Reviewerly/exHarmony huggingface dataset:
The Role of Humble Leadership Behaviors in Restricting Organizational Silence: An Investigative Study of Faculty Members’ Opinions in the College of Administration and EconomicsatTikrit University

The purpose of this research is to investigate the relationship between humbleleadership behaviors and organizational silence of faculty members in the College of Business and Economics at Tikrit University.(145) questionnaires were givento faculty members in the College of Administration andEconomics atTikrit University, who represent the research community. (140) valid questionnaires were obtained for statistical analysis with a response rate of (96.5%) and wereanalyzed by SPSS program.The theoretical results showed that there wasa knowledge gap related to determining the nature of the relationship between humbleleadership behaviors and organizational silence. The practical results showed thatthere aretwo types of relationships between the research variables, a direct effect relationship and a direct relationship between humble leadership behaviors and organizational silence.
THE IMPORTANCE AND CONDITIONS OF STUDYING GEOMETRIC MATERIAL IN THE ELEMENTARY SCHOOL

The article contains theoretical questions and information on geometric material in elementary school. The importance of the development of students in the process of teaching geometric thinking which is a type of visual-figurative and geometric thinking is highlighted. The study of geometric material for a primary school student is carried out by his logical thinking and provided by various mental processes. Analyzes of systems for studying geometric concepts and relationships both in traditional and alternative systems of teaching mathematics in primary school have been carried out. This article provides structural analyzes of the system for studying geometric material of the two most popular systems for teaching mathematics to primary school students. Analyzes of primary school mathematics textbooks by authors I.B. Bekboev and N.I. Ibraeva, L.G. Peterson are considered.
The Effects of Storytelling on User Experience and Behavior-related Variables

The emergence of brand stories helps build brands and enhance brand value. This study examines the effects of storytelling on user experience (UX) with an online shop and identifies its effects on brand- and behavior-relevant variables. The results obtained indicate that the use of storytelling in an online shopping context enhances the website's performance in terms of hedonic and pragmatic UX and overall attractiveness. Explicit and implicit brand attitude and explicit and implicit ecological sustainability, explicit social sustainability and the behavioral variable of the willingness to pay a higher price for the products of the brand reached higher scores under the condition of storytelling. Moreover, the implicit and explicit ecological sustainability and implicit social sustainability were increased through the condition. In addition, implicit brand attitudes and behavioral intentions were increased, as well as implicit ecological and social sustainability. The present results add significant empirical insights to the literature on the underlying mechanisms of storytelling, which is related to UX, brand-related outcomes and the interrelation between these variables. Hence, the present study further provides a more holistic understanding of human perception and behavior in online contexts.
Cyber Sleuths: The Transformative Role of Computers in Forensic Analysis

This document highlights the importance of computers in the field of digital forensics. As the world progresses towards digitalization, various industries are increasingly recognizing the advantages of incorporating digital processes into their operations. Digitalization facilitates efficient and rapid workflows, and it can be achieved through the use of computers or systems. To perform digital forensic tasks, the installation of specific forensic software tools on a computer is essential. This enables faster and more streamlined procedures. For instance, computers played a significant role in expediting COVID-19 testing through positive forensic tests in 2020. The utilization of computers greatly simplifies the testing process.
Enhancing Security and Access Control: A Biometric-Based Campus Area Network (CAN) Design for North Eastern Mindanao University (NEMSU) - Tagbina Campus

In this rapidly changing landscape of modern institutions, biometric technology has become a crucial part of strengthening security, enhancing productivity, and refining the overall campus experience. This study focuses on the implementation of a Campus Area Network (CAN) at the North Eastern Mindanao State University (NEMSU) - Tagbina Campus, with a specific emphasis on leveraging biometric technology for non-teaching and teaching personnel. By employing advanced methods such as fingerprint recognition and facial identification, institutions can ensure precise attendance records in real-time, replacing error-prone traditional methods. The proposed Campus Area Network (CAN) will serve as an integrated structure that establishes connectivity in both geographically separated Local Area Networks (LANs) and Metropolitan Area Networks (MANs) facilitating efficient data transfer and communication among various campus facilities. The primary goal of this study is to not only streamline administrative operations but also to establish a safer and more accountable educational environment, which will benefit both professors and students in the long run
Talkhands: Communicating With The Silenced Using A Machine Learning Powered Sign Language Converter

n our interconnected world, communication is a basic human right often hindered by barriers, particularly for the deaf and hard of hearing. TalkHands, an innovative project, employs cutting-edge machine learning to create a state-of- the-art Sign Language Converter. This technology aims to facilitate seamless communication between sign language users and non-users, fostering inclusivity and equality by giving silent voices a means to be heard and understood
Using Gaze Tracking on Sensory Integration Therapy for Eye-movement Detection

