Reviewerly/exHarmony huggingface dataset:
Facial Recognition using Locality Preserving Projection Algorithm

Facial Recognition Technology (FRT) has become the research object of many in the recent times considering the challenges with enormous increase in human population. The face as the main axis in social relations plays an important role in the representation of human identity, which requires an increased level of security and the creation of exchange tricks for safe and recognizable evidence and innovations of individual authentication. Apparently, facial recognition is widely adopted for security reasons, and the uniqueness of human characteristics has increased the popularity of facial recognition technology systems worldwide. Law enforcement agencies faced the problem of the impossibility of proper investigation of criminal cases. Suspects are often difficult to catch and the wrong persons may be arrested due to the basis and methods adopted for the investigation. This study proposes a facial recognition system for the identification of criminals using Locality Preserving Projection (LPP) Algorithm. The development of the system involves specification of the functions resulting from the performance analysis obtained from other related systems, and the translation of the developed model into the design of the proposed system. The framework was evaluated by matching the faces of people extracted through special cameras with the images of people on a watch list. Watch lists that contain images of people, including people not suspected of wrong doings. Strategically, the implementation of the facial recognition system was achieved with Locality Preserving Projection (LPP) Algorithm to improve the feature extraction methods and dimensionality reduction techniques. Cascading Style Sheets (CSS) was used for describing the presentation of the document written in HTML language and JavaScript for the front-end for optimum web compatibility. A Python binding of the cross-platform Qt GUI toolkit and Python plugin were used to implement the graphical user interface for detecting and recognizing images and the presence of an individual upon entering the database library. Interestingly, the developed framework recorded accuracy and recognition &gt;95% capacity under normal conditions, including lighting and distance from camera and at a reasonably cheaper cost in comparison with previously proposed Facial Recognition Technology
impact of using smart applications, “Sehaty Application”, on health care at primary care center in Al-Qurayyat Region

The research aimed to investigate the impact of using smart applications, specifically the "Sehhaty" application, on healthcare in primary care centers in the Al Qurayyat province. The study used a descriptive-analytical methodology and relied on an electronic questionnaire as a tool for data collection. The study sample consisted of 213 residents of Al Qurayyat and 211 individuals from major cities. The study found that the most common method used to obtain healthcare services inside and outside the province was through the "Sehaty" application, but the residents of Al Qurayyat were the least likely to use it. The study also found that the level of knowledge and ease of use of the application was slightly lower among residents of the province. Additionally, the study found that residents of the province benefited more from the services of the application and that residents of major cities were more satisfied with the application's services. The most significant problem encountered by both residents of the province and major cities when using the application was the need for a high-speed internet connection. The study recommended several measures to optimize the use of the application.
Using Support Vector Machine For Classification And Feature Extraction Of Spam In Email

We provide an overview of recent and successful content-based e-mail spam filtering algorithms in this article. Our main focus is on spam filters based on machine learning and variants influenced by them. We report on significant ideas, methodologies, key endeavors, and the field's current state-of-the-art. The initial interpretation of previous work demonstrates the fundamentals of spam filtering and feature engineering in e-mail. We finish by looking at approaches, procedures, and evaluation standards, as well as exploring intriguing offshoots of recent breakthroughs and proposing directions of future research.
Big Data Information System Helps Online System Research and Development of Education Reform in the New Era

This paper analyzes the effective combination of extreme curriculum education big data and school teaching reform from the perspectives of teaching management, subject lesson preparation, classroom teaching, after-school tutoring, home-school co-education, etc.It is possible to accurately improve teachers' ability to interpret the curriculum standards and professional level.Especially in the situation of "tight days", it is more necessary to use technological innovation to improve the quality of teaching.Entering higher vocational education in the context of the Internet era, and using artificial intelligence to improve management teaching programs, we should correctly understand the role of artificial intelligence technology in daily life.In the process of education and teaching, teachers should change traditional concepts, adapt to the development of the times to learn new technologies, and improve teaching methods to help students develop.
An Efficient Memory Management Method for Embedded Vector Processors

For processors with vectorial computing units like DSP, it is very important to ensure vector load/store operations alignment of memory blocks, and minimize space wastage when making memory allocations. In this paper, we design and implement a memory management method, vector memory pool, suitable for embedded vector processors. By partitioning an entire block of memory space into many aligned vector objects and making efficiently use of vector processing units, the processing of memory manipulation library functions such as memset/memcpy is accelerated. The implementation and comparative verification of vector memory pool on RT-Thread Nano based on SWIFT DSP was completed, and the running efficiency reached a tens of times improvement compared to the original method.
A Self-Organizing Multi-Layer Agent Computing System for Behavioral Clustering Recognition

