Reviewerly/exHarmony huggingface dataset:
Optimization of the Abstract Text Summarization Model Based on Multi-Task Learning

Automatic text summtarization technology is a crucial component in the field of natural language processing, utilized to address the demands of processing extensive textual data by effectively extracting key information to enhance task efficiency. With the rise of pretrained language models, abstract text summarization has progressively become mainstream, producing fluent srummaries that encapsulate core content. Nonetheless, abstract text summarization unavoidably faces problems of inconsistency with the original text. This paper introduces a sequence tagging task to achieve multi-task learning for abstract text summarization models. In this sequence tagging task, we meticulously designed annotated datasets at both entity and sentence levels based on an analysis of the XSum dataset, aiming to enhance the factual consistency of generated summaries. Experimental results demonstrate that the optimized BART model yields favorable performance in terms of ROUGE and FactCC metrics.
Optimizing Particle Filtering Based on Improved Cuckoo Search Algorithm

In response to the issue of particle depletion caused by resampling in standard particle filters, which affects the accuracy of tracking systems, an improved target tracking method using the Cuckoo Search Algorithm for particle filter optimization is proposed. In this approach, particles are treated as host nests for cuckoos, simulating the behaviour of cuckoos searching for host nest locations. This process involves two phases: global search and local search, which guide particles to move towards high-likelihood regions. Furthermore, enhancements are made to the optimization mechanism of the Cuckoo Search Algorithm by introducing an improved method for dynamic global search step size. This improvement strengthens the algorithm's convergence speed and achieves improved tracking performance with fewer iterations and particles. Finally, simulations are conducted for tracking dynamic targets in one-dimensional space. The simulation results confirm that the enhanced algorithm indeed improves the accuracy of the tracking system for dynamic targets.
Re-encryption and pseudonym-exchange-base location service

Providing location service for vehicle users is the important application in Vehicle Ad Hoc Networks (VANETs). However, while location-based services provide convenient life services, they also cause potential risks of revealing user privacy information. Therefore, a Re-encryption and pseudonym-exchange-base location service (RPLS) is proposed in this paper. RPLS makes use of trajectory of vehicle to provide location service for vehicle user. And protect the trajectory through identity-based re-encryption strategy. In order to prevent pseudonym connection attack, pseudonym-exchange strategy based on similar behavior of vehicles is adopted, which increase the difficulty of being tracked. Simulation results show that the proposed RPLS algorithm not only guarantees the performance of location service query, but also improves the performance of protecting location privacy.
Research on False Comment Detection Model Based on the Fusion of Convolutional Neural Network and GRU

Deep learning neural networks have significantly improved the detection rate of false comments, but their processing of text is still serializable, and there are still shortcomings in addressing the implicit connections between false comments. Therefore, a false comment detection model based on CNN GRU is proposed. For this model, first analyze the characteristics of false comments in real websites, preprocess the dataset, and then use Doc2vec to extract word vectors from the text, so that the input data can be combined with contextual context and preserve word order information. Then, CNN is used for text feature representation, GRU is introduced for comment classification, and the final CNN GRU model is constructed. By comparing the performance of models with different parameters in false comment detection through experiments, the most suitable model structural parameters are obtained. Finally, a performance comparison was conducted with commonly used machine learning algorithms and neural network models to verify the effectiveness of the CNN-GRU model.
An Efficient Multi-view Stereo Reconstruction Method Based On MA-MVSNet

To address the problems of multi-view stereo reconstruction, such as low reconstruction completeness and poor reconstruction accuracy, we propose a novel multi-view reconstruction network named MA-MVSNet based on the improved attention mechanism. This network takes the basic MVSNet as the backbone and introduces Local-grouped Self-attention (LGSA) and Global Adaptive Average-pooling Attention (GAAA) into the reconstruction framework to make the network have both long-range dependence and local receptive field, which solves the problem that the existing convolutional neural network-based methods can not efficiently model the global contextual information of images and improves the reconstruction quality. The experiment shows that the proposed network can achieve excellent performance on DTU dataset, especially in terms of reconstruction completeness. Compared with the existing benchmark network MVSNet, our network has improved reconstruction accuracy by 5% and reconstruction completeness by 50%.
Code Defect Detection Model with Multi-layer Bi-directional Long Short Term Memory based on Self-Attention Mechanism

