Reviewerly/exHarmony huggingface dataset:
Trajectory Privacy Protection Method Based on Sensitivity Analysis

With the widespread popularity of mobile devices, users continue to generate a variety of trajectory data in their daily activities. However, trajectory data often contains sensitive information of users, and direct disclosure of such data may lead to the leakage of user privacy. In order to solve the above problems, In this paper, we propose a Trajectory Privacy Protection Method Based on Sensitivity Analysis (TPPSA), combined with differential privacy-preserving strategies to improve data availability while ensuring privacy-preserving utility. In the data preprocessing stage, the data is segmented using temporal and spatial dimensions to effectively reduce noise injection. Semantic location and check-in frequency are introduced to construct a sensitivity analysis model for trajectory data, and the privacy budget is allocated according to the semantic relevance and sensitivity of historical trajectories to improve privacy protection effectiveness and data availability. By constructing a prefix tree, the data is hierarchically noised based on Markov probabilistic prediction model, and the noised trajectories and their counts are published. We verify the privacy and utility of this scheme by conducting experiments on real datasets.
Research on Plane Marking Algorithm of Aluminum Ingot Based on GA-RANSAC Fitting

When laser marking the surface of aluminum ingots, accurate plane parameters must be determined. Due to the presence of noise points and outliers in the acquisition process of aluminum ingot surface point clouds, the fitting accuracy of plane markers will be seriously affected. For the aluminum ingot plane parameter fitting problem, the random sampling consistency (RANSAC) algorithm can effectively estimate the plane parameters and has a certain degree of robustness. However, in order to obtain accurate plane fitting results, the number of iterations needs to be increased, which affects the operating efficiency of the system. In response to the above problems, this paper proposes a point cloud fitting plane (GA-RANSAC) algorithm that combines genetic algorithm (GA) and RANSAC algorithm to shorten the system running time by reducing the number of iterations. Experimental results show that compared with the RANSAC algorithm and other plane fitting algorithms, the GA-RANSAC algorithm can accurately estimate plane models in point clouds of tens of thousands of orders of magnitude, improving the operating efficiency of the system. The GA-RANSAC algorithm is a plane fitting algorithm with strong robustness and high accuracy, which lays a good foundation for the subsequent work of surface marking of aluminum ingots.
A Dual-Stream Fusion CNN-LSTM Gesture Recognition Algorithm for Command and Control Systems

In this study, we propose a novel gesture recognition algorithm based on dual-stream feature fusion CNN-LSTM, aimed at addressing the limitations and deficiencies of existing gesture recognition methods in human-machine interaction systems within the military command and control sphere. We employed Blackman windowing along with wavelet thresholding and dynamic zero-padding algorithms to suppress clutter and enhance data robustness. Utilizing the Multiple Signal Classification technique, we computed the angular parameters, which were synergized with distance and velocity parameters to create range-Doppler matrix plots and angle-time graphs. These feature maps were fed into a dual-stream CNN-LSTM network for feature extraction and classification, thereby achieving high-precision gesture recognition. Notably, this advanced system achieves a stable and precise gesture recognition accuracy rate of 98.25%, offering an efficient and secure avenue for enhanced human-machine interaction in the military domain.
Meta-learning few shot object detection algorithm based on channel and spatial attention mechanisms

The two-branch based meta-learning few shot detection algorithm will first use the shared backbone network to get the support features and query features, use the query features for bounding box generation and get the bounding box features, then use the aggregation algorithm to aggregate the bounding box features with the support features, and then use the aggregated features to completion classification and regression tasks. Since the simple channel multiplication aggregation algorithm misses valuable high-level semantic information such as channel and space, it performs poorly in the detection of novel classes of targets. To address this problem, a meta-learning few shot object detection algorithm based on channel and spatial attention mechanisms is proposed. The algorithm proposes a convolutional attention mechanism module with residual block, which includes a channel attention mechanism and a spatial attention mechanism. The support features processed using this module better characterize the semantic information that there are individual categories on channel and spatially. Aggregation of this information with bounding box features can be used to better accomplish classification and detection tasks. In addition, a decoupling module for classification and regression is proposed, which decouples the classification and regression tasks to improve the performance of the algorithm. In this module, different aggregation algorithms are used for classification and regression tasks respectively. To verify the effectiveness of the algorithm, experiments are conducted on the public dataset PASCAL VOC. The experimental results show that the performance of the algorithm gets a performance improvement over the baseline model.
Research and Implementation of Gesture Recognition Mechanical Arm System Based on LeapMotion

