Reviewerly/exHarmony huggingface dataset:
FedAvg-DWA: A Novel Algorithm for Enhanced Fraud Detection in Federated Learning Environment

The research on credit card fraud detection can protect consumers and banks from economic losses. The current research direction mainly focuses on improving the efficiency and accuracy of fraud detection using machine learning technology. This paper mainly studies how to use federated learning to improve the efficiency and effect of credit card fraud detection. The experiment uses the credit card transaction data of European cardholders and uses the Dirichlet distribution to randomly and proportionally allocate the sample data to 10 simulated banks. In response to the limited prediction accuracy of existing federated learning algorithms such as FedAvg (Federated Averaging) for imbalanced data, this paper proposes a FedAvg-DWA algorithm (Federated Averaging with Distance-based Weighted Aggregation). This algorithm considers the weight of small class samples and innovates in the model distance aspect of the aggregation strategy. The experiment achieved good results, providing a new method for dealing with the problem of class imbalance.
Lightweight IoT-based Authentication Scheme in 5G Circumstance

With the popularization of internet devices and 5G technology, the Internet of Things has become one of the most popular technologies today. However, the massive amount of data generated by various intelligent devices is one of the biggest problems in IoT scenarios. In order to solve the problems of data theft and network congestion in large-scale IoT, scholars have quickly studied authentication technologies in IoT and 5G environments. However, there is currently no achievement that combines data security and low transmission latency. In this study, we propose an authentication scheme based on a combination of IoT architecture and 5G servers. To solve the problem of excessive authentication overhead caused by massive terminal access to the network, we adopt lightweight encryption modules such as one-way hash functions and XOR operations. This reduces the computational burden and makes the proposed authentication scheme suitable for objects with limited resources, such as sensors or IoT devices.
Ghost Detection Using Spatial Features-Intensity Fusion for Autonomous Mining Trucks

The multi-dust environment in open-pit mines presents a great challenge for the perception of autonomous mining trucks, leading to a large number of ghost detections (i.e., dust is detected as an obstacle), which seriously affects the production efficiency and increases the risk of accidents involving other traffic participants. To solve the ghost detection problem, this paper proposes a Spatial Features-Intensity Fusion method based on LiDAR point clouds collected from real mining areas. This method first constructs an offline data bank of obstacles and dust, in which the prior feature distributions of surface distance, angle, area, and reflected intensity of point clouds are analyzed. In the online classification pipeline, the grid clustering algorithm extracts point cloud clusters in a specific experimental scenario. Then the similarity between features of extracted point cloud clusters and prior feature distributions in the data bank is calculated using the Mahalanobis distance, which determines whether the current point cloud cluster is a ghost detection. The results show that for all detected point cloud clusters of the same category, the classification accuracy of the proposed method achieves 100% for obstacles and 96% for dust. Moreover, misjudging little dust as an obstacle will not lead to risks, and in most cases does not significantly affect the transportation efficiency of autonomous mining trucks. This method has the potential to help autonomous mining trucks eliminate interferences from adverse weather conditions such as dust, rain, snow, and fog, which is necessary for driving safety and efficient production in autonomous mining areas.
Improved Streaming Computing Method Based on IoT Components of Virtual Power Plant

The virtual power plant is an important part of the new power system. Its IoT components need to collect and process energy equipment measurement data in real time, which inevitably uses stream computing. This paper improves the original streaming computing method. By sorting the data according to the reference relationship, it avoids the centralized storage of a large amount of data, reduces the concurrent access to the data, and realizes that each step is parallel; by splitting the data, the data processing in the branch stream is simplified; by encapsulating computing units, the free distribution of computing on distributed computing nodes is realized, avoiding the bottleneck limitation of a single node, and realizing lateral expansion. This ensures stable system operation and efficient use of computing resources.
Short Text Classification Model Based on DeBERTa-DPCNN

Aiming at the sparsity, real-time and irregularity of news headline text, a Chinese short text classification algorithm based on DeBERTa-DPCNN is proposed. The short text vector is extracted through DeBERTa’s attention decoupling mechanism and the enhanced mask decoder, and the short text information is extracted deeply with the help of DPCNN to obtain more global features of the text. The F1 value of the comprehensive evaluation index of the DeBERTa-DPCNN fusion model is 5.12%, 1.03%, 0.47%, 13.68%, 4.44%, 3.3%, 2.78%, 0.12% higher than that of the BERT model, RoBERT model, XLNet model, joint DPCNN network model, BERT-DPCNN model, ERNIE-DPCNN model, ERNIE-DPCNN-BiGRU model and DeBERTa model respectively.
A Multi-dimensional Generic Evaluation Framework for the Security of Large Language Models

