Reviewerly/exHarmony huggingface dataset:
Violence Detection In Videos via Motion-Guided Global and Local Views

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset.
Quantitative Dynamic Scalability Model and Analysis of Blockchain Database System

The scalability of blockchain database systems has garnered considerable attention. This paper introduces a quantitative dynamic scalability (QDS) model and presents two metrics: throughput growth (TG) following an increase in available resources, and the number of resources that users find acceptable under the maximum latency (UL) level. The TG model's calculation necessitates the slope of throughput growth versus available resources, which can be determined using the universal scalability law (USL) model. We found that the non-sharded blockchain database cannot be entirely fitted and that the USL model cannot provide accurate slope values. Therefore, we developed a novel universal scalability law plus (USL+) model based on the classic USL model. Regarding the experimental evaluation, we verified the USL+'s less accuracy, particularly with a roughly 52% relative error (USL) and a 15% relative error (USL+) for larger node numbers. We then numerically simulated the QDS model and examined the impact of different data sets, finding that the error was smaller with more similar configurations. Finally, we investigated different protocols using the QDS algorithm and found that RingBFT's scalability was better with only two shards, while SharPer was better with 50 shards.
Software Vulnerability Detection with GPT and In-Context Learning

Code vulnerability detection is a software security analysis technique that focuses on recognizing and resolving possible code vulnerabilities and weaknesses. Its primary objective is to mitigate the chances of malicious attacks and system failures. Vulnerabilities encompass mistakes, defects, or insecure programming methodologies found within the code, which can lead to security risks, service denials, data leaks, and various other concerns. Previous research has predominantly focused on deep learning models such as VulDeePecker, Russell, and SySeVR. With the advent of large language models, impressive advancements have been made in various domains, including natural language generation, text classification, and sentiment analysis. However, there is currently no effective method for utilizing large language models in vulnerability detection. Therefore, this study explores and validates the application of such models for code vulnerability detection. In this paper we present a context-based learning approach to enhance the capability of code vulnerability detection named VUL-GPT. Our method combines code retrieval and code analysis, leveraging in-context learning to improve the performance of the GPT model in vulnerability detection. Specifically, we use GPT to generate analysis content for the test code and employ code retrieval methods such as BM-25 and TF-IDF to retrieve the most similar code snippet and its vulnerability information from the training set. Subsequently, we input them along with the test code and its analysis into the GPT model, leveraging the contextual learning ability of the large language model for vulnerability detection. Our experiments demonstrate that combining with code retrieval and code analysis, the GPT models can detect code vulnerability detection more effectively.
Knowledge Graph Embedding via Adaptive Negative Subsampling

Knowledge graph embedding is an important approach for addressing the task of knowledge graph completion, which has received extensive attention and research in recent years. However, some existing methods, such as non-Euclidean space embedding models, suffer from high computational complexity and time costs, as well as high embedding dimensions, without significant improvements over classical models. In this study, we apply adaptive negative sampling to the classical knowledge graph embedding model transE. We have performed optimization on the loss function while preserving the fundamental principles of embedding. As a result, our model is referred to as ANStransE. We also experimentally investigate the impact of different dimensions and evaluate our approach on three commonly used datasets (WN18RR, FB15k-237, and YAGO3-10). The experimental results demonstrate a significant performance improvement achieved by our method, enabling a simple model to achieve results close to the state-of-the-art. Through adaptive negative sampling, we are able to effectively utilize negative samples for model training, thereby enhancing the accuracy and efficiency of knowledge graph representation learning. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The relevant code has been made publicly available at https://github.com/xiaoluoElon/ANStransE.
An Image Emotion Classification Method Based on Supervised Contrastive Learning

This article proposes an image emotion classification model based on supervised contrastive learning, which includes two modules of low-level feature extraction and deep emotion feature extraction, and feature fusion is used to enhance the overall perception of image emotion. In the low-level feature extraction module, the LBP (Local Binary Patterns) algorithm is used to extract texture features of images. In the deep emotion feature extraction module, supervised contrastive learning is introduced to narrow the intra-class distance between the same emotion images and expand the inter-class distance between different emotion images, so as to improve the classification performance of the overall model. Comprehensive experiments on the FI emotion dataset show that the proposed model has good performance for image emotion classification.
Towards Accurate and Efficient Super Spreader Detection with Sketching

