Reviewerly/exHarmony huggingface dataset:
Utilizing Blockchain for Safeguarding IoT-Based Robotic Networks from Spoofing Attacks

In the dynamic realm of IoT-based robotic networks, autonomous robots, with their intricate integration of sensors and data-driven functionalities, become prime targets for cyber threats. Their heightened dependence on sensor data for path planning and control makes them particularly vulnerable. Within these multi-robot ecosystems, the process of achieving consensus is vital for ensuring coordinated communication. However, this consensus mechanism can become a weak point, susceptible to adversarial intrusions such as spoofing attacks. In this chapter, we introduce a novel approach that leverages blockchain to facilitate path planning coordination among a network of robots. Furthermore, we present a consensus algorithm for detecting compromised robots by analyzing blockchain data transferred from the robots. Through simulation results, we demonstrate that the proposed approach enhances the resilience of a robot network by rapidly identifying spoofed client robots or compromised servers. We conclude with a comprehensive discourse on the multifaceted benefits of deploying blockchain as a defensive measure in these networks while also shedding light on the emergent challenges and their feasible countermeasures.
A Study of Rule Extraction from Double Random Forest to Identify the Characteristics of Working Poor in Jakarta Province, Indonesia

Double Random Forest (DRF) outperforms Random Forest (RF) models, particularly when the RF model is underfitting. DRF generates more diverse and larger trees that significantly improve prediction accuracy. By applying association rule technique, the extracted rules from the DRF model provide an easily understandable interpretation of the characteristics of individuals identified as the working poor in Jakarta. The findings show that DRF performs good predictive performance in classifying poor workers in Jakarta, achieving an AUC value of 79.02%. The extracted rules from this model highlights interactions between education levels, working household member proportion, and job stability that significantly affect the classification of working poor. Specifically, worker with lower education levels, particularly high school or below, show a higher probability of being classified as poor workers. In addition, households with fewer employed members, especially those involving worker in self-employed/employee/freelancer roles, face a greater risk of falling into the poor category due to job instability and limited workforce participation. This implies that the interaction between the low proportion of working household members and low education, the interaction between unstable job position and low proportion of working household members, and the interaction between low education and unstable job position are the most important characteristics of the working poor in Jakarta.
Survey on Resource Management Solutions to Speed up Processing Small Files in Hadoop Cluster

High performance data analytics is a computing paradigm involving optimal placement of data, analytics and other computational resources such that superior performance is achieved with lesser resource consumption. Resource allocation and scheduling are the two major functionalities to be addressed in Hadoop clusters to satisfy the service level agreements of users for High performance data analytics applications. Though many solutions have been proposed for optimal resource allocation and scheduling, those schemes are designed for large Hadoop files. Recently with Internet of Things (IoT) convergence with big data, there is need to process large volumes of small files whose size is lower than block size of Hadoop. This creates huge storage overhead and exhausts Hadoop clusters computational resources. This survey analyzes the existing works on resource allocation and scheduling in Hadoop clusters and their suitability for small files. The aim is to identify the problems in existing resource allocation and scheduling approaches while handling small files. Based on the problems identified, prospective solution architecture is proposed.
A Comparison Small Area Estimation for Skewed Data with EBLUP and Hierarchical Bayes Approaching using Rao-Yu Model

Small Area Estimation (SAE) is a method based on modeling for estimating small area parameters, that applies the Linear Mixed Model (LMM) as its basic. It is conventionally solved with Empirical Best Linear Unbiased Prediction (EBLUP). The main requirement for LMM to produce high precision estimates is normally distributed for its sampling error. However, the researching data namely per capita food expenditure for food crop farmers’ households in Southeast Sulawesi Province has been positively skewed. Applying EBLUP for positively skewed data will produce less accurate estimates. Meanwhile, doing a transformation process will potentially produce biased estimates. The Hierarchical Bayes (HB) approaching often known as Full Bayesian is more flexible regarding normality assumptions and can determine distribution based on data. Because this research will carry out estimates at the district/city level throughout Southeast Sulawesi Province, totaling 17 regions, the efficiency of the estimates can be increased by using the SAE Rao-Yu model which includes area and time random effects. For this reason, this study compares SAE modeling with the EBLUP and HB approaches which are assumed to follow normal, log-normal, and skew-normal distributions. By comparing the Relative Root Mean Square Error (RRMSE), Deviance Information Criterion (DIC), and Coefficient of Variation (CV) values, it is concluded that estimates from the log-normal and skew-normal SAE HB models are more efficient than the SAE EBLUP and normal HB models. However, the SAE estimate that is closer to the direct estimation results is the skew-normal SAE HB.
Application of Principal Component Method Analysis in Identifying Factors that Influence the Occurrence of Inflation in the Bandung City in 2023

