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20th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2019

作者： Du, Haixin Zhang, Jiankui Sha, Shihao Ye, Cai Luo, Qiuming College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of Popular HPC Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (纸本)9781728126166

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. This paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. The testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. The max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA). © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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LSTM based model for real-time stock market prediction on unexpected incidents

LSTM based model for real-time stock market prediction on un...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Aslam, Saba Rasool, Abdur Jiang, Qingshan Qu, Qiang National University of Computer and Emerging Sciences Department of Computer Science Faisalabad Pakistan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Key Lab for High Performance Data Mining Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781665436786

Sudden and unlooked events can be adverse to the world financial community, especially stock exchange markets. Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden events' effects on a minute-by-minute basis to evaluate market reactions precisely for a better investment strategy. Despite the web crawler's availability, it is challenging to mine biased data;however, we build an artificial intelligence (AI) based model to make a better investment strategy that uses unexpected incidents like terrorist attacks and other natural disasters like floods and earthquakes. For this, we used Pakistan's real-time terrorist attacks, floods, and earthquakes data and Pakistan stock market KSE100 index data to feed the model which predicts swift market changes. We study unexpected real-world incident data and stock market data from 2001 to 2020. First of all, a web crawler crawls the data from different websites using AI techniques. Then this data is fed into the information extractor module, which extracts the meaningful information based on AI. This unexpected incident data and stock market data are then used to train the AI-based LSTM to predict the market direction. Results prove that our model outperforms in measuring the influence of these shocking incidents and consequently detecting abrupt alterations in stock market directions. © 2021 IEEE.

关键词： Financial markets

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Design of Automatic Vulnerability Detection System for Web Application Program

Design of Automatic Vulnerability Detection System for Web A...

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2013 IEEE 4th International Conference on Software Engineering and Service Science

作者： Binbin Qu Beihai Liang Sheng Jiang Chutian Ye School.of Compute Science Huazhong University of Science & Technology Shenzhen Key Laboratory of High Performance Data Mining

ISBN: (纸本)9781467349970

At present,Web application vulnerability safety problems widely exist because of the lack of effective validation and filtering mechanism for untrusted user *** paper shows the design of the prototype system against SQL injection and cross-site scripting *** main steps of the detection are as follows:construct the taint dependency graph for the program by the static analysis of source code;use finite state automata to represent the value of tainted string;verify whether the program has effective safe handling for the user input by matching with the attack pattern and then implement the prototype system for automatic detection on the Java Web program vulnerability based on taint dependency *** experimental results show that the system is comprehensive and accurate for the detection of related vulnerabilities.

关键词： web application vulnerability static analysis taint dependency analysis

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Interactive key-Value Memory-augmented Attention for Image Paragraph Captioning 28

Interactive Key-Value Memory-augmented Attention for Image P...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Xu, Chunpu Li, Yu Li, Chengming Ao, Xiang Yang, Min Tian, Jinwen Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Huazhong University of Science and Technology China Shanghai Jiao Tong University China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781952148279

Image paragraph captioning (IPC) aims to generate a fine-grained paragraph to describe the visual content of an image. Significant progress has been made by deep neural networks, in which the attention mechanism plays an essential role. However, conventional attention mechanisms tend to ignore the past alignment information, which often results in problems of repetitive captioning and incomplete captioning. In this paper, we propose an Interactive key-value Memory-augmented Attention model for image Paragraph captioning (IMAP) to keep track of the attention history (salient objects coverage information) along with the update-chain of the decoder state and therefore avoid generating repetitive or incomplete image descriptions. In addition, we employ an adaptive attention mechanism to realize adaptive alignment from image regions to caption words, where an image region can be mapped to an arbitrary number of caption words while a caption word can also attend to an arbitrary number of image regions. Extensive experiments on a benchmark dataset (i.e., Stanford) demonstrate the effectiveness of our IMAP model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep neural networks

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Pre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering

arXiv

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arXiv 2024年

作者： Ni, Shiwen Cheng, Hao Yang, Min Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shenzhen University of Advanced Technology China

