Reviewerly/exHarmony huggingface dataset:
Digitization of the 17th and 18th Centuries’ Dutch East India Company (VOC) Archives for The Archives’ Preservation

This study discusses the process of digitizing the Dutch East India Company (VOC) treasures archive from the 17th century to the mid-20th century during the Dutch East Indies colonial government (1818&ndash;1942). This VOC archive has been recognized by UNESCO as a world historical heritage, namely, the Memory of the World Register, in March 2004. The National Archives of the Republic of Indonesia (ANRI), as a national archival institution, is responsible for opening these archives online for the public, students, scientists, and all those in need around the world. Therefore, it is necessary to carry out a digitization process for easy access to the VOC archives. The purpose of this study was to see how the process of digitizing the VOC archives was carried out and what obstacles were encountered in the process. This research method uses a qualitative approach with a case study method through a narrative description of data analysis that focuses on digitizing activities carried out by the ANRI, especially in the Reproduction and Archive Digitization Sub-Directorate. Data were collected through observation of digitization activities, interviews with archivists involved, analysis of digitizing documents, and literature studies. The results that can be reviewed are the process of digitizing the VOC archives, starting from the archive identification stage, metadata preparation, archive digitization, digitizing quality control, digital archive storage, and making reports of the activity of digitization. The digitized archives are presented on the website of &ldquo;Sejarah Nusantara-Arsip Nasionalthat&rdquo;, which can be freely accessed by the public. The conclusion obtained is that the digitization of the VOC archives has been carried out well according to the standard procedures previously established and the practice theory of archive digitization in general.
Novel Framework to Secure User Authentication Details During the Financial Transaction Using Blockchain Codes in the Cloud Environment

Purpose: In the digital era, secured financial transaction is a challenging task for the banking sector when involving a third-party service provider. With the extended knowledge about networks and security, hacking and attacks are also being updated accordingly. The hackers always aim to attack the financial transaction server to duplicate the session or to impose on the user through their identity. This type of attack always occurs in a weak server where the transactions were not protected by a proper methodology. Design/Methodology/Approach: This model is developed using the elliptical curve encryption technique integrated with the chaotic map encryption algorithm for the Blockchain networks in the cloud environment. The security of the transaction is assured through the hash functions generated during each transaction and the different factor authentication of the user and the server. Findings/ Results: The computational time of the proposed model is reduced by 33% than the existing models and the encryption and decryption costs were also minimized by 18 % and 15% respectively proving the efficiency of the proposed model. Paper Type: Experimental Research
An approach to predict the task efficiency of web pages

Usability is generally considered as a metric to judge the efficacy of any interface. This is also true for the web pages of a website. There are different factors - efficiency, memorability, learnability, errors, and aesthetics play significant roles in order to determine usability. In this work, we proposed a computational model to predict the efficiency with which users can do a particular task on a website. We considered seventeen features of web pages that may affect the efficiency of a task. The statistical significance of these features was tested based on the empirical data collected using twenty websites. For each website, a representative task was identified. Twenty participants completed these tasks using a controlled environment within a group. Task completion times were recorded for feature identification. The one Dimensional ANOVA study reveals sixteen out of the seventeen are statistically significant for efficiency measurement. Using these features, a computational model was developed based on the Support Vector Regression. Experimental results show that our model can predict the efficiency of web pages’ tasks with an accuracy of 90.64%.
Countering online hate speech through legislative measures: The Ethiopian approach from a comparative perspective

It is apparent that the phenomenon of online hate speech is increasing at a meteoric rate. Meanwhile, governments around the world are resorting to legislative measures in an attempt to address this pernicious social problem. Yet, while much of the discourse has been on how these legislative efforts may inadvertently affect individuals’ right to freedom of expression in established liberal democracies, little is known about the collateral costs of similar trends in developing countries with a burgeoning democratic culture. For this reason, it is imperative to assess how and to what extent legal frameworks for the moderation of user-generated online content may portend dire consequences for freedom of speech in non-democratic countries like Ethiopia, where there are distinct media and socio-political environments. Using comparative legal research methodology, this research article seeks to offer an overview of the legislative responses to online hate speech in different jurisdictions, drawing from normatively preferable policy frameworks, to provide some insights into how gaps in the new Ethiopian hate speech and disinformation prevention law can be filled.
Linked Argumentation Graphs for Multidisciplinary Decision Support

Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm.
Detection of Deepfake Video Using Residual Neural Network and Long Short-Term Memory

The appearance of web-based media has implied genuine and anecdotal stories introduced in such a comparative manner that it can now and then be hard to differentiate the two. Similarly, manipulation of real photos, audios or videos with the help of Artificial Intelligence techniques is done such that it is difficult to distinguish between the real and fake thus called Deepfake. It can happen to big celebrities, politicians, and to layman as well for some malicious purpose. Consequently, this procedure can end up being very threat to human culture subsequently expected to identify it appropriately. This paper intends to tackle this issue by proposing a model that uses Residual Neural Network (ResNet50) and Long Short-term Memory (LSTM) to detect video as fake or real. This approach tries to find flaws in the fake data left behind while its creation using neural based techniques like generative adversarial networks (GAN).
Performance Analysis of Deep Convolutional Network Architectures for Classification of Over-Volume Vehicles

The number of vehicle accidents has increased in recent years due to overloaded goods carriers. Off-road driving, mountain roads, and sharp edges on a road are the main causes of an imbalance in overloaded trucks. In rural areas, where smaller roads cannot accommodate high volume vehicles, such vehicles cause many problems for cars, bikes, bicycles, and other small vehicles, as well as an increase in traffic congestion in those areas. This has become a major problem in the daily lives of drivers in rural areas as well as major urban areas. Solutions are needed to detect over-volume goods carriers and alert drivers to slow down or control the volume in their trucks. This work mainly focuses on a solution that uses deep CNN models. In this work, different deep convolutional architectures are evaluated for their ability to classify goods based on their volume. The model implemented is based on a dataset-specific transfer learning process with CNN layers generated in ImageNet in which only dense layers are learned. The primary objective of this work is to identify a classification method that exhibits proven results with respect to the accuracy parameters. In this work, different deep architectures were tested, and among the efficient networks, Net-B3 was found to perform with 95% accuracy on average. The different architectures were evaluated based on their accuracy, confusion matrix, ROC curve, and AUC score with a real-time dataset.
Enhanced Graph Neural Network with Multi-Task Learning and Data Augmentation for Semi-Supervised Node Classification

Graph neural networks (GNNs) have achieved impressive success in various applications. However, training dedicated GNNs for small-scale graphs still faces many problems such as over-fitting and deficiencies in performance improvements. Traditional methods such as data augmentation are commonly used in computer vision (CV) but are barely applied to graph structure data to solve these problems. In this paper, we propose a training framework named MTDA (Multi-Task learning with Data Augmentation)-GNN, which combines data augmentation and multi-task learning to improve the node classification performance of GNN on small-scale graph data. First, we use Graph Auto-Encoders (GAE) as a link predictor, modifying the original graphs’ topological structure by promoting intra-class edges and demoting interclass edges, in this way to denoise the original graph and realize data augmentation. Then the modified graph is used as the input of the node classification model. Besides defining the node pair classification as an auxiliary task, we introduce multi-task learning during the training process, forcing the predicted labels to conform to the observed pairwise relationships and improving the model’s classification ability. In addition, we conduct an adaptive dynamic weighting strategy to distribute the weight of different tasks automatically. Experiments on benchmark data sets demonstrate that the proposed MTDA-GNN outperforms traditional GNNs in graph-based semi-supervised node classification.
Person Event Detection Method in Computer Discipline Domain Based on BiGRU and CNN in Series

