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Big Data & Artificial Intelligence & software engineering (ICBASE), International Conference on

作者： He Cai Dihan Li Renyu Jiang Yichao Shen Information Management and Information System Harbin Institute of Technology Weihai China Software Engineering Xiamen University Xiamen China Computer Science and Technology Nanjing University Jinling College Nanjing China Artificial Intelligence Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665427104

In recent years, the rapid development of deep learning has promoted the improvement of medical imaging. This paper uses computer vision technology to alleviate the classification problem of malignant melanoma images. The incidence and mortality of malignant melanoma have gradually increased in recent decades and data show that detecting melanoma early can significantly improve patients’ survival rate. We conducted a systematic review of existing literature, finding there are several algorithms that can recognize melanoma. Most of these algorithms classify the original image directly or use the K-NearestNeighbor (KNN) method, but these methods’ recognition efficiency and accuracy are not high and need a large number of pictures to train. In this paper, our model first uses Fully Convolutional Networks (FCN) to find and cut out the possible diseased part, then resorts to Convolutional Neural Networks (CNN) classifier for classification and recognition, which improves the performance and accuracy of the model. The effectiveness of this model has been verified on the dataset ISIC. Our work achieves an accuracy of 95.56%, precision of 88.57%, recall of 82.67%, false positive ratio of 2.63%, and F1 score of 85%, which is markedly better than the existing methods.

关键词： Deep learning Computer vision systematics Image recognition Melanoma Classification algorithms Convolutional neural networks

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Matching Based Escape Routing Algorithm with Variable Design Rules and Constraints

A Matching Based Escape Routing Algorithm with Variable Desi...

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Design Automation Conference

作者： Qinghai Liu Disi Lin Chuandong Chen Huan He Jianli Chen Yao-Wen Chang Center for Discrete Mathematics Fuzhou University Fuzhou China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fuzhou China Department of Microelectronics Fuzhou University Fuzhou China Hangzhou Huawei Enterprises Telecommunication Technologies Co. Ltd Hangzhou China State Key Lab of ASIC & System Fudan University Shanghai China Graduate Institute of Electronics Engineering National Taiwan University Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

Escape routing is a critical problem in PCB routing, and its quality greatly affects the PCB design cost. Unlike the traditional escape routing that works mainly for the BGA package with unique line width and space, this paper presents a high-performance escape routing algorithm to handle problems with variable design rules and manual constraints, including variable line widths/spaces, the neck mode of wires, and the pad entry for differential pairs. We first propose a novel obstacle-avoiding method to project pins to the boundary and construct a channel projection graph. We then construct a bi-projection graph and propose a matching-based hierarchical sequencing algorithm to consider manual constraints. We perform global routing for each pin/differential pair by congestion-avoiding path initialization and rip-up and reroute path optimization. Finally, we complete detailed routing in every face, ensuring the wire angle and pad entry constraints. Experimental results show that our algorithm can achieve 100% routability without any design rule violation for all given industrial PCB instances, while two state-of-the-art routers cannot complete routing.

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A Virtual Reality Simulation system for Coal Safety Based on Cloud Rendering and AI Technology 3rd

A Virtual Reality Simulation System for Coal Safety Based on...

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3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Li, Mei Sun, Zhenming Jian, Zhan Tan, Zheng Chen, Jinchuan Institute of Remote Sensing and Geographical Information System Peking University Beijing100871 China College of Resources and Safety Engineering China University of Mining and Technology Beijing100083 China Beijing Longruan Technologies Beijing100190 China

ISBN: (纸本)9783030639402

Coal mining, regarded as a high-risk industry, has strongly demand on Virtual Reality (VR) training environment for safety mining and emergency rescue. In order to solve the problems of the current VR platform of lack of immersive experience and interest, a Browser/Client VR simulation system are designed and realized. Three key techniques are studies, including cloud rendering, AI behavior tree and mining disaster animation. Unlike WebGL and HTML5, cloud rendering provides photo-realistic quality. 2 AI characters are designed to guide users to have an overall understanding of the mine and experience the effect mine disasters. The system has been successfully applied in the Virtual Reality Teaching and Experiment Laboratory for undergraduate in China University of Mining & Technology-Beijing. The research, as a new tool for miner training and disaster drilling, has a signification meaning of the work safety IT construction. © 2020, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Virtual reality

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Group-wise Inhibition based Feature Regularization for Robust Classification

Group-wise Inhibition based Feature Regularization for Robus...

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International Conference on Computer Vision (ICCV)

作者： Haozhe Liu Haoqian Wu Weicheng Xie Feng Liu Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of feature diversity for final judgment. In our method, we propose to dynamically suppress significant activation values of CNN by group-wise inhibition, but not fixedly or randomly handle them when training. The feature maps with different activation distribution are then processed separately to take the feature independence into account. CNN is finally guided to learn richer discriminative features hierarchically for robust classification according to the proposed regularization. Our method is comprehensively evaluated under multiple settings, including classification against corruptions, adversarial attacks and low data regime. Extensive experimental results show that the proposed method can achieve significant improvements in terms of both robustness and generalization performances, when compared with the state-of-the-art methods. Code is available at https://***/LinusWu/TENET_Training.

关键词： Training Computer vision Codes Heuristic algorithms Robustness Classification algorithms Convolutional neural networks

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DataPAL: Data Protection and Authorization Lifecycle framework 6

DataPAL: Data Protection and Authorization Lifecycle framewo...