Autism spectrum disorder is a condition that often occurs in infancy (from birth to three years old). A common problem includes eyesight disorders, where children cannot focus on a specific item. We use the method of Convolutional Neural Networks (CNN) to detect eyes and collect eye positions. Then, we use a linear regression model to best fit the movement of the eyeballs through the track of positions. After that, we use an Analytic Hierarchy Process (AHP) model for a multi-perspective judgment on children's performance in eye movement.
Investigative Capabilities of Crime Investigators in Handling Eight Focus Crimes

This study examined the investigative capabilities of crime investigators and the problems encountered in handling eight focus crimes in CALABARZON using the descriptive method of research. The instrument was distributed conveniently to 365 criminal investigator-respondents with a turnout of 325 or 89.04% questionnaire retrieval rate. Basic and inferential statistical tools were used to analyze and interpret the data gathered. The maximum number of cases filed by investigators is 500, and the minimum is zero or none. Physical injury is the case with most convictions; however, it is the most dismissed case due to noncooperation of the victim/complainant. It was also revealed that the criminal investigators are very highly capable as to their skills, attitude, and knowledge in the conduct of their duties and responsibilities. However, even though they are highly capable in handling the eight focus crimes, they still encountered serious problems in investigating the index crimes. In light of the study’s findings, the Philippine National Police can devise incentive programs to encourage criminal investigators to improve themselves and become more effective in the performance of their duties.
Sustainability Development Goals Initiatives of Multinational firms: Empirical Evidence from Emerging Economy

MNCs are regarded as one of the essential actors within the private sector that have a greater capability and outreach to address these wicked global challenges identified by the UN in 2015. Surprisingly, despite their important role in achieving SDGs, there is a lack of knowledge on the nature and extent of MNCs’ engagement in sustainable development programs (van der Waal and Thijssens, 2020). This study aims to address this issue by examining the characteristics (organizational size, and location) that influence firms’ engagement in SDGs initiatives that target to increase positive externalities. The results suggest that size and location exert a positive impact on the firm’s involvement in activities that increase positive externalities. These results have important theoretical and policy implications.
An investigation on brand hate: understanding negative psychology of consumers in the service sector

The purpose of this research is to examine the relationship among the brand hate reasons, antecedents, and its consequences. This study follows the four-step model of reasons, antecedents, and consequences of brand hate. Reasons included are product related such as poor food quality, Safety &amp; hygiene. These reasons lead to antecedents of brand hate such as dissatisfaction, which leads to brand hate and its consequences, which are brand avoidance and complaining behavior. The study model is based on the Triangular theory of hate. This study collected data from primary sources of data through the use of a structured questionnaire. This research study follows a causal research design. The target population of the study was all food consumers of Pakistani, who had experienced any food brand in the last 06 months and had extremely negative feelings about that brand. Data screening was through Excel and was analyzed through SPSS and WarpPLS. Results showed a positively significant relationship between the reasons, antecedents, and consequences of brand hate. The findings indicated that the root cause of brand hate is customer dissatisfaction due to poor food quality and concerns about the safety &amp; hygiene of food, which leads to brand avoidance and complaining behavior of food consumers. The outcomes of this research hold significance for the band managers, who can be helped through superior information and comprehension of such a brand hate study.
Neural network for step anomaly detection in head motion during fMRI using meta-learning adaptation

Quality assessment and artifact detection in functional magnetic resonance imaging (fMRI) data is essential for clinical applications and brain research. Subject head motion remains the main source of artifacts - even the tiniest head movement can perturb the structural and functional data derived from the fMRI. In this paper, we propose an end-to-end neural network technology for detecting step anomalies with training on partially synthetic data with adaptation to a specific small set of real data. A procedure for generating a synthetic dataset for training and a module for automated labeling of real data is developed. A recurrent neural network model for detecting step anomalies is proposed. A method for the model adaptation to a small set of real data based on one-step meta-learning is developed. An experimental verification of the accuracy is carried out in the problem of detecting step anomalies using a sliding window of 10, 15, and 24 pixels. The experiments have shown the proposed technology to provide the detection of stepwise anomalies with an accuracy of 0.9546.
Sirka: An alternative to the formalin fixative