Legal question answering (QA) has attracted increasing attention from people seeking legal advice, which aims to retrieve the most applicable answers from a large-scale database of question-answer pairs. Previous methods mainly use a dual-encoder architecture to learn dense representations of both questions and answers. However, these methods could suffer from lacking domain knowledge and sufficient labeled training data. In this paper, we propose a three-stage (pre-training, fine-tuning and re-ranking) framework for legal QA (called PFR-LQA), which promotes the fine-grained text representation learning and boosts the performance of dense retrieval with the dual-encoder architecture. Concretely, we first conduct domain-specific pretraining on legal questions and answers through a self-supervised training objective, allowing the pre-trained model to be adapted to the legal domain. Then, we perform task-specific fine-tuning of the dual-encoder on legal question-answer pairs by using the supervised learning objective, leading to a high-quality dual-encoder for the specific downstream QA task. Finally, we employ a contextual re-ranking objective to further refine the output representations of questions produced by the document encoder, which uses contextual similarity to increase the discrepancy between the anchor and hard negative samples for better question re-ranking. We conduct extensive experiments on a manually annotated legal QA dataset. Experimental results show that our PFR-LQA method achieves better performance than the strong competitors for legal question answering. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Fastmap projection for high-dimensional data: A cluster ensemble approach

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第12期9卷 311-330页

作者： Khan, Imran Ivanov, Kamen Jiang, Qingshan Shenzhen Key Laboratory of High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory for Low-cost Healthcare Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

high-dimensional data with many features present a significant challenge to current clustering algorithms. Sparsity, noise, and correlation of features are common properties of high-dimensional data. Another essential aspect is that clusters in such data often exist in various subspaces. Ensemble clustering is emerging as a leading technique for improving robustness, stability, and accuracy of high-dimensional data clusterings. In this paper, we propose FastMap projection for generating subspace component data sets from high-dimensional data. By using component data sets, we create component clusterings and provides a new objective function that ensembles them by maximizing the average similarity between component clusterings and final clustering. Compared with the random sampling and random projection methods, the component clusterings by FastMap projection showed high average clustering accuracy without sacrificing clustering diversity in synthetic data analysis. We conducted a series of experiments on real-world data sets from microarray, text, and image domains employing three subspace component data generation methods, three consensus functions, and a proposed objective function for ensemble clustering. The experiment results consistently demonstrated that the FastMap projection method with the proposed objection function provided the best ensemble clustering results for all data sets. © 2016 SERSC.

关键词： Clustering algorithms

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Pattern mining, semantic label identification and movement prediction using mobile phone data

Pattern mining, semantic label identification and movement p...

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8th International Conference on Advanced data mining and Applications, ADMA 2012

作者： Xie, Rong Luo, Jun Yue, Yang Li, Qingquan Zou, Xiaoqing International School of Software Wuhan University Wuhan 430079 China Shenzhen Institutes of Advanced Technology CAS Shenzhen 518055 China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055 China Shenzhen University Shenzhen China State Key Lab. of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079 China Faculty of Land Resource Engineering Kunming University of Science and Technology Kunming China

ISBN: (纸本)9783642355264

data collected from mobile phones have potential knowledge to provide with important behavior patterns of individuals. In this paper, we present approaches to discovering personal mobility and characteristics based on mobile phone location information and semantic analysis. We discuss three aspects related to very common mobile phone-related applications such as pattern mining, semantic label identification and movement prediction. We use real mobile phone data to perform functions of discovering these behavior patterns and demonstrate effectiveness of our approaches. © Springer-Verlag 2012.

关键词： Cellular telephones

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Identification of key Genes as Potential Drug Targets for Gastric Cancer

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Tsinghua Science and Technology 2023年第4期28卷 649-664页

作者： Md.Tofazzal Hossain Md.Selim Reza Yin Peng Shengzhong Feng Yanjie Wei Center for High Performance Computing Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology UniversityGopalganj 8100Bangladesh Guangdong Provincial Key Laboratory for Genome Stability and Disease Prevention and Regional Immunity and Diseases Department of PathologyShenzhen UniversityShenzhen 518060China

