Reviewerly/exHarmony huggingface dataset:
Opportunities and Challenges in Digital Transformation of Education

The World is facing a fast and radical change due to the expedited development of digital technologies. Advances in digital technology are constantly driving the digital transformation of society as a whole. The concept of “digital transformation” first emerged in 1968 and became distinctive in the research literature after the year of 2014 (Reis et al, 2018). In its broad sense, digital transformation refers to the process that new digital technologies are integrated into all sectors of human activity, fundamentally changing all aspects of human life. This leads to a world that is increasingly experienced with, through, and by information technology (Stolterman &amp; Fors, 2004).
Notice of completion and acceptance of the Ministry of Education’s supply-demand matching employment education project

In order to further improve the whole process management and result assessment of the supply-demand matching employment education project, the National College Graduates Employment and Entrepreneurship Steering Committee (hereinafter referred to as the National Steering Committee) decided to organize the first and second phases of the supply-demand matching employment education project Final acceptance work. See document notification for details. Before March 31, 2024, the person in charge of the project university shall submit the final materials to the enterprise. Before April 15, 2024, the project company will complete the project acceptance and submission, and send relevant materials: pdf version of the completion report, Attachment 2, and Attachment 3 (if any) to the email address of the relevant industry committee.
AI Model Stability in Industrial IoT Intrusion Detection: Leveraging the Characteristics Stability Index

In Industrial Internet of Things (IIoT) environments, the reliability and adaptability of machine learning models are crucial for accurate decision-making. This paper introduces the Characteristic Stability Index (CSI) to monitor and ensure the stability of models in the context of heterogeneous IIoT sensor data. The CSI quantifies the variations in feature importance rankings, enabling the early detection of data drift and shifts. The experimentation results validate the performance of the decision tree algorithm to provide actionable insights, facilitating domain experts' adaptability and enhancing decision-making while minimizing operational risks and costs in the choice of intrusion detection systems model.
Kinerja Pelayanan Dinas Perpustakaan Dan Kearsipan Provinsi Sulawesi Selatan

This research was conducted with the aim of knowing how the process of managing parking retributions in Makassar City, what are the obstacles experienced in its management so far, the mechanism of collection, deposit until it arrives at the acceptance process into PAD which leads to / is beneficial for the development of Makassar City. In the field of library and information services, the library provides library services in various fields of science such as social sciences, humanities, science and technology and many others. One of the areas of circulation that provides information services to the public, libraries are the places most visited by people who want to get information. In this way, service officers at this library are required to improve their performance in order to provide good quality service to the community. However, in reality the existing service structure at the South Sulawesi Province Library and Archives Service is not yet fully in line with what the community expects. Library development and empowerment has not been optimal and library services are also not satisfactory. The existing phenomenon is that the performance of employees and contract workers in providing services is not fully in line with what users and the public expect, so the quality of library services still needs to be improved. This research was conducted to determine the performance of library services at the South Sulawesi Province Library and Archives Service. &#x0D; &#x0D;
WORM-VIRUS DETECTION METHOD ACCORDING TO MULTI-CLASS CLASSIFICATION

The work presents the results of research on worm viruses and methods of their detection. Malware distribution happens all the time. The analyzed modern tools and systems for prevention, detection and countermeasures against malicious software and computer attacks are quite effective, provide a high percentage of detection and function at an adequate level. But criminals constantly study the capabilities of such tools and systems, improve malicious software and computer attacks, and achieve certain results. Therefore, developers of tools and systems for prevention, detection and countermeasures against malicious software and computer attacks must constantly improve them. The protection of corporate networks is relevant. They can be effectively configured to increase computing resources when solving the tasks of warning, detecting and countering malicious software and computer attacks to protect corporate networks. Therefore, the article defines as an urgent scientific task - the development of methods to improve the efficiency of the functioning of distributed systems with partial centralization for detection of malicious software and computer attacks in computer networks and detection of malicious software with their use due to the synthesis of their architecture in such a way that the principles of functioning of such systems make it difficult for criminals to understand them. The work considers a set of worm viruses, which covers network features as much as possible. Therefore, to study the effectiveness of methods of creating distributed systems and the systems themselves based on them, worm viruses were considered. The purpose of the work is to develop a method for detecting worm viruses in corporate networks. The work developed a method of detecting worm viruses using their division into classes based on common features and defined criteria according to the classification of objects according to many classes and taking into account its implementation in the architecture of partially centralized distributed systems to obtain a complete sensor and make a decision regarding the classification of worms virus to a certain class. This improved the reliability of detection by 8-11% compared to using the method without directly involving the elements and components of the system. As a result of setting up experiments and conducting them, results were obtained that confirm the correct functioning of a partially centralized distributed system for the detection of worm viruses.
GAZE DIRECTION MONITORING MODEL IN COMPUTER SYSTEM FOR ACADEMIC PERFORMANCE ASSESSMENT

