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5th International Conference on Telecommunications and Communication engineering, ICTCE 2022

作者： Fang, Jie Li, Wenzao Wang, Yue Sun, Xiulan College of Communication Engineering Chengdu University of Information Technology China Network and Data Security Key Lab of Sichuan Pro. University of Electronic Science and Technology of China China Institute of Data Application Research Educational Informalization and Big Data Center China

ISBN: (纸本)9781450397797

In environmental monitor architecture, the usual way of processing data is centralized. On the one hand, this leads to a long time of data processing, and with the increasing number of monitoring sensors, the pressure of data processing in data centers increases sharply. The edge computing provides a feasible solution to relieve the processing pressure and decrease the delay of data processing in data center. However, in the environmental monitor system, the edge servers are often load unbalance because of the nonuniform distribution of sensors. While, it is usually ignored in recently researches. In this paper, we proposed a cooperative offloading strategy (MECOGA) based on genetic algorithm (GA). It aims to ease the data processing pressure and reduce the data processing time in the data center. In addition, the load balance of edge servers is also one of our optimization goals. In this paper, we aim at decreasing task processing delay and improving the system load balancing. Contrast to other algorithms, our MECOGA decreases the delay near 20% before collaborative loading. And the balance variance is 15% smaller than the Balance strategy. © 2022 Association for Computing Machinery.

关键词： Genetic algorithms

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction 30

A Semantic Mention Graph Augmented Model for Document-Level ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Probe Machine Based Computing Model for Solving Satisfiability Problem 14th

Probe Machine Based Computing Model for Solving Satisfiabili...

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Cui, Jianzhong Yin, Zhixiang Yang, Jing Geng, Xianya Zhang, Qiang School of Electronic and Information Engineering Anhui University of Science and Technology HuainanAnhui232001 China School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China School of Mathematics and Big Data Anhui University of Science and Technology HuainanAnhui232001 China Department of Computer Huainan Union University HuainanAnhui232038 China School of Computer Science Dalian University of Technology DalianLiaoning116024 China

ISBN: (纸本)9789811534140

Probe Machine (PM) is a recently reported theoretical model with massive parallelism. Particularly, Turing Machine (TM) had been proven to be the special case of PM. The paper proposed a PM based computing model for satisfiability problem. PM is capable of searching pairs of data fiber that are complementary to pre-devised probes, leading to the connection of pairs of data in parallel. We encoded the assignment to variables in the given 3-SAT formula into data fibers and devised probes between pairs of data fiber, the unique satisfying assignment of a hard 3-SAT formula could be generated within just one step of probe operation. More generally, for an arbitrary 3-SAT formula with variables and clauses, we presented a method for deciding the satisfiability using the concept of potential probe. Complexity analysis shows the encoding complexity and time complexity of the proposed model are o(n) and o(1), respectively. The distinguishing characteristics of the proposed model lie in two aspects. On one hand, solution to NP-complete problem was generated in just one step of probe operation rather than found in vast solution space. On the other hand, the proposed model is highly parallel. Most important of all, the parallelism increases with problem size. This marks a giant step in computational theory. With the parallel search capability inherited in PM, the size of NP-complete search problems is expected to be increased further. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Probes

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Open domain question answering with character-level deep learning models

Open domain question answering with character-level deep lea...

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作者： Lei, Kai Deng, Yang Zhang, Bing Shen, Ying Institute of Big Data Technologies Shenzhen Key Lab for Cloud Computing Technology and Applications School of Electronic and Computer Engineering Peking University Shenzhen518055 China

ISBN: (纸本)9781538636749

Single-relation factoid question answering (QA) is strongly supported by rich sources of facts from knowledge bases (KB). However, there are many irrelevant information in questions and overwhelming number of facts in knowledge bases, making it difficult to capture goal entity and relation involved in a question. In order to settle these issues, firstly, a state-of-the-art sequence tagging model (BiLSTM-CRF) is adopted to detect the entity mention in a question. Then, we propose a n-gram match (NGM) algorithm with Chinese-specific rules and an attention-based siamese bidirectional long-short term memory (ASBLSTM) model to measure the lexical and semantic similarity between questions and candidate facts. Our whole method requires no hand-crafted template or feature engineering. In addition, character-level models are proved to be effective in solving the out of vocabulary (OOV) issue and improving the accuracy in Chinese KBQA task. Experiment results show that our system outperforms the best system with deep learning models in the KBQA share task of the Conference on Natural Language Processing and Chinese Computing (NLPCC) 2016 and our system achieves an AverageF1 measure of 80.97% and 37.18% on test dataset in NLPCC 2016 and 2017 respectively. © 2017 IEEE.

关键词： Entity extraction Entity linking Knowledge base question answering Relation recognition

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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Optimization of human body attitude detection based on mask RCNN 8

Optimization of human body attitude detection based on mask ...

