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Cybersecurity: Sight and Foresight 28th

Cybersecurity: Sight and Foresight

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28th international conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Brodie, Kevin Olatunbosun, Samuel Norfolk State University NorfolkVA United States

ISBN: (纸本)9783031859328

This paper’s focus is on information systems (IS), emerging threats, and advancement of information technology, (IT). Many of the modern-day cybersecurity threats are targeting the newer technology used in IS. These include smartphones and other portable hand-held devices. Social media has also become a target in the hands of cybercriminals where perpetrators are increasingly exploiting the vulnerabilities within the general computer systems critical hardware. The paper highlights common attacks that are used in malware attacks. Stationary hardware is no longer the focal target, but attackers are now exploiting whatever means are possible to attain private and confidential information of users [9]. The growth of global Internet connectivity and the technological advancement of business concerns have greatly increased the attack vectors available to cybercriminals and without the proper countermeasures and security control implementation, IS are susceptible to compromise at all levels [9]. There have been many malicious exploits over the world wide web of computer interconnections with known cybercriminal attack patterns, and this paper also examine the mitigating capabilities of existing security mechanisms and countermeasures that are used to prevent cybercriminal threats [9]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computer viruses

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Oracle Character Prototype-Guided Cyclic Disentanglement for Oracle Bone Inscriptions Detection 4th

Oracle Character Prototype-Guided Cyclic Disentanglement for...

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4th international conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Li, Bang Zhang, Jingping Yu, Nana Zhang, Zihao Liu, Yongge Han, Yahong School of Computer and Information Engineering Anyang Normal University Anyang455000 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang455000 China Digital Media Art Department Shanghai Theatre Academy Shanghai200040 China Key Laboratory of Integrated Innovation of Digital Performing Arts Ministry of Culture and Tourism Shanghai200040 China College of Intelligence and Computing Tianjin University Tianjin300072 China

ISBN: (纸本)9789819787043

Oracle Bone Inscriptions (OBI), as the oldest form of Chinese writing, bears the precious heritage of ancient Chinese civilization. Deciphering OBI has been a monumental challenge in archaeology and philology. However, the preservation method of OBI through rubbings faces significant challenges due to severe noise interference, adversely affecting the accuracy of OBI detection. Additionally, existing traditional object detection algorithms inadequately consider the unique characteristics of OBI, making it difficult to effectively identify Oracle characters on OBI rubbings. To address these challenges, this paper proposes a novel approach to OBI detection, Oracle Character Prototype-guided cyclic disentanglement for OBI detection. These features are amalgamated into distinct character structure prototypes by capturing shared character structure features among different Oracle characters. This approach highlights the distinctive forms of Oracle characters, enhancing feature clarity and recognition. The Oracle Character Prototypes enable the model to learn common features across different character forms, improving its adaptability to various Oracle character structures and enhancing its capability to handle complexity. Moreover, to mitigate the impact of noise on detection, we introduce a cyclic disentanglement method for Oracle Bone Inscriptions. This process, utilizing the Oracle Character Prototypes and contrastive loss as supervisory signals, cyclically decouples Oracle features, isolating information rich in character structure features. This effectively mitigates the influence of noise on OBI detection. Comprehensive evaluations were conducted, comparing our method with state-of-the-art object detection algorithms, validating its effectiveness both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

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Predicting Test Smell Categories based on Issue Descriptions using information Retrieval and Machine Learning Techniques

Predicting Test Smell Categories based on Issue Descriptions...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Jiramed Withunsapphasiri Passakorn Phannachitta Chartchai Doungsa-Ard College of Arts Media and Technology Chiang Mai University Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Effective software testing is essential for achieving high-quality software products. Various strategies and methods are used to ensure the software meets requirements and functionality, but test smell is one of the challenges to achieving effective software testing. These smells contain various issues, such as execution behavior, semantic inconsistencies, dependency management, inefficiencies, and code-related problems. This study investigates applying information retrieval (IR) and machine learning techniques to predict test smell categories by analyzing natural language issue descriptions and shifting from code-based to issue-based approaches. The result reveals that preprocessing techniques impact model performance. Techniques such as Spacy demonstrated better performance due to their sophisticated language models, while TF-IDF proved effective in distinguishing semantic inconsistencies in textual issue descriptions. Oversampling appeared as a crucial method for handling data imbalance and enhancing model performance across all test smell categories. However, N-gram requires further investigation to capture the relationship in issue descriptions. These findings show the possibility of combining natural language processing with machine learning to predict and address test smell categories, contributing to more reliable software testing practices and reducing the mistakes that will happen. This study can potentially explore additional preprocessing strategies, oversampling techniques, and machine learning models to optimize predictive performance.

