Reviewerly/exHarmony huggingface dataset:
Work self-efficacy and its role in promoting psychological health of the staff of educational organizations in the conditions of war and post-war revival: a theoretical analysis of the problem

Introduction. Today, psychologists are facing an important and urgent task of finding factors that promote educational staff's work performance and psychological health in war conditions. One of these factors is overall self-efficacy and, in particular, work self-efficacy of the individual. Aim: to make a theoretical analysis of the concept of work self-efficacy and determine its role in promoting the psychological health of the staff of educational organizations in the conditions of war and post-war revival. Results: In Western organizational psychology, the problem of self-efficacy is studied in relation to work efficiency and job satisfaction, as a staff's personal characteristics correlate, as an occupational stress-and burnout-reducing factor, and as a mental health and subjective well-being factor. Work self-efficacy is a type of personal self-efficacy and it is considered as part of professional self-concept and is defined as a person's belief in their ability to successfully perform work tasks. Work self-efficacy in foreign studies is considered as a significant correlate of such organizational indicators as career, job satisfaction, dedication, involvement in work and work productivity in general. Work self-efficacy acts as a "buffer" from occupational stress and burnout. In the conditions of war, the development of self-efficacy acquires special importance. On the one hand, self-efficacy in certain population categories has decreased, but on the other hand, self-efficacy plays a key role in reactions to stress and coping with threatening situations and traumatic events. In addition, self-efficacy is an important factor in post-war personal recovery and can be developed via psychological interventions. Conclusions: The results of the theoretical research can be helpful in empirical studies of educational staff's work self-efficacy in the conditions of war and for the development of special work self-efficacy promotion psychological technologies.
Analysis of the k-Means Method in Clustering Acceptance of PKH Aid in Pulau Rakyat Tua Village

The Family Hope Program (PKH) is a program that provides cash assistance to Very Poor Households (RSTM) which are required to fulfill requirements related to efforts to improve the quality of human resources. In selecting residents to be recipients of the Family Hope Program (PKH) in Pulau Rakyat Tua Village, the problem that often arises is that the provision of Family Hope Program assistance is often considered not to be on target. In addition, errors often occur because the selection is still done manually and requires a long time in selecting participants, which can be influenced by the objective assessment of PKH companions. The research objective is to apply the k-means clustering algorithm in selecting prospective beneficiaries of the Family Hope Program (PKH). The method used uses the application of data mining with the k-means clustering algorithm. Based on the results of applying the k-means clustering algorithm, the results of the system being built can make it easier to select potential recipients of Family Program assistance. The results of the k-means clustering algorithm test produced Cluster 1 in the Eligible category totaling 29 PKH beneficiary data and Cluster 2 in the Ineligible category totaling 1 PKH beneficiary data.
Real-Time Notifier for Blockchain Events

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events.
Data Acquisition System for Forecasting Applications of Photovoltaic Power Production

Photovoltaic power production is dependent on atmospheric factors that change rapidly. Therefore a weather monitoring system is required to better observer modifications that can alter the power production of a PV power plant. This paper proposes a low-cost data acquisition system constructed with the help of a Raspberry Pi and available Open Source tools. Additional sensors are for temperature, humidity and pressure monitoring, a fisheye camera for all-sky imaging and a low-cost SDR system used for satellite image acquisition. The presented study is used only for the data acquisition part of the forecasting methodology.
Employee Overtime Administration Information System at Production Division 3 PT. Konimex Indonesia

Production division of PT. Konimex Indonesia has problems in terms of collecting data on employee overtime hours. This happens because the data collection process is still carried out using notebooks that cause problems, including book media that is easily lost and damaged. The process of collecting overtime hours is also a problem because data collection and counting of hours are still carried out by recording directly in the employee's overtime logbook. In addition, the payroll administration department also has difficulties in the process of preparing employee overtime data reports. Based on these problems, the author formulates a problem, namely how to design and create an employee overtime administration information system. The author gives a limit on this study, which only discusses the payroll system in the production division of 3 PT. Konimex Indonesia. The system inputs that the author discusses are employee data and overtime data. The system output that the author discusses is an employee overtime data report. The author uses the SDLC (System Development Life Cycle) method in the information system developer method. The information system is designed and created using the PHP programming language and the MyQL database. The author's goal in this study is to create an information system for the administration of employee overtime data in the production division that can be used by the administration to process overtime data up to the report creation process.
Elastic DNN Inference with Unpredictable Exit in Edge Computing

Multi-exit neural networks have recently boomed in edge computing to maximize the computing power of different devices. However, many real-time tasks running on edge computing applications have encountered unpredictable exiting frequently due to system power outages, high-priority preemption, etc., which have been overlooked by multi-exit models until now. To tackle this issue, it is critical to decide at which branch the multiexit model exits so that the unpredictable exit will always come with desirable results. In this paper, we propose EINet, a samplewise planner of real-time multi-exit deep neural networks, which achieves efficient Elastic Inference with unpredictable exit while guaranteeing best-effort accuracy on different edge platforms. Therefore, a given trained deep neural network is first partitioned into multiple blocks with one exit each by EINet. Then EINet obtains the block-wise model profiles, including the block-wise accuracy and inference time. Using the model profiles, EINet is able to dynamically determine which exits to take during the inference task for each sample. We introduce Confidence Score Predictors to dynamically adapt the uniqueness of the input samples, and the Search Engine to efficiently find the near-optimal plan during the elastic inference. EINet is evaluated extensively using multiple DNNs and datasets with unpredictable exits. Results show that EINet can achieve the highest average accuracy compared with multiple baselines.
P<sup>2</sup>Auth: Two-Factor Authentication Leveraging PIN and Keystroke-Induced PPG Measurements

