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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Yan Yang, Zhenyu Hu, Wenyue Xu, Baojie Zhang, Zhibo Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9781665410205

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP. © 2024 IEEE.

关键词： Inverse problems

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Boosting Performance and QoS for Concurrent GPU B+trees by Combining-Based Synchronization 23

Boosting Performance and QoS for Concurrent GPU B+trees by C...

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28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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FAST Observations of Four Comets to Search for the Molecular Line Emissions between 1.0 and 1.5 GHz Frequencies

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Research in Astronomy and Astrophysics 2024年第10期24卷 130-140页

作者： Long-Fei Chen Chao-Wei Tsai Jian-Yang Li Bin Yang Di Li Yan Duan Chih-Hao Hsia Zhichen Pan Lei Qian Donghui Quan Xue-Jian Jiang Xiaohu Li Ruining Zhao Pei Zuo School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550025China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550025China Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311100China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Key Laboratory of Radio Astronomy and Technology Chinese Academy of SciencesBeijing 100101China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhai 519082China Núcleo de Astronomía Facultad de Ingenieríay CienciasUniversidad Diego PortalesChile Department of Astronomy College of Physics and Electronic EngineeringQilu Normal UniversityJinan 250200China Department of Electronic and Optical Engineering Space Engineering UniversityBeijing 101416China Laboratory for Space Research Faculty of ScienceThe University of Hong KongHong Kong(SAR)China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China CAS Key Laboratory of Optical Astronomy National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Sciences University of Chinese Academy of SciencesBeijing 100049China

We used the Five-hundred-meter Aperture Spherical radio Telescope(FAST)to search for the molecular emissions in the L-band between 1.0 and 1.5 GHz toward four comets,C/2020 F3(NEOWISE),C/2020 R4(ATLAS),C/2021 A1(Leonard),and 67P/Churyumov-Gerasimenko during or after their perihelion *** of molecular transition lines fall in this low-frequency range,many attributed to complex organic or prebiotic *** conducted a blind search for the possible molecular lines in this frequency range in those comets and could not identify clear signals of molecular emissions in the *** several molecules have been detected at high frequencies of greater than100 GHz in comets,our results confirm that it is challenging to detect molecular transitions in the L-band frequency *** non-detection of L-band molecular lines in the cometary environment could rule out the possibility of unusually strong lines,which could be caused by the masers or non-LTE *** the line strengths are predicted to be weak,for FAST,using the ultra-wide bandwidth receiver and improving the radio frequency interference environments would enhance the detectability of those molecular transitions at low frequencies in the future.

关键词： astrochemistry ISM:molecules comets:general line:identification

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An importance-and-semantics-aware approach for entity resolution using MLP 1

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4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Xu, Yaoli Li, Zhanhuai Qi, Wanhua School of Computer Science and Engineering Northwestern Polytechnical University Xi’anShaanxi710072 China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology Xi’an China Anyang Institute of Technology AnyangHenan455000 China

ISBN: (数字)9789811322037

ISBN: (纸本)9789811322020

Entity resolution (ER), as the process of identifying records which depict the same real-world entity, plays a fundamental role in data integration and data cleaning tasks. Although deep learning techniques of data science have transformed various applications, there are few efforts to leverage these techniques to deal with entity resolution. We also observe the importance of overlapped tokens and the semantic similarity from pre-trained word vectors can benefit ER. To this end, we propose a deep learning based framework for ER, which can leverage the state-of-the-art techniques in deep neural network communities. We also propose an importance-and-semantics-aware approach for ER using a multilayer perceptron (MLP), to combine the importance of overlapped tokens, semantic similarity and textual similarity of corresponding attribute values of pairs. Comparative experiments demonstrate that our method outperforms the traditional method. © Springer Nature Singapore Pte Ltd. 2018.

关键词： data integration

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Design of a reconfigurable antenna array with discrete phase shifters using differential evolution algorithm

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Progress In Electromagnetics Research B 2011年第31期 29-43页

作者： Li, X. Yin, M. College of Computer Science Northeast Normal University Changchun 130117 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

The reconfigurable design problem is to find the element that will result in a sector pattern main beam with side lobes. The same excitation amplitudes apply to the array with zero-phase that should be in a high directivity, low side lobe pencil shaped main beam. Multi-beam antenna arrays have important applications in communications and radar. This paper presents a new method of designing a reconfigurable antenna array with quantized phase excitations using a new evolutionary algorithm called differential evolution (DE). In order to reduce the effect of mutual coupling among the antenna-array elements, the dynamic range ratio is minimized. Additionally, compared with the continuous realization and subsequent quantization, experimental results indicate better performance of the discrete realization of the phase-excitation value of the proposed algorithm.

关键词： Antenna lobes

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Comparative analysis of identification methods for wheat leaf diseases

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International Agricultural engineering Journal 2016年第3期25卷 197-203页

作者： Zhang, D.Y. Xie, X.H. Liang, D. Huang, L.S. Zhang, X.Q. Teng, L. Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei230039 China Anhui Engineering Laboratory of Agro-Ecological Big Data Anhui University School of Electronic and Information Engineering Hefei Anhui230601 China

How to fast, accurately and robustly recognize wheat diseases, particularly for those diseases with mild-to-moderate severity, is a challenge for prevention and control of crop disease timely. In this study, image processing technique was applied to segment the infected regions of disease leaves. Twenty disease features were extracted, and eighteen larger weight features were selected by Relief-F algorithm to generate the models of Support Vector Machine (SVM), Relevance Vector Machine (RVM) and Back Propagation Neural Network (BPNN). Subsequently, these models were used to identify two kinds of wheat diseases, namely, wheat stripe rust and powdery mildew. Total 136 samples, including 68 training samples and 68 test samples with different infection severities were used to study the recognition capabilities of the three models. Results showed that high predictive accuracies in identification of two wheat diseases with varying severity for all three models. Overall accuracy of RVM was 89.71%, which was superior to 83.82% of SVM and inferior to 92.64% of BPNN. Meanwhile, the recognition accuracies of SVM, RVM and BPNN models for mild-to-moderate disease were 83.33%, 88.33% and 91.67%, respectively. The prediction time of RVM was less than those of SVM and BPNN, with differences as large as 7.96 and 31.68 times, respectively. Therefore, RVM appeared to be the most suitable for real-time identifying wheat leaf diseases among the three models, which can provide important technical support for wheat diseases management.

