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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Peng Shao, Shuai Technology and Engineering Center for Space Utilization Chinese Academy of Science China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350308365

The daily task planning problem (DTPP) in the ground control of satellites has been a significant focus for space scientists. Although various optimization methods have been devoted to solving this problem, most of them can only deal with simple DTPPs with a few variables and naive objectives, which cannot obtain multiple optimal solutions that meet the complex requirements of real-world scenarios. To address this issue, this paper formulates DTPPs into a large-scale multi-objective optimization problem and solves it using an evolutionary algorithm embedded with heuristic strategies. The proposed algorithm can quickly approach the optimal solution of DTPPs and can be seamlessly embedded into various evolutionary algorithms. In order to evaluate the performance of the proposed algorithm, statistical experiments are carried out on the DTPP with nine real-world datasets. The experimental results demonstrate that the proposed algorithm has significant advantages over several representative evolutionary algorithms. © 2024 IEEE.

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Tracking and Security Features Enhancement in a Smart School Bus Using IoT

Tracking and Security Features Enhancement in a Smart School...

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2023 International Conference on IoT, Communication and Automation technology, ICICAT 2023

作者： Abhay Surya, R.L. Likith, A.S. Guruprasad, N. Global Academy of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350302820

Public transportation systems are often not taken into account by the parents as a mode of transport, due to the risks and lack of safety measures they possess. The Internet of Things (IoT) based applications and devices implemented in these School buses, rectify the concerns amongst the parents to have a trustworthy infrastructure. The system provides a solution to meet safety requirements by consistent student tracking using RFID and GPS technologies to report boarding and deboarding time of the student passing through 2 checkpoints, real-time bus location, and speed tracking to abide by the rules. Furthermore, a dual authentication to confirm that no unauthorised person can access the bus system, and surveillance IP camera for indoor monitoring. Sensors like Door sensors and Proximity sensors connected to Arduino Uno are used to mark the onboard and post-deboarding safety to depict a child's presence in the red zones, respectively. This can nullify or drastically bring down the occurrence of any undesirable instances. A mobile application displaying all the necessary information is made available for the ease of parents. Overall, the proposed system can uplift the safety of school children while travelling, providing parents with a stress-free and worriless environment. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Technique and Disinformation Detection using Pre-Trained Language Models On Imbalanced Datasets 1

AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Techni...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： El-Sayed, Ahmed Nasr, Omar El-Madany, Nour El-Din Department of Computer Engineering Arab Academy for Science and Technology Egypt Department of Electronics And Communication Arab Academy for Science and Technology Egypt

ISBN: (纸本)9781959429272

This paper presents the pipeline developed by the AAST-NLP team to address both the persuasion technique detection and disinformation detection shared tasks. The proposed system for all the tasks’ sub-tasks consisted of preprocessing the data and finetuning AraBERT on the given datasets, in addition to several procedures performed for each subtask to adapt to the problems faced in it. The previously described system was used in addition to Dice loss as the loss function for sub-task 1A, which consisted of a binary classification problem. In that sub-task, the system came in eleventh place. We trained AraBERT for task 1B, which was a multilabel problem with 24 distinct labels, using binary cross-entropy to train a classifier for each label. On that sub-task, the system came in third place. We utilised AraBERT with Dice loss on both subtasks 2A and 2B, ranking second and third among the proposed models for the respective subtasks. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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Performance Analysis of Machine Learning Algorithms for Password Strength Check 1

Performance Analysis of Machine Learning Algorithms for Pass...

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1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Divya, R. Gaganashree Devamane, Shridhar B Dharshini, V. Deepika, S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350339727

Passwords are a vital component of system security, providing a simple, direct means of protecting a system and representing the identity of an individual for a system. However, the same patterns that people use to create passwords also render them vulnerable to attack. Since the bulk of records has weak passwords or well-known password patterns, password cracking algorithms have been developed to anticipate the password both offline and online. In order To prevent these vulnerabilities from being exploited, it is critical for companies to realize the risks that passwords provide and to create robust policies governing the selection and use of passwords. The proposed model employs multiple Machine Learning (ML) methods, including Decision Tree (DT), Logistic Regression (LR), Random Forest Classifier (RF), and K Nearest Neighbor, pushing users to select a strong password as a protection against these online and offline attacks. The Random Forest Classifier had the best results during the testing of the models over the test set, with an accuracy of 98%, and Logistic Regression achieved the lowest accuracy of 82%. © 2023 IEEE.

