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Gender Prediction Using Real-time Convolutional Neural Network

Procedia computer science 2025年 258卷 497-506页

作者： Emmanuel O. Balogun Sunday Adeola Ajagbe Abidemi E. Adeniyi Temitope O. Olayinka Elijah Adetoyese Adeogun Gbadegesin Adetayo Taiwo Ogheneruona Maria Esegbona-Isikeh Pragasen Mudali ICT Unit Ajayi Crowther University Oyo Oyo Nigeria Department of Computer Engineering Abiola Ajimobi Technical University 200255 Nigeria Department of Computer Science Bowen University Iwo Nigeria Department of Computer Science University of Maine Orono United State Department of Computer Science Ladoke Akintola University of Technology Ogbomoso Nigeria School of Computing Science & Engineering University of Salford Manchester United Kingdom Amey Services Limited United Kingdom

This work presents real time gender prediction using Convolutional Neural Network. Automatic classification of gender has become an area that has garnered importance due to the emergence of breakthroughs in the world of computing particularly with the advent of machine learning and Artificial intelligence. The goal of gender prediction in computer vision involves accurate prediction of gender from visual data. Gender prediction is an indispensable biological metric as it plays a significant role in many human applications. The application varies from immigration, border access, law enforcement, defence and intelligence, citizen identification and banking. Image processing combined with machine learning algorithms have been employed to build solutions from image representation of biological attributes such as facial images, human skeletal radiographs (most notably skull and pelvic bones), gait, smiles and non-biological features such as social media activities, names and other means that could be employed to determine gender by extracting regions of interest, applying necessary filters and normalizing the matrix values obtained. For the purpose of this work, a total of 6760 hand-bone radiographs were acquired from the Radiological Society of North America Repository. The dataset was divided into 70% training datasets and 30% test dataset using Random Sampling Cross-Validation Method. The acquired data images were pre-processed using image cropping, histogram equalization and segmentation techniques. Performance evaluation was carried out on the developed system using the metrics: Accuracy, False Positive Rate (FPR), Recognition Time, Specificity and Sensitivity, these metrics were evaluated at threshold values of 0.25, 0.35, 0.5 and 0.75. Optimal values were gotten at optimum threshold value of 0.75, the values are; 4.79, 93.67, 95.21, 94.53 and 137.87 respectively for metrics (FPR, Sensitivity, Specificity, Accuracy and Time).

关键词： Convolutional Neural Network Gender Prediction computer Vision Image Processing Classifiers Image Recognition

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An Instance-Level Motion-Aware Graph Model for Multi-target Multi-camera Tracking

An Instance-Level Motion-Aware Graph Model for Multi-target ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaotong Fan Huicheng Zheng School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multi-target multi-camera tracking (MTMCT), focusing on inferring trajectory across multiple surveillance videos, is holding significant practical utility. While numerous studies aim to learn visual features robust to illumination variation, occlusions, and other issues, the spatio-temporal information within a multi-camera system is not explored sufficiently. Existing methods generally exploit coarse-grained spatio-temporal information by modeling the distribution of time intervals between cameras. However, they tend to neglect the specific motion state of individual objects, which may hinder accurate cross-camera association. In this paper, we introduce a novel motionaware graph (MAG) model designed to extract instance-level spatio-temporal information that aligns with visual features and seamlessly aggregate these two kinds of information within a unified graph framework for MTMCT. Specifically, we propose a motion encoder-decoder module that predicts spatio-temporal consistency scores between objects based on their instance-level motion states. These scores are then integrated with visual similarity scores to generate discriminative feature representations for data association via a graph attention mechanism. Experimental evaluations and ablation studies on the large-scale MTA dataset demonstrate the superiority of our proposed model.

