Reviewerly/exHarmony huggingface dataset:
Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms

Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152).
Combination of Cooperative Grouper Fish -- Octopus Algorithm and DBSCAN to Automatic Clustering

Density-based spatial clustering of applications with noise (DBSCAN) has been used to cluster data with arbitrary shapes which clustering is done based on the density among objects in data. Given that DBSCAN is a proper tool for identifying outliers and clustering non-convex data, it can be used for automatic clustering of non-convex data and covered the weakness of most automatic clustering algorithms in not recognizing non-convex clusters. So, in this chapter, a new automatic clustering algorithm is introduced which is a combination of DBSCAN and a new metaheuristic algorithm called grouper fish – octopus (GFO) algorithm. GFO-DBSCAN finds the best number of clusters in two main steps in an iterative manner. In the first step, the values of esp and minpts are generated by GFO algorithm, and in the second step, the clustering of data is performed using DBSCAN algorithm with eps and minpts that are generated in the previous step. After each clustering, using correct data labels, and cluster centroids, the Calinski-Harabasz (CH) index is calculated. Finally, after passing some iterations of GFO algorithm, the best number of clusters is reported. In this study, three categories of data are used to measure the performance of the GFO-DBSCAN algorithm. Also, DBSCAN is compared with ACDE, DCPSO, and GCUK algorithms. According to the results, GFO-DBSCAN has achieved the optimal number of clusters in most data and has outperformed other well-known algorithms.
Research on the Status Quo and Prevention of Juvenile Delinquency from the Perspective of Internet

With the continuous development of social economy, the Internet era has evolved rapidly. The information that minors come into contact with is more and more complete and uncontrollable. Due to the immature mind, the knowledge is narrow, the legal awareness is weak, and the impulsive crime is obvious. The education of the family, school and society is not perfect. According to statistics, the popularity of the Internet of minors in China reached 98% in 2021. Some pornographic and violent games and some related film and television works and popular short videos quickly participated in the life of minors, resulting in the lack of correct education and guidance on the violence and sexual curiosity and impulses of minors. In some specific In this case, it causes illegal or even criminal acts. The delinquency of minors faces many new situations and problems. For example, some online rumors, online banking theft, minors are addicted to online games and other phenomena. To this end, this paper analyzes the causes of online crime among minors and puts forward targeted preventive measures in order to minimize the impact of bad online information on minors.
Blockchain-Enabled Smart Packaging: Enhancing Food Traceability and Consumer Confidence in the Chinese Food Industry

This paper explores blockchain-based intelligent packaging in the food industry to establish a transparent traceability system. The main objective is to address information asymmetry, enhancing consumer confidence in food safety. By leveraging blockchain technology, key data points are recorded in a decentralized manner, bridging the gap between producers and consumers. The proposed decentralized food traceability system improves market supervision efficiency and curbs counterfeit products. Consumers gain easy access to trustworthy traceability details, fostering trust in the food supply chain. Ultimately, this technology-driven approach elevates enterprise brand value and reputation. In conclusion, blockchain-based traceable smart packaging revolutionizes food supervision, providing comprehensive oversight across the supply chain. It enhances consumer confidence and promotes transparency in the food industry, driving advancements in food traceability for a safer and more trustworthy supply chain.
Research on Exercise Design Method Based on Cultivation of Students' Mathematical Thinking Ability

In the new curriculum standard, students are required to have the spirit of innovation. Good exercise training can not only stimulate students' spirit of innovation but also cultivate students' ability of mathematical thinking. In junior middle school mathematics teaching, we should not only pay attention to the improvement of students' mathematical literacy, but also pay attention to the improvement of students' learning quality. Exercise teaching and design is an important part of middle school mathematics classroom teaching. Exercise training can improve students' ability to solve mathematical problems, improve mathematical literacy and mathematical thinking ability. Therefore, in order to cultivate students' mathematical thinking ability as the purpose, this paper summarizes a better way of design, which is of great significance to improve the quality of mathematics classroom teaching.
A Study on Live Streaming Bandwagon of CCTV Hosts in the Context of the New Era

