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SSRN 2024年

作者： Obaid, Mahmoud Odeh, Suhail M. Ashqar, Huthaifa I. Abumwais, Allam Hodrob, Rami Computer System Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Software Engineering Department Bethlehem University Bethlehem Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine

Background and objective: Breast cancer is the most common form of cancer among women in Palestine, accounting for over 34% of cancer diagnoses and 12% of cancer-related deaths. The Palestinian healthcare system faces challenges, including limited resources and inadequate access to advanced diagnostic tools. Traditional diagnostic techniques often struggle to differentiate between benign and malignant tumors, leading to potential misdiagnosis. The study aims to improve breast cancer detection accuracy and efficiency by introducing a novel two-step deep learning model designed to identify and characterize cancerous areas in breast tissue. This model, which distinguishes between benign and malignant tumors, is intended as a scalable, cost-effective solution for low-resource settings like Palestine. Methods: The study employs a two-step deep learning approach: first, a U-Net architecture combined with VGG16 is used for segmenting abnormalities from mammography images, trained using the CBIS-DDSM dataset, which includes 1592 mammograms showing mass abnormalities. Second, VGG16 classifies segmented regions as benign or malignant, using a dataset from Palestine Hospital, comprising images from 34 patients, for testing. The CBIS-DDSM dataset provides annotations and ground truth binary masks, while the Palestine dataset consists of raw images without ground truth masks. Results: The proposed model achieved a 91% accuracy rate in classifying masses in the Palestine Hospital dataset and demonstrated robust performance with an average Intersection over Union (IoU) score of 0.70 on a diversified public dataset. Additionally, the U-Net model achieved a mean IoU score of 0.7 on the test set, indicating precise tumor region outlines. Conclusion: This study successfully presents a deep learning framework for breast cancer detection and classification, showing excellent performance in both segmentation and classification tasks. The model's high accuracy and robustness confirm its p

关键词： Image segmentation

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DPP-HSS: Towards Fast and Scalable Hypervolume Subset Selection for Many-objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Gong, Cheng Nan, Yang Shang, Ke Guo, Ping Ishibuchi, Hisao Zhang, Qingfu City University of Hong Kong Department of Computer Science Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

Hypervolume subset selection (HSS) has received significant attention since it has a strong connection with evolutionary multi-objective optimization (EMO), such as environment selection and post-processing to identify representative solutions for decision-makers. The goal of HSS is to find the optimal subset that maximizes the hypervolume indicator subject to a given cardinality constraint. However, existing HSS algorithms or related methods are not efficient in achieving good performance in high-dimensional objective spaces. This is primarily because HSS problems become NP-hard when the number of objectives exceeds two, and the calculation of hypervolume contribution is very time-consuming. To efficiently solve HSS problems while maintaining a good solution quality, we propose a fast and scalable hypervolume subset selection method for many-objective optimization based on the determinantal point process (DPP), named DPP-HSS, which is fully free of hypervolume contribution calculation. Specifically, DPP-HSS constructs a hypervolume kernel matrix by extracting the convergence and diversity representations of each solution for a given HSS problem. This matrix is then used to build a DPP model. Subsequently, the original HSS problem is reformulated as a new maximization optimization problem based on the constructed model. A greedy DPP-based hypervolume subset selection algorithm is implemented to solve this transformed problem. Extensive experiments show that the proposed DPP-HSS achieves significant speedup and good hypervolume performance in comparison with state-of-the-art HSS algorithms on benchmark problems. Furthermore, DPP-HSS demonstrates very good scalability with respect to the number of objectives. © 1997-2012 IEEE.

关键词： Optimization algorithms

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GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Gene Mutation

GeneDroid Fuzz: An Android Intent Fuzzing Method Based on Ge...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Lu, Runfeng Sun, Yuzhu Sun, Haofeng Fu, Xiao Luo, Bin Du, Xiaojiang Shi, Jin Aitsaadi, Nadjib Guizani, Mohsen Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Nanjing University School of Information Management Nanjing China Université Paris-Saclay David Uvsq Versailles France Mohamed Bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350351255

With the rapid expansion of mobile internet usage, the prevalence of the Android operating system on smartphones is steadily growing. However, improper utilization of the Intent mechanism within Android applications can result in security vulnerabilities. Presently, the majority of Android security testing methods, which rely heavily on fuzzing, are predominantly focused on UI interactions, lacking sufficient testing capabilities for Intents. The motivation of this paper is to find a more effective testing method to improve the security detection capabilities of Intents. This paper introduces an Intent fuzzing method based on genetic mutation principles. Initially, we establish an Intent seed library using a text classification model, followed by employing Jaccard distance and minimum edit distance to refine high-quality seeds. Subsequently, we augment the seeds through extensive mutation using genetic algorithms, generating numerous test cases that exhibit structural similarity but contain varied content. During testing, we compare the state before and after Intent testing using image similarity to detect anomalies. Experimental results demonstrate that this method effectively enhances test coverage and identifies potential issues in edge cases. This approach offers an efficient means of conducting Intent security testing and enhances Android app robustness and security. © 2024 IEEE.