This paper designs an interactive control system for gesture recognition mechanical arm based on LeapMotion. The LeapMotion somatosensory controller is used to replace the traditional human-machine interaction method. The control circuit of the mechanical arm is designed based on the STM32, and hand data is collected by visual Information. The recognized gesture actions are analyzed and processed by the computer, and control information is transmitted to the development board through wireless transmission, And then control the mechanical arm to imitate the movements of the actual human hand. This mechanical arm is easy to make, operate, and learn, with a focus on solving the problems of inflexibility and difficulty in lifting heavy objects for the elderly. Through implementation, the interactive control system could realize the remote control and synchronous coordination of the human hand to the mechanical arm.
Learning Neural Voxel Fusion Field for Editable Scene Rendering

Since the introduction of neural radiance fields, neural rendering has rapidly developed, displaying significant advantages in novel view synthesis and reconstruction fields. Recently, there has also been substantial exploration in editable scene rendering. Unfortunately, many existing high-quality neural rendering techniques do not support editable scene rendering. Additionally, technologies that enable editable scene rendering face challenges when it comes to learning multiple neural radiance fields to reconstruct each object and the background separately. It becomes difficult to translate high-quality but complex network methods into practice. In this paper, we introduce Neural Voxel Fusion Field (NVFF), a new method for editable scene rendering based on a neural voxel fusion field. Specifically, we observe the shortcomings of the feature extraction in traditional voxel-based editable scene rendering, and propose a more effective feature fusion strategy. This allows us to achieve higher-quality editable scene rendering with only a slight increase in memory overhead. We conducted tests on the ToyDesk dataset and obtained results with PSNR=22.10, SSIM=0.88, and LPIPS=0.22. Compared to other editable scene rendering approaches, our method achieves the same or even higher rendering quality at a faster training speed.
The Design of Electronic assessment System Based on Object Oriented and School oriented

Aiming at the problem that the traditional examination methods of various colleges and universities are not unified and can not meet the diversified examination needs of current teaching, this paper proposes the design of the Electronic assessment system based on object-oriented technology. The overall architecture of the system is designed by investigating the demand functions around the role of using the system, and using software engineering and other technologies. The system is designed to meet the needs of various exam types, especially for online coding exams such as web front-end coding. The system design includes the functions of database building, test paper formation, Electronic assessment, marking, etc. in the three-dimensional process assessment, which can meet the requirements of current course teaching for assessment.
Graph Convolutional Networks Based on Neighborhood Expansion

Graph neural networks (GNNs) are an efficient framework for learning graph-structured data and achieving state-of-the-art performance on many tasks, including node classification, link prediction, and graph classi-fication. Over-smoothing problem is troublesome in GNNs, that is, increasing the depth of the network leads to significant degradation in model performance. A large number of methods address it through regu-larization or residual concatenation, but these methods only mitigate the over-smoothing in some extent, which persists as the network depth increases. In this paper, we propose the Graph Convolutional Network Based on Neighborhood Expansion (NEGCN), which enables the shallow network aggregate multi-hop neighbor features information for node embedding. specifically, NEGCN uses Depth-First-Search to ex-pand 1-hop neighbors with similarity, so as to avoid the problem of over-smoothing. The robustness of NEGCN is further improved by combining with attention mechanism and graph sparsification methods. Extensive experiments demonstrate that our model has a superior performance on real-world graph learning benchmarks.
STFN: Spatio-Temporal Fusion Network to Detect Ethereum Phishing Scams

Due to the decentralized and transparent characteristics of the blockchain ecosystem, malicious activities such as phishing scams on the Ethereum platform result in significant financial losses for users. The current methods for detecting phishing largely rely on analyzing original transactions, which makes uncovering hidden transaction patterns challenging. To tackle this limitation, we introduce the Spatio-Temporal Fusion Network (STFN) designed to identify phishing scams on the Ethereum network. Specifically, STFN incorporates two key components: the transactions subgraph encoder for formalizing spatial features, and the transaction sequence BERT encoder for capturing temporal features. By fusing these spatio-temporal features, we facilitate their integration into a machine learning algorithm for classifying phishing accounts. The experimental outcomes underscore the effectiveness of the STFN, achieving an AUC of 93.26% and a Recall of 94.53%, outperforming previous methods for Ethereum phishing detection.
Dynamic Feature Collaborative Variational Auto-Encoders for Academic Paper Recommendation

