Reviewerly/exHarmony huggingface dataset:
Research on Metaverse Security and Forensics

As a subversive concept, the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic, industrial, and investment circles. However, while the metaverse brings unprecedented opportunities for transformation to human society, it also contains related risks. Metaverse is a digital living space with information infrastructure, interoperability system, content production system, and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities. Through social incentives and governance rules, the metaverse reflects the digital migration of human society. This article will conduct an in-depth analysis of the metaverse from the perspective of electronic data forensics. First, from the perspective of Internet development, the background and development process of the metaverse is discussed. By systematically elaborating on the concept and connotation of the metaverse, this paper summarizes the different views of current practitioners, experts, and scholars on the metaverse. Secondly, from the perspective of metaverse security, the social risk and crime risks of the metaverse are discussed. Then the importance of metaverse forensics is raised. Third, from the perspective of blockchain, smart wearable devices, and virtual reality devices, the objects and characteristics of metaverse forensics have been studied in depth. Taking smart wearable devices as an example, this paper gives the relevant experimental process of smart bracelet forensics. Finally, many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.
Towards Arabic Image Captioning: A Transformer-Based Approach

The automatic generation of textual descriptions from images, known as image captioning, holds significant importance in various applications. Image captioning applications include accessibility for the visually impaired, social media enhancement, automatic image description for search engines, assistive technology for education, and many more. While extensive research has been conducted in English, exploring this challenge in Arabic remains limited due to its complexity. Arabic is one of the world’s most widely spoken languages. Around 420 million native people speak this language. It is also the official language of 22 nations in the Middle East and North Africa. Image captioning in Arabic will foster inclusion, improve communication, and provide technological breakthroughs. Therefore, this study aims to develop an Arabic image caption generator using the Flicker30k dataset. The proposed model comprises an encoder, decoder, and translation transformer, which generate descriptive captions for Arabic images. The encoder component utilizes ResNet101, a powerful convolutional neural network, to extract rich visual features from the input images. The decoder module consists of an attention block that allows the model to focus on different parts of the image, and an LSTM model, used to generate the captions. Finally, the generated captions are translated using an English-to-Arabic dialect translation transformer. The model achieved a BLEU-4 score of 0.1253 for English-generated captions and medium user satisfaction for Arabic-generated captions.
MAA: Modulation-adaptive Acoustic Gesture Recognition

In this paper, we propose a modulation-adaptive acoustic gesture recognition system with smartphones (termed, MAA), which can achieve a high recognition accuracy under various modulation schemes and quickly adapt to a new modulation at low cost. Specifically, MAA creates an acoustic channel model to capture temporal and spatial features, and leverages a domain adversarial network to eliminate the difference among modulation schemes when performing the same gesture. The proposed framework includes a data collection module, a signal preprocessing module, a channel construction module, and a domain adaptation module. For data collection, we determine the appropriate signal length and bandwidth for transmitted acoustic signals. In the signal preprocessing module, channel estimation and background noise removal are incorporated. Then, we develop a tensor reconstruction network and a feature mapping network in the channel construction module to directly map features to specific gestures. For domain adaptation, we train the above two networks in the source domain, and use an adversarial network to adapt to the target domain. Experimental results show that the proposed MAA achieves a good performance on gesture recognition with different modulation schemes, with better adaptation to new modulation schemes than several state-of-the-art baselines.
MV-VVQA: Multi-View Learning for No-Reference Volumetric Video Quality Assessment

Recently, volumetric video has gained growing research interest as it allows for the creation of immersive and realistic experiences by representing the full volume of 3D content. However, due to the limitation of storage space and transmission bandwidth in common applications, volumetric videos are inevitably bothered with compression and simplification distortions, which severely harms users' quality of experience (QoE). Moreover, current volumetric video quality assessment (VVQA) is mainly focused on full-reference or reduced-reference metrics, which can not be applied in the absence the reference information. Therefore, in this paper, we propose a novel deep learning based no-reference volumetric video quality assessment method based on multi-view learning. Specifically, we first project volumetric videos to 2D video sequences from various viewpoints. Then a 3D-CNN backbone is utilized to extract quality-aware features from the projected video sequences. Then a quality regression module is designed to fuse the features learned from the multiple viewpoints and jointly regress the features into quality scores. The experimental results show that our method outperforms current state-of-the-art objective volumetric video quality assessment metrics on the vsenseVVDB2 database, which validates the effectiveness of the proposed method.
Research on Growth Factors in Enterprise Organizations

