Reviewerly/exHarmony huggingface dataset:
THE OPPORTUNITIES AND CHALLENGES OF CRYPTOCURRENCIES: A SYSTEMATIC REVIEW

Cryptocurrencies have taken center stage in the financial sector of the globe. A cryptocurrency is a form of digital and virtual currency that is secured and encrypted by blockchain and cryptography technologies. A new type of global investment in cryptocurrencies. Even though, this new virtual decentralized has benefits and harms. A PRIMA method was applied to collect data from search engines. Data were analyzed through Atlas.ti for finding the themes to answer the objectives. The result shows six challenges via Complex and changeable activities, crimes, crypto expenses, illegal activities, the need for specialization, and others that can be controlled and hacked. Moreover, seven themes for opportunities such as cryptocurrency transactions, decentralization, new academic topics, new industry, new monetary system, new money type, and new technological control are presented.
About Modeling Digital Twins of a Social Group

The object of the study is mathematical modeling methods. The subject of the study is the application of mathematical methods in modeling digital twins of a social group. The aim of the work is to model the digital counterparts of a social group. A digital double is a digital copy of a physical object or process, with the right approach, it helps to improve the main and auxiliary business processes. This concept is part of the fourth industrial revolution and is designed to help detect problems faster, find out what will happen to the original in different conditions and, as a result, produce better products. In this article, some applied aspects are considered, the main provisions of the mathematical theory of digital twins of social groups are presented. To solve the problem of creating a digital double of a social group (students) as one of the tools, the authors proposed to use the technologies of population algorithms. The novelty of the research consists in the application of the swarm part algorithm for modeling digital twins of a social group. The particle swarm method was chosen as a research tool. As the social group under study finds the optimal position in space, so the element of the digital twin of the particle swarm model based on them can search in space, in particular, the extremes of functions. Which, for example, is applicable to finding the minimum of the loss function in machine learning. A graphical simulation in the Java Script language was performed using the three library.js. Data processing was performed using the C# Job System, which provides parallelization of computing processes and is integrated into the Entity Component System. A program was implemented that simulates the activity of a student group as one of the constituent elements of a digital twin of a social group. Swarm algorithms are promising in the field of practical application. On their basis, it is possible not only to solve the problems of digital twins, but also to manage groups of robots, robotic systems and complexes.
Cyber Crime: Legal Issues and its Legal Remedies

Cybercrime, e-crime, Internet scams, etc. are all terms used to describe illicit activities using one system or a whole network. With the development of technology and also the Internet’s secretive character, it is now possible for people with little technical ability to generate money fraudulently without leaving their house, and cybercrime is rising as a vocation. These dangers are growing every day; in fact, recently some of the largest attacks ever were recorded and predictions indicate that things will only get worse from here on out. The need for cyber laws and worries of the government authorities are brought on by the rising number of crimes. This paper provides overview of several countries’ cyber laws, highlighting the problems and potential legal repercussions. Here, it is examined whether and to what extent these laws are sufficient to safeguard us. These rules cannot keep up with the fast-evolving cybercrime scenario.
INTEGRATIVE MODEL OF HOLISTIC KNOWLEDGE OF SCHOOLCHILD’s INDIVIDUALITY BY TEACHER. MESSAGE 1. THEORETICAL SUBSTANTIATION AND OBJECTIVES OF THE RESEARCH

From the standpoint of a systematic approach, the process of teacher’s cognition of the integral individuality of a student is considered. The purpose of the study: through the integration of various theories and approaches, to substantiate a new system-integrative approach to pedagogical cognition of the integral individuality of students by a teacher. Research objective: to show that the systems "subject", "integral individuality", "cognition" form a trinity that ensures the functioning of the resulting polysystems: "integral individuality - cognition - object", "integral individuality - cognition - subject", "integral individuality - cognition - integral individuality". Thus, the most complete knowledge of student’s integral individuality by a teacher is ensured. Pedagogical knowledge is defined as a multi-level process of studying the student's individuality as an object and as a subject of cognition, based on scientific and theoretical knowledge of the properties of the student's integral individuality in dialectical unity with practical knowledge about the student and the teacher's reflexive knowledge about himself, formed in the process of interpersonal interaction in the dyad "teacher - student". The resultative and procedural directions of the study of teacher’s cognition of the student are singled out, namely: the study of the relationship between the properties of the integral individuality of the student and their “life manifestations” observed by the teacher, the role of reflexivity and objective factors of pedagogical activity in the process of cognition. The novelty and theoretical significance of the work is determined by the fact that it fits into the current direction of psychological research - the development of psychodiagnostic tools for systemic psychological research that is in demand in pedagogical and social practice. The practical result of the study is the creation of a model of holistic cognition of the integral individuality of a student by a teacher, including the development of a program for scientific and practical observation of a teacher over the "life manifestations" of the properties of the integral individuality, as well as a questionnaire for the integral individuality of schoolchildren of different ages.
Typological Catalogue of the ancient Roman Scribal Tool Known as a Bone Rule

