Reviewerly/exHarmony huggingface dataset:
Accelerated identification method of industrial instrument based on yolov5

There are many problems in industrial instrument detection, such as complex background and visual Angle, etc. Traditional machine learning methods are difficult to achieve high-precision object detection and real-time monitoring applications. This paper proposes an accelerated identification method of industrial instruments based on YOLOv5. First, sample data are collected on site for image annotation, and YOLOv5 network structure is designed for transfer learning training, so as to realize image recognition of four types of industrial instruments. After that, the model is accelerated based on TensorRT on Jetson TX2 platform. The speed of instrument online detection reached 30 fps and mAP0.5 is 0.989.
A Layer-wise Training and Pruning Method for Memory Efficient On-chip Learning Hardware

In this paper, we present a layer-wise on-chip learning and pruning algorithm to enable the training of deep neural networks (DNNs) that are too large to be directly trained using the limited memory resources of edge devices. To perform on-chip learning of large DNN in a limited memory space, the proposed algorithm performs pruning in the middle of the training by initializing only the first layer of the network and training that layer using direct random target projection (DRTP) algorithm. After training of the first layer is completed, a simple magnitude based pruning is applied to create empty memory space for the second layer and this process is repeated until the last layer. The proposed algorithm allows training larger DNN on a smaller physical memory compared to conventional algorithms. The proposed algorithm achieves 95.06% classification accuracy with a network of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$28 \times 28-500-500-10$</tex> structure for the MNIST dataset while using 39.5% smaller physical weight memory compared to conventional learning methods.
Application of Blockchain Technology in Community-Based Home Elderly Care Services under Population Aging

In recent years, against the background of the deepening aging population, traditional elderly care services are facing the need for transformation. The application of new technologies such as blockchain has provided new ideas to solve the problem of aging population and promoted the upgrading of the elderly service industry. It is an inevitable trend to improve home-based, community-based and institutional-based elderly care service networks. The development of community-based home elderly care services is in line with the traditional concept of home-based elderly care and also brings new incentives to the silver economy. The government should work together with social forces to build a service system of community-based home elderly care services, so as to improve the quality of elderly care service and provide inclusive elderly care services. In this paper, a system using blockchain technology is designed for community-based home elderly care services. On the one hand, this system integrates the volunteer service resources and precisely matches the service demands of the elderly so as to provide accurate and professional services in the community. On the other hand, the system converts these service hours into "points", which is recorded in point center, to support the volunteers' future elderly care services. In addition, the application of blockchain in the intelligent elderly care service system can realize the data interconnection between multiple subjects such as elderly care institutions and medical institutions. Therefore, it can see that the improvement of community-based home elderly care services and the continuous upgrading of elderly care service technology will bring better care experience to the elderly.
Fine-tuning Arabic Pre-Trained Transformer Models for Egyptian-Arabic Dialect Offensive Language and Hate Speech Detection and Classification

Offensive language and Hate Speech are rampant on social media platforms (Facebook, Twitter, etc.) in Egypt for quite a while now, appearing in Tweets, Facebook posts and comments, etc., It is an increasingly outreaching problem that needs immediate attention. This paper focuses on the problem of detecting and classifying both offensive language and Hate Speech using State-of-the-art techniques in text classification. Pre-trained transformer models have gained a reputation of astounding general language understanding that could be fine-tuned for language-specific tasks like Text classification, We collected an Egyptian-Arabic dialect Custom dataset of about 8,000 text samples manually labelled into 5 distinct classes: (Neutral, Offensive, Sexism, Religious Discrimination, Racism), It was used to fine-tune and evaluate multiple different Arabic pre-trained transformer models based on different transformer architectures and pre-training approaches for the Natural Language Processing downstream task of text classification. We achieved an average accuracy of about 96% across all fine-tuned transformer models.
An Ontological Approach to the Development of Analytical Platform Language Toolkits