Personal Identification Number (PIN), as one of the primary means of protecting digital properties and privacy on mobile devices, has been suffering from shoulder surfing attacks and weak password guessing for the long term. Recent years witness the growing interest in two-factor authentication that takes advantage of two different ways for mutual verification, thereby strengthening user authentication's accuracy and reliability. Especially with the popularity of smartwatches, more physiological signals are readily available to facilitate two-factor authentication. This paper presents a lightweight and unobtrusive two-factor authentication scheme, P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Auth, integrating the PIN and unique keystroke-related Photoplethysmography (PPG) measurement on wearables. Specifically, we propose the transformation of the multivariate PPG signal induced by the keystrokes to extract reliable biometric features. We develop short-time energy-based methods to identify the input cases, thus enabling support the authentication for both one-handed and two-handed input cases. Furthermore, we also consider the situation where there is no fixed PIN and design a new enhanced privacy scheme by combining the PPG measurements of different keystrokes to improve authentication security. The experiments involving 15 volunteers demonstrate that our prototype system can achieve an average authentication accuracy of over 95% for one-handed cases and over 90% for two-handed cases.
Investigating the effectiveness of deep learning approaches for deep fake detection

As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This paper aims to provide an in-depth discussion about the challenge of generated fake face image detection. We explain the different datasets and the various proposed deep learning models for fake face image detection. The models used were trained on a large dataset of real data from CelebA-HQ and fake data from a trained generative adversarial network (GAN) based generator. All testing models achieved high accuracy in detecting the fake images, especially residual neural network (ResNet50) which performed the best among with an accuracy of 99.43%.
A critical review of the state of computer security in the health sector

There is growing concern about IT security in the healthcare sector due to the number of cyberattacks. The objective of the review is to analyze the state of adoption of computer security in the healthcare sector and provide valuable knowledge to researchers and health organizations interested in this field of study. An exhaustive search of international and regional articles on computer security in healthcare organizations was conducted using Scopus, Dimensions, and pubMed databases. Preferred reporting items for systematic reviews and meta-analysis (PRISMA) statement was used for the selection of articles published between 2018 and 2022. The final number of articles considered is 50. The review explored approaches related to computer security types, mechanisms, and technologies. The findings reveal that blockchain is the most widely used technology to protect medical information. In addition, network, software, and hardware security approaches are employed, using mechanisms such as data encryption, authentication, and access control. Based on these findings, a perimeter security model for the protection of medical information is proposed. In conclusion, these results highlight the importance of adopting robust security measures in terms of networks, software, and hardware, as well as adopting blockchain technology to improve data security in the healthcare sector.
Variable Velocity Planning Algorithm for Autonomous Robot or Car

The aim of this paper is to propose a planning techniques for the longitudinal and angular velocity profile for autonomous robot or car. This paper addresses this problem proposing a new approach that consists of introducing a velocity planning in the trajectory planner. The angular and longitudinal acceleration limits are imposed due to anti slipping constraints.
HOG Feature-Based Offline Handwritten Malayalam Word Clustering with Lexicon Reduction

Handwriting recognition is an active research in the field of pattern recognition, image processing and computer vision. The handwriting recognition system can recognize handwritten words extracted from the scanned document image. For a better recognition model, the most inevitable thing is a good dataset for training purpose. Here, the clustering is performed on the segmented handwritten words to group the matching words for easing the process of labelled dataset creation. In this, after doing some pre-processing operations, as a feature descriptor, histogram of oriented gradient features is extracted holistically on the word image. For feature reduction, Principal Component Analysis is applied on the extracted feature set and finally K-means clustering used for grouping the matched handwritten words together from the unlabelled dataset. Here, training the recognition model for keyword searching, the clustering helps to create a lexicon reduced dataset and the non-repeated segmented words are neglected after clustering. This will make the most complicated and time-consuming manual task, grouping and labelling the handwritten words for dataset creation easier.
Industrial espionage from a human factor perspective

Industrial espionage is a significant threat in a fiercely competitive environment which increases the risk of information security and safety being compromised and leads to concerns about business ethics. The main aim of this paper is to examine industrial espionage from the perspective of the insider human factor, explore the motivations that may lead to industrial espionage, and identify ways of maintaining information security and safety to reduce insider threats. The research involved qualitative in-depth interviews among twenty-one stakeholders from seven European countries. The transcripts were analysed using grounded theory methodology. Results show that main factors that may lead to industrial espionage include intensifying market competition, financial compensation offered in exchange for information, decreasing loyalty among the younger generation, psychological issues of personal grievance and psychological disorders, and poorly developed information-security infrastructure. This study recommends that managers and policymakers plan and implement protection and prevention measures, undertake risk analyses to reduce the potential consequences of insider threats, and establish a critical business information tracking system. Further recommendations include maintaining an appropriate company culture, ensuring employee satisfaction, and fostering information safety education while creating adequate security infrastructure.
RBSAC: Rolling Balance Controller Based on Soft Actor-Critic Algorithm of the Unicycle Air Robot

