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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Zhijie Shen Bowen Liu Xiaolong Xu Lianyong Qi Fei Dai Wanchun Dou State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China College of Computer Science and Technology China University of Petroleum (East China) Qingdao China School of Computer Science Nanjing University of Information Science and Technology Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China

As a standardized and systematic management method, business process management plays an important role in the industrial Internet of Things (IIoT) scenario. With the rapid development of mobile Internet and the increasing amount of data generated by the Internet of Things devices, the traditional storage scheme can no longer meet the requirements of managing the IIoT mobile business data. The cloud-edge collaborative data storage scheme provides mass storage capability by the cloud data center, and meanwhile takes access delay into account by storing data in the edge servers near to user, making it an effective solution to manage mobile business data in the IIoT scenario. However, the storage capacity of edge servers is relatively limited. How to design a reasonable data placement strategy to maximize the speed of data reading and tasks execution in IIoT business process has become an urgent problem to be solved. In order to shorten the overall running time, reduce data transmission costs and enhance users experience, we propose a data placement strategy based on Bayesian optimization algorithm, which can reasonably allocate storage capacity in the edge side, and further determine which edge server the data should be placed on. The results of the experiment show that the strategy based on our proposed algorithm performs better in a variety of different scenarios than the ones that based on other kind of optimization algorithms which have been adopted in similar studies.

关键词： Cloud computing Systematics Memory User experience Bayes methods Servers Task analysis

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A MILP Based Two-Stage Load Scheduling Approach for Building Load’s Peak-to-Average Ratio Reduction

A MILP Based Two-Stage Load Scheduling Approach for Building...

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Power and Electrical engineering (ACPEE), 5th Asia Conference on

作者： Runze Deng Fengji Luo Gianluca Ranzi Zehua Zhao Yan Xu School of Civil Engineering The University of Sydney Sydney Australia School of Big Data and Software Engineering Chongqing University Chongqing China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152820

Building energy management is recognized as a useful technique to enhance building automation and facilitate demand response. This paper proposes a simple and useful building energy management system (BEMS) that simultaneously minimizes the user's electricity bill while assistants the utility to reduce the Peak-to-Average Ratio (PAR) of the regional load. The working mechanism of the proposed BEMS is based on a simple observation: the PAR reduction of an area can be achieved by reducing the PAR of individual buildings. The system consists of two stages: (1) in the first stage, the appliances are temporarily scheduled to obtain the minimum electricity cost of the unit; and (2) in the second stage, the appliances are re-scheduled with the objective to minimize the unit's PAR, while ensuring the building's electricity cost does not exceed the optimal cost determined in the first stage. Our numerical results show that by applying the proposed BEMS to individual units, the overall PAR of a group of buildings can be effectively reduced.

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A novel hybrid ensemble approach for wind speed forecasting with dual-stage decomposition strategy using optimized GRU and transformer models

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Energy 2025年 329卷

作者： Ullah, Sajid Chen, Xi Han, Han Wu, Junhao Dong, Jinghan Liu, Ruiqing Ding, Weijie Liu, Min Li, Qingli Qi, Honggang Huang, Yonggui Yu, Philip Lh East China Normal University Shanghai200241 China School of Geographic Sciences East China Normal University Shanghai200241 China Software Engineering Institute East China Normal University Shanghai200026 China State Key Laboratory of Estuarine and Coastal Research East China Normal University Shanghai200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Peking University Chongqing Research Institute of Big Data Chongqing High-tech Zone Science Valley Building 10 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong

Wind energy has attracted global interest owing to its sustainable and environmentally friendly characteristics. Nevertheless, precisely forecasting wind speed can be challenging due to its volatile and unpredictable nature. This paper presents a new hybrid forecasting approach based on dual stage decomposition mechanism, namely TMQGDT for wind speed prediction. At first, a decomposition technique called time-varying filtered based empirical mode decomposition (TVFEMD) is utilized to decompose the original wind speed data into several intrinsic mode functions (IMFs). Afterwards, multi-scale permutation entropy (MPE) is used to assess the complexity of each IMF. Based on the entropy values, the IMFs are further classified into high-frequency and low-frequency IMFs. To address the significant volatility observed in the high-frequency IMFs, discrete wavelet transform (DWT) method is employed to perform secondary decomposition. The low-frequency IMFs are forecasted using gated recurrent unit (GRU) model optimized with quantum particle swarm optimization (QPSO) algorithm, while the high-frequency IMFs are forecasted with the Transformer model. The proposed model is trained and validated using four wind speed time series datasets collected from Germany and China. Five individual models and six hybrid models are compared against the proposed model to validate the forecasting performance of the proposed TMQGDT model. The prediction outcomes reveals that the R2 of the model is 0.973, 0.968, 0.956, and 0.996 on the four dataset test sets, which has improved by 3.39 %, 3.93 %, 5.53 %, and 0.50 %, respectively, compared to the TVFEMD-MPE-QPSO-GRU-DWT-Autoformer model. The excellent accuracy performance of the TMQGDT model indicates that developing a hybrid model based on deep learning techniques using secondary decomposition mechanism and optimization algorithm can enhance the precision of wind speed prediction. © 2025

