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IEEE International Symposium on Quality Electronic Design

作者： Zhengyang Chen Yuhan Zhu Zhen Chen Zhisheng Chen Genggeng Liu College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Fuzhou China School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350309270

ISBN: (纸本)9798350309287

In recent years, microfluidic biochips have been widely applied in various fields of human society. The emergence of distributed channel-storage architecture enables fluid to be directly cached within the flow channels without being transported to the dedicated storage. However, the volume of the flow channels is limited, it is important to carefully consider the fluid volume to be cached to ensure it can be accommodated within the appropriate flow channels. In this paper, we propose a high-level synthesis method considering actual volume management and channel storage through integer linear programming, providing a rational analysis of fluid caching and the temporal relationships between different fluid-handling tasks. Experimental results conducted on multiple benchmark tests demonstrate the effectiveness of the proposed method in reducing the completion time of bioassay, the number of caches, the maximum fluid caching volume, and the number of fluid-handling tasks.

关键词： Fluids Biochips Estimation Cells (biology) Integer linear programming Benchmark testing Task analysis

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Depth map optimization based on adaptive chrominance image segmentation

Depth map optimization based on adaptive chrominance image s...

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International Conference on Communication Software and networks, ICCSN

作者： Li Zhou Tao Sun Zhong Lv Sufen Wei School of Information Science and Engineering Shandong University Jinan Shandong China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Ji''nan Jinan Shandong China Information Engineering College Jimei University Xiamen Fujian China

The development of Free Viewpoint Video (FVV) technique is the most promising multimedia processing fields in recent years. Depth map prediction is the key method in FVV. There are many methods to estimate depth map. Among them, combined temporal and interview prediction method is the most practical which can save much data processing bandwidth, without additional hardware resources and computation time consumption. But block estimation based depth map has much block effect and prediction noises, especially at the object boundary and large continuous areas. This paper presents an adaptive chrominance image segmentation method to process the depth map prediction noises getting by combined temporal and interview prediction method. Experiment results show that this method can enhance the depth map quality efficiently with less calculation complexity, and is practical for hardware circuit realization and high performance real time processing.

关键词： Image segmentation

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A multimedia SoC performance evaluation method based on memory access model

A multimedia SoC performance evaluation method based on memo...

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Congress on Image and Signal processing, CISP

作者： Li Zhou Sufen Wei Tao Sun Qi Guo School of Information Science and Engineering Shandong University Jinan Shandong China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Ji''nan Jinan Shandong China Information Engineering College Jimei University Xiamen Fujian China

More and more complex application cases bring much heavy bandwidth loading problem to SoC architecture. To design a high performance SoC system which supports large amount and high speed data processing capability, an efficient, systematic, and quick performance evaluation of SoC architecture becomes the base requirement for chip success. A novel performance evaluation method based on memory access model is presented in this paper. It can give out accurate and confident estimation result to aid both system level and module level design. It can be used to establish SoC architecture, find the performance bottleneck at the very beginning design step of chip architecture determination, and give out comparing result among different architecture definition. Both design effort and design iteration cycles could be saved.

关键词： System-on-a-chip Clocks Performance evaluation Computer architecture Delay Bandwidth Codecs

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Encoding-Aware Data Placement for Efficient Degraded Reads in XOR-Coded Storage Systems

Encoding-Aware Data Placement for Efficient Degraded Reads i...

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Reliable Distributed Systems

作者： Zhirong Shen Patrick P.C. Lee Jiwu Shu Wenzhong Guo College of Mathematics and Computer Science Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Department of Computer Science and Engineering The Chinese University of Hong Kong Department of Computer Science and Technology Tsinghua University Key Laboratory of Spatial Data Mining and Information Sharing Minstry of Education Fuzhou China

ISBN: (纸本)9781509035144

Erasure coding has been increasingly used by distributed storage systems to maintain fault tolerance with low storage redundancy. However, how to enhance the performance of degraded reads in erasure-coded storage has been a critical issue. We revisit this problem from two different perspectives that are neglected by existing studies: data placement and encoding rules. To this end, we propose an encoding-aware data placement (EDP) approach that aims to reduce the number of I/Os in degraded reads during a single failure for general XOR-based erasure codes. EDP carefully places sequential data based on the encoding rules of the given erasure code. Trace-driven evaluation results show that compared to two baseline data placement methods, EDP reduces up to 37.4% of read data on the most loaded disk and shortens up to 15.4% of read time.

关键词： Encoding Distributed databases Layout Fault tolerant systems Redundancy Computer science

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Color PNG Image Authentication Scheme Based on Rehashing and Secret Sharing Method

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Journal of information Hiding and Multimedia Signal processing 2015年第3期6卷 523-533页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou Fujian350108 China

An authentication scheme that uses rehashing and secret sharing methods to verify the reality of a color image’s content is proposed in this paper. First, the proposed scheme uses Shamir’s secret sharing method to share image features. Second, the proposed scheme innovatively utilizes the rehashing method to overcome easy collisions of the random numbers to enhance the effectiveness against attacks. Third, the proposed method utilizes Portable network Graphics images to hide authentication messages. Using the rehashing method, the collisions take up little in address space, making the process suitable for processing authentication messages. The experimental results indicated that the proposed scheme had very low false acceptance and rejection ratios and verified that the rehashing method can be used in image authentication applications. © 2009 ISSN 2073-4212.