Super spreaders are the flow that have a large number of distinct connections (also called spread), which related with many threats to networks. Estimating flow spread is the crucial step in super spreader detection. However, existing methods cannot achieve flow spread estimation in terms of accurate, efficient, and reversible simultaneously. All these characteristics is highly required for high-speed network measurement. In this paper, we propose MorphSketch, a new data structure that estimates flow spread for super spreader detection with high accuracy, memory efficiency, high throughput and reversibility. MorphSketch combines hashing with sampling to process packets in order to improve throughput. It uses self-morph bitmap to record spread information, which can adaptively enlarge the upper bound of spread estimation under limited memory usage to ensure accuracy and memory efficiency. Moreover, MorphSketch can track candidate super spreader by comparing corresponding spread information, which realizes reversibility in super spreader detection. We perform a series of performance evaluations on real world traffic trace. Experiment results demonstrate that under same memory usage, the MorphSketch significantly outperforms existing work in terms of accuracy and efficiency.
Improved Grey Wolf Optimization Algorithm Based on Arctangent Inertia Weight

An improved Grey Wolf Optimization (AGWO) algorithm based on arctangent inertia weight strategy is proposed to address the slow convergence speed and low optimization accuracy of the Grey Wolf Optimization (GWO) algorithm. The AGWO algorithm has non-linear characteristics and can better balance the global and local search capabilities of the algorithm. Under special situation, AGWO can degenerate into GWO, AGWO is an extent of GWO. The search performance of AGWO is tested on five classic test functions. The experimental results show that AGWO has higher optimization performance than four classical swarm intelligence algorithms.
The Intelligent Information Search System IISI and Its Key Technologies for Internet Big Data

With the development of cyberspace and applications, Internet big data has become ubiquitous, which contains important information and knowledge. Correspondingly, the demand for intelligent search of Internet big data has also arisen. IISI, an intelligent information search system, can automatically collect Internet big data and use big data analysis, natural language processing and artificial intelligence technologies to intelligently analyze and process Internet big data, obtain valuable information and knowledge contained within and construct knowledge graphs. By accurately understanding the user's search intention, the system offers intelligent search of Internet big data. This research focuses on the key technologies of IISI system, which provides an intelligent search framework for Internet big data, based on which intelligent search engines in different application fields can be constructed. Function and performance tests show that the IISI system has high real-time performance and precision in the intelligent search for Internet big data, which meets the needs of intelligent search for Internet big data. IISI provides important reference for the implementation of the intelligent information search system for Internet big data.
Research on Recommendation Algorithms Based on Asymmetric Attention Mechanism Models

Although models such as matrix factorization and graph neural networks have achieved good performance in rating prediction, they still face some problems, such as cold start and data sparsity. In this paper, we propose a model called Asymmetrical Local-Global Attention Model(ALGM) to address these issues by combining user comments and rating information. Firstly, to alleviate the data sparsity problem faced by previous recommendation algorithms that only use rating information, we introduce additional user comment information. Secondly, in order to extract valuable information from comments more effectively, we utilize dynamic word embedding tools for modeling. Finally, to accurately depict user and item representations, we adopt attention mechanisms and an asymmetric approach to model user and item features. The proposed algorithm in this paper is evaluated on datasets such as Amazon, using mean squared error (MSE) as the evaluation metric. Experimental results show that the ALGM algorithm proposed in this paper outperforms the selected baseline models by 2% in performance.
An Unsupervised Graph Embedding Method Based on Dynamic Graph Attention Networks and Infomax for Link Prediction

Due to the lack of labeled data in many real-world scenarios, Graph Neural Network models that require a large volume of labeled data are not effective, and research based on unsupervised learning frameworks has become essential. To solve the problem of sparsity in labeled graph data, this paper proposes an unsupervised graph representation learning method based on graph infomax and dynamic graph attention mechanism(IMDGA). This method conducts unsupervised learning based on the idea of contrastive learning and optimizes the encoder by calculating the maximum mutual information between the graph's local information and global information. At the same time, we use a dynamic Graph Attention Network as an encoder model to prevent the Graph Convolutional Neural network's inability to adapt to neighbor nodes with different weights. Finally, link prediction experiments on three public data sets of cora, citeseer, and pumbed show that the proposed model has achieved superior results compared with other graph representation learning, showing its effectiveness and competitiveness.
Multimodal Features Enhanced Named Entity Recognition Based on Self-Attention Mechanism

