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arXiv 2023年

作者： Varsamis, G.D. Karafyllidis, I.G. Gilkes, K.M. Arranz, U. Martin-Cuevas, R. Calleja, G. Dimitrakis, P. Kolovos, P. Sandaltzopoulos, R. Jessen, H.C. Wong, J. Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi67100 Greece National Centre for Scientific Research Demokritos Athens15342 Greece EY Global Innovation Quantum Computing Lab Greece Department of Molecular Biology and Genetics Democritus University of Thrace Alexandroupolis68100 Greece

Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2B base pairs, and each one could be stored with 2 bits of information, so one human genome would take 6.4B bits or ~760MB of storage ("National Institute of General Medical Sciences," n.d.). Today’s most powerful tensor processing units cannot handle the volume of DNA data necessitating a major leap in computing power. It is, therefore, important to investigate the usefulness of quantum computers in genomic data analysis, especially in DNA sequence alignment. Quantum computers are expected to be involved in DNA sequencing, initially as parts of classical systems, acting as quantum accelerators. The number of available qubits is increasing annually, and future quantum computers could conduct DNA sequencing, taking the place of classical computing systems. We present a novel quantum algorithm for reference-guided DNA sequence alignment modeled with gate-based quantum computing. The algorithm is scalable, can be integrated into existing classical DNA sequencing systems and is intentionally structured to limit computational errors. The quantum algorithm has been tested using the quantum processing units and simulators provided by IBM Quantum, and its correctness has been confirmed. © 2023, CC BY-NC-ND.

关键词： DNA

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A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 169-178页

作者： Mohammad Al-Sharman David Murdoch Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek the Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L 3G1Canada the Cognitive Autonomous Driving Lab University of WaterlooWaterloo N2L 3G1Canada the School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 999002Singapore the Faculty of Science Engineering and ComputingKingston University LondonLondon SW153DWUK the Khalifa University Center for Autonomous Robotic Systems Department of Aerospace EngineeringKhalifa UniversityAbu Dhabi 127788UAE

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the *** a typical CPS,the braking system is crucial for the vehicle design and safe ***,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of *** this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified *** techniques are utilized to obtain more accurate model for brake pressure state estimation *** proposed model is trained using real experimental training data which were collected via conducting real vehicle *** vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving *** on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated *** results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

关键词： Brake pressure state estimation cyber-physical system(CPS) deep learning dropout regularization approach

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GoMORE: Global Model Reuse for Resource-Constrained Wireless Federated Learning

arXiv

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arXiv 2023年

作者： Yao, Jiacheng Yang, Zhaohui Xu, Wei Chen, Mingzhe Niyato, Dusit Southeast University Nanjing210096 China The Zhejiang Lab Hangzhou311121 China The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The School of Computer Science and Engineering Nanyang Technological University 308232 Singapore

Due to the dynamics of wireless environment and limited bandwidth, wireless federated learning (FL) is challenged by frequent transmission errors and incomplete aggregation from devices. In order to overcome these challenges, we propose a Global MOdel REuse strategy (GoMORE) that reuses the outdated global model to replace the local model parameters once a transmission error occurs. We analytically prove that the proposed GoMORE is strictly superior over the existing strategy, especially at low signal-to-noise ratios (SNRs). In addition, based on the derived expression of weight divergence, we further optimize the number of participating devices in the model aggregation to maximize the FL performance with limited communication resources. Numerical results verify that the proposed GoMORE successfully approaches the performance upper bound by an ideal transmission. It also mitigates the negative impact of non-independent and non-identically distributed (non-IID) data while achieving over 5 dB reduction in energy consumption. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Energy Efficient Semantic Communication over Wireless Networks with Rate Splitting

arXiv

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arXiv 2023年

作者： Yang, Zhaohui Chen, Mingzhe Zhang, Zhaoyang Huang, Chongwen Zhejiang Lab Zhejiang Hangzhou311121 China The College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, the problem of wireless resource allocation and semantic information extraction for energy efficient semantic communications over wireless networks with rate splitting is investigated. In the considered model, a base station (BS) first extracts semantic information from its large-scale data, and then transmits the small-sized semantic information to each user which recovers the original data based on its local common knowledge. At the BS side, the probability graph is used to extract multi-level semantic information. In the downlink transmission, a rate splitting scheme is adopted, while the private small-sized semantic information is transmitted through private message and the common knowledge is transmitted through common message. Due to limited wireless resource, both computation energy and transmission energy are considered. This joint computation and communication problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under computation, latency, and transmit power constraints. To solve this problem, an alternating algorithm is proposed where the closed-form solutions for semantic information extraction ratio and computation frequency are obtained at each step. Numerical results verify the effectiveness of the proposed algorithm. © 2023, CC BY-SA.

关键词： Wireless networks

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Multi-Level Speaker Representation for Target Speaker Extraction

Multi-Level Speaker Representation for Target Speaker Extrac...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ke Zhang Junjie Li Shuai Wang Yangjie Wei Yi Wang Yannan Wang Haizhou Li Key Laboratory of Intelligent Computing in Medical Image Northeastern University China SDS SRIBD The Chinese University of Hong Kong Shenzhen China The Hong Kong Polytechnic University Hong Kong China Tencent Ethereal Audio Lab Tencent Shenzhen China Machine Listening Lab (MLL) University of Bremen Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Target speaker extraction (TSE) relies on a reference cue of the target to extract the target speech from a speech mixture. While a speaker embedding is commonly used as the reference cue, such embedding pre-trained with a large number of speakers may suffer from confusion of speaker identity. In this work, we propose a multi-level speaker representation approach, from raw features to neural embeddings, to serve as the speaker reference cue. We generate a spectral-level representation from the enrollment magnitude spectrogram as a raw, low-level feature, which significantly improves the model’s generalization capability. Additionally, we propose a contextual embedding feature based on cross-attention mechanisms that integrate frame-level embeddings from a pre-trained speaker encoder. By incorporating speaker features across multiple levels, we significantly enhance the performance of the TSE model. Our approach achieves a 2.74 dB improvement and a 4.94% increase in extraction accuracy on Libri2mix test set over the baseline.

