Reviewerly/exHarmony huggingface dataset:
An Interpretable Hybrid Recommender Based on Graph Convolution to Address Serendipity

This paper proposes a hybridized recommender system built to overcome the disadvantages of its individual components by combining them in a way that balances contrasting metrics. The individual com-ponents are a content-based and collaborative filtering-based model, a neural network model, and a graph convolutional network model. The individual models used in today’s scientific landscape focus entirely on a single metric to be tuned and optimized. Through this paper, we introduce a way to balance all metrics, while retaining excellent precision and recall and improving on less focused metrics such as coverage and serendipity. We also explore a novel hybridization technique that represents the recommendation scenario as a graph and infers edges from the dataset to map relevant relationships. Interpretability is the comprehension of a model’s fundamental decision-making. This helps improve users’ trust in the model and is an attempt at understanding the features and their relevance. The results of the hybrid recommender are explained using post hoc interpretability techniques. Additionally, serendipity is used to capture user satisfaction and the factor of “pleasant surprise” with the recommendations. To overcome the subjective nature of evaluating serendipity, we also propose a new distance-based method to calculate it. The results provide a comparison of contrasting and competing metrics of the individual and hybrid models, and also show the balance of metrics achieved by the hybrid models.
Verbal Group

Chapter 3 explores verbal group system and structure. In doing so for English and Spanish, it concentrates on what in SFL is referred to as univariate structure. Univariate structures are structures involving a single variable, which is repeated over and over again; they thus function as the realisation of recursive systems. The unit complexes introduced above (clause complexes, group complexes, word complexes and morpheme complexes) are structures of this kind. And some languages develop more delicate clause and group systems organised along these lines. The recursive tense systems in English and Spanish which we describe in this chapter are good examples. Chinese verbal groups on the other hand do not involve recursive systems realised by iterating structures and so have to be approached from a multivariate perspective.
Pendampingan Pengelolaan Jurnal Online di Universitas Selamat Sri (UNISS) Kendal

&lt;p align="justify"&gt;&lt;em&gt;The existence of scientific journals is fundamental as a place for the publication of scientific articles for academics to show the results of their research work. Along with the development of technology and information, it demands the management of online-based journals for the convenience and transparency of the articles in them. Management of journals with an online system is still not fully understood by academics in several institutions. As one of the universities located not far from UIN Walisongo Semarang, Selamat Sri University (UNISS) Kendal has sufficient resources and according to needs to be a place for community service activities to be carried out with the theme of assisting in the management of online journals based on the Open Journal System (OJS). The purpose of this assistance is to introduce online scientific journal management using the OJS system at UNISS. The method used is lectures and hands-on practice regarding online journal management. The results of this community service activity are knowing information about the availability of infrastructure, articles, Human Resources (HR), and regulations that support the start of managing online journals using OJS. In addition, understanding and awareness of the need to have online journals for the benefit of scientific publications for researchers or academics are also obtained which is the requirement as stated in the Tri Dharma of Higher Education.&lt;/em&gt;&lt;/p&gt;
[A region-level contrastive learning-based deep model for glomerular ultrastructure segmentation on electron microscope images].

We propose a novel region- level self-supervised contrastive learning method USRegCon (ultrastructural region contrast) based on the semantic similarity of ultrastructures to improve the performance of the model for glomerular ultrastructure segmentation on electron microscope images.USRegCon used a large amount of unlabeled data for pre- training of the model in 3 steps: (1) The model encoded and decoded the ultrastructural information in the image and adaptively divided the image into multiple regions based on the semantic similarity of the ultrastructures; (2) Based on the divided regions, the first-order grayscale region representations and deep semantic region representations of each region were extracted by region pooling operation; (3) For the first-order grayscale region representations, a grayscale loss function was proposed to minimize the grayscale difference within regions and maximize the difference between regions. For deep semantic region representations, a semantic loss function was introduced to maximize the similarity of positive region pairs and the difference of negative region pairs in the representation space. These two loss functions were jointly used for pre-training of the model.In the segmentation task for 3 ultrastructures of the glomerular filtration barrier based on the private dataset GlomEM, USRegCon achieved promising segmentation results for basement membrane, endothelial cells, and podocytes, with Dice coefficients of (85.69 ± 0.13)%, (74.59 ± 0.13)%, and (78.57 ± 0.16)%, respectively, demonstrating a good performance of the model superior to many existing image-level, pixel-level, and region-level self-supervised contrastive learning methods and close to the fully- supervised pre-training method based on the large- scale labeled dataset ImageNet.USRegCon facilitates the model to learn beneficial region representations from large amounts of unlabeled data to overcome the scarcity of labeled data and improves the deep model performance for glomerular ultrastructure recognition and boundary segmentation.
A Novel Approach for Privacy Preserving Technique in IoT Fog and Cloud Environment

