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A Novel Deep Learning-Based Method for Real-Time Face Spoof Detection

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SSRN

SSRN 2023年

作者： Malik, Muhammad Amir Mazhar, Tehseen Haq, Inayatul Shahzad, Tariq Ghadi, Yazeed Yaseen Mallek, Fatma Hamam, Habib Department of Computer Science and Software Engineering International Islamic Unversity Islamabad44000 Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan School of Information Engineering Zhengzhou University Zhengzhou450001 China Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada National College Of Business Administration & Economics Canada

This research aims to improve the detection accuracy of face spoof recognition systems by employing a hybrid model of machine learning and computer vision-based approaches. Differences, including Decision Tree, Nave Bayes, K-nearest Neighbor, Support Vector Machine, Convolutional Neural Network (CCN), and Recurrent Neural Networks are used for face spoof detection. For face spoof detection, the proposed model is a hybrid variant of the CNN-based classifier used in the proposed face spoof detection model. This study improves real-time face fake detection using machine learning and computer vision. The proposed system is based on a CNN-based classification approach with optimized hyper parameters that detect real-time face spoofing attacks using print, video, and repeat attacks, improving detection accuracy. IDIAP, USSA & and MSFD datasets are used in the simulation;the proposed model has achieved a maximum accuracy of 87.5%. a high sensitivity score of 92.45%,. © 2023, The Authors. All rights reserved.

关键词： Decision trees

Natural Language Explanation for Autonomous Navigation

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Natural Language Explanation for Autonomous Navigation

Conference on Digital Avionics Systems (DASC)

作者： Lena Trigg Brandon Morgan Alexander Stringer Lacey Schley Dean F. Hougen School of Computer Science University of Oklahoma Norman Oklahoma USA 76 Software Engineering Group United States Air Force Oklahoma City Oklahoma USA Oklahoma Aerospace Defense Innovation Institute University of Oklahoma Norman Oklahoma USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Navigating an aircraft is a complicated problem as it requires analyzing extensive operational data to identify an optimal and safe path. Recently, many studies have employed deep reinforcement learning (DRL) to address the navigation problem, and it has been demonstrated to be a powerful tool for addressing these kinds of problems. Despite being a promising tool, the opaque nature of the reasoning mechanisms of DRL methods make them difficult to troubleshoot performance issues and fosters mistrust, thereby constraining their application in critical scenarios. This deficiency necessitates the need for the development of explanation methods aimed at clarifying the reasoning processes of DRL models for users. Additionally, governments around the world are beginning to require the inclusion of such explanation methods in autonomous systems used for safety critical applications. In this paper, we propose NavChat, an explanation framework designed to justify the trajectory suggested by a trained DRL model for an efficient and safe path in an unknown environment. NavChat is a post-hoc approach that leverages SHapley Additive exPlanations (SHAP), a Large Language Model (LLM), and prompt engineering to generate natural language explanations. Using SHAP, we identify the most important features that affect the agent's decision. The LLM provides natural language explanations which facilitate faster comprehension and more lucidity for non-expert users in comparison with visual explanations. We compare the explanations provided by NavChat for various prompts and then evaluate the accuracy and consistency of its explanations via a user study. This method facilitates understanding of the decisions made by the automated navigation model and communicates the reasoning process.

关键词： Visualization Large language models Natural languages Government Deep reinforcement learning Cognition Aircraft navigation Trajectory Safety Prompt engineering

Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images

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arXiv 2023年

作者： Du, Bowei Huang, Yecheng Chen, Jiaxin Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Object detection on drone images with low-latency is an important but challenging task on the resource-constrained unmanned aerial vehicle (UAV) platform. This paper investigates optimizing the detection head based on the sparse convolution, which proves effective in balancing the accuracy and efficiency. Nevertheless, it suffers from inadequate integration of contextual information of tiny objects as well as clumsy control of the mask ratio in the presence of foreground with varying scales. To address the issues above, we propose a novel global context-enhanced adaptive sparse convolutional network (CEASC). It first develops a context-enhanced group normalization (CE-GN) layer, by replacing the statistics based on sparsely sampled features with the global contextual ones, and then designs an adaptive multilayer masking strategy to generate optimal mask ratios at distinct scales for compact foreground coverage, promoting both the accuracy and efficiency. Extensive experimental results on two major benchmarks, i.e. VisDrone and UAVDT, demonstrate that CEASC remarkably reduces the GFLOPs and accelerates the inference procedure when plugging into the typical state-of-the-art detection frameworks (e.g. RetinaNet and GFL V1) with competitive performance. Code is available at https://***/Cuogeihong/CEASC. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

