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2nd International Conference on Big Data Engineering, BDE 2020

作者： Zhang, Changqiang Chen, Ming Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology China

ISBN: (纸本)9781450377225

In this paper, we propose an approach to assess the ability of developers based on their behavior data from OSS. Specifically, we classify developers' ability into code ability, project management ability, and social ability. Code efficiency is related to the developer's commit record and the pull-request record. The developer's project management ability is achieved by tracking the developer's commit record. We use regular matching to map the commit behavior to the project management behavior and calculate the developer's project management ability according to the proportion of different behaviors. The social ability of developers is related to the data that developers interact with in the open-source community. We dug for developer reviews on commit, issue, and gist fragments. By calculating the proportion of positive emotions in developer reviews and the proportion of developers interacting with others in the reviews, the social ability of developers is obtained. We get behavioral data from 50 random developers. Twitter's data is used to test the effect of different machine learning algorithms on the accuracy of developer comment polarity judgments. It is found that the combination of SVM, xgboost and random forest have the highest prediction accuracy. Finally, we select 5 students to use Likert scale to score the results. Our score shows that the results are basically in line with expectations. © 2020 ACM.

关键词： Decision trees

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Modeling Full Information with Graph Network for Joint Entity-Relation Extraction 21

Modeling Full Information with Graph Network for Joint Entit...

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Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Qian Wan Luona Wei Xinhai Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China College of Systems Engineering National University of Defense Technology China Laboratory of Software Engineering for Complex Systems National University of Defense Technology China

ISBN: (纸本)9781450385053

Fully capturing contextual information and analyzing the association between entity semantics and type is helpful for joint extraction task: 1) The context can reflect the part of speech and semantics of entity. 2) The entity type is closely related to the relation between entities. Previous research used to simply embed the contextual information into shallow layer of the model, ignoring the association between entity semantics and type. In this paper, we propose a graph network with full-information modeling to explicitly model different-level information in the text. The contextual information of entity is dynamically embedded in each span representation to improve the reasoning ability. To capture the fine-grained association between the semantics and type of entity, the graph network uses the feature of entity types to generate edge information between different nodes. Experimental results show that our model outperforms previous models on the CoNLL04 dataset and obtains competitive results on the SciERC dataset in both entity recognition and relation extraction. Extensive additional experiments further verify the effectiveness of the model.

关键词： Natural Language processing Named Entity Recognition Graph Network Deep Learning Relation Extraction

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud Computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud Computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud Computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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A Memory Saving Mechanism Based on Data Transferring for Pipeline parallelism

A Memory Saving Mechanism Based on Data Transferring for Pip...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Jiang Rui Xu Sheng Ma Qiong Wang Xiang Hou Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

The correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. The multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. The memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.

关键词： Training Computational modeling Pipelines Neural networks Memory management Bandwidth parallel processing

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A pipelining strategy for accelerating convolutional networks on arm processors 10th

A pipelining strategy for accelerating convolutional network...

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10th International Symposium on parallel Architectures, Algorithms and Programming, PAAP 2019

作者： Zhou, Xin Li, Rongchun Zhang, Peng Liu, Yuntao Dou, Yong National Key Laboratory for Parallel and Distribution Processing National University of Defense Technology Changsha450001 China

Convolutional neural networks (CNN) is playing an important role in many fields. Many applications are able to run the inference process of CNN with pre-trained models on mobile devices in these days. Improving performance of embedded processors such as ARM-based CPUs makes it possible to meet the requirement of real-time processing. In this paper, a pipelining strategy is proposed to accelerate convolution networks on ARM processors. We implement a 3 × 3 convolution with Neon instructions which are single instruction and multiple data (SIMD) instructions supported by ARM processors. In order to reduce stalls in the pipeline, issue orders of instructions are rearranged according to the out-of-order execution and dual-issue mechanism on ARM processors. A tiling method is exploited to increase data reuse. The input feature map is divided into multiple 6 × 6 tiles, and the computations within the tile is highly optimized using our proposed pipelining strategy. The speedup of proposed method is 2.88 compared with gcc compiled codes on RK3288. The effect of our optimizing method is measured by a performance profiling tool, cycles and cache misses are decreased significantly. The multi-thread version implemented with openMP achieve speedup of 6.8 compared with single-thread gcc complied version. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Pipelines

