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arXiv 2024年

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024, CC BY-NC-ND.

关键词： Infrared imaging

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A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 39-47页

作者： Keyvan Majd Mohammad Razeghi-Jahromi Abdollah Homaifar School of Computing Informaticsand Decision Systems EngineeringArizona State UniversityTempeAZ 85281 USA ABB Corporate Research Center RaleighNC 27606 USA Department of Electrical and Computer Engineering North Carolina A&T State UniversityGreensboroNC 27403 USA

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.

关键词： Global asymptotic stability input-output linearization optimal control trajectory tracking

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Interest Flooding Attack in Named Data Network: Case Study on Palapa Ring Topology

Interest Flooding Attack in Named Data Network: Case Study o...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Jupriyadi Syaiful Ahdan Adi Sucipto Eki Ahmad Zaki Hamidi Hasan Nur Arifin Nana Rachmana Syambas School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia

This research focuses on the interest flooding attack model and its impact on the consumer in the Named Data Networking (NDN) architecture. NDN is a future internet network architecture has advantages compared to the current internet architecture. The NDN communication model changes the communication paradigm from a packet delivery model based on IP addresses to names. Data content needed is not directly taken from the provider but stored in a distributed manner on the router. Other consumer request data can served by nearest router. It will increase the speed of data access and reduce delay. The changes communication model also have an impact on the existing security system. One attack that may occur is the threat of a denial of service (DoS) known as an interest flooding attack. This attack makes the network services are being unavailable. This paper discussed examining the interest flooding attack model that occurred and its impact on the performance of NDN. The result shows that interest flooding attacks can decrease consumer satisfied interest.

关键词： Distributed databases Network architecture Data models Topology Delays Floods Security

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High-Fidelity Coherent-One-Way QKD Simulation Framework for 6G Networks: Bridging Theory and Reality

arXiv

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arXiv 2025年

作者： Brazaola-Vicario, Aitor Kouvakis, Vasileios Trevlakis, Stylianos E. Ruiz, Alejandra Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Niyato, Dusit Spain The Department of Research and Development InnoCube P.C. 79 17is Noemvriou Str. Thessaloniki55534 Greece The Department of Electrical and Computer Engineering University of Western Macedonia ZEP Area Kozani50100 Greece The Department of Informatics & Telecommunications University of Thessaly Lamia35100 Greece The College of Computing and Data Science Nanyang Technological University 639798 Singapore

Quantum key distribution (QKD) has been emerged as a promising solution for guaranteeing information-theoretic security. Inspired by this, a great amount of research effort has been recently put on designing and testing QKD systems as well as articulating preliminary application scenarios. However, due to the considerable high-cost of QKD equipment, a lack of QKD communication system design tools, wide deployment of such systems and networks is challenging. Motivated by this, this paper introduces a QKD communication system design tool. First we articulate key operation elements of the QKD, and explain the feasibility and applicability of coherent-one-way (COW) QKD solutions. Next, we focus on documenting the corresponding simulation framework as well as defining the key performance metrics, i.e., quantum bit error rate (QBER), and secrecy key rate. To verify the accuracy of the simulation framework, we design and deploy a real-world QKD setup. We perform extensive experiments for three deployments of diverse transmission distance in the presence or absence of a QKD eavesdropper. The results reveal an acceptable match between simulations and experiments rendering the simulation framework a suitable tool for QKD communication system design. © 2025, CC BY.

关键词： Bit error rate

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Real-Time Adaptive Safety-Critical Control with Gaussian Processes in High-Order Uncertain Models

