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IET International Conference on Wireless Sensor Network 2010, IET-WSN 2010

作者： Li, Jinbao Zhang, Desheng Guo, Longjiang School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin Heilongjiang 150080 China

ISBN: (纸本)9781849192392

Limitations of current multi-channel MAC protocols include requiring of strict time synchronization, existing large number of multi-channel hidden terminals, and lacking single-hop broadcast support and sleep mechanism, i.e. duty cycling. This paper proposes an asynchronous duty cycle based multichannel MAC protocol, called DCM, for wireless sensor networks. Instead of requiring time synchronization among nodes, DCM takes advantage of receiver-initiated cross channel handshake mechanism to make a channel reservation for data transmission, and employs receiver adjustment mechanism to tackle the problem of the loss of broadcasting data and to improve broadcast reliability. According to theoretical analysis using Queueing theory, this paper obtains the average number of available channels. The simulation and testbed experimental results show that DCM reduces the number of multi-channel hidden terminals effectively, and achieves significant improvement in network throughput with increasing benefit as traffic loads increase while maintaining higher energy efficiency.

关键词： Wireless sensor networks

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PSHCAR: A position-irrelevant scene-aware human complex activities recognizing algorithm on mobile phones 1

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

作者： Jia, Boxuan Li, Jinbao Xu, Hui School of Computer Science and Technology Heilongjiang University HarbinHeilongjiang150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province HarbinHeilongjiang150080 China

ISBN: (数字)9789811322037

ISBN: (纸本)9789811322020

Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human complex activities using mobile phones in recent years. However, previous works often restrain the position and orientation of cell phones which limit the applicability of their methods. To overcome this limitation, we propose a novel position-irrelevant activities identification method named PSHCAR, which efficiently utilize information from multiple sensors on smartphones. Moreover, besides commonly-used features such as accelerometer and gyroscope, PSHCAR also employ the knowledge about scenes of activities, which is helpful but ignored by previous works, to identify complex activities of mobile phone users. Comparative experiments show that our method performs better than several strong baselines on the task of human complex activities recognition. In conclusion, our method achieves state-of-the-art performance without any limitation on position or orientation of mobile phones. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Ubiquitous computing

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M&M: A multi-channel MAC protocol with multiple channel reservation for wireless sensor networks

M&M: A multi-channel MAC protocol with multiple channel rese...

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International Conference on Cyber-Enabled Distributed computing and Knowledge Discovery

作者： Li, Jinbao Zhang, Desheng School of Computer Science and Technology Heilongjiang University Harbin Heilongjiang 150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin Heilongjiang 150080 China

ISBN: (纸本)9780769542355

In this paper, a multi-channel MAC protocol, called M&M, with multiple channel reservation is proposed to tackle the channel conflict problem. M&M is fully distributed with no requirements of time synchronization or multi-radio, so it is very practical to implement M&M in resource-constrained sensor nodes. M&M can make nodes to choose one actually idle channel for communications from all assumed idle channels. Therefore, M&M can greatly alleviate, if not completely eliminate, packet collisions resulted by the channel conflict, and thus conserve more energy to prolong the lifetime of WSNs. To investigate the values of multiple channel reservation, extensive simulation and real testbed experiments are conducted. The results show that when the number of channels becomes larger or loads are heavy, M&M improves energy efficiency and throughput significantly. © 2010 IEEE.

