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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xingtong Liu Meng Shen Laizhong Cui Ke Ye Jizhe Jia Guangchun Yue School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency.

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GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

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IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

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Human-AI Co-Creation of Art Based on the Personalization of Collective Memory 6

Human-AI Co-Creation of Art Based on the Personalization of ...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Wu, Zhuohao Li, Yanni Ji, Danwen Wu, Dingming Shidujaman, Mohammad Zhang, Yuan Zhang, Chenfan Communication University of China School of Animation and Digital Arts Beijing China Tongji University College of Design and Innovation Shanghai China Shenzhen University College of Computer Science and Software Engineering Shenzhen China American International University-Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh School of Design Politecnico di Milano Milan Italy

ISBN: (纸本)9781665453110

Artificial intelligence (AI) is trained with data, especially texts, numbers, images, videos and music on Internet. These data all together across time and space make a collective memory of the world. The latest large-scale AI models give people a chance to create out of a large pool of this collective memory, which they won't be able to access before, and communicate with AI in both human natural language and the unique machine supported ways. As demonstrated and discussed in this paper, effective and efficient workflows can be built up for human and AI to co-create meaningful results based on both the collective memory of the world and the personalized ideas and tastes. This kind of human-AI co-creation has a new force with great potential, and expect a new strategy and philosophy to guide human-AI collaboration. © 2022 IEEE.

关键词： Artificial intelligence

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NeRFTAP: Enhancing Transferability of Adversarial Patches on Face Recognition using Neural Radiance Fields

arXiv

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arXiv 2023年

作者： Liu, Xiaoliang Shen, Furao Han, Feng Zhao, Jian Nie, Changhai State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

Face recognition (FR) technology plays a crucial role in various applications, but its vulnerability to adversarial attacks poses significant security concerns. Existing research primarily focuses on transferability to different FR models, overlooking the direct transferability to victim’s face images, which is a practical threat in real-world scenarios. In this study, we propose a novel adversarial attack method that considers both the transferability to the FR model and the victim’s face image, called NeRFTAP. Leveraging NeRF-based 3D-GAN, we generate new view face images for the source and target subjects to enhance transferability of adversarial patches. We introduce a style consistency loss to ensure the visual similarity between the adversarial UV map and the target UV map under a 0-1 mask, enhancing the effectiveness and naturalness of the generated adversarial face images. Extensive experiments and evaluations on various FR models demonstrate the superiority of our approach over existing attack techniques. Our work provides valuable insights for enhancing the robustness of FR systems in practical adversarial settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

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computers, Materials & Continua 2021年第12期69卷 3549-3561页

作者： Wei Sun Xuan Chen Xiaorui Zhang Guangzhao Dai Pengshuai Chang Xiaozheng He Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic Institute TroyNY12180USA

Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera *** has gradually become a core technology of intelligent transportation *** existing vehicle re-identification models adopt the joint learning of global and local ***,they directly use the extracted global features,resulting in insufficient feature ***,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional *** solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this *** model consists of global and local *** global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation *** addition,multi-scale pooling operations are used to obtain multiscale *** the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local *** features from both branches are combined to provide a more comprehensive and distinctive feature *** experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.

关键词： Vehicle re-identification region batch dropblock multi-feature learning local attention

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Di-Net: Decomposed Implicit Garment Transfer Network for Digital Clothed 3d Human

SSRN

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SSRN 2024年

作者： Zhong, Xiaojing Su, Yukun Wu, Zhonghua Lin, Guosheng Wu, Qingyao School of Software Engineering South China University of Technology China School of Computer Science and Engineering Nanyang Technological University Singapore Key Laboratory of Big Data and Intelligent Robot Ministry of Education China Peng Cheng Laboratory China Tencent Wechat Department China SenseTime Research

3D virtual try-on tasks aim to generate realistic try-on results for full- body garments, allowing them to be observed from arbitrary perspectives. Recent methods often represent the 3D human form with a fixed topol- ogy, limiting garment representation and leading to higher computational demands. Furthermore, the challenge of flexible transfer over various body parts remains. To address these problems, we introduce DI-Net(Decomposed Implicit Garment Transfer Network), where garment transfer is decomposed into 2D image layout transfer and 3D texture layout transfer. We propose a complementary warping module that leverages dense correspondence learn- ing and sparse flow learning to align the body appearance and garments with the target pose for image layout transfer. Subsequently, the transferred garments are lifted to the 3D human mesh by constructing a pixel-aligned implicit function and predicting the texture based on geometric priors. By applying different semantic masks for garment transfer, we efficiently control the texture of the generated mesh. Experimental results show the effective- ness and superiority of our method in the 3D virtual try-on task. © 2024, The Authors. All rights reserved.

