Reviewerly/exHarmony huggingface dataset:
Agent Functionals in Monitoring Information Systems

The processes of construction and use of agent functionalities in monitoring information systems (MIS) are investigated in the work. Several data-converting agents are combined by the monitoring information system into global and local multi-agent structures to provide the ability to perform monitoring tasks while reducing the informativeness of the input data arrays. The relevance of these studies is determined by the fact that there is a need for methods to identify the properties of monitoring objects in the process of changing their states. At the same time, the informativeness of the results of already conducted observations decreases in the process of discovering new properties of the monitored objects. There is a need to make new observations and to identify new properties of objects based on the results of already observed observations. The definition of basic concepts and principles of construction of agent functionalities is given. Hypotheses are formulated to increase the efficiency of monitoring by building agent functionalities. An experimental test of the formulated hypotheses was performed. To do this, the process of constructing an agent functional in monitoring the individual patient's response to the use of a standard treatment regimen was studied. Blood glucose was a simulated measure of health. It is experimentally proved that the method of ascending synthesis of elements allows ensuring the integrity of the agent functional. In this case, the SLE of the simulation results from the actual values decreases by more than 34%.
Alih Fungsi Lahan di Bojong Kondang dalam Perspektif Ekoteologi

Excessive land conversion causes negative impacts such as floods, landslides, climate change, and other disasters. This happened in Bojong Kondang, where the slopes of the mountains were used as residential buildings. The purpose of this paper is to describe the main research topics: 1) Community understanding of land conversion in Bojong Kondang; 2) Ecotheological analysis in the perspective of Seyyed Hossein Nasr on land conversion in Bojong Kondang. This research is a field research, with descriptive qualitative method, and the ecotheological theory of Seyyed Hosein Nasr as an analytical tool. The findings in this study are first, there are pros and cons between the community and the government on the housing development. Second, the perpetrators of the housing construction were not native Bojong Kondang residents, but migrants. Third, the indigenous people of Bojong Kondang are very concerned about the environment and uphold ecotheological values based on the principles of Islamic teachings. Fourth, the elders in Bojong Kondang still adhere to mythology.
Artificial intelligence and machine learning

Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday life. The history of the development of artificial intelligence is described, technologies of machine learning and neural networks are analyzed. An overview of already implemented projects for the use of artificial intelligence is given, a forecast of the most promising, according to the authors, directions for the development of artificial intelligence technologies for the next period is given. This article provides an analysis of how educational research is being transformed into an experimental science. AI is combined with the study of science into new ‘digital laboratories’, in which ownership of data, as well as power and authority in the production of educational knowledge, are redistributed between research complexes of computers and scientific knowledge.
Analisis Pemanfaatan Search Engine Optimization (SEO) sebagai Upaya Meningkatkan Volume Lalu Lintas Website Akademik di Google Search Engine

The purpose of making a website as course materials for Informatics Engineering students.One of the problems is the website not indexed on Google Search Engine, so there are no visitors.This is proven through further searches through the Google Search Engine.Optimization technique to increase the volume of website traffic using the implementation of Search Engine Optimization (SEO).This type of research is conducted using qualitative research, with data collection methods by intervieweing website administrator and making website observations on Google Search Engine.The SEO method that is applied uses 2 types, On-Page SEO (on the website) and Off-Page SEO (outside the website).The results showed that using the SEO method was proven to be successful in boosting website traffic organically on the Search Engine Result Page (SERP), reach 578 page views.This can be seen with the movement of traffic volume using the help of Google Search Console and Google Analytics tools which have been integrated with the website.The organic traffic that has been successfully increased is an indicator if the website can be found on Google Ssearch Engine using related keywords.
A Credit Management Mechanism for Peer-to-Peer Electricity Trading in Blockchain Environment

With the rapid development of distributed generation, peer-to-peer (P2P) electricity trading between microgrids in blockchain environment has become one of the indispensable ways to consume the local renewable energy. The existing credit management for P2P electricity trading generally realized through the smart contract of penalty, which cannot show the risk identification and risk aversion behaviors of each microgrid. Therefore, this study proposes a newly credit management mechanism to reduce the defaults, which achieves credit-based risk control optimization and credit-based continuous double auctions. In the first stage, microgrids make robust control optimize to submit a trading strategy that follows contracts as far as possible. In the second stage, which realizes rewards and penalties through credit-based equivalent price conversion. The experimental results show that the proposed credit management mechanism can improve microgrids' interests and ensure fair environment.
Stability analysis based parameter tuning of Social Group Optimization