关键词： Accuracy Feature extraction Acoustics Speech processing Spectrogram Overfitting

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Explainable Artifacts for Synthetic Western Blot Source Attribution

Explainable Artifacts for Synthetic Western Blot Source Attr...

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IEEE International Workshop on Information Forensics and Security (WIFS)

作者： João P. Cardenuto Sara Mandelli Daniel Moreira Paolo Bestagini Edward Delp Anderson Rocha Artificial Intelligence Lab. Recod.ai Institute of Computing Universidade Estadual de Campinas (UNICAMP) Campinas SP Brazil Department of Computer Science Loyola University Chicago Chicago IL USA Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challenge even for expert scientists habituated to working with such content. When exploited by organizations known as paper mills, which systematically generate fraudulent articles, these technologies can significantly contribute to the spread of misinformation about ungrounded science, potentially undermining trust in scientific research. While previous studies have explored black-box solutions, such as Convolutional Neural Networks, for identifying synthetic content, only some have addressed the challenge of generalizing across different models and providing insight into the artifacts in synthetic images that inform the detection process. This study aims to identify explainable artifacts generated by state-of-the-art generative models (e.g., Generative Adversarial Networks and Diffusion Models) and leverage them for open-set identification and source attribution (i.e., pointing to the model that created the image).

关键词： Forensics Conferences Paper mills Closed box Organizations Generative adversarial networks Diffusion models Security Convolutional neural networks Fake news

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Performance Optimization for Intelligent Reflecting Surface Assisted Multicast MIMO Networks

Performance Optimization for Intelligent Reflecting Surface ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Songling Zhang Zhaohui Yang Mingzhe Chen Danpu Liu Kai-Kit Wong H. Vincent Poor Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Zhejiang University Hangzhou China Zhejiang Lab Hangzhou China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering University College London London UK Department of Electrical and Computer Engineering Princeton University Princeton NJ USA

ISBN: (纸本)9781665435413

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

关键词： Manifolds Multicast algorithms Array signal processing Surface waves Simulation Performance gain Matrices

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Secure Semantic Communications: Fundamentals and Challenges

arXiv

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arXiv 2023年

作者： Yang, Zhaohui Chen, Mingzhe Li, Gaolei Yang, Yang Zhang, Zhaoyang The College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310007 China Zhejiang Lab Hangzhou31121 China The School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China The School of Information and Communication Engineering BUPT The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

Semantic communication allows the receiver to know the intention instead of the bit information itself, which is an emerging technique to support real-time human-machine and machine-to-machine interactions for future wireless communications. In semantic communications, both transmitter and receiver share some common knowledge, which can be used to extract small-size information at the transmitter and recover the original information at the receiver. Due to different design purposes, security issues in semantic communications have two unique features compared to standard bit-wise communications. First, an attacker in semantic communications considers not only the amount of stolen data but also the meanings of stolen data. Second, an attacker in semantic communication systems can attack not only semantic information transmission as done in standard communication systems but also attacks machine learning (ML) models used for semantic information extraction since most of semantic information is generated using ML based methods. Due to these unique features, in this paper, we present an overview on the fundamentals and key challenges in the design of secure semantic communication. We first provide various methods to define and extract semantic information. Then, we focus on secure semantic communication techniques in two areas: Information security and semantic ML model security. For each area, we identify the main problems and challenges. Then, we will provide a comprehensive treatment of these problems. In a nutshell, this article provides a holistic set of guidelines on how to design secure semantic communication systems over real-world wireless communication networks. © 2023, CC BY-SA.

关键词： Semantics

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

arXiv

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

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On the Correctness of Speculative Consensus

arXiv

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arXiv 2022年

作者： Hellings, Jelle Rahnama, Sajjad Gupta, Suyash Sadoghi, Mohammad Department of Computing and Software McMaster University HamiltonON Canada Exploratory Systems Lab Department of Computer Science University of California Davis DavisCA United States RISELab Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States

The introduction of Bitcoin fueled the development of blockchain-based resilient data management systems that are resilient against failures, enable federated data management, and can support data provenance. The key factor determining the performance of such resilient data management systems is the consensus protocol used by the system to replicate client transactions among all participants. Unfortunately, existing high-throughput consensus protocols are costly and impose significant latencies on transaction processing, which rules out their usage in responsive high-performance data management systems. In this work, we improve on this situation by introducing the Proof-of-Execution consensus protocol (PoE), a consensus protocol designed for high-performance low-latency resilient data management. PoE introduces speculative execution, which minimizes latencies by starting execution before consensus is reached, and PoE introduces proof-of-executions to guarantee successful execution to clients. Furthermore, PoE introduces a single-round check-commit protocol to reduce the overall communication costs of consensus. Hence, we believe that PoE is a promising step towards flexible general-purpose low-latency resilient data management systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Information management

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