Reviewerly/exHarmony huggingface dataset:
A Photovoltaic Electric Vehicle Automatic Charging and Monitoring System

This paper researches and designs an automatic charging control and monitoring system for PV electric vehicle batteries, which can automatically charge the low-voltage and high-voltage batteries by obtaining the operation mode of PV electric vehicles, avoiding battery maintenance to the maximum extent, preventing damage to the battery cell caused by over-discharge, and increasing the range of the whole vehicle. Suppose the PV EV is in parking mode. In that case, it will obtain the power of the low-voltage battery in real-time and charge the low-voltage battery according to the request, and then it will obtain the power of the high-voltage battery and send a power-up request to charge the high-voltage battery according to the request; if the PV EV is in driving mode, it will obtain the power of the low-voltage battery in real-time and generate a power-up request according to the power-up request. When the PV electric vehicle is in driving mode, it will obtain the low voltage battery power condition in real-time and send a request to recharge the low voltage battery.
Statistical Features Versus Deep Learning Representation for Suspicious Human Activity Recognition

For many years, researchers have been interested in human activity recognition for many reasons, including preventing crimes before they occur, preventing acts of sabotage, and securing people and facilities. Human activity recognition using deep learning is the focus of many researchers. Techniques for human activity recognition that rely on sensors worn by a person with his limbs and torso are impractical and the only useful way to recognize the human activity especially in public places is through video clips of surveillance cameras. In this paper i compared the performance of CNN with different models such as Inception, ResNet, Inception-ResNet, MobileNet V2, NASNet and PNASNet and the performance of handcrafted features such as statistical features (shape moments like mean, Skew, Kurtosis, etc.) in terms of their human activity recognition accuracy and also compared the results with the state-of-the-art methods. In my research i used suspicious activities included in the HMDB data set (falling to the floor, punching, kicking and shooting a gun) to evaluate the statistical features and also to evaluate the different modern CNN architectures. The experimental results in the case of using statistical features confirmed the superiority of the Support vector machine (SVM) than other classifiers. My experimental results indicated that the CNN with NASNet architecture achieves the best performance of the six CNN architectures but when comparing the performance with the statistical features method, I found the superiority of statistical features with a support vector machine classifier. This paper contributes to studying the effectiveness of using modern CNN architectures in recognizing suspicious human activity and found that these techniques depend on the quality of images, whether the activity is individual or group. This study enables the researcher to evaluate and compare the different modern CNN architectures in suspicious human activity recognition and compare them also with models that use the hand-crafted features represented in the statistical features on an objective and fair basis The remainder of this paper is organized as follows: Section 1 provides the related work. Section 2 describes an action recognition system using Hand-crafted features. Section 3 presents the experimental results and Section 4 concludes the paper.
Linear Prediction Coefficients based Copy-Move Forgery Detection in Audio Signal

With the advancement digitalization, the issue of digital multimedia security has become one of the essential research areas. Digital multimedia security includes the analysis and classification of many digital data such as audio, image and video, etc. The most important problem with audio data is the difficulty of detecting audio forgery as auditory and visually. Considering the studies in the audio forgery field, the lack in the number of research in this field is a problem emphasized by many other researchers. In this study, we have developed a copy-move forgery detection system for audio signals. Linear Prediction Coefficients (LPC) are obtained to detect copy-move forgery and audio forgery is detected using Pearson Correlation Coefficient (PCC). The study basically consists of 3 stages. 1) By using the Yet Another Algorithm for Pitch Tracking (YAAPT) method, the silent and unsilent regions in the audio signals are detected and the audio signals have been divided into segments. 2) LPC coefficients of 5, 10 and 15 are obtained from these segments. 3) The similarity between the coefficients is calculated by PCC. In the experimental part, the effects of different coefficients on the success of the study are analyzed and the results is given in tables.
RepShardChain: A Reputation-Based Sharding Blockchain System in Smart City

With the rapid development of smart cities with emerging technologies as the core, the attendant problems of data sharing and data security have attracted extensive attention. As an emerging distributed storage technology, blockchain has the characteristics of decentralization, transparency and credibility, tamper proof, and traceability, which is very suitable for data sharing and storage in smart cities. However, the scalability and consensus mechanisms of blockchain are not applicable to real-time data sharing requirements and resource-constrained Internet of things (IoT) devices in smart cities. According to the needs of smart cities, this paper designs a data sharing model RepShardChain based on reputation and sharding blockchain, designs a reputation-based proof-of-work (PoW) consensus mechanism to adapt to the IoT devices with limited resources, designs a reputation-based random sharding scheme to avoid malicious attacks, designs an incentive mechanism that takes into account the rewards and punishments of nodes, and incentivizes nodes to perform normal behavior to obtain more rewards, so as to realize the safety and efficiency of the blockchain. And we analyze the security of the proposed RepShardChain against various attacks. Simulation experiments show that the proposed model has higher throughput and higher security than the traditional blockchain.
A Deep-Learning-Based Optimal Auction for Vehicular Edge Computing Resource Allocation