This paper focuses on the research and application of eye movement and gaze direction analysis in online testing systems. The practical novelty of the proposed model of monitoring the direction of gaze in a computer-based knowledge control system lies in the possibility of automated remote control over a large audience of students. The practical significance lies in creating the same conditions for computer testing for all students and increasing the correspondence of knowledge level to the received test results. The implemented and tested system is relevant and necessary in higher educational institutions, particularly in Ukraine, where remote education has emerged as the safest means of acquiring knowledge. This is especially true for fields of study where practical tasks and laboratory work do not necessitate a student's physical presence at the institution. That is why the application of the latest information technologies is extremely important. The dynamic authentication based on the sequence of eye movements proposed in the model allows error-free user's eye area detection for further analysis of the test subject's behavior. The proposed authentication method excludes the need to enter passwords or CAPTCHAs, ensures the speed of determining the user's presence. Further analysis of the user's direction of vision includes responding to the information received, such as skipping a question or the need for re-authorization. Question skipping occurs when the system decides that the user has not been looking at the screen for an extended period (looking sideways, down, up for more than 30 seconds). Re-authentication becomes necessary if the user exits the test or if there is a user replacement. Real-time gaze control capability is implemented by using massive parallel processing system (NVIDIA GeForce GTX 1650 graphics card) for calculations. The analysis of the results obtained shows that the proposed approach allows gaze detection at different illumination ranges (from 0.3 lux to 10,000 lux), as well as to detect states of the user's eyes that violating test rules for further system response (skipping a question or re-authentication request).
Accounting for digital currencies: A review of challenges and standardization efforts

In the wake of the Fourth Industrial Revolution, the ascent of digital currencies heralds a transformative era in the global financial landscape, challenging traditional paradigms of currency, commerce, and regulation. This study delves into the emergence, integration, and implications of digital currencies within the global economy, with a particular focus on the accounting and regulatory challenges they pose. Through a meticulous examination of global adoption trends, technological advancements, and the historical evolution of digital currencies, this paper aims to dissect the multifaceted impact of these novel financial instruments on financial reporting, transparency, and regulatory frameworks. Employing a qualitative literature review strategy alongside thematic analysis, the study identifies key challenges in volatility, valuation, transaction anonymity, and the pressing need for standardized accounting practices to enhance comparability and transparency in financial reporting. The investigation extends to the scrutiny of current accounting standards, regulatory responses, and the concerted efforts towards standardization and best practices in digital currency accounting. The findings underscore a critical juncture for practitioners and regulators alike, navigating the intricate accounting landscape shaped by digital currencies. The study culminates in advocating for adaptive regulatory frameworks and standardized accounting practices, emphasizing the necessity of international collaboration and continuous research to harness the potential of digital currencies while safeguarding financial stability and integrity. Recommendations for future research and policy development are proposed to address the evolving dynamics of digital currencies, aiming to support innovation, ensure financial transparency, and maintain regulatory efficacy in the digital age.
Navigating the nexus of security and privacy in modern financial technologies