In the process of digital transformation, enterprises develop and launch numerous business application systems. Practice indicates that the application development process often introduces many logical security defects. Auditing the source code before software goes live is considered an effective means of defect discovery. This paper proposes a defect detection model based on a multi-layer Bi-LSTM with self-attention mechanism for CWE vulnerabilities. It forms an intermediate representation of structured information from code data flow analysis, inputs it into a multi-layer Bi-LSTM network for training, and then uses the self-attention mechanism to deeply extract features, ultimately generating a code defect detection model. The model is used to detect defects in CWE-399 vulnerability, and experimental results show that the model has an accuracy of 93.51%, precision of 96.81%, recall of 86.19%, and an F1 score of 91.19%. It demonstrates high performance in vulnerability detection, validating the effectiveness of the model.
Research on signal denoising method based on improved wavelet threshold

In order to filter out the noise in the signal and improve the transmission efficiency, a signal noise reduction method based on improved wavelet threshold function is proposed. Based on the traditional soft and hard threshold denoising methods, a new wavelet threshold is proposed to remove background noise. Obtain higher output signal-to-noise ratio, and the transmission efficiency of the signal is improved. In this method, the noise signal is decomposed by wavelet, the low-resolution signal is retained, and the high-resolution is zeroed. The processed signal is reconstructed and then the filtered signal is obtained. The experimental results show that the improved wavelet threshold function proposed in this paper significantly improves the signal to noise ratio and reduces the mean square error. It provides theoretical support for efficient signal transmission.
Smart Scan: An Approach to Detect Denial of Service Vulnerability in Ethereum Smart Contracts

In this research, we introduce Smart Scan, an innovative approach designed to enhance the security of Ethereum smart contracts by detecting and mitigating Denial of Service (DoS) vulnerabilities. Smart Scan achieved an impressive accuracy rate of 89% in DoS vulnerability detection, surpassing the baseline accuracy of 84%. Additionally, the analysis of smart transactions initially achieved a baseline accuracy of 40%, shedding light on potential anomalies within Ethereum contracts, which combines static and dynamic analysis with a broad ensemble of machine learning algorithms. A 40% accuracy rate was also reached in the initial study of smart transactions, which helped to illuminate the possibility of anomalies in Ethereum contracts. By scanning for vulnerabilities before they can be exploited, Smart Scan helps developers and contract auditors make Ethereum's building blocks more bulletproof. To further solidify blockchain's position as the digital future's secure and transparent backbone, Smart Scan contributes to its continued development and adoption.
Spammer Groups Detection Based on Hypergraph Embedding And Autoencoder Classifier Model

Aiming at the limitations of existing spammer group detection approaches, we propose a novel approach for detecting spammer groups based on hypergraph embedding and autoencoder-classifier. First, we use time periodicity and rating behavior to model the review dataset as a heterogeneous hypergraph. Second, we combine the autoencoder and attention mechanism to design a hypergraph neural network to learn the high-quality low-dimensional vector representations of nodes from the heterogeneous hypergraph and use the MeanShift clustering algorithm to obtain candidate spamming groups by filtering items, ratings, and time nodes. Finally, we apply an abnormal detection model based on an autoencoder-classifier to detect the spammer groups. The experimental results on real-world review datasets show that the proposed method has better detection performance than the baselines.
Research on MBSE-based conceptual modeling framework for data link operations

A modeling framework for data link operational concepts is the basis and prerequisite for standardizing and guiding the development of data link operational concepts. Based on the MBSE method, a data link operational concept modeling framework covering the general description, operational environment, operational adversary, operational mission, operational capability requirements, operational force system, operational activities, operational concept of operations and other view models is proposed, the logical relationship and detailed composition among the view models are analyzed, and the development strategy of the data link operational concept modeling framework is specifically designed. It can provide theoretical methods and means to support the systematization research and engineering design of data link combat concepts.
Design of Fall Detection System based on YOLOv3

Aiming to address the risk of falls among the elderly, it proposes a human fall detection system based on YOLOv3. To improve the convergence and detection performance of the original YOLOv3 network, a loss function based on Intersection Over Union is employed for improvement. With the same detection time, the improved YOLOv3 network achieved a detection accuracy of 98.3%, which is 1.3% higher than before the improvement. After training completion, the pt format model was converted into the wk format that can be recognized by the development board and ultimately transplanted onto Hisilicon Hi3559AV100 device to run, completing the hardware implementation of the fall detection system. Testing showed an accuracy rate of 93.5% in different scenarios.
Application of ensemble learning in evaluating the effectiveness of military training