The essential research elements of Enterprise organization engineering include four essential life elements: enterprise organization gene, enterprise organization seed cell, enterprise organization life scaffold, and enterprise organization growth factor. The in-depth research of these four essential life elements of enterprise organization constitutes the core theoretical content of Enterprise organization engineering. Chapter 12 of this book systematically analyzes enterprise organization growth factor, includes significance of growth factor research in enterprise organization (determining the vital work of Enterprise organization engineering, determining the research program of Enterprise organization engineering, determining the state of the enterprise organization engineering work), human tissue engineering growth factors (human tissue engineering growth factor meaning, role of human growth factors, types of growth factors), meaning of growth factors in enterprise organizations (preliminary definition of the meaning of growth factors in enterprise tissues, the essential difference between enterprise organization growth factors and human growth factors, essential differences between growth factors and incentives, essential differences between growth factors and management systems, growth factor and enterprise organization culture), composition of growth factors (growth factors composed of tacit knowledge, spiritual culture constitutes growth factors, the psychological contract constitutes growth factors), basic ideas of growth factor application research (research on growth factor identification, research on growth factor production, maturation promotion, and activation).
The Q-Fractionalism Reasoning Learning Method

As the title suggests, in this work, a modern machine learning method called the Q-fractionalism reasoning is introduced. The proposed method is founded upon a synergy of the Q-learning and fractional fuzzy inference systems (FFISs). Unlike other approaches, the Q-fractionalism reasoning not only incorporates the knowledge base to understand how to perform but also explores a reasoning mechanism from the fractional order to justify what it has performed. This method suggests that the agent choose actions aimed at the characterization of reasoning. In fact, the agent deals with states termed as primary and secondary fuzzy states. The primary fuzzy states are unobservable and uncertain, for which the agent chooses actions. However, the projection of primary fuzzy states onto the knowledge base results in secondary fuzzy states, which are observable by the agent, allowing it to detect primary fuzzy states with degrees of detectability. With a practical experiment implemented on a linear switched reluctance motor (LSRM), the results demonstrate that the application of the Q-fractionalism reasoning in the real-time position control of the LSRM leads to a remarkable improvement of about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ 70\% $</tex-math> </inline-formula> in the accuracy of the control objective compared with a typical fuzzy inference system (FIS) under the same setting.
A Reduced Feature-Set OCR System to Recognize Handwritten Tamil Characters using SURF Local Descriptor

High dimensionality in variable-length feature sets of real datasets negatively impacts the classification accuracy of traditional classifiers. Convolutional Neural Networks (CNNs) with convolution filters have been widely used for handling the classification of high-dimensional image datasets. However, these models require massive amounts of high-dimensional training data, posing a challenge for many image-processing applications. In contrast, traditional feature detectors and descriptors, with a minor trade-off in precision, have shown success in various computer vision tasks. This paper introduces the Nearest Angles (NA) classifier tailored for a handwritten character recognition system, employing Speeded-Up Robust Features (SURF) as local descriptors. These descriptors make local decisions, while global decisions on the test image are accomplished through a ranking-based classification approach. Image similarity scores generated from the SURF descriptors are ranked to make local decisions, and these ranks are then used by the NA classifier to produce a global class similarity score. The proposed method achieves recognition rates of 96.4% for Tamil, 96.5% for Devanagari, and 97 % for Telugu handwritten character datasets. Although the proposed approach shows slightly lower accuracy compared to CNN-based models, it significantly reduces the computational complexity and the number of parameters required for the classification tasks. As a result, the proposed method offers a computationally efficient alternative to deep learning models, lowering the computational time multiple times without a substantial loss in accuracy.
Data Set Design and Data Labeling

In this section, the book reveals the most tricky and advanced data processing techniques in the easiest way for the readers. More importantly, the book chapter reveals how to read data from audio, speech, image, and text in different modes and techniques for data sanitization and scaled data processing systems. The book also explains statistical methods for interpreting and analyzing data for different deep learning models; the Maxwell-Boltzmann statistic technique is deeply described specifically with image signal processing, which demonstrates its main use in open CV libraries. MNIST data handling is presented with training, test, and deployment mechanism. More importantly, a novel technique, pixel normalization for image processing, is presented including the global standards that facets the sequences in prediction, classification, and sequence generation and sequence classification.
Encryption and Decryption Using Deep Neural Network