Real-Time Adaptive Safety-Critical Control with Gaussian Pro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yu Zhang Long Wen Xiangtong Yao Zhenshan Bing Linghuan Kong Wei He Alois Knoll Department of Informatics Technical University of Munich Germany Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents an adaptive online learning framework for systems with uncertain parameters to ensure safety-critical control in non-stationary environments. Our approach consists of two phases. The initial phase is centered on a novel sparse Gaussian process (GP) framework. We first integrate a forgetting factor to refine a variational sparse GP algorithm, thus enhancing its adaptability. Subsequently, the hyperparameters of the Gaussian model are trained with a specially compound kernel, and the Gaussian model’s online inferential capability and computational efficiency are strengthened by updating a solitary inducing point derived from newly samples, in conjunction with the learned hyperparameters. In the second phase, we propose a safety filter based on high order control barrier functions (HOCBFs), synergized with the previously trained learning model. By leveraging the compound kernel from the first phase, we effectively address the inherent limitations of GPs in handling high-dimensional problems for real-time applications. The derived controller ensures a rigorous lower bound on the probability of satisfying the safety specification. Finally, the efficacy of our proposed algorithm is demonstrated through real-time obstacle avoidance experiments executed using both simulation platform and a real-world 7-DOF robot.

关键词： Adaptation models Adaptive systems Uncertainty Computational modeling Gaussian processes Real-time systems Safety

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PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutralization Prediction

PESI: Paratope-Epitope Set Interaction for SARS-CoV-2 Neutra...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhang Wan Zhuoyi Lin Shamima Rashid Shaun Yue-Hao Ng Rui Yin J. Senthilnath Chee-Keong Kwoh School of Computer Science and Engineering Nanyang Technological University Singapore Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore School of Chemical and Biomolecular Engineering Nanyang Technological University Singapore Department of Health Outcome and Biomedical Informatics University of Florida Gainesville FL USA

Prediction of neutralization antibodies is important for the development of effective vaccines and antibody-based therapeutics. Traditional methods rely on features based on first principles derived from the binding interface. However, they are burdened by arduous data preprocessing from a limited quantity of protein structures. In comparison, deep learning allows automatic substructure characterization and representation without hand-crafted feature engineering. In particular, large language models (LLMs) based method predicts neutralization using Fv sequences of antibody and antigen. Despite LLM’s success, incorporating full-length Fv sequences suffers from: 1) inaccurate sequence-level labels in existing datasets, 2) inefficient modeling due to noisy non-contributing motifs, and 3) ignorance of non-bonded interactions that play a key role in facilitating epitope-paratope pairing. In this paper, we propose a novel approach that incorporates only the paratope and epitope for antibody-antigen neutralization prediction while adopting a novel set modeling that regards the paratope and epitope as bags of residues. Specifically, we hand-crafted a dataset containing neutralizing paratope-epitope pairs where epitopes are potentially generalizable to future unseen variants of SARS-CoV-2. Training on such a dataset enables deep learning models to predict neutralizing antibodies for prospective mutated variants of SARS-CoV-2, meanwhile addressing the problem of inaccurate sequence-level labels. A higher modeling efficiency is also achieved by disregarding non-contributing motifs. Furthermore, we also propose paratope-epitope set interaction (PESI), a set modeling model inspired by first principles that learns intra-inter non-covalent interactions through a global attention mechanism. To validate PESI, we perform a 10-fold cross-validation on our dataset. Experimental results show that PESI achieves a more balanced overall performance and a significant improvement on MCC as c

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Load Balancing on Named Data Networking, Case Study: UIN Topology in Indonesia

Load Balancing on Named Data Networking, Case Study: UIN Top...

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International Conference on Telecommunication Systems, Services, and Applications (TSSA)

作者： Eki Ahmad Zaki Hamidi Syaiful Ahdan Jupriyadi Adi Sucinto Hasan Nur Arifin Nana Rachmana Syambas School of Electrical Engineering and Informatics Instsitut Teknologi Bandung Bandung Indonesia Electrical Engineering Department UIN Sunan Gunung Djati Bandung Bandung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandarlampung Indonesia

Named Data Networking (NDN) is a network with a future internet architecture that changes the point of view in networking from host-centric to data-centric. Named data networking provides a network system where the routing system is no longer dependent on traditional IP. Network packets are routed through nodes by name. When many manufacturers produce packages with different names for several consumers, routing with load balancing is necessary. The case study carried out is to conduct a simulation by connecting all UIN campuses into a topology with the name UIN Topology in Indonesia, using several scenarios to describe the effectiveness of the load balancer on the UIN topology in Indonesia. This study focuses on load balancer applications to reduce delays in Named Data Networking (NDN), the topology of UIN in Indonesia.

