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7th International Conference on Computational Data and Social networks, CSoNet 2018

作者： Li, Jinzhong Liu, Guanjun Department of Computer Science and Technology College of Electronic and Information Engineering Jinggangshan University Ji’an343009 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610054 China Department of Computer Science and Technology College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China

ISBN: (纸本)9783030046477

Most existing approaches of learning to rank treat the effectiveness of each query equally which results in a relatively lower ratio of queries with high effectiveness (i.e. rich queries) in the produced ranking model. Such ranking models need to be further optimized to increase the number of rich queries. In this paper, queries with different effectiveness are distinguished, and the queries with higher effectiveness are given higher weights. We modify the gradient in the LambdaMART algorithm based on a new perspective of Matthew effect to highlight the optimization of the rich queries and to produce the rich ranking model, and we present a consistency theorem for the modified optimization objective. Based on the effectiveness evaluation criteria for information retrieval, we introduce the Gini coefficient, mean-variance and quantity statistics to measure the performances of the ranking models. Experimental results show that the ranking models produced by the gradient-modified LambdaMART algorithm based on Matthew effect exhibit a stronger Matthew effect compared to the original LambdaMART algorithm. © Springer Nature Switzerland AG 2018.

关键词： Artificial intelligence

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A Multi-label Classifier for Human Protein Subcellular Localization Based on LSTM networks

A Multi-label Classifier for Human Protein Subcellular Local...

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Zhiying Gao Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Research Center of Big Data and Network Security Tongji University Key Laboratory of Embedded System and Service Computing Tongji University

Nowadays, with the increasing number of protein sequences all over the world, more and more people are paying their attention to predicting protein subcellular location. Since wet experiment is costly and time-consuming, the automatic computational methods are urgent. In this paper, we propose a variant model based on Long Short-Term Memory(LSTM) to predict protein subcellular location. In this model, we employ LSTM to capture long distance dependency features of the sequence data. Moreover, we adjust the loss function of the loss layer to solve multi-label classification problem. Experimental results demonstrate that, compared with the traditional machine learning methods, our method achieves the best performance in various evaluation metrics.

关键词： LSTM multi-label classification protein subcellular localization

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Dynamic negotiation strategy based parallel-offer in cloud computing

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Journal of Computational Information Systems 2015年第10期11卷 3691-3699页

作者： Hu, Jun Xu, Ru Xiao, Deyu College of Computer Science and Electronics Engineering Hunan University Changsha China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China

Cloud computing can dynamically provide service based on service level agreements that between cloud services and consumers. Establish a viable and efficient dynamic negotiation strategy is one of hot research in cloud negotiation. This paper proposes a dynamic negotiation strategy based on parallel-offer under cloud computing environment, which combines dynamic concession strategy with parallel-offer tradeoff mechanism. The strategy can adjust negotiation strategy according to the supply demand relationship and tradeoff strategy of multi-issue, to achieve flexible and efficient cloud computing negotiation. This work firstly, establishes predicting market supply-demand relationship model, to build the dynamic concession strategy. Then on the base of above, introduces a parallel offer multi-issue tradeoff strategy into negotiation strategy, to achieve the dynamic concession tradeoff strategy (DCT). Finally, DCT mechanism is implemented in CloudSim test bed, and empirical results show DCT negotiation mechanism reach faster agreements and achieve higher utilities than other related approaches in cloud computing environment. ©, 2015, Binary Information Press. All right reserved.

关键词： Cloud computing

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Graph Ensemble Neural network