Chinese Named Entity Recognition (NER) is a very important subtask in the information extraction domain. Its purpose is to locate named entities in the text and classify them into predetermined categories. The key point of NER is to learn a high-quality representation of tokens. Recently, representation learning techniques have been introduced into NER due to their excellent performance in mining the semantics of texts and mastering their organization. In the field of Chinese, many studies introduce multimodal feature extraction schemes to enrich token representations, such as radical and word. However, the learning scheme of these auxiliary features is relatively complex and has difficulty learning the interactive relationship between features with a concatenation or MLP fusion method. To address these challenges, a Multimodal Chinese NER Model based on Self-attention Mechanism named MNER is proposed, which consists of a multimodal feature fusion module and an entity classification module. To study the informative characterization of tokens, a multimodal feature fusion module is proposed to exploit radical, character, and word information. In the multimodal feature fusion module, a self-attention mechanism is designed to integrate multimodal features based on the correlation between the features, which addresses the problem that existing methods have difficulty exploiting interaction information between features. A semantic-aware category modifier is proposed to enhance the CRF classification layer's performance. It increases entity discrimination by adjusting the embeddings of features according to the similarity between the token embeddings and embedding of each entity category, which widens the encoding gap between different entities and narrows the search scope for entity classification. Finally, the proposed MNER is compared with ten state-of-the-art methods on Weibo and Resume datasets, and the results show the superiority of our model on three metrics.
Universal Adversarial Perturbation From Dominant Feature For Speaker Recognition

Deep neural networks (DNNs) have greatly improved speaker recognition performance. However, DNNs are vulnerable to imperceptible adversarial example, which has attracted the attention in both academic and industrial areas in recent years. Although many elegant adversarial attack methods have been proposed, few of them studied Universal Adversarial Perturbation (UAP) against speaker recognition. In this paper, we propose a novel algorithm from the perspective of dominant features to generate UAP to mislead speaker recognition DNNs. Our method uses dominance as an optimization objective, and designs a tiny neural network to improve the efficiency. We conduct experiments on the TIMIT dataset and our method has achieved state-of-the-art results regarding attack success rate and imperceptibility. Moreover, leveraging the concept of dominant feature, our method significantly improves the training speed and can generate UAP more efficiently.
A Practical Non-Intrusive Cyber Security Vulnerability Assessment Method for Cyber-Insurance

Cybersecurity insurance is one of the important means of cybersecurity risk management and the development of cyber insurance is inseparable from the support of cyber risk assessment technology. Cyber risk assessment can not only help governments and organizations to better protect themselves from related risks, but also serve as a basis for cybersecurity insurance underwriting, pricing, and formulating policy content. Aiming at the problem that cybersecurity insurance companies cannot conduct cybersecurity vulnerability assessment on policyholders before the policy is signed without the authorization of the policyholder or in legal, combining with the need that cybersecurity insurance companies want to obtain network security vulnerability risk profiles of policyholders conveniently, quickly and at low cost before the policy signing, this study proposed a practicial non-intrusive cyber security vulnerability risk assessment method. Our model uses only open source intelligence and publicly available network information data, employing an ensemble machine learning approach to assess the cyber vulnerability risk of an organization, achieving a precision of 75.6% compared to a rating based on comprehensive information by cybersecurity experts.
Collaborative Learning for Cross-Organizational Data Sharing Using Hyperledger Fabric

The rise of edge networking presents both opportunities and challenges. The ever-expanding array of IoT devices generates vast amounts of data, enabling collaboration and smart applications. However, managing decentralized data, resolving heterogeneity, and ensuring privacy poses challenges. The combination of blockchain and federated learning offers a solution. But trust management, inefficiencies, and ambiguous assessment of data contribution have become new obstacles. In this paper, we propose a data sharing model that supports cross-organizational collaborative learning based on Hyperledger Fabric, in order to enhance the high-quality data sharing of collaborative learning and improve the effect of learning. The experimental results show that the proposed model can realize decentralized and efficient data sharing and improve the accuracy and stability of collaborative learning under acceptable communication load.
Leveraging Concept-Driven Pre-Training Model for Shot-Text Conceptualization Task