In order to solve the problem of subjective adjustment of evaluation index weights in the Analytic Hierarchy Process (AHP) for military training effectiveness evaluation, and the inability to accumulate historical evaluation experience, which leads to unscientific comparison of effectiveness evaluation results between different units, this study proposes an integrated learning model applied in the field of military training effectiveness evaluation. This model is based on the existing Analytic Hierarchy Process and constructs an indicator system based on historical evaluation data of each unit, Train multiple sub models separately, and through the designed model fusion device, fuse the multiple sub models into the military training effectiveness evaluation model proposed in this paper. Using a certain training dataset as the data source, the samples were divided into training and testing sets in a ratio of 8:2. After training, the proposed model achieved an accuracy of 98.98% in the testing set, with an average absolute error of 1.08%. This model effectively avoids subjective evaluation and can provide scientifically comparable evaluation results.
RCAN based MRI super-resolution with applications

High-quality Magnetic Resonance Imaging (MRI) provides an aid to reliable diagnosis. By processing medical images, doctors are able to view pathological features more easily; however, since pathological features are usually small in the early stages, we would like to obtain medical images that are as clear as possible so that doctors can observe relevant features and even finer textures. Therefore, applying image super-resolution technology to medical images can reconstruct high-resolution medical images without increasing hardware equipment, which helps doctors make better diagnoses of patients' conditions. The use of deep learning methods can effectively reconstruct high-resolution images. In this paper, RCAN network is applied to the medical image dataset IXI and formed a control experiment with SAN, EDSR and T2Net network, and RCAN achieves good results both in terms of data metrics and in terms of recovering images for analysis.
Parallel Personal Protective Equipment Recognition Network Based On Multimodal Fusion

The emergence of the Omicron variant in February 2022 resulted in widespread challenges in Shanghai, necessitating proper monitoring of Personal Protective Equipment (PPE) in mobile cabin hospitals for staff and patient safety. Camera-based PPE approaches offer effective monitoring for compliance and safety at work. However, existing camera-based approaches proved inefficient in accurately detecting diverse PPE components. To address this issue, we propose the Parallel Multimodal PPE Recognition Network (PMPRN), an end-to-end solution that combines target and keypoint detection in parallel. PMPRN integrates the finetuned MobileNetv2-YOLOX object detector and MobileNetv2-CID model to enhance detection performance. We introduce the novel PPELoss function to improve geometric position judgments and detection efficiency in various scenarios. Additionally, an adaptive Hybrid fusion strategy handles the exponential growth of equipment categories, ensuring precise detection and identification of PPE items like gloves. Compared to existing detectors, PMPRN demonstrated a significant 27.6-point accuracy improvement and a 12.76 Fps speed increase. Notably, it accurately detects gloves held in hand and identifies improper usage of glasses, masks, and other equipment. The proposed PMPRN was validated on a dataset of prevention personnel, and the test results demonstrated the effectiveness and real-time performance of the proposed model and framework.
Research on the efficient performance algorithm of fragmented blockchain for industrial Internet of Things applications

This article addresses the challenge of optimizing performance in a fragmentation blockchain system for industrial Internet of things (IIOT) applications. It introduces an enhanced version of the traditional Whale Optimization Algorithm (WOA) known as the Chaotic Whale Optimization Algorithm. This enhanced algorithm is designed to optimize the parameters of the fragmentation blockchain system with the goal of maximizing real-time memory utilization in the fragmentation nodes while minimizing block consensus time. The proposed method identifies optimal blockchain system parameters within diverse network environments, taking into account factors such as node computing power and inter-node transmission rates. To mitigate the issue of local optima inherent in traditional algorithms, the Chaotic Whale Optimization Algorithm transforms the optimization problem in a more intuitive manner. This algorithm strikes a fine balance between diversity and convergence. The effectiveness of this method is assessed through simulation experiments, with results indicating its superior performance compared to alternative approaches. Overall, the approach presented in this article significantly reduces consensus time within the fragmentation blockchain system and enhances real-time memory utilization in the fragmented blocks.
Path planning algorithm design integrating ultra-wideband positioning and Beidou satellite navigation

In mobile robot design, the most commonly used path planning algorithm is A*. To address the issues of static planning and the inability to perceive the robot's current position in traditional A* algorithm, a path planning algorithm based on ultra-wideband (UWB) positioning and Beidou satellite navigation is proposed. Indoor positioning is achieved using a UWB quad-system for obtaining positional coordinates, while outdoor positioning relies on Beidou technology to obtain positional coordinates. Additionally, the search neighborhoods of traditional A* algorithm are improved to shorten path lengths, reduce the search of irrelevant nodes, and enhance the efficiency of path planning. The introduction of higher-order Bezier curves removes sharp points from the path, making it smoother and more suitable for practical mobile robot motion planning.
Human Behavior Recognition Algorithm Based on OpenPose Group Graph Convolution

