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IEEE International Conference on data Mining (ICDM)

作者： Kang Miao Zhao Zhang Jiahuan Ren Mingbo Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China School of Information Science and Technology Donghua University Shanghai China Department of Computer Science Harbin Institute of Technology (Shenzhen) Shenzhen China

Recent advances in single image super-resolution (SISR) have achieved remarkable performance through deep learning. However, the high computational cost hinders the deployment of SISR models on edge devices. Instead of proposing new SISR models, a new trend is emerging to improve network efficiency by reducing parameters, FLOPs, and inference time through slight modifications to the original models. However, recent methods usually focus on reducing only one of three metrics, i.e., FLOPs, parameters and inference time, which inevitably increases the other two metrics. In this paper, we propose a novel Adaptive Student Inference Network (ASIN) on popular SISR models, which aims at reducing FLOPs and inference time while maintaining the number of parameters and restoring clearer high-resolution images. Specifically, our ASIN divides a SISR model into three components (head, body and tail) and adopts various strategies for each part. For head and tail parts, to ensure the restored images contain more detailed information, a novel auxiliary Enhanced Teacher Network (ETNet) is designed, which is trained with the ground-truth images to obtain more prior knowledge to guide student network to extract more accurate textures using a new knowledge distillation method. For the body part, owing to the varying difficulties of the reconstructions in different regions, we propose an Adaptive Depth Predicted Module (ADPM) to dynamically shorten average depth of network to reduce the computational cost of overall network. Extensive experiments on two datasets demonstrate the effectiveness and state-of-the-art performance of our ASIN compared to its counterparts.

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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Chinese Control and Decision Conference, CCDC

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 School of Computer Science and Information Engineering Hefei University of Technology Hefei Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Hefei Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Heifei

ISBN: (纸本)9781665478977

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraordinarily serious natural disasters (e.g., floods and earthquakes) occur. Central to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew in the static road network, which is unable to adapt to the dynamic changes of the road network caused by secondary disasters, such as rock slides and debris flows. Consequently, this work is concentrated on multicrew dynamic scheduling and routing in road network restoration. Specifically, a model of multicrew dynamic scheduling and routing is first presented. Next, the Markov decision process is adopted to construct a decision model for repair crews, based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing strategy. Finally, experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex, dynamic emergency scenarios of post-disaster.

关键词： Schedules Q-learning Roads Heuristic algorithms Maintenance engineering Markov processes Dynamic scheduling

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Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective

arXiv

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arXiv 2022年

作者： Zhao, Yunrui Xu, Qianqian Jiang, Yangbangyan Wen, Peisong Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS China State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few labeled positive examples with many unlabeled ones. Compared with ordinary semi-supervised learning, this task is much more challenging due to the absence of any known negative labels. While existing cost-sensitive-based methods have achieved state-of-the-art performances, they explicitly minimize the risk of classifying unlabeled data as negative samples, which might result in a negative-prediction preference of the classifier. To alleviate this issue, we resort to a label distribution perspective for PU learning in this paper. Noticing that the label distribution of unlabeled data is fixed when the class prior is known, it can be naturally used as learning supervision for the model. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions, which is formulated by aligning their expectations. Moreover, we further adopt the entropy minimization and Mixup regularization to avoid the trivial solution of the label distribution consistency on unlabeled data and mitigate the consequent confirmation bias. Experiments on three benchmark datasets validate the effectiveness of the proposed method. Code available at: https://***/Ray-rui/Dist-PU-Positive-UnlabeledLearning-from-a-Label-Distribution-Perspective. Copyright © 2022, The Authors. All rights reserved.

关键词： Entropy

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Communication with children for older adults' life satisfaction: Non-face-to-face and/or face-to-face?

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Gerontechnology 2016年第4期14卷 191-203页

作者： Sun, Kai Zuo, Meiyun Kong, Dong Key Laboratory of Data Engineering and Knowledge Engineering School of Business Renmin University of China Beijing China Research Institute of Smart Aged Care School of Information National Academy of Development and Strategy Renmin University of China Beijing China

Objectives Communication with children is an important way to reduce the effect of empty nest syndrome and increase life satisfaction of older adults. Under new network environment, more and more young people rely on non-face-to-face (nFTF) technology to communicate with parents, instead of face-to-face (FTF) communication due to the constraints of living condition and busy schedule of job. We are wondering whether it is good for older adults' life satisfaction. Specifically, can nFTF provide the same benefit as FTF does to increase older adults' life satisfaction? How to use nFTF to benefit older adults best? Theories From the perspective of family support (for instance, emotional support and instrumental support), this paper mainly uses Media-Richness Theory and Social Presence Theory to analyse the different capability of FTF and nFTF to increase older adults' life satisfaction. Besides, we try to examine in which situation increasing nFTF can benefit older adults. Design The data of China Health and Retirement Longitudinal Study (CHARLS) is used to test our hypotheses. Results FTF is better than nFTF to increase life satisfaction. Only in the context that FTF is harder or less available (frequency of FTF is less than once every six months), nFTF is positively related to increasing life satisfaction;when the frequency of FTF is more than once every six months, more nFTF will not provide an extra benefit for older adults. In the present paper, we systematically analyse the difference of FTF and nFTF, and extend previous studies, which focus on nFTF's advantage, to in what context nFTF can cooperate with FTF to increase older adults' life satisfaction.