关键词： Visualization Attention mechanisms Tracking Surveillance Neural networks Lighting Predictive models

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Solving Constrained Multi-objective Optimization Problems with Passive Archiving Mechanism 1

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17th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2022

作者： Jia, Huijuan Zhang, Kai Shen, Chaonan School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430065 China

ISBN: (数字)9789819915491

ISBN: (纸本)9789819915484

During these years, many advanced constrained multi-objective evolutionary algorithms (CMOEAs) have been developed to solve constrained multi-objective optimization problems (CMOPs). However, some existing constrained multi-objective algorithms do not use an archiving mechanism, which leads to easy loss of the searched feasible solutions during the search process. In addition, even if some algorithms use an archiving mechanism, an unsuitable archiving update strategy can cause difficulty in balancing the diversity and convergence of the archive set. To address these challenges, a two-stage constrained multi-objective evolutionary algorithm based on a passive archiving mechanism (PA-CMOEA) is proposed in this paper. In the first stage, the constrained multi-objective problem is modeled as an unconstrained multi-objective problem, which allows the population to fully explore the decision space. The archive set is passively updated in a constraint-domination principle (CDP) criterion using population information. The goal of this stage is for the archive set to converge to the constrained Pareto front region of the problem with the help of population information. In the second stage, a local search is performed on the archive set. Specifically, the convergence and diversity of the archived solution set is improved while ensuring that the feasibility of the archived set is not worse. Finally, the algorithm outputs the archive set. To estimate the performance of the proposed algorithm, experiments are conducted on the LIRCMOP and CF benchmarks in comparison with some of the most popular algorithms. The experimental results demonstrate that the proposed algorithm has a competitive advantage compared with the comparative algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Benchmarking

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FAGC: Free Space Fragmentation Aware GC Scheme based on Observations of Energy Consumption

FAGC: Free Space Fragmentation Aware GC Scheme based on Obse...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lihua Yang Zhipeng Tan Fang Wang Yang Xiao Wei Zhang Biao He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology College of Science and Technology National University of Defense Technology China Huawei Technology Co. Ltd China

Smartphones are everyday necessities with limited power supply. Charging a smartphone twice a day or more affects user experience. Flash friendly file system (F2FS) is a widely-used log-structured file system for smartphones. Free space fragmentation of F2FS consists of invalid blocks mainly causing performance degradation. F2FS reclaims invalid blocks by garbage collection (GC). We explore the energy consumption of GC and the effect of GC on reducing free space fragments. We observe the energy consumption of one background GC is large but its effect on reducing free space fragments is limited. These motivate us to improve the energy efficiency of GC. We reassess how much free space is a free space fragment based on data analysis, use the free space fragmentation factor to measure the degree of free space fragmentation quickly. We suggest the free space fragmentation aware GC scheme (FAGC) that optimizes the selection for victim segments and the migration for valid blocks. Experiments on real platform show that FAGC reduces GC count by 82.68% and 74.51% respectively than traditional F2FS and the latest GC optimization of F2FS, ATGC. FAGC reduces the energy consumption by 164.37 J and 100.64 J compared to traditional F2FS and ATGC respectively for a synthetic benchmark.

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Tree-IDS: An Incremental Intrusion Detection system for Connected Vehicles

Tree-IDS: An Incremental Intrusion Detection System for Conn...

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Conference on Local computer Networks (LCN)

作者： Yuxin Zhao Zixiang Bi Guosheng Xu Chenyu Wang Guoai Xu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China

The rapid development of Internet of Vehicles technology has led to the continuous upgrading of the functions of connected vehicles. While connected vehicles bring convenience to people’s life, there are also many security threats. Connected vehicles not only have intra-vehicle networks communication, but also communicate with the external network. The diversity of communication methods makes the attack surface wider, and some new attacks are constantly emerging. In order to ensure vehicle security, This paper focuses on the attacks that are vulnerable to vehicles, and proposes an incremental intrusion detection system, which can not only detect attacks, but also incrementally learn new types of attacks. Experimental results illustrate that the proposed system can incrementally learn new attacks and avoid catastrophic forgetting problems, and can detect various types of known attacks with 99.99% accuracy on the Car-Hacking Dataset and 99.37% accuracy on the CICIDS2017.