关键词： Sensor nodes

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Double Loss Block Neural Architecture Search 10

Double Loss Block Neural Architecture Search

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10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022

作者： Liu, Yang Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

Neural architecture search aims to automatically design network architectures by machines, which is expected to bring about a new revolution in machine learning. Despite high expectations, the effectiveness and efficiency of existing neural architecture search solutions are unclear, and the inefficiency of neural architecture search solutions is mainly attributed to inaccurate architecture evaluation. In this work, this paper divides the large search space of neural architecture search into multiple modules, effectively reducing the error caused by shared parameters to ensure that the candidate architectures in the modules are fully trained. By adopting a block search method, this paper can Evaluate all candidate architectures in a module, addressing the problem of inaccurate evaluation. In addition, high-quality data is necessary. Under the guidance of the teacher model, combined with the ground truth, the neural architecture search is supervised by the double loss function. Under the constraint of the same amount of computation, the method in this paper achieves 77.3% top-level accuracy on ImageNet., which is 1.0% higher than the efficient network EfficientNet-B0, and 0.3% higher than the DNA method without additional computation. Therefore, the double loss block neural architecture search model proposed in this paper significantly improves the effectiveness of neural architecture search. © 2022 IEEE.

关键词： Distillation

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Research on combinatorial encryption method

Research on combinatorial encryption method

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2014 International Conference on Information Science, Electronics and Electrical Engineering, ISEEE 2014

作者： Lu, Jun Mo, Juan Lv, Xingfeng Zhang, Zhuo College of Computer Science and Technology Heilongjiang University College of CS HLJU Harbin China Key Laboratory of Database and Parallel Computing Heilongjiang Province KLDPC HLJU Harbin China

ISBN: (纸本)9781479931965

Combinatorial encryption algorithm is advanced. Combinatorial encryption method makes use of the variety of ordinal in universal combination code. Firstly, the theory of universal combinatorial coding is expatiated. Secondly, researches are done in combinatorial encryption characteristic, key generating method, encryption and decryption algorithms. At last, key space is analyzed. Combinatorial encryption technology breaks through the thought of encryption. It is based on combinatorial theory and the code element is adopted as the basic unit in encryption computing. Furthermore, the key generating method is unique. Combinatorial encryption technology makes a new attempt to cipher system. © 2014 IEEE.

关键词： Cryptography

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The parallel processing for promoter data base on OpenMP

The parallel processing for promoter data base on OpenMP

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International Conference on Electronic Engineering and Information Science, ICEEIS 2015

作者： Shi, Y. Lu, J. Zheng, J.J. College of Computer Science and Technology Heilongjiang University China College of Computer Science and Technology Heilongjiang University Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin China

ISBN: (纸本)9781138027725

The promoters of Arabidopsis and the Poplar are parallel processed and analyzed in this paper. The mainly works include three parts: First, The Motif data are parallel matched with both plant promoter data based on OpenMP. Then, the matching result is processed by frequent mining technology. The experimental result shows that the parallel processing time based on OpenMP is far less than the serial time. Second, to increase the calculation accuracy, the algorithm of solving the P value is improved by the prime split method. At last, according to the frequent process result, the shared frequent items of the two plants are analyzed. © 2015 Taylor & Francis Group, London.

关键词： Application programming interfaces (API)

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Cross-Modality Encoder Representations Based On External Attention Mechanism 3

Cross-Modality Encoder Representations Based On External Att...

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3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

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

ISBN: (纸本)9798350335972

With the prevalence of deep learning, people use multi-modality information for interpretation and reasoning. In this paper, a cross-modality encoder CMEEA (cross-modality encoder representation based on external attention mechanism) is adopted to improve the accuracy of the model. The two external storage units of CMEEA can be regarded as a dictionary of the whole dataset to improve the performance of the network, while learning more representative features of the input and reducing the computational cost. This paper uses five pretraining tasks that help the model learn internal modal and cross-modal relationships. It also verifies the generalizability of the model by applying the pretraining cross-modal model to the visual inference task NLVR2, and improves the previous results by 0.1%. By fine-tuning the pretrain parameters, the model improves by 1.3% on visual question answering (VQA). © 2023 IEEE.

关键词： Deep learning

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Session-Recommendation Based on Gated Neural Network to Extract Item Structure Feature Graph 22

Session-Recommendation Based on Gated Neural Network to Extr...