In light of the widespread adoption of large language models, their susceptibility to security vulnerabilities cannot be overlooked. As a result, it has become imperative to evaluate their proficiency in addressing issues such as toxicity, bias, and disinformation. However, current research focused on appraising and mitigating security risks has predominantly concentrated on specific facets, leading to disparities in evaluation criteria. In contrast, there has been relatively limited attention given to multidimensional and universal frameworks for security evaluation. In this context, this paper delves into the realm of generic evaluation frameworks for security that offer support for cross-language and multi-category analysis. We underscore the existing challenges associated with prominent large language models concerning security issues and develop a comprehensive test data set to furnish researchers with a tool for quantifying security aspects. Through comprehensive evaluations across three major benchmark tests, we identify distinct strengths and weaknesses exhibited by each open source large language model to varying degrees. By employing a multi-dimensional security evaluation framework, we can attain a more holistic comprehension of the performance exhibited by each model across diverse security dimensions. This approach holds significant value in advancing the domain of security research and facilitating the practical application of language models.
Research on Relation Extraction in the Field of Aviation Electrical Fault Diagnosis

In order to reduce the troubleshooting time of aviation electrical maintenance and effectively utilize unstructured textual information related to electrical systems, we proposed an improved model for relationship extraction using CASREL. This model incorporates entity recognition and relationship extraction techniques to identify faults and extract relationships, aiming to enhance the efficiency of reading and searching through manuals and facilitate knowledge sharing among maintenance staff. For handling overlapping relationships among electrical fault entities, we adopted a cascaded binary labeling framework. To strengthen the model’s ability to capture local features, we employed BERT-wwm-ext as the encoder and introduced a self-attention module based on scaled dot product. Additionally, we enhanced the nonlinear capability and complex feature learning capability by replacing the single Linear layer in the annotator with a multi-layer perceptron. To evaluate the effectiveness of our improvements, we conducted several sets of comparative ablation experiments. The results demonstrate a 7.5% improvement in the F1 value of our improved model compared to the original model, achieving a value of to 96.7%. It provides a solid foundation for subsequent implementations of aviation electrical knowledge graph and intelligent fault diagnosis system.
A Multi-Agent Action Prediction Algorithm Incorporating Decision Trees and Residual Networks

The strategies of traditional multi-agent are difficult to understand from the outside through human observation due to the large joint state space and complex action behavior. Therefore, this paper investigates the utilization of expert log data in multi-agent and proposes a method for fitting strategies with residual networks based on expert datasets. The method can predict the agent’s forthcoming action and action parameters from the current state, so that the problem of predicting the opponent’s action in real time can be solved by using the expert data obtained from a large number of simulation environments. Results obtained from the RoboCup2D soccer simulation environment demonstrate that the algorithm effectively predicts agent actions and fits strategy models for different teams.
A New Method for Comparing the Difficulties of Chinese Texts Based on Natural Language Processing Technology

Research on the difficulty comparison of different Chinese texts involves multiple fields. Some commonly used methods: Vocabulary Complexity Analysis, Grammar and Semantic Complexity Analysis, Theme and Domain Specialization Analysis, etc. The article attempts to explore a new method for comparing text difficulty by calculating the numerical value of mean dependency distance (MDD). Research analysis: The higher the MDD value, the greater the difficulty of the text; The difficulty of the textbook text increases as the order increases, which also means that the difficulty of the text gradually increases; The difficulty level of Chinese texts in different periods is around the average of 3.662, which also indicates that the MDD value of Chinese texts is higher than that of other languages, and there is statistical support for the higher difficulty level of Chinese texts compared to other languages.
RFD:Detecting SAR Coastal Ship Targets Based On Reducing Feature Decay

Synthetic aperture radar (SAR) image has been widely used in the detection of ship target. Especially with the development of artificial intelligence, machine learing has been applied for ship detection in SAR images. However, it is hard to detect small scale ship in the SAR image using the current methods of SAR ship detection due to the image characteristics such as inshore complex background, geometric distortion, low pixel values, and limited useful information. In response to these problems, we propose a detection method based on feature reducing decay which could decrease the loss of effective information, i.e., reducing feature decay (RFD) module. First, one of the feature extraction models named RFD-ELAN model is suggested to reduce the loss of valuable information caused by multiple convolutions and activation functions. Then, the Refine-IoU loss function is used for loss calculation, which could provide a relatively accurate representation of the difference between prediction bounding box and ground truth bounding box. Finally, we utilize clustering analysis to improve the adaptability of anchor boxes and reduce redundancy in prediction areas. The experimental results show that the detection accuracy of the proposed algorithm has increased by 2.77% and 3.9% respectively, compared with the baseline for mAP_0.5:0.95 in the SIRSDD and HRSID coastal ship dataset. The experimental results show that the detection accuracy of the proposed algorithm is higher than that of baseline for both mAP_0.5:0.95 in the SIRSDD and HRSID coastal ship dataset, which improvement is 2.77% and 5.1% respectively.
Graph Convolutional Networks with Sliding Window Attention for Skeleton-Based Action Recognition