关键词： Textures

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MICAS: Multi-grained In-Context Adaptive Sampling for 3D Point Cloud Processing

arXiv

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arXiv 2024年

作者： Shao, Feifei Liu, Ping Wang, Zhao Luo, Yawei Wang, Hongwei Xiao, Jun Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University China Computer Science and Engineering University of Nevada Reno United States Ningbo Innovation Center Zhejiang University China School of Software Technology Zhejiang University China College of Computer Science and Technology Zhejiang University China

Point cloud processing (PCP) encompasses tasks like reconstruction, denoising, registration, and segmentation, each often requiring specialized models to address unique task characteristics. While in-context learning (ICL) has shown promise across tasks by using a single model with task-specific demonstration prompts, its application to PCP reveals significant limitations. We identify inter-task and intra-task sensitivity issues in current ICL methods for PCP, which we attribute to inflexible sampling strategies lacking context adaptation at the point and prompt levels. To address these challenges, we propose MICAS, an advanced ICL framework featuring a multi-grained adaptive sampling mechanism tailored for PCP. MICAS introduces two core components: task-adaptive point sampling, which leverages inter-task cues for point-level sampling, and query-specific prompt sampling, which selects optimal prompts per query to mitigate intra-task sensitivity. To our knowledge, this is the first approach to introduce adaptive sampling tailored to the unique requirements of point clouds within an ICL framework. Extensive experiments show that MICAS not only efficiently handles various PCP tasks but also significantly outperforms existing methods. Notably, it achieves a remarkable 4.1% improvement in the part segmentation task and delivers consistent gains across various PCP applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Query processing

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VIKOR decision making method based on multi-granularity hesitant fuzzy linguistic term sets

VIKOR decision making method based on multi-granularity hesi...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Jin Qian Wei Jin Xuanyuan Wang Ying Yu Mingchen Zheng Duoqian Miao School of Software East China Jiaotong University Nanchang Jiangxi School of Transportation Engineering East China Jiaotong University Nanchang Jiangxi Department Computer Science and Technology Tongji University Shanghai China

In the existing multi-attribute decision making researches, most of the hesitant fuzzy linguistic multi-attribute decision making methods assume the decision makers use the linguistic term set under the same granularity to express their linguistic evaluation informations. However, in actual decision making, due to different cultural and educational backgrounds, decision makers may use different sets of linguistic terms at different granularities, which leads to the mismatch of decision information and inaccuracy of decision results. To address this problem, this paper proposes VIKOR decision making method based on multi-granularity hesitant furzy linguistic term sets, which consider both the overall benefits of the group and the regret values of individuals to select the optimal solution. Specifically, a VIKOR decision making method based on the multi-granularity hesitant fuzzy linguistic term set is first proposed, which employs to convert the multi-granularity hesitant fuzzy linguistic term set of each decision maker to the same granularity for aggregation, and obtains the corresponding membership level linguistic term set. Secondly, using the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked based on their proximity to the positive ideal solution. This ranking facilitates the selection of the optimal solution. Secondly, combined with the VIKOR method, the positive and negative ideal solutions are determined, and all alternatives are ranked in order of merit according to the magnitude of the closeness of the preference value setting of each alternative to the positive ideal solution, so as to achieve the selection of the optimal solution. Finally, an arithmetic example is used to prove the accuracy, rationality and feasibility of the method, and the stability of the proposed method is further illustrated by experimentally analyzing the decision mechanism coefficients.

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Smarter Movement Control Application 5

Smarter Movement Control Application

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5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022

作者： Hafeez, Yasir Ali, Syed Saad Azhar Moiana, Isaias Johane Shaheen, Ateeq-Ur-Rehman Adil, Syed Hasan Amin, Hafeez Ullah University of Nottingham Malaysia School of Computer Science Faculty of Science and Engineering Semenyih43500 Malaysia King Fahd University of Petroleum and Minerals Aerospace Engineering Department Dhahran31261 Saudi Arabia Universiti Teknologi Petronas Electrical and Electronics Engineering Department Perak Seri Iskandar32610 Malaysia University of Sargodha College of Engineering and Technology Department of Electrical Engineering Punjab Sargodha40100 Pakistan Iqra University Software Engineering Department Karachi Pakistan

ISBN: (数字)9781665459327

ISBN: (纸本)9781665459327

The coronavirus disease 2019 (Covid-19) has devastated the entire globe in a short period of time and was declared a global pandemic by the World Health Organization (WHO) on March 11th, 2020. It rapidly increased in fatality rate and has become an international public health crisis, culminating in social and economic calamity. However, mobile applications are being introduced globally to minimize the coronavirus's continuous spread by tracing people's circulation or mobility using digital software and smartphones. There is no digitally equipped tool that monitors the movement of the people, particularly in the public places. This work on Smarter Movement Control Application (SMCApp) aims to develop methods that will assist Malaysian people to move around with the aid of mobile tracing application. Therefore, the SMCApp was designed and developed as a mobile application software which stands to improve compliance with the mandated SOP measures across the country, as well as to provide digital support to those who wish to travel to various parts of the country. As a result, it is concluded that the usage of Smarter Movement Control Application (SMCApp) is hoped to bring about safe and effective movement of people throughout these two regions, as it will constantly alert individuals of any suspicious close-contact or the state of the location. Once the application is in effect, with at least 90% of users installing the App on their smartphones, it is projected to increase tranquility and elevate compliance with SOP measures. © 2022 IEEE.

关键词： COVID-19

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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IEEE International Conference on Big Data

作者： Fredy Rojas Samaneh Madanian John Michael Templeton Christian Poellabauer Sandra L. Schneider Department of Computer Science and Software Engineering Auckland University of Technology (AUT) Auckland New Zealand Department of Computer Science and Engineering University of South Florida Tampa FL USA School of Computing and Information Sciences Florida International University Miami FL USA Department of Communicative Sciences & Disorders Saint Mary’s College Notre Dame IN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings.

关键词： Deep learning Analytical models Medical services Predictive models Brain modeling Data models Stability analysis Transient analysis Brain injuries Sports

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