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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Federated Learning for 6G Edge Intelligence: Concepts, Challenges and Solutions 10th

Federated Learning for 6G Edge Intelligence: Concepts, Chal...

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10th international conference on Artificial Intelligence and Mobile Services, AIMS 2021 held as Part of the Services conference Federation, SCF 2021

作者： Wang, Han Hu, Jianhe Xing, Chunxiao Zhang, Liang-Jie Research Institute of Information Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Computer Science and Technology Institute of Internet Industry Tsinghua University Beijing100084 China Department of Economics College of Letters and Science University of California Davis DavisCA95616 United States National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Company Limited Shenzhen518057 China

ISBN: (纸本)9783030960322

Along with continuous evolution, the future 6G network will become a converged "Cloud-Edge-Terminal" ecosystem which can carry various crucial AI applications on edge computing units, formulating an ubiquitous "Edge Intelligence" paradigm to enable differentiated service innovations and empower intelligent transformation of vertical industries. However, due to issues of data security, user privacy, wireless network transmission capability and etc., it is not feasible for conventional machine learning methods to build AI models by directly collecting massive distributed edge data together, and hence resulting a large number of "isolated data islands" in the edge units. In order to break the data sharing barrier and drive cross-edge data cooperation, this paper studies a federated learning based AI model training method by which sensitive raw data can be maintained and protected in its original edge units. Based on the general scheme, some challenging problems are discussed to implement this new paradigm in practical scenarios, and the corresponding promising solutions and key techniques are proposed to inspire further researches. © 2022, Springer Nature Switzerland AG.

关键词： Digital storage

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Zero-shot Video Emotion recognition via Multimodal Protagonist-aware Transformer Network 21

Zero-shot Video Emotion Recognition via Multimodal Protagoni...

引用

29th ACM international conference on Multimedia (MM)

作者： Qi, Fan Yang, Xiaoshan Xu, Changsheng Hefei Univ Technol Hefei Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9781450386517

Recognizing human emotions from videos has attracted significant attention in numerous computer vision and multimedia applications, such as human-computer interaction and health care. It aims to understand the emotional response of humans, where candidate emotion categories are generally defined by specific psychological theories. However, with the development of psychological theories, emotion categories become increasingly diverse and fine-grained, samples are also increasingly difficult to collect. In this paper, we investigate a new task of zero-shot video emotion recognition, which aims to recognize rare unseen emotions. Specifically, we propose a novel multimodal protagonist-aware transformer network, which is composed of two branches: one is equipped with a novel dynamic emotional attention mechanism and a visual transformer to learn better visual representations;the other is an acoustic transformer for learning discriminative acoustic representations. We manage to align the visual and acoustic representations with semantic embeddings of fine-grained emotion labels through jointly mapping them into a common space under a noise contrastive estimation objective. Extensive experimental results on three datasets demonstrate the effectiveness of the proposed method.

关键词： multimodal affective computing zero-shot learning

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Edge computing Enabled Technologies for Secure 6G Smart Environment-An Overview 12th

Edge Computing Enabled Technologies for Secure 6G Smart Envi...

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12th international conference on Soft computing and pattern recognition, SoCPaR 2020 and 16th international conference on Information Assurance and Security, IAS 2020

作者： Jamil, Syed Usman Khan, M. Arif Rehman, Sabih ur Charles Sturt University Wagga Wagga Australia

ISBN: (纸本)9783030736880

In this article, we present a theoretical framework for a secure smart architecture based on futuristic edge resource allocation with a key focus on the task off-loading and intelligent enabling technologies. this article aims to highlight the growing need of embedded off-loading standards in the context of the Internet of Everything (IoE), fog, and cloud paradigms enabled technologies in the overall design framework of smart architecture. Efficient task off-loading strategies perform a vital role in mobility, heterogeneity, data and energy management, load balancing, establishing security mechanisms, reducing the propagation delay and information processing. the motive behind presenting such an embedded intelligent and secure architecture is to address the growing concerns of upcoming smart networks and its sub-network based on different futuristic scenarios, applications and devices. Furthermore, we discuss the off-loading criteria and enabling technologies, in view of the new advancement and critical requirements with rising demands of up-coming intelligence-based secure off-loading frameworks, in order to meet the requirements of the secure smart environment. Finally, through our discussion, we develop the specific research questions and explain its motivations in order to design and evaluate the off-loading criteria and its associated enabling technologies to accommodate the secure technological advancements and resource allocation in the sixth generation (6G) wireless communication era. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of Everything

