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9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Sun, Boliang Chen, Hai You, Ziqi National University of Defense Technology Laboratory for Big Data and Decision ChangSha China National University of Defense Technology College of System Engineering ChangSha China

ISBN: (纸本)9798350330076

This paper presents a scientometric analysis of the research landscapes, hotspots, and emerging frontiers of combinatorial optimization algorithms on graphs from 1987 to 2022. The study investigates technical maturity, country/region collaboration, institutional cooperation, journal co-citation, dual graph overlay, and hotspot frontier analysis based on co-citation and keyword co-occurrence using tools like CiteSpace, Bibliometrics R, and VOSviewer. The analysis reveals that research in this field has entered a mature stage, and countries such as the United States, China, and Japan have produced rich output in this field. The University of Tokyo, MIT, the Chinese Academy of Sciences, Shanghai Jiao Tong University, Stanford University, and the University of California have formed 10 distinct collaborative clusters. The findings also suggest that research in the field of combination optimization mainly comes from two clusters, namely, computer science and mathematics, and that cross-disciplinary knowledge is also involved. In the field of graph-based combination optimization, deep learning, machine learning, graph neural networks, and reinforcement learning are now areas of intense investigation. The frontier direction of research in this field is represented by quantum computing technologies such as coherent ising machines, hybrid quantum-classical algorithms, and quantum algorithms. The research problems in this field are categorized into three main types from a keyword perspective: graph theory problems, classical optimization problems, and open problems. The corresponding methods for solving these problems are also categorized into combinatorial optimization techniques, stochastic optimization methods, and machine learning-based optimization methods. The study highlights the need for interdisciplinary collaboration, more research on quantum computing technologies, advanced optimization techniques, and the exploration of emerging research frontiers in combination o

关键词： Graph theory

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Predicting Friendship Using a Unified Probability Model 7th

Predicting Friendship Using a Unified Probability Model

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7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Kou, Zhijuan Wang, Hua Yuan, Pingpeng Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811518980

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter data set, High school Call data set) show that our model works as expected. © Springer Nature Singapore Pte Ltd 2019.

关键词： Social networking (online)

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A method for accelerating relations search over big data scenario 5

A method for accelerating relations search over big data sce...

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5th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2022

作者： Liu, Lihua Wang, Mao Xiao, Kaiming Chen, Haiwen College of System Engineering National University of Defense Technology China and Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781450398343

We propose an acceleration method for relations search over big data scenario. The relations search is in essence an induced subgraph search of graph data model, which is to search all edges with all adjacent vertices falling into a given vertex set. For example, in the risk-control scenario for anti-fraud, for a swindling gang of highly similar fraud behaviors, figuring out all relations between these swindlers can be greatly beneficial to precisely attack all similar gangs. In-memory induced subgraph search requires lots of random accesses, while in big data scenario, relations data tend to be stored on disk when the memory is limited, and the corresponding random lookups over disk incorporate huge overhead. Also, even a singleton disk access could be costly since there are usually many properties data in a relation. Hence, existing methods would suffer considerable performance bottlenecks when applied over data on disk. We build graph over disk-based edge index, and propose multiple BFS (Bread First Search) level gaps based induces subgraph acceleration method, with efficient performance guarantee when the memory is limited. We avoid imbalance in performance for query evaluation with edge index based data organization, with which we further reduce redundant I/O accesses. We filter most invalid no-result edge queries based our BFS level gaps framework. We find that, for each BFS, we can confirm the no edge status between a pair of vertices if their BFS accessing level are of distance more than 1, which indicates the nonexistence of edge. Extensive experiments over real world graph datasets confirm the effectiveness of our method. We find that more than 97% invalid edge queries are filtered by our method, which greatly improve the system performance. Hence, our method with multiple BFS level gap encoding over vertices could greatly filter no-result edge queries and improve the relation search performance. Out method also reduce the redundant I/O access over disk wi

关键词： big data

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Generative Positive-Unlabeled Classification for Hunting Small Open Reading Frames

Generative Positive-Unlabeled Classification for Hunting Sma...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance data Generation Gene Annotation Positive-Unlabeled Learning

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Space-Oblivious Compression and Wear Leveling for Non-Volatile Main Memories 36

Space-Oblivious Compression and Wear Leveling for Non-Volati...

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36th International Conference on Massive Storage systems and technology, MSST 2020

作者： Liu, Haikun Ye, Yuanyuan Liao, Xiaofei Jin, Hai Zhang, Yu Jiang, Wenbin He, Bingsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore

Emerging Non-Volatile Main Memory (NVMM) technologies generally feature high density, low energy consumption and cost per bit compared to DRAM. However, the limited write endurance of NVMM poses a significant challenge of using NVMM as an substitute for DRAM. This paper proposes a space-oblivious compression and wear-leveling based memory architecture to improve the write endurance of NVMM. As memory blocks of many applications usually contain a large amount of zero bytes and frequent values, we propose a nonuniform compression encoding scheme that integrates Zero Deduplication with Frequent Value Compression (called ZD-FVC) to reduce bit-writes on NVMM. Moreover, we leverage the memory space available through compression to achieve an intra-block wear leveling policy. It rotates the writing positions of a compressed data block within the data’s initial memory space, and thus enhances write endurance by balancing the bit-writes per NVMM cell. ZD-FVC can be integrated into the NVMM module and implemented entirely by hardware, without any intervention of Operating systems. We implement ZD-FVC in Gem5 and NVMain simulators, and evaluate it with several programs from SPEC CPU2006. Experimental results shows that ZD-FVC is much better than several state-of-the-art approaches. Particularly, ZD-FVC can improve data compression ratio by 55% compared to Frequent Value Compression. Compared with the data Comparison Write, ZD-FVC is able to significantly improve the lifetime of NVMM by 3.3X on average, and also reduces NVMM write latency by 31% and energy consumption by 19% on average. © 2020 36th International Conference on Massive Storage systems and technology, MSST 2020. All Rights Reserved.

关键词： Dynamic random access storage

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RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

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20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

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Clause-level Relationship-aware Math Word Problems Solver

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Machine Intelligence Research 2022年第5期19卷 425-438页

作者： Chang-Yang Wu Xin Lin Zhen-Ya Huang Yu Yin Jia-Yu Liu Qi Liu Gang Zhou Anhui Province Key Laboratory of Big Data Analysis and Application School of Data ScienceUniversity of Science and Technology of ChinaHefei 230026China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China Laboratory of Mathematical Engineering and Advanced Computing Information Engineering UniversityZhengzhou 450001China

Automatically solving math word problems,which involves comprehension,cognition,and reasoning,is a crucial issue in artificial intelligence *** math word problem solvers mainly work on word-level relationship extraction and the generation of expression solutions while lacking consideration of the clause-level *** this end,inspired by the theory of two levels of process in comprehension,we propose a novel clause-level relationship-aware math solver(CLRSolver)to mimic the process of human comprehension from lower level to higher ***,in the lower-level processes,we split problems into clauses according to their natural division and learn their *** the higher-level processes,following human′s multi-view understanding of clause-level relationships,we first apply a CNN-based module to learn the dependency relationships between clauses from word relevance in a local ***,we propose two novel relationship-aware mechanisms to learn dependency relationships from the clause semantics in a global ***,we enhance the representation of clauses based on the learned clause-level dependency *** expression generation,we develop a tree-based decoder to generate the mathematical *** conduct extensive experiments on two datasets,where the results demonstrate the superiority of our framework.

关键词： Artificial intelligence(AI) artificial neural network(ANN) computational mathematics machine intelligence machine learning

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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Many Objectives Autonomous Robot Path Planning with Improved MOEA/D 13

Many Objectives Autonomous Robot Path Planning with Improved...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhou, Jin Chieng, David Lee, Boon Giin Ji, Junkai Li, Jianqiang University of Nottingham Ningbo China Next-Generation Internet of Everything Laboratory Department of Electrical and Electronic Engineering Ningbo China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350308365

Path planning is the core of autonomous robot navigation, which helps the robot to find a collision-free path to the destination based on the environment information. Most current path planning methods only consider the path length, but the optimal path may deviate from the shortest when considering other environmental factors such as uneven terrain or regions with varying traversal costs. Similarly, in scenarios prioritizing energy efficiency, a sole focus on path length may lead to suboptimal solutions. In this paper, an improved Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) with adaptive weight vector, external archive, and constrained update strategy namely the MOEA/D-EAWA is proposed. This algorithm not only considers the path length but also four additional objectives such as smoothness, traveling time, terrain (elevation), and speed limit (expected delay). In addition, MOEA/D-EAWA is better suited for such many-objective path planning problem which has an irregular, discrete, and sparse Pareto front. The simulation results from 90 map instances demonstrate that the proposed method outperforms the existing approaches. © 2024 IEEE.

关键词： Robot programming

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Partitioning large-scale property graph for efficient distributed query processing 21

Partitioning large-scale property graph for efficient distri...

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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and systems, HPCC/SmartCity/DSS 2019

作者： Pang, Haohan Gan, Peng Yuan, Pingpeng Jin, Hai Hua, Qiangsheng National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab. Clusters and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

Recently, many communities have published their domain knowledge in the form of graphs. A significant trend is that the relations between pairs of nodes in the graph have become so complex that these data can be treated as property graphs. Complex properties imply semantic constraints which can distinguish elements of property graph into different semantic-related aspects. If the property graph can be partitioned according to these aspects, cross-partition communication would be reduced when evaluating SPARQL queries, for the reason that a query can often be evaluated in a single aspect. Conventional graph partition methods, however, pay little attention to semantic constraints in property graphs. To solve this problem, we propose a new property graph partition method for query optimization. First, we propose the property semantic reachable path as basic partition element, with which to capture the semantic-related aspects in a property graph. Second, to combine paths into partitions, we develop a space-efficient algorithm which merges vertices rather than entire paths. Extensive experiments over representative property graph data confirm the effectiveness of our semantic awareness approach. The performance of our approach is comparable with leading competitors, in terms of load balance, data redundancy, and network overhead. © 2019 IEEE.

关键词： Semantics

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