关键词： Android Fuzzing Gene Mutation Intent

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Equity-aware Load Shedding Optimization

arXiv

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arXiv 2024年

作者： Fang, Xin Wang, Wenbo Ding, Fei Department of Electrical & Computer Engineering Mississippi State University StarkvilleMS United States Energy System Integration National Renewable Energy Laboratory GoldenCO United States

Load shedding is usually the last resort to balance generation and demand to maintain stable operation of the electric grid after major disturbances. Current load-shedding optimization practices focus mainly on the physical optimality of the network power flow. This might lead to an uneven allocation of load curtailment, disadvantaging some loads more than others. Addressing this oversight, this paper introduces an innovative equity-aware load-shedding optimization model that emphasizes a fair allocation of load curtailment across the network. By proposing a novel equity indicator for load shedding and integrating it into an ACOPF-based optimization framework, we offer grid operators a more balanced and equitable load shedding strategy. Case studies highlight the importance of equity considerations in determining optimal load curtailment between buses. Copyright © 2024, The Authors. All rights reserved.

关键词： Electric load flow

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On the Selection of Intermediate Length Representative Periods for Capacity Expansion

arXiv

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arXiv 2024年

作者： Anderson, Osten Yu, Nanpeng Oikonomou, Konstantinos Wu, Di Department of Electrical and Computer Engineering University of California Riverside RiversideCA United States System Evaluation Group Pacific Northwest National Laboratory RichlandWA United States

As the decarbonization of power systems accelerates, there has been increasing interest in capacity expansion models for their role in guiding this transition. Representative period selection is an important component of capacity expansion modeling, enabling computational tractability of optimization while ensuring fidelity between the representative periods and the full year. However, little attention has been devoted to selecting representative periods longer than a single day. This prevents the capacity expansion model from directly simulating interday energy sharing, which is of key importance as energy generation becomes more variable and storage more important. To this end, we propose a novel method for selecting representative periods of any length. The method is validated using a capacity expansion model and production cost model based on California’s decarbonization goals. We demonstrate that the representative period length has a substantial impact in the results of the capacity expansion investment plan. © 2024, CC BY-NC-ND.

关键词： Decarbonization

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The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv

引用

arXiv 2024年

作者： Wang, Cheng Yuan, Zenghui Zhou, Pan Xu, Zichuan Li, Ruixuan Wu, Dapeng Oliver Hubei Engineering Research Center on Big Data Security Key Laboratory of Distributed System Security of Hubei Province School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Software The Key Laboratory for Ubiquitous Network Service Software of Liaoning Province Dalian University of Technology Dalian116024 China Intelligent and Distributed Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon999077 Hong Kong

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network’s edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Network security

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Securing Overlay Cognitive Radio Networks Over Cascaded Channels with Energy Harvesting

Securing Overlay Cognitive Radio Networks Over Cascaded Chan...

引用

IEEE Globecom Workshops

作者： Deemah H. Tashman Soumaya Cherkaoui Walaa Hamouda Sidi Mohammed Senouci Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada Department of Electrical and Computer Engineering Concordia-University Montreal Canada DRIVE Laboratory EA1859 Univ. Bourgogne Franche Comté Nevers France

This paper investigates the physical-layer security (PLS) for an overlay cognitive radio network (CRN). Due to the unreliability of direct communication between the primary users (PUs), secondary users (SUs) are enlisted to facilitate communication between two PUs. As several SUs are available, a particular one is chosen to transmit the PUs' messages in addition to its own. This SU uses the time switching protocol to harvest power from the PU's signal. Additionally, an eavesdropper is available to capture the messages conveyed by the PUs. Given this, the SU receiver collects energy from the relayed messages through the power splitting protocol and utilizes this energy to transmit jamming signals in order to degrade the wiretap channel and improve the privacy of the PUs' communication. The signals' transmission is modeled more realistically under the assumption of cascaded Rayleigh channels. The performance of PLS is evaluated using the probability of a non-zero secrecy capacity and the intercept probability. Moreover, the SUs' Ergodic rate is studied to analyze the impact of this assistance on their transmission reliability.