The rapid growth of scientific research has contributed to an overwhelming surge of information in academic papers, and scholarly paper recommendation systems have developed rapidly. However, existing systems often underutilize valuable feature information and fail to consider changes in the attractiveness of papers over time. Therefore, this paper proposes a novel approach called the dynamic feature-based collaborative variational auto-encoders (DFC-VGAE) model for the academic paper recommendation. In this study, we preprocess the features using a pre-trained natural language model and capture structural features through co-authorship and citation networks. The model also modifies the encoding and decoding. Promising results were achieved through a real-world experiment using a comprehensive academic paper recommendation dataset.
SVNet: Supervoxel Network for Video Oversegmentation

Supervoxel segmentation for video is a video pre-processing technique that groups voxels with similar spatiotemporal features into supervoxels, effectively reducing the number of elemental voxels for downstream computer vision applications, or applications in other fields, e.g., hardware design or video visualization. Existing methods for video supervoxel generation primarily rely on traditional techniques, such as graph theory, mean shift, and clustering, which have yielded promising results. Recent deep learning-based methods mainly worked on object segmentation or semantic segmentation from videos, paying less attention to video oversegmentation. However, the quality of supervoxels directly affects the results of subsequent tasks. In this paper, we introduce a novel approach SVNet which enables direct end-to-end segmentation of voxels into supervoxels from a deep iterative clustering network. The process begins by utilizing the spatiotemporal features learned by the deep neural network to construct a soft association map between voxels and supervoxels. Subsequently, through an iterative update process, the features of supervoxels and the soft association map between voxels and supervoxels are continually refined to enhance the accuracy of voxels segmentation into supervoxels with the supervised loss using the reconstructed video features and labels. We evaluate our method and the representative supervoxel algorithms for their capability on the performance of video segmentation. Experiments show that our SVNet excels particularly in terms of the BRD metric, and its accuracy is roughly on par with the compared methods.
MMW radar ConfMap abnormaly detection based on Generative Adversarial Network

Millimeter wave radar target detection technology has now been widely used in the field of autonomous driving. The main sensors for autonomous driving are cameras and radars. Among them, the most commonly used radar target detection method is RODNet, which is a method that analyzes radar radio frequency images. However, in autonomous driving scenarios, false alarms may be generated by abnormal RODNet ConfMap in the scene of targets which close to the radar occupying several radar azimuth cells. For solving this problem, this study regards the irregular ConfMap as an anomaly and proposes a GAN-based anomaly detection method to distinguish false positives. Since the generative adversarial network has the ability to generate high-dimensional image spaces and infer latent spaces, the regular ConfMap in this study is regarded as a positive example, and the irregular false alarm ConfMap is regarded as a negative example. Based on this, a method of establishing a generative model from latent space to regular confmap through a generative adversarial network is proposed, and a method of distinguishing true from false by comparing with the generated distribution is proposed.Three widely used GAN are used for anomaly detection. After using different GAN for ConfMap anomaly detection, we found that GANomaly has the best performance. In addition, the generation and adversarial network loss function for ConfMap anomaly detection contains multiple hyperparameters, which are optimized based on the dataset in this paper. After verification with actual data, it is proved that network anomaly detection based on GANomaly can achieve an accuracy of 91.67%.
LlmRe: A zero-shot entity relation extraction method based on the large language model

Entity relation extraction aims to extract knowledge triples from unstructured or semi-structured text data and can be applied to various fields, including medicine, finance knowledge graph construction and intelligent question-answering. Traditional entity relation extraction requires a large amount of labeled data, consumes a lot of labor and time, and the trained model lacks generalization ability, which is difficult to migrate to other fields. Zero-shot entity relation extraction relieves the dependence on labeled data in traditional method. Based on unlabeled text data, zero-shot entity relation extraction has strong domain adaptability, which is a very challenging and practical task. Recent work on large language models shows that large models can effectively complete downstream tasks through natural language instructions and have good generalization ability. Inspired by this, we explore the use of large models for information extraction. Due to the randomness of large language model generation, we introduce in-context learning in entity relation extraction task to guide large language model to output data in a specified format to help obtain structured data. At the same time, we propose a three-stage extraction framework for decomposing entity relation extraction tasks, and each stage is conducted in the form of question and answer to reduce the complexity of extraction. We evaluated the knowledge triples extraction performance of the model on three self-built test datasets in different fields, and the experimental result showed that our proposed method achieved impressive performance in the zero-shot entity relation extraction task, surpassing the comparison model on multiple metrics, proving the effectiveness and domain adaptability of the proposed method.
EIP Security Analysis: Application Program Standards, Attack Events, and Security Vulnerabilities