关键词： Software testing Semantics Machine learning Predictive models information retrieval Software Natural language processing Data models Software reliability Software engineering

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Event-based Video Person Re-identification via Cross-Modality and Temporal Collaboration

Event-based Video Person Re-identification via Cross-Modalit...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Renkai Li Xin Yuan Wei Liu Xin Xu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Video-based person re-identification (ReID) has become increasingly important due to its applications in video surveillance applications. By employing events in video-based person ReID, more motion information can be provided between continuous frames to improve recognition accuracy. Previous approaches have assisted by introducing event data into the video person ReID task, but they still cannot avoid the privacy leakage problem caused by RGB images. In order to avoid privacy attacks and to take advantage of the benefits of event data, we consider using only event data. To make full use of the information in the event stream, we propose a Cross-Modality and Temporal Collaboration (CMTC) network for event-based video person ReID. First, we design an event transform network to obtain corresponding auxiliary information from the input of raw events. Additionally, we propose a differential modality collaboration module to balance the roles of events and auxiliaries to achieve complementary effects. Furthermore, we introduce a temporal collaboration module to exploit motion information and appearance cues. Experimental results demonstrate that our method outperforms others in the task of event-based video person ReID.

关键词： Data privacy Correlation Event detection Collaboration Transforms Streaming media Signal processing Video surveillance Speech processing Identification of persons

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M2PAIR: A High-Quality Acoustic Impulse Response Computation Model

M2PAIR: A High-Quality Acoustic Impulse Response Computation...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhiyu Li Xinpei Zhao Jing Wang Xinyuan Qian Xiang Xie School of Information and Electronics Beijing Institute of Technology Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acoustic Impulse Response (AIR) provides crucial spatial information about the environment, significantly enhancing audio immersion. However, achieving high perceptual quality while computing AIR in real-time for interactive audio-video media (IAVM) presents a challenging problem. This study proposes the Mesh to Parametric AIR (M2PAIR), a method for computing AIR designed for IAVM. M2PAIR integrates neural networks with psychoacoustics. It takes the 3D scene mesh, the listener positions, and the sound source positions as inputs, utilizes perceptual parameters as intermediaries, and computes the desired high-quality AIR signal based on these parameters. Experimental results demonstrate that M2PAIR improves the perceptual quality of AIR output compared to existing methods while reducing the model complexity. Additionally, it meets the requirements of IAVM, including real-time computation, high sampling rates, and flexible duration for the output AIR.

关键词： Psychoacoustics Three-dimensional displays Computational modeling Atmospheric modeling Neural networks media Signal processing Real-time systems Psychoacoustic models Speech processing

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Fake News Detection Across Multiple Domains Using Fuzzy Association Rules 24th

Fake News Detection Across Multiple Domains Using Fuzzy Asso...

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24th international conference on Algorithms and Architectures for Parallel processing, ICA3PP 2024

作者： Xu, Xiaofeng Zhang, Weisha Huang, Yuanyuan Lu, Hongyu Lu, Jiazhong School of Cybersecurity Xin Gu Industrial College Chengdu University of Information Technology Chengdu610225 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China SUGON Industrial Control and Security Center Chengdu610225 China School of Foreign Languages University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615247

The detection of fake news is not confined to a single domain, and due to domain transfer, the reliance on detection methods specific to a single domain becomes inadequate when dealing with multi-domain issues. When a multi-domain detection occurs, labels from one domain focus exclusively on the news characteristics of that particular domain, ignoring the possibility that part of the news may include functions from more than one domain. To address these challenges, we propose a multi-domain fake news detection model based on fuzzy rules, incorporating a fuzzy mechanism and an optimization module for fuzzy association rules. In this model, we first introduce a fuzzy mechanism that applies fuzzy processing to domains through neural networks, constructing fuzzy domain labels for each news domain. Subsequently, we incorporate fuzzy association rules to extract a broader range of sample features, thereby enhancing detection effectiveness. Experiments conducted on a fake news dataset demonstrate that our model achieves commendable detection performance with a relatively low training cost, reaching an F1 score of over 97.13% for sample detection. This model successfully overcomes the limitations of single-domain detection in fake news identification and improves the precision of feature extraction through fuzzy rules, showing superior performance in handling multi-domain fake news detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： fake news detection fuzzy rules multi-domain social media

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Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios

Learn from Balance: Rectifying Knowledge Transfer for Long-T...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xinlei Huang Jialiang Tang Xubin Zheng Jinjia Zhou Wenxin Yu Ning Jiang School of Computer Science and Technology Southwest University of Science and Technology School of Information Science and Technology Great Bay University School of Computer Science and Engineering Nanjing University of Science and Technology Graduate School of Science and Engineering Hosei University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Knowledge Distillation (KD) transfers knowledge from a large pre-trained teacher network to a compact and efficient student network, making it suitable for deployment on resource-limited media terminals. However, traditional KD methods require balanced data to ensure robust training, which is often unavailable in practical applications. In such scenarios, a few head categories occupy a substantial proportion of examples. This imbalance biases the trained teacher network towards the head categories, resulting in severe performance degradation on the less represented tail categories for both the teacher and student networks. In this paper, we propose a novel framework called Knowledge Rectification Distillation (KRDistill) to address the imbalanced knowledge inherited in the teacher network through the incorporation of the balanced category priors. Furthermore, we rectify the biased predictions produced by the teacher network, particularly focusing on the tail categories. Consequently, the teacher network can provide balanced and accurate knowledge to train a reliable student network. Intensive experiments conducted on various long-tailed datasets demonstrate that our KRDistill can effectively train reliable student networks in realistic scenarios of data imbalance.