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Multi-task Pre-training Language Model for Semantic Network Completion

arXiv

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arXiv 2022年

作者： Li, Da Yang, Sen Xu, Kele Yi, Ming He, Yukai Wang, Huaimin Tencent Beijing100084 China Beijing Institute of Microbiology and Epidemiology Beijing100071 China State Key Laboratory of Pathogen and Biosecurity Beijing100071 China National University of Defense Technology Changsha410073 China National Key Lab of Parallel and Distributed Processing Changsha410073 China

Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language processing, it suffers from incompleteness. This paper focuses on knowledge graph completion by predicting linkage between entities, which is a fundamental yet critical task. Semantic matching is a potential solution for link prediction as it can deal with unseen entities, while the translational distance based methods struggle with the unseen entities. However, to achieve competitive performance as translational distance based methods, semantic matching based methods require large-scale datasets for the training purpose, which are typically unavailable in practical settings. Therefore, we employ the language model and introduce a novel knowledge graph architecture named LP-BERT, which contains two main stages: multi-task pre-training and knowledge graph fine-tuning. In the pre-training phase, three tasks are taken to drive the model to learn the relationship information from triples by predicting either entities or relations. While in the fine-tuning phase, inspired by contrastive learning, we design a triple-style negative sampling in a batch, which greatly increases the proportion of negative sampling while keeping the training time almost unchanged. Furthermore, we propose a new data augmentation method utilizing the inverse relationship of triples to improve the performance and robustness of the model. To demonstrate the effectiveness of our proposed framework, we conduct extensive experiments on three widely-used knowledge graph datasets, WN18RR, FB15k-237, and UMLS. The experimental results demonstrate the superiority of our methods, and our approach achieves state-of-the-art results on the WN18RR and FB15k-237 datasets. Significantly, the Hits@10 indicator is improved by 5% from the previous state-of-the-art result on the WN18RR dataset while reaching 100% on the UMLS dataset. Copy

关键词： Inverse problems

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Co-designing the Topology/Algorithm to Accelerate distributed Training

Co-designing the Topology/Algorithm to Accelerate Distribute...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiang Hou Rui Xu Sheng Ma Qiong Wang Wei Jiang Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

With the development of Deep Learning (DL), Deep Neural Network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. The hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. Then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Training Deep learning Network topology Multiprocessor interconnection Computational modeling Training data Switches

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Surrogate Supervision-based Deep Weakly-supervised Anomaly Detection

Surrogate Supervision-based Deep Weakly-supervised Anomaly D...

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Zhiyue Wu Hongzuo Xu Yijie Wang Yongjun Wang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665424288

Many anomaly detection applications can provide partially observed anomalies, but only limited work is for this setting. Additionally, a number of anomaly detectors focus on learning a particular model of normal/abnormal class. However, the intra-class model might be too complicated to be accurately learned. It is still a non-trivial task to handle data with anomalies/inliers in skewed and heterogeneous distributions. To address these problems, this paper proposes an anomaly detection method to leverage Partially Labeled anomalies via Surrogate supervision-based Deviation learning (denominated PLSD). The original supervision (i.e., known anomalies and a set of explored inliers) is transferred to semantic-rich surrogate supervision signals (i.e., anomaly-inlier and inlier-inlier class) via vector concatenation. Then different relationships and interactions between anomalies and inliers are directly and efficiently learned thanks to the neural network’s connection property. Anomaly scoring is processed via the trained network and the high-efficacy inliers. Extensive experiments show that PLSD significantly prevails state-of-the-art semi/weakly-supervised anomaly detectors.

关键词： Learning systems Conferences Detectors Gaussian distribution Benchmark testing Task analysis Anomaly detection

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection

Word Embedding-based Context-sensitive Network Flow Payload ...

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International Conference on Applied Machine Learning (ICAML)

作者： Yizhou Li Yijie Wang Li Cheng Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421263

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due to its various possible characters and complex semantic context, and thus identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which applies the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method.

关键词： Art Semantics Clustering algorithms Intrusion detection Machine learning Feature extraction Task analysis

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Fden: Mining Effective Information of Features in Detecting Network Anomalies

Fden: Mining Effective Information of Features in Detecting ...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Bin Li Yijie Wang Mingyu Liu Kele Xu Zhongyang Wang Li Cheng Yizhou Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En pk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En pk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En pk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the Network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： Knowledge engineering Conferences Telecommunication traffic Signal processing Feature extraction Numerical models Security

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