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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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Attention Model-Driven Maddpg Algorithm for Delay and Cost Aware Placement of Service Function Chains in 5g

SSRN

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SSRN 2024年

作者： Munshi, Joy Sultana, Sumaya Hassan, M.D. Jahid Roy, Palash Razzaque, M.D. Abdur Alkhamees, Bader Fahad Uddin, M.D. Zia Hassan, Mohammad Mehedi Department of Computer Science and Engineering Green University of Bangladesh Narayanganj Dhaka 1461 Bangladesh Green Networking Research Group Department of Computer Science and Engineering University of Dhaka Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Sintef Digital Oslo Norway

The rapidly expanding applications of 5G networks necessitate strategic placement of Virtual Network Functions (VNFs) within Service Function Chains (SFCs) to minimize placement costs while delivering real-time services to users. The dual objectives of this efficient placement strategy are to simultaneously reduce resource usage costs and application service delays in the 5G network. Previous studies have limitations, typically constrained by fixed resource costs or by adopting a greedy approach for resource selection from nearby nodes. In this paper, we introduce a multi-objective linear programming (MOLP) based optimization framework designed for the placement of VNFs in SFC requests, considering a real-time pricing scheme of the resources and the demands of user applications. This framework allows for the analysis of the boundary performances regarding cost and delay, facilitating a balanced trade-off between the two. Given that this problem is proven to be NP-hard in large networks, we have also developed a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which leverages an attention model-based approach for the placement of SFC VNFs. This method focuses on neighboring nodes to help agents reduce the complexity of the solution and effectively capture the dynamic nature of the network environment. Simulation experiments demonstrate that our proposed system model surpasses existing state-of-the-art approaches in terms of resource placement cost and service latency. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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On authenticated skyline query processing over road networks

On authenticated skyline query processing over road networks

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作者： Zhu, Xiaoyu Wu, Jie Chang, Wei Zakirul Alam Bhuiyan, Md Raymond Choo, Kim-Kwang Qi, Fang Liu, Qin Wang, Guojun School of Computer Science and Engineering Central South University Changsha China Center for Networked Computing Temple University PhiladelphiaPA United States Department of Computer and Information Sciences Saint Joseph's University PhiladelphiaPA United States Department of Computer Science Fordham University New York CityNY United States Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Computer Science and Electronic Engineering Hunan University Changsha China School of Computer Science Guangzhou University Guangzhou China

In recent times, many location-based service providers (LBSPs) choose to outsource data query services to third-party cloud service providers (CSPs). This allows users to easily search for points of interests (POIs), such as restaurants and parking lots in their vicinity, using their mobile devices and in-vehicle infotainment units. Skyline query is one potential technique to be deployed for road networks. However, the untrusted CSPs may forge or omit query results, intentionally or not. Therefore, in this article, we posit that by observing the unique properties of skyline query results in road networks, we can bind each POI with four nearby POIs with special properties using signature chain technology. Our proposed approach not only provides users with skyline query result authentication ability over the road network, but also have low communication overhead. Specifically, the overhead analysis and experimental results show that our proposed approach decreases the communication overhead. © 2020 John Wiley & Sons, Ltd.

关键词： Location based services

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Neural Gas Counter Propagation Neural Network (NG CPNN): A Novel Model for Face Recognition

Neural Gas Counter Propagation Neural Network (NG CPNN): A N...

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science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Kazeem Michael Olagunju Adeleye Samuel Falohun Alice Oluwafunke Oke Marion O. Adebiyi Directorate of Centre for Systems and Information Services Landmark University Omu-Aran Nigeria Department of Computer Engineering LAUTECH Ogbomoso Nigeria Department of Computer Science Landmark University Omu-Aran Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The advent of biometric technology has enhanced security in various ways, especially by lowering the propensity for circumvention which was obtainable in traditional recognition measures such as the use of passwords, tokens, smart cards, PIN etc. Particularly facial recognition is currently a hot topic, which has evolved over time. A face recognition system is supposed to automatically recognize faces in pictures and videos by receiving a two-dimensional face image as input and comparing it to a predefined database of faces to outputs a discernible face image in the end. Despite the various algorithms used in face recognition, the problems of occlusion, light conditions and facial variation still persist. However, this study has implemented and presented Neural Gas Counter Propagation Neural Network (NG CPNN) as a novel model for face recognition *** implementation of model was carried out using 600 face images, pre-processed by histogram equalization, while face features were extracted by Principal Component Analysis. The novelty of NG CPNN was established by utilizing computational time, sensitivity, and accuracy as performance assessment measures at four distinct threshold values. The result shows computation time of 72.36s, accuracy of 96.25%, sensitivity of 98.33% at 0.8, 0.8, and 0.2 thresholds respectively. Finally, it was concluded based on empirical result and statistical evidence that NG-CPN is a novel model for face recognition. NG CPNN can be employed in access control systems, international borders, smartphone technology, and many more.

关键词： Analytical models Sensitivity Accuracy Face recognition Computational modeling Biological system modeling Neural networks

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Citrus Limon L. Ripeness Identification Using Feature Extraction of Mean RGB, HSV, LBP and K-Nearest Neighbor

Citrus Limon L. Ripeness Identification Using Feature Extrac...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Priyo Utomo, Ade Cahyo Sari, Christy Atika Rachmawanto, Eko Hari Hadi, Heru Pramono Mostafa, Salama A. Al-Dayyeni, Wisam Subhi Talib University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Ada University School of Information Technologies and Engineering Baku Azerbaijan

ISBN: (纸本)9798350339215

Citrus Limon L. (Lemon) is a type of fruit that is currently widely consumed, because it contains abundant vitamin C, fiber, and antioxidants. This fruit have high potential in the agribusiness sector and is widely cultivated by traditional farmers. However, during harvest time, traditional farmers generally still use manual methods using the human eye in distinguishing the maturity level of lemons which is less efficient because it has a low accuracy. Digital image processing is one solution to this problem. In the research on the classification of lemon maturity levels using digital image processing with the feature extraction method of the Mean RGB, HSV, and LBP methods and the K-Nearest Neighbor classification algorithm in this study, a total of 120 lemon images were used which were divided into 80 training image data and 40 image data. testing. The results of model performance measurements in the form of the highest accuracy level in the Mean RGB method of 100%, the highest accuracy in the HSV method of 98%, and the highest accuracy in the LBP method of 82.5%. © 2023 IEEE.