Gastric cancer(GC)is one of the most common cancers and ranks the third in cancer mortality all over the *** goal of this study was to identify potential hub-genes,highlighting their functions,signaling pathways,and candidate drugs for the treatment of GC *** used publicly available next generation sequencing(NGS)data to identify differentially expressed(DE)*** top DE genes were mapped to STRING database to construct the protein-protein interaction(PPI)network and top hub genes were selected for further *** found a total of 1555 DE genes with 870 upregulated and 685 downregulated genes in *** selected the top 400(200 upregulated and 200 downregulated)genes to construct a PPI network and extracted the top 15 hub *** gene ontology(GO)term and kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses of the 15 hub genes exposed some important functions and signaling pathways that were significantly associated with GC *** survival analysis of the hub genes disclosed that the lower expressions of the three hub genes CDH2,COL4A1,and COL5A2 were associated with better survival of GC *** three genes might be the candidate biomarkers for the diagnosis and treatment of ***,we considered 3 key proteins(genomic biomarkers)(COL4A1,CDH2,and CO5A2)as the drug target proteins(receptors),performed their docking analysis with the 102 meta-drug agents,and found Everolimus,Docetaxel,Lanreotide,Venetoclax,Temsirolimus,and Nilotinib as the top ranked 6 candidate drugs with respect to our proposed target proteins for the treatment against GC ***,the proposed drugs might play vital role for the treatment against GC patients.

关键词： gastric cancer hub genes candidate genes molecular docking candidate drugs

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Towards Convergence of Blockchain and Self-sovereign Identity for Privacy-Preserving Secure Federated Learning 3rd

Towards Convergence of Blockchain and Self-sovereign Identit...

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3rd International Conference on Big data and Security, ICBDS 2021

作者： Haque, Rakib Ul Hasan, A. S. M. Touhidul Daria, Apubra Qu, Qiang Jiang, Qingshan School of Computer Science and Technology University of Chinese Academy of Sciences Shijingshan District Beijing100049 China Department of Computer Science and Engineering University of Asia Pacific Dhaka1205 Bangladesh Institute of Automation Research and Engineering Dhaka1205 Bangladesh Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9789811908514

More and more researchers are eager to train their Machine Learning (ML) model by the distributed dataset in a federated manner. However, there are numerous privacy and security concerns in federated learning, i.e., adversarial attacks, authentication, and model inversion attacks. In this paper, we integrate self-sovereign identity for privacy-preserving secure federated learning. The proposed framework represents identity management and authentication scheme for edge devices, which are participated in FL. It guarantees participants’ privacy, where users’ private data are not shared with the centralized training module. The trust triangle and Blockchain ensure the authenticity of the distributed federated learning, where data privacy is handled with Differential Privacy. Result analysis is done based on measured score and time complexity. In terms of accuracy, BCWD, HDD, and DD achieve 94.69 %, 81.05 % and 78.00 %, respectively on standard ML, whereas 94.45 %, 80.59 % and 76.43 % on ϵ DP-based FL with privacy budget, ϵ = 3. For the time complexity measurement, the total number of transactions employed in the system is 1000. Transactions time and their verification took 3323.15 s. Moreover, the proposed system shows robust performance in both cases. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Prior Dirichlet Distribution Based Feature Selection in Naive Bayes on high Dimensional data Classification 6

Prior Dirichlet Distribution Based Feature Selection in Naiv...

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6th International Conference on Big data and Information Analytics, BigDIA 2020

作者： Chen, Hui Chen, Lifei Jiang, Qingshan Guo, Shun Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China College of Mathematics and Informatics Fujian Normal University Fuzhou China Shenzhen Key Lab for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Science Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Classification is an essential technology in data mining, which has been widely used in real-world problems. A massive amount of information is pouring into our lives since the development of science and technology. The data size and dimension are growing rapidly in various fields and challenging data mining and machine learning approaches. Feature selection reduces the dimension of data and selects significant features, which is an important research direction. However, some norm measures of feature datasets may be imprecise. Many feature selection methods have the characteristics of high cardinality and high computational complexity. Therefore, there is a strong need to study effective feature selection methods. Naive Bayes is a simple and effective learning theory that does not need various parameters. However, its conditional independence assumption limits its classification results in practical applications. Meanwhile, the curse-of-dimensionality brings noises and messy interaction between features. In this paper, we propose a feature selection method named as the Prior Dirichlet Distribution of Naive Bayes (PDDNB) which optimizes the Bayesian theory based on Dirichlet priori distribution. This method simplifies the computational ways to an analytical solution, which significantly improve the calculation efficiency. The executive experiments on the ALL-AML datasets and the other five real-world datasets turn out our method to effectively increase classification accuracy. © 2020 IEEE.

关键词： Feature Selection

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