Reviewerly/exHarmony huggingface dataset:
Assessing the Influence of Service and Government Performance on Public Satisfaction

This study aims to analyze the influence of government and service performance on customer satisfaction. This study is crucial because it presents a new and previously neglected viewpoint in academic circles by highlighting the importance of efficient service delivery and effective government performance in improving consumer satisfaction using a random sampling technique. These findings have significant relevance for the Abu Dhabi Police Department, as they emphasize the imperative of ongoing progress in police services to not just meet, but beyond, public expectations. The findings of this study are based on the assumption that enhancing police service performance is essential for attaining long-term organizational objectives and cultivating a more robust rapport with the public. This is consistent with the overarching goal of the Abu Dhabi Police Department to uphold public safety while maintaining the provision of top-notch services; the findings present substantial practical and theoretical consequences. Essentially, it offers a structure for the Abu Dhabi Police Department to increase its approaches to providing services, consequently enhancing the way the public perceives and feels about their services. In theory, this enhances the current literature by presenting a novel model that can serve as a standard for future research in this domain. Although this research has made valuable contributions, it also recognizes specific limitations, which provide opportunities for further academic investigation. These restrictions encourage scholars to delve deeper into and broaden the literature, perhaps resulting in more extensive and nuanced insights into the connections between government performance, service quality, and consumer happiness within police agencies.
Multi-Visit Interactive Recalibration Network for Drug Recommendation with a Triple Graph Encoder

Electronic health records (EHRs) comprehensively describe the health status of many patients during their visits. Combining the records in EHRs with the patient’s current medical treatment status can generate personalized medication combinations. However, the increasing number of drugs poses significant challenges for clinical experts to recommend combination drugs. In recent years, deep learning models have been widely studied and applied in drug recommendation task. Currently, the existing models either lack sufficient mining of patients’ health data or ignore the modelling of patients’ longitudinal medical information. Therefore, we propose a multi-visit interactive recalibration network (MIRNet) for drug recommendation with a triple graph encoder. Specifically, we design a medical recalibration module to capture the feature representations in patient diagnosis and procedure information through cascaded convolutions. To achieve friendly interaction of medical codes between relatively necessary historical visits and the current visit in the drug recommendation process, we propose a multi-visit filter module. Furthermore, we design a triple graph encoder to fuse molecular, EHR, and Drug-Drug Interation (DDI) graphs, which aims to extract implicit drug feature representations from different medical knowledge. We perform experiments on the real-world MIMIC-III dataset, and the experimental results reveal that the model MIRNet outperforms other competitive baselines regarding major indicators.
Development of Trans-nail PPG Controller Using Fingertip Blood Volume Changes to Enable Highly Accurate Motion Prediction

The widespread adoption of Virtual Reality (VR)/Augmented Reality (AR) has increased the demand for versatile controllers for VR/AR. Most conventional controllers for VR/AR have issues with a lack of immersive sensation, comfort, and stability. This study proposes a trans-nail PPG (photoplethysmography) controller using fingertip blood volume changes to enable accurate motion prediction. This controller utilizes the fact that the mainly DC component of the PPG signal varies with the state and movement of the hand and fingers. Based on reservoir computing, this controller also learns the PPG signals under various conditions and predicts the state and movement of the hand and fingers. Since this controller is attached to the fingernail, it does not compromise immersion, comfort, or stability. Three PPG sensor circuits in the trans-nail PPG sensor were designed, prototyped, and assembled with an LED on a nail-sized PCB. The output signals of the PPG sensor for various motions such as thumb raising and lowering, wrist raising and lowering, and wrist twisting were used for the learning and predictions of reservoir computing. As a result, the motion predictions with accuracies of over 90% were achieved successfully.
Harnessing Information Communication Technology in the Development of Oil and Gas Business in Nigeria

The purpose of this study is to investigate the impact of Information Communication Technology in effectively managing the logistics and supply chain business of a 4th party logistics oil and gas service firm with a focus on TECON Oil Services Limited. This is predicated on the fact that Logistics is the process of ‘strategically managing the acquisition, movement and storage of materials, parts and unfinished inventory (and the related information flows) through the organization and its marketing channels in such a way that current and future profitability is maximised through the cost effect fulfilment of orders’. A structured questionnaire was duly administered to selected management staff of the company with 60.7% retrieval rate. The collated responses were analysed and the hypotheses were tested using the Eview software. At 5% level of significance, test of all five hypotheses showed p-values less than 0.005 (P &lt; 0.05), indicating positive relationship between the tested ICT parameters and effective business performance. With overall significant regression, the coefficient of determination ranged between 87.9% and 99.7% of variation in the model. In conclusion therefore, the study ascertains a positive relationship between effective ICT and the Logistics and Supply Chain management of the firm’s business operations. It further recommended, among others, the need for the development of policies in the area of ICT to support the performance of various logistics networks for effective overall performance of the sector. Additional or further studies are required on the impact of effective supply chain management in the Oil and Gas Sector of the Nigeria Economy.
КАЗАХСТАНСКИЙ И ЗАРУБЕЖНЫЙ ОПЫТ ЦИФРОВИЗАЦИИ УНИВЕРСИТЕТОВ

The article is devoted to Kazakhstani and foreign experiences in the field of digital transformation of universities. The transitional experiences of domestic and universities abroad, the appropriate research published in open sources, the advanced university’s policy and original studies led to the categorization of digitalization experiences of the world universities in online support, in personalization and interaction along with the missing components of digitalization at Kazakhstani universities. Comparative analysis of IITU’s experience of digitalization and the world technical universities, in particular the Heriot-Watt University, the high ranked Scottish and British higher school as the best universities in the world allow to sum up the ways of transformation of the Kazakhstan University. According to Independent Distance Learning Resources Policy, the general elements and their functions were classified. A number of web-based applications available to perform key tasks such as remote e-mail access, use of a virtual learning environment, online tuition and residency fees were presented.
Towards Dynamic Causal Discovery with Rare Events: A Nonparametric Conditional Independence Test

Causal phenomena associated with rare events occur across a wide range of engineering problems, such as risk-sensitive safety analysis, accident analysis and prevention, and extreme value theory. However, current methods for causal discovery are often unable to uncover causal links, between random variables in a dynamic setting, that manifest only when the variables first experience low-probability realizations. To address this issue, we introduce a novel statistical independence test on data collected from time-invariant dynamical systems in which rare but consequential events occur. In particular, we exploit the time-invariance of the underlying data to construct a superimposed dataset of the system state before rare events happen at different timesteps. We then design a conditional independence test on the reorganized data. We provide sample complexity bounds for the consistency of our method, and validate its performance across various simulated and real- world datasets, including incident data collected from the Caltrans Performance Measurement System (PeMS).
Visibility-Constrained Control of Multirotor via Reference Governor

For safe vision-based control applications, perception-related constraints have to be satisfied in addition to other state constraints. In this paper, we deal with the problem where a multirotor equipped with a camera needs to maintain the visibility of a point of interest while tracking a reference given by a high-level planner. We devise a method based on reference governor that, differently from existing solutions, is able to enforce control-level visibility constraints with theoretically assured feasibility. To this end, we design a new type of reference governor for linear systems with polynomial constraints which is capable of handling time-varying references. The proposed solution is implemented online for the realtime multirotor control with visibility constraints and validated with simulations and an actual hardware experiment.
Applying Sentiment Analysis on Children’s Stories

Today in the world, the rapid and widespread adoption of modern technologies has changed the way that we can access information from news, conversations, and exchange of views about any topics that could attract people's attention. Current communication on the Internet allows people in the community to present and share their ideas in publications and interact with others on different subjects. These data contain a large amount of common knowledge that can be used in scientific and humanitarian research to generalize the benefit to all people everywhere. With the advancement of artificial intelligence and its branches and by using these data, we can solve the various problems that face us in life. Natural language processing is one of the most important issues that have arisen in parallel with the developments of artificial intelligence. Sentiment analysis is regarded as a computational technique that aids in determining and evaluating people's emotions through written documents. Therefore, in this study, we will focus on the sentiment analysis by applying Random Forest and Decision Tree algorithms on the dataset containing children’s stories. The results showed that the accuracy rate attained by Random Forest was 98.32% while Decision Tree gave 97.74%.
Application of Big Data-based Information Technology in Business English Courses in Colleges and Universities

Abstract In this paper, after using Cool Edit Pro2.1 software to complete the speech signal acquisition work, the DTW method is used to pre-emphasize the speech signal. Due to the small continuous correlation between adjacent frames, the use of windowing and frame-splitting operation makes the amplitude change of the speech signal smooth and obtains the spectral envelope of the speech signal. The negative impact of MFCC feature parameter extraction is optimized by parameterizing the volume intensity and fundamental trajectory, and the application of the English scoring mechanism in business English education is explored. According to the current situation of business English teaching in colleges and universities, research problems and sampling objects are identified, and statistical analysis is applied to analyze the teaching effect of business English informatization by example. The results show that the speech evaluation system of spoken business English based on DTW does not differ much from the similar results of the scoring of the master’s degree of English majors in our university, and its difference value always stays between 1 and 10. In terms of the simple correlation coefficient and significance level (0.01), the simple correlation coefficient between teaching effectiveness and the use of technological media is 0.385 (P=0.00), which means that there is a significant correlation between both teaching effectiveness and the use of technological media. This study improves the standardization of English pronunciation among students, which is important for the development of business English in colleges and universities.
Development of A Multi-Level Data Security System for Internet of Things using Dimensionality Reduction Techniques and Machine Learning

With the increase in the use of the Internet, most applications now have an online presence, which has increased Internet of Things-based applications. Therefore, implies that most of our applications could be exposed to malicious activity with-in the network, and the burden of protecting the network and detecting any attack quickly becomes more significant as new attacks surface and cyberattacks in-crease. The development of a multi-level data security system for the Internet of Things using dimensionality reduction techniques was first realized by analyzing existing data security systems. This was achieved by developing a multi-level data security system using Dimensionality Reduction Technique. Machine Learning Technique was used to dynamically detect malicious activity beyond the Intruder Detection System (IDS). Firstly, Principal Component Analysis (PCA) was used to authenticate the status of the source of data transmitted. The first layer of IDS was achieved using Naïve Bayes classifier, while Random Forest was adopted in the second layer. The work was able to identify the normal and malicious packets in the first layer, while in the second layer, the type of attacks on the IoT network were identified. Finally, PCA enhanced security by classifying malicious packets. The results demonstrate that our proposed PCA detection model gives a relatively high Precision rate with 95%, 94.4%, 97.5%, 95%, and 96% for the Normal, Flooding, Scheduling, Gray hole, and Blackhole attacks, respectively. The re-search shows that the PCA Technique performed well when deployed, and the test with relevant performance evaluation metrics shows better accuracy regarding detecting malicious Nodes.
Words, Acronyms, Languages, and Perceptions: The Human Mind, Life, and Semantics Interoperability

From the time I imigrated to the United States, I have always been very surprised by the constant use of acronyms in schools, industries, and government. In November 1978, I started my first full-time job with International Business Machines Corporation (IBM) after finishing my doctorate at University of California, Los Angeles (UCLA). I was to be part of a national team that tried to understand and define the computer requirements of faculty, particularly researchers at Caltech, University of Southern California (USC), and UCLA, on how they would use computers. Because of my biomedical engineering (BME) background, I was assigned to the healthcare/hospital marketing team. During my first week, I overheard a team member say: “Just came back from the LA County-USC Medical Center and there is a problem in the CCU. ”
Glass Packaged Mineral Water Recognition System Based On Logo Using The Histogram Of Oriented Gradients Method

Application of Glass Packaged Mineral Water Recognition System Based on Logo Using Method Histogram of Oriented Gradients is an application program used to introduce brands or logos of glass bottled mineral water. This application was designed on the Windows operating system and uses Python and Open CV software. The methods used in this design are Histogram of Oriented Gradients as a method for feature extraction, Method Euclidean Distance as a method for measuring similarity distance, and k-Nearest Neighbor as a method of recognizing a logo or brand. The average accuracy of the test is 13.575%.
Electronic Archive Design With RC4 Cryptographic Based File Security

The transformation of archival document storage systems is starting to shift from physical formats that require a lot of space and storage equipment to the electronic or digital realm (often called electronic archives). This is considered to reduce the costs of procuring equipment and storage space. In line with changes in paperless storage patterns, the issue of data security and confidentiality becomes important, so that information from documents to be archived can be maintained and cannot be used by irresponsible people. One technique for securing documents digitally is to use cryptography and the algorithm chosen is Rivest cipher 4. The RC4 (Rivest Chiper 4) algorithm was chosen because the execution speed in file encryption is faster than other algorithms. This article aims to implement the RC4 algorithm into an electronic archive (e-Archieve) application. The application development method uses the waterfall method with 5 stages. The application was built using the PHP programming language and MySQL database, as well as the Rivest Cipher 4 cryptographic algorithm. The result of the application development is an electronic archive website. Every file uploaded to the server can be encrypted by the admin. Encrypted files will change to random characters like a virus. The application was tested using black box testing techniques, where all features worked as expected
ANALISIS BIBLIOMETRIK DALAM PENELITIAN PERANGKAT LUNAK MIKE 21 DALAM INDUSTRI KONSTRUKSI

Mike 21's technological developments contribute to the progress of infrastructure and the living environment, providing efficient and sustainable solutions. This study aims to measure the development of the use of Mike 21 software in the field of civil engineering in Indonesia. The study used bibliometric analysis methods with the help of VOSViewer software. Further analysis of related terms resulted in 237 terms, 204 of which were selected for mapping visualization that considered relevance and correlation with Mike 21’s research. The mappings prove that the development of research in the field of civil engineering has direct correlations with the use of Mike 21 software. The results of the research through a literature review using the Publish or Perish reference management application, identified 991 Google Scholar indexed journal articles relevant to the theme 'Mike 21 in civil engineering publication development in Indonesia'. The study matrix found that the entire article had a total of 22,227, with an average of 22.32 citations per article and an annual average of 2.469.67 and the highest number of publications published in 2020. Research that uses Mike 21 in the field of Civil Engineering is on the rise, making a positive contribution to future infrastructure and living environmental progress
Dynamic Mining Interval to Improve Blockchain Throughput

Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities i n these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining Interval (DMI) mechanism that adjusts mining intervals in response to block size and trading volume to enhance the transaction throughput of Blockchain platforms. Besides, in the context of public Blockchains such as Bitcoin, Ethereum, and Litecoin, a shift towards transaction fees dominance over coin-based rewards is projected in near future. As a result, the ecosystem continues to face threats from deviant mining activities such as Undercutting Attacks, Selfish Mining, and Pool Hopping, among others. In recent years, Dynamic Transaction Storage (DTS) strategies were proposed to allocate transactions dynamically based on fees thereby stabilizing block incentives. However, DTS’ utilization of Merkle tree leaf nodes can reduce system throughput. To alleviate this problem, in this paper, we propose an approach for combining DMI and DTS. Besides, we also discuss the DMI selection mechanism for adjusting mining intervals based on various factors.
Drift-aware Anomaly Detection for Non-stationary Time Series

Anomaly detection of time series is vital in various scenarios with explosively growing time series data. However, the non-stationary time series degrade the performance of current anomaly detection methods, where data drift causes unpredictable changes. This paper proposes a Drift-aware Anomaly Detection (DAD) method for detecting anomalies in non-stationary time series. DAD adopts a self-attention mechanism to learn an embedding, distinguishing the anomaly embeddings from the normal embeddings. Next, the KL divergence calculates the drift deviation between two data segments at adjacent periods. Then, the drift deviation module combined with the latent vector which is used to reconstruct the original vector. During the encoding stage of the time series, the latent code is modeled using different Gaussian mixture distributions and the data reconstruction error at each time tick is regarded as an anomaly metric. Furthermore, we propose a new metric to measure the degree of drift deviation for a dataset used for a fair experiment comparison. Experimental results on several public datasets and a newly collected sensor dataset demonstrate that for the non-stationary time series anomaly detection task, DAD outperforms state-of-the-art anomaly detection models up to 11.5% on the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score.
Ethical Hacking in Digital Forensics

The continuous adoption of technology in our daily lives has created an avenue for malicious individuals to exploit, especially as we regularly interact with these devices, sharing sensitive information such as health records, financial data, and personal details. Consequently, in our technologically advanced world, it is highly probable that crimes will encompass electronic devices, necessitating digital forensics investigations to unearth vital information for resolving these cases. However, due to the advances of these devices, digital forensic analysts may need to utilize hacking skills to either get into and/or investigate these devices. For the evidence acquired to be deemed acceptable in court, these hacking skills must be ethical. Ethical hacking in the field of digital forensics has become increasingly prevalent and valuable. By using the techniques and tools employed by the attacker, investigators can collect valuable digital evidence, preserve the integrity of evidence, and provide a comprehensive analysis for legal proceedings. However, the use of ethical hacking in digital forensics also presents ethical considerations. It is crucial to adhere to legal and ethical guidelines such as the anonymization of data, defining the scope of the investigation, and obtaining formal consent as part of ethical hacking activities. In this paper, we discuss the implications of using ethical hacking tools and procedures in digital forensic investigations, along with recommendations and guidelines to preserve the privacy, transparency, intellectual property, and data integrity of the evidence. Furthermore, we introduce four scenarios to show that ethical violations can occur during digital forensic investigations when the analyst uses hacking techniques.