关键词： Authentication

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Learnable Graph Convolutional network and Feature Fusion for Multi-view Learning

arXiv

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arXiv 2022年

作者： Chen, Zhaoliang Fu, Lele Yao, Jie Guo, Wenzhong Plant, Claudia Wang, Shiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Faculty of Computer Science University of Vienna Vienna1090 Austria Research Network Data Science @ Uni Vienna University of Vienna Vienna1090 Austria

In practical applications, multi-view data depicting objectives from assorted perspectives can facilitate the accuracy increase of learning algorithms. However, given multi-view data, there is limited work for learning discriminative node relationships and graph information simultaneously via graph convolutional network that has drawn the attention from considerable researchers in recent years. Most of existing methods only consider the weighted sum of adjacency matrices, yet a joint neural network of both feature and graph fusion is still under-explored. To cope with these issues, this paper proposes a joint deep learning framework called Learnable Graph Convolutional network and Feature Fusion (LGCN-FF), consisting of two stages: feature fusion network and learnable graph convolutional network. The former aims to learn an underlying feature representation from heterogeneous views, while the latter explores a more discriminative graph fusion via learnable weights and a parametric activation function dubbed Differentiable Shrinkage Activation (DSA) function. The proposed LGCN-FF is validated to be superior to various state-of-the-art methods in multi-view semi-supervised classification. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Diagnosis of Alzheimer's disease based on logistic regression algorithm

Diagnosis of Alzheimer's disease based on logistic regressio...

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computing, Robotics and System Sciences (ICRSS), International Conference on

作者： YuSi Chen Ning Liu PeiKun Chen School of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Quanzhou China School of Public Health Hangzhou Normal University Hangzhou China Department of mathematics and computer science Quanzhou Normal University Fujian Provincial Key Laboratory of Data-Intensive Computing Quanzhou China Xiamen Silicon Field System Engineering Co. Ltd. Xiamen China

Language impairment may be an important early manifestation of dementia. In this study, we propose a multi-feature fusion to distinguish MCI and AD patients. In this paper, a set of acoustic, demographic and linguistic features were selected, and after data processing, logistic regression algorithm was used to diagnose MCI and AD patients. In order to get better results, comparative experiments are also effectively conducted, including different combination methods and different group distinctions with the three most popular algorithms. Studies have shown that this method is more advantageous and the classification results are more accurate.

关键词： Fuses Linguistics Feature extraction Data processing Acoustics Classification algorithms Data mining

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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A Genetic Algorithm Based Piecewise Linear Representation of Time Series

A Genetic Algorithm Based Piecewise Linear Representation of...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Xiyang Yang Changxin Zhai Fang Li Longshu Liu Youhua Zhang Key laboratory of Intelligent Computing and Information Processing of Fujian Province Quanzhou Normal University Quanzhou China School of Mathematics and Computer Science Quanzhou Normal University Quanzhou China School of Mathematical Science Beijing Normal University Beijing China

ISBN: (数字)9781728123486

ISBN: (纸本)9781728123493

Line Segment Representation (LSR) refers to represents a time series by a few of line segments, such that the original time series and the piecewise line segment series have shapes as similar as possible. Because of its simple expression, LSR based time series are often easier to be understood and computed for some time series datamining tasks than the original raw data. Two kinds of continuous LSR methods, namely, 1 1 trend filtering and mix-integer programming (MILP) method, are discussed in this paper. To overcome the poor representation ability of l 1 trend filtering, and the high computational complexity of MILP, this paper proposes a hybrid method combining GA and linear programming (GA-LP) to find the optimal LSR time series efficiently. In GA-LP, locations of the breakpoints of the piecewise linear segment are fixed by GA, and values on these locations are fixed by a LP method. Numerical experiments reveal that GA-LP can reduce representation error by comparisons with l 1 trend filtering and MILP method, and its computing time is much less than that of MILP.

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Decoupling Learning and Remembering: a Bilevel Memory Framework with Knowledge Projection for Task-Incremental Learning

Decoupling Learning and Remembering: a Bilevel Memory Framew...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenju Sun Qingyong Li Jing Zhang Wen Wang Yangli-Ao Geng Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University

The dilemma between plasticity and stability arises as a common challenge for incremental learning. In contrast, the human memory system is able to remedy this dilemma owing to its multilevel memory structure, which motivates us to propose a Bilevel Memory system with Knowledge Projection (BMKP) for incremental learning. BMKP decouples the functions of learning and remembering via a bilevel-memory design: a working memory responsible for adaptively model learning, to ensure plasticity; a long-term memory in charge of enduringly storing the knowledge incorporated within the learned model, to guarantee stability. However, an emerging issue is how to extract the learned knowledge from the working memory and assimilate it into the long-term memory. To approach this issue, we reveal that the parameters learned by the working memory are actually residing in a redundant high-dimensional space, and the knowledge incorporated in the model can have a quite compact representation under a group of pattern basis shared by all incremental learning tasks. Therefore, we propose a knowledge projection process to adaptively maintain the shared basis, with which the loosely organized model knowledge of working memory is projected into the compact representation to be remembered in the long-term memory. We evaluate BMKP on CIFAR-10, CIFAR-100, and Tiny-ImageNet. The experimental results show that BMKP achieves state-of-the-art performance with lower memory usage 1 1 The code is available at https://***/SunWenJu123/BMKP.

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