Reviewerly/exHarmony huggingface dataset:
A Machine Learning Classification Paradigm for Automated Human Fall Detection

For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.&#x0D; The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.&#x0D; Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.
Comparative Analysis of Data Security and Cloud Storage Models Using NSL KDD Dataset

Cloud computing is becoming increasingly important in many enterprises, and researchers are focusing on safeguarding cloud computing. Due to the extensive variety of service options it offers, A significant amount of interest from the scientific community has been focused on cloud computing. The two biggest problems with cloud computing are security and privacy. The key challenge is maintaining privacy, which expands rapidly with the number of users. A perfect security system must efficiently ensure each security aspect. This study provides a literature review illustrating the security in the cloud with respect to privacy, integrity, confidentiality and availability, and it also provides a comparison table illustrating the differences between various security and storage models with respect to the approaches and components of the models offered. This study also compares Naïve Bayes and SVM on the accuracy, recall and precision metrics using the NSL KDD dataset.
Entanglement-enabled decoherence-free transmission oftwo-color photons through single mode fiber

We demonstrate decoherence-tolerant transmission of a Bell state through a single-mode fiber (SMF) using the photon frequency degree of freedom. To this end, a slightly non-degenerate polarization-entangled singlet , |Ψ − ⟩=(|HV⟩−|VH⟩)/2, is localized at the SMF as the depolarization channel subject to random noise due to incessant fiber bending. Two-photon beats and quantum state tomography jointly verify the absence of collective decoherence, showing that |Ψ − ⟩ is the sought-after one-dimensional decoherence-free subspace (DFS) pertaining to SMF. Efficient splitting and combining of photon streams in our DFS help outperform the DFS in time domain. This motivates us to attempt DFS-enabled fault-tolerant fiber transmission of biphoton qubits. Two-photon BB84 protocol is implemented in a polarization-maintaining fiber to which dephasing noise is relevant so that a two-dimensional DFS is appropriate. A low bit error rate 5.4% is achieved by encoding one-qubit information onto the biphoton state in spite of significant polarization fluctuation. Our scalable frequency-based DFS has a natural affinity for wavelength division multiplexing in fiber communication by design and as such is extensible to multi-particle entanglement.
Edge AI for Real-Time Video Analytics in Surveillance Systems

More and more surveillance systems are being used to increase security and safety for the general public. However, the conventional method of processing all video data in the cloud can be ineffective and slow response times. By performing video analytics at the network's edge, close to where the data is created, Edge AI is a promising new strategy that can address these issues.&#x0D; The most recent developments in edge AI for real-time video analytics in security systems are discussed in this paper. We discuss the different techniques that are being used, as well as the applications that are being enabled by edge AI. The paper also discusses the challenges and limitations of edge AI, and the future research directions in this area.
Lane Detection and Traffic Sign Detection using Deep Learning and Computer Vision for Autonomous Driving Research Using CARLA Simulator

Lane identification and traffic sign detection is the most challenging and promising problem for self-driving or autonomous vehicles with unintentional lane departure and ignorance of traffic signs being major contributing factors to motor vehicle collisions around the world. To tackle this problem the proposed work aims to detect both lane and traffic signs for autonomous vehicles. This article proposes semantic segmentation and object detection model for implementing Advanced Driver Assistance System (ADAS) applications. The applications are implemented using a variant of Convolutional Neural Networks (CNN) deep learning model such as SegNet and You Only Look Once (YOLO) algorithm. Due to dynamic and adverse environment conditions, devising and testing a system which yields effective performance in all urban driving scenarios is challenging. Hence the environment is set up virtually with the help of the Car Learning to Act (CARLA) simulator. With aid of developed models, lane and traffic signs were successfully detected and tested under various constraints. Obtained results are evaluated with various performance metrics. Models are deployed for separate created datasets. The semantic segmentation model developed for lane detection using SegNet gives a mean average precision (mAP) of 93.33%, an overall accuracy of 94.80%, F-score of 93.42% and a minimal error rate of 5.20%. Model developed for Traffic sign detection, a mean average precision of 93.67%, an accuracy of 95.56%, recall of 92.67%, F-score of 93.16% and error rate of 4.44% have been achieved.
Design of a Mobile Access Control Application to Optimize Security and Efficiency in the Entry and Exit of University Students in Peru