In recent years, Internet technology has propelled live streaming with product endorsements to become the most popular sales model. In early 2020, the outbreak of the COVID-19 pandemic had a particular impact on the real economy. In this context, to expedite the resumption of production, promote economic growth, and stimulate economic vitality, the central station CCTV initiated a live-streaming campaign. As a result, CCTV hosts transitioned from the newsroom to the electric power live streaming room, transforming their image from traditional hosts to live-streaming anchors. This paper aims to analyze the participation deficiencies of CCTV hosts during the live-streaming process and provide recommendations for future practices in this area.
Analyzing SQL payloads using logistic regression in a big data environment

Abstract Protecting big data from attacks on large organizations is essential because of how vital such data are to organizations and individuals. Moreover, such data can be put at risk when attackers gain unauthorized access to information and use it in illegal ways. One of the most common such attacks is the structured query language injection attack (SQLIA). This attack is a vulnerability attack that allows attackers to illegally access a database quickly and easily by manipulating structured query language (SQL) queries, especially when dealing with a big data environment. To address these risks, this study aims to build an approach that acts as a middle protection layer between the client and database server layers and reduces the time consumed to classify the SQL payload sent from the user layer. The proposed method involves training a model by using a machine learning (ML) technique for logistic regression with the Spark ML library that handles big data. An experiment was conducted using the SQLI dataset. Results show that the proposed approach achieved an accuracy of 99.04, a precision of 98.87, a recall of 99.89, and an F -score of 99.04. The time taken to identify and prevent SQLIA is 0.05 s. Our approach can protect the data by using the middle layer. Moreover, using the Spark ML library with ML algorithms gives better accuracy and shortens the time required to determine the type of request sent from the user layer.
Performance and application analysis of quantum memory

Light is the best carrier of information in quantum network. By exploring the interaction of light with matter, quantum memory technology can be further developed. Quantum memory can simultaneously receive and obtain optical quantum information on demand, which is the basis for establishing large-scale quantum computing and long-distance quantum communication. However, the performance of quantum memory directly affects its practical application process and the progress of quantum information technology. In the past two decades, quantum memory in various physical systems and quantum information protocols has been intensively studied, its performance has been significantly improved, and its relevant applications have been widely demonstrated. In this paper, we firstly sort the research progress of quantum memory metrics in the past ten years, and discuss the development of efficiency, lifetime, fidelity and mode capacity in detail according to the characteristics of cold atom systems and solid-state doped ion crystal systems. Secondly, the recent typical applications of quantum memory in quantum entanglement, memory-enhanced multi-photon processes, and quantum interference of different particles are introduced. Finally, the future development of quantum storage is prospected and summarized.
Automatic software bug prediction using adaptive golden eagle optimizer with deep learning

Abstract In the software maintenance and development process, the software bug detection is an essential problem because it related with the complete software successes. So, the earlier software bug detection is essential to enhance the software efficiency, reliability, software quality and software cost. Moreover, the efficient software bug prediction is a critical as well as challenging operation. Hence, the efficient software bug prediction model is developed in this article. To achieve this objective, optimized long short-term memory is developed. The important stages of the proposed model is preprocessing, feature selection and bug detection. At first the input bug dataset is preprocessed. In preprocessing, the duplicate data instances are removed from the dataset. After the preprocessing, the feature selection is done by Adaptive Golden Eagle Optimizer (AGEO). Here the traditional GEO algorithm is altered by means of opposition-based learning (OBL). Finally, the proposed approach utilizes a long short-term memory (LSTM) based recurrent neural network (RNN) for bug prediction. Long Short-Term Memory (LSTM) network is a type of recurrent neural network. The promise and NASA dataset are considered as the input for bug prediction. the performance of proposed approach is analysed based on various metrics namely, accuracy, F- measure, G-measure and Matthews Correlation Coefficient (MCC).
Planet Anima: a virtual graduation experience in the metaverse

In this paper, we present Planet Anima, a virtual environment designed for hosting virtual graduation events in the metaverse. Through this digital space, we successfully organized an online event under the COVID-19 circumstance, including a graduation ceremony and a student art exhibition. Our user study analyzes the Planet Anima experience with a focus on collaborative engagement and creative expression. Furthermore, we explore the impact of platform choice (headset VR vs. desktop VR) on metaverse user experience. Our findings demonstrate that Planet Anima can facilitate the co-experiences that foster creativity. Notably, the results indicate that headset VR outperforms desktop VR in terms of presence, social presence, and emotional involvement. This study also highlights the gaps between virtual and physical experiences, identifying the challenge of cybersickness associated with headset VR. These insights contribute to enhancing the user experience within the metaverse.
First end‐to‐end PQC protected DPU‐to‐DPU communications