The vehicular edge computing technology extends the Internet of Vehicles(IoV) from cloud computing to edge computing, enabling IoV to support in-vehicle applications such as autonomous driving, high-definition video, and navigation planning with low latency and low bandwidth consumption costs. Due to the high deployment cost and maintenance cost of edge computing nodes, to improve the revenue of service providers and encourage edge computing service providers to deploy computing nodes, it is necessary to design an incentive mechanism for edge computing service providers. Auctions are an effective incentive design solution. This paper designs an optimal auction mechanism to maximize the revenue of edge computing service providers, which ensures the two important attributes of individual rationality and incentive compatibility and ensures the feasibility of allocation and efficient use of resources. Specifically, we designed a system model for pricing and allocating edge computing service providers in the Internet of Vehicles environment, and transformed the optimal auction problem of resources under the Internet of Vehicles into a mathematical programming model of the optimal auction with constraints. And designed a matching algorithm, allocation algorithm, and price calculation algorithm based on a neural network. Finally, we experiment and analyze the algorithm. The simulation results show that the proposed scheme is superior to the VCG algorithm in terms of revenue and resource utilization.
A New Era for Smart Manufacturing Solutions with Emerging Technologies Apps

In the current manufacturing ecosystem, industry 4.0 automation plays an important role with technologies such as Artificial Intelligence (AI), internet of things (IoT), and Blockchain. Everything will be connected to everything including people, partners, and businesses. It is necessary for a data analyst to have a deep insight into problem identification and root cause analysis on a real-time basis in a manufacturing plant. Artificial Intelligence will increasingly play a role in uncovering deeper insights from complex data, and helping manufacturers make better decisions faster. Smart manufacturing solutions will be able to leverage these technologies not only to increase productivity and improve existing processes but also to create new business models. The focus of this paper is to discuss the impact of the current emerging technologies on smart manufacturing solutions. In particular, the benefits of the current applications of AI, IoT, and Blockchain technology in smart manufacturing solutions.
A Fast and Secured Peer-to-Peer Energy Trading Using Blockchain Consensus

The architecture and functioning of the electricity markets are rapidly evolving in favour of solutions based on real-time data sharing and decentralised, distributed, renewable energy generation. Peer-to-peer (P2P) energy markets allow two individuals to transact with one another without the need of intermediaries, reducing the load on the power grid during peak hours. However, such a P2P energy market is prone to various cyber attacks. Blockchain technology has been proposed to implement P2P energy trading to support this change. One of the most crucial components of blockchain technology in energy trading is the consensus mechanism. It determines the effectiveness and security of the blockchain for energy trading. However, most of the consensus used in energy trading today are traditional consensus such as Proof-of-Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). These traditional mechanisms cannot be directly adopted in P2P energy trading due to their huge computational power, low throughput, and high latency. Therefore, we propose the Block Alliance Consensus (BAC) mechanism based on Hashgraph. In a massive P2P energy trading network, BAC can keep Hashgraph's throughput while resisting Sybil attacks and supporting the addition and deletion of energy participants. The high efficiency and security of BAC and the blockchain-based energy trading platform are verified through experiments: our improved BAC has an average throughput that is 2.56 times more than regular BFT, 5 times greater than PoW, and 30% greater than the original BAC. The improved BAC has an average latency that is 41% less than BAC and 81% less than original BFT. Our energy trading blockchain (ETB)'s READ performance can achieve the most outstanding throughput of 1192 tps at a workload of 1200 tps, while WRITE can achieve 682 tps at a workload of 800 tps with a success rate of 95% and 0.18 seconds of latency.
Towards robust autonomous driving systems through adversarial test set generation

Correct environmental perception of objects on the road is vital for the safety of autonomous driving. Making appropriate decisions by the autonomous driving algorithm could be hindered by data perturbations and more recently, by adversarial attacks. We propose an adversarial test input generation approach based on uncertainty to make the machine learning (ML) model more robust against data perturbations and adversarial attacks. Adversarial attacks and uncertain inputs can affect the ML model's performance, which can have severe consequences such as the misclassification of objects on the road by autonomous vehicles, leading to incorrect decision-making. We show that we can obtain more robust ML models for autonomous driving by making a dataset that includes highly-uncertain adversarial test inputs during the re-training phase. We demonstrate an improvement in the accuracy of the robust model by more than 12%, with a notable drop in the uncertainty of the decisions returned by the model. We believe our approach will assist in further developing risk-aware autonomous systems.
Hybrid university education in COVID-19 times: resources used by teachers

