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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhao, Yangyang Xiao, Fuyuan School of Computer and Information Science Southwest University Chongqing400715 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9783031200953

Intuitionistic fuzzy set (IFS) has attracted much attention because it can deal with fuzziness and uncertainty more flexibly than traditional fuzzy set. Complex intuitionistic fuzzy set (CIFS) extends intuitionistic fuzzy to the complex plane, which can better express and process uncertain information. In this paper, a novel distance measure complex intuitionistic fuzzy distance (CIFD) is proposed for CIFSs. Firstly, inspired by Tanimoto coefficient, a new similarity measure between CIFSs is proposed. Then, based on the similarity measure, the CIFD is proposed and its non-negativity, non-degeneracy, and symmetry are analyzed. Moreover, when CIFS degenerates into classical IFS, CIFD is also applicable to measure the differences between IFSs. Finally, to illustrate the effectiveness of CIFD, an example is given at the end. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy sets

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Sharing Food with FoodLifeSavr Smartphone App 2

Sharing Food with FoodLifeSavr Smartphone App

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Andres, Arfandi Tanjaya, Edwin Setiawan Ghaniyardi Sandhi, Joshua Christian Warnars, Harco Leslie Hendric Spits Bina Nusantara University Information Systems Department School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program - Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

In this era, people often overeat, make parties or events, where there are excessive availability of food. As well as shops or restaurants that sell food, has ready-to-eat foods that only lasts one day and then treated as expired food. Often this precious food is thrown in the trash, and meanwhile, many destitute people cannot eat enough per day. Therefore, this research paper aims to mobilize people who overeat and donate their excess food that can still be consumed to those in need. This smartphone application is equipped with activities such as registering for new donors who want to join the mobile application and logging in for donors who are already registered in the mobile application. Donors can monitor couriers taking photos of recipients with food donated from donors. In addition, donors can select the food category for donation and input amount, track the donated food that the courier has picked up, and send it to the recipient. This mobile application is also equipped with donors to share experiences, ideas, and views based on topics created by staff and available threads. © 2022 IEEE.

关键词： Mobile computing

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Person Tracking and Modelling for a Human Interaction Robot 25

Person Tracking and Modelling for a Human Interaction Robot

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Adam Golding Wafa Johal Claude Sammut Computer Science and Engineering UNSW Sydney Sydney Australia School of Computing & Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

For a robot to perform personalised human support in a home, it is essential to uniquely identify the people it is working for. Each individual has their own attributes, knowledge of the environment, and experiences which the robot should learn and keep in its memory. One of the current challenges is for a robot to identify and re-identify an individual while dealing with visual occlusion, changes in appearance, and identity switching. This paper presents a novel approach to address these challenges in robot vision by extending current state-of-the-art solutions to enhance person memorisation by taking multiple viewpoints to extract features for matching and maintaining this information in a world model of the robot.

关键词： Visualization Robot vision systems Human-robot interaction Switches Feature extraction Planning Robots Faces Testing Identification of persons

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Hyperautomation for Air Quality Evaluations: A Perspective of Evidential Three-way Decision-making

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Cognitive Computation 2024年第5期16卷 2437-2453页

作者： Ding, Juanjuan Zhang, Chao Li, Deyu Sangaiah, Arun Kumar School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University Shanxi Taiyuan030006 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Hyperautomation acts as a real digital transformation with the support of several cutting-edge cognitive computation methods that include robotic process automation, natural language processing, artificial intelligence, and other emerging ones, which is conducive to processing complex industrial processes via extending the range of various data-driven cognitive decision-making algorithms. The study of air quality evaluations (AQE) plays a significant role in ensuring healthy atmospheric environments. In view of the objective existence of uncertainties, AQE can be modeled and addressed by a typical data-driven automated decision-making problem, and hyperautomation can provide a reasonable solution via associating with a variety of techniques. This article explores hyperautomation for AQE via evidential three-way large-scale group decision-making (LSGDM) in an intuitionistic fuzzy (IF) setting. First, the notion of intuitionistic fuzzy sets (IFSs) is incorporated into the paradigm of multi-granularity (MG) three-way decisions (TWD), and the model of adjustable MG IF probabilistic rough sets (PRSs) is developed. Second, an IF clustering analysis with the improved technique for order preference by similarity to ideal solution (TOPSIS) method is conducted to affirm representative members within a decision group. Third, a novel IF LSGDM method is built via adjustable MG IF PRSs and the evidence reasoning (ER) method. Finally, a case study in the setting of AQE is studied by using the presented evidential three-way LSGDM method. Corresponding experimental analyses are carried out for illustrating the efficiency of hyperautomation for AQE. In general, the proposed method improves the performance of information fusion by virtue of adjustable MG IF PRSs, and the TOPSIS method avoids the influence of subjective factors on decision results. Meanwhile, the evaluation information of decision-makers (DMs) is fully analyzed by means of the ER method, which can provide more explaina

关键词： Decision making

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Polarity Classification of Twitter Data Using Machine Learning Approach

Polarity Classification of Twitter Data Using Machine Learni...