Abstract Swarm-based optimization algorithms have been popularly used these days for optimization of various real world problems but sometimes it becomes hard to estimate the associated characteristics due to their stochastic nature. To ensure a steady performance of these techniques, it is essential to have knowledge about the range of variables, in which a particular algorithm always provides stable performance and performing stability analysis of an algorithm can help in providing some knowledge regarding the same. Many researchers have performed the stability analysis of several optimization algorithms and analyzed their behavior. Social Group Optimization (SGO) is a newly developed algorithm which has been proven to yield promising results when applied to many real world problems but in literature no work can be found on stability analysis of SGO. In this paper, Von Neumann stability analysis approach has been used for performing stability analysis of Social Group Optimization (SGO) to analyze the behavior of its algorithmic parameters and estimate the range in which they always give stable convergence. The results obtained have been supported by sufficient experimental analysis and simulated using eight benchmark function suite along with their shifted and rotated variations which prove that the algorithm performs better within the stable range and hence convergence is ensured.
Digital Transformation: The Age of Innovations in Business and Society

This chapter considers main factors of digital transformation in business and society. The latest studies of digital ad spend in Europe and a number of marketing technology solutions all over the world within the period 2011–2020 have confirmed a rapid development of digital technologies in business and society. It was justified that due to digitalization process companies have to revise their priorities and modify their main strategic goals for simultaneous achievement of commercial efficiency and a social effect. These goals need some adjustment to specific requirements and wishes of main process participants that form five levels of approval. Proposed marketing management model in digital transformation of business and society contains basic components, which lie in the digital contour. Responses of millennials (the people born at the turn of the century) on the use of digital technologies in different spheres of life are analyzed. The pilot research allowed to distinguish three groups of millennials depending on how actively they use digital technologies in different spheres of life. Main directions of innovative marketing management development are defined. The opportunities of implementing functionality of artificial intelligence, neuromarketing, augmented and virtual reality, marketing technologies MarTech in company business-processes are studied.
Capturing Expert Knowledge to Guide Data Flow and Structure Analysis of Large Corporate Databases

Maintaining and improving existing, large-scale systems, that are based on relational databases has proven to be a challenging task.Among many other aspects, it is crucial to develop actionable methods for estimating costs and durations in the process of assessing new feature requirements.This is a very frequent activity during the evolution of large database systems and data warehouses.This goal requires the analysis of program code, data structures and business level objectives at the same time, which is a daunting task if made manually by experts.Our industrial partner started to develop a static database analysis software package that would automate and ease this process in order to make more accurate estimations.The goal of this work was to create a quality assessment model that can effectively help developers to assess the data flow (lineage) quality and the database structure quality of data warehouse (DWH) and online transaction processing (OLTP) database systems.Based on the relevant literature, we created different models for these two interconnected topics, which were then evaluated by independent developers.The evaluation showed that the models are suitable for implementation, which are now included in a commercial product developed by our industrial partner, Clarity.
Partitive and Hierarchical Clustering of Distributional Data using the Wasserstein Distance

The chapter presents partitive and hierarchical clustering algorithms to analyze a set of observations described by numeric distributional variables. The two distance-based algorithms use the 2-norm Wasserstein distance as a building block, exploiting its interpretative advantages. The first is a dynamic clustering algorithm that generalizes the well-known k-means. The second is an extension of the hierarchical agglomerative clustering algorithm. Clustering outputs are evaluated according to internal indices, which can be interpreted through the type and the size of the variability explained by the obtained clusters. The chapter presents an application of the clustering algorithms on a temperature dataset.
Digital Competence of Students from Economic Universities in Vietnam in the Digital Economy