关键词： Knowledge engineering Training Heavily-tailed distribution Focusing Tail Signal processing media Reliability Speech processing Knowledge transfer

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28th international conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

28th International Conference on Image Processing, Computer ...

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ISBN: (纸本)9783031859328

The proceedings contain 28 papers. The special focus in this conference is on Image processing, computer Vision, and Pattern Recognition. The topics include: Image-Based Seal Recognition: Approaches and Challenges in Current Automated Systems;Deep Learning Techniques for Lunar Impact Crater Identification Based on CCD and DEM Data;silicon Wafer Map Defect Classification Using Artificial Intelligence Models;low Light Image Enhancement Using Autoencoder-Based Deep Neural Networks;towards Elephants Intelligent Monitoring in Zakouma National Park, Chad;a Review of Multi-modal and Multi-view Applications in Hand-Drawn Sketch Images;residential Real Estate Image Classification for Property Valuation;Novel Method to Investigate Decay in Rotting Bananas Using RGB Color Images;lalitha: A Hand Gesture-Based computer Control System;dishari: A Novel Gesture-Based Educational Application for Specially Challenged People;mobility Anomaly Detection with Intelligent Video Surveillance;Weakly-Supervised Video Anomaly Detection Using Modified Anomaly Score Module and Modified BERT;Contour Detection of Seeds Based on Traditional and Convolutional Neural Network (CNN) Based Algorithms;Early Detection of Lameness in Dairy Cattle Using Activity Data, Image Analysis, AI and ML - An Approach for Improved Animal Welfare and Economic Impact;implications for Designing Hawks Detection with Data Augmentation and Network Optimizations;a Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image processing Techniques;using Linkage Context for Automated Correction in Unsupervised Entity Resolution;the Rising Threat Against Modern technology in Cybersecurity;an Online Bookstore Design and Implementation;cybersecurity: Sight and Foresight;the Application of Blockchain technology in the Transmission of Semiconductor Process Recipes;the Use of Social media and the Internet for the Facilitation of Human Trafficking.

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A Fuzzy C-Means Clustering Algorithm for Real Medical Image Segmentation

A Fuzzy C-Means Clustering Algorithm for Real Medical Image ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Feifei Zhang Fei Shi Dayong Ren Yue Li School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image processing, the presence of noise can often result in the obfuscation of crucial details, which in turn can have a detrimental impact on the accuracy of clinical diagnoses. In order to effectively remove noise and improve segmentation performance, this paper proposes a refined Fuzzy C-Means (FCM) algorithm, designated as MKL-FCM. The method commences with Poisson denoising, which enables more effective handling of the noise characteristics inherent to medical images. Subsequently, multi-scale Kullback-Leibler divergence is utilised to analyse local image information across varying scales, facilitating the differentiation between tissues and pathological regions. Furthermore, tight wavelet frames are capable of capturing fine image details, while reverse optimisation of the objective function serves to correct errors in feature reconstruction, thereby enhancing the accuracy of the segmentation process. The experimental results demonstrate that MKL-FCM enhances both image clarity and segmentation accuracy, and outperforms existing methods in terms of efficiency.

关键词： Image segmentation Accuracy Noise reduction Noise Signal processing algorithms Clustering algorithms Linear programming Partitioning algorithms Speech processing Medical diagnostic imaging

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Feature Disentangling Dual-stream Network for User Bias Alleviation in Social media Prediction

Feature Disentangling Dual-stream Network for User Bias Alle...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wenhao Hu Weilong Chen Weimin Yuan Xiaolu Chen Han Yang Yanru Zhang Zhu Han College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China University of Houston Houston USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Social media popularity prediction is increasingly crucial for optimizing user engagement and guiding content recommendation systems. However, existing methods suffer from an excessive reliance on user information, which disproportionately influences predictions and leads to the neglect of content diversity. This oversight results in user bias, which adversely impacts the accuracy of predictions. In this paper, an approach named Feature Disentangling Dual-Stream Network (FDDN) is introduced to address this gap. In FDDN, we introduce the Multimodal Extraction Module to extract content features from different modalities. Additionally, the User Popularity Extraction Module helps to analyze the impact of user features on popularity. The Disentangled Adaptation Module distinguishes the impact of user features from content features, thus alleviating user bias and ensuring a more comprehensive prediction. Extensive experiments on a large public dataset demonstrate the robustness and effectiveness of our approach, indicating superior performance compared to existing methods.

关键词： Micromechanical devices Social networking (online) Dams Signal processing media Feature extraction Robustness Cultural differences Speech processing Recommender systems

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