ABSTRACT Formalin fixation is the most essential step of routine histopathology practice. During the last few years, various fixatives have been developed for use in histopathology practice as an alternative to formalin, to overcome its side effects on health. Here we have demonstrated an interesting and novel idea of using sirka or sugar cane vinegar as an alternative to the formalin with the adequate result.
FORENSIC HANDWRITING EXAMINATION POSSIBILITIES IN THE INVESTIGATION OF RAIDING

The formation and development of the economic security of the state requires the adoption of effective measures to counteract and minimize the negative impact of criminal offenses that encroach on social relations in the sphere of economic activity, the legal activity of economic entities, etc. To organize a proper response to them, representatives of law enforcement agencies use all measures provided for by criminal procedural legislation. An important role in solving questions that require the application of knowledge during a special investigation of raiding is played by forensic examination, particularly forensic handwriting examination. In connection with this purpose, the article is formulated as a definition of possible and problematic issues performed at the stage of appointment of a forensic handwriting examination. To achieve this, the materials of investigative judicial and expert practice, and theoretical developments of Ukrainian scientists were analyzed, and a textual analysis of current legal acts was carried out. For this, general scientific, philosophical, and special methods of scientific knowledge were used. Based on the results of the conducted research, we offer to justify a qualified expert examination of handwriting material under a forensic handwriting examination, to send a decision on the task of identifying the executor of the handwritten text or a decision on a complex of non-identification works provided for by the appropriate methodology, which is carried out based on the corresponding court decision, the decision of the pre-trial investigation body, or based on of the contract concluded with the customer. In the context of the rating investigation, certain specifics of the purpose of forensic handwriting expert research are defined. Mandatory conditions must be met when appointing a forensic handwriting examination. It was established that the conclusion of the expert drawn up based on the results of the forensic psychological and handwriting examination of the expert from the Red Office should be critically assessed, its assessment should be ensured taking into account other evidence collected during the investigation, as well as establishing the fact of the violation of a criminal offense.
Purpose: The purpose of this research was to examine the fit of the creativity-based curriculum components model. Method: For this purpose, a quantitative research approach based on descriptive-survey method was used. In this research, the method of inferential analysis and data description and the use of factor analysis were used to analyze the questionnaire data and present the results. The statistical population includes all elementary school teachers in Tehran province who are working in …

Background and Aim: The issue of justice has been widely studied by thinkers and researchers, but the discussion of justice in the field of education has not been widely investigated; Therefore, the general purpose of the current research was to examine the educational justice based on the dynamics of Payam Noor University students in Tehran province with a qualitative approach. Methods: The research method was qualitative. The statistical population of the research included (academic experts, faculty members, and university officials) in order to reach a consensus in the Delphi method, the sample size in the qualitative section was determined to be 17 people considering the saturation law. The data collection method was library method, and the measurement tool was semi-structured interview in the qualitative part. Determining the validity of the questionnaire in this research was done by two methods: 1. Form and 2. Content. In the form method, the questionnaire was given to a group of experts, and in the content method, the calculations related to the AVE and CR coefficients related to the validity of the questionnaire items were confirmed. The reliability coefficient was confirmed using Cronbach's alpha test. In order to analyze the data in the qualitative part, Delphi technique was used. Results: The findings showed that the effective factors on educational justice, which includes three dimensions (1-distributive justice, 2-interactive justice, 3-procedural justice), amount to 45% in the dynamics of students, which includes two dimensions (1- They are technology-oriented, 2-learner-oriented, they have an effective role. Conclusion: According to the results obtained, it was suggested that Payam Noor universities of Tehran province should act step by step and gradually according to a road map and a long-term strategic plan, and in order to implement it, it is necessary to develop an operational plan. has it. Having a paradigmatic approach with a local and regional perspective also helps to realize it.
Unveiling Latent Spaces with Variational Autoencoders

Variational Autoencoders learn the latent representation space of the input domain by learning to compress and decompress from a stochastic latent representation space. This allows the information contained in the sample to be compressed into a lower dimension. Therefore, when used on classifiable datasets, it can retain the classification factors when transformed into the latent vector. By learning the conditional distribution within the latent space of each label set within the domain, it is possible to perform categorical generation of artificial samples. In this work, we study the MNIST dataset and demonstrate a novel approach to conditional generation and that even a simple statistical analysis instead of using complex generators can result in a high-accuracy generative model.
Bridging the Gap in Phishing Detection: A Comprehensive Phishing Dataset Collector