In recent years, digital competence has become one of the most significant fundamental competences in the digital economy.Digital competence is a key aspect for student majoring in economic in order to study and work in the world of the global economy.The development of digital competence for students include the determination of the conceptual content and current status of students' digital competence.For countries conducting digital transformation in the later stages like Vietnam, inheriting the achievements of previous countries is the optimal choice.After analyzing and synthesizing experiences from previous research results and the digital competence measurement frameworks for students from Vietnam and other countries, we chose an appropriate digital competence framework -the DLGF Framework of UNESCO to conduct measurement and assessment of the available digital competences of students.This study will be a basis for proposing the training programs to develop their digital competence for students in Vietnam.
QKD field-trial in Padua: a resource-effective implementation with the iPOGNAC encoder

Quantum key distribution (QKD) is one of the most mature among the quantum technologies that allows two remote users to generate secret keys with unconditional security. To increase its adoption, simple, low-cost, and robust systems are necessary, together with demonstrations in real environments. Here, we present a QKD field-trial over optical fibers deployed in the city center of Padua, Italy. Our system exploits two key technologies developed by our group: a low-error, self-stabilized polarization encoder, called iPOGNAC, and a novel synchronization technique, called Qubit4Sync, which allows us to minimize the experimental complexity of our system.
Application of Cryptography and Network Security in the Internet of Things (IoT)

This chapter discusses the importance of cybersecurity, its origin, and various aspects. Basic concepts related to cryptography are given for the reader to understand the importance of cryptography for cybersecurity, and then the concept of the cybersecurity and network security is also explained and clarified the meaning of both. The discussion ends with recent technological developments of cybersecurity. The future of this technology is considered as Internet of things (IoT). The IoT basics, working of IoT devices, applications of IoT for various fields, and problems associated with IoTs are explained with simple examples. Whatever the process and processing devices they are facing cyberat-tacks and hacking, so this chapter explains the concept of various security options and measures along with drawbacks of each type of security system and then discusses in length about the weakness of the security where the possible threats exist. The IoT security aspects and related issues are explained in detail. The chapter ends with the latest and future cybersecurity trends and issues along with the IoT and future possible developments in the field of cybersecurity and IoT technologies.
Enhanced Iterated Local Search for Scheduling of Scientific Workflows

Recently, cloud computing has been used to host and implement scientific applications that have large data and are processing-intensive. This is because cloud computing provides a cost-effective, scalable, and pay-per-use deployment environment. Optimizing workflow scheduling is one of the areas of research activities currently and one of the most important issues in the cloud environment and this problem is considered NP-complete There are many improvement objectives in workflow scheduling, one of the most important of which is to reduce the makespan. The iterated local search (ILS) has been shown as an effective approach for tackling numerous real-world difficult problems as it simply adapted to different research instances. In this paper, an enhanced ILS algorithm is proposed for achieving this aim. Several enhancing modifications have been proposed for developing the ILS algorithm; two methods for developing an initial solution of the algorithm, and four proposed methods for developing the perturbation phase. Experiments are conducted on well-known scientific workflows of various sizes and types and with WorkflowSim in order to test the six proposed methods. Experimental results show that some of these enhancing modifications outperform the standard ILS as they obtained better results compared with this ILS.
Disentangle Saliency Detection into Cascaded Detail Modeling and Body Filling

Salient object detection has been long studied to identify the most visually attractive objects in images/videos. Recently, a growing amount of approaches have been proposed, all of which rely on the contour/edge information to improve detection performance. The edge labels are either put into the loss directly or used as extra supervision. The edge and body can also be learned separately and then fused afterward. Both methods either lead to high prediction errors near the edge or cannot be trained in an end-to-end manner. Another problem is that existing methods may fail to detect objects of various sizes due to the lack of efficient and effective feature fusion mechanisms. In this work, we propose to decompose the saliency detection task into two cascaded sub-tasks, i.e., detail modeling and body filling. Specifically, detail modeling focuses on capturing the object edges by supervision of explicitly decomposed detail label that consists of the pixels that are nested on the edge and near the edge. Then the body filling learns the body part that will be filled into the detail map to generate more accurate saliency map. To effectively fuse the features and handle objects at different scales, we have also proposed two novel multi-scale detail attention and body attention blocks for precise detail and body modeling. Experimental results show that our method achieves state-of-the-art performances on six public datasets.
Artificial intelligence is not a panacea: policing content on social media platforms, three dilemmas and their ethical and legal implications