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5th International Conference on Data Storage and Data Engineering, DSDE 2022

作者： Zhang, Wei Yang, Yan Zhong, Yingli College of Computer Science and Technology Heilongjiang University China Heilongjiang Key Laboratory of Database and Parallel Computing Heilongjiang University China School of Computer Science and Technology Heilongjiang University China

ISBN: (纸本)9781450395724

The issue of inaccurate user portrait recommendations for short-Term conversations is solved. The classical recommendation is based on an assumption: The user's historical behavior can represent the user's stable long-Term preferences, and the result is generated based on the user's historical behavior. However, there is sparseness in the user's historical behavior information record in the actual data set, and the obtained data cannot accurately represent the user's preferences. Research on a session recommendation focuses on user behavior data in the short term, and aims at predict anonymous user behavior based on in-session behavior information. It plays a vital role in application scenarios where user behavior information is scarce. Feature extraction is performed on the user's anonymous behavior information graph generated in a fixed period of time as the embedding representation of user's interest preference;the graph structure of the same item in the global click interaction graph is combined with the input a gated graph neural network as the item attribute embedding representation. The prediction part adopts simulated interactive calculation to produce results. With the help of gated graph neural network, features extraction and analysis of data in short-Term sessions: session-based user behavior data and in-session item attributes are obtained from the graph data structure, which reduces errors caused by inconsistent data processing methods. The user's current interest is captured based on the behavior information in the session, the global inter-item conversion graph is generated based on the sequence of click moments, and the feature vector is obtained through graph neural network processing. The generation of recommendation results is more time-sensitive, complete the anonymity problem due to the scarcity of user's information, and improves the accuracy of recommendations. © 2022 ACM.

关键词： Graph neural networks

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Minimum latency data aggregation scheduling in MR-MC wireless sensor networks

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Tongxin Xuebao/Journal on Communications 2014年第10期35卷 192-199页

作者： Li, Jin-Bao Wang, Meng Guo, Long-Jiang School of Computer Science and Technology Heilongjiang University Harbin150080 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

The problem of minimum latency data aggregation scheduling in single-radio single-channel wireless sensor networks is a NPC problem and many solutions have been proposed. In multi-radio multi-channel networks, the latency may be lower as a node may receive transmission data from other different nodes simultaneously. It formulates the minimum latency data aggregation scheduling into an optimization problem with joint consider of tree structure, time slot assignment, channel assignment and radio assignment. The problem is divided into constructing data aggregation tree and designing a node schedule subproblems. For each subproblem, heuristic algorithm has been proposed. The simulation results show that the proposed algorithm achieve good performance.

关键词： Scheduling

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Density and distance based probabilistic broadcasting algorithm in mobile sensor networks

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2014年第1期51卷 151-160页

作者： Shen, Yue Guo, Longjiang Li, Jinbao School of Computer Science and Technology Heilongjiang University Harbin 150080 China Key Laboratory of Database and Parallel Computing Heilongjiang Province Harbin 150001 China

Broadcasting is the most basic manner to transmit messages in mobile wireless sensor networks. However, existing relevant broadcasting algorithms in mobile wireless sensor networks need a large number of intermediate forwarding nodes. The large number of intermediate forwarding nodes causes a mass of redundant message packages. Massive redundant messages will consume much energy and lead to a short life period of mobile wireless sensor networks. This paper proposes a broadcasting algorithm named node density and distance-based probability (NDDP for short). The average forwarding ratio of the algorithm is only 5S/(Nπr2), where S is the area of the networks, N is the amount of nodes in the networks, and r is communication range. Then the average receipt ratio of NDDP is more than 95 percent in theoretical analysis and more than 92 percent in the ns-2 simulation results. Denser the network is, the more energy saving the algorithm is. Simulation results also show that NDDP outperforms the two algorithms in Smite and Sidewinder not only on the aspect of stability but also on energy conservation.

关键词： Energy conservation

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