The main objective of this research work is to design a mobile access control application to optimize security and efficiency in the entry and exit of university students in Peru. For the development of this work, the RUP methodology was used and among its different stages the purpose of the work and the way in which this application was designed are explained. Also, we know that the technological advance with the passing of them is increasing is beneficial to use for problems that may arise in society, education, etc. in this case for the safety and efficiency of entry and exit of university students. Finally, a well-designed and properly implemented mobile application can be a powerful tool to optimize security and efficiency in the access of university students in Peru, thus contributing to a more effective and convenient educational experience for all involved.
Quantum Computing Algorithms for Solving Complex Mathematical Problems

The power of quantum mechanics, that is too complex for conventional computers, can be solved by an innovative model of computing known as quantum computing. Quantum algorithms can provide exponential speedups for some types of problems, such as many difficult mathematical ones. In this paper, we review some of the most important quantum algorithms for hard mathematical problems. When factoring large numbers, Shor's algorithm is orders of magnitude faster than any other known classical algorithm. The Grover's algorithm, which searches unsorted databases much more quickly than conventional algorithms, is then discussed.
Stochastic Gradient Deep Multilayer Neural Network based Linear Congruential Generative Cryptosystem for Secured Data Communication in Cloud

Cloud computing is a kind of distributed computing that use a vast network of interconnected resources accessible over the internet. Security is a crucial concern in cloud computing due to the fact that users save their data on the cloud for convenient access from any location and at any time. Consequently, many users are worried about safeguarding their sensitive data in an unsafe location. Therefore, cloud computing architecture requires an innovative cryptographic method that ensures the secrecy, authenticity, integrity, and non-repudiation of data transfer in the cloud. A new technique called SEMcrypt, which stands for Stochastic Gradient DEep Multilayer Neural Network based Linear Congruential Generative Cryptography, has been developed to enhance secure data transmission. SEMcrypt ensures higher data confidentiality and reduces the time required for communication between the cloud user (i.e., patient) and the server. The SEMcrypt approach has two distinct processes: categorization and secure data transport. Initially, the data is gathered from the patients and is used as input for the stochastic gradient regularised deep multilayer neural network. The deep neural network consists of one input layer, two hidden layers, and one output layer. At first, the information is gathered from the patients and sent to the input layer. Next, the patient data that has been gathered is examined in hidden layer 1 using the generalised Tikhonov regularisation function. The patient data that has been analysed is sent to hidden layer 2. The hyperbolic tangent activation function is used at that layer to classify the patient data. Subsequently, the categorised data undergoes encryption via the use of Linear Congruential Generative Goldwasser-Micali encryption, ensuring safe transfer of the data. Subsequently, the encrypted data is sent to the cloud server. The patient data is encrypted on the server side using the Linear Congruential Generative Goldwasser-Micali decryption technique to prevent unauthorised access or assaults. Consequently, the authorised recipient receives the unaltered information, which is then kept in the database for further analysis. Secured data transmission is achieved by ensuring better levels of data confidentiality and reducing the time required for the process. The experimental assessment focuses on criteria such as the time it takes to generate keys, the level of data confidentiality and integrity, the computing time, and the accuracy of categorization. The empirical findings demonstrate that our suggested SEMcrypt approach delivers efficient performance outcomes by attaining superior levels of data confidentiality and integrity within a minimal timeframe.
The role of electronic human resource practices in the sustainable human resource management

Through a theoretical framework, this study aimed to identify definitions of sustainable human resource management by covering the definitions proposed by many researchers and attempting to reach a new definition. Many previous studies have identified many benefits that can be achieved by using sustainable human resource management in organizations. In addition, this study summarized these benefits and identified the most frequently mentioned benefits by reviewing previous studies. The study measured the impact of Electronic -Human Resource practices on Sustainable Human Resource Management in the Iraqi Ministry of Higher Education and Scientific Research by serving 180 employees. The partial least squares (PLS) method in the Smart PLS statistical program (version 4.0.8.9) was used as a statistical method for data analysis. The results of the study showed a relatively acceptable effect of e-human resource practices on sustainable human resource management in the Ministry of Higher Education and Scientific Research. The most important recommendation of this study was that if the Ministry wants to encourage the adoption of new working methods such as sustainable human resource management, it should introduce digital technologies such as artificial intelligence and machine learning in the continuous development and training of employees.
Exploring Quantum Machine Learning Algorithms for Enhanced Data Analysis