The development of complex software systems is impossible without using modern modeling tools. At the design process, various models are developed: when solving each task, the attributes of processes and systems, which are significant for solving this task, are to be presented in the model. Developing analytical systems for data-intensive areas has specifics, which determine new requirements for the system functionality and architecture (interoperability, adaptability, etc.). These requirements can be implemented in the system based on a combination of two approaches: knowledge-driven development and model-driven development. This article presents an approach to creating a knowledge-driven analytical platforms, which integrate language toolkits allowing to create "on the fly" new domain-specific languages (DSLs). DSLs provide "user interfaces" which are customizable to the specifics of the tasks, solved by users with modeling tools, and to the corresponding users' domains. The architecture of the analytical platform, the graph model, and the metalanguage which are the basis of the language toolkits implementation are described in the paper. The multifaceted ontology, which is the core of the analytical platform, is presented too.
Complex Multivalue Logic with Two Sequentially Connected Logic Structure

The present work proposes and investigates a method for building a complex multi-valued logic, including two sequentially connected propositional logics: real L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> with two states T and F, and imaginary L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> with states i and ¬i. The four states are not equivalent to each other. At the same time, the multi-valued logic is not built hierarchically. Imaginary logic is completely superior in truth to real logic. This is achieved by additional constraints between states F and i. Research results have been obtained for the proposed multivalued logic model. It is shown that the three logics - L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</inf> , L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</inf> correspond to the axioms of the Boolean algebra and the distributive lattice. Some possibilities for the use of these multivalued logics in research and applied fields are pointed out.
A Novel Blockchain-Integrated Distributed Data Storage Model with Built-in Load Balancing

Most companies relay on centralized servers, which are considered as a fast and easy to deploy solution for file sharing, but they have many downsides, like security and trust issues, which can be solved using DLT (blockchain) solutions. However, DLT-based solutions have many challenges, such as nodes orchestration, adjusting reliability and double-spending, as well as load balancing. This research proposes a DLT (Distributed Ledger Technology) based model for files storage for companies using p2p networks, which takes into account both data reliability and storage space efficiency. More efficient data storage is achieved through the use of the Erasure coding technique. For load balancing, a set of algorithms that can be implemented in the form of a smart contract were proposed. The described model can become the basis of the IT infrastructure for trusted exchange of documents, technical and scientific information, games distributions, mobile applications, updates of scientific, regulatory and technical databases in b2b and b2c format
A classification of online training courses according to the methods of presentation and educational content

This article analyzes the different types of online training courses. The classification is made by analyzing both the methods of implementation and the specifics of the type of educational content and the way of presenting the information. The advantages and disadvantages of different types of online courses are outlined in terms of their effectiveness and required development resources.
The Social Impact of Artificial Intelligence

Artificial intelligence (AI) has become important in modern life. The authors study in this article about the social impact of AI; they also highlight its social importance, as it facilitates community services and satisfies the requirements of societal sectors. Among the prominent advantages of using AI in society are facilitating individual services such as customer service centers, gaining customer satisfaction, and facilitating electronic dealings. Its disadvantages are the unemployment problem such as replacing employees by robots, limited options in the labor market, negative impacts on the youth especially teenagers, and the inability of robots in dealing with the elders for giving them the sympathy and social support. AI also faces many challenges such as concerns about disinformation by fake news, secret surveillance, and lack of solutions for laid-off employees because AI systems have replaced them, which may lead to limited jobs in the future.
Artificial Intelligence-Based Behavioral Biometrics

Recognition of reliability of people entering into a system has been a vital downside in numerous business concerns. Biometrics, which provides recognition of personnel using their distinctive traits, has the potential to become an authentic approach and hence are irreplaceable as a part of several identification systems. Uni-modal biometric systems have been designed to validate user identity. These systems use single biometric traits that can be used for recognition. But most of the drawbacks in uni-modal biometric system are due to the use of single attribute of biometric. As a solution to this problem, multimodal biometric identification systems have been developed recently, which are useful for authentication of the users. These systems avoid security threats better and provide higher security to the systems. In this article, the authors present these aspects of behavioral biometrics based on artificial intelligence and provide a comparative study of the existing approaches and systems. Also, some suggestions for future enhancements are presented for better security of the systems.
Review on Reliability and Energy-Efficiency Issues in Cloud Computing

In the current era of information technology, data is increasing exponentially due to the large number of devices in the network. This leads to the rise of a concept called cloud computing, which becomes the need of time and also results in the rise in popularity of cloud computing. It becomes critical to provide on-demand services that adapt to the needs of the customer. Two important factors that trigger cloud computing systems (CCS) are reliability and energy efficiency. This article provides a comprehensive overview of available strategies for ensuring reliability and energy efficiency in cloud computing, combined approaches, the current status, as well as their trade-offs.
Speedy Management of Data Using MapReduce Approach