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GREECOPE: Green computing with Piezoelectric Effect 10

GREECOPE: Green Computing with Piezoelectric Effect

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10th international conference on Smart Cities and Green ICT Systems (SMARTGREENS)

作者： Kshirsagar, Meghana Lahoti, Rutuja More, Tanishq Ryan, Conor Univ Limerick Biocomp & Dev Syst Grp Lero Limerick Ireland Govt Coll Engn Dept Informat Technol Aurangabad Maharashtra India

ISBN: (纸本)9789897585128

the growing interest in the search and use of alternative resources for renewable energy can lead the future towards substantially decreasing carbon footprint and reduce the effects of global warming. the proposed research explores the possibility of harnessing piezoelectric energy from the environment of moving vehicles on road. Although the technology is still immature, it has the advantage of having zero carbon footprints thus making it ideal to investigate the potential for green energy generation. the main objective is to develop regression models that can estimate energy generated from vehicular traffic. Energy is generated when force is applied to piezoelectric transducers which depend on significant factors such as the number of piezoelectric transducers and their arrangement, load applied and frequency. We design Support Vector Machine (SVM) and Generalised Linear Model (GLM) for predicting energy. the best features for training the model were selected by incorporating feature selection techniques such as Pearson's correlation coefficient and Mutual Information Statistics. the experimental setup makes use of simulated data which takes into account vehicle count of different vehicles with and without load. the accuracy achieved from SVM and GLM are 99.6% and 99.7% respectively. the energy savings achieved by making use of generated piezoelectric energy is discussed with a sample scenario of Motorway50 of Dublin, the Irish Capital city. through this work, we propose to investigate deeper into the feasibility towards cost-effectiveness by utilizing energy which is wasted by human and vehicular locomotion.

关键词： Intelligent Transportation Piezoelectric Effect Smart Cities Road Transport

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Identifying Quality Attributes of FODA and DSSA Methods in Domain Analysis using a Case Study 10

Identifying Quality Attributes of FODA and DSSA Methods in D...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Bhushan, Megha Kumar, Ashok Samant, Piyush Bansal, Sakshi Tiwari, Sharad Negi, Arun DIT University School of Computing Dehradun India Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Chandigarh University AIT-CSE Punjab Gharuan Mohali India NIT Silchar Electronics and Instrumentation Engineering Department Assam India Vignan Foundation of Science and Technology ECED Andra Pradesh India Deloitte USI Hyderabad India

ISBN: (纸本)9781665439701

the concept of reuse is addressed in software product line engineering (SPLE) by distinguishing between two types of processes for development: application and domain engineering. In domain engineering (DE), the focus is to define and manage all the valid combinations of reusable artefacts participating in the product line (PL) and the relationships between them. Domain analysis (DA) is the activity in DE, which describes the variability and commonalities in a domain. Instead of being applicable to a single software system, DA is applicable to multiple related software systems. Each DA method supports different quality attributes which are preserved while reuse. In this paper, two DA methods namely domain specific software architecture (DSSA) ad feature oriented domain analysis (FODA) are explained to model common and variable requirements of PL(s). A case study on Automated Teller Machine (ATM) is discussed using these two methods to identify quality attributes supported by these methods. this paper also discuss two different DA methods, which allows reusability for the identification, organization and knowledge modelling regarding the domain solution, so as to provide reuse among each element of domain. © 2021 IEEE.

关键词： Reusability

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Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet Deep Learning Architecture 10

Brain Tumor Segmentation and Tumor Prediction Using 2D-VNet ...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Rastogi, Deependra Johri, Prashant Tiwari, Varun Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9781665439701

Segmentation of brain tumors is a difficult task because of the enormous variation in the intensity and size of gliomas. the tumor type Glioma is the highly prevalent malignant tumor in the brain, with a high death rate and a morbidity rate of more than 3%. In the clinic, MRI is the most common way of detecting brain cancers. Automatic segmentation is difficult because of the overlapping area between the intensity distributions of healthy, enhancing, non-enhancing and edema regions. Segmenting brain tumour areas utilising multi-modal MRI scan pictures can help with treatment observation, post-diagnosis monitoring, and patient impacts evaluation. Manual segmentation, on the other hand, is still the most common procedure in clinical brain tumour segmentation, which takes time and results in significant performance variations across operators. For this reason, the development of accurate and consistent automatic segmentation technology is required. Convolutional neural networks (CNNs), have shown promise in brain tumor segmentation due to their powerful learning capacity. this article suggests an 2D-VNet model for brain tumor segmentation and enhancing the prediction. the presented model was successfully segmented brain tumors and predict the result in enhancing tumor and real enhancing tumor. Experiment with BRATS2020 benchmarks dataset, we found that Loss (for Training:.0025, Testing:.0032 and Validation:.0031), Dice Coefficient (for Training:.9974, Testing:.9967 and Validation:.9968) and Accuracy (for Training:.9971 Testing:.9963 and Validation:.9964). © 2021 IEEE.