关键词： Decision trees

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Generating Adversarial Patterns in Facial Recognition with Visual Camouflage

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Journal of Shanghai Jiaotong University (science) 2024年 1-12页

作者： Bao, Qirui Mei, Haiyang Wei, Huilin Lü, Zheng Wang, Yuxin Yang, Xin Key Laboratory of Social Computing and Cognitive Intelligence of Ministry of Education School of Control Science and EngineeringSchool of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China Systems Engineering Institute Academy of Military Sciences Beijing100141 China

Deep neural networks, especially face recognition models, have been shown to be vulnerable to adversarial examples. However, existing attack methods for face recognition systems either cannot attack black-box models, are not universal, have cumbersome deployment processes, or lack camouflage and are easily detected by the human eye. In this paper, we propose an adversarial pattern generation method for face recognition and achieve universal black-box attacks by pasting the pattern on the frame of goggles. To achieve visual camouflage, we use a generative adversarial network (GAN). The scale of the generative network of GAN is increased to balance the performance conflict between concealment and adversarial behavior, the perceptual loss function based on VGG19 is used to constrain the color style and enhance GAN’s learning ability, and the fine-grained meta-learning adversarial attack strategy is used to carry out black-box attacks. Sufficient visualization results demonstrate that compared with existing methods, the proposed method can generate samples with camouflage and adversarial characteristics. Meanwhile, extensive quantitative experiments show that the generated samples have a high attack success rate against black-box models. © 2023, Shanghai Jiao Tong University.

关键词： Face recognition

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Extending generalized unsupervised manifold alignment

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science China(Information sciences) 2022年第7期65卷 139-156页

作者： Xiaoyi YIN Zhen CUI Hong CHANG Bingpeng MA Shiguang SHAN Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science and Engineering Nanjing University of Science and Technology CAS Center for Excellence in Brain Science and Intelligence Technology

Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment(EGUMA)method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate(1) simultaneous alignment and discovery of manifolds;(2) complete unsupervised learning structure without any prerequisite correspondence;(3) more concise local geometry for the embedding space;(4) efficient alternative optimization;(5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.

关键词： unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm

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OpenAnnotate2: Multi-Modal Auto-Annotating for Autonomous Driving

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-13页

作者： Zhou, Yijie Cai, Likun Cheng, Xianhui Zhang, Qiming Xue, Xiangyang Ding, Wenchao Pu, Jian School of Computer Science Fudan University Shanghai China Independent Researcher Shanghai China SenseAuto Inc Shanghai China Computer Science Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Institute of Science and Technology for BrainInspired Intelligence Fudan University Shanghai China

The demand for high-quality annotated data has surged in recent years for applications driven by real-world artificial intelligence, such as autonomous driving and embodied intelligence. Consequently, the development of a tool that can assist humans in the highly automated and high-quality annotation of large-scale, multi-modal data is of significant importance and urgency for both academic research and practical applications. Most existing multi-modal data annotation tools require frame-by-frame, object-by-object annotation with keyboard and mouse, making it challenging to provide high-quality and real-time annotations for 2D images and 3D point clouds in highly open scenarios like autonomous driving. To address these challenges, we propose OpenAnnotate2, which understands human intentions based on natural language prompt, and formulates plans to decompose and execute complex multi-modal data annotation tasks. Additionally, the tool can continually enhance its cognitive and annotation capabilities with minimal human-computer interaction, through an ever-updating external knowledge base. This significantly simplifies the annotation workflow, paving the way for the creation of massive datasets suitable for large-scale visual models. The source code will be released at https://***/Fudan-ProjectTitan/OpenAnnotate. IEEE

关键词： Three dimensional displays

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Adaptive Sensor Fault Detecting Cyber-Physical Security System for Automated Carriers

Adaptive Sensor Fault Detecting Cyber-Physical Security Syst...

