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DASCDC: Dual Attention-based Sparse Common Deep Clustering Method 9

DASCDC: Dual Attention-based Sparse Common Deep Clustering M...

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9th IEEE international conference on Behavioural and Social computing, BESC 2022

作者： Dong, Yao Shi, Jin Deng, Yahan Dong, Yongfeng Wang, Ziqiu School of Artificial Intelligence Hebei University of Technology Tianjin300401 China Hebei Province Key Laboratory of Big Data Computing Tianjin300401 China Data Driven Industrial Intelligent Hebei Engineering Research Center Tianjin300401 China

ISBN: (数字)9798350398144

ISBN: (纸本)9798350398144

Graph convolutional network (GCN) has been successfully applied in deep clustering on the high dimensional dataset, which is usually processed by extracting the feature and structure information. However, it lacks an effective fusion mechanism of common information between the feature and structure information and does not perform well on the sparse dataset. Therefore, we propose a dual attention-based sparse common deep clustering method(DASCDC). Firstly, we advance a sparse feature representation method with an adversarial loss function to solve the data sparsity problem and improve the discriminator's performance. Then, an information interaction operator is designed to deeply mine the common information and enhance the information fusion capability. Finally, considering the different contribution degrees of various neighbour nodes and information, we promote a dual self-attention mechanism from the node and model levels. Experiment results indicate that the proposed DASCDC algorithm overperforms other state-of-art deep clustering algorithms on non-graph and graph datasets with high dimension and sparsity. © 2022 IEEE.

关键词： Cluster analysis

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A new multi-rules approach to improve the performance of the Chi fuzzy rule classification algorithm

A new multi-rules approach to improve the performance of the...

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IEEE international conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational intelligence (IEEE WCCI) / international Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Jara, Leonardo Gonzalez, Antonio Perez, Raul Univ Granada DaSCI Andalusian Res Inst Granada Spain

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

The Chi algorithm has been used in many applications to obtain a set of fuzzy rules from the data. Its main advantage is that it requires low learning time in relation to others learning algorithms and that makes it a good candidate for tackling big data problems. However, this algorithm has some shortcomings, among them, its limited generalization capability, which leads to obtaining a high number of uncovered examples over new unseen examples. An improved version of this algorithm is presented in this paper. The main difference lies in the process of assigning a candidate rule to each example, the original algorithm assigns only one, and in the new proposal, several rules are assigned. The inclusion of more rules improves the generalization capacity of the acquired knowledge, which in turn leads to an improvement in accuracy. The paper includes an extensive experimental study to show the performance of the proposal.

关键词： Fuzzy Rule-Based Classification Systems Fuzzy rule learning Fast Fuzzy Inference Soft computing

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Challenges and future directions for security and privacy in vehicular fog computing

Challenges and future directions for security and privacy in...

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2022 international conference on Innovation and intelligence for Informatics, computing, and Technologies, 3ICT 2022

作者： Nazih, Ossama Benamar, Nabil Lamaazi, Hanane Chaoui, Habiba Ibn Tofail University National School of Applied Sciences Kenitra Morocco Moulay Ismail University of Meknes School of Technology Meknes Morocco Uae University College of Information Technology Al Ain United Arab Emirates

ISBN: (数字)9781665451932

ISBN: (纸本)9781665451932

Cooperative Intelligent Transportation System (CITS) has been introduced recently to increase road safety, traffic efficiency, and to enable various infotainment and comfort applications and services. To this end, a bunch technologies have been deployed to maintain and promote ITS. In essence, ITS is composed of vehicles, roadside infrastructure, and the environment that includes pedestrians, and other entities. Recently, several solutions were suggested to handle with the challenges faced by the vehicular networks (VN) using future internet architectures. One of the promising solutions proposed recently is Vehicular Fog computing (VFC), an attractive solution that supports sensitive service requests considering factors such as latency, mobility, localization, and scalability. VFC also provides a virtual platform for real-time big data analytic using servers or vehicles as a fog infrastructure. This paper surveys the general fog computing (FC) concept, the VFC architectures, and the key characteristics of several intelligent computing applications. We mainly focus on trust and security challenges in VFC deployment and real-time BD analytic in vehicular environment. We identify the faced challenges and future research directions in VFC and we highlight the research gap that can be exploited by researchers and vehicular manufactures while designing a new secure VFC architecture. © 2022 IEEE.