COVID-19 has been a crisis that has caused significant changes around the world. This supervening global pandemic situation has affected all areas of society, including universities. In this regard, face-to-face learning scenarios were converted into online or hybrid teaching with the use of information and communication technologies as a meeting point between teaching staff and students. This work is part of the University Teaching Research Networks Programme of the Institute of Education Sciences of the University of Alicante (call 2020-21) and its general objective is analyse the resources used by teachers in hybrid teaching in the context of COVID-19. A quantitative approach with a non-experimental and cross-sectional design was adopted. A questionnaire was designed ad hoc for data collection, which was completed by a sample of 367 students of the bachelor’s degree in Teaching at the University of Alicante. The data were collected online through Google Forms during class. The results showed that PowerPoint is the resource most used by teachers for theory and practice classes. In turn, notes, materials in digital format uploaded to the virtual campus, are the resource most frequently provided by the teaching staff. Likewise, the university’s virtual classroom is the resource most frequently used by teaching staff to interact with students. In conclusion, it can be said that the change of teaching modality has led to an adaptation of face-to-face teaching to virtual scenarios, without any profound innovation in the university educational model.
Natural language Processing and Ontology based Decision Support System for Diabetic Patients

Diagnosis of diabetes disease is very promising because it may create various other acute or chronic health problems in the human body. This study proposes a recommendation system for diagnosis and treatment of Diabetes Mellitus as suggested by the Clinical Practice Guidelines (CPG). A treatment plan is designed which includes medications and lifestyle changes, according to the patient information, symptoms, and lab tests. Various tasks such as gathering, modelling, and implementing diabetic domain expertise knowledge collected from the experts, CPG, and other sources are used to construct an ontology for diabetes and a decision support system to manage diabetes in its early stages. This work also uses an Optical Character Recognition (OCR) and a Natural Language Processing (NLP) model that help directly to read doctor's prescription and extract useful information useful for developing the ontology. Web Ontology Language (OWL) is used to build the proposed ontology, and the Semantic Web Rule Language (SWRL) is used to create the rules for providing restrictions on the ontology.
Building and evaluation of an Algerian Cultural Heritage dataset using convolutional neural networks

Preservation of cultural heritage is a field of high importance. Recently people are sharing architectural monument images on social media. In this paper, we try to recognize architectural monuments in digital photographs of Algerian cultural heritage using a convolutional neural network (CNN). As no datasets support the diversity of Algerian monuments, adapted for CNN training, we built a new dataset and made available to the public. AlgHeritage dataset consists of 20,000 images that can serve as a benchmark for various research fields, as it combines numerous real-world challenges. We evaluate our dataset with three CNN models MobileNetV3, InceptionV3 and InceptionResNetV2, and compare it with two other datasets. MobileNetV3 with fine-tuning produces a good accuracy of 93.29% on the AlgHeritage dataset in comparison with other datasets. The AlgHeritage dataset is available at https://bit.ly/3O38FOe.
An Explainable Self-Labeling Grey-Box Model

The massive success in machine learning in recent years has led to a wide spread of Artificial Intelligence (AI) models. Due to their enormous complexity, most of these AI models, notably the most effective type, Deep Learning Models, are classified as Black-box models, making them difficult to comprehend. Therefore, the goal of deployable, transparent AI models is the focus of the current research field known as Explainable Artificial Intelligence (XAI).Humans can learn how machine learning algorithms generate decisions through explanation, which leads to novel data-driven insights. In this work, we study an explanation approach so-called the Grey-Box model. The developped Grey-Box model uses a self-labeling framework based on a semi-supervised methodology. The key idea of the Grey-Box model is to exploit the benifits of black-box and white-box models. For this purpose, we implement a mechanism to increase a small initial labeled dataset. It allows to incorporate the model's most reliable predictions from a large unlabeled dataset. The proposed approach results in an efficient black box model that is accurate and interpretable.
Fine-Grained Sentiment Analysis on PeduliLindungi Application Users with Multinomial Naive Bayes-SMOTE