This study discusses the city of Bandung which is one of the regions that has a fairly high contribution to the national inflation rate. Bandung City is the city with the third highest contribution to the formation of West Java inflation after Bogor City and Bekasi City. Based on BPS data (2009) the combined inflation that occurred in 3 cities in West Java Province (Bandung, Cirebon, Tasikmalaya) contributed 8.33% of national inflation. Because there are many factors behind inflation, research is conducted using principal component method to identify these factors. The principal component method is multivariate statistical analysis which can be used to reduce some of the original variables to new orthogonal variables while maintaining the total diversity of the original variables and correlating with others. The variables analyzed include Food, Beverages, and Tobacco (X1), Clothing and Footwear (X2), Housing, Water, Electricity, and Household Fuel (X3), Health (X4), Transportation (X5), Information, Communication, and Financial Services (X6), Food and Beverage / Restaurant Provision (X7), Personal Care and Other Services (X8). The results showed that there were 3 components formed from 8 variables analyzed, namely personal care factors and other services, health and housing, water, electricity and fuel.
Prediction of FKRTL Services Diagnosed with Type 2 Diabetes Mellitus Using the Hierarchical Agglomerative Clustering Time Series Method

Diabetes Mellitus (DM) is a disease characterized by hyperglycemia or increased blood sugar levels and metabolic disorders. DM is a disease with a high prevalence rate in Indonesia. DM is included in the 10 most frequent Advanced Referral Health Facility (FKRTL) visits diagnoses. This research aims to find out provincial groupings based on data from FKRTL visits of participants diagnosed with T2DM using the Hierarchical Agglomerative Clustering method (single linkage, complete linkage, average linkage) and to find out predictions of participants diagnosed with T2DM at FKRTL visits using the Autoregressive Integrated Moving Average (ARIMA) method. The method used is Hierarchical Agglomerative Clustering Time Series. This research shows that the best algorithm is average linkage with a number of clusters of two. Forecasting provides a pattern that tends to decline.
:<i>Stereophonica: Sound and Space in Science, Technology, and the Arts</i>

Previous articleNext article No AccessBook ReviewsGascia Ouzounian. Stereophonica: Sound and Space in Science, Technology, and the Arts. Cambridge, MA: MIT Press, 2021. x+231 pp.; 64 black-and-white illustrations, notes, bibliography, index. $40.00.Hannah M. GranthamHannah M. GranthamUniversity of Delaware Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by Winterthur Portfolio Volume 57, Number 2-3Summer/Autumn 2023 Published for the Henry Francis du Pont Winterthur Museum, Inc. Article DOIhttps://doi.org/10.1086/727748 For permission to reuse, please contact [email protected].PDF download Crossref reports no articles citing this article.
Internal system for ensuring academic integrity in higher education institutions

The integration of the domestic higher education system into the European Higher Education Area has led to the implementation of a system for upholding academic integrity standards in the practices of higher education institutions. At the legislative level, the new edition of the Law of Ukraine "On Higher Education" was adopted in 2017. The National Agency for Quality Assurance in Higher Education played a significant role in introducing the system of ethical norms compliance in domestic higher education institutions. Since 2019, the agency has been accrediting educational programs, during which participants in the educational process are required to disclose their practices and procedures for adhering to academic integrity. In October 2019, the National Agency approved "Recommendations for Higher Education Institutions on the Development and Implementation of a University System for Ensuring Academic Integrity" based on the best European and domestic practices. A content analysis of the recommendations indicates that the university system for ensuring academic integrity consists of the following components: 1) normative framework (codes, regulations); 2) structural units and authorized commissions (commissions, support groups); 3) informational base (websites, sections, informational and methodological materials); 4) tools for implementing the principles of academic integrity in the educational and research activities of the university (professional development, online courses, lectures and training, informational-consultative support); 5) tools for monitoring compliance with academic integrity in educational and research activities (surveys of participants in the educational and research process, checking works for signs of academic plagiarism).
Typical Mechanisms of Criminal Invasion of Privacy on the Internet

The author describes typical mechanisms of criminal violations of privacy on the Internet by singling out such their elements as: necessary conditions that create an opportunity for the perpetrators to implement the chosen crime mechanism; actions constituting preparation for the crime; actions constituting the crime itself; actions aimed at concealing the fact of crime or the traces left by the perpetrators. It is noted that the object of crime has its specific features — this information constitutes personal or family secrets. The author presents examples of investigation and court practice that characterize crime mechanisms. The description of typical models of crime mechanisms is a source of forensically relevant information, which could be used as a basis for building versions regarding the circumstances of the violations and the persons involved.
A study of binary decision diagram characteristics of bent Boolean functions