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2023 Human-Centered Cognitive systems, HCCS 2023

作者： Bangyal, Waqas Haider Iqbal, Muddesar Bashir, Ahsan Ubakanma, George Kohsar Univeristy Murree Dept. of Computer Science Muree Pakistan London South Bank University School of Engineering School of Engineering Cognitive Systems Research Centre United Kingdom Abasyn University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350359183

Sentiment analysis has become very important nowadays because there are lots of social media platforms peoples are using to express their opinion. Twitter is one of the most popular social media platforms which is used for microblogs. People use to express their opinion on current affairs, and there is a challenge for researchers to classify the sentiment accurately. In this research study, we proposed a greatly efficient technique for the detection of fake news on covid-19. The data set of fake news is taken from the corpus and executes the NLP cycle. In this research, we applied five machine learning to predict the sentiment of fake or real news. Support Vector Machine, Logistic Regression, KNN, Decision Trees, and Random Forest are machine learning classifiers used in this research, and results are compared. © 2023 IEEE.

关键词： Sentiment analysis

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Non-Orthogonal Broadcast and Unicast Transmission Based on Novel Centralized Frequency Reuse for Multibeam Satellite System

Non-Orthogonal Broadcast and Unicast Transmission Based on N...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Zhiqiang Han, Shuai Li, Cheng Benslimane, Abderrahim Harbin Institute of Technology School of Electronics and Information Engineering Harbin150001 China Simon Fraser University School of Engineering Science BurnabyBCV5A 1S6 Canada University of Avignon Department of Computer Science and Engineering Avignon84029 France

ISBN: (纸本)9798350351255

The multibeam satellite system is crucial for the next generation communication, providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within limited spectrum resources presents formidable challenges. In response, rate-splitting multiple access (RSMA) has emerged, leveraging non-orthogonal transmission and precoding strategies concurrently. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using RSMA. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose a novel centralized frequency reuse strategy, exhibiting commendable performance alongside reduced computational complexity. Furthermore, we maximize the weighted sum rate (WSR) and introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problem into tractable convex counterpart. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications. © 2024 IEEE.

关键词： Convex optimization

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection 24

Dual-Modeling Decouple Distillation for Unsupervised Anomaly...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400706868

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. © 2024 ACM.

关键词： Students

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Developing an Adaptive Security Framework for Real-Time Threat Detection and Response in Cloud-Network systems

Developing an Adaptive Security Framework for Real-Time Thre...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vimal Kumar, M. Guru Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St.Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha University Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

The study is centred on examining an intelligent security system for real-time threat identification and mitigation in cloud-network systems. The more cloud computing environments are being constructed;conventional models of security do not offer optimal solutions to counteract changing cyber threats. This framework incorporates the use of sophisticated algorithms in machine learning and real-time data analysis to achieve a more proactive form of defence measures. It incorporates anomaly detection, classification algorithms and predictive analysis in the improvement of threat detection and response time. The framework also has active response mechanisms for timely deliberation on anything that may be seen to have a detrimental effect on the cloud. Analyzes reveal high levels of detection, timely responses and scalability proving the suitability of the framework to handle new threats and/or blend with existing cloud components. The findings of this study can be considered as valuable contribution to cloud security since the approach proposed in this paper can be effectively employed to enhance protection against new types of threats in specific organizational contexts. © 2024 IEEE.

关键词： Cloud security

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Graph-Based Embedding Improvement Feature Distribution in Videos

Graph-Based Embedding Improvement Feature Distribution in Vi...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Zhang, Junbin Tsai, Pei-Hsuan Tsai, Meng-Hsun National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan National Cheng Kung University Institute of Manufacturing Information and Systems Tainan Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350324174

Video understanding is a significant computer vision research subject since online video content is growing exponentially. Feature extraction and representation play a crucial role in video understanding tasks such as classification, segmentation, and recognition. However, the model's learning is ambiguous since adjacent video frames typically have similar RGB features. To address this issue, we present graph-based embedding to enhance video feature distribution. We construct a graph-structured of videos by connecting similar features. Node embedding is generated by utilizing a graph model. Experiments demonstrate that our approach effectively improves feature distribution. The graph attention network (GAT) improves accuracy and editing score by 4% over the visual model. © 2023 IEEE.

关键词： Graphic methods

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BRNet: A Bilateral Residual Network for Real-Time Semantic Segmentation 6

BRNet: A Bilateral Residual Network for Real-Time Semantic S...

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6th International Conference on Artificial Intelligence and Big Data, ICAIBD 2023

作者： Xu, Jie Liu, Xiaofeng Xu, Liming Shi, Chengkun Zhao, Xukang Wu, Yajuan China West Normal University School of Electronic Information Engineering Nanchong China China West Normal University School of Computer Science Nanchong China

ISBN: (纸本)9781665491259

The lightweight semantic segmentation network makes use of various excellent residual designs, and these residual modules not only improve the efficiency of semantic segmentation but also enhance the accuracy of semantic segmentation. Inspired by the structure of residuals, we propose a lightweight network, based on the modified BiSeNet V2 model, termed the Bilateral Residual Network (BRNet). Additionally, the Three Branches Residual Stem (TBR-Stem) module is also designed. Specifically, the two-branch structure of the original Stem Block is modified to a three-branch structure, and the asymmetric convolution operation is adopted, while depth-wise separable convolution and dilated convolution are introduced. As a result, TBR-Stem significantly reduces the computation, expands the receptive field, and enhances the feature extraction capability. Besides, the Channel Attention Module (CAM) is added to the Semantic Branch to reduce channel noise. The method achieves 73.3% MIoU on the CityScapes validation set and 143 FPS inference speed on a single NVIDIA GeForce GTX 1080Ti, finally, demonstrating that the method improves segmentation accuracy. © 2023 IEEE.

关键词： Convolution

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