One problem with the Internet of Things (IoT) is that user data and identities could be used in ways that aren’t what they were meant to be used for. Researchers have come up with different ways to lower privacy risks. But most of the existing solutions still have problems. They also have heavy cryptosystems and policies that are applied on both sensor devices and in the cloud. To solve these privacy problems, fog computing has been added to the edges of IoT networks to help with low latency, computation, and storage. This research uses deep learning models and hashing techniques that give the properties of User validation, Data confidentiality, Data verifiability, and Data integrity, and Data obfuscation that are done using MQTT protocol which derives more confidentiality, making data obscure. The future scope will be applied to AI models which can optimise the setup time for learning and training classification models.
IP Network Multicast Technology and Application Simulation

IP network multicast technology effectively solves the problems of unicast and broadcast in point-to-multipoint applications, saves network bandwidth and reduces network load, and is widely used in multimedia conferences, video-ondemand, and IPTV fields. This paper analyzes the advantages and disadvantages of unicast, broadcast and multicast, introduces the basic architecture, service model and IP address mapping relationship of multicast, summarizes the operation mechanism of group membership management, compares and analyzes the working modes of the two multicast routing protocols, and finally Based on eNSP, the enterprise network multicast video stream is simulated and realized, which provides a good reference for the research and application of multicast technology.
Blockchain-Based E-Voting System with Face Recognition

Given the increasing importance of technology in meeting human needs, its utilization has become crucial. In contemporary democracies, where public trust in governments is declining and elections play a pivotal role, the widespread adoption of technology has led to new challenges. Elections hold significant importance as they determine the future leaders of countries or organizations. However, certain computerized voting systems have faced criticism for their lack of transparency. Establishing public trust in the government is a formidable task due to the lack of transparency and susceptibility to exploitation in existing voting procedures. Both traditional and current digital voting systems are ineffective due to their vulnerabilities. The main objective is to address issues in conventional and electronic voting systems, including errors and unfairness that may arise during the voting process. Integrating blockchain technology into the electoral process can ensure fair elections and reduce unfair practices. The computerized voting methods do not meet the necessary standards for widespread usage, and the physical voting systems also face numerous issues. This underscores the importance of finding a solution to protect the democratic principles of citizens. By offering a fast and secure voting method, this system has the potential to bring about a revolutionary change in the electoral process. It could lead to higher voter participation and more accurate election results. The proposed approach presents a framework for digital voting using blockchain technology, eliminating the need for physical polling locations. Our suggested design incorporates adaptable consensus algorithms to support a scalable blockchain. Smart contracts ensure secure interactions between users and the network during transaction execution. The security aspects of the blockchain-based voting mechanism have also been addressed, including the use of cryptographic hashes for transaction encryption and prevention of 51% attacks. Furthermore, blockchain technology has been utilized to establish transaction systems throughout the voting process. Performance studies of the proposed system demonstrate its feasibility for deployment in large populations.
Attack prediction in Internet of Things using knowledge graph

Internet of Things (IoT)has numerous applications in the industry and society, thanks to its ability to achieve automation and connectivity in a range of activities. Despite its great potentials, IoT is susceptible to physical and cyber-attacks, which causes security threats (e.g., financial risk and leakage of privacy). To address this problem, an approach for attack prediction is proposed for IoT. Aiming at a high degree of flexibility, an intelligent model is designed to construct knowledge graph by integrating equipment information CPE, vulnerability information CVE and attack pattern information CAPEC disclosed by the National Institute of Standards and Technology (NIST) and the security organization MITRE. Based on the knowledge graph, the safety analysis and operation analysis of many IOT information are carried out. To conclude the possible attack, knowledge representation learning method that fuses the triple information and semantic path combination information of the knowledge graph (FTSPC) was employed. We transform the attack prediction task into the link prediction problem. The suggested method is evaluated on a public dataset and our dataset, the results demonstrated that the method can predict the attack of IoT infrastructure, providing rich IoT security knowledge to security researchers and professionals and a useful reference for active defense.
A Tightly Secure ID-Based Signature Scheme Under DL Assumption in AGM

