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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Lu Ji Rui Liu Dongsheng Zhou Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art.

关键词： Legged locomotion Deep learning Image recognition Correlation Fitting Neural networks Image restoration

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A real-time one-time pad DNA-chaos image encryption algorithm based on multiple keys

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Optics & Laser Technology 2021年 143卷

作者： Shihua Zhou Key laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China

Image encryption is an important necessity when sharing data over public networks, and many image encryption algorithms have been proposed for the task. However, most of these algorithms are complex and slow. We propose a real-time one-time pad DNA-chaos image encryption algorithm using multiple keys. The proposed encryption algorithm uses a greatly simplified mechanism and a large, unpredictable sequence to encrypt the image. Our algorithm has three main advantages. First, its reliance on the original image for four of the keys makes it sensitive to changes in the original image. Small changes in the image produce large changes in the keys, making the algorithm resistant to differential attacks. Second, we integrate the natural DNA sequence into the process of generating the key, and the algorithm uses the massive DNA sequences stored in the DNA gene bank to solve the problem that it is difficult to store one-time pad. The keys have the property of one-time pad, and the key space is considerablely large. Finally, our algorithm encrypts quickly enough to make it suitable for real-time applications. On the premise of ensuring that the results of this paper are not worse than those of the comparison literature in other performance indicators, in terms of encryption time, even if we use a computing environment that is worse than all the comparison literature computer configurations, the algorithm of this paper can still save at least 14%–86%. As the picture size becomes larger, the advantages of the algorithm in this paper are more obvious.

关键词： Image encryption Multiple keys DNA coding Chaos 00-01 99-00

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DNA logic circuits based on FokI enzyme regulation

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New Journal of Chemistry 2020年第5期44卷 1931-1941页

作者： Sue Zhao Yuan Liu Bin Wang Changjun Zhou Qiang Zhang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China School of Computer Science and Technology Dalian University of Technology Dalian 116024 China College of Computer Science and Engineering Dalian Minzu University Dalian China

In biomolecular programming, researchers have attempted to use DNA molecules as a new kind of highly reliable raw material, due to the high predictability and good programmability of DNA molecules, and construct DNA circuits that can realize logical calculation. In DNA circuits with a multi-step cascade, autonomy, and complex behavior, it is very important to be able to controllably activate and regulate toeholds. Therefore, we propose a strategy using an enzyme-assisted cleavage regulation system to solve the above problems. In this strategy, the DNA input strand regulates the recognition domain of FokI and initiates the activity of FokI due to the reconfiguration of programmable information. In addition, FokI induced conformational changes regulate the toehold-mediated strand displacement reaction, which optimizes the complex function and the signal transmission of logic circuits. In order to prove the feasibility of this strategy, we constructed a series of logic gates (YES, AND, and INHIBIT), and on this basis, we successfully implemented both a demultiplexer circuit and a multiplexer cascade circuit. The proposed enzyme-assisted cleavage regulation strategy provides a new approach for constructing more complex molecular computing systems, with applications in biosensors and medical detection systems.

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A proof-of-majority consensus protocol for blockchain-enabled collaboration infrastructure of 5G network slice brokers 2

A proof-of-majority consensus protocol for blockchain-enable...

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2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure, BSCI 2020, Co-located with AsiaCCS 2020

作者： Lin, Wenmin Xu, Xiaolong Qi, Lianyong Zhang, Xuyun Dou, Wanchun Khosravi, Mohammad R. Institute of VR and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Nanjing University of Information and Technology Nanjing China Qufu Normal University Rizhao China Department of Computing Macquarie University Sydney Australia State Key Laboratory of Novel Software Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Department of Computer Engineering Persian Gulf University Bushehr Iran

ISBN: (纸本)9781450376105

With network slicing technology, 5G changes the classical mobile network business model from a network-operator-oriented business to a more open system with multiple stakeholders. In this context, a network slice broker plays the role of agency between network resource providers and vertical service providers, to lease and provide on-demand network slice services. In practice, a single network slice broker normally owns limited types of network slices. And a collaboration infrastructure could be built up to enable network slice sharing among multiple network slice brokers, so as to improve user experience, and enhance the resource utilization rate. However, a major challenge with such an infrastructure is how to support fair and secure slice trading process among multiple un-trusted network slice providers. Blockchain technology, on the other side, brings in high opportunities to implement a fair and secure trading system without a trusted third-party. In view of this observation, this paper proposes a design of blockchain-enabled collaboration infrastructure for network slice brokers, where a group of registered brokers can trade their network slices in a fair and secure manner. To enhance the accountability of slice trading records, transactions are verified and sorted by each broker node, which are further packed into blocks and appended to a shared ledger maintained by each member in the collaboration network. More specifically, we design a Proof-of-Majority (PoM) consensus protocol to sort slice trading transactions before packing transaction blocks of the shared ledger. Furthermore, an optimization for the basic PoM protocol is discussed, with dependency resolving strategy to improve system performance. Experiments are conducted to verify the feasibility of our proposal. © 2020 ACM.