Video behavior recognition often needs to focus on object motion processes. In this work, a self-organizing computational system oriented toward behavioral clustering recognition is proposed, which achieves the extraction of motion change patterns through binary encoding and completes motion pattern summarization using a similarity comparison algorithm. Furthermore, in the face of unknown behavioral video data, a self-organizing structure with layer-by-layer accuracy progression is used to achieve motion law summarization using a multi-layer agent design approach. Finally, the real-time feasibility is verified in the prototype system using real scenes to provide a new feasible solution for unsupervised behavior recognition and space-time scenes.
Evidence from Iraqi Banks on the Reporting of Sustainability Determinants and Their Impact on Market Returns

The objective of this study is to define the concept of sustainability, its dimensions, and the importance of reporting it; to measure the level of reporting on the dimensions of sustainability for banks listed on the Iraq Stock Exchange; and to determine the effect of the level of reporting on market returns and the cost of equity financing. A sample of commercial banks registered on the Iraqi market was chosen for the study objective. The study relied on the applied side of the employment of environmental, social, and governance (ESG) indicators and the standards of the Global Reporting Initiative (GRI) to analyze the content of the reports of the commercial banks and to determine the extent of their commitment to reporting on the dimensions of sustainability. Commercial banks issued ten (10) securities during the period (2016–2021). The market returns of shares were calculated using the annual closing and opening share prices. Between weak and very weak, the general average level of reporting information related to the economic pillar was 34.22. In comparison, it was 7.23 for the environmental and 15.70 for the social pillars. The governance axis had a reporting rate of 24.95%, while the average reporting rate for other public disclosures was 43.72%. The study also uncovered a direct and statistically significant relationship between the market returns of the study sample institutions' shares and the dimensions of sustainability.&#x0D;
The Transformation of Talent Training in Cultural Industry in Colleges and Universities under the Background of Epidemic Normalization

According to the analysis of the development speed and situation in recent years, the progress of the national economy and comprehensive strength.The demand for highquality and high-capacity talents is increasing.However, the emergence of the epidemic at the beginning of 2020 has had a huge impact on the development of talent cultivation.As an important tray for the long-term development of China's culture, the cultural industry talent cultivation is particularly important.At present, the epidemic situation has entered the normalization stage, and there are a series of problems in the development of talent cultivation in the cultural industry, such as the importance of education activities on campus over society, the importance of education work on theory over practice, the importance of cultivation methods on tradition and the lack of innovation.The existence of these problems greatly limits the effectiveness of talent cultivation in the cultural industry in colleges and universities.For this reason, this paper makes a diversified analysis of the cultivation of talents in the cultural industry of colleges and universities from the aspects of integration of industry and education, project practice, and discipline integration, hoping to form an effective reference for the relevant talent cultivation work.
Upaya Meningkatkan Hasil Belajar Siswa Kelas X-MIPA-2 Melalui Metode Observasi Yang Divariasikan dengan LKS Word Square pada Materi Klasifikasi Hewan di SMAN 1 Bluto Tahun Pelajaran 2018-2019

Abstract&#x0D; &#x0D; Based on the observations of class X-MIPA-2 SMAN 1 Bluto, most of the learning atmosphere is still monotonous and student activity is lacking. For that we need an effort that can increase the effectiveness of student learning. This research was conducted in class X-MIPA-2 because the characteristics of learning outcomes in Animal Classification materials are still low. Efforts to improve the quality of learning are through various methods and according to the material. This research was designed through Classroom Action Research (CAR). The purpose of this study was to determine the increase in learning outcomes of Animal Classification material through the application of the observation method which was varied with Wordsquare worksheets in class X-MIPA-2 SMAN 1 Bluto. The subject of this research is class X-MIPA-2 SMAN 1 Bluto with 40 students. This research was conducted in two cycles. Each cycle consists of 4 stages, namely: planning (planning), implementation (acting), observation (observing), and reflection (reflecting). The indicators of the success of this research are (1) an increase in the percentage of students who get a score of 65 or the number of students who study thoroughly increases to 85%, (2) completeness of classical activities 75%. Based on the results of the study, it was obtained that the percentage increase in the achievement of classical learning mastery in the first cycle was 77.5% and the second cycle was 87.5%, while the classical activity in the first cycle was 61.25% and the second cycle was 76.25%. The conclusion of this study is that through the application of the observation method that is varied with the Wordsquare worksheet on the Animal Classification material, it can improve student learning outcomes in class X-MIPA-2. Meanwhile, the suggestion of this research is that the observation method that is varied with the Wordsquare worksheet needs to be applied to other biological materials because the observation method that is varied with the Wordsquare worksheet makes it easier for students to understand the material being studied.&#x0D; Kata kunci: Hasil Belajar, Metode Observasi Variasi LKS Word Square, Materi Klasifikasi Hewan
A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning

As a new distributed machine learning (ML) approach, federated learning (FL) shows great potential to preserve data privacy by enabling distributed data owners to collaboratively build a global model without sharing their raw data. However, the heterogeneity in terms of data distribution and hardware configurations make it hard to select participants from the thousands of nodes. In this paper, we propose a multi-objective node selection approach to improve time-to-accuracy performance while resisting malicious nodes. We firstly design a deep reinforcement learning-assisted FL framework. Then, the problem of multi-objective node selection under this framework is formulated as a Markov decision process (MDP), which aims to reduce the training time and improve model accuracy simultaneously. Finally, a Deep Q-Network (DQN)-based algorithm is proposed to efficiently solve the optimal set of participants for each iteration. Simulation results show that the proposed method not only significantly improves the accuracy and training speed of FL, but also has stronger robustness to resist malicious nodes.
Exploring the Effects of SEONMEDI VR Nature Meditation on Simulator Sickness and Presence

이 연구는 우리 고유의 선(仙)명상과 가상현실(VR) 기술을 결합하여 사회적 위기 상황 속에서 정신 질환의 관리와 치료를 위한 VR자연명상과 애플리케이션 ‘선메디’를 개발하고, 디지털 치료제로서의 발전 가능성을 확인하기 위해 사용자 경험을 조사하였다. 한국의 자연 풍경을 360도 실사 촬영하는 방식으로 4개의 가상 자연을 구현하였으며, 품질에 대한 지표를 확인하기 위하여 대학생 16명을 대상으로 2주간 2회씩 VR자연명상을 체험하게 한 후, 사이버 멀미, 실재감, 만족도 검사를 각각 실시하였고, 평균 및 표준편차를 비교하였다. 연구결과 첫째, 선메디 VR자연명상 체험 시 사이버 멀미로 인한 불편감이 크지 않은 것으로 나타났다. 둘째, VR자연 명상의 실재감 평균이 높은 것으로 나타났다. 셋째, VR자연명상의 만족도 점수가 매우 만족하는 것으로 조사되었다. 이에 따라 디지털 치료제의 영역에서 선메디 VR자연명상을 활용한 치료 개입이 새로운 대안이 될 수 있음을 제안하였다. This study aimed to develop a VR natural meditation application called "SEONMEDI" that utilizes a form of Korean Seon meditation in a virtual reality(VR) setting to manage and treat psychological disorders during times of acute distress. The study investigated the user experience of Seonmedi to examine its potential role in the future of digital therapeutics. The study utilized four virtual environments that featured areas of outstanding natural beauty in South Korea, rendered with 360-degree real-life photography. Sixteen university students participated in the research, checking in twice a week. Researchers measured quality indicators, such as instances of simulator sickness, degree of "presence," and volunteer satisfaction. First, the study found that the simulator induced very few instances of simulator sickness in the volunteers. Second, volunteers recorded high scores for "presence" throughout their VR natural meditation sessions. Finally, users reported very high satisfaction scores. As a result, SEONMEDI VR natural meditation is recommended as a promising alternative treatment intervention in the field of digital therapeutics.
Bitcoin and Stock Returns: An Empirical Study

This paper investigates the profitability of Bitcoin and US equity. More concretely, we inspect the performances of the S&P 500 index and Bitcoin by comparing their returns and volatilities. As a result, we obtain the following significant findings. First, our regression analysis clarifies that for the period after the sudden appearance of COVID-19, there was a weak nexus between the S&P 500 index and Bitcoin returns. In addition, our return and return spread analysis evidences that for this period, on average, Bitcoin returns were much higher than the S&P 500 index returns. Moreover, our volatility and volatility spread analysis reveals that for this period, on average, the volatilities of Bitcoin returns were much higher than those of the S&P 500 index returns.
Analysing Changes in Official Use of the Design Concept Using SweCLARIN Resources

We investigate changes in the use of four Swedish words from the fields of design and archi- tecture. It has been suggested that their meanings have been blurred, especially in governmental reports and policy documents, so that distinctions between them that are important to stakeholders in the respective fields are lost. Specifically, we compare usage in two governmental public reports on design, one from 1999 and the other from 2015, and additionally in opinion responses to the 2015 report. Our approach is to contextualise occurrences of the words in different representations of the texts using word embeddings, topic modelling and sentiment analysis. Tools and language resources developed within the SweClarin infrastructure have been crucial for the implementation of the study.
Passive Radar Tracking in Clutter Using Range and Range-Rate Measurements