The dataset contains a typological catalogue of archaeological small finds of an ancient device known as a “bone rule” or “bone ruler” or “bone spatula,” together with scaled drawing and full metadata for each item in the catalogue. The ancient device has been identified as some type of scribal tool [8, 9, 13, 28, 45]. The dataset is assembled from publications; from online resources; and from individual scholars. The PDF Bone_Rules_Catalogue file records all pertinent available information, including find spots, inventory number, measurements, scaled thumbnails, and notes including inventory numbers, bibliographical references, and citations for plates and drawings. The PDF Bone_Rules_Catalogue file is best for a conspectus of known specimens. The PDF Bone_Rules_Catalogue file also records other bone objects improperly claimed as bone rules, and a full bibliography, both of which are included also as separate PDF files (PDF Other_Bone_Objects, PDF Bibliography) as a convenience. The CSV Catalogue_Proper file contains all fields in the PDF catalogue in csv format, but also breaks spatial and measurement data into separate fields and regularizes field contents. The CSV Catalogue_Proper file is therefore best for data processing. The full-scale drawings with metadata form the remainder of the core dataset. The catalogue and full dataset are meant to accompany the analysis and discussion in William A. Johnson, “Scribal Tools of the Trade: Bone Rules, Dividers, and Lamps as Writing Aids,” Segno e Testo 2023 [41].
A Descriptive Analysis of Lively Accounting Acceptance from the View of Accounting Education: The Integration of Academic Flashcards and Augmented Reality Technology

Due to the Covid-19 global pandemic, the use of technological advancement is increasingly becoming a great determinant in education which demands accounting educators to consistently establish new innovative teaching and learning methods.Accounting education seeks to develop concepts, rules and theories for solving accounting problems.However, teaching accounting in accounting education is not an easy task.Students ordinarily encounter the complexities of not just understanding the technical mechanisms, but also to apply the terminology and fundamentals of accounting.The main objective of this study is to develop primary results on the practicality of the main features of Lively Accounting which integrates flashcards with augmented reality application and to identify other expanded TAM variables towards technology acceptance of Lively Accounting.Reminiscing and understanding accounting theories is the main issue experienced by students.Therefore, Lively Accounting as a flexible educational tool creates a different style of experience where users can learn both offline and online.Descriptive approach was used in this present study.For data collection, questionnaires were distributed through an online platform and an illustrative video about Lively Accounting was disseminated to accounting students in UiTM Tapah.The major findings of this study discovered that the main attributes of Lively Accounting do satisfy the needs and preferences of students as it enhances students' knowledge and understanding for the related course.The acceptance of Lively Accounting is believed to be contributed initially by the constructs of perceived usefulness, perceived ease of use, perceived enjoyment and attitude toward use.It seems that students with a pleasant feeling about the usefulness of technology will be motivated to use and accept this new learning experience.Due to the demand for an innovative accounting learning tool, Lively
ACTUALIZATION OF CUSTOMER IMPRESSION FORMATION IN THE HOSPITALITY INDUSTRY