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6th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Bandopadhyay, Subhajit Dimitrakos, Theo Diaz, Yair Hariri, Ali Dilshener, Tezcan la Marra, Antonio Rosetti, Alessandro German Research Center Huawei Technologies Duesseldorf GmbH Munich Germany School of Computing University of Kent Canterbury United Kingdom European Business School University Munich Germany IT All Software Solutions Munich Germany Security Forge Pisa Italy Department of Computer Science and Information Engineering University of Trento Trento Italy Institute for Cyber Security School of Mathematics Computer Science and Engineering City University of London London United Kingdom

ISBN: (纸本)9781665427425

This paper introduces a new model for handling data privacy throughout data lifecycle via the introduction of a policy profile using the Abbreviated Language For Authorization (ALFA) policy language. Our approach extends previous models in three complementary ways: (1) By introducing Administration and Delegation Profile (ADP) in ALFA policy where users and companies can restrict the scope of access/usage policies related to data as well as specify a chain of custody for data (moreover such an approach eases up the tasks of handling users' consent);(2) Thanks to our framework Usage Control system Plus (UCS+) users can monitor the usage of data and revoke its usage upon specific conditions or at will;(3) By introducing new states for policy evaluation, i.e. Admissible/NotAdmissible to filter out those applicable policies that were unauthorized in the first place. © 2021 IEEE

关键词： Data privacy

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AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing

AFF-Dehazing: Attention-based feature fusion network for low...

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作者： Zhou, Yu Chen, Zhihua Sheng, Bin Li, Ping Kim, Jinman Wu, Enhua Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong School of Information Technologies The University of Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively. © 2021 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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Label Self-Adaption Hashing for Image Retrieval

Label Self-Adaption Hashing for Image Retrieval

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International Conference on Pattern Recognition

作者： Jianglin Lu Zhihui Lai Jingxu Lin Qinghong Lin Jie Zhou Institute of Computer Mathematics and Information Technologies Kazan Federal University Kazan Russia SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China College of Computer Science and Software Engineering Shenzhen Univeristy Shenzhen China

Hashing has attracted widespread attention in image retrieval because of its fast retrieval speed and low storage cost. Compared with supervised methods, unsupervised hashing methods are more reasonable and suitable for large-scale image retrieval since it is always difficult and expensive to collect true labels of the massive data. Without label information, however, unsupervised hashing methods can not guarantee the quality of learned binary codes. To resolve this dilemma, this paper proposes a novel unsupervised hashing method called Label Self-Adaption Hashing (LSAH), which contains effective hashing function learning part and self-adaption label generation part. In the first part, we utilize anchor graph to keep the local structure of the data and introduce joint sparsity into the model to extract effective features for high-quality binary code learning. In the second part, a self-adaptive cluster label matrix is learned from the data under the assumption that the nearest neighbor points should have a large probability to be in the same cluster. Therefore, the proposed LSAH can make full use of the potential discriminative information of data to guide the learning of binary codes. It is worth noting that LSAH can learn effective binary codes, hashing function and cluster labels simultaneously in a unified optimization framework. To solve the resulting optimization problem, an Augmented Lagrange Multiplier based iterative algorithm is elaborately designed. Extensive experiments on three large-scale data sets indicate the promising performance of the proposed LSAH.

关键词： Training Image resolution Image retrieval Binary codes Feature extraction Data models Data mining

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A Fuzzy Delphi Based Inference system for Detecting and Controlling Rice Weeds

Research Square

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Research Square 2021年

作者： Nafchi, Najmeh Fatahi Asemi, Adeleh Tahaei, Hamid Safahan Institute of Higher Education Isfahan Iran Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer System & Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

In this research, the purpose was to design a fuzzy expert system based on fuzzy delphi method to detect and control the rice weed. The statistical population was elites and experts with regard to the science, experience and field of activity;15 experts were selected as the sample. Two questionnaires were used to design the desired fuzzy expert: i) Fuzzy Delphi Technique Weed Detection Questionnaire, ii) Delphi Technique Weed Control Questionnaire. The design of the desired expert system was done with MATLAB software and the fuzzy logic tool box. That is, after obtaining an appropriate range of factors, through attributing the fuzzy trapezoidal membership functions to these ranges and generating the input functions, designing the rule base of this system and combining the output results of each factor, a system was designed whose input was the weed factor and the output was scores assigned to weeds. MATLAB guide was also used to design the graphical user interface. Then, for validation the designed system was tested. The answers of system and individual expert were then analyzed using paired t-test. Root Mean Square Error and Middle Absolute Value Deviation tests were used to calculate the system errors. The results were 0.12 and 0.01, respectively. This indicates that the designed fuzzy expert system has sufficient accuracy. Finally, given that all but two of the examined rules are the same as the diagnosis of an individual expert, then in 94% of the cases, the diagnosis of the system is the same as the diagnosis of an individual expert. © 2021, CC BY.

关键词： MATLAB

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RIS-Empowered Integrated Location Sensing and Communication with Superimposed Pilots

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IEEE Transactions on Communications 2025年

作者： Xia, Wenchao Zhao, Ben Tang, Wankai Zhu, Yongxu Wong, Kai-Kit Lambotharan, Sangarapillai Shin, Hyundong Jiangsu Key Laboratory of Wireless Communications and IoT China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing210003 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China University College London Department of Electronic and Electrical Engineering LondonWC1E 7JE United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Loughborough University Institute for Digital Technologies LondonE20 3BS United Kingdom Kyung Hee University Department of Electronics and Information Convergence Engineering 1732 Deogyeong-daero Giheung-gu Gyeonggi-do Yongin-si17104 Korea Republic of

In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher information matrix (FIM) analysis and derive Craḿer-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme. © 1972-2012 IEEE.

关键词： Fast Fourier transforms

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