To combat phishing attacks - aimed at luring web users to divulge their sensitive information - various phishing detection approaches have been proposed. As attackers focus on devising new tactics to bypass existing detection solutions, researchers have adapted by integrating machine learning and deep learning into phishing detection. Phishing dataset collection is vital to developing effective phishing detection approaches, which highly depend on the diversity of the gathered datasets. The lack of diversity in the dataset results in a biased model. Since phishing websites are often short-lived, collecting them is also a challenge. Consequently, very few phishing webpage dataset repositories exist to date. No single repository comprehensively consolidates all phishing elements corresponding to a phishing webpage, namely, URL, webpage source code, screenshot, and related webpage resources. This paper introduces a resource collection tool designed to gather various resources associated with a URL, such as CSS, Javascript, favicons, webpage images, and screenshots. Our tool leverages PhishTank as the primary source for obtaining active phishing URLs. Our tool fetches several additional webpage resources compared to PyWebCopy Python library, which provides webpage content for a given URL. Additionally, we share a sample dataset generated using our tool comprising 4, 056 legitimate and 5,666 phishing URLs along with their associated resources on GitHub [1]. We also remark on the top correlated phishing features with their associated class label found in our dataset. Our tool offers a comprehensive resource set that can aid researchers in developing effective phishing detection approaches.
A coercion-resistant online voting protocol based on blind signatures and fake credentials

In this paper, we present an initial proposal for a practical implementation of an online voting system based on blind signatures, to provide both ballot secrecy and integrity, together with universal verifiability. Moreover, the proposed system offers coercion-resistance by means of a credential scheme based on fake passwords, that the voter can use when under coercion. The idea is that fake passwords seem indistinguishable from valid passwords, and by allowing the voter to use an arbitrary number of fake passwords, no adversary is capable of determining if a coerced voter is casting a valid ballot or a fake ballot.
E-Prevention: The ICASSP-2023 Challenge on Person Identification and Relapse Detection from Continuous Recordings of Biosignals

The e-Prevention challenge concerns the analysis and processing of long-term continuous recordings of biosignals recorded from wearable sensors, i.e., accelerometers, gyroscopes and heart rate monitors embedded in smartwatches, as well as sleep information and daily step count, in order to extract high-level representations of the wearer’s activity and behavior, termed as digital phenotypes. The ability of these digital phenotypes to quantify behavioral patterns and traits will be evaluated in two different tasks: 1) Person Identification, and 2) Relapse Detection in patients in the psychotic spectrum. The long-term data that will be used in this challenge have been acquired during the course of the e-Prevention project, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders (i.e, schizophrenia and bipolar disorder). Specifically, the data were continuously collected from patients for a monitoring period of up to 2.5 years, while from the control subgroup for a period of 3 months, constituting one of the largest of its kind ever recorded.
Experimenting with open science practices at the STI 2023 conference

The adoption of open science practices has become a prominent topic of study for the science studies community. However, the research practices of the community itself are still quite traditional.
Where do scholars move? Measuring the mobility of researchers across academic institutions

In recent years, bibliometric databases have substantially improved the consistency and quality of the metadata extracted from publications, particularly the author-affiliations linkages from scientific publications. Furthermore, author-name disambiguation algorithms have been implemented for most large bibliometric databases, such as Web of Science, Scopus, and Dimensions.
Optimizing Solar Energy Harvesting: Supervised Machine Learning-Driven Peak Power Point Tracking for Diverse Weather Conditions

Solar Power is one of the significant prevalent forms of clean energy due to its perceived to be pollution-free and easily accessible. The market for renewable energy was established by the rapid development in electrical energy consumption and the diminution of conventional energy resources (CER). Under varying weather condition extracted energy from solar system is not constant and maximum. This study suggests the applicability of machine learning algorithm (MLA) in Peak power point tracking (P3T) methods to maximize power of a PV arrangement under varying weather conditions. Machine learning methods optimize peak power point tracking in solar photovoltaic systems by bringing agility, data-driven decision-making, and increased accuracy. MLAs improve the overall efficiency, stability, and dependability of these systems by handling the unpredictability of solar energy production under varying weather circumstances and PSCs Because MLAs are able to learn and adjust to non-linear relationships between solar intensity and PVS output. In this study, the squared multiple squared exponential Gaussian process regression method SGPRA tested in three rapidly varying ecological conditions. The performance of ML-P3T methods is validated using Matlab/Simulink, and the simulation outcome are compared with one of the most used algorithms, the variable step size incremental conductance algorithm (VINA). The Matlab/Simulink findings show that SGPRA operates significantly better under varying weather circumstances, harnessing more peak power efficiency 90%, shorter tracking time 0.13 sec, a mean error of 0.042, and superior stability.