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization Software defined networking

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BCPPAS : Blockchain-Based Cross-Domain Identity Authentication Scheme for IoT with Privacy Protection

BCPPAS : Blockchain-Based Cross-Domain Identity Authenticati...

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International Conference on computer Supported Cooperative Work in Design

作者： Qianqian Li Yubing Han Qi Liu Guijuan Wang Hao Yu Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

关键词： Privacy Federated learning Aggregates Authentication Blockchains Computational efficiency Internet of Things Servers Protection Information exchange

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Select Informative Samples for Night-Time Vehicle Detection Benchmark in Urban Scenes

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Remote Sensing 2023年第17期15卷 4310-4310页

作者： Wang, Xiao Tu, Xingyue Al-Hassani, Baraa Lin, Chia-Wen Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China School of Computer Science Wuhan University Wuhan430081 China Department of Electrical Engineering Industrial Technology Research Institute National Tsing Hua University Hsinchu30013 Taiwan

Night-time vehicle detection plays a vital role due to the high incidence of abnormal events in our daily security field. However, existing studies mainly focus on vehicle detection in autonomous driving and traffic intersection scenes, but ignore urban scenes. There are vast differences between these scenes, such as viewpoint, position, illumination, etc. In this paper, the authors present a night-time vehicle detection dataset collected from urban scenes, named Vehicle Detection in Night-Time Urban Scene (VD-NUS). The VD-NUS dataset consists of more than 100 K challenging images, comprising a total of about 500 K labelled vehicles. This paper introduces a vehicle detection framework via an active auxiliary mechanism (AAM) to reduce the annotation workload. The proposed AAM framework can actively select the informative sample for annotation by estimating its uncertainty and locational instability. Furthermore, this paper proposes a computer-assisted detection module embedded in the AAM framework to help human annotators to rapidly and accurately label the selected data. AAM outperformed the baseline method (random sampling) by up to 0.91 AP and 3.0 MR−2 on the VD-NUS dataset. © 2023 by the authors.

关键词： Vehicles

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AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)

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Neural computing and Applications 2024年 1-20页

作者： Zhang, Ying Xu, Shimin Zhang, Linghui Jiang, Weiwei Alam, Sameer Xue, Dabin College of General Aviation and Flight Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory of Air Traffic Management System Nanjing China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Air Traffic Management Research Institute School of Mechanical & ampAerospace Engineering Nanyang Technological University Singapore Singapore Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic University Hong Kong

Accurate sector-based air traffic flow predictions are essential for ensuring the safety and efficiency of the air traffic management (ATM) system. However, due to the inherent spatial and temporal dependencies of air traffic flow, it is still a challenging problem. To solve this problem, some methods are proposed considering the relationship between sectors, while the complicated spatiotemporal dynamics and interdependencies between traffic flow of route segments related to the sector are not taken into account. To address this challenge, the attention-enhanced graph convolutional long short-term memory network (AGC-LSTM) model is applied to improve the short-term sector-based traffic flow prediction, in which spatial structures of route segments related to the sector are considered for the first time. Specifically, the graph convolutional networks (GCN)-LSTM network model was employed to capture spatiotemporal dependencies of the flight data, and the attention mechanism is designed to concentrate on the informative features from key nodes at each layer of the AGC-LSTM model. The proposed model is evaluated through a case study of the typical enroute sector in the central–southern region of China. The prediction results show that MAE reduces by 14.4% compared to the best performing GCN-LSTM model among the other five models. Furthermore, the study involves comparative analyses to assess the influence of route segment range, input and output sequence lengths, and time granularities on prediction performance. This study helps air traffic managers predict flight situations more accurately and avoid implementing overly conservative or excessively aggressive flow management measures for the sectors. © The Author(s) 2024.

关键词： Forecasting

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