关键词： Particle swarm optimization (PSO)

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MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zhang, Yanan Mo, Wenyi Zheng, Changwen Su, Bing Xiong, Hui University of Chinese Academy of Sciences Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China China Institute of Software Chinese Academy of Sciences Southern Marine Science and Engineering Guangdong Laboratory Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Guangzhou HKUST Fok Ying Tung Research Institute China

As a successful approach to self-supervised learning, contrastive learning aims to learn invariant information shared among distortions of the input sample. While contrastive learning has yielded continuous advancements in sampling strategy and architecture design, it still remains two persistent defects: the interference of task-irrelevant information and sample inefficiency, which are related to the recurring existence of trivial constant solutions. From the perspective of dimensional analysis, we find out that the dimensional redundancy and dimensional confounder are the intrinsic issues behind the phenomena, and provide experimental evidence to support our viewpoint. We further propose a simple yet effective approach MetaMask, short for the dimensional Mask learned by Meta-learning, to learn representations against dimensional redundancy and confounder. MetaMask adopts the redundancy-reduction technique to tackle the dimensional redundancy issue and innovatively introduces a dimensional mask to reduce the gradient effects of specific dimensions containing the confounder, which is trained by employing a meta-learning paradigm with the objective of improving the performance of masked representations on a typical self-supervised task. We provide solid theoretical analyses to prove MetaMask can obtain tighter risk bounds for downstream classification compared to typical contrastive methods. Empirically, our method achieves state-of-the-art performance on various benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Redundancy

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ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters

ToFM: Topic-specific Facet Mining by Facet Propagation withi...

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IEEE International Conference on big Knowledge (ICBK)

作者： Hongxuan Li Bifan Wei Jun Liu Zhaotong Guo Jingchao Qi Bei Wu Yong Liu Yuanyuan Shi School of Computer Science and Technology Xi'an Jiaotong University Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an China School of Continuous Education Xi'an Jiaotong University Xi'an China Servyou Software Group Co. Ltd.

ISBN: (纸本)9781665438599

Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms.

关键词： Conferences Knowledge based systems Clustering algorithms Encyclopedias Binary search trees Internet Online services

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Deep Sentiment Learning Network for Temporal-aware Recommendation Based on User Reviews

Deep Sentiment Learning Network for Temporal-aware Recommend...

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IEEE International Conference on big data Analysis (ICBDA)

作者： Xinxin Li Tianqi Shang Dezhong Peng Xiaoyu Shi School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China College of Computer Science Sichuan University Chengdu China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China

Recently, many websites encourage users to fill in reviews on items and services to improve the quality of personalized recommendation, because of the rich sentiment information hidden in user reviews. However, most existing recommendation methods based on reviews heavily emphasize extracting the rich sentiment information from reviews by using deep learning technologies as far as possible and always ignore the effects of temporal dynamical of user preferences. To address the above issues, we propose DSL- TR, a deep sentiment learning framework for the temporal-aware recommendation, using an intricate combination of bidirectional long short-term memory (BLSTM) and Convolution Neural Network (CNN). It consists of four layers: embedding layer, BLSTM layer, CNN layer and feature fusion layer. In the embedding layer, we explicitly combine the time information in the review embedding layer by regarding the review time as an independent factor. Then, we utilize the BLSTM and CNN networks to extract the long and short-terms of latent features of user and item in parallel. Finally, the factorization machine technique, as a rating predictor, is introduced on the last layer to estimate the rating based on the learned user and item latent features. We conduct extensive experiments on Amazon's four data sets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.