This chapter discusses three dilemmas relating to 1) public trust, 2) freedom of information, and 3) liability, faced by social media platforms in terms of their policies and practices of content moderation and the implications for ethics and law. These dilemmas, which cannot be resolved merely by the advent and application of artificial intelligence (AI), reflect the complex relationship between social media platforms, users, and governments. This relationship transforms alongside changes in politics and societies. In the light of cleaning up social media content, the chapter contends that states, social media companies and users should each play their respective role. It would be beneficial to increase collaboration between states and social media firms in tackling the related problems, although both state and corporate powers should be restricted. Social media users also need to share the responsibility of maintaining social media hygiene with awareness of limiting and balancing the powers of states and corporate companies.
Building 1U CubeSat as a Tool to Promote Project-Based Learning in Paraguay, a Case Study

I am a Electromechanical Engineering student, currently developing my final project in order to graduate.I find space development and technologies very fascinating, as well as innovative solutions to any engi-
A Visual Question Answering System using YOLO Model

A Visual Question Answering System (VQAS) is an intelligent system that can provide answers to different user queries about images. Such a system may be helpful for the visually impaired person to know the environment or about the images verbally by asking specific questions. Also, processing a large number of images and fetching the required information quickly from them is a challenging task. The majority of the image retrieval systems use the captions assigned to the image data and perform matching with the user query terms to fetch the relevant images. Image retrieval based on content analysis with respect to different user-entered queries in natural languages is a challenging task and is an active area of research. In this paper, we present a natural language query-based Visual Question Answering (VQA) system which can retrieve the images based on their content match as per the user query and also can provide answers to different user queries about the images. The presented model uses the YOLO (You Only Look Once) object detection model to identify different objects present in images and to decide the context. The presented model is tested on a varied number of user queries about different images and the results obtained show the effectiveness of the model in presenting answers to different user queries. The presented model achieves an average accuracy of 96% on the considered testing image data set and user queries.
A Perfect Recovery for Fragile Watermarking by Hamming Code on Distributed Pixels

Fragile watermarking is the task of embedding a watermark in an image such that even small changes, called tamper, in the image can be detected or even recovered to preserve unauthorized alteration. A well-known type of spatial fragile watermarking embeds the watermark in the least significant bits of the image to measure correctness of the most significant bits. In addition, Hamming code is a coding algorithm in communication that transmits the data-bits by augmenting some check-bits to exactly detect and recover single-bit modifications. This property is used, in this paper, to detect and perfectly recover the images modified by a tamper with diameter less than a half of the diameter of the image. Based on our knowledge, this is the first time that perfect reconstruction is guaranteed in a high probable condition. To achieve this goal, the Hamming code is applied on a distributed pixel of which bits are extracted from far sufficient pixels in the image. According to the experimental results, the proposed method achieves better performance, in terms of recovering the tampered areas, in comparison to state-of-the-art.
Application of Class Based Association Rule Pruning to Generate Optimal Association Rules in Healthcare

The association rule mining approach produces uninteresting association rules. When the set of association rules become large, it becomes less interesting to the user. In order to pick interesting association rules among peak volumes of found association rules, it is critical to aid the decision-maker with an efficient post-processing phase. Theymotivate the need for association analysis performance. Practically it is an overhead to analyze the large set of association rules. In this work, association rule pruning technique called Class Based Association Rule Pruning (CBARP). This pruning techniques is proposed to prune the weak association rules of the healthcare system. The results are compared with Semantic Tree Based Association Rule Mining (STAR) technique and it demonstrate that the CBARP method outperforms other methods for the given support values.
Local Binary Complement Pattern for Color-Inversion Invariant Texture Classification

Existing Local Binary Pattern (LBP) methods cannot well handle the color-inversion changes. To address this problem, we propose a novel color descriptor called Local Binary Complement Pattern (LBCP) for color-inversion invariant texture classification. The proposed LBCP consists of three local operators, i.e., LBCP_S, LBCP_C and LBCP_O, all of which use the complement information to achieve the color-inversion invariance. Specifically, LBCP_S encodes the sign information of local neighboring differences in each channel. LBCP_C and LBCP_O encode the binary color values and the ordering information of central pixels, respectively, across color channels. After encoding the image using these three operators, we obtain multiple histograms and concatenate them as the LBCP descriptor. Experiments on several color texture databases demonstrate the effectiveness of LBCP for texture classification under color-inversion changes.