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Customized Transformer Adapter with Frequency Masking for Deepfake Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5904-5918页

作者： Shi, Zenan Chen, Haipeng Jia, Yixin Zhang, Dong Lu, Wei Yang, Xun Jilin University College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University College of Software Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Sun Yat-sen University School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China

The evolution of advanced artificial intelligence generated content approaches has heightened concerns about deepfake, due to the sophisticated forgeries and concealed appearances they produce. To this end, the pre-trained Vision Transformer (ViT) model has become a de facto choice for deepfake detection, thanks to its powerful learning capability. Despite favorable results achieved by existing ViT-based methods, they have inherent limitations that could result in suboptimal performance in scenarios with continuously evolving forgery techniques, such as overfitting to single forgery patterns or placing excessive emphasis on dominant forgery regions. In this paper, we propose CUTA, a simple yet effective deepfake detection paradigm that utilizes ViT adapters as the medium and fully exploits the spatial- and frequency-domain features of given images to overcome the limitations of existing methods. Specifically, CUTA focuses on frequency domain masking within the input space, which obscures parts of the high-frequency image to intensify the training challenge while preserving subtle forgery cues in the frequency domain to facilitate comprehensive forgery representations. Furthermore, we propose two task-customized modules within the ViT model, i.e., the texture enhancement module and the multi-scale perceptron module, to seamlessly integrate local texture and rich contextual features. These two modules ensure an organic interaction between the task-specific forgery patterns and general semantic features within the pre-trained ViT framework. The experimental results on several publicly available benchmark datasets demonstrate CUTA’s superiority in performance, particularly showcasing its significant advantages in both cross-dataset and cross-manipulation scenarios. 2005-2012 IEEE.

关键词： Frequency response

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Reasoning about Strategic Abilities in Stochastic Multi-agent systems

arXiv

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arXiv 2024年

作者： Zhang, Yedi Song, Fu Chen, Taolue Wu, Xuzhi Department of Computing Science National University of Singapore Singapore State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computing and Mathematical Sciences Birkbeck University of London United Kingdom College of Computer Science and Software Engineering Shenzhen University China

Reasoning about strategic abilities is key to AI systems comprising multiple agents, which provide a unified framework for formalizing various problems in game theory, social choice theory, etc. In this work, we propose a probabilistic extension of the alternating-time μ-calculus (AMC), named PAMC, for reasoning about the strategic abilities of agents in stochastic multi-agent systems. We show that PAMC subsumes two existing logics AMC and PμTL (a probabilistic extension of the modal μ-calculus), but is incomparable with the probabilistic alternating-time temporal logic (PATL). We study the problems of model checking and satisfiability checking for PAMC. We first give a model checking algorithm by leveraging algorithms for solving normal-form games and AMC model checking. We establish that the model checking problem of PAMC remains in UP∩co-UP, the same complexity class as the model checking problem for AMC and PμTL. We also provide a new reduction from the satisfiability problem of PAMC to solving parity games, by which we obtain an EXPTIME decision procedure, as well as the small model property which allows to construct a model for each satisfiable PAMC formula. Satisfiability in PAMC has the same complexity as in the modal μ-calculus, unlike PCTL and PATL whose satisfiability checking problems remain open. We have implemented both the model checking and satisfiability checking algorithms as open-source tools. Experimental results are reported, showcasing the practical applications and effectiveness of our approaches. © 2024, CC BY.

关键词： Stochastic systems

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Emotion Recognition Empowered Human-computer Interaction with Domain Adaptation Network

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Chen, Junxin Northeastern University School of Computer Science and Engineering Shenyang110004 China Dalian University of Technology China The School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China Dalian University of Technology School of Software Dalian116621 China

Multi-modal emotion recognition plays a vital role in the human-computer interaction (HCI) for consumer electronics. Nowadays, many studies have developed multi-modal fusion algorithms for this ***, two challenging issues remain unsolved, i.e., inefficient multi-modal feature fusion and unclear distance in feature *** this end, we develop a novel framework, namely LAFDA-Net, for cross-subject emotion recognition using EEG and eye movement *** is based on low-rank fusion and domain adaptation *** specifically, the multi-modal signals are input into the feature extraction branch in parallel to generate ***, these features are fused by the low-rank fusion branch, reducing complexity and avoiding ***, the fused features are flattened and sent to the classification branch to determine the emotion *** training, these features are input into the domain adaptive branch to bridge the gap between the source domain and target *** benchmark datasets, i.e., SEED, SEED-IV, and SEED-V, are employed for performance *** results demonstrate that the proposed LAFDA-Net is robust, effective, and has advantages over peer methods. © 1975-2011 IEEE.

关键词： Emotion Recognition

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