关键词： 5G mobile communication systems

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An adaptive clustering algorithm of gaussian curve fitting the histogram based on K-means

An adaptive clustering algorithm of gaussian curve fitting t...

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2019 IEEE International Conferences on Ubiquitous computing and Communications and Data Science and Computational Intelligence and Smart computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Ma, Guangkun Yang, Jinzhu Song, Xiaoxu Zhao, Hong School of Computer Science and Engineering Northeastern University Shenyang China School of Software Shenyang University of Technology Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781728152097

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation. © 2019 IEEE.

关键词： K-means clustering

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao Xiaojie Zhang Xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms Data models Task analysis Business

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Achieving handle-level random access in an encrypted DNA archival storage system via frequency dictionary mapping coding

Patterns

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Patterns 2025年

作者： Ben Cao Xue Li Bin Wang Tiantian He Yanfen Zheng Xiaokang Zhang Qiang Zhang School of Computer Science and Technology Dalian University of Technology Dalian 116024 China School of Computer Science and Technology China University of Petroleum Qingdao 266580 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China School of Computer Science and Engineering Nanyang Technological University Singapore 639798 Singapore Centre for Frontier AI Research Agency for Science Technology and Research (A∗STAR) Singapore 138632 Singapore

DNA storage provides a high storage density and durability, but current systems experience high latency and lack data security. This study proposed the use of frequency dictionary mapping coding (FDMC) to enable handle-level random access of DNA archival storage. In addition, a hybrid e-molecular encryption strategy and a multi-level error-correction algorithm were introduced to ensure data security and integrity. The simulation and wet experiment results demonstrated that FDMC achieved handle-level random access in a lossless encrypted DNA storage system, which balanced security and robustness. Even in extreme cases, in which there was a 10% loss of DNA sequences, this system still recovered 91.74% of the original data while ensuring a storage density above 1.80 bits per nucleotide. In summary, FDMC enhances the application potential of DNA as a storage medium and bridges the gap between DNA storage and traditional storage modes.

关键词： DNA storage random access frequency dictionary mapping coding data security

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ON THE OPPORTUNITIES OF GREEN computing: A SURVEY

arXiv

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arXiv 2023年

作者： Zhou, You Lin, Xiujing Zhang, Xiang Wang, Maolin Jiang, Gangwei Lu, Huakang Wu, Yupeng Zhang, Kai Yang, Zhe Wang, Kehang Sui, Yongduo Jia, Fengwei Tang, Zuoli Zhao, Yao Zhang, Hongxuan Yang, Tiannuo Chen, Weibo Mao, Yunong Li, Yi Bao, De Li, Yu Liao, Hongrui Liu, Ting Liu, Jingwen Guo, Jinchi Zhao, Xiangyu Wei, Ying Qian, Hong Liu, Qi Wang, Xiang Chan, Wai Kin Li, Chenliang Li, Yusen Yang, Shiyu Yan, Jining Mou, Chao Han, Shuai Jin, Wuxia Zhang, Guannan Zeng, Xiaodong Tsinghua University China Shanghai Jiaotong University China University of Science and Technology of China China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Huazhong University of Science and Technology China Shanghai Institute of AI for Education School of Computer Science and Technology East China Normal University China Xi’an Jiaotong University China College of Computer Science Nankai University China School of Computer Science China University of Geosciences China School of Information Science and Technology Beijing Forestry University China Engineering Research Center for Forestry-Oriented Intelligent Information Processing National Forestry and Grassland Administration China Guangzhou University China Nanyang Technological University Singapore City University of Hong Kong Hong Kong Wuhan University China State Key Laboratory for Novel Software Technology Nanjing University China Ant Group China