As the financial service sector rapidly evolves with the integration of cutting-edge technologies, the intersection of security and privacy becomes paramount. This paper delves into the intricate landscape of security and privacy issues within the financial service sector, offering a comprehensive analysis of the challenges and opportunities presented by emerging technologies. From blockchain to artificial intelligence, the paper explores the vulnerabilities inherent in these innovations and the consequential threats to sensitive financial data. Through an examination of recent case studies, regulatory frameworks, and technological advancements, this work aims to provide a nuanced understanding of the evolving threat landscape. Additionally, the paper proposes strategic solutions and best practices to fortify the security and privacy architecture surrounding financial technologies, fostering a resilient and trustworthy ecosystem. This research contributes to the ongoing dialogue surrounding the imperative of safeguarding financial systems, ensuring that innovation aligns seamlessly with the imperatives of confidentiality, integrity, and availability in an era where financial services and technological advancements are inextricably linked.
Advancing Digital Transformation in Chinese Education: A Comprehensive Analysis of Current Trends, Opportunities, Challenges, and Strategic Imperatives

This study assesses the progress and identifies the pivotal advancements in China's digital education, including infrastructure improvements, the growth of online learning platforms, policy support, and technological innovations. It underscores digital education's significance in promoting educational equality, updating teaching methods, raising educational standards, expanding resources, and encouraging lifelong learning. Nevertheless, digital education's expansion encounters significant challenges: the digital divide, educators' insufficient digital skills, inconsistent quality of educational content, difficulties in developing students' self-learning skills, and concerns over data security and privacy. To address these issues, the study outlines strategic directions specific to China's educational context: enhancing infrastructure and technology access, increasing digital literacy among teachers and students, supporting personalized and adaptive learning approaches, improving digital resources and evaluation methods, integrating online and offline learning experiences, and expanding international cooperation. These strategies are designed to address existing challenges and stimulate ongoing innovation and improvement in China's digital education sector.
Optimizing Recommender Model: Integrating Knowledge Graph Information Fusion and Attention Mechanism

Abstract Graphs are versatile for capturing relationships between objects, as seen in social networks, enabling the implementation of algorithms like community discovery and clustering. The growing interest in deep learning within the graph domain has led to the development of various graph neural network algorithms in recent years. These algorithms offer an effective solution to address graph learning challenges by incorporating graph operations into traditional deep learning models and leveraging both graph structure and attribute information to handle the intricacies of graph data. Graph neural network algorithms represent an extension of traditional deep learning methods, like convolution, into the realm of graph data. These algorithms incorporate the concept of data propagation to formulate deep learning approaches specifically designed for graphs. Notably, they have demonstrated success in diverse domains such as social networks, recommendation systems, knowledge graphs, and others. In addressing the aforementioned challenges, this paper introduces a novel approach utilizing a graph neural network. To overcome the lack of temporal information in the recommendation network's neighbor structure, an ordered input-based gated cyclic unit is incorporated for state aggregation and updating. The model leverages the unit's capacity for capturing contextual relationships, thereby enhancing its ability to capture temporal features in the neighbor structure and improve predictions on new datasets. Additionally, the paper places emphasis on the shallow output of the intermediate layer. A multi-headed attention mechanism is employed to integrate information from multiple layers of output, ensuring that the shallow structural features provided by the intermediate layer play a more significant role in the scoring prediction task. This enhancement further refines the application of graph neural networks in recommendation systems.
Escalating High-dimensional Imaging using Combinatorial Channel Multiplexing and Deep Learning

Abstract Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each individual protein, inherently limiting their throughput and scalability. Here, we present CombPlex (COMBinatorial multiPLEXing), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of proteins that can be measured from C up to 2 C − 1, and is applicable to any mass spectrometry based or fluorescence-based microscopy platform. In CombPlex, every protein can be imaged in several channels, and every channel contains agglomerated images of several proteins. These combinatorically-compressed images are then decompressed to individual protein-images using deep learning and optimization. We perform feasibility experiments in silico and achieve accurate (F1=0.98, R=0.99) reconstruction for compressing the stains of twenty-two proteins to five imaging channels. We test our approach experimentally and obtain accurate (F1=0.97, R=0.93) images of seven proteins using three channels, both in fluorescence microscopy and in mass-based imaging. We demonstrate that combinatorial staining coupled with deep-learning decompression can serve to escalate the number of proteins measured using any imaging modality, without the need for specialized instrumentation. Coupling CombPlex with instruments for high dimensional imaging could pave the way to image hundreds of proteins at single-cell resolution in intact tissue sections.
Evaluation of Instagram's Neural Machine Translation for Literary Texts: An MQM-Based Analysis

