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FairCF: fairness-aware collaborative filtering

science China(Information sciences) 2022年第12期65卷 127-141页

作者： Pengyang SHAO Le WU Lei CHEN Kun ZHANG Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artifcial Intelligence Hefei Comprehensive National Science Center

Collaborative filtering(CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation systems to help users find potentially desirable items. Most CF models optimize recommendation accuracy; however, they may lead to unwanted biases for particular demographic groups. Thus, we focus on learning fair representations of CF-based recommendations. We formulate this problem as an optimization task with two competing goals: embedding representations better meet accuracy requirements of recommendations, and simultaneously obfuscate information hidden in the embedding space, which is related to the users' sensitive attributes for fairness. Here,the intuitive idea is to use fair representation learning from machine learning to train a classifier with a sensitive attribute predictor from the user side to satisfy the fairness goal. However, such fair machine learning models assume entity independence, which differs greatly from CF because users and items are correlated collaboratively via user-item behaviors. Therefore, sensitive user information can be exposed from the users' preferred items. Consequently, defining only fairness constraints on users cannot achieve fairness in recommendation systems. In this paper, we propose FairCF framework for fairness-aware collaborative *** particular, we first define fairness constraints in a fair embedding space, where both a user classifier and an item classifier are employed to fit the fairness constraints. We then design an item classifier without item sensitive labels. The proposed framework can be trained in an end-to-end manner under most embedding based CF models. Extensive experiments conducted on three datasets(Movie Lens-100K, Movie Lens-1M,and Lastfm-360K) clearly demonstrate the superiority of the proposed FairCF framework relative to various fairness metrics(i.e., performance of newly-trained classifiers) than other state-of-the-art fairness-aware CF mode

关键词： recommendation systems fairness adversarial learning

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A self-driving solution for resource-constrained autonomous vehicles in parked areas

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High-Confidence Computing 2024年第1期4卷 32-42页

作者： Jin Qian Liang Zhang Qiwei Huang Xinyi Liu Xiaoshuang Xing Xuehan Li College of Information Engineering Taizhou UniversityTaizhou 225300China Engineering Research Center of Integration and Application of Digital Learning Technology Ministry of EducationBeijing 100039China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China The School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China

Autonomous vehicles in industrial parks can provide intelligent,efficient,and environmentally friendly transportation services,making them crucial tools for solving internal transportation *** the characteristics of industrial park scenarios and limited resources,designing and implementing autonomous driving solutions for autonomous vehicles in these areas has become a research *** paper proposes an efficient autonomous driving solution based on path planning,target recognition,and driving decision-making as its core *** designs for path planning,lane positioning,driving decision-making,and anti-collision algorithms are *** analysis and experimental validation of the proposed solution demonstrate its effectiveness in meeting the autonomous driving needs within resource-constrained environments in industrial *** solution provides important references for enhancing the performance of autonomous vehicles in these areas.

关键词： Autonomous vehicles Path planning Target recognition Driving decision-making Self-driving

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Fluorescence Nanoscopy in Neuroscience

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engineering 2022年第9期16卷 29-38页

作者： Yangyundou Wang Jian Lin Qiming Zhang Xi Chen Haitao Luan Min Gu Center for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Fluorescence nanoscopy provides imaging techniques that overcome the diffraction-limited resolution barrier in light microscopy,thereby opening up a new area of research in biomedical imaging in fields such as ***,we review the foremost fluorescence nanoscopy techniques,including descriptions of their applications in elucidating protein architectures and mobility,the real-time determination of synaptic parameters involved in neural processes,three-dimensional imaging,and the tracking of nanoscale neural *** conclude by discussing the prospects of fluorescence nanoscopy,with a particular focus on its deployment in combination with related techniques(e.g.,machine learning)in neuroscience.

关键词： Fluorescence imaging Diffraction limit Nanoscale resolution Neuroscience

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Cleo: Smart Glasses to Monitor Consumption of Alcohol and Cigarettes

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SN computer science 2023年第1期4卷 1-13页

作者： Sethuraman, Sibi Chakkaravarthy Kompally, Pranav Mohanty, Saraju P. Raju, Bksp Kumar Cruz, Meenalosini Vimal School of Computer Science and Engineering and Center of Excellence in Artificial Intelligence and Robotics (AIR) VIT-AP University Amaravati India Department of Computer Science and Engineering University of North Texas Denton 76207 TX United States Department of Information Technology Allen E. Paulson College of Engineering and Computing Georgia Southern University Statesboro GA United States

It is estimated that over 60% of people around the globe consume alcohol and cigars daily. Many people use them beyond the permitted limit, which causes lung cancer, liver and kidney failure. If there is a system that could monitor their intake level, it will alert them in case of excess consumption, which could help them control the intake. To help the users monitor their consumption, we introduce Cleo Eyeglasses in this paper. Cleo is a wearable spectacle device with a mounted camera and single onboard computer that performs custom trained object recognition to identify alcoholic beverages and cigarettes. Upon recognition, a log is automatically maintained in the corresponding mobile application. If the intake exceeds the user threshold, then our system generates an alert to both the user and concerned medical personnel. Cigarette/alcohol consumption can immediately affect the body without noticeable symptoms. Cleo addresses this with keen monitoring of vital body parameters and generates an alert when abnormalities are detected. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Alcohol monitoring CNN computer vision Deep learning Smart glasses Smoke monitoring

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Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

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computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

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A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-Hoc Networks

