data into a not readable cipher form. There are many individual symmetric and asymmetric algorithms that are implemented to secure the files but have their own disadvantages. Proposed technique in this paper is to use a combination of symmetric and asymmetric encryption algorithms and compare the performance with the existing systems. The file is divided into n-parts depending on the size then each part of the file is encrypted with any of the AES, DES, and RSA algorithms. Results of this Hybrid technique provides a stronger security. The comparison is made on the basis of time taken by the proposed and existing systems in milliseconds to encrypt different numbers of bytes of text and image data.