Dual-Tower Model with Semantic Perception and Timespan-Coupled Hypergraph for Next Basket Recommendation

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SSRN

SSRN 2023年

作者： Zhou, Yangtao Chu, Hua Li, Qingshan Li, Jianan Zhang, Shuai Zhu, Feifei Hu, Jingzhao Wang, Luqiao Yang, Wanqiang School of Computer Science and Technology XiDian University Xi’an710071 China Intelligent Financial Software Engineering New Technology Joint Laboratory XiDian University Xi’an710071 China Shanghai Fairyland Software Corp. Ltd. Shanghai200233 China

Next Basket Recommendation (NBR) is an essential task within the realm of recommendation systems, dedicated to the anticipation of user preferences in the next moment based on the analysis of users’ historical sequences of engaged baskets. Current NBR models utilize unique identities (IDs) information to represent distinct users and items, and focus on capturing the dynamic preferences of users through sequential encoding techniques such as recurrent neural networks and hierarchical time decay modeling, which have dominated the NBR field for over a decade. However, these models exhibit two significant limitations, resulting in suboptimal representations for both users and items. Firstly, the dependence on unique IDs information for the derivation of user and item representations, ignores the rich semantic relations that interweave the items. Secondly, the majority of NBR models remain bound to modeling an individual user’s historical basket sequence, thereby neglecting the broader vista of global collaborative relations among users and items. To address these limitations, we introduce a dual-tower model with semantic perception and timespan-coupled hypergraph for the next basket recommendation. It is carefully designed to integrate the semantic and collaborative relations into both the user and item representations. Specifically, to capture rich semantic relations effectively, we propose a hierarchical semantic attention mechanism with a large language model to integrate multi-aspect textual semantic features of items for basket representation learning. Simultaneously, to capture global collaborative relations explicitly, we design a timespan-coupled hypergraph convolutional network to efficiently model high-order structural connectivity on hypergraph among users and items. Comprehensive experiments on two public datasets demonstrate that our proposed model significantly outperforms the mainstream NBR models on two classical evaluation metrics, Recall and NDCG. © 20

关键词： Semantics

Split Learning without Local Weight Sharing To Enhance Client-side Data Privacy

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Pham, Ngoc Duy Phan, Tran Khoa Abuadbba, Alsharif Gao, Yansong Nguyen, Van-Doan Chilamkurti, Naveen La Trobe University School of Computing Engineering and Mathematical Sciences VIC Australia CSIRO's Data61 Cybersecurity CRC Australia The University of Western Australia Department of Computer Science and Software Computing Australia

Split learning (SL) aims to protect user data privacy by distributing deep models between the client-server and keeping private data locally. In SL training with multiple clients, the local model weights are shared among the clients for the local model updates. This paper first reveals data privacy leakage exacerbated by local weight sharing among the clients in SL through model inversion attacks. Then, to reduce the data privacy leakage issue, we propose and analyze privacy-enhanced SL (P-SL) (or SL without local weight sharing). We further propose a parallelized P-SL to expedite the training process by duplicating multiple server-side model instances without compromising accuracy. Finally, we explore P-SL with late participating clients and devise a server-side cache-based training method to address the forgetting phenomenon in SL when late clients join. The experiment results demonstrate that P-SL helps reduce up to 50\% of client-side data leakage, which essentially achieves a better privacy-accuracy trade-off than the current trend by using differential privacy mechanisms. Moreover, P-SL and its cache-based version achieve comparable accuracy to baseline SL under various data distributions, while incurring lower costs for computation and communication. Additionally, caching-based training in P-SL mitigates the negative effect of forgetting, stabilizes learning, and enables practical and low-complexity training in a dynamic environment with late-arriving clients. © 2004-2012 IEEE.

关键词： Sensitive data

A Survey of Source Code Search: A 3-Dimensional Perspective

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arXiv 2023年

作者： Sun, Weisong Fang, Chunrong Ge, Yifei Hu, Yuling Chen, Yuchen Zhang, Quanjun Ge, Xiuting Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing210093 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this paper, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future *** Codes 68-04 © 2023, CC BY.