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8th International Conference on Orange technology, ICOT 2020

作者： Yin, Zhenhua Wang, Xiaogang Li, Lingli Anhui Institute of Information Engineering Computer and Software Engineering Department Wuhu China Changzhou College of Information Technology Software and Big Data Department Changzhou China Beijing Huasheng Jingshi Information Technology Co. Ltd Computer and Software Engineering Department Beijing China

ISBN: (纸本)9781665418522

Mask RCNN has the advantage of higher accuracy than other human body attitude detection models, and has been widely used for multi-person human body attitude detection. However, Mask RCNN has a low detection speed. Therefore, we propose a MobileNet-based Mask RCNN optimization model to accelerate the inference calculation speed of Mask RCNN. At the same time, in order to further improve the detection accuracy of Mask RCNN, we propose a method by using pixel segmentation results to assist the detection of key points in the human body, which improves the speed of reasoning and reduces the rate of false detection to some extent. © 2020 IEEE.

关键词： Pixels

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Character-Level Street View Text Spotting Based on Deep Multisegmentation Network for Smarter Autonomous Driving

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 297-308页

作者： Zhang, Chongsheng Tao, Yuefeng Du, Kai Ding, Weiping Wang, Bin Liu, Ji Wang, Wei Henan Provincial Key Laboratory of Big Data Analysis and Processing Laboratory of the Yellow River Cultural Heritage Henan University Kaifeng475001 China School of Information Science and Technology Nantong University Nantong226019 China School of Electrical Engineering Zhejiang University Hangzhou310058 China Big Data Lab Baidu Inc. Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China

Urban scenes are full of street entities with sign boards. Therefore, in autonomous driving, street view text spotting techniques will play a significant role in the precise understanding of surrounding scenes during driving, because texts contained in the images usually provide important clues for accurate image understanding, while it is often ambiguous for existing computer vision algorithms to understand scene images without texts. In this work, we propose a Multi-Segmentation network for character-level scene Text Detection (MSTD). The MSTD introduces a densely connected atrous spatial pyramid pooling module to enlarge the receptive field of the feature extraction layer, so as to localize long as well as large-sized text instances. Moreover, it devises a double segmentation subnetwork to utilize two independent but inherently complementary losses to co-optimize the network and increase the reliability of the confidence scores in predicting the text/nontext areas. With the character instances detected by the MSTD, one can easily perform scene text spotting with classic object recognition networks such as ResNet and DenseNet. We carried out extensive experiments on nine scene text datasets to demonstrate the outstanding performance of the MSTD on character-level and line-level text instance localization and scene text recognition, where the MSTD significantly outperforms the state-of-the-art scene text detection methods and the sequence-to-sequence-learning-based scene text recognizers. © 2020 IEEE.

关键词： Character recognition

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Assessing and characterizing oilseed rape freezing injury based on MODIS and MERIS data

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International Journal of Agricultural and Biological engineering 2017年第3期10卷 143-157页

作者： She Bao Huang Jingfeng Zhang Dongyan Huang Linsheng Institute of Agricultural Remote Sensing and Information Technology Application Zhejiang UniversityHangzhou 310058China Key Laboratory of Agricultural Remote Sensing and Information System Zhejiang ProvinceHangzhou 310058China Key Laboratory of Environment Remediation and Ecological Health Ministry of EducationHangzhou 310058China Anhui Engineering Laboratory of Agro-ecological Big Data Anhui UniversityHefei 230601China

The oilseed rape growing in the lower reaches of Yangtze River in China belongs to winter varieties and suffers the risk of freezing *** this research,a typical freezing injury event occurred in anhui Province was taken as a case study,the freezing damage degree of oilseed rape was assessed,and its development characteristics based on the vegetation metrics derived from MODIS and MERIS data were *** oilseed rape was mapped according to the decline of greenness from bud stage to full-bloom period,with the phenological phases identified adopting time-series *** was more sensitive to freezing injury compared with other commonly used vegetation indices(VIs)calculated using MODIS bands,e.g.,EVI,GNDVI and *** freezing damage degree employing the difference between post-freeze growth and the baseline level in adjacent damage-free growing seasons was *** remote sensing-derived damage levels were supported by their correlation with the cold accumulated temperatures at the county *** performance of several remote sensing indicators of plant biophysical and biochemical parameters was also investigated,i.e.,the photosynthetic rate,canopy water status,canopy chlorophyll content,leaf area index(LAI)and the red edge position(REP),in response to the advance of the freezing *** was found that the photosynthetic rate indicator—Photochemical Reflectance Index(PRI)responded strongly to freezing *** injury caused canopy water loss,which could be detected though the magnitude was not very ***-LAI showed a slow and lagging response to low temperature and restored rapidly in the recovery phase;additionally,REP and the indicator of canopy chlorophyll content—MERIS Terrestrial Chlorophyll Index(MTCI),did not appear to be influenced by freezing *** was concluded that the physiological functions,canopy structure,and organic content metrics showed a descending order of vulnerabilities to freezing injury.

关键词： Brassica napus oilseed rape freezing injury crop monitoring MODIS MERIS

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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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