Graph Convolutional Networks (GCNs) have been widely applied and achieved significant results in skeleton-based action recognition. However, existing methods mostly focus on modeling static spatial features of the skeleton structure, while overlooking the highly coordinated temporal and spatial relationships among joints. In this work, we propose Sliding Window Attention Graph Convolution (SWAGC) method which introduces local sliding windows to expand spatial feature extraction in the temporal dimension and propagates joint features to capture key joint information in different temporal periods. Furthermore, to better model temporal dynamics, we introduce Global Multi-Scale Temporal Convolution (GMSTC). GMSTC combines multi-scale temporal convolutions with self-attention mechanism to extract global multi-scale temporal dynamics and enhance the modeling capability for different types of actions. Combining SWAGC and GMSTC, we develop a graph convolutional network named SWA-GCN, which achieves performance comparable to state-of-the-art methods on 3 main stream datasets: NTU RGB+D, NTU RGB+D 120, and NW-UCLA.
Improving SMS Spam Detection Through Machine Learning: An Investigation of Feature Extraction and Model Selection Techniques

When it comes to SMS, the topic of spam is a big obstacle that requires urgent attention. The majority of SMS messages that fall under the category of spam are commercial communications, which are obviously illegal and unethical, followed by phishing scams and messages that may disturb the user's peace of mind. Users may find this to be extremely inconvenient as it consumes energy to erase those messages, slows down websites, and may even infect computers with viruses. We must therefore distinguish between SMS messages that are spam and those that are not in order to prevent this. We are recognizing this in this project utilizing machine learning algorithms. In this paper, we will talk about the algorithms we employ, compare them all to the dataset we use, and finally select the algorithm that will be most effective at determining which SMS messages fall under the spam category. Based on comparisons of precision and accuracy, we choose the optimum algorithm, then for the results of the research in our paper, we found that the best non-pretrained machine learning model was GRU with the best feature extractor being GloVe, with an accuracy of 91.9171% and precision of 91.9187%. However, the pretrained machine learning model BERT still outperformed the non-pretrained model with an accuracy of 99.0166% and precision of 99.0179%.
Gamification-Based To-Do List Mobile Application Development

The paper discusses the development of mobile to-do list applications using the concept of gamification. The purpose of developing this application is to assist users in motivating themselves to complete tasks encountered in everyday life. The gamification concept applied to this application consists of several elements, such as the use of a point system, level system, and badge system as a representation of the user. In this paper, the application is developed using the waterfall method. Then, testing the application involves several users as participants. The results of the test show that application users feel more motivated and more productive in completing their tasks after using this application. In addition, users also provide positive feedback regarding the gamification elements applied to the application. Overall, developing a mobile to-do list application using the concept of gamification can be an effective solution to help users increase their motivation and productivity in completing daily tasks.
Analysis the Influence Factors of Intention to Use NFT Application

Non-Fungible Token (NFT) is a cryptographic asset and each unit is distinct from other assets in its own right. Cryptocurrency is an important element of NFT transactions. In 2022, cryptocurrency has experienced a tremendous decline, but the number of NFT transactions had significantly increased. According to a previous studies, NFT has its own popularity and followers. In this case, this research was carried out to examine the information quality model-based NFT's actual content in order to determine the elements that can affect the intention to use aspects. 207 respondents completed the survey, but only 198 were used to process the data. This study used the quantitative method with the structural equation modeling and partial lease square (SEM-PLS) methodologies. It was discovered that knowledge and motivation were two factors that did not affect the intention to use, while the other five did. This study identified 91.3% as a strong influencing factor (R2). This study is very beneficial to a variety of sectors, particularly those involved in NFT and digital marketing (trending advertising).
Coffee Distribution Model with Blockchain Technology to Increase The Transparency of Local Coffee Distribution