Identity-based signatures (IBS) can be verified using the signer identity information as the public key, and hence, there is no need for certificate management that proves the corresponding public key ownership. Unfortunately, none of the existing IBS schemes has security proven as tight as the discrete logarithm (DL) problem, the hardest problem in the cyclic group setting, under the standard EUF-CMA security model. Recently, the introduction of proving security in the algebraic group model (AGM), where the adversary’s computation is algebraic, enables some ordinary signature schemes to be proven tightly reducible under DL assumption and EUF-CMA. To date, however, it remains unknown whether IBS schemes can also be proven as secure as the DL problem in the AGM. Achieving tight security in IBS schemes under standard EUF-CMA is challenging, due to the need to take extra precautions against adaptive queries on user private keys by the adversary. In this work, we show, for the first time, an IBS scheme with tight security under DL assumption and EUF-CMA in the AGM. The scheme features a minimal signature size of two group elements, with a reduction loss factor of two.
The Mediating Role of Knowledge Management on the Relationship between Human Resource Management Practices and Organizational Performance: Case Study of Faculty of Economic and Commercial Sciences and Management Sciences in Tahri Muhammed University Becher -Algeria

The study aimed to identify the direct impact of HRM practices on organizational performance, and the indirect impact through knowledge management at Tahri M. Bechar University, Algeria. In order to achieve the goal of the study, a questionnaire was designed based on relevant studies to collect data from the study sample,which is represented by 48 teachers. The study found a number of results, the most important are:The existence of a statistically significant effect of HRM practices on organizational performance, and the existence of a statistically significant effect of knowledge management as a mediator in the relationship between HRM practices and organizational performance at Tahri. M Bechar University, Algeria. The most important recommendations are: The need to use HRM practices effectively in order to be able to develop its performance, and the necessity of alignment between the strategy of HRM with the strategy of applying knowledge management to ensure a high level of organizational performance
Quantum Key Distribution Through a Multi-Core Optical Fiber

The prospects of using multi-core fiber for constructing quantum networks have been analyzed. It has been shown that multi-core fiber has several advantages in implementing the technology of quantum key distribution among multiple groups of subscribers. Test experiments were conducted using scientific and educational complexes to simultaneously implement two quantum and real classical communication channels at speeds of 100 Mbps, 1 Gbps, and 10 Gbps through a single multi-core fiber. The possibility of simultaneous transmission of classical and quantum data through multi-core fiber using phase encoding has been demonstrated.
Business-Psychological Approach in the System of Educational Activity Mgimo

Thanks to the cooperation of business psychologists from Russia and Great Britain, an approach to business psychology has been formed, which attracts specialists from the field of education because of its focus on comprehensive and versatile training bachelor's and master's degree students in economics. A business-psychological approach in a university with an economic field of training expands the opportunities for students to acquire a unique set of social skills, professional competencies and relevant personal qualities for the subsequent provision of high demand among potential employers, strengthening confidence in independent successful professional activity. In a modern university, "at various stages of training, various formats of initiation and support of personal desire for the development of personal skills and qualities, as well as conscious choice and implementation of an individual professional trajectory are assumed." Students of the Bachelor's degree and Master's degree programs in economic areas of training can be considered both objects and subjects business psychological approach at the university: mastering basic psychological knowledge during the educational process, they become active students of practical business psychology at specific workplaces in companies. The Moscow State Institute of International Relations (University) (MGIMO), according to the charter, is accountable The Ministry of Foreign Affairs (MFA) of the Russian Federation, which, in fact, determines the specifics of its educational activities. Being a system-forming educational institution of higher education of the Federal Educational-methodical combining the enlarged group of specialties and areas of training "Political Sciences and regional studies" and performing the functions of a basic educational institution of higher education in the areas of training "International Relations", "Foreign regional studies", "Advertising and Public Relations", MGIMO has a variety of opportunities for organizing, conducting and developing both fundamental and and applied scientific research, as well as their implementation in the educational process of the university. Business psychology as a modern branch of applied psychology is no exception in this sense. In the structure of MGIMO's approach to business psychology, two types of components can be distinguished — institutional and educational (subject).
FORECASTING OF COVID–19 WITH AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD IN EAST JAVA PROVINCE

Background: The COVID-19 pandemic has had a major impact on the world's health system, including Indonesia. The national health system is facing challenges with increasing cases of COVID-19. With the forecasting of COVID-19 cases, it is hoped that it can be one of the references in dealing with COVID-19 and one form of mitigation in dealing with COVID-19. Purpose: This research aims to predict COVID-19 cases in East Java Province for the coming year using the Autoregressive Integrated Moving Average (ARIMA) method based on patient data from March 2020 to January 2022. Methods: This type of research is analytic. Forecasting future COVID-19 cases using the Autoregressive Integrated Moving Average (ARIMA) method based on COVID-19 data from March 2020 to January 2022. Results: Based on the results of ARIMA analysis, the best forecasting model for confirmed cases of COVID-19 is the model (1:0:1) with AIC values ​​(14.22672), SIC (14.33357), while for cured cases is the model (1:2: 3) with the value of AIC (13.93054), SIC (13.03738), and for the case of death is the model (1:2:1) with the value of AIC (10.76105) and SIC (10.86790). Conclusion: From the results of this study, it is predicted that there will be an increase in COVID-19 cases in July 2022, January 2023 and June 2023.
impact of incentives on employee performance and job satisfaction at Madinah General Hospital