Addressing the global increase in social media users, platforms such as Instagram introduced automatic translation to broaden information dissemination and improve cross-cultural communication. Yet, the accuracy of these platforms' machine translation systems is still a concern. Therefore, this paper aims to explore the potential of Neural Machine Translation utilized by Instagram in producing high-quality translations. In doing so, this study attempts to scrutinize the reliability of Instagram's "See Translation" feature in the translation of literary texts from Arabic to English. A selection of auto-translated Instagram captions is analyzed through the identification, classification, and assignment of error types and penalty points, utilizing the MQM core typology. Subsequently, the Overall Quality Score of the error-based analysis is calculated automatically using the ContentQuo platform. Furthermore, the study investigates whether Instagram Neural Machine Translation can effectively convey the intended message within literary texts. From 30 purposively selected Instagram captions with literary content, the study found Instagram's machine translation lacking in 90% of cases, particularly in accuracy, fluency, and style. Among these, 61 errors were identified: 26 in fluency, 25 in accuracy, and 10 in style, adversely affecting the quality and failing to convey the original message. The findings suggest a need for enhanced algorithms and linguistic architecture in Neural Machine Translation systems to better recognize linguistic variants and text genres for more accurate and fluent translations.
Study of automated hematology analyzer’s scatterplot patterns in white blood cell disorders

Background: Automated hematology analyzers produce scattergrams that can be used as screening tool for various hematological conditions and efficiently shorten turnaround times. Aim was to study scattergram patterns of various while blood cell disorders and assess their efficacy compared to a peripheral blood smear for diagnosis of various disorders. Methods: Scattergram findings generated by UniCel® DxH 800 automated hematology analyzer, a 5-part differential analyzer. The graphic displays have been compiled over a period of 3 months from blood samples received for CBC. Samples that the counter flagged as abnormal for white blood cell were chosen. Based on the scatterplots, a preliminary diagnosis was formed. It was compared with the peripheral blood smear (PBS) findings which were taken as the gold standard. Results: The scatterplots showed unique patterns for various disorders on the basis of location, shape, size, density of the cells and their clustering. The scattergram analyser showed 90% sensitivity and 88% specificity for diagnosing hematological disorders. A 97-100% accuracy rate was reported showing excellent correlation between PBS result and WBC parameter result in cell counter analyzers. Conclusions: Not all cases of haematological malignancy exhibit cytopenias or cytosis at initial presentation. Therefore, these scatter plots offer helpful information that prompts a hematopathologist to suspiciously screen the peripheral smear in cases with normal counts. Scattergram analysis suspects a diagnosis earlier than peripheral smear examination. Given their strong correlation with a variety of WBC disorders and confirmed by PBS, WBC scatterplots can be used as a screening tool.
Enhanced Scalar Multiplication Algorithm over Prime Field Using Elliptic Net

Scalar multiplication in elliptic curve cryptography is the most expensive and time-consuming operation. The elliptic curve cryptography attracted interest due to the development of modern technology since it could offer the equivalent high level of security with a reduced length of key. Therefore, improving elliptic curve scalar multiplication performance has always been the primary goal of cryptography. In this paper, a novel scalar multiplication algorithm based on the modified double and double add via elliptic net with Karatsuba method was proposed in order to enhance the efficiency of scalar multiplication. In the experimental results, the elliptic net equivalence sequence was applied to the Twisted Edwards curve together with safe curves of numsp384t1 and numsp512t1. At the point operational level, the proposed method reduced the cost of multiplication by 46.15% and 42.30% for double and double add, respectively, when compared to elliptic net using eight blocks method. The proposed double lowered the multiplication cost by 12.5% and the squaring cost by 20% when compared to elliptic net using ten temporary variables method. Following this, proposed double add cost reductions of 6.25% and 20% were obtained to multiplication and squaring. At the field operational level, in comparison to the binary method, the eight-block elliptic net method, and the elliptic net method with ten temporary variables for the 384 bits scenario, the developed scalar multiplication algorithm obtained cost reductions of 57.6%, 31.3%, and 13.2%, respectively. On 512 bits with similar comparison, the designed algorithm exhibited better performance by averages 59.2%, 31.0% and 13.2%. The results signified that the designed algorithm over prime field performed better at the point and field operational levels with larger scalar bit size.