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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

14th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

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Edge-cloud cooperation driven surface roughness classification method for selective laser melting

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Advanced engineering Informatics 2025年 66卷

作者： Shuaiyin Ma Yuming Huang Yang Liu Zhiqiang Yan Jingxiang Lv Wei Cai School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an 710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an 710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an 710121 China Shaanxi Union Research Centre of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi’an University of Posts and Telecommunications Xi’an 710121 China Department of Management and Engineering Linköping University SE-581 83 Linköping Sweden Industrial Engineering and Management University of Oulu 90570 Oulu Finland Key Laboratory of Road Construction Technology and Equipment of MOE Chang’an University Xi’an 710064 China College of Engineering and Technology Southwest University Chongqing 400715 China

Additive manufacturing (AM) technology is extensively utilized in aerospace and industrial manufacturing. However, parts built using AM are susceptible to spheroidization, porosity, cracks, and poor surface quality, making it difficult to establish an actionable product quality degree. Hence, developing a reasonable method to equate product quality with a new degree and further analyzing the product quality based on these standards has proven effective for enhancing part quality in AM. To achieve this goal, this paper proposes a surface roughness classification method that utilizes surface roughness analysis and sample enhancement. This method leverages edge cloud cooperation to efficiently analyze and integrate data from different sensors, enabling real-time monitoring and adjustment of the manufacturing process. Subsequently, the quality degree analysis system was developed utilizing matter-element extension cloud model. Furthermore, a bidirectional-gated recurrent unit (Bi-GRU) based model for quality classification and recognition has been established, with Wasserstein generative adversarial network (WGAN) employed for sample enhancement to address the issue of imbalanced column classification and to enhance the accuracy of both classification and recognition. Finally, the results obtained from this case study demonstrate through comparative experiments that the proposed method for classifying surface roughness can accurately identify 98% of prepared samples.

关键词： Additive manufacturing Bi-GRU Clustering analysis Edge-cloud cooperation WGAN

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DST: Personalized Charging Station Recommendation for Electric Vehicles Based on Deep Reinforcement Learning and Spatio-Temporal Preference Analysis

DST: Personalized Charging Station Recommendation for Electr...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaoying Fan Yongqiang Gao Fujun Zhang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development and popularization of electric vehicles (EVs), the increased demand for charging stations (CSs) has made configuring charging facilities and optimizing charging station recommendations crucial. Consequently, personalized recommendation services for charging stations have emerged as a crucial strategy to mitigate the "mileage anxiety" experienced by drivers. Previous work has typically focused on optimizing CSs’ resource allocation or drivers’ costs, with little consideration of the complexity of the charging behavior of individuals, such as the distribution of Point Of Interests (POIs) surrounding the CS, the similarity of driver selection in different temporal scales, and the tradeoff between different users’ inherent preferences and external cost (e.g., travel times and electric prices), which may result in lower satisfaction, decreased accuracy of recommendations, and sub-optimal decision-making. To address this problem, we propose a charging station recommendation framework based on Actor-Critic Deep Reinforcement Learning, called DST, to assist electric vehicle drivers in finding the proper spots for charging. In the DST, both the actor and critic networks are implemented by Deep Neural Networks (DNNs). The Actor networks, utilizing a Semantic-based Bidirectional Long Short-Term Memory Network and a Stacked Convolutional Neural Network with Multiple Channels, extract the geographical semantic features of CSs and specific preference patterns from different temporal scales as hidden state representations. The critic networks employ a Twin-Critic architecture for joint optimization of inherent preference rewards and external environmental rewards. Extensive experiments on two real-world datasets demonstrate that the DST achieves the best comprehensive performance compared with seven baseline approaches.

关键词： Costs Web services Semantics computer architecture Charging stations Feature extraction Electric vehicles Deep reinforcement learning Spatiotemporal phenomena Vehicles

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Pre-classification Supporting Reasoning for Document-level Relation Extraction 21

Pre-classification Supporting Reasoning for Document-level R...