关键词： Deep learning

Influence of Key Nodes in Industrial Control Malicious Code Modeling

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Influence of Key Nodes in Industrial Control Malicious Code ...

Electronic Information engineering and computer science (EIECS), 2021 International Conference on

作者： Wei Yang Hongwei Yan Chao Xu Yu Yao Jinlei Zhou Baile Wang Danni Wang Software College Northeastem University Shenyang China School of Computer Science And Engineering Northeastem University Shenyang China Department of Cybersecurity Monitoring State Grid Liaoning Information and Communication Company Shenyang China

Key nodes in complex networks play an important role in the structure of the entire network. Different nodes in the network often have different degrees, and the node with the highest degree in the network is usually called a key node. However, the heterogeneity of nodes is mostly neglected in the research of malware propagation models by scholars at home and abroad. Therefore, based on the SUIQMR model proposed in the industrial control coupling network and considering the role of key nodes in the network, an improved malware suppression strategy for key nodes in the immune network is obtained. The simulation results verify the effectiveness of the improved malware suppression strategy, showing that the immune key nodes can effectively suppress the spread of malware.

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Classification or Prompting: A Case Study on Legal Requirements Traceability

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arXiv 2025年

作者： Etezadi, Romina Abualhaija, Sallam Arora, Chetan Briand, Lionel School of Electrical Engineering & Computer Science University of Ottawa Canada SnT Centre for Security Reliability and Trust University of Luxembourg Luxembourg Faculty of Information Technology Monash University Melbourne Australia Lero Research Ireland centre for Software Research University of Limerick Ireland

New regulations are continuously introduced to ensure that software development complies with the ethical concerns and prioritizes public safety. A prerequisite for demonstrating compliance involves tracing software requirements to legal provisions. Requirements traceability is a fundamental task where requirements engineers are supposed to analyze technical requirements against target artifacts, often under limited time budget. Doing this analysis manually for complex systems with hundreds of requirements is infeasible. The legal dimension introduces additional challenges that only exacerbate manual effort. In this paper, we investigate two automated solutions based on large language models (LLMs) to predict trace links between requirements and legal provisions. The first solution, Kashιf, is a classifier that leverages sentence transformers and semantic similarity. The second solution prompts a recent generative LLM based on Rice, a prompt engineering framework. On a benchmark dataset, we empirically evaluate Kashιf and compare it against a baseline classifier from the literature. Kashιf can identify trace links with an average recall of ≈67%, outperforming the baseline with a substantial gain of 54 percentage points (pp) in recall. However, on unseen, more complex requirements documents traced to the European general data protection regulation (GDPR), Kashιf performs poorly, yielding an average recall of 15%. On the same documents, however, our Rice-based solution yields an average recall of 84%, with a remarkable gain of about 69 pp over Kashιf. Our results © 2025, CC BY.

关键词： Semantics

Metaverse: A Vision, Architectural Elements, and Future Directions for Scalable and Realtime Virtual Worlds

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arXiv 2023年

作者： Ismail, Leila Buyya, Rajkumar Lab School of Computing and Information Systems The University of Melbourne Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

With the emergence of Cloud computing, Internet of Things-enabled Human-computer Interfaces, Generative Artificial Intelligence, and high-accurate Machine and Deep-learning recognition and predictive models, along with the Post Covid-19 proliferation of social networking, and remote communications, the Metaverse gained a lot of popularity. Metaverse has the prospective to extend the physical world using virtual and augmented reality so the users can interact seamlessly with the real and virtual worlds using avatars and holograms. It has the potential to impact people in the way they interact on social media, collaborate in their work, perform marketing and business, teach, learn, and even access personalized healthcare. Several works in the literature examine Metaverse in terms of hardware wearable devices, and virtual reality gaming applications. However, the requirements of realizing the Metaverse in realtime and at a large-scale need yet to be examined for the technology to be usable. To address this limitation, this paper presents the temporal evolution of Metaverse definitions and captures its evolving requirements. Consequently, we provide insights into Metaverse requirements. In addition to enabling technologies, we lay out architectural elements for scalable, reliable, and efficient Metaverse systems, and a classification of existing Metaverse applications along with proposing required future research *** Codes ***, *** © 2023, CC BY.

关键词： Energy efficiency