Abstract The appearance of quantum computing in the short foreseeable future and its capability to break conventional cryptographic algorithms forces to change the paradigm of secure real‐time communications. Thus, government organizations, data centers, and enterprises among others are migrating their public key infrastructure towards using post‐quantum cryptography (PQC) algorithms in order to mitigate the security threats posed by quantum computers. This letter presents the first quantum resilient secure end‐to‐end communication link based on PQC algorithms operating between two data‐processing units DPU. Both data‐processing units employ on‐board ARM processors to perform the computationally expensive cryptographic building blocks—in that case CRYSTALS‐Kyber as a key encapsulation mechanism and CRYSTALS‐Dilithium for digital signature scheme in combination with advanced encryption standard with 256‐bit key.
Dual-Channel Decisions under Blockchain and Returns

Blockchain is currently used in a wide range of industries to improve the efficiency of the circulation of goods and effectively reduce counterfeiting in supply chains. In order to improve consumer trust in their purchases and reduce returns, the paper develops four models of consumers return based on blockchain technology from a consumer utility perspective. We conducted a Stackelberg game to analyze the impact of return modes and blockchain technology on optimal decisions and consumers, where consumers can return goods through the original channel, all through the online channel, and all through the offline channel. The major results of our study show that when blockchain technology is not used, the costs of return hassles in one channel can have an impact on other channels, and the adoption of online returns is advantageous to both consumers and the retailer. When blockchain technology is used, the manufacturer offers the retailer a lower wholesale price as a subsidy for the unit validation fee, which is always advantageous to the retailer. In most situations, implementing blockchain technology can boost consumer surplus. Only if the fixed cost of blockchain technology is low would the manufacturer adopt it.
An on‐chain trading model of real world asset backed digital assets

Abstract Metaverse is a digital value interaction network based on blockchain technology, with an important economic system component. While both traditional financial industries and crypto‐native industries have made significant progress by leveraging blockchain, the value stream of each remains limited to separate ecosystems. To bridge this gap between off‐chain and on‐chain economic systems, an on‐chain trading model was proposed using HD key derivation technique for direct uploading onto chains without going through centralized services for IoT data transmission. To improve the current status of NFTs as static assets, a token protocol binding each NFT with a unique account address was proposed. Additionally, oracle technique was leveraged with a decentralized and distributed trust model spanning across on‐chain and off‐chain components which securely pushes data between smart contracts and Web‐APIs. A decentralized trading model was developed based on smart contracts implementing automated market makers according to CFMM algorithm. Parallel transaction computing was executed based on the DAG model to ensure high operational performance and security standards of underlying blockchain. Finally, the on‐chain trading system of real world asset backed digital assets was developed integrating all the above key techniques that correspond to crucial functions of a complete economic system in Metaverse.
BlockDetective: A GCN‐based student–teacher framework for blockchain anomaly detection

Abstract The anonymous and tamper‐proof nature of the blockchain poses significant challenges in auditing and regulating the behaviour and data on the chain. Criminal activities and anomalies are frequently changing, and fraudsters are devising new ways to evade detection. Moreover, the high volume and complexity of transactions and asymmetric errors make data classification more challenging. Also, class imbalances and high labelling costs are hindering the development of effective algorithms. In response to these issues, the authors present BlockDetective, a novel framework based on GCN that utilizes student–teacher architecture to detect fraudulent cryptocurrency transactions that are related to money laundering. The authors’ method leverages pre‐training and fine‐tuning, allowing the pre‐trained model (teacher) to adapt better to the new data distribution and enhance the prediction performance while teaching a new, light‐weight model (student) that provides abstract and top‐level information. The authors’ experimental results show that BlockDetective outperforms state‐of‐the‐art research methods by achieving top‐notch performance in detecting fraudulent transactions on the blockchain. This framework can assist regulators and auditors in detecting and preventing fraudulent activities on the blockchain, thereby promoting a more secure and transparent financial system.
Perancangan Aplikasi E-Voting pada Pemilihan Ketua Osis Berbasis Mobile