SSRN

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SSRN 2023年

作者： Duan, Rui Yan, Chungang Wang, Junli Jiang, Changjun Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Ensemble methods have been shown to improve graph neural networks (GNNs). Existing ensemble methods on graphs determine a strong classifier by combining a set of trained base classifiers, i.e., combining the final outputs of base classifiers for prediction. However, these methods fail to promote many popular GNNs to perform well under heterophily (in graphs where many connected nodes havedifferent class labels), which limits their applicability. Furthermore, they ignore the hierarchical nature of GNNs, which results in no interaction between base classifiers when neighbors are aggregated (during training). Two issues arise from this: low applicability and shallow ensemble. We propose Graph Ensemble Neural network (GEN) for addressing above issues, which is not a simple ensembleof GNNs, but instead integrates ensemble into GNNs to fuse a set of graphs. GEN deepens single ensemble into multiple ensembles during training and applies to homophily and heterophily graphs. In GEN, we design structure augmentation to generate some graphs for training and design feature augmentation for attenuating errors brought by the initial features. Different from existing graphensemble methods that execute only one ensemble, GEN executes multiple deep ensembles throughout the neighbor aggregation to fuse multiple graphs generated by structure augmentation. Extensive experiments show that GEN achieves new state-of-the-art performance on homophily and heterophily graphs for the semi- and full-supervised node classification. The source code of GEN is publicly available at https://***/graphNN/GEN1. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Infra-YOLO: Efficient Neural network Structure with Model Compression for Real-Time Infrared Small Object Detection

arXiv

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arXiv 2024年

作者： Chen, Zhonglin Geng, Anyu Jiang, Jianan Lu, Jiwu Wu, Di The College of Electrical and Information Engineering Hunan University Hunan Changsha410082 China The Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Hunan Changsha410082 China

Although convolutional neural networks have made outstanding achievements in visible light target detection, there are still many challenges in infrared small object detection because of the low signal-to-noise ratio, incomplete object structure, and a lack of reliable infrared small object dataset. To resolve limitations of the infrared small object dataset, a new dataset named InfraTiny was constructed, and more than 85% bounding box is less than 32x32 pixels (3218 images and a total of 20,893 bounding boxes). A multi-scale attention mechanism module (MSAM) and a Feature Fusion Augmentation Pyramid Module (FFAFPM) were proposed and deployed onto embedded devices. The MSAM enables the network to obtain scale perception information by acquiring different receptive fields, while the background noise information is suppressed to enhance feature extraction ability. The proposed FFAFPM can enrich semantic information, and enhance the fusion of shallow feature and deep feature, thus false positive results have been significantly reduced. By integrating the proposed methods into the YOLO model, which is named Infra-YOLO, infrared small object detection performance has been improved. Compared to yolov3, mAP@0.5 has been improved by 2.7%;and compared to yolov4, that by 2.5% on the InfraTiny dataset. The proposed Infra-YOLO was also transferred onto the embedded device in the unmanned aerial vehicle (UAV) for real application scenarios, where the channel pruning method is adopted to reduce FLOPs and to achieve a tradeoff between speed and accuracy. Even if the parameters of Infra-YOLO are reduced by 88% with the pruning method, a gain of 0.7% is still achieved on mAP@0.5 compared to yolov3, and a gain of 0.5% compared to yolov4. Experimental results show that the proposed MSAM and FFAFPM method can improve infrared small object detection performance compared with the previous benchmark method. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

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A key frame extraction algorithm oriented for video copy detection based on PCC and the histogram of video frames

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Journal of Computational Information Systems 2014年第16期10卷 6999-7006页

作者： Peng, Fei Qian, Qin Long, Min College of Computer Science and Electronics Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China College of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

Aim to improve the accuracy and the robustness of the video copy detection, a novel key frame extraction algorithm is proposed. The shot segmentation is accomplished by evaluating the Pearsons correlation coefficients (PCC) between adjacent frames, while the key frames are extracted by combining the boundary-based and visual content-based methods. Experimental results show that the proposed key frame extraction algorithm is effective, and it is robust against some content preserving video processing attacks for some existed video hash methods. It provides a novel effective method for key-frames extraction in the video copy detection. 1553-9105/Copyright © 2014 Binary Information Press

关键词： Extraction

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A Novel ICD Coding Method Based on Associated and Hierarchical Code Description Distillation

arXiv

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arXiv 2024年

作者： Zhang, Bin Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. ICD coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated ICD coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. However, most of them ignore the code hierarchy, leading to improper code assignments. To address these problems, we propose a novel method based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code *** utilize the code description and the hierarchical structure inherent to the ICD codes. Therefore, in this paper, we leverage the code description and the hierarchical structure inherent to the ICD codes. The code description is also applied to aware the attention layer and output layer. Experimental results on the benchmark dataset show the superiority of the proposed method over several state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning

Routing Strategy for SDN Large Flow Based on Deep Reinforcem...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yu Ke Junli Wang Chungang Yan Jiamin Yao Key Laboratory of Embedded System and Service Computing Collaborative Innovation Center for Financial Network Security (Tongji University) Ministry of Education National (Province-Ministry Joint) Tongji University Shanghai China

With the expansion of network scale, traditional routing strategies in Software-Defined networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes a different routing method, called Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning (RSDRL). Considering the links in the network that can carry large flow are limited, RSDRL can schedule large flow firstly and generate routing strategies adaptively. At the same time, in order to avoid the occurrence of congestion, RSDRL quantifies the degree of congestion in the network to implement routing. Experiments show that the proposed model has better Quality of Service (QoS) performance under the test traffic, realizing a reasonable schedule of traffic.

关键词： Deep learning Training Knowledge engineering Schedules Adaptation models Reinforcement learning Quality of service

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Minimizing Schedule Length of Energy Consumption Constrained Parallel Applications on Heterogeneous Distributed Systems

Minimizing Schedule Length of Energy Consumption Constrained...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xiongren Xiao Guoqi Xie Renfa Li Keqin Li College of Computer Science and Electronic Engineering Hunan University China Key Laboratory for Embedded and Network Computing of Hunan Province China Department of Computer Science State University of New York USA

ISBN: (纸本)9781509032068

Energy consumption is one of the primary design constraints in heterogeneous parallel and distributed systems ranging from small embedded devices to large-scale data centers. The problem of minimizing schedule length of an energy consumption constrained parallel application has been studied recently in homogeneous systems with shared memory. To adapt the heterogeneity and distribution of high-performance computing systems, this study solves the problem of minimizing schedule length of an energy consumption constrained parallel application on heterogeneous distributed systems based on dynamic voltage and frequency scaling (DVFS) energy-efficient design technique. Such problem is decomposed into two sub-problems in this study, namely, satisfying energy consumption constraint and minimizing schedule length. The first sub-problem is solved by transferring the energy consumption constraint of the application to that of each task, and the second sub-problem is solved by heuristically scheduling each task with low time complexity. Experiments with Fast Fourier transform parallel applications show that not only the actual energy consumptions always do not exceed and are close to given energy consumption constraints, but also the minimum schedule lengths are generated by using the proposed algorithm.

关键词： Energy consumption Program processors Schedules Time-frequency analysis Processor scheduling Optimization Job shop scheduling

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Correct after Answer: Enhancing Multi-Span Question Answering with Post-Processing Method

arXiv

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arXiv 2024年

作者： Lin, Jiayi Zhang, Chenyang Tong, Haibo Zhang, Dongyu Hong, Qingqing Hou, Bingxuan Wang, Junli Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Collaborative Innovation Center for Financial Network Security Tongji University Shanghai201804 China

Multi-Span Question Answering (MSQA) requires models to extract one or multiple answer spans from a given context to answer a question. Prior work mainly focuses on designing specific methods or applying heuristic strategies to encourage models to predict more correct predictions. However, these models are trained on gold answers and fail to consider the incorrect predictions. Through a statistical analysis, we observe that models with stronger abilities do not predict less incorrect predictions compared with other models. In this work, we propose Answering-Classifying-Correcting (ACC) framework, which employs a post-processing strategy to handle incorrect predictions. Specifically, the ACC framework first introduces a classifier to classify the predictions into three types and exclude "wrong predictions", then introduces a corrector to modify "partially correct predictions". Experiments on several MSQA datasets show that ACC framework significantly improves the Exact Match (EM) scores, and further analysis demostrates that ACC framework efficiently reduces the number of incorrect predictions, improving the quality of predictions.1 © 2024, CC BY-NC-ND.

关键词： Prediction models

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