关键词： Deep learning Convolution Conferences Neural networks big data Feature extraction data mining

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An efficient face recognition attack method based on generative adversarial networks and cosine metrics

An efficient face recognition attack method based on generat...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Hu Ding Shumeng He Yanwen Wu Yongli Jin Lin Gan Gaodi Xu Houqun Yang School of Computer Science and Technology Hainan University Haikou Hainan School of Big Data and Software Engineering Chongqing University Chongqing School of Computer Science and Technology Anhui University Hefei Anhui Elmore Family School of Computer and Electrical Engineering Purdue University West Lafayette USA Hainan Association for Artificial Intelligence Haikou Hainan

ISBN: (纸本)9781665426312

Deep neural networks are vulnerable to attacks on adversarial samples. These attacks are caused by adding small magnitude perturbations to the input samples, which may lead to misclassification of the deep neural network. Based on the study of the adversarial sample attack network model, we propose an attack sample based on the generative adversarial network HNUGAN, incorporating the cosine metric of disparity recognition, for the features of the face dataset, to construct an attack sample to attack the face recognition system. Using these adversarial samples can reduce the recognition accuracy of models such as GoogleNet and ResNet to a very low level, and thus complete the attack on the target model. Interestingly, we can obtain a batch of adversarial samples through adversarial training to expand the dataset and retrain the target model to improve its robustness and resistance to attacks.

关键词： Training Measurement Deep learning Resistance Target recognition Face recognition Perturbation methods

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An improved smith-waterman algorithm based on spark parallelization

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International Journal Bioautomation 2019年第1期23卷 117-129页

作者： Liu, Yanfeng Li, Leixiao Gao, Jing College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot010018 China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Inner Mongolia University of Technology Hohhot010080 China

This paper proposes the design and the implementation of a Spark parallelization plan for improving the Smith-Waterman (SW) algorithm, named the Spark-OSW algorithm. Then, the Spark-OSW was verified through accuracy, performance and acceleration tests. The results show that the proposed algorithm achieved 100% accuracy, ran much faster than the SW, and performed well in cluster environment. The research findings shed important new light on the database search for gene sequences. © 2019 INT. J. Bioautomation.

关键词： Search engines

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Personalized POI recommendation based on check-in data and geographical-regional influence

Personalized POI recommendation based on check-in data and g...

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作者： Chuang Song Junhao Wen Shun Li The School of Big Data and Software Engineering Chongqing University

Nowadays, many people like to share the places they visited to their friends in the location-based social networks(LBSNs). Therefore, LBSNs have accumulated large-scale user check-in data and the availability of these data enables many locationbased services to users. As a location-based service, point-ofinterests(POI) recommendation can provide services to people and promote merchants. Many researchers utilize user-based collaborative filtering and geographical influence for POI recommendation. However, existing studies have two limitations:(1) when modeling user-based CF, users' POI preferences are not fully considered;(2) when modeling geographical influence, geographical features have not been explored deeply. In this paper, we propose a POI recommendation approach by improved userbased CF and geographical-regional influence. Firstly, we construct the user-POI matrix by normalized check-in frequencies, which can effectively represent user preferences. Secondly, we find that each user's check-in POIs can be divided into several regions. Accordingly, we integrate geographical influence with the regional feature to produce recommendation. Finally, we utilize a unified framework to combine improved user-based CF with geographical-regional influence for POI recommendation. Our experimental results on real-world dataset show that the proposed approach outperforms the state-of-the-art POI recommendation methods substantially.

关键词： POI recommendation Location-based social networks Collaborative filtering Geographical influence

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Multi-Objective Optimization Recommendation Algorithm Based on Collaborative Filtering and Item Similarity

Multi-Objective Optimization Recommendation Algorithm Based ...

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作者： Chaosheng Zhao School of Big Data & Software Engineering Chongqing University

The traditional collaborative filtering recommendation algorithm generates recommendations for users by calculating the similarity between users or items,without considering other factors and data sparsity *** appearance of personalized tags can reflect the characteristics of *** algorithms try to optimize the recommendation results by setting weight coefficients of various ***,it is unreasonable to set the same weight coefficients for different *** solve these problems,we propose a multi-objective optimization recommendation algorithm based on collaborative filtering and item *** algorithm calculates the similarity between users by improved user-based collaborative filtering recommendation algorithm and generates a candidate recommendation ***,the algorithm uses the coupled object similarity measure method to calculate the similarity between ***,we use NNIA algorithm to optimize the former two goals and generate the final recommendation *** results on the Movielens dataset shows that the proposed algorithm has good recommendation effect.

关键词： collaborative filtering item similarity multi-objective optimization NNIA

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