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International Conference on computer, Communication, Control and Information technology (C3IT)

作者： Projat Banerjee Subhashis Das Blockchain Researcher and Developer IBM India Private Limited India Computer Science and Engineering Academy of Technology Adisaptagram India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

Security is a vital facet for any automatic carrier, supported by the detected situation, the carrier should respond consequently to make sure that the environment is secure. Hence, the carrier should be expedited with a cyber-physical atmosphere. We tend to showcase a cyber-physical security mechanism for a carrier in low light environments. Detection of vulnerability via optical device, supersonic (US), infrared (IR), motion sensors and dynamic higher cognitive process with applicable response via the actuators of the carrier is the stress of the present proposal. Focus is given to running on practical hardware and code faults of the sensors and actuators which can dynamically occur with time, with appropriate bypass and remedial actions to be taken at runtime to ensure security. Finally, with the aid of cloud infrastructure, we broadcast the matter situation to different carriers and forestall congestion blockchain infrastructure to make certain that the information once kept in the ledger cannot be tampered with.

关键词： Wheels Surface emitting lasers Sensor phenomena and characterization Robot sensing systems Acoustics Sensor systems Safety Security Photodiodes Automobiles

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Clinical Decision Support System for Anemia Identification and Severity Stratification 1

Clinical Decision Support System for Anemia Identification a...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Vidhya Shree, H. Yukthi, H.S. Vandana, W.M. Chirchi, Khushi E. Saha, Jayita Dayananda Sagar University Computer Science and Engineering Bengaluru India Manipal Institute of Technology Bengaluru Computer Science and Engineering Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350371802

The research addresses the pervasive challenges faced by anemic individuals globally. This innovative solution goes beyond traditional approaches by incorporating real-time data collection, including critical parameters such as Complete Blood Count (CBC), age, and gender providing a dynamic and holistic view of an individual's health status. The integration of CBC data, coupled with demographic information, enhances the precision of the monitoring process, allowing for a more accurate assessment of anemia risk and severity. The collected real-time data is seamlessly transmitted to a centralized cloud platform, where advanced machine learning models, specifically recurrent neural networks (RNNs), come into play. Trained on a diverse dataset that includes CBC counts, age, and gender, these models excel in capturing temporal patterns and subtle variations associated with anemia. The result is a robust classification system that achieves a remarkable 97% accuracy, facilitating early detection and tailored interventions. By combining real-time data collection with sophisticated machine learning and deep learning models, the proposed system represents a significant advancement in addressing the challenges posed by anemia. This holistic and personalized approach not only ensures timely intervention but also contributes to improved healthcare outcomes for individuals affected by this widespread condition. © 2024 IEEE.

关键词： Recurrent neural networks

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A High-Quality Adaptive Video Reconstruction Optimization Method Based on Compressed Sensing

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computer Modeling in engineering & sciences 2023年第10期137卷 363-383页

作者： Yanjun Zhang Yongqiang He Jingbo Zhang Yaru Zhao Zhihua Cui Wensheng Zhang School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan030024China Department of Big Data and Intelligent Engineering Shanxi Institute of TechnologyYangquan045000China School of Computer Science Beijing University of TechnologyBeijing100124China The Institute of Automation Chinese Academy of Sciences(CAS)Beijing100049China

The video compression sensing method based onmulti hypothesis has attracted extensive attention in the research of video codec with limited ***,the formation of high-quality prediction blocks in the multi hypothesis prediction stage is a challenging *** resolve this problem,this paper constructs a novel compressed sensing-based high-quality adaptive video reconstruction *** includes the optimization of prediction blocks(OPBS),the selection of searchwindows and the use of neighborhood ***,the OPBS consists of two parts:the selection of blocks and the optimization of prediction *** combine the high-quality optimization reconstruction of foreground block with the residual reconstruction of the background block to improve the overall reconstruction effect of the video *** addition,most of the existing methods based on predictive residual reconstruction ignore the impact of search windows and reference frames on ***,Block-level search window(BSW)is constructed to cover the position of the optimal hypothesis block as much as *** maximize the availability of reference frames,Nearby reference frame information(NRFI)is designed to reconstruct the current *** proposed method effectively suppresses the influence of the fluctuation of the prediction block on reconstruction and improves the reconstruction *** results showthat the proposed compressed sensing-based high-quality adaptive video reconstruction optimization method significantly improves the reconstruction performance in both objective and supervisor quality.

关键词： Compressed sensing OPBS block-level search window nearby reference frame information evolutionary algorithm

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