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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Pengqi Duan Yudong Zheng Jun Lu College of Computer Science and Technology Heilongjiang University Heilongjiang University HLJU Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province College of Computer Science and Technology Heilongjiang University Heilongjiang University HLJU Harbin China

ISBN: (纸本)9781665437585

Even though pre-trained language models like BERT and XLNet have produced significant consequences on a variety of tasks of natural language processing, they are difficult to deploy in practical applications due to their large number of parameters, and huge calculation and memory costs. In order to improve the training speed and inference speed of the pre-trained model, the FasterBERT model is proposed in this paper. This model combines batch data preprocessing method with knowledge distillation. It increases the model to increase the model speed and maintains the original accuracy. Furthermore, double loss function is incorporated during the knowledge distillation process in this paper, so that the model can better learn the knowledge of the teacher model and the knowledge of the real labels. The accuracy of the model is improved. The effectiveness of the FasterBERT model is demonstrated on 12 datasets in this paper. Training speed of the FasterBERT is 1.1-2.2 times higher than FastBERT and 2-43 times higher than BERT. In addition, the accuracy is up to 0.6% higher than that of FastBERT, and the accuracy is better than that of the BERT model on three datasets. The experiments show that the FasterBERT model proposed in this paper performs well on six Chinese datasets and six English datasets. The speed is greatly improved while the accuracy of the task also has a significant effect.

关键词： Training Costs Bit error rate Data preprocessing Data models Natural language processing Information and communication technology

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NKMH: A Neural Efficient Recommendation Based on Neighborhood key Information Aggregation of Modified Hawkes

NKMH: A Neural Efficient Recommendation Based on Neighborhoo...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xin Xu Nan Wang Hu Jin Yang Liu Kun Li Key Laboratory of Database and Parallel Computing of Heilongjiang Province School of Computer Science and Technology Heilongjiang University Harbin China Postdoctoral Program of Heilongjiang Hengxun Technology Co. Ltd School of Computer Science and Technology Heilongjiang University Harbin China

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The rapid development of neural networks has con-tributed to the increasing maturity of recommendation systems. However, deep neural networks have poor interpretability for models and do not show strong advantages for sparse data and noisy data. Recently, Hawkes process has become more and more focused for its good interpretability with probabilistic models. Based on this, we proposes A Neural Efficient Recommendation Model Based on Neighborhood key Information Aggregation of Modified Hawkes(NKMH). The model utilizes a neural network and designs three modules to jointly fit the modified Hawkes process. It not only inherits the high interpretability of Hawkes, but also effectively solves the problem of poor prediction ability of the Hawkes process. Besides, we present a novel key information search strategy(KISS), which can effectively remove the noise in a session and alleviate the sparsity of the data to some extent. Extensive experiments on two datasets show that the NKMH model outperforms many current popular models.

关键词： Deep learning Conferences Neural networks Predictive models Search problems Probabilistic logic Software

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Power adjustable cross-layer network protocol in dense wireless sensor networks

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Ruan Jian Xue Bao/Journal of Software 2016年 27卷 11-20页

作者： Ni, Lin-Yu Li, Jin-Bao School of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

A cross layer network strategy with power regulation is proposed for the wireless sensor networks with dense nodes. Under this strategy, nodes use different transmission power for data transmission and so that each node has a predetermined number of its neighbor nodes, or node degree. It thus guarantees the network connectivity, and can effectively reduce the competition conflict between nodes. The nodes in the network are divided according to the hierarchy. The upper node with the minimum transmission power is selected as the father node to construct the routing tree, and the candidate father node sets for the nodes are calculated. In the case of the failure of the father node, candidate father nodes are selected for transmission to solve the problem. The experimental results show that this strategy can improve the network throughput and the rate of data transmission. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Throughput

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Study on distributed channel allocation and routing forwarding strategy in Dual-Radio sensor networks

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Tongxin Xuebao/Journal on Communications 2010年第9 A期31卷 81-89页

作者： Fan, Bin Li, Jin-Bao Guo, Long-Jiang Liu, Xu Ren, Qian-Qian School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

Considering the limited orthogonal channels in wireless sensor networks (WSN), a distributed channel allocation algorithm (DCA) for Dual-Radio WSN was presented. Based on DCA, a routing forwarding strategy (RFS) was proposed as well. DCA avoids conflictions within a path and reduces conflictions among different paths with a small number of channels. Furthermore theoretically prove that when K≥Δ+1, the upper bound of the network throughput is |M|+1, where K is the number of orthogonal channels in WSN, Δ is the largest degree of the network topology graph, and |M| is the number of non-leaf nodes in the network routing tree. Nodes switch channels timely in RFS, and then further dropping the conflict between the paths, effectively transmit data in parallel. Simulation results indicate that DCA and RFS can reduce the latency of data forwarding and the average energy consumption of nodes, as well as increase the throughput significantly.

关键词： Energy utilization

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keywords generation algorithm based on bipartite graph

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Tongxin Xuebao/Journal on Communications 2011年第9 A期32卷 146-152页

作者： Yang, Yan Zhu, Jing-Hua Jin, Hu Li, Wei Department of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang University Harbin 150080 China

To improve the efficiency of keywords generation, a bipartite graph based keywords generation(BGKG) algorithm was proposed. It generated keywords based on search engine logs and built a bipartite graph between query terms and the clicked URLs. It took into account the rank of the URLs in result pages and the order of users' clicking. Experiments were done with real query logs. The results show that keywords generated by BGKG can satisfy the needs of enterprise clients and BGKG is more efficient than other keyword generation algorithms.