The attention to the presented research topic is based on the growing dominance of the individual's needs in getting an impression in all sectors of the economy and, especially, in the hospitality industry. However, the absence of a developed discourse on this problem emphasizes the existence of a real contradiction between the theoretical understanding of the problem and a person’s growing interest in satisfying his emotional needs. In this regard, the subject of analysis in this context is the peculiarities of perception as a psychological phenomenon of consumers of the hospitality industry. The goal is an unconditional requirement to reveal the instrumental significance of impressions in the development of the competitive ability of the hospitality industry as one of the flagships of the domestic economy. The methodological resource should include a number of methods used, such as analytical, descriptive, the method of gestalt psychology, instrumental, in general, which allowed reconstructing the ways of creating and managing the impression of individuals. The source material used in the article can be conditionally differentiated in two directions: psychological and material on the economics of impressions. Of particular importance for the realization of the goals were the works described below, relating to the analysis of open and closed gestalt images, as well as to the description of the features of self-expression and self-presentation of the individual, her interest in the game as one of the ways of life. It can be considered a new promising scientific direction with multidimensional aspects of impression economics. This direction should include the monographs "The Economy of Impressions" by authors Pine and Gilmort, the work of A. Slivotsky "Migration of values", the article by V. Pekar "Introduction to the economy of Impressions", etc. In the context, the emphasis is placed on the statement of the emergence of a new value, which is the impression, prevailing in the scientific body of literature on a tourist destination. This statement, from the point of view of the authors, is debatable. The argumentation given here against this statement, of course, cannot be assessed as absolute truth, but only as the initial moment of scientific controversy. The article presents psychological techniques, the use of which can greatly increase the interest of individuals in obtaining new impressions by an individual in a tourist destination. The practical value consists in demonstrating techniques, the creative understanding of which can enable the hospitality industry to increase its economic status.
CURRENT DIRECTIONS OF PROFESSIONAL DEVELOPMENT OF EMPLOYEES OF THE CENTERS "MY DOCUMENTS"

The presented article is the result of a large-scale survey conducted among the employees of the "My Documents" centers. The purpose of the study was to identify professional gaps, deficient competencies, which, according to the MFC employees themselves, should be filled in the process of mastering additional professional programs. The result of the study was the development of relevant areas of professional development of MFC employees, that is, a range of measures that allow increasing the pace and efficiency of educational processes for specialists of the My Documents centers within the framework of obtaining additional professional education.
IMKGA-SM: Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling

Multimodal knowledge graph link prediction aims to improve the accuracy and efficiency of link prediction tasks for multimodal data. However, for complex multimodal information and sparse training data, it is usually difficult to achieve interpretability and high accuracy simultaneously for most methods. To address this difficulty, a new model is developed in this paper, namely Interpretable Multimodal Knowledge Graph Answer Prediction via Sequence Modeling (IMKGA-SM). First, a multi-modal fine-grained fusion method is proposed, and Vgg16 and Optical Character Recognition (OCR) techniques are adopted to effectively extract text information from images and images. Then, the knowledge graph link prediction task is modelled as an offline reinforcement learning Markov decision model, which is then abstracted into a unified sequence framework. An interactive perception-based reward expectation mechanism and a special causal masking mechanism are designed, which "converts" the query into an inference path. Then, an autoregressive dynamic gradient adjustment mechanism is proposed to alleviate the insufficient problem of multimodal optimization. Finally, two datasets are adopted for experiments, and the popular SOTA baselines are used for comparison. The results show that the developed IMKGA-SM achieves much better performance than SOTA baselines on multimodal link prediction datasets of different sizes.
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models

Language models learn a great quantity of factual information during pretraining, and recent work localizes this information to specific model weights like mid-layer MLP weights. In this paper, we find that we can change how a fact is stored in a model by editing weights that are in a different location than where existing methods suggest that the fact is stored. This is surprising because we would expect that localizing facts to specific model parameters would tell us where to manipulate knowledge in models, and this assumption has motivated past work on model editing methods. Specifically, we show that localization conclusions from representation denoising (also known as Causal Tracing) do not provide any insight into which model MLP layer would be best to edit in order to override an existing stored fact with a new one. This finding raises questions about how past work relies on Causal Tracing to select which model layers to edit. Next, we consider several variants of the editing problem, including erasing and amplifying facts. For one of our editing problems, editing performance does relate to localization results from representation denoising, but we find that which layer we edit is a far better predictor of performance. Our results suggest, counterintuitively, that better mechanistic understanding of how pretrained language models work may not always translate to insights about how to best change their behavior. Our code is available at https://github.com/google/belief-localization
Elastic Cash

Elastic Cash is a new decentralized mechanism for regulating the money supply. The mechanism operates by modifying the supply so that an interest rate determined by a public market is kept approximately fixed. It can be incorporated into the conventional monetary system to improve the elasticity of the US Dollar, and it can be used to design new elastic cryptocurrencies that remain decentralized.
Adversarial Alignment for Source Free Object Detection