In this paper, aiming at the problems that the current human behavior recognition method is easily affected by the background environment when extracting video behavior feature information, resulting in incomplete extraction of feature information, local information, excessive number of parameters and large amount of computation, this paper proposes a human behavior recognition (HAR) based on the combination of skeleton key points and group graph convolution (GGCN) Method. This method constructs a global pose map by grouping the key point information of multiple people, and uses the grouping graph convolution to convolution the pose map, which reduces the number of parameters and calculations while extracting features. Based on the training and testing of the model on UCF-101 and HMDB-51 public datasets, and the horizontal comparison test with the current mainstream advanced algorithms, the simulation results show that the proposed method has higher accuracy in human behavior recognition tasks, which verifies the feasibility of the model.
Research on a Scale-Based Feature Image Retrieval Algorithm

With the continued evolution of information retrieval technology, the necessity for prompt and precise image information depiction has become paramount. Traditional methods primarily focus on extracting image features, encompassing texture, color, and spatial relationships. This study, however, introduces a scale-based feature image retrieval algorithm. This technique serves as a method for local feature vector extraction, capturing scale angle variations and exhibiting affine invariant characteristics. In assessments, this algorithm consistently outperforms its conventional counterparts, especially in matching scenes from varied viewpoints.
Student Behavior Recognition in Classroom Based on Improved YOLOv7

Student behavior recognition in the classroom through deep learning is of great significance for smart education. However, there is currently a lack of public available datasets in this area, and the recognition accuracy is still low. A behavior recognition method based on an improved Yolov7 is proposed. The ConvNeXt module is introduced to improve the extraction of image features. In addition, the channel attention module and the spatial attention module are incorporated into the upsampling and downsampling of the feature pyramid network, which obtains more geometric and semantic information. Furthermore, Wise IoU is used to effectively streamline the learning process for objects of different sizes. A dataset of students' classroom behavior covering nine types is constructed for experiments. The experimental results show that the improved network achieves 80.29% in the accuracy of student behavior recognition, an improvement of 4.78% over the original YOLOv7, and reduces the missed recognition rate.
Quality Assurance in Digital Forensic Investigations: Optimal Strategies and Emerging Innovations

Digital forensic investigations are critical in modern law enforcement, cybersecurity, and legal proceedings. Ensuring digital evidence’s accuracy, integrity, and reliability is paramount in these contexts. This review article explores the challenges and best practices associated with quality control in digital forensic investigations and the emerging technologies that are reshaping the field. The article begins by discussing the foundational concepts of quality control in digital forensics, emphasizing the need for standardized procedures, documentation, and validation techniques. It delves into the potential sources of errors and bias that can arise during the acquisition, preservation, analysis, and presentation of digital evidence. It highlights the importance of continuous monitoring and review to mitigate these risks. The review article further examines the evolving landscape of digital forensic tools and technologies advancing quality control efforts. It covers advancements in data acquisition methods, including live forensics and memory analysis, and discusses the role of artificial intelligence and machine learning in automating quality control processes. The integration of Blockchain and cryptographic techniques for ensuring the integrity of digital evidence is also explored. In addition, the article addresses the challenges and opportunities presented by cloud computing, IoT devices, and the proliferation of digital data sources. It emphasizes adaptability and agility in quality control approaches to accommodate the changing digital landscape. Through a comprehensive analysis of established practices and emerging technologies, this review article offers practitioners, researchers, and policymakers’ insights into enhancing the reliability and trustworthiness of digital forensic investigations. By adopting robust quality control measures and embracing innovative technologies, the digital forensics community can ensure its findings hold up to scrutiny in the courtroom and beyond.
Revisiting Artificial Intelligence in HRM

The study aims to provide a descriptive understanding about the adoption of artificial intelligence in HRM by the business organizations in the contemporary business environment. Literature review has been adopted as a research tool in the study. All sources of literature were searched and reviewed. The literature review has revealed that the corporate sector is rarely utilizing AI in the overall management of human resources. Even many companies were found to be not showing readiness behavior towards robotic process animated techniques because of its complex nature. However, literature review based on systematic literature review/bibliometric literature review/meta-analysis is suggested to increase the thoroughness of the review process in future researches to facilitate the generalisation of the results. Another limitation of the study is that no empirical evidence has been collected. The study has Industry Implications as through AI, businesses can enable computers/machines to act as robotic assistant tools to perform functions that are generally implemented by human cognition. In the age of technologies, artificial intelligence (AI) serves as a real breakthrough for human resource development as it assists the present stressful corporate world by replacing human intelligence into machine intelligence. The study highlighted the theoretical significance of Artificial Intelligence in the field of Human Resource Management. Along with that, the study concludes that businesses should try to adopt AI more in the management of its human resources in order to remain competitive in the present and future world of work. But, at the same time, it will not replace emotional intelligence significantly required for human resources management.