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Proceedings of the 10th International Joint Conference on Knowledge Graphs

作者： Jiehao Zhao Guiduo Duan Tianxi Huang School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China Department of Fundamental Courses Chengdu Textile College China

ISBN: (纸本)9781450395656

The document-level relation extraction task aims to extract relational triples from a document consisting of multiple sentences. Most previous models focus on modeling the dependency between the entities and neglect the reasoning mechanism. Some other models construct paths implicitly between co-sentence entities to find semantic relations. However, they ignore that there are interactions between different triples, especially some triples play an import role in predicting others. In this short research paper, we propose a new two stage framework PCSR(Pre-classification Supporting Reasoning) which captures the interactions between triples and utilizes these information for reasoning. Specifically, we make a pre-classification for each entity pair in the first stage. Then we aggregate the embeddings of predicted triples to enhance entity representation and make a new classification. Since the second classification could find triples missed in the first stage, we take the result as the supplement of the prior one. Experiments on DocRED show that our method achieves an F1 score of 62.11. Compared with the previous state-of-the-art model, our model increase by 0.81 on the test set, which demonstrates the effectiveness of our reasoning mechanism.

关键词： BERT Information Extraction Document-level Relation Extraction Reasoning Mechanism

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Joint Task Offloading and Resource Allocation for NOMA-Based Vehicular Networks

Joint Task Offloading and Resource Allocation for NOMA-Based...

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International Conference on computer Supported Cooperative Work in Design

作者： Yunfei Song Yongqiang Gao Yipei He Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Vehicle Edge computing (VEC) is a critical technology that can achieve low latency and energy consumption for Telematics. However, with the high-speed mobility of new energy-electric vehicles and their cross-regional nature, performing high-quality service of vehicle tasks on the VEC model is still challenging. Considering the electric vehicle range problem, this paper plans to jointly optimize task offloading, task result forwarding and computational resource allocation (OOFR) within the maximum tolerable delay of vehicle tasks to minimize vehicle tasks' delay and energy consumption. The non-orthogonal multiple access (NOMA) technology is used with roadside units (RSUs) to achieve multiplexing of limited spectrum resources. This allows multiple vehicle users to perform task transmission simultaneously, thus reducing vehicle task transmission delay. In addition, we propose a cooperative game approach based on NOMA for task grouping to reduce the signal interference of vehicle task transmission. Finally, a deep reinforcement learning method is proposed for task offloading decision selection. A simulation platform is built to compare with MEC, COMO and MADDPG methods, combined with simulation results, showing that the superiority of our proposed scheme is verified.

关键词： NOMA Energy consumption Simulation Games Electric vehicles Deep reinforcement learning Telematics

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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OpenSim: A Simulator of OpenStack Services

OpenSim: A Simulator of OpenStack Services

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Asia International Conference on Modeling & Simulation

作者： Dinkar Sitaram Phalachandra Hallymysore Sudheendra Harwalkar Supriya Kathare Anirudh Srinivasan Archana Reddy Konark Kumar Sahana Sekhar Department of Computer Science and Engineering PES University Center for Cloud Computing and Big Data Bangalore India Dept. of Comput. Sci. & Eng. PES Univ. Bangalore Bangalore India

cloud computing is still an emerging field experiencing rapid advancement in both industry and academia. cloud computing has solved many problems initially faced by internet application developers such as acquisition of a server with a fixed capacity to handle expected peak application demand which led to under-utilization of provisioned resources, by enabling a new consumption and delivery model for IT services. It involves provisioning of dynamically scalable and virtualized resources as a service over the Internet. Evaluation of algorithms and policies in an exhaustive manner directly on a cloud is infeasible as it involves a lot of time and effort. Moreover, utilization of real cloud infrastructure limits the experiment to the scale of the infrastructure. A desirable alternative would be utilization of simulation tools that enables evaluation of a hypothesis in a repeatable, controlled and timely manner prior to software development in a cloud environment. Taking into consideration these issues and the ever-growing popularity of OpenStack, in this paper we propose OpenSim a simulator of OpenStack services. It has been developed by the authors of this paper with the intention of simulating new algorithms and also to enable a user to study the behavior of an application running under various deployment configuration in an OpenStack environment.

关键词： Virtual machining Servers Computational modeling cloud computing Neutrons IP networks Scheduling algorithms

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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