Artificial Intelligence (AI) has achieved significant advancements in technology and research with the development over several decades, and is widely used in many areas including computing vision, natural language processing, time-series analysis, speech synthesis, etc. During the age of deep learning, especially with the arise of Large Language Models, a large majority of researchers’ attention is paid on pursuing new state-of-the-art (SOTA) results, resulting in ever increasing of model size and computational complexity. The needs for high computing power brings higher carbon emission and undermines research fairness by preventing small or medium-sized research institutions and companies with limited funding in participating in research. To tackle the challenges of computing resources and environmental impact of AI, Green computing has become a hot research topic. In this survey, we give a systematic overview of the technologies used in Green computing. We propose the framework of Green computing and devide it into four key components: (1) Measures of Greenness, (2) Energy-Efficient AI, (3) Energy-Efficient computing Systems and (4) AI Use Cases for Sustainability. For each components, we discuss the research progress made and the commonly used techniques to optimize the AI efficiency. We conclude that this new research direction has the potential to address the conflicts between resource constraints and AI development. We encourage more researchers to put attention on this direction and make AI more environmental friendly. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon footprint

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Knowledge-routed visual question reasoning: Challenges for deep representation embedding

arXiv

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arXiv 2020年

作者： Cao, Qingxing Li, Bailin Liang, Xiaodan Wang, Keze Lin, Liang School of Intelligent Systems Engineering Sun Yat-sen University China DarkMatter AI Research China Department of statistics University of California Los AngelesCA90095 United States School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Engineering Research Center for Advanced Computing Engineering Software of Ministry of Education China

Though beneficial for encouraging the Visual Question Answering (VQA) models to discover the underlying knowledge by exploiting the input-output correlation beyond image and text contexts, the existing knowledge VQA datasets are mostly annotated in a crowdsource way, e.g., collecting questions and external reasons from different users via the internet. In addition to the challenge of knowledge reasoning, how to deal with the annotator bias also remains unsolved, which often leads to superficial over-fitted correlations between questions and answers. To address this issue, we propose a novel dataset named Knowledge-Routed Visual Question Reasoning for VQA model evaluation. Considering that a desirable VQA model should correctly perceive the image context, understand the question, and incorporate its learned knowledge, our proposed dataset aims to cutoff the shortcut learning exploited by the current deep embedding models and push the research boundary of the knowledge-based visual question reasoning. Specifically, we generate the question-answer pair based on both the Visual Genome scene graph and an external knowledge base with controlled programs to disentangle the knowledge from other biases. The programs can select one or two triplets from the scene graph or knowledge base to push multi-step reasoning, avoid answer ambiguity, and balanced the answer distribution. In contrast to the existing VQA datasets, we further imply the following two major constraints on the programs to incorporate knowledge reasoning: i) multiple knowledge triplets can be related to the question, but only one knowledge relates to the image object. This can enforce the VQA model to correctly perceive the image instead of guessing the knowledge based on the given question solely;ii) all questions are based on different knowledge, but the candidate answers are the same for both the training and test sets. We make the testing knowledge unused during training to evaluate whether a model can unde

关键词： Knowledge based systems

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DNA circuits driven by conformational changes in DNAzyme recognition arms

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RSC advances 2020年第13期10卷 7956-7966页

作者： Xinyi Sun Xuedong Zheng Sue Zhao Yuan Liu Bin Wang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian 116622 China wangbinpaper@***. College of Computer Science Shenyang Aerospace University Shenyang 110136 China xuedongzheng@***. School of Computer Scicence and Technology Dalian University of Technology Dalian 116024 China Liuyuan.dlut@***.

DNA computing plays an important role in nanotechnology due to the unique programmability and parallelism of DNA molecules. As an important tool to realize DNA computation, various logic computing devices have great application potential. The application of DNAzyme makes the achievements in the field of logical computing more diverse. In order to improve the efficiency of the logical units run by DNAzyme, we proposed a strategy to regulate the DNA circuit by the conformational change of the E6-type DNAzyme recognition arms driven by Mg. This strategy changes the single mode of DNAzyme signal transmission, extends the functions of E6-type DNAzyme, and saves the time of signal transmission in the molecular scale. To verify the feasibility of this strategy, first, we constructed DNA logic gates (YES, OR, and AND). Second, we cascade different logic gates (YES-YES, YES-AND) to prove the scalability. Finally, a self-catalytic DNA circuit is established. Through the experimental results, we verified that this DNAzyme regulation strategy relatively reduces the cost of logic circuits to some extent and significantly increases the reaction rate, and can also be used to indicate the range of Mg concentrations. This research strategy provides new thinking for logical computing and explores new directions for detection and biosensors.

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