E-voting or electronic voting is a way of voting that utilizes electronic tools to facilitate the voting process. E-voting has advantages such as a fast election process, fast vote counting process, real-time vote counting where every incoming vote will be visible on the application, information is more easily accessible, reduces operational costs in conducting elections or voting and minimizes the shortcomings that exist in conventional voting methods. The rapid development of technology and smartphone users, voting by e-voting is certainly very helpful and speeds up the process of an election. The results of the recapitulation of the 6 test criteria that have been carried out obtained the results of the number of answers from respondents have a value of 100% in accordance with testing the functionality of the system using blackbox testing.
DeepTPA-Net: A Deep Triple Attention Network for sEMG-Based Hand Gesture Recognition

The use of hand gestures for human-computer interaction (HCI) has gained popularity due to its ability to provide natural and intuitive communication in human dialogues. Hand gesture recognition (HGR) using surface electromyography (sEMG) signals is more reliable and user-friendly than traditional computer vision-based methods. This study proposes a deep network named DeepTPA-Net that utilizes multi-channel sEMG signals to recognize hand gestures. DeepTPA-Net employs a ResNet50 network as an automated feature extractor and a novel triple attention (3Attn) block that connects spatial, temporal, and channel attention modules in parallel to signify important features for HGR. We evaluated the performance of DeepTPA-Net using five publicly available benchmark sEMG hand gesture datasets, including CapgMyo DB-a, Csl-hdemg, NinaPro DB1, NinaPro DB2, and SeNic. The effectiveness of the proposed 3Attn block for HGR is demonstrated through a performance comparison with other attention mechanisms. We compared the performance of DeepTPA-Net with various baseline models, including its variations and other existing methods. The results show that DeepTPA-Net significantly outperforms the baseline models for all five benchmark datasets, indicating the superiority of DeepTPA-Net for sEMG-based HGR.
Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a principled approach to aggregate information and offers mathematical analysis by exploring tools from graph signal processing. This analysis provides insights into the equivariance properties of GCNNs; spectral behaviour of the learned filters; and the stability to graph perturbations, which arise from support perturbations or uncertainties. However, extending the convolutional learning and respective analysis to the spatiotemporal domain is challenging because spatiotemporal data have more intrinsic dependencies. Hence, a higher flexibility to capture jointly the spatial and temporal dependencies is required to learn meaningful higher-order representations. Here, we leverage product graphs to represent the spatiotemporal dependencies in the data and introduce Graph-Time Convolutional Neural Networks (GTCNNs) as a principled architecture. We also introduce a parametric product graph to learn the spatiotemporal coupling. The convolution principle further allows a similar mathematical tractability as for GCNNs. In particular, the stability result shows GTCNNs are stable to spatial perturbations. owever, there is an implicit trade-off between discriminability and robustness; i.e., the more complex the model, the less stable. Extensive numerical results on benchmark datasets corroborate our findings and show the GTCNN compares favorably with state-of-the-art solutions. We anticipate the GTCNN to be a starting point for more sophisticated models that achieve good performance but are also fundamentally grounded.
Inclusive Consistency-Based Quantitative Decision-Making Framework for Incremental Automatic Target Recognition

When new unknown samples are captured continually in the open-world environment, the concept diversity accumulation of existing classes and the identification/creation of new concept classes should be considered simultaneously. Since the initial training set of existent classes may be under-prepared, adhering to immediate decisions will inevitably lead to reduced open set recognition performance and higher costs of labeling/updating. Inspired by quantitative indicators in predictive reliability assessment and semi-supervised/active learning, the inclusive-consistency-based quantitative decision-making framework (ICQdm) is proposed for incremental automatic target recognition (ATR) to evaluate the identifiability and typicality of new unknown samples, which could give the decision-making guide of recognition and updating. For recognition decision-making, the first consistency indicator calculates the reliability of the unknown sample being included by one specific training class. The test samples with low reliability should be the wrongly classified samples and new unknown classes’ samples, which are difficult to be labeled by recognition models themselves. For updating decision-making, the second consistency indicator is designed to be the sample distribution density under the inclusive constraint, which could highlight the dense sample distributions of new unknown samples outside the known training distribution. Experiments verify that the proposed ICQdm outperforms other comparison methods on the open set recognition reliability evaluation and labeling/updating efficiency.