关键词： Intelligent systems

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PSeer: Performance Prediction for Partially Co-located Distributed Deep Learning 23

PSeer: Performance Prediction for Partially Co-located Distr...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ding, Weijia Ding, Ziwei Zhao, Laiping Qiu, Tie College of Intelligence and Computing Tianjin University Tianjin Key Laboratory of Advanced Networking China

ISBN: (纸本)9781665494571

This paper studies the problem of predicting the deep learning (DL) jobs' training time which is the fundamental guide for training resources allocating and job scheduling. The existing prediction approaches, however, are greatly challenged for distributed deep learning (DDL) jobs due to the partial interference, namely, the resource contention that only occurs at partial time or locations of a job. We observe that the maximum difference in training time under different combinations of partial interference DDL can reach 2.2×. Thus partial interference is not a negligible factor for predicting the training time of DDL jobs. In this paper, we present PSeer, a partial interference-aware training time predictor for DDL workloads. It adopts a combined dual-submodel (the step number submodel and the step speed submodel), which is highly accurate in predicting by encoding the spatially and temporally varied partial interference into the model design. It is also lightweight: the step number submodel only requires the initial training loss value, and the step speed submodel can be constructed using the CPU and GPU resource utilization information of a few pre-trained steps. Empirical evaluation shows the prediction error generated by PSeer could achieve as low as 3.93% when employing RFR in the training speed submodel, and the prediction overhead is © 2021 IEEE.

关键词： Training Deep learning Graphics processing units Interference Predictive models Encoding Resource management

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VFLChain: Blockchain-enabled Vertical Federated Learning for Edge Network data Sharing

VFLChain: Blockchain-enabled Vertical Federated Learning for...

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2022 IEEE international conference on Unmanned Systems, ICUS 2022

作者： Cheng, Ziwen Pan, Yongqi Liu, Yi Wang, Bowen Deng, Xin Zhu, Cheng National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

With the widespread use of Internet of things(IoT), a large amount of data will be generated in the edge of network, which can facilitate a significant transformation in edge intelligent services by integrating with edge computing, 5G and artificial intelligence. However, since the intelligent edge services seriously rely on big data and computing resource, it challenges the traditional centralized data processing model. data sharing is a promising way to tackle this problem, but some critical technical challenges still remain, such as fragile data privacy protection, inefficient data exchange and low quality of data fusion. To address these problems, a privacy-enhanced and intelligence-preserved data sharing system, name VFLChain, is proposed in this article. The proposed VFLChain is designed based on consortium blockchain and vertical federated learning, which can ensure trustworthy and secure data sharing without relying on any center platforms or third parties. Furthermore, a blockchain-assisted decentralized vertical federated learning is presented to adapt to the decentralized system and support privacy-preserved, intelligent and efficient edge data sharing, while improving quality of data through learning with different characteristic data samples. Then, a data sharing processing workflow in VFLChain is also described to demonstrated details of data sharing. The simulation experiments confirm that the proposed system and mechanism have good accuracy and stability, and guarantee an effective data sharing. © 2022 IEEE.

关键词： Internet of things

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A Joint Learning Sentiment Analysis Method Incorporating Emoji-Augmentation 8

A Joint Learning Sentiment Analysis Method Incorporating Emo...

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8th IEEE international conference on Cloud computing and intelligence Systems, CCIS 2022

作者： Chen, Jie Luo, Luping Ji, Bojing Zhao, Shu Zhang, Yanping Anhui University The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology China Chizhou University School of Big Data and Artificial Intelligence China

ISBN: (纸本)9781665477352

Social media is the platform for most people to share their opinions, emojis are also widely used to express moods, emotions, and feelings on social media. There have been many researched on emojis and sentiment analysis. However, existing methods mainly face two limitations. First, since deep learning relies on large amounts of labeled data, the training samples of emoji are not enough to achieve the training effect. Second, they consider the sentiment of emojis and texts separately, not fully exploring the impact of emojis on the sentiment polarity of texts. In this paper, we propose a joint learning sentiment analysis method incorporating emoji-augmentation, and the method has two advantages compared with the existing work. First, We optimize the easy data augmentation method so that the newly generated sentences can also preserve the semantic information of emojis, which relieves the problem of insufficient training data with emojis. Second, it fuses emojis and text features to allow the model to better learn the mutual emotional semantics between text and emojis, jointly training emojis and words to obtain the sentence representations containing more semantic information of both emojis and text. Our experimental results show that the proposed method can significantly improve the performance compared with several baselines on two datasets. © 2022 IEEE.

