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International Conference on Information Technology: Research and Education (ITRE)

作者： Chao-Tung Yang Kuan-Ching Li High Performance Computing Laboratory Department of Computer Science and Information Engineering Tunghai University Taichung Taiwan Parallel and Distributed Processing Center Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)0780389328

Cluster and grid computing is a relatively new interdisciplinary field, where computer science, engineering and computational biology are its core supporting disciplines. The rise of cluster and grid computing discipline brings to computer science faculty members new opportunities and challenges, both in education and in research. In this paper, we explore issues in teaching cluster and grid computing, the developing of curricula, and thoughts to foster student to work on research projects in cluster and grid computing.

关键词： Anatomy Grid computing Distributed computing Concurrent computing computer networks Brain modeling Predictive models computer science Education Space technology

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Efficiently indexing sparse wide tablesin cloud computing

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Information Technology Journal 2013年第20期12卷 5415-5423页

作者： Huang, Bin Peng, Yuxing Department of Computer Huaihua University 418008 Huaihua China School of Computer Science Wuhan University 430072 Wuhan China National Laboratory of Parallel and Distributed Processing National University of Defense Technology 410073 Changsha China

For its perception of unlimited resources and infinite scalability, Cloud Computing has emerged as a pervasive paradigm for hosting? data-centric applications in large computing infrastructures. The data produced by these applications are essentially sparse and wide and may change schema frequently, traditional relational data model is inappropriate for their data management. A new data model, called Sparse Wide Table, was introduced for this task. Unfortunately, we have to face many challenges in building the secondary index for Sparse Wide Table in cloud, as the distributed and column-oriented storage which eliminates a number of NULLs. In this study, we present a three-level index scheme for efficient data processing in the Cloud. Our approach can be summarized as follows. First, we build an index for each column by which the records can be rebuilt easily. Second, we build a bitmap index for each storage node which only indexes the data residing on the node. Third, we organize the storage nodes as a structured overlay and each node maintains a portion of the global index for the all different data. The global index is a bitmap index to indicate the node each data resides in. Finally, based on the three-level index scheme, some query algorithms are implemented. We conduct extensive experiments on a LAN and the results demonstrate that our indexing scheme is dynamic, efficient and scalable. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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RBP-Former: Joint Prediction of RNA-protein Binding Sites on Full-length RNA Transcripts for Multiple RBPs

RBP-Former: Joint Prediction of RNA-protein Binding Sites on...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Yichong Liu, Xiaojian Cheng, Fan Pan, Xiaoyong Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai200240 China

ISBN: (纸本)9798350386226

RNA-binding proteins (RBPs) are essential for gene expression, and the complex RNA-protein interaction mechanisms require analysis of global RNA information. Therefore, accurate prediction of RBP binding sites on full-length RNA transcripts is crucial for understanding these mechanisms and their roles in diseases. While machine learning methods can predict RBP binding to RNA fragments, extending this to full-length transcripts presents challenges due to sequence length and data imbalance. In this paper, we introduce RBP-Former, a binding site joint prediction model designed specifically for full-length RNA transcripts that can be used for multiple RBPs. This model processes information at both coarse and fine-grained levels to fully exploit sequence data and its interactions with multiple RBPs. We develop multi-level imbalance learning strategies, achieving favorable results on imbalanced data. Our method outperforms existing methods in predicting binding sites on full-length RNA transcripts for multiple RBPs, demonstrating its effectiveness in handling imbalanced label and sample distributions. © 2024 IEEE.

关键词： Binding sites

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Flexible Routing in Grouped DHTs

Flexible Routing in Grouped DHTs

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International Conference on Peer-to-Peer Computing

作者： Yiming Zhang Dongsheng Li Lei Chen Xicheng Lu National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

In most DHTs proposed so far, all nodes are assumed to be homogeneous, and all messages are routed using a common algorithm. In practice, however, nodes in large-scale systems might be heterogeneous with respect to their capabilities, reputations, affiliations of administrative domains, and so on, which consequently makes it preferable to distinguish the heterogeneity of participant nodes. To achieve this, in this paper we present grouped tapestry (GTap), a novel tapestry-based DHT that supports organizing nodes into groups and allows flexible DHT routing. The effectiveness of our proposals is demonstrated through theoretical analysis and extensive simulations.

关键词： Routing Fingers Peer to peer computing Maintenance engineering Security Availability Data structures

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Feature-rich named entity recognition for bulgarian using conditional random fields

Feature-rich named entity recognition for bulgarian using co...