The rapid increase of records generation throughout diverse domain names has necessitated the development of superior records analysis techniques. Quantum gadget mastering (QML) has emerged as a promising paradigm that harnesses the computational power of quantum systems to beautify information evaluation duties. This studies paper explores the software of quantum machine getting to know algorithms to cope with demanding situations in information analysis, highlighting their capacity for progressed performance and scalability. We provide an overview of key QML algorithms, talk their blessings, and gift case research demonstrating their effectiveness in actual-global statistics evaluation eventualities. Finally, we outline the cutting-edge kingdom of QML research and speak destiny directions for its software in records evaluation.
Reflections on the Legal Boundaries of Scientific Research Processing of Personal Data from a Comparative Law Perspective

This article aims to explore the legal boundaries of handling personal data in scientific research from a comparative law perspective. Currently, there are some deficiencies in the legal framework in China regarding the handling of personal data in scientific research. Therefore, it is worth considering the addition of provisions related to "scientific research" in the Personal Information Protection Law of the People's Republic of China. Additionally, it is possible to mitigate the personal rights of data subjects by appropriately exempting researchers from data processing obligations and imposing additional conditions, as well as adjusting the consent mechanism, in order to establish incentive-compatible requirements for data processing behavior. Through these improvement measures, it is hoped that clearer and more flexible legal boundaries can be provided for scientific research while respecting the rights of personal data subjects.
Research on the Cultivation Model of Innovation and Entrepreneurship Ability for Accounting Majors in the Context of Big Data

"Mass entrepreneurship and innovation" (hereinafter referred to as "mass entrepreneurship and innovation") is an important support for China's economic development. Relying on greater stimulation of market vitality and social creativity, it can withstand the downward pressure of the economy and maintain the long-term fundamentals of China's economy. On the other hand, it is also an important measure to enhance students' employment ability and expand the scope of employment. Enhancing students' awareness and ability of "innovation and entrepreneurship" is an important driving force in supporting the country's "growth and employment stability". Taking this as the starting point, the article first analyzes the problems in innovation and entrepreneurship education in the accounting profession under the current education system. Then, starting from the background of big data, it fully studies the impact of big data on the accounting profession, and analyzes the problems from three aspects: curriculum system construction, talent team construction, and teaching assessment method construction that meet the requirements of "innovation and entrepreneurship", Finally, a reform strategy for innovation and entrepreneurship teaching in accounting majors under the background of big data was proposed.
AN ANALYSIS OF STUDENTS’ ABILITY IN CONSTRUCTING CONDITIONAL SENTENCES (AT THE ELEVENTH GRADE STUDENTS OF SMA NEGERI 1 WARINGINKURUNG)

Today, many students struggle to construct proper English sentences, particularly in conditional sentences. The teaching methods teachers used failed to engage students. Also, some experts believe that the difficulties students face with conditionals arise from the interaction between meaning and form. So, understanding this relationship and how it frequently appears in conditional sentences is crucial. To address this issue, a study was conducted to assess the proficiency of eleventh-grade students at SMA Negeri 1 Waringinkurung in constructing conditional sentences. The research involved 32 students, and the methodology used was qualitative content analysis, which included tests and interviews. The students' sentences were coded and sorted using a method called summative content analysis. This method involves identifying and measuring specific words or material in the text to understand how they are used. The researcher used various categories like formula of conditional sentences, main and if clauses, modal verbs, verb tense, and word order for further analysis. This analysis helped the researcher evaluate the students' understanding and proficiency in constructing conditional sentences.The study found that students commonly made mistakes in verb tenses and the use of modal verbs. Interviews with the students also confirmed that verb tenses were the most challenging aspect for them. Overall, the research results showd that the students had a good ability to construct conditional sentences, with an average score of 71.75. The findings pinpointed specific areas where students encountered challenges, providing valuable insights for educators to effectively address these issues and enhance overall proficiency.
IMPROVING UNDER-RESOURCED SECONDARY SCHOOL TEACHERS’ LITERACY INSTRUCTION: AN INTERVENTIONAL ETHNOGRAPHIC STUDY