The knowledge graph of computer discipline domain plays a critical role in computer education, and the person event is an important part of the discipline knowledge graph. Adding person events to the graph will make the discipline knowledge graph richer and more interesting, and enhance enthusiasm of students for learning. The most crucial step in building the person event knowledge graph is the extraction of trigger words. Therefore, this paper proposes a method based on the serial fusion of gated recurrent neural network and convolutional neural network (SC-BiGRU-CNN) for person event detection in the computer discipline domain. We extract the global features of the text from the person event sentences through the BiGRU model, and input the extracted global features into the CNN model to further extract the fine-grained features of the text. And then the extracted features are used to classify the event trigger words. In addition, a dataset (CD-PED) for person event detection in the computer discipline domain is constructed to obtain trigger words and their types. We perform experiments on the public dataset MAVEN and the domain dataset CD-PED, respectively. The experimental results show that our approach has significantly improved the [Formula: see text] value compared with the baseline model on the domain dataset CD-PED.
Distribution of the number of cycles in directed and undirected random regular graphs of degree 2

We present analytical results for the distribution of the number of cycles in directed and undirected random 2-regular graphs (2-RRGs) consisting of $N$ nodes. In directed 2-RRGs each node has one inbound link and one outbound link, while in undirected 2-RRGs each node has two undirected links. Since all the nodes are of degree $k=2$, the resulting networks consist of cycles. These cycles exhibit a broad spectrum of lengths, where the average length of the shortest cycle in a random network instance scales with $\ln N$, while the length of the longest cycle scales with $N$. The number of cycles varies between different network instances in the ensemble, where the mean number of cycles $\langle S \rangle$ scales with $\ln N$. Here we present exact analytical results for the distribution $P_N(S=s)$ of the number of cycles $s$ in ensembles of directed and undirected 2-RRGs, expressed in terms of the Stirling numbers of the first kind. In both cases the distributions converge to a Poisson distribution in the large $N$ limit. The moments and cumulants of $P_N(S=s)$ are also calculated. The statistical properties of directed 2-RRGs are equivalent to the combinatorics of cycles in random permutations of $N$ objects. In this context our results recover and extend known results. In contrast, the statistical properties of cycles in undirected 2-RRGs have not been studied before.
Nisotak

This paper outlines the design, development, and preliminary usability study of a system comprising 1) a web-based Indigenous lesson-creation interface and 2) an accompanying mobile app for studying the lessons. The Nisotak project was developed in response to the need for the preservation of Indigenous languages and to support reconciliation within Canada. In this paper, the authors discuss the technological aspects of the project and the less tangible decision-making that helped navigate software development in ways that support and honour Indigenous languages, Indigenous knowledge, and Indigenous people while, at the same time, making space for non-Indigenous allies. The key decisions that guided this project included privileging the target language(s), accommodating multiple dialects, creating an easy-to-use and engaging interface for non-technical users, and designing for easy transfer of ownership and management. Finally, the authors share the results of a small usability study.
Investigations Towards the Measurement of Displacement using Lateral Shearing Interferometry and Fourier Fringe Analysis Technique

Abstract In the present communication we report the measurement of small displacement of reflecting surface (plane mirror) using wedge plate lateral shearing interferometry. With the help of simple mathematical analysis relation between specimen displacement and difference phase is undertaken. Fourier based fringe analysis technique is used for determination of difference phase. Theoretical and experimental investigations are carried out to check the sensitivity of the technique. The detailed discussion regarding sources of errors and uncertainty analysis is also incorporated and the expanded uncertainty is found to be ±0.00236mm. The technique is simple and can be used in industry environment.
Content-Based Image Search While Maintaining Privacy with Cloud Image Repository

If you don't know the names of the images, it is important to pick a set of photos that look like informational images and use a search structure that uses the CBIR rule. In general, the CBIR framework breaks down the visual aspects. B. Diversity, image boundaries, surfaces, and nomenclature consistency between input photos and dataset images. The rendering strategy is CNN and the restoration method is cosine similarity. This white paper addresses the problem of large-scale image recovery with a focus on improving accuracy and flexibility. We focus on factors that can affect search performance, such as: B. Varying lighting conditions, object size and structure, and possibly obstacles and crowded facilities. These factors are particularly large and track very large data sets with great variability. Suggest REMAP. REMAP is the original CNN-based global descriptor that started and learned on Trio Bungle and is added to an ever-evolving deep set of features from several CNN layers. REMAP is broadly stable across different semantic levels of visual reflection and retains the associated absolute identifiers.
Involvement of Students Majoring in Advertising and Public Relations in Academic Research Activities