With the ongoing advancement and widespread adoption of blockchain technology, Ethereum has established itself as the foremost platform for executing smart contracts.As a focal point of the industry, the security and stability of the Ethereum ecosystem have become crucial. Ethereum Improvement Proposals (EIPs) play a vital role in promoting the technological progress of Ethereum and providing direction for innovation in the blockchain field. ERCs, as a type of EIP, define application-level standards and conventions that are crucial for promoting innovation and ensuring the scalability and maintainability of the system. In particular, some ERCs, such as the ERC-20 token standard, have become the foundation of the Ethereum ecosystem. However, if EIPs have security issues, any security vulnerabilities or design flaws can have severe consequences, including financial losses, data leaks, and damage to the reputation of the entire Ethereum network. This paper primarily focuses on the security analysis of application standard proposals (ERC). We conducted research on the current state of EIP security, performed case studies, and provided security recommendations. The goal is to gain a comprehensive understanding of the security features and potential risks of these proposals, and to propose practical solutions to enhance the security of EIPs.
A Proactive Decision Making Method for Big Streaming Data

As one of the core technologies of Cyber—Physical System, proactive complex event processing technology is capable of processing the big streaming data and providing decision in real time. In order to improve the efficiency of complex event processing, this paper proposes a proactive decision model based on reinforcement learning. The model first processes and predicts the big streaming data through probabilistic event processing network and adaptive Bayesian network, and then uses n-step off-policy temporal difference method for reinforcement learning to get the decision result with high efficiency. In experiments based on the intelligent transportation system in Cyber—Physical System shows that this method can effectively reduce the congestion level of nodes.
TEACHERS’ CREATIVITY IN USING DIGITAL LEARNING MEDIA DURING THE COVID 19 PANDEMIC AT SMP NEGERI 13 MAKASSAR

This research aimed to find out the form of teachers’ creativity in using digital learning media during the COVID 19 pandemic, the problems that faced by teacher in using digital learning media and how the teacher overcome the problem in using digital learning media. Three English teachers from SMP Negeri 13 Makassar were the subjects of the study. The method used was descriptive qualitative research that describes teachers’ creativity in using digital learning media during the covid 19 pandemic. The researcher used observation and interviews as an instrument in data collection. In analyzing the data, the researcher used three steps, the first was data reduction, the second was data display, and the last was data verification. The result of the analysis showed that the English teacher has three forms of creativity. The first fluency was teachers have the initiative and idea in using digital learning media. The second is flexibility, teachers have varied ideas in terms of combining the media. And the last originality, teacher has been unable to create new media. With limited abilities, teachers tend still to use the available media. On the other hands, the problem faced by the teachers in using digital learning media was signal interference which caused the learning process stop, and not all students have smartphones, so learning was not optimal. Meanwhile, the way to overcome problems in using digital media was to make learning videos that can be accessed even when the internet was down, so that students view the material without fear of signal interference. Regarding students who do not have smartphones, the teacher provides flexible time for students to take part in learning.
The impact of picture and picture learning model on EFL students’ writing ability

Writing has been a very challenging skill for students especially for eight grade learners of one Islamic junior high school in Central Aceh. This research was conducted to see whether the picture and picture-learning model could increase the students’ capability in writing. To investigate it, the quantitative approach, i.e., experimental method was employed. The population of this study was four classes, totaling 124 students. By using random sampling, classes VIII1 and VIII3 consisting of 30 and 32 students, respectively, were selected as the sample of this research. To collect the data, both pretest and posttest were given to the students before and after applying the picture-learning model. The results showed that the learners performed better after the intervention for both groups; however, the experimental group made better improvement in their writing (post-test = 84; pre-test = 60). In addition, the t-test also displayed that t-test (3.603) was higher than t-table (3.0) indicating that Ha was accepted and Ho was rejected. The conclusion, therefore, was that the picture and picture-learning model increased the students’ writing ability.
Types of figurative language in Christina Perri’s songs

This study examined the types of figurative language found in Christina Perri’s songs. This descriptive study used a qualitative approach. Data were taken from the lyrics of Christina Perri’s songs. Content analysis was used to examine the data in this study. The results of this study revealed that there were several kinds of figurative language in Christina Perri’s songs. They were metaphor, simile, personification, symbolism, alliteration, assonance, consonance, hyperbole, oxymoron, and imagery. The study on figurative languages in songs provides important educational implications such as promoting language proficiency, encouraging literary analysis and critical thinking, raising understanding of cultures, stimulating the creative process, and building emotional intelligence for students and teachers during teaching and learning process.