Due to the complexity of the robot’s dynamics model coupled with multiple outputs, most model-based or proportional-integral-derivative type controllers are unable to effectively solve the problem of attitude control of a Unicycle Air Robot (UAR) rolling in ground mode. In this paper, we formulate the attitude control problem in ground mode as a continuous-state, continuous-action Markov decision process with unknown transfer probabilities. Based on deterministic policy gradient theorems and neural network approximations, we propose RBSAC: a model-free rolling balance Reinforcement Learning (RL) controller based on Soft Actor-Critic (SAC) algorithm, which learns the state feedback controller from the attitude sampling of the ground mode. To improve the performance of the algorithm, we further integrate a batch learning method by playing back previously prioritized trajectories. We illustrate through simulations that our model-free approach RBSAC outperforms a feedback-based linear PID controller. After conducting simulation verification, it has been observed that the RBSAC controller enables stable rolling of the UAR on the ground, resulting in a 60% improvement in speed compared to the PID controller. Moreover, the RBSAC controller exhibits enhanced robustness and autonomous response to external disturbances during motion.
Automatic building of annotated image datasets for training neural networks

As a rule, specially trained neural networks are engaged in the recognition and classification of objects in RGB and RGB-D images. The quality of object recognition depends on the quality of neural network training. Since the network cannot go far beyond the limits of the training set, the problems of forming datasets and their correct annotating are of particular relevance. These tasks are time-consuming and can be difficult to perform in real-world conditions since it is not always possible to create the required real-world illumination and observation conditions. So, synthesized images with a high degree of realism can be used as input data for deep learning. To synthesize realistic images, it is necessary to create appropriate realistic models of scene objects, illumination and observation conditions, including ones achieved with special optical devices. However, this is not enough to create a dataset, since it is necessary to generate thousands of images, which is hardly possible to do manually. Therefore, an automated solution is proposed, which allows us to automatically process the scene to observe it from different angles, modify the scene by adding, deleting, moving, or rotating individual objects, and then perform automatic annotation of a desired scene image. As a result, not only directly visible scene object images but also their reflections may be annotated. In addition to the segmented image, a segmented point cloud and a depth map image (RGB-D) are built, which helps in training neural networks working with such data. For this, a Python scripting interpreter was built-in into a realistic rendering system. It allows us to perform any actions with the scene that are allowed in the user interface to control the automatic synthesis and segmentation of images. The paper provides examples of automatic dataset generation and corresponding trained neural network results.
Entanglement Distribution in Satellite-based Dynamic Quantum Networks

Low Earth Orbit (LEO) satellites present a compelling opportunity for the establishment of a global quantum information network. However, satellite-based entanglement distribution from a networking perspective has not been fully investigated. Existing works often do not account for satellite movement over time when distributing entanglement and/or often do not permit entanglement distribution along inter-satellite links, which are two shortcomings we address in this paper. We first define a system model which considers both satellite movement over time and inter-satellite links. We next formulate the optimal entanglement distribution (OED) problem under this system model and show how to convert the OED problem in a dynamic physical network to one in a static logical graph which can be used to solve the OED problem in the dynamic physical network. We then propose a polynomial time greedy algorithm for computing satellite-assisted multi-hop entanglement paths. We also design an integer linear programming (ILP)-based algorithm to compute optimal solutions as a baseline to study the performance of our greedy algorithm. We present evaluation results to demonstrate the advantage of our model and algorithms.
A classroom facial expression recognition method based on attention mechanism

Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration.
The Effect of School Principals' Agile Leadership Behaviors on Entrepreneurial Teacher Behaviors through Innovative Organizations

The purpose of this study is to reveal the mediating role of innovative organizations in the effect of school administrators' agile leadership behaviors on entrepreneurial teacher behaviors. Based on the relationship between the variables, a model was proposed and the proposed model was tested. Before testing the SEM model, the relational survey model was used to determine the relationship between the variables and the model was tested. The study group of the research consists of 479 teachers working in Kartal, Tuzla, Maltepe and Sultanbeyli districts of Istanbul in 2023. According to the research findings, the agile leadership characteristics of school principals positively affect the entrepreneurial behaviors of teachers and the innovative structure of schools. As a result of the analysis of SEM model, innovative organizations have a partial mediating role between agile leadership behaviors of school principals and entrepreneurial teacher behaviors. This research contributes both theoretically and practically to the effect of agile leadership on teachers' entrepreneurial behaviors and innovative organizational structure of organizations, and the effect of innovative organizations on entrepreneurial teacher behaviors. In the light of the findings, implications related to agile leadership, teachers' entrepreneurial behaviors and innovative organizational structures are discussed and suggestions are made for future research.