关键词： Tumors

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Analysis of the Clustering Effect of Sports Big Data Transmission Based on Ant Colony Intelligent Algorithm 10th

Analysis of the Clustering Effect of Sports Big Data Transmi...

引用

10th international conference on Frontier computing, FC 2020

作者： Han, Shen Xi’an Institute of Eurasia Shaanxi Xi’an710000 China

ISBN: (纸本)9789811601149

Cloud computing provides strong support for big data processing from two aspects of distributed storage and distributed computing and gradually becomes the mainstream of big data mining platform. However, when dealing with large-scale datasets in the cloud platform, the typical clustering mining algorithm has some shortcomings. therefore, a big data based on group intelligence algorithm is proposed K-means clustering mining algorithm. Firstly, the data storage capacity of cloud computing Hadoop framework and the map reduce computing model are analyzed, and then the group is adopted the intelligent algorithm improves the traditional K-means clustering algorithm of data mining to solve the problem of local optimization. the experimental results show that compared with the weighted K-means aggregation Class algorithm, the improved algorithm shows better clustering accuracy and running speed, which can be applied to large-scale data clustering mining. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Optimization Design of Instant Translation System Based on Cloud computing Mode 10th

Optimization Design of Instant Translation System Based on C...

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10th international conference on Frontier computing, FC 2020

作者： Shuo, Zhang Lvshun Development Zone Shunle Street No. 33 Liaoning Dalian211052 China

ISBN: (纸本)9789811601149

Most of the existing instant translation systems use language rules and language databases for translation, which takes too long. To solve this problem, a new real-time translation system is designed based on cloud computing mode. the hardware of the system is divided into four layers: user layer, service layer, computing layer and storage layer. the storage structure, translation structure and retrieval structure are designed, respectively. Using Windows 10 system to build the software development structure, through data input, page loading, online translation, offline translation, information input and other steps to complete the translation process, the software also has seven functions: online translation, offline translation, thesaurus information query, historical information query, new words, voice translation and user settings. In order to test the working effect of the system, the experimental results show that the translation time of the real-time translation system based on cloud computing is less than that of the traditional system. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Aggregating Correlation Filter with Multiple Kernels Learning for Robust Visual Object Tracking 22

Aggregating Correlation Filter with Multiple Kernels Learnin...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. the handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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An Analysis of Indian Election Outcomes using Machine Learning 10

An Analysis of Indian Election Outcomes using Machine Learni...

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10th international conference on System Modeling and Advancement in Research Trends, SMART 2021

作者： Singh, Harmanjeet Shukla, Anand Kumar University Institute of Computing Chandigarh University Mohali India

ISBN: (纸本)9781665439701

What other people think"has always been makes us curious while indulge in decision making process. Earlier, many of us asked our personal acquaintances to recommend a political candidate or political party to whom we can vote or consult a broker to decide in which share we should invest our money. With the invent of Internet and world wide web have now let it happen to find out large pool of online users to share recommendation, opinion, views about an event. Although these online users are never our personal acquaintance nor professional critics. An irony of situation is many people are sharing opinions available to completely unknown person via Internet. In the beginning of 21st century, social networking websites taken exponential growth in popularity among online users by providing them a platform for discussion and exchange information. Apart from posting photos and videos for event, now online users are posting their comments, views, opinion about event. With this platform, most researchers are analyzing posts or opinion, performing computational methods to come to conclusion about popularity of event. this analysis part is becoming more powerful day by day to gauge user's opinion and would play a significant role for someone to make major decision in future. the social microblogging sites such as Pinterest, Tumblr, Apsense, Scoop and specially Twitter are leading pools of online users and contains massive information that can be useful by extracting knowledge from information and that knowledge become asset for business specialist, political specialist, marketing specialist etc. © 2021 IEEE.

关键词： Sentiment analysis

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