The Key Social Disability Policy (PSBB) requires highly mobile people to use the PeduliLindungi application. One application has several reviews from positive and negative users. Review data can be labeled with two types of emotions: negative sentiment and positive sentiment. Fine-grained sentiment analysis is a type of sentiment analysis that can be used to identify user reactions. One method of sentiment analysis is Multinomial Naïve Bayes. In this research, we used Multinomial Naïve Bayes to perform fine-grained sentiment analysis for users of the PeduliLindungi application. The data used is from the Google Play store. The sentiment class labeling results for the PeduliLindungi review data resulted in 9021 reviews, including a total of 6244 negative reviews and 2777 positive reviews. This research uses a data-sharing model that divides 80% of training data and 20% of test data. Many data imbalances for the two sentiment classes can be overcome by using the SMOTE method. SMOTE has been shown to improve classification accuracy more effectively than non-SMOTE, as applying SMOTE has been shown to improve the performance of imbalanced data. The proper classification method used to classify PeduliLindungi's user ratings is Multinomial Naïve Bayes-SMOTE, which has the highest AUC value.
Development of quality assurance program for digital pathology by the Korean Society of Pathologists

Digital pathology (DP) using whole slide imaging is a recently emerging game changer technology that can fundamentally change the way of working in pathology. The Digital Pathology Study Group (DPSG) of the Korean Society of Pathologists (KSP) published a consensus report on the recommendations for pathologic practice using DP. Accordingly, the need for the development and implementation of a quality assurance program (QAP) for DP has been raised.To provide a standard baseline reference for internal and external QAP for DP, the members of the Committee of Quality Assurance of the KSP developed a checklist for the Redbook and a QAP trial for DP based on the prior DPSG consensus report. Four leading institutes participated in the QAP trial in the first year, and we gathered feedback from these institutes afterwards.The newly developed checklists of QAP for DP contain 39 items (216 score): eight items for quality control of DP systems; three for DP personnel; nine for hardware and software requirements for DP systems; 15 for validation, operation, and management of DP systems; and four for data security and personal information protection. Most participants in the QAP trial replied that continuous education on unfamiliar terminology and more practical experience is demanding.The QAP for DP is essential for the safe implementation of DP in pathologic practice. Each laboratory should prepare an institutional QAP according to this checklist, and consecutive revision of the checklist with feedback from the QAP trial for DP needs to follow.
Low Computational Complexity Algorithm for Hand Gesture Recognition using mmWave RADAR

Radio detection and ranging (RADAR) technology has attracted a lot of attention recently, especially for hand gesture recognition. Contactless hand gesture recognition can be applied in many areas, such as in-car entertainment systems and clean room operations. In this work, a computationally efficient and fast hand gesture feature extraction approach based on frequency-modulated continuous-wave (FMCW) RADAR is proposed, which is highly beneficial for real-time applications. Unlike conventional image recognition, the features of the hand gesture are extracted directly in an efficient manner. Our approach adopts 2-dimensional Fast Fourier Transform (FFT) to form a Range-Doppler matrix, and background modelling to remove clutter. Furthermore, we use best bin selection to locate the target in the Range-Doppler matrix in order to obtain both range and velocity of targets. Fourier beam steering is employed to obtain the angle of targets. Four classifiers are trained to perform hand gesture recognition. Cross-validation is used to evaluate their performance. Experimental results indicate that the features extracted by our approach can be fed directly into the classifiers for recognition which leads to an average recognition accuracy of 98.74% across all classifiers. Compared to image based recognition, the additional feature extraction process can be skipped, saving significant processing time. Our approach could be useful in many areas such as in-car entertainment systems, smart homes and others.
Enhanced Salp Search Algorithm for Optimization Extreme Learning Machine and Application to Dew Point Temperature Prediction

Abstract Extreme learning machine (ELM) is popular as a method of training single hidden layer feedforward neural networks. However, the ELMs optimized by the traditional gradient descent algorithms cannot fundamentally solve the influence of the random selection of the input weights and biases. Therefore, this paper proposes a method of extreme learning machine optimized by an enhanced salp search algorithm (NSSA-ELM). Salp search algorithm (SSA) is a metaheuristic algorithm, to improve the performance of SSA exploration and avoid getting stuck in local optima, the neighborhood centroid opposite‑based learning is used to optimize SSA. This method maintains the diversity of the population, which is conducive to avoid local optimization and accelerate convergence. This paper performs classification tests on NSSA and other metaheuristic-optimized ELMs on ten datasets, and regression tests on 5 datasets. Finally, the prediction ability of dew point temperature is evaluated. The meteorological data of five climatically representative cities in China from 2016 to 2022 were collected to predict the dew point temperature. The experimental results show that the NSSA-ELM is the best model, and its generalization performance and accuracy are better than other models.