The previous contribution utilizes two assignments in practical programming: Map and Reduce. MapReduce is another preparing structure. Hadoop is its open-source execution on a solitary processing hub or groups. Contrasted and existing preparing ideal models, MapReduce and Hadoop enjoy two benefits. The deficiency lenient capacity brings about dependable information handling by reproducing the registering errands and cloning the information lumps on various figuring hubs across the processing bunch. The high-throughput knowledge preparation employs a cluster handling structure and the Hadoop disseminated document framework. Information is put away in the HDFS and made accessible to the slave hubs for calculation. The suggested work uses hashing methodology to increase speed in searching the required data. The suggestion reduces the mapping using hashing method based on attributes by 29.6% compared to previous work.
Probing the Efficacy of Hardware-Aware Weight Pruning to Optimize the SpMM Routine on Ampere GPUs

The Deep Learning (DL) community found in pruning techniques a good way to reduce the models' resource and energy consumption. These techniques lead to smaller sparse models, but sparse computations in GPUs only outperform their dense counterparts for extremely high levels of sparsity. However, pruning up to such sparsity levels can seriously harm the accuracy of the Neural Networks (NNs). To alleviate this, novel performance-aware pruning techniques favor the generation of more regular sparse matrices that can improve the exploitation of the underlying hardware. Nevertheless, an important drawback is that these techniques heavily condition the location of the non-pruned values, which can strongly degrade the accuracy of the models.
Improving Radar-Camera Fusion-based 3D Object Detection for Autonomous Vehicles

Object detection is essential for autonomous driving as it provides knowledge of the state of the surrounding objects. Aside from the objects' 3D bounding boxes, 3D object detection also estimates the objects' velocity and attribute. Radar-camera fusion-based object detection methods have the potential to deliver accurate and robust results even in harsh conditions while being more affordable than lidar-based solutions. However, current radar-camera fusion-based methods are still significantly outperformed by their lidar-based counterparts, even when radar has access to direct velocity measurement which lidar lacks. In this work, we propose a feature-level radar-camera fusion-based object detection architecture. Our architecture associates preliminary detections produced by an image-based pipeline with radar detection clusters using a frustum-based association mechanism. By using radar detection clusters instead of individual points, we can extract features that allow the model to use the geometry of the clusters to refine the preliminary detections. We evaluated our architecture on the nuScenes dataset and achieved a nuScenes detection score (NDS) of 0.465. We also find that our architecture achieved significantly better objects' orientation, velocity, and attribute estimation results compared to other radar-camera fusion-based methods.
Dematerialization of Public Procurement Approach Based on Hyperledger Fabric Blockchain using OCDS

The development of Blockchain technology presents an opportunity for the dematerialization of government processes, particularly in the field of public procurement. It has the characteristics of guaranteeing transparency, integrity, traceability and immutability of data. However, in the existing solutions, including web-based, there is a lack of standardization of data contracts and the confidentiality could be improved. This paper aims to study the combination of Blockchain technology and OCDS format to standardize public procurement data through smarts contracts. We propose a Blockchain-based system using Hyperledger Fabric by defining chain codes for every step of the public procurement process: planning, tendering, awarding and contracting, including implementation. Then, the proposed solution presents a mechanism to secure the process and guarantee the confidentiality of offers in Blockchain using secret shared keys and private data collection.
Evaluation-Self-regulated Learning in Higher Education in a Digital Environment Under Vulnerable Conditions and Confinement Due to Covid-19

Education is one of the areas in which the trend toward using digital tools in educational processes and methodologies is increasingly unavoidable. In Mexico, the first internet connection occurred just over three decades ago. Since then, internet access has enabled the gradual advancement of education into the digital age. The Covid-19 pandemic has forcibly and massively challenged us to digitalize educational work. The present research is an experimental study of self-regulated learning in a higher-education digital environment with a group of first-year university students attending a private institution in northern Mexico. A study recommendation is that before implementing any strategy to develop self-regulated learning using digital tools and virtual environments, all significant contextual aspects that could affect the effectiveness of the intervention strategy must be considered
Developing the habit of learning English using Duolingo application