Bent Boolean functions exist only for an even number of variables, moreover, they are unbalanced. Therefore, they are used in coding theory and in many areas of computer science. General form of bent functions is still unknown. One way of representing Boolean functions is with a reduced ordered binary decision diagram (ROBDD). The strength of ROBDDs is that they can represent Boolean functions data with a high level of redundancy in a compact form, as long as the data is encoded in such a way that the redundancy is exposed. This paper investigates characteristics of bent functions with focus on their ROBDD parameters. Decision diagram experimental framework has been used for implementation of a program for calculation of the ROBDD parameters. The results presented in this paper are intended to be used to create methods for the construction of bent functions using a ROBDD as a data structure from which the bent functions can be discovered.
Problems of Criminal Law Regulation and Qualification of Acts Committed Using Electronic Means of Payment

The article presents an analysis of the provisions of the criminal law providing for responsibility for the theft of funds in a bank account and electronic money. Special attention is paid to the problems of qualifying deception as a method of committing fraud using electronic means of payment, its difference from deception, which can be used to secretly steal electronic funds. The article reveals the need to formulate the concept of deception in a crime under Art. 159.3 of the Criminal Code of the Russian Federation. The topical issues of qualification of fraud using electronic means of payment are investigated, taking into account changes in the Criminal Code of the Russian Federation and the wording of the resolution of the Plenum of the Supreme Court of the Russian Federation. The analysis of judicial practice of various subjects of the Russian Federation on the qualification of crimes using payment cards is carried out. The main problems of qualification of thefts, special elements of fraud, other related elements of crimes, for which electronic means of payment are used, are considered. It is concluded that there is no consensus among lawyers and law enforcers on the qualification of theft using payment bank cards, which leads to disparate judicial practice on the crimes in question. Proposals are put forward to solve the identified problems. A forecast is made about an increase in the number of crimes using electronic means of payment. The legislator is invited to develop a unified approach in describing the signs of crimes committed with the help of modern computer technologies, and law enforcement agencies to develop relevant methods for preventing, detecting and counteracting them, as well as to develop a unified approach to the qualification of these crimes.
Cybervictimization: An Assessment of Consequences

The author analyses the consequences of cybervictimization, i.e. the situation when a person becomes a victim in virtual space. A number of terms are defined, specifically, “the consequences of cybervictimization”, “the damage inflicted by cybervictimization”, their essential differences are pointed out. Besides, the characteristics of the damage inflicted by cybervictimization are described in detail, including: regressivity, ambivalence, irradiation, polymorphism, situational character and disproportionality. A typology of the forms of damage is proposed, which is based on both one-dimensional and poli-dimensional models of analyzing consequences. A detailed description is provided for thirty main forms of harm that could result from the victimization of a person online. Based on the suggested classification, as well as the recording of the degree of the inflicted damage, the author compiles a sample list for evaluating the degree of victimization on the Internet.
Color Image Filtering Using Convolution Fuzzy Neural Network

In this chapter, color image filtering using convolution fuzzy neural network is dealt with. Different types of filters are applied, and the resultant images are obtained using MATLAB. The original image is fuzzified, and the corresponding membership image is found. The image obtained by applying Gaussian filter for different membership and crisp values is depicted. The procedure of convolution operation using Gaussian filter is explained. Features of the images are taken as trapezoidal fuzzy numbers. Hue- and saturation-modified images for different membership values are found. The images for different types of filters such as mean, median, bilateral, Lab, noise, unsharp, standard, Gaussian filters, and the corresponding membership images are obtained. Hamming and Euclidean distances between filtered images and corresponding membership images are calculated, and using these distances, similarity index is evaluated. It is found that images acquired by using mean and bilateral filters are almost similar.
Measuring Similarity Between ETL Processes Using Graph Edit Distance

Maintaining data warehouses and ETL processes is becoming increasingly difficult. For this reason, we introduce a similarity measure on ETL processes, based on the edit distance of a graph, which models the process. We show both the exact way how to calculate it and heuristic approaches to compute the estimated similarity more quickly. We propose methods to improve graph edit distance based on the assumption that the ETL process model is a directed acyclic graph.
Historical, Economic, Social and Legal Conditionality for Criminalization of Acts Using Electronic Payment Means