关键词： Face-to-face Life satisfaction Non-face-to-face Older adults

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Visual Feature Combination Approach for Zero-Shot Learning

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Ruan Jian Xue Bao/Journal of Software 2018年 29卷 16-29页

作者： Yang, Gang Liu, Jin-Lu Li, Xi-Rong Xu, Jie-Ping School of Information Renmin University of China Beijing100872 China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China

Zero-Shot learning is an important research in the field of machine learning and image recognition. Zero-Shot learning methods normally use the semantic information among unseen classes and seen classes to transfer the knowledge which is learned from examples of seen classes to unseen classes, so as to recognize and classify the examples of unseen classes. In this study, a zero-shot learning approach based on construction of visual feature combination is proposed. The approach generates many examples of unseen class on visual feature level by the way of feature combination, which is first proposed, and thus transforms zero-shot learning problem to be a traditional classification problem solved by supervised learning. The approach mimics human cognition process of associative memory, and includes four steps: feature-attribute relation extraction, example construction, example screening, and domain adaption. On training examples of seen classes, the relationship between class attributes and dimensions of feature is extracted;on visual feature level, examples of unseen classes are generated by visual feature combination;dissimilarity representation is introduced to filter the generated examples of unseen classes;semi-supervised and unsupervised feature domain adaption are proposed to linearly transform the generated examples of unseen classes to be more effective. The proposed approach shows superior performance on three benchmark datasets (AwA, AwA2, and SUN), especially on dataset AwA, it obtains 82.6% top-1 accuracy which is the best result as far as we know. Experiment results demonstrate the effectiveness and superiority of the proposed approach. © Copyright 2018, Institute of Software, the Chinese Academy of Sciences. All rights reserved.

关键词： Image classification

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A new BP algorithm for network marketing performance evaluation of agricultural products

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Journal of Applied Sciences 2013年第20期13卷 4332-4335页

作者： Zou, Chun-Fang Huang, Li-Juan School of of Business Administration Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China School of Information Technology Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China

Network marketing performance evaluation can sum up and improve the network marketing activities for network enterprises. So, how to evaluate network marketing performance has become a matter of concern in the field related. The study mainly takes agricultural products for example and presents a new algorithm for evaluating network marketing performance based on improved BP neural network algorithm. First, a evaluation indicator system for evaluating network marketing performance of agricultural products is constructed;Second the study analyzes the reasons of the defects of BP neural network algorithm and then designs a new BP algorithm and improves it through changing its learning rate to speed up the calculation and simplify the structure of the original BP algorithm;Finally, the improved algorithm is realized with three e_commerce enterprises are taken for examples to evaluate the network marketing performance and verify the validity and feasibility of the model and the experimental results show that the model can evaluate the network marketing performance of different enterprises practically. © 2013 Asian Network for Scientific Information.

关键词： Agricultural products

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Adaptive-Non-Dominated Sorting Genetic Algorithm Based Multi-Objective Optimization Energy-Balanced Opportunistic Network Routing Strategy

SSRN

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SSRN 2023年

作者： Song, Ming Tang, Qi Xu, Gang Zhang, Feng Hao, Xinyu Huang, Baoqi College of Computer Science Inner Mongolia University Inner Mongolia Hohhot China Inner Mongolia A.R. Key Laboratory of Data Mining and Knowledge Engineering China Computer Science and Information Technology Shanxi University Shanxi Province Taiyuan China

In opportunistic networks, the uneven energy consumption of nodes and the polarization of cache utilization are important factors that impact network sustainability and overall performance. To address these issues, this paper proposes a multi-objective optimization energy-balanced routing strategy for opportunistic networks based on adaptive non-dominated sorting algorithm (ANMOR). The strategy aims to maximize the forwarding capability of nodes while achieving energy balance. The algorithm can find all Pareto-optimal solutions in the entire search space, which represent the trade-off relationships between different objectives and can provide a range of feasible solutions for routing. Experimental results show that this strategy can significantly improve the energy balance and cache utilization of opportunistic networks, enhance network performance, and prolong network lifetime. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Learning Representations from Local to Global for Fine-grained Patient Similarity Measuring in Intensive Care Unit

Learning Representations from Local to Global for Fine-grain...

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IEEE International Conference on data Mining (ICDM)

作者： Xianli Zhang Buyue Qian Yang Li Zeyu Gao Chong Guan Renzhen Wang Yefeng Zheng Hansen Zheng Chen Li School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Beijing Chaoyang Hospital Capital Medical University Tencent Shenzhen Guandong China School of Mathematics and Statistics Xi’an Shaanxi China

Patient similarity measurement is an essential step in discovering clinically meaningful subgroups and building case retrieval systems. Most existing studies implement this procedure using similarity measurement algorithms on the multivariate clinical time-series (input space) or the low-dimensional patient representation (representation space) learned by a representation learning model. However, they either suffer from the adverse effects of irrelevant variables in the data or fail to assess the fine-grained similarity underneath the disease progress. In this paper, we propose a method to measure more fine-grained patient similarity in the state space, where each patient is represented by a series of state representations that reveal the dynamic health status. We discuss three desiderata, including stability, personality, and interpretability, for the state representations, and on this basis, develop a supervised predictive model that learns good state representations for identifying similar patients and predicting patient outcomes. Experimental results on the publicly available dataset MIMIC-III show that our method offers a promising direction for precisely identifying similar patients at the state trajectory level, as well as accurately predicting outcomes.

关键词： Training Representation learning Measurement units Precision medicine MIMICs Predictive models Extraterrestrial measurements

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Trustworthy preference completion in social choice

arXiv

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arXiv 