The study is a part of the government-sponsored literacy program to improve the Indonesian secondary students’ literacy performance. Particularly, this study focuses on the examination of the program as the attempt to improve the secondary teachers’ literacy instruction at the under-resourced schools. The program implemented in the Bahasa Indonesia subject by employing Reading to Learn pedagogy (Rose, 2016) in teaching the explanatory text. Using an interventional ethnographic study design, which involves three main phases i.e. Reading to Learn pedagogy workshop, the R2L implementation with mentoring, and evaluation, the current study investigates the experience of two female eighth graders’ contract-based teachers, in their early thirties, at two under-resourced secondary schools in, Bandung, West Java, Indonesia. The data collected in this three-month study were observation, interviews with the teachers, principal, and students, recorded teaching sessions, recorded reflections sessions, and lesson plans and teaching materials. The findings of the study reveal that the teachers’ literacy instruction in utilizing stages in the R2L pedagogy indicated significant improvement from their previous practice. The intensive mentoring provided during the workshop and school implementation of the R2L implementation have equipped the teachers with more solid pedagogical foundation in internalizing and implementing the R2L pedagogy. In addition, the program has promoted the teachers’ professional development and assisted the school to gain larger access to literacy pedagogy for better literacy participation.
EXPLORING THE PROFILE OF SCIENTIFIC ARTICLE WRITING ABILITY AMONG TERTIARY STUDENTS: INSIGHTS FROM FIVE INSTITUTIONS IN INDONESIA

The motivation for this study stems from the observation that the culture and ability of tertiary students to write scientific articles are at a low level. This study aims to describe the profile of students’ scientific writing abilities. A descriptive method was used to gather data from five tertiary institutions in West Java. This study used a qualitative approach. The samples included 5 lecturers and 211 students. Data were collected using student needs questionnaires, interviews with lecturers, and analysis of student scientific articles. Data collection techniques included questionnaires and interviews and were analyzed using descriptive data analysis. The findings indicate that students’ ability to write scientific articles falls within the sufficient category. Despite this reality, the questionnaire results reveal that students still encounter difficulties in writing scientific articles and require a learning model that makes the process easier and helps to overcome problems in writing. The analysis of interviews with lecturers of scientific writing courses suggests that students’ ability to write scientific papers is adequate. Lecturers have implemented various learning methods during lectures because not all students like writing scientific articles. Moreover, most students are still confused about writing scientific articles correctly, and their motivation to read is low.
Beyond Keywords: Comparing Insights from Unstructured Data using Generative GPT Search and Hybrid Extractive Search in Petroleum Exploration

Summary The oil and gas industry faces a significant challenge in dealing with unstructured data, such as text documents, emails, audio recordings, and video footage. To tackle this issue, innovative techniques like Natural Language Processing (NLP) and data integration are being employed to analyze and extract insights from diverse sources. In the petroleum exploration sector, the analysis of open-file datasets, often consisting of scanned images of reports, is a time-consuming manual process. However, companies are now adopting automated approaches like NLP, text mining, OCR, machine learning, and intuitive search platforms to streamline the analysis and uncover valuable information from unstructured data. Hybrid extractive search techniques, combining keyword matching and contextual understanding, excel at extracting specific information and quantitative measurements, while generative search techniques powered by large language models excel at understanding qualitative relationships and generating human-like responses. The choice between these techniques depends on the objectives and characteristics of the data being analyzed, with hybrid extractive searches being effective for precise information extraction and generative searches being valuable for comprehending qualitative relationships in well-structured sentences and documents.
A terminating intuitionistic calculus

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The Construction of Deep Learning in English Newspaper Reading Course under Blended Teaching Mode

This paper discusses how to integrate deep learning techniques into English newspaper reading courses in colleges and universities in order to improve the quality of English education. Deep learning can be used to achieve personalized learning paths, multi-modal learning with real-time feedback and assessment, virtual experiments and immersive learning, social learning and collaboration, and encourage lifelong and self-directed learning. The article provides a practical approach to constructing an English newspaper reading course in higher education, including course design, selection of teaching materials and multimedia resources, learning platforms and tools, teacher training and support, and assessment and improvement. These approaches help to improve students’ English proficiency and develop their information acquisition and analysis skills to meet evolving needs.