关键词： Citrus fruits

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A Deep Features Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification

arXiv

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arXiv 2024年

作者： Arslan, Muhammad Mubeen, Muhammad Akram, Arslan Abbasi, Saadullah Farooq Ali, Muhammad Salman Tariq, Muhammad Usman Department of Computer Science Loyola University Chicago Chicago United States Department of Computer Science University Of the People CA United States Facultty of Computer Science and Information Technology The Superior University Lahore Lahore Pakistan Department of Electronic Electrical and Systems Engineering University of Birmingham Birmingham United Kingdom

The versatile nature of Visual Sentiment Analysis (VSA) is one reason for its rising profile. It isn't easy to efficiently manage social media data with visual information since previous research has concentrated on Sentiment Analysis (SA) of single modalities, like textual. In addition, most visual sentiment studies need to adequately classify sentiment because they are mainly focused on simply merging modal attributes without investigating their intricate relationships. This prompted the suggestion of developing a fusion of deep learning and machine learning algorithms. In this research, a deep feature-based method for multiclass classification has been used to extract deep features from modified ResNet50. Furthermore, gradient boosting algorithm has been used to classify photos containing emotional content. The approach is thoroughly evaluated on two benchmarked datasets, CrowdFlower and GAPED. Finally, cutting-edge deep learning and machine learning models were used to compare the proposed strategy. When compared to state-of-the-art approaches, the proposed method demonstrates exceptional performance on the datasets presented. © 2024, CC BY.

关键词： Contrastive Learning

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A Cyclic Small Phase Theorem for MIMO LTI systems*

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IFAC-PapersOnLine 2023年第2期56卷 1883-1888页

作者： Chao Chen Wei Chen Di Zhao Jianqi Chen Li Qiu Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Mechanics and Engineering Science & State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Control Science and Engineering & Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between -π and π. The proposed theorem complements the celebrated small gain theorem.

关键词： Small phase theorem segmental phase cyclic feedback systems stability analysis lead-lag compensation

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An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

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computers, Materials & Continua 2021年第10期69卷 1003-1019页

作者： Mahmood ul Hassan Shahzad Ali Khalid Mahmood Muhammad Kashif Saeed Amin Al-Awady Kamran Javed Ansar Munir Shah Department of Computer Skills Preparatory yearNajran UniversityNajran66241Saudi Arabia Department of Computer Science Jouf UniversityTubarjal74769Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMahayil Asir63317Saudi Arabia Department of Computer Science Community College Mahayil AsirKing Khalid UniversityMahayil63718Saudi Arabia Department of Computer Engineering National University of Technology(NUTECH)Islamabad44000Pakistan Department of Computer Science Institute of Southern PunjabMultan60000Pakistan

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile *** failure of nodes leads to network partitioning that may compromise the basic operation of the sensor *** deal with such situations,a rapid recovery mechanism is required for restoring inter-node *** to the immense importance and need for a recovery mechanism,several different approaches are proposed in the ***,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity ***,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy *** paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node *** achieves energy efficiency by transmitting a minimal number of control *** is also coverage-aware as it relocates minimal nodes while trying to restore *** the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node *** also show that ECRT exchanges a much smaller number of packets than other ***,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.

关键词： Cut-vertex failure recovery network connectivity node relocation wireless sensor network node failures

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases

Hybrid Convolutional Neural Networks for Enhanced Detection ...

引用

Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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Residual Autoencoder Deep Neural Network for Electrical Capacitance Tomography

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computers, Materials & Continua 2022年第12期73卷 6307-6326页

作者： Wael Deabes Kheir Eddine Bouazza Department of Computer Science in Jamoum Umm Al-Qura UniversityMakkah25371Saudi Arabia Computers and Systems Engineering Department Mansoura UniversityMansoura35516Egypt Laboratoire D’Informatique et des Technologies de L’Information D’Oran(LITIO) University of Oran31000OranAlgeria

Great achievements have been made during the last decades in the field of Electrical Capacitance Tomography(ECT)image ***,there is still a need to make these image reconstruction results faster and of better ***,Deep Learning(DL)is flourishing and is adopted in many *** DL is very good at dealing with complex nonlinear functions and it is built using several series of Artificial Neural Networks(ANNs).An ECT image reconstruction model using DNN is proposed in this *** proposed model mainly uses Residual Autoencoder called(ECT_ResAE).Alarge-scale dataset of 320 k instances have been generated to train and test the proposed ECT_ResAE *** instance contains two vectors;a distinct permittivity distribution and its corresponding capacitance *** capacitance vector has been modulated to generate a 66×66 image,and represented to the ECT_ResAE as an *** scalability and practicability of the ECT_ResAE network are tested using noisy data,new samples,and experimental *** experimental results show that the proposed ECT_ResAE image reconstruction model provides accurate reconstructed *** achieved an average image Correlation Coefficient(CC)of more than 99%and an averageRelative ImageError(IE)around 8.5%.

关键词： ECT image reconstruction deep learning residual autoencoder IE CC

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