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International Conference on Recent Advances in Natural Language processing, RANLP-2009

作者： Georgiev, Georgi Nakov, Preslav Ganchev, Kuzman Osenova, Petya Simov, Kiril Ontotext AD 135 Tsarigradsko Ch. Sofia 1784 Bulgaria Department of Computer Science National University of Singapore 13 Computing Drive Singapore 117417 Singapore Department of Computer and Information Science University of Pennsylvania Philadelphia PA United States Linguistic Modelling Laboratory Institute for Parallel Processing Bulgarian Academy of Sciences 25A Acad. G. Bonchev St. 1113 Sofia Bulgaria

The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other languages with language-specific lexical, syntactic and morphological information. In particular, we make use of the rich tagset annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive suitable task-specific tagsets (local and nonlocal). We further add domain-specific gazetteers and additional unlabeled data, achieving F 1=89.4%, which is comparable to the state-of-the-art results for English.

关键词： Learning systems

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A distributed algorithm for SI transactions serializability in cloud computing

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Information Technology Journal 2013年第24期12卷 8435-8445页

作者： Huang, Bin Peng, Yuxing Peng, Xiaoning School of Computer Science Wuhan University Wuhan 430072 China Department of Computer Huaihua University Huaihua 418008 China National Laboratory of Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China

There are well known anomalies permitted by snapshot isolation that can lead to violations of data consistency by interleaving transactions that individually maintain consistency. Until now, there are some ways to prevent these anomalies only in single computer and there are not the corresponding solving methods in cloud computing. This paper describes our PDCC algorithm to detect cycles in a snapshot isolation dependency graph and abort transactions to break the cycle in cloud computing. The algorithm ensures serializable executions for SI transactions in cloud computing. Based on the transaction concurrency control of Percolator, we have implemented our algorithm in an open source cloud database system (HBase) and our performance study shows that PDCC throughput and scalability are good. © 2013 Asian Network for Scientific Information.

关键词： Cloud computing

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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science China(Information sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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Optimizing Reference Gene Set for Inter-Chip Normalization Using Genetic Algorithm

Optimizing Reference Gene Set for Inter-Chip Normalization U...

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International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Fei Li Wenjian Xu Peng Li XiaoChen Bo Yuxing Peng Department of Computer Science National Laboratory of Parallel & Distributed Processing National University of Defense Technology Changsha China Beijing Institute of Radiation Medicine Beijing China

Due to the existence of systematic bias in the measurements, inter-chip normalization is required before integrated data analysis of multiple microarray datasets. Current inter-chip normalization methods rely on some assumptions, which can rarely be verified by experiments, such as the invariant expression level of housekeeping genes or the stability of statistical data distribution. In this paper, we present a highly verifiable inter-chip normalization method using genetic optimization algorithm, which depends only on a small number of housekeeping genes, and therefore facilitates experimental validation without any accuracy lost.

关键词： Genetic algorithms Gene expression Laboratories Data analysis Optimization methods Genomics Bioinformatics Statistical distributions Probes computer science

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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Cross-lingual Adaptation as a Baseline: Adapting Maximum Entropy Models to Bulgarian

Cross-lingual Adaptation as a Baseline: Adapting Maximum Ent...

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2009 International Workshop on Adaptation of Language Resources and Technology to New Domains, AdaptLRTtoND 2009 - held in conjunction with the International Conference RANLP 2009

作者： Georgiev, Georgi Nakov, Preslav Osenova, Petya Simov, Kiril Ontotext AD 135 Tsarigradsko Chaussee Sofia1784 Bulgaria Department of Computer Science National University of Singapore 13 Computing Drive Singapore117417 Singapore Linguistic Modelling Laboratory Institute for Parallel Processing Bulgarian Academy of Sciences 25A Acad. G. Bonchev St. Sofia1113 Bulgaria

ISBN: (纸本)9789544520090

We describe our efforts in adapting five basic natural language processing components to Bulgarian: sentence splitter, tokenizer, part-of-speech tagger, chunker, and syntactic parser. The components were originally developed for English within OpenNLP, an open source maximum entropy based machine learning toolkit, and were retrained based on manually annotated training data from the BulTreeBank. The evaluation results show an F1 score of 92.54% for the sentence splitter, 98.49% for the tokenizer, 94.43% for the part-of-speech tagger, 84.60% for the chunker, and 77.56% for the syntactic parser, which should be interpreted as baseline for Bulgarian. © 2009 International Workshop on Adaptation of Language Resources and Technology to New Domains

关键词： Syntactics

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