The modern world is distinguished by the dynamic development of electronic means of payment. They can pay for almost all existing goods and services. According to the Central Bank, every year the share of payments made with their use in the total volume of non-cash payments of the population grows rapidly. At the same time, the economic and legal nature and essence of this phenomenon have not been sufficiently studied from a scientific point of view. The objectives of this work are: to determine the existing approaches to the concept of electronic means of payment; analize the stages of formation and development of the legislative regulation of Art. 159.3 in the Criminal Code of the Russian Federation and the features of the judicial interpretation of the norm at each stage; establish the sources of problems of law enforcement of the norm under consideration. The methods used in the study are analysis of the scientific and information base, the comparative historical, statistical method, etc.. The methodological and theoretical basis of the study is presented with the legislative acts regulating the concept of electronic means of payment and providing for liability for fraud with them, works of domestic and foreign scientists in the economic and criminal law sphere. As a result of the study, the controversial issues of the concept "electronic means of payment" were considered; identified and analyzed the features of the judicial interpretation of the norm provided for by Art. 159.3 of the Criminal Code of the Russian Federation, depending on various legislative regulations (by stages); the state and dynamics of fraud using electronic means of payment in 2019–2022 were determined; analyzed some determinants of fraud using electronic means of payment; concluded that the problem of enforcement of Art. 159.3 of the Criminal Code of the Russian Federation is associated with insufficient scientific development of signs of a crime, as well as with a lack of legislative regulation of the main categories and recommendations for their qualification. The results of the analysis can be useful in further development of the problem under study, as well as in educational processes.
Attractiveness of the Workplace Environment of Educational Institutions in the Context of Sustainable Development

Abstract Overcoming the challenges of Covid-19, every person at an educational institution has faced changes in the workplace environment from working time, scope and content to the transformation of psychological, emotional and physical relationships. The attractiveness factors of workplace environment are essential in thinking of, developing and accepting sustainable attitude, sustainable education and sustainable development in everyday life. The aim of the article is to present the results of the research on the attractiveness factors of workplace environment and organizational culture of educational institutions from the point of view of teachers and heads of educational institutions, evaluating them in the context of sustainable development of education, based on theoretical analysis of literature sources and empirical research. Quantitative research method – survey – was used in the study. 97 participants (teachers (n=84) and heads of educational institutions (n=13)) from different educational institutions of one of the biggest cities in Latvia, Daugavpils, took part in the study. The data were processed in accordance with research ethics and personal data protection, visualized on the basis of numerical data units. The gained data indicate that the most important factors for the attractiveness of the workplace environment for both teachers and heads of educational institutions are relationships, work fit and security. Respondent groups cite ‘responsibility’ and ‘location’ as the least important factors in making the workplace environment attractive. Workplace attractiveness factors are a combination of workplace environment and organizational culture, and they are interrelated concepts. The results of the study show that both teachers and heads of educational institutions value an innovative culture in the organization that is characterized by the following statements: ‘willing to take risks, a result-oriented, creative, stimulating, challenging’ and they appreciate a supportive culture in the organization that is characterized by the statements ‘willing to cooperate, encouraging and sociable, recognizing personal freedom’. There are statistically significant differences in opinions on the attractiveness of the workplace environment (such as ‘contribution’, ‘recognition’, ‘innovation’ and ‘responsibility’) among teachers and heads of educational institutions.
Overview of SQL Injection Attack Detection Techniques

In today's rapid development of digital economy and digital transformation, SQLIA (SQL injection attack) against data infrastructure as a means of illegal access manipulation is a serious threat to data security, SQLIA methods are becoming more and more diverse and complex, and data security is facing serious challenges. SQLIA detection technology has become a research hotspot in the field of data security. At present, many domestic and foreign SQLIA detection technology methods are diverse and have their own characteristics, the SQLIA detection technology can be summarised to better open up new research ideas. In this paper, the current status and research progress of SQLIA detection technology are systematically reviewed. Firstly, the SQL injection attack mechanism is introduced and various injection methods and attack types are summarized. Then, the main techniques of SQLIA detection are analyzed, classified and summarized from four aspects: data analysis, feature training, syntactic semantics and pattern matching. Finally, the principles, advantages and disadvantages of the above four detection methods are summarized and evaluated, and SQLIA detection tools and technologies are compared from multiple indicators. The challenges existing in SQLIA detection methods and corresponding optimization technologies are discussed, and the key research directions in the future are prospeced. It is expected to provide some basic theoretical support and technical direction guidance for the subsequent integration of multiple detection methods, solving the problem of second-order SQL injection and creating a new theory of SQL attack and defense.