Coffee is a popular drink all over the world. Its composition and characteristics are influenced by several factors, such as geographical and plant origin, harvesting and roasting conditions, and the brewing method used. With the increasing consumption of coffee, the demand for its high quality and authenticity naturally increases as well. Unfortunately, at the same time, there is a lot of counterfeiting of coffee due to the lack of distribution transparency to support coffee sales, and this is a problem that becomes an issue in this research. The low transparency of coffee distribution is one of the crucial problems faced by the coffee industry. This problem arises because there is no transparent and accountable coffee distribution monitoring mechanism. On the other hand, blockchain technology can make transactions with high transparency and data security. Therefore, as a solution to the problem of coffee counterfeiting, we offer the adoption of distribution and sales using blockchain technology to increase the transparency of coffee distribution from coffee farmers to customers. The research method uses a qualitative approach by identifying problems through literature reviews and interviews with stakeholders involved in coffee distribution. The result of this research is a conceptual model of coffee distribution using blockchain technology. At the end, a simulation with sample data will be shown using the blockchain algorithm.
Purchasing Power Analysis Using K-Means Algorithm

The purpose of this study is to analyze the purchasing power of district and city communities in West Java based on their spending levels, using data from Jabar Open Data for the years 2018 to 2021. This study focuses on one of the factors that affects purchasing power, namely spending, and uses the K-means algorithm to analyze it. This algorithm helps to group data into clusters, which enables a better understanding of purchasing power based on the expenditure levels of people in districts and cities in West Java. The research was conducted using the CRISP-DM method with the K-means algorithm, which is applied to perform data mining on expenditure level data of district and city communities in West Java, listed in West Java Open Data. The data consists of 320 regions including cities and regencies in West Java Province, from 2010 to 2021, with provincial codes, regency codes, and cities. From this data, the research aims to identify regional groups that have high purchasing power, which are considered areas with good opportunities for establishing businesses. The results of this study indicate that urban areas in West Java have high purchasing power, which makes them good locations for establishing businesses. Based on this finding, the researchers plan to establish culinary businesses in urban areas in West Java, particularly in the city of Bandung.
Digital Artwork Marketplace Web Application Design using Blockchain Technology

This research was conducted to apply other ways of transacting modern digital art using blockchain technology. This technology can help artists easily sell their works of art by verifying the ownership of digital art, where previously only physical art could do it. The use of NFT (Non-Fungible Tokens) in publishing digital artworks in a digital marketplace media platform can be a solution for the copyright of work in the future for artists in Indonesia. The developed Marketplace platform applies the Polygon Matic blockchain technology and a smart contract from a third party, namely MetaMask, to secure and process every transaction that occurs to provide security and convenience in digital commerce.
Security Risks and Best Practices for Blockchain and Smart Contracts: A Systematic Literature Review

The rise of blockchain technology and smart contracts has brought widespread attention due to their capacity to transform multiple industrial sectors through decentralized, transparent, secure transactions. However, despite their promise to revolutionize various fields worldwide, lingering concerns regarding security risks impede their adoption rate. Addressing these concerns is crucial now more than ever; therefore, we conducted a comprehensive literature review within our study's scope that focused on published papers between 2014-2023 centered around security risks concerning blockchain and smart contracts. Our systematic approach using the PRISMA checklist analyzed nine categorized research model-based primary studies while recognizing vulnerabilities in smart contract development and providing best practices to mitigate such issues. These findings benefit both researchers and practitioners as they showcase how acknowledging these vulnerabilities can further develop into exploring more significant aspects of blockchain technology's security issues and smart contract development processes. Our study contributes significantly by expanding knowledge in this field while providing novel insights valuable for individuals involved in designing or implementing blockchain technologies.
User Experience Evaluation of the Booking Website using System Usability Scale and Usability Testing (study case sports arena)

Saraga is a website that aims to assist users in searching, finding, and booking sports arenas according to the availability of time, location, and facilities needed. This website has sufficient features and information related to sports arenas and facilities. The preliminary survey of 31 respondents showed that 38.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> gave a score of 2 out of 5 for the sports arena booking system. Users find it difficult to choose a sports arena because the process is on separate pages. For users to have an easier time using the sports arena booking system, it is necessary to conduct a user experience evaluation by measuring the level of usefulness through usability testing and system usability scale to gauge user satisfaction with the sports arena booking system. Based on the results of the system usability scale analysis of 63 new respondents, it is known that 54 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of respondents gave an awful score. However, usability testing results show an appropriate category for aspects of learn ability at 65.71 %, effectiveness at 62.85%, efficiency at 63.29%, memorability at 70%, and errors at 24 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in the category of less good. Therefore, improving the sports arena booking system's functionality, features, and content is recommended.