关键词： Search engines

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Energy-efficient algorithm for mobile target prediction and tracking in wireless sensor networks

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Tongxin Xuebao/Journal on Communications 2010年第9 A期31卷 135-143页

作者： Liu, Xu Li, Jin-Bao Guo, Long-Jiang Fan, Bin Zhu, Jing-Hua School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

Aiming at the problem of target tracking in wireless sensor networks, an energy efficient algorithm for mobile targets prediction and tracking was proposed. Sleep scheduling mechanism was used to reduce the cost, as well as to guarantee the real-time tracking. The algorithm predicts the trajectory of the target with the Markov chain theory, and the sleeping nodes in the predicted area would be waked up to monitor the targets. In order to predict the target accurately, a location algorithm based on distance vector was triggered to estimate its position. The sensors sleep initiatively to save energy when they did not have the sensing task. Simulation and testbed experimental results indicate that the algorithms can accurately describe the target trajectory, and efficiently reduce the energy consumption.

关键词： Target tracking

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Channel allocation and routing strategy in multi-radio multi-channel wireless sensor networks

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Ruan Jian Xue Bao/Journal of Software 2014年 25卷 103-112页

To address the problems of transmission delay, transmission conflicts and low throughput in Wireless sensor networks, this paper proposes a channel allocation and routing strategy in Multi-Radio Multi-Channel Networks. The strategy is to dynamically establish a k-ary n-cube topology. It not only uses the optimal static channel allocation algorithm to improve the throughput of nodes but also employs the dimension order routing algorithm to reduce transmission conflicts. This method is suitable for the network of density nodes with many communication conflicts. It can also be used in both single hop and multi hop network environment. The experiment results show that compared with traditional methods, the channel allocation and routing strategy based on the k-ary n-cube topology can effectively reduce end to end delay, network conflicts and energy consumption, prolong the network lifetime and increase the throughput of network. ©2014 ISCAS.

关键词： Throughput

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Cross-layer design for multi-power multi-radio wireless sensor networks

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2011年第SUPPL. 2期31卷 114-118页

作者： Guo, Xiao-Hang Li, Jin-Bao School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin 150080 China

In this paper, we investigate on the optimal routing jointly scheduling, channel and power assignment in multi-power multi-radio WSNs. We first formulate the optimal routing as a linear programming problem, and then design a polynomial time heuristic algorithm. Experiments show that the proposed cross-layer routing significantly reduce the energy consumption and the end-to-end transmission delay.

关键词： Wireless sensor networks

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Hybrid Recommendation Algorithm Based on Social Trust Clustering

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 21-31页

作者： Zhu, Jing-Hua Wang, Chao Ma, Sheng-Chao School of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang Province Key Laboratory for Database and Parallel Computing Harbin150080 China

Recommender system can solve the information overload problem effectively, and collaborative filtering (CF) is one of the techniques that is widely used in recommendation system. However, the traditional CF technology has problems such as poor scalability, sparse data, and low accuracy of recommendation results. In order to improve the quality of recommendations, this article integrates the trust relationship into the recommendation system in which the trust relationship is clustered by using the clustering (FCM) method. Using the trust cluster to predict implicit trust between users, the trust relationship is finally combined with the user-item relationship to give recommendations. The experimental results on the data set of Douban and Epinions show that compared with traditional CF algorithm, trust based recommendation algorithm and recommendation algorithm for user item clustering, the presented algorithm can greatly improve the recommendation quality and time efficiency. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Collaborative filtering

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An implementation of the QR iterations for finding eigenvalues of matrices with CUDA on GPU

An implementation of the QR iterations for finding eigenvalu...

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International Conference on Mechatronic Science, Electric Engineering and Computer

作者： Meirui Meihui Weiping Zhang Tao Wang Ning Tian Jinbao Li Longjiang Guo School of Computer Science and Technology Heilongjiang University Harbin 150080 China Harbin Research Institute of Electrical Instrumentation 150028 China School of Computer Science and Technology Heilongjiang University Harbin China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781479925667

Based on the principle of QR loop iterations, this paper implements a parallel algorithm based on the hardware of GPU (Graphic Process Unit) by using routines from CUDA (Computer Unified Device Architecture) to find the eigenvalues of general matrices. CPU and GPU computing card form a client-server computing framework. Here, CPU can be regarded as a client and GPU card is considered as a computing server. For the experiment environment, this paper chooses a GPU card with the model of NVIDIA GeForce GTX460 as a server side and a CPU with the model of Intel Core i5-760 quad-core as a client side. Win7 64-bit is selected as the operating system. The parallel implementation consists of two parts:PA H and PA QR. PA H is a procedure that transforms a matrix A into the Hessenberg matrix B. PA QR is the actual parallel algorithm of the QR iterations that is imposed on the Hessenberg matrix B for finding eigenvalues of the general matrix A. The speedup ratio of the proposed algorithm is jarless when the number of iterations becomes greater. The experimental results show that the parallel implementation with CUDA on GPU only makes use of less running time than traditional sequential algorithms. The speedup ratio of PA H is between 1.79 and 7.81. The speedup ratio of PA QR is between 3.24 and 118.9. Especially, when the order of general matrix equals 8192, the amount of iterations becomes 10000. The speedup ratio of the PA-H and the PA QR can run up to 7.81 and 118.9 respectively.

关键词： Graphics processing units Eigenvalues and eigenfunctions Hardware Kernel Instruction sets

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