The aim of the study is to substantiate the need to involve students in academic research work, participation in scientific conferences of various levels. The authors conducted a study “Participation of students majoring in Advertising and Public Relations in academic research work” on the basis of the Moscow Aviation Institute in order to understand what needs to be changed in the educational process, what forms of organization of research activities can and should be used so that students are in demand in the labor market and join scientific work. The study is novel in that it determines the main reasons for the motivation and demotivation of participation of students majoring in Advertising and Public Relations in scientific work. As a result, the methodological recommendations developed during the study, which will help university teachers to involve students in academic research work and can be used in the educational process, have been presented.
Emotive-Reflexive Approach to the Formation of Phonetic Competence in Russian among Foreign Students

The aim of the study is to identify the emotive-reflexive approach as an optimal pedagogical approach to the formation of phonetic competence in the context of the methodology of teaching Russian as a foreign language. The article proposes a problematization of the term “emotive-reflexive approach” in foreign language education, illustrates the methodological techniques of forming the phonetic competence of foreign students (A1-B1). The scientific novelty of the research lies in the development and theoretical substantiation of the types of tasks for teaching Russian as a foreign language on the basis of the emotive-reflexive approach. As a result, exercises are presented that contribute to the intensification of the formation and development of phonetic competence in the Russian language among foreigners and their classification is determined.
Assessment of Readiness for Professional Self-Development of Students in the Direction of “Special (Defectological) Education”

The aim of the study is to substantiate the need to form readiness for professional self-development among students in Bachelor Degree Program in the direction 44.03.03 Special (Defectological) Education. The article analyses the content of Bachelor Degree programs in the direction 44.03.03 Special (Defectological) Education and the requirements for obtaining the qualification category of teachers (highest, first) in order to determine the universal competencies and general professional competencies necessary for the professional self-improvement of students; the readiness of students - future teachers-defectologists of Moscow City University and Moscow Region State Pedagogical University for further professional self-development was assessed. The scientific novelty lies in the comparison of the competencies provided by the Federal State Educational Standard in the direction of training 44.03.03 Special (Defectological) Education and the criteria for awarding the qualification category of pedagogical workers. The results of the study are the following: the level of readiness of students studying in the direction of “Special (Defectological) Education” for professional self-development was revealed; a number of features of professional competencies formation among third-year and fourth-year students in the corresponding area of training were formulated.
Development of Reading Skills in a Foreign Language among Primary Schoolchildren Using Online Educational Books

The aim of this study is a theoretical substantiation of the expediency of using online books for the development of foreign language reading skills in primary schoolchildren. The paper considers the features of teaching semantic reading at foreign language lessons in primary school, describes the types of communicative reading, the stages of working with a text when teaching a foreign language in primary school. The scientific novelty of the study lies in identifying the criteria for selecting texts for teaching foreign language reading at an early stage of learning; defining the notion of “online book”; highlighting the requirements for the content of electronic books as educational materials; developing the structure of an online book and the content of the stages of work on developing reading skills using online books at foreign language lessons in primary school. As a result of the study, didactic material was created in the format of an online book for the development of foreign language reading skills in primary schoolchildren by the material of the English language.
Professional Self-Determination as a Mechanism for the Development of Educational Independence of a Profession-Oriented School Student

The paper aims to identify the content and organisational aspects of implementation of professional self-determination as a mechanism for the development of educational independence of a profession-oriented school student. The paper differentiates the notion of “educational independence” from the related notions of “learning independence”, “cognitive independence”, justifies the choice of professional self-determination as a mechanism for the development of a student’s educational independence in profession-oriented training. The scientific novelty of the study lies in determining the mechanisms of educational independence development as a professionally significant quality of a modern high school student - a future university student. As a result, the factors justifying the choice of professional self-determination as a mechanism for the development of educational independence of a profession-oriented school student have been identified, the content and forms of organisation of professional self-determination of a high school student in profession-oriented training have been specified.