关键词： Network topology Load management Routing Data models Topology Delays IP networks

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An Empirical Study on the Use of Quantum Computing for Financial Portfolio Optimization

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SN computer Science 2022年第5期3卷 1-29页

作者： Gomes, Cláudio Falcao, Gabriel Paquete, Luís Fernandes, João Paulo Department of Informatics Engineering Faculty of Engineering of the University of Porto Porto Portugal Artificial Intelligence and Computer Science Laboratory (LIACC) Porto Portugal Carnegie Mellon University Pittsburgh USA Faculty of Sciences and Technology of the University of Coimbra Coimbra Portugal Instituto de Telecomunicações Coimbra Portugal Centre for Informatics and Systems of the University of Coimbra Polo II Coimbra Portugal

Quantum Computing (QC) is regarded with a mix of amazement, excitement, and skepticism. While quantum computers have been shown to outperform classical ones in particular computational tasks, their effective applicability to general-purpose problems remains under-studied. We shed light on the practical use of QC to tackle a combinatorial optimization problem in Finance, the Portfolio Optimization Problem (POP). We present an in-depth empirical study on the influence that configurable parameters of both a state-of-the-art adiabatic quantum computer and POP itself can have on the overall quality of the solutions we obtain. Our results show that some of these parameters, such as chain strength and a number of reads, have a significant statistical effect, while others, such as anneal schedule and embedding, do not. Our results also show that the quality of the solutions returned by a quantum computer, given a quadratic unconstrained binary optimization formulation of POP from the literature, is still far from the quality of the solutions produced by a classical computer using an exact algorithm. We believe the conclusions drawn from our study are valuable contributions to the utilization of adiabatic quantum computers in practice, not only in the context of POP but also for other application domains.

关键词： Combinatorial optimization Portfolio optimization Quadratic unconstrained binary optimization Quantum annealing

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Non-Orthogonal Multiple Access Based Multi-Objective Optimization in Emergency Task Offloading

Non-Orthogonal Multiple Access Based Multi-Objective Optimiz...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Chen, Caijuan Zhang, Chaofeng Li, Gaolei Liu, Yuchen National Institute of Informatics The Information Systems Architecture Science Research Division Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Nc State University Department of Computer Science NC United States

ISBN: (纸本)9798350368369

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints. © 2025 IEEE.

关键词： Multiobjective optimization

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Comparison of Baseline Reduction Methods for Emotion Recognition Based on Electroencephalogram Signals 6

Comparison of Baseline Reduction Methods for Emotion Recogni...

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6th International Conference on informatics and Computing, ICIC 2021

作者： Agus Wirawan, I Made Wardoyo, Retantyo Lelono, Danang Kusrohmaniah, Sri Asrori, Saifudin Universitas Pendidikan Ganesha Education of Informatics Engineering Department Bali Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Gadjah Mada Department of Psychology Yogyakarta Indonesia UIN Syarif Hidayatullah Faculty of Social and Political Sciences Jakarta Indonesia

ISBN: (纸本)9781665421553

Emotions play an essential role in human social interactions. Its importance has sparked research on emotion recognition mainly based on electroencephalogram signals. However, differences in individual characteristics significantly affect the electroencephalogram signal pattern and impact the emotion recognition process. Several studies have used the baseline reduction approach with the Difference method to represent the differences in individual characteristics on electroencephalogram signals. On the other hand, the baseline reduction process on signal data, in general, can also use the Relative Difference and Fractional Difference methods. Therefore, the contribution of this research is to compare the performance of the three baseline reduction methods on emotion recognition based on electroencephalogram signals. In this study, feature extraction and representation were also carried out using Differential Entropy and 3D Cube. Furthermore, Convolutional Neural Network and Decision Tree methods are used to classify emotions. The experimental results using the DEAP dataset show that the Relative Difference and Fractional Difference methods are superior in reducing the baseline electroencephalogram signal compared to the Difference method. In addition, the Relative Difference and Fractional Difference methods produce a smoother electroencephalogram signal pattern in the baseline reduction process. © 2021 IEEE.

关键词： Emotion Recognition

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