关键词： Sentiment analysis

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Design and Implementation of Deep Learning Real-Time Streaming Video data Processing System 1

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7th international conference on Smart computing and Communication, SmartCom 2022

作者： Zhao, Qiming Wu, Jing Wu, Xiang Fan, Jie Wang, Linhao Wu, Fengling College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： big data

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ABF-FNN: A new fuzzy neural network for predicting coal mine gas concentration hazard

ABF-FNN: A new fuzzy neural network for predicting coal mine...

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2023 IEEE international conference on big data, bigdata 2023

作者： Sun, Yimin Zhang, Xiaobo Zhang, Zhehao Wu, Yunyang Tang, Haihao Luo, Haonan Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China

ISBN: (纸本)9798350324457

Serious accidents in coal mines are often caused by high concentration of gas. Therefore, accurate prediction of mine gas concentration is vital for ensuring the safety of coal mine production. Hence, a new method for predicting the risk of gas content in underground coal mines (ABFFNN, adaptive bandwidth feedback fuzzy neural network) is proposed in this paper. Namely, 1) Fuzzy theory and neural networks are combined in the method, and feedback mechanisms are incorporated to enhance network learning capability and robustness. 2) This method designs a novel approach to calculate adaptive bandwidths and dynamically adapts to changes in univariate and multivariate time series data. Weight training is performed using particle swarm optimization algorithm. 3) The evaluation of the developed prediction model is conducted using two publicly available datasets as well as actual industrial data collected at a mining site. The public datasets used include the data of Qinggangping coal mine monitoring system in Shaanxi Province and the Box-Jenkins gas stove dataset. It is shown by the results that good prediction results are achieved by the developed prediction model on two public data sets and one private data set. In addition, it can be seen that the gas concentration hazard in underground coal mines can be better predicted by the method proposed in this paper when compared with other models. © 2023 IEEE.

关键词： adaptive bandwidth feedback fuzzy theory prediction

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Robot Cloud computing and Al Services - State-of-the-art Solutions, Challenges, and Needs 16

Robot Cloud Computing and Al Services - State-of-the-art Sol...

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16th IEEE international conference on Service-Oriented System Engineering, SOSE 2022

作者： Gao, Jerry Wang, Dayong San Jose State University Department of Computer Engineering San Jose United States

ISBN: (数字)9781665475341

ISBN: (纸本)9781665475341

According to the PRNewswire research report, Cloud Robotics Market will witness a YOY growth of 21.19% in 2022 at a CAGR of 22.26% during the forecast period. This strong demand has brought more research interest in cloud platforms for robotics integration services. Most of the available research results and commercial application technology solutions try to build robot cloud services from robot computing offloading, map data sharing, and human-robot interaction back-end support. However, there is a lack of research results for building a robot cloud that fully supports comprehensive business such as robot resource management, AI service support, robot big data analysis, and business intelligence. In this work, we present complete robotic cloud construction requirements and specific technical issues at different levels. The robotic cloud architecture design issues are discussed in several dimensions, including the service model, deployment model, business application model, and edge computing model of the robotic cloud, and finally, an overall robotic cloud architecture design is proposed. © 2022 IEEE.

关键词： Information management

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Development Road Map and Planning Mode of Artificial intelligence Technology Under the Background of Internet Information 1

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5th EAI international conference on 6G for Future Wireless Networks, 6GN 2022

作者： Tang, Zheqing Sun, Xiqiang Luo, Yang School of Information Engineering Heilongjiang Vocational College Harbin150022 China

ISBN: (数字)9783031360145

ISBN: (纸本)9783031360138

With the progress of various software and hardware technologies, the data processing and computing power of the computer are further enhanced. Artificial intelligence technology has entered a period of rapid development. The current "Internet + " boom and the continued prosperity of the big data industry have further accelerated the integration of artificial intelligence technology with other related industries. This paper summarizes the Internet information, static information collection technology and artificial intelligence technology, analyzes the neural network, puts forward RRT path planning, and studies the pheromone increment dynamic update strategy. The results show that when LK is less than lbest, the increase of the difference will sharply increase the pheromone increment on the path, which makes the algorithm easy to fall into local optimization. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： data handling

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