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich source domain to an unlabeled target domain without seeing source data. While most existing SFOD methods generate pseudo labels via a source-pretrained model to guide training, these pseudo labels usually contain high noises due to heavy domain discrepancy. In order to obtain better pseudo supervisions, we divide the target domain into source-similar and source-dissimilar parts and align them in the feature space by adversarial learning. Specifically, we design a detection variance-based criterion to divide the target domain. This criterion is motivated by a finding that larger detection variances denote higher recall and larger similarity to the source domain. Then we incorporate an adversarial module into a mean teacher framework to drive the feature spaces of these two subsets indistinguishable. Extensive experiments on multiple cross-domain object detection datasets demonstrate that our proposed method consistently outperforms the compared SFOD methods.
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use of execution-time AML attacks against MARL and the defences against those attacks. We surveyed related work in the application of AML in Deep Reinforcement Learning (DRL) and Multi-Agent Learning (MAL) to inform our analysis of AML for MARL. We propose a novel perspective to understand the manner of perpetrating an AML attack, by defining Attack Vectors. We develop two new frameworks to address a gap in current modelling frameworks, focusing on the means and tempo of an AML attack against MARL, and identify knowledge gaps and future avenues of research.
Order-Preserving Database Encryption with Secret Sharing

The order-preserving encryption (OPE) problem was initially formulated by the database community in 2004 soon after the paradigm database-as-a-service (DaaS) was coined in 2002. Over the past two decades, OPE has drawn tremendous research interest from communities of databases, cryptography, and security; we have witnessed significant advances in OPE schemes both theoretically and systematically. All existing OPE schemes assume that the outsourced database is modeled as a single semi-honest adversary who should learn nothing more than the order information of plaintext messages up to a negligible probability. This paper addresses the OPE problem from a new perspective: instead of modeling the outsourced database as a single semi-honest adversary, we assume the outsourced database \textit{service} compromises a cluster of non-colluding servers, which is a practical assumption as all major cloud vendors support multiple database instances deployed to exclusive sub-networks or even to distinct data centers. This assumption allows us to design a new stateless OPE protocol, namely order-preserving database encryption with secret sharing (ODES), by employing secret-sharing schemes among those presumably non-colluding servers. We will demonstrate that ODES guarantees the latest security level, namely IND-FAOCPA, and outperforms the state-of-the-art scheme by orders of magnitude.
On the influence of clipping in lossless predictive and wavelet coding of noisy images

Especially in lossless image coding the obtainable compression ratio strongly depends on the amount of noise included in the data as all noise has to be coded, too. Different approaches exist for lossless image coding. We analyze the compression performance of three kinds of approaches, namely direct entropy, predictive and wavelet-based coding. The results from our theoretical model are compared to simulated results from standard algorithms that base on the three approaches. As long as no clipping occurs with increasing noise more bits are needed for lossless compression. We will show that for very noisy signals it is more advantageous to directly use an entropy coder without advanced preprocessing steps.
Recognising geometric primitives in 3D point clouds of mechanical CAD objects

The problem faced in this paper concerns the recognition of simple and complex geometric primitives in point clouds resulting from scans of mechanical CAD objects. A large number of points, the presence of noise, outliers, missing or redundant parts and uneven distribution are the main problems to be addressed to meet this need. In this article we propose a solution, based on the Hough transform, that can recognize simple and complex geometric primitives and is robust to noise, outliers, and missing parts. Additionally, we can extract a series of geometric descriptors that uniquely characterize a primitive and, based on them, aggregate the output into maximal or compound primitives, thus reducing oversegmentation. The results presented in the paper demonstrate the robustness of the method and its competitiveness with respect to other solutions proposed in the literature.
Counteracts: Testing Stereotypical Representation in Pre-trained Language Models

Recently, language models have demonstrated strong performance on various natural language understanding tasks. Language models trained on large human-generated corpus encode not only a significant amount of human knowledge, but also the human stereotype. As more and more downstream tasks have integrated language models as part of the pipeline, it is necessary to understand the internal stereotypical representation in order to design the methods for mitigating the negative effects. In this paper, we use counterexamples to examine the internal stereotypical knowledge in pre-trained language models (PLMs) that can lead to stereotypical preference. We mainly focus on gender stereotypes, but the method can be extended to other types of stereotype. We evaluate 7 PLMs on 9 types of cloze-style prompt with different information and base knowledge. The results indicate that PLMs show a certain amount of robustness against unrelated information and preference of shallow linguistic cues, such as word position and syntactic structure, but a lack of interpreting information by meaning. Such findings shed light on how to interact with PLMs in a neutral approach for both finetuning and evaluation.