An auto-encoder which can be split into two parts is designed. The two parts can work well separately. The top half is an abstract network which is trained by supervised learning and can be used to classify and regress. The bottom half is a concrete network which is accomplished by inverse function and trained by self-supervised learning. It can generate the input of abstract network from concept or label. It is tested by tensorflow and mnist dataset. The abstract network is like LeNet-5. The concrete network is the inverse of the abstract network. Through test, encryption and decryption can be achieved by abstract network and concrete network add jump connection and negative feedback with absolute function. When binary encoding is used, although the encrypted vector is four bits, the result of decryption has the same quality as one-hot encoding because of the jump connection and negative feedback. The parameter of DNN is secondary key, the architecture of DNN is primary key. Secondary key can be shared by all the people, primary key can be shared by sender and receiver. The key can be generated by training the DNN. When big dataset is used for encryption, the classes are far bigger, the label may be something in the world, numbers, words, or attributes represented by float number. The label can use the mix of one-hot encoding and binary encoding, it is harder to attack. Through analysis, it is safe for most situations.
Awareness of using chatbots and factors influencing usage intention among nursing students in South Korea: a descriptive study

Purpose: Artificial intelligence (AI) has had a profound impact on humanity; in particular, chatbots have been designed for interactivity and applied to many aspects of daily life. Chatbots are also regarded as an innovative modality in nursing education. This study aimed to identify nursing students' awareness of using chatbots and factors influencing their usage intention.Methods: This study, which employed a descriptive design using a self-reported questionnaire, was conducted at three university nursing schools located in Seoul, South Korea. The participants were 289 junior and senior nursing students. Data were collected using self-reported questionnaires, both online via a Naver Form and offline.Results: The total mean score of awareness of using chatbots was 3.49±0.61 points out of 5. The mean scores of the four dimensions of awareness of using chatbots were 3.37±0.60 for perceived value, 3.66±0.73 for perceived usefulness, 3.83±0.73 for perceived ease of use, and 3.36±0.87 for intention to use. Significant differences were observed in awareness of using chatbots according to satisfaction with nursing (p&lt;.001), effectiveness of using various methods for nursing education (p&lt;.001), and interest in chatbots (p&lt;.001). The correlations among the four dimensions ranged from .52 to .80. In a hierarchical regression analysis, perceived value (β=.45) accounted for 60.2% of variance in intention to use.Conclusion: The results suggest that chatbots have the potential to be used in nursing education. Further research is needed to clarify the effectiveness of using chatbots in nursing education.
A Forensic Video Upscaling Colorizing and Denoising Framework for Crime Scene Investigation

Digital videos have been widely used as key evidence sources in Forensic crime scene investigations. Resolution is one of the most dominating parameter which affects the overall quality of the video. The main goal of this paper is to find an efficient forensic video analysis framework to assist the forensic crime scene investigation. A forensic video analysis framework (FVAF) that employs an efficient video enhancing deep learning model for increasing resolution of the low quality videos is used. The low resolution video is fed as input to the model. First, the video is pre-processed using fastai deep learning library. Large videos are cropped to manage runtime efficiently. Second, the video is rescaled for increasing the resolution by Spatial Resolution method. The framework successfully increases the resolution of the video from SD-standard definition Resolution type of 480p with Aspect Ratio 4:3 of Pixel size 640 × 480 to Full Ultra HD Resolution type of 8K or 4320p with Aspect Ratio 16∶9 of Pixel Size 7680 × 4320. The rescaled videos are submitted for colorization process. DeOldify deep learning model using Self-Attention Generative Adversarial Network and Two Time-Scale Update Rule is adopted by FVAF framework for colorizing the videos. Also, the colorized videos are trained and tested by various video enhance AI models model Gaia High Quality 4K rendering and Theia fine Tune detail. 4K not rendered and Theia Fine Tune Fidelity: 4K not rendered and video denoise AI models model Standard, clear, lowlight, severe noise and Raw. The upscaled and colorized video is also trained and tested using denoise video enhance AI and video denoise AI models. The results of each model are stored for comparison. From the stored results best video enhance AI model and the best video denoise AI models is selected. Lowlight AI model and Gaia high quality 4K rendering are used in this FVAF to produce high standard video for Forensic Analysis. We run this model using GPU to efficiently pre-process the video. By this framework, we increase the resolution of the video footages to further assist the forensic crime investigation.
Keeping secrets in a quantum world