Language learning mobile application soft-wares (apps) for smart mobile devices such as smartphones and tablet computers have grown dramatically in recent years. These applications were consistently reported to have a high potential for assisting students in learning the target language. This study worked with a free application named Duolingo which assists learners in learning various languages. In the studied context, Duolingo has been used as a tool to help students build their English learning habits via a challenge called “22-day challenge to learn English with Duolingo”. The participants of this study were 15 students who were participating in this challenge in the Spring semester of 2022. Semi-structured interview was employed to collected their learning experience and perceptions towards the app. Generally, the students had positive experience and perceptions of this app. The participants indicated interest in several Duolingo aspects, such as the forum where learners can discuss and exchange information, the podcast, the recognizable subjects in each lesson, and even the owl emblem and the way the owl reminds learners to study daily. However, two disadvantages have been identified, namely the occasional trouble with voice recognition and the limitation of a high degree of linguistic proficiency.
FedCLS:A federated learning client selection algorithm based on cluster label information

Federated learning is a common distributed machine learning framework. Through the training of the global model, the problems of large communication overhead and data privacy protection in traditional centralized machine learning are solved. But in real distributed scenarios, Non-Independent and Identically Distributed(Non-IID) of data reduces the speed of learning and the accuracy of global model. To solve this problem, this paper proposes a federated learning client selection algorithm based on cluster label information(FedCLS). FedCLS realizes efficient federated learning by optimizing the selection of clients in each round of training. Through extensive simulations, we demonstrate that compared with traditional FedAVG based on random extraction, FedCLS has better learning performance and less resource overhead.
Exploiting Blockchain for Smart Charging of Electric Vehicles: A Proof of Stake Algorithm

The energy sector is undergoing a vast transformation. Innovations in renewable energy production, electric vehicles (EVs) and smart meters are pushing the energy sector towards decentralisation. In this context, determining the potential of distributed ledger technology (DLT) through smart contracts and interoperability between different sections of the grid is an open point. Implementation of blockchain as a DLT into the electricity distribution network with the combination of multiple components, assists decentralisation and the connection of end-users directly to the grid. In this paper, a new method is proposed to charge EVs based on fuzzy logic and distributed ledger using a proof of stake algorithm. In this way, the risk of manipulation and loss of data, which could be detrimental to the energy sector, is reduced. The decentralisation of controlling EV's charging is achieved using this technique and the data floating inside the network is immutable and protected by the consensus mechanism of blockchain.
A privacy-preserving, sealed double-auction smart contract for Local Energy Markets

The continued proliferation of renewable energy sources (RES) in the residential sector enables private households to increasingly participate in the energy market. However, it also poses an infrastructural challenge as their spatial dispersion and the simultaneity of their energy generation increase the strain on the main grid. Local energy markets (LEMs) have been proposed to amend this effect by leveraging market dynamics to efficiently allocate privately generated energy in the immediate vicinity. Blockchain has been suggested as suitable information technology to reflect and support the decentralization and the parity characteristic of such LEMs. While some research on the topic has been conducted, concerns regarding anonymity in the proposed markets remain. In this study, we hence conceptualize a Blockchain-based LEM which expands on previous designs by introducing a one-time-address-based anonymization technique. The proposed concept also extends the double auction mechanisms of existing LEMs by a sealed offering protocol, providing information symmetry. The concept is prototypically implemented and validated using a Proof of Authority Ethereum network. It is shown that the implementation is sufficiently scalable for proposed LEM sizes.
Generation Performance Analysis for Installed Photovoltaic Systems on the Maltese Islands

Advanced data analytics of big data applied to PV systems can offer the advantages to both the plant operators and distribution system operators. This paper analyses and compares the generation output over a year of 56 grid-connected photovoltaic systems in Gozo, the second largest island of the Maltese archipelago. The datasets consist of production from a large PV plant and from small residential rooftop PV systems. The normal performance ratio was compared to the NREL weather-corrected performance ratio. For the considered dataset, daily adjustments due to the weather-corrected approach were observed to vary between 2.39% and -7.35% in May 2020. For October 2020, the daily adjustments due to the weather-corrected approach were observed to vary between 1.87% and -8.45%. The resultant median normal and weather corrected PRs were seen to marginally vary over the considered months.