关键词： Support vector machines

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Targeted Pareto Optimization for Subset Selection With Monotone Objective Function and Cardinality Constraint

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Shang, Ke Wu, Guotong Pang, Lie Meng Ishibuchi, Hisao National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Subset selection, a fundamental problem in various domains, is to choose a subset of elements from a large candidate set under a given objective or multiple objectives. Pareto optimization for subset selection (POSS) has emerged as a powerful paradigm for addressing subset selection problems. Recently, some POSS variants have been proposed to further improve its performance. In this paper, we propose a new POSS variant, named Targeted Pareto Optimization for Subset Selection (TPOSS). TPOSS differs from POSS in four aspects: problem formulation, population initialization, mutation, and environmental selection. The main idea of TPOSS is to focus the search on the target region of subset selection with respect to the subset cardinality in order to improve the search efficiency. We conduct comprehensive experiments to compare TPOSS with six state-of-the-art algorithms on three subset selection tasks (i.e., sparse regression, unsupervised feature selection, and hypervolume subset selection) where the size of the candidate sets ranges from 20 to 400. Experimental results show that with respect to the objective value of the best feasible subset, TPOSS outperforms the other algorithms on all the three tasks, which suggests the potential of TPOSS to enhance subset selection in various domains. IEEE

关键词： Linear programming

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A study on improved mrmf algorithm on uncertain graph 2nd

A study on improved mrmf algorithm on uncertain graph

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2nd International Cognitive Cities Conference, IC3 2019

作者： Wen, Kejie Yang, Juan Liu, Wei Yang, Yanchun Zhang, Baili School of Computer Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information Integration of Ministry of Education Southeast University Nanjing211189 China Research Center for Judicial Big Data Supreme Count of China Nanjing211189 China

ISBN: (纸本)9789811561122

With the increased size and density of graph, the performance of the space decomposition-based algorithm (SDBA) decreases rapidly in terms of the most reliable maximum flow problem (MRMF). To solve this problem, the double filter space decomposition-based algorithm (DF-SDBA) is proposed by applying the probability filter and the cut-set filter. First, among the intervals to be processed in the process of space decomposition, DF-SDBA algorithm uses the probability constraint to filter out the intervals which own smaller probability of the lower boundary distribution than the current maximum flow, reducing the number of iterations effectively. Then the cut-set constraint is applied to unspecified intervals. According to the rule that all the edges in the cut-sets must exist in the upper bound of each interval, the unspecified intervals are filtered out, cutting down the number of partition intervals once again. The experimental results reveal that compared with the SDBA algorithm, the DF-SDBA algorithm can reduce the number of the intervals needed to be divided can obtain better performance. © Springer Nature Singapore Pte Ltd 2020.

关键词： Flow graphs

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Deep knn for medical image classification 23rd

Deep knn for medical image classification

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhuang, Jiaxin Cai, Jiabin Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Human-level diagnostic performance from intelligent systems often depends on large set of training data. However, the amount of available data for model training may be limited for part of diseases, which would cause the widely adopted deep learning models not generalizing well. One alternative simple approach to small class prediction is the traditional k-nearest neighbor (kNN). However, due to the non-parametric characteristics of kNN, it is difficult to combine the kNN classification into the learning of feature extractor. This paper proposes an end-to-end learning strategy to unify the kNN classification and the feature extraction procedure. The basic idea is to enforce that each training sample and its K nearest neighbors belong to the same class during learning the feature extractor. Experiments on multiple small-class and class-imbalanced medical image datasets showed that the proposed deep kNN outperforms both kNN and other strong classifiers. © Springer Nature Switzerland AG 2020.

关键词： Nearest neighbor search

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A Text Clustering Algorithm Hybriding Invasive Weed Optimization with K-Means

A Text Clustering Algorithm Hybriding Invasive Weed Optimiza...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Chunmei Fan Taohong Zhang Zhiyong Yang Li Wang Department of Computer University of Science and Technology Beijing (USTB) Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China

ISBN: (纸本)9781467372121

Invasive Weed Optimization (IWO) is an optimization algorithm with powerful explorative and exploitive capability. K-MEANS method is a clustering algorithm sensitive to the initial point selection and easy to fall into local optimum. In order to improve the performance of traditional K-MEANS, in this paper, a clustering algorithm framework hybirding IWO with K-MEANS is argued. This paper mainly focus on dicussing different manner of combining those two algorithms, we try two methods and apply them to the Chinese text clustering. To our knowledge, such applications of IWO-KMEANS hasn't been reported in other literatures. The experimental results shows that compared with the traditional K-MEANS algorithm, as well as the Differential Evolution optimization based K-MEANS(DE-KMEANS) algorithm, employing IWO optimization to select cluster center outperforms all aforementioned methods.

关键词： Clustering algorithms Optimization Sociology Statistics Algorithm design and analysis Computers Standards

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