Cryptographers are preparing for new quantum computers that will break their ciphers. Cryptographers are preparing for new quantum computers that will break their ciphers.
Development of intelligent tutor for enhancing Think-Pair-Share in learning of Electrical Machines in Colleges of Education (Technical) in North East, Nigeria

This study developed an Intelligent Tutor for enhancing Think-Pair-Share in learning of Electrical Machines in Colleges of Education (Technical) in North East, Nigeria that will improve access to students and give them flexibility in learning. The study adopted a multi-method research design involving Research and Development (R &amp; D) and Descriptive Survey research designs. The population of the study was 25 persons consisting of 10 lecturers of Electrical/Electronic Programme in Federal College of Education (Technical) Potiskum and Federal College of Education (Technical) Gombe, 15 industrial personnel, 5 each from Ashaka Cement, Bajoga, Savannah Sugar Plantation, Numan and Adama Beverages, Yola. Three research questions and three null hypotheses guided the study. The instruments were subjected to face and content validation by experts. The instrument, Intelligent Tutor Questionnaire for Electrical Machines (ITQEM) was then incorporated in the Intelligent Tutor (IT) for enhancing Think-Pair-Share in learning of Electrical Machines. Data collected from industrial personnel and lecturers’ validation were analyzed using mean and standard deviation, two-tail t-test was used for test of significance between the responses of industrial personnel and Electrical/Electronic lecturers at 0.05 level of significance. The findings of the study showed that three topics and 60 items were found to be appropriate for inclusion in the Intelligent Tutor. It was recommended among others that, since Research grants such as Tetfund NRF, Bench-work and PTDF should give priorities to scholars working on Intelligent Tutors and Artificial Intelligences in general to allow equal access for learners from all angles of the country which would in turn harnesses the untapped resourcefulness of the lecturers in Electrical Machines Principles of Operation.
TEACHER OF THE FUTURE SCHOOL AND DIGITAL TECHNOLOGIES: CONSOLIDATION OF OPPORTUNITIES

Currently, there is no agreed understanding of the prospects for the development of education and the place and role of the teacher in the future. Scientifi c and educational communities, as well as the public, are divided in opinion on what will be the basis for the school of the future: digital technologies, competencies, achievements of sciences, personal orientation. With different grounds for the prospects of education development, they can mutually complement each other and be combined within the framework of a systematic personality development approach. The purpose is to study the personal and professional (including digital technologies) resources for personal and professional development of a teacher, providing a set of conditions for the development, upbringing, and training of students at the future school. The study was conducted with a specially developed program, including  methods, on a sample of teachers (n=) of Moscow schools. Statistical methods included discriminant, regression, factor analysis. The results of multiple regression analysis (MRA) showed that the main factors determining the high level of personal and professional development in teachers are personal resources: refl exive design, constructive coping, emotional involvement. The technological components are identifi ed as additional. In the new era of continuing education, the emphasis must inevitably shift from the technological component to the personal one, including the values. The teacher’s value system will include traditional cultural and educational values (personality, interaction, creativity) as well as individual values, personal and professional characteristics. At the same time, technological processes, including digitalization, will expand and complement the teacher’s resource capabilities for the development of students at the school of the future.
A Comparative Study of Cloud Data Portability Frameworks for Analyzing Object to NoSQL Database Mapping from ONDM's Perspective

Cloud computing revolves around storing and retrieving data in a portable manner. However, practical data portability across multiple Database-as-a-service (DBaaS) cloud data stores is challenging. This becomes even more complicated when data needs to be migrated between different types of data storage, such as SQL and NoSQL databases. NoSQL databases have gained significant popularity among developers due to their ability to provide high availability, fault tolerance, and scalability, making them suitable for managing big data in large-scale infrastructures. However, the varied data models in NoSQL databases make it difficult to migrate or port data among data repositories. Object to NoSQL database mappers (ONDMs) solves this problem. However, only a few ONDMs are available for C#.NET development, and the ONDM market used in Java development could be more stable. To address this issue, we propose building a middleware solution using the .NET framework to support cloud data portability, leveraging the capabilities of ONDMs. In this study, we evaluate several frameworks and compare them to our suggested middleware solution through empirical research. Our middleware solution can perform open network data management (ONDM) and object-relational mapping (ORM).