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IEEE Transactions on Intelligent Transportation Systems 2024年第9期25卷 12621-12630页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Alenazi, Mohammed J. F. Das, Ashok Kumar University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500032 India

The rise of smart cities is directly connected to the increasing use of vehicles. The growing vehicle utilization has driven the emergence of Vehicular Ad-hoc Networks (VANETs), facilitating instant information exchange among vehicles. The system provides essential information regarding road conditions, traffic patterns, and more relevant data. VANETs encompass two fundamental categories of communication exchanges, namely Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2I technology facilitates the integration of cars and transportation infrastructure, enabling effective communication between vehicles and infrastructure. Nevertheless, the potential of V2I communication has various security concerns arising from prevalent security threats. Current authentication techniques encounter challenges regarding complexity, security, and privacy considerations. We designed a hash-based lightweight and anonymous authentication scheme to address the aforementioned restrictions and enhance the effectiveness of authentication in V2I architecture. This scheme effectively combines identity, password, and bio-metric to enhance resistance against impersonation, denial of service, and privileged insider attacks. The devised scheme distinguishes itself by a comparative analysis and security proofs, highlighting its superior capability in guaranteeing secure authentication in V2I communication. The comprehensive security analysis conducted formally and informally showcases the robustness of the proposed solution against several threats. The performance evaluation results show that our scheme demonstrates a decrease in the computational cost of 51.40% approximately and a reduction in communication overhead of around 22.57%. These results establish the efficiency and scalability of the proposed scheme as a viable solution for V2I architecture. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Exploiting Cross-Modal Cost Volume for Multi-sensor Depth Estimation 17th

Exploiting Cross-Modal Cost Volume for Multi-sensor Depth E...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Kim, Janghyun Shin, Ukcheol Heo, Seokyong Park, Jinsun Department of Information Convergence Engineering Pusan National University Busan Korea Republic of Robotics Institute Carnegie Mellon University Pittsburgh United States School of Computer Science and Engineering Pusan National University Busan Korea Republic of

ISBN: (纸本)9789819609680

Single-modal depth estimation has shown steady improvement over the years. However, relying solely on a single imaging sensor such as RGB and near-infrared (NIR) cameras can result in unreliable and erroneous depth estimation, particularly in challenging lighting conditions such as low-light or sudden lighting change scenarios. Thereby, several approaches have leveraged multiple sensors for robust depth estimation. However, the effective fusion method that maximally utilizes multi-modal sensor information still requires further investigation. With this in mind, we propose a multi-modal cost volume fusion strategy with cross-modal attention, incorporating information from both cross-spectral and single-modality pairs. Our method initially constructs low-level cost volumes that consist of modality-specific (ı.e., single modality) and modality-invariant (ı.e., cross-spectral) volumes from multi-modal sensors. These cost volumes are then gradually fused using bidirectional cross-modal fusion and unidirectional LiDAR fusion to generate a multi-sensory cost volume. Furthermore, we introduce a straightforward domain gap reduction approach to learn modality-invariant features and depth refinement techniques through cost volume-guided propagation. Experimental results demonstrate that our method achieves SOTA (State-of-the-Art) performance under diverse environmental changes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Depth perception

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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On Multi-image Random Grids by Arbitrary Transformations

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Human-Centric Intelligent Systems 2024年第1期4卷 93-110页

作者： Buckley, Neil Nagar, Atulya Kumar Secco, Emanuele Lindo AI Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool UK Robotics Lab School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool UK

Random grids are a method for visual secret sharing, whereby a secret image is encoded into a number of shares, each of which is maximally entropic. There has been growing interest in hiding multiple images in a scheme, such that additional images are revealed by stacking the shares in different ways. This paper proposes a metaheuristic method for generating schemes that allow for a wide range of transformations, or even combinations thereof, to reveal an arbitrary number of secret images. Up to 10 multi-secrets are shown in this paper, as well as hiding multiple secrets in a general access structure. To remove noise from these schemes an algorithm is proposed to extract the information from the noise, and in all cases, relative contrasts are given. In one example, six images are hidden in two shares, such that the mean relative contrast under OR-stacking is 0.152, under XOR-stacking 0.278, and with noise removed, it is 0.74. In an example hiding 10 images in two shares, the values are respectively 0.136, 0.206 and 0.679, respectively.

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Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference

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Journal of Communications and Information Networks 2024年第4期9卷 330-339页

作者： Chao Gong Ping Li Panlong Yang Zhao Lv School of Computer Science and Technology Anhui UniversityHefei 230000China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of TechnologyMa’anshan 243032China School of Computer science School of Cyber Science and EngineeringNanjing University of Information Science and TechnologyNanjing 210000China

In the realm of the Internet of things(IoT),radio frequency identification(RFID)technology is essential for linking physical objects to digital *** introduction of harmonic technology has expanded RFID’s applications by improving sensitivity and enabling communication with wireless fidelity(Wi-Fi)***,this trend faces challenges,notably interference from strong Wi-Fi signals,which impacts RFID-based sensing *** paper proposes the WiFID(Wi-Fi and RFID)algorithm,which enables Wi-Fi devices to sense weak RFID harmonic *** subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic *** validation on the universal software radio peripheral(USRP)platform demonstrates that WiFID successfully detects 90% of RFID harmonics at−30 dBm,with only a minimal 4%decrease in Wi-Fi throughput.

关键词： RFID harmonic wireless sensing Wi-Fi interference avoidence

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