The conceptualization of short-texts is playing an increasingly important role in text comprehension and other applications, with the aim of mapping a short-text to a set of pre-defined concepts. This work investigates and measures the adaptations of pre-training driven model in short-text conceptualization task. To this end, we discreetly design a flexible architecture as well as the corresponding procedure flow, to absorb and adopt two objectives (Concept Association Reasoner (CAR) objective and Masked-Concept Language Model (MCLM) objective respectively) into conventional pre-training architecture (e.g., BERT tested here), which have been verified on other Natural Language Processing domains. Finally, we evaluate the proposed model using several standard short-text conceptualization datasets and metrics, and experimental results showed that our model outperformed the most advanced models on real-world datasets. Especially, empirical results verify the contributions of disambiguation released by the aforementioned objectives related with concept semantics.
Based on Linkable Ring Signature Map Review Scheme for Zero-Trust Autonomous Vehicles

The real-time map plays a vital role in the Internet of Vehicles (IoV) navigation. Compared with the existing map review, the real-time map review method is based on crowdsensing enabling faster data updates and shortening the map period from map collection to service. However, the crowdsensing method requires frequent interaction between the vehicle and nearby devices or servers, increasing the risk of user privacy leakage. How to ensure the privacy of users is a challenge in the real-time map review. Starting from the typical architecture of IoV and combining the core idea of zero-trust “never trust, continuous verification”, IoV-ZT conceptual model architecture is designed. This paper proposes a based on linkable ring signature map review scheme for zero-trust autonomous vehicles (LRSMR). In the LRSMR, a linkable ring signature based on the SM2 digital signature algorithm is used to protect user identity privacy and ensures while ensuring signature linkability by hiding the identity of the actual signer in the ring. Through the requirement for user credit, the reliability of the review data is improved. At the same time, the distribution of rewards increases users' motivation. At last, the security analysis indicates that the LRSMR satisfies correctness, unforgeability, unconditional anonymity, linkability and nonslanderability. The simulation results illustrate that the LRSMR is efficient in terms of communication overhead and computation cost.
Toward the Secure Transmission of Large-Scale Images in Autonomous Driving

The rapid advancement of autonomous driving technology and the increasing complexity of in-vehicle systems present novel challenges to system safety. The substantial volume of data collected by in-vehicle sensors greatly supports the intelligent enhancement of autonomous driving systems. However, traditional public key cryptographic systems, which rely on computational complexity, may fail to meet the requirements for secure transmission and real-time response of the vast image information. Resource-intensive AI algorithms often necessitate computing and storage resources offered by cloud platforms for training and predicting. Securely integrating AI into autonomous driving systems has emerged as an imminent problem that demands attention during its development process. To address the aforementioned issues, this paper focuses on analyzing secure image transmission technologies and trusted AI algorithms for autonomous driving. To tackle the challenge of traditional public key-based data protection methods for the real-time secure transmission of massive data in autonomous driving systems, this paper introduces a secure and efficient image partitioning scheme based on visual cryptography (VC), which resolves the conflict between data protection in autonomous driving and real-time responsiveness.
Syntactic Enhanced Euphemisms Identification Based on Graph Convolution Networks and Dependency Parsing

To evade content moderation policies, social media users adopt euphemisms (also called algospeak, which refers to a benign-looking alternative to sensitive content) to obfuscate their true intentions. Euphemisms identification task aims at understanding the confusing expressions and interpreting the true meanings of euphemisms, which plays a key role in recent euphemisms analysis. The leading euphemisms identification method defines this problem as a text classification task, which establishes a mapping between euphemisms and corresponding real-meaning words in a specific area through fine-grained classification. However, the traditional classification methods leverage explicit textual semantic features, where specific terms and oral swearing in user speech from forums may introduce bias to the classification results on specific areas and limit the performance of euphemisms identification. In this work, we propose SyGCN for euphemisms identification task. Specifically, SyGCN captures the explicit syntactic features by using dependency parsing tree as extended features and integrates syntactic features and semantic features into the Graph Convolution Network to enhance the identification performance. We conduct experiments on three sensitive categories and demonstrate the effectiveness of our method. The experimental results show that our model can effectively utilize the syntactic structure information of dependency parsing to improve the performance of euphemisms identification.