The aim of this study was to identify the incentive system used by public hospitals in Saudi Arabia and its impact on employee performance and satisfaction in Medina General Hospital in particular. The study also aimed to determine the contribution of material and moral incentives provided to employees in Medina General Hospital, as well as to identify the extent to which incentives are applied in reality in the hospital and their impact on improving the performance and satisfaction of employees in Medina General Hospital. The study population included all employees in Medina General Hospital, and the study used simple random sampling. The sample size was 100 individuals. The study found a statistically significant negative correlation between incentives and job performance at 0.01, with job performance in Medina General Hospital increasing by 0.479% for every 1% increase in incentives applied. Similarly, there was a statistically significant negative correlation between incentives and job satisfaction at 0.01, with job satisfaction in Medina General Hospital increasing by 0.411% for every 1% increase in incentives applied.
High-accuracy digital fringe projection system for three-dimensional optical sensing using cell phone device

As the demand for faster and more accurate 3D shape measurement with portable devices increases due to the growing interest in augmented reality (AR) and virtual reality (VR), there is a need to explore the potential use of mobile phones for this purpose. While some phones already use lidar for 3D shape measurement, the resolution is still too low to enable meaningful AR or VR. Among all 3D shape measurement techniques, structured light systems offer high resolution and accuracy. In this research, we utilize a MOTO Z model mobile phone, which includes both a high-resolution camera and a digital light processing (DLP) projector. The DLP projector used for digital fringe projection provides highly accurate 3D geometry of the object being measured.
Predicting solar power and energy production over the course of a full day

The majority of air and ground vehicle systems are reliant on specialized diesel fuel. This reliance increases the likelihood that operations may be operating in an energy constrained or contested environment given the state of international relations between global energy providers and consumers. Such a vulnerability has the potential to reduce operational effectiveness or efficiency if logistical supply chains were interrupted or impeded. The most effective and efficient methodology to reduce reliance on specialized diesel fuel is to hybridize our energy and power (E&P) systems, and support more diverse E&P solutions including renewable energy generation (photovoltaic (PV) arrays, wind generation, wave energy converters), nuclear, or decaying isotopes. In this paper/presentation, we present our advances in developing a set of predictive artificial intelligence and machine learning (AI/ML) algorithms that forecast E&P capabilities of a photovoltaic array indirectly and directly. These milestones are a product of two separate types of AI/ML approaches: (1) developing AI/ML based algorithms that predict ambient and panel temperature from various atmosphericbased sensor data which can then be used in combination with an irradiance profile and a MATLAB Simulink model to predict the E&P capabilities of the PV array (indirect method), and (2) developing AI/ML which predicts the resulting E&P capabilities of the PV array, using various atmospheric-based sensor data (direct method).
Speckle interferometry single-shot applications with multiple carrier-fringe information

The need for robust equipment that allows identifying defects or measuring small displacements in a harsh environment has been a requirement in the aeronautical and principally in the oil and gas industry. In this field, Digital Speckle Pattern Interferometry and shearography had been the optical techniques more used. Recently, advances in the process of phase images through multiple carrier frequencies had allowed compact optical configurations that can combine multiple acquisitions or even multiple techniques in an simple process of capture. This article shows the different applications, versatility, and compactness of the use of carrier frequencies through the multiple aperture principle.
Measuring material thickness variations through tri-aperture DSPI

A configuration for the measurement of thickness changes in materials through one-shot digital speckle pattern interferometry (DSPI) was developed. The phase maps calculation was made by adding carrier fringes by the multiple aperture principle and Fourier Transform Method (FTM). With this setup, interferometry configurations verified that the simultaneous and instantaneous visualization of two opposite faces of a surface is possible. In addition, the combination of the simultaneous results obtained from both sides of the material makes it possible to determine displacements with greater sensitivity or to identify changes in their thickness. The validation and demonstrative tests were carried out with a 1-mm thick aluminum plate with a 5-mm diameter through hole coated. Thickness changes until 2 μm was measured.