Passive bistatic radar research is essential for accurate 3D target tracking, especially in the presence of missing or low-quality bearing information. Traditional extended Kalman filter (EKF) methods often introduce bias in such scenarios. To overcome this limitation, we propose employing the unscented Kalman filter (UKF) for handling the nonlinearities in 3D tracking, utilizing range and range-rate measurements. Additionally, we incorporate the probabilistic data association (PDA) algorithm with the UKF to handle cluttered environments. Through extensive simulations, we demonstrate a successful implementation of the UKF-PDA framework, showing that the proposed method effectively reduces bias and significantly advances tracking capabilities in passive bistatic radars.
DEVELOPMENT OF TEACHERS' PROFESSIONAL COMPETENCE IN THE CONDITIONS OF TERRITORIAL COMMUNITIES

According to Art.59 of the Law of Ukraine «On Education», educational institutions, in which teaching staff works, must promote professional development and advanced training. Taking into account the regular updating of the legislation on advanced training improving the teaching staff, the rapid development of e-education, increasing the teachers’ freedom to choose the forms, methods, and terms of advanced training for professional growth is discussed. The professional development of employees turns into a flexible subsystem of lifelong professional education. At the same time, opportunities for professional and personal development have been significantly expanded. It makes possible not only to develop professional competence effectively but improve qualifications, choose a direction of education and improve skills in accordance with the personal needs and interests of teaching staff. The changes that took place made it necessary to rethink traditional approaches in the professional development of teaching staff. Increased attention is paid to the training and retraining of teaching staff on the formation and development of their professional competences, lifelong education. In this context, teachers’ professional development is a purposeful, specially organized process of systematic updating of professional competence in the system of formal, nonformal and informal education. The current system of advanced training is a complex of means and processes of individual formation and professional needs in the form of course and inter-course periods. Currently, it is significantly updated in terms of content, forms, scientific and methodological support, as it is open and flexible.
Improved Model for Identifying the Cyberbullying based on Tweets of Twitter

The surge of cyberbullying on social media platforms is a major concern in today's digital age, with its prevalence escalating alongside advancements in technology. Thus, devising methods to detect and eliminate cyberbullying has become a crucial task. This research meticulously presents a refined model for identifying instances of cyberbullying, building on previous methodologies. The process of devising the model involved a thorough literature review, object-oriented design, and decision tree methodologies to shape the labelling procedure and build the classifier. Data pre-processing was executed using RapidMiner, considering six intrinsic components. The final model successfully classified Indonesian-language tweets into five distinct categories: animal, psychology and stupidity, disabled person, attitude, and general bullying, with an accuracy rate of 99.56%.
EE557 Artificial Intelligence: Optimizing the Efficiency of Screening Protocols for Women with Dense Breasts

Breast-cancer detection faces a ‘four-fold’ screening challenge in women with dense breasts. Firstly, greater breast density is associated with an elevated cancer risk. Secondly, X-ray mammography (XM) is less accurate at detecting breast cancer in women with dense breasts (Boyd et al. 2007). Thirdly, while guidelines recommend supplemental screening for women with dense breasts (Mann et al 2017), there is a lack of clarity on which supplemental modalities are preferred, and there is restricted access to supplemental modalities. Finally, assessment and reporting of breast density in XM is not performed, going against guideline recommendations across Europe (2015-2022, DenseBreast-info, Inc). This analysis outlines the potential for artificial intelligence (AI) technologies to improve health economic outcomes when using supplemental screening modalities in women with dense breasts.
EE439 Prevention of Needlestick Injuries and Blood Exposures Among Kenyan Healthcare Workers Using Safety Engineered Devices

Exposure to blood-borne pathogens poses a serious risk to health care workers (HCW). In Kenya, 76.6% of occupational exposures to HIV/AIDs among HCWs were attributed to needle stick injuries (NSIs). Blood exposures (BEs) are strongly associated with lack of safety device use and inadequate safety practices. Safety engineered devices (SEDs) can be a cost-effective solution as they can minimize the involved risks.
Bangla to English Translation Using Sequence to Sequence Learning Model Based Recurrent Neural Networks

Translation is a way to convert one language to another for effective communication. Researchers have discovered many machine translation methods to translate the human language. Natural Language Processing (NLP) is a breakthrough innovation to process human language. Since the Bangla language holds complex grammatical rules and an enormous vocabulary, it appears quite challenging to translate the Bangla language into another. In this paper, an efficient machine translator technique is proposed to translate the Bangla language to English. Instead of the traditional rule-based method, we develop a deep learning-based model for a successful machine translator. A sequence-to-sequence (Seq2Seq) learning model with attention layer-based recurrent neural networks (RNN) is utilized in the deep neural network model. Since the Seq2Seq-based learning for Bangla to English conversion is a recent model, a Seq2Seq-based Bangla to English database is developed for evaluating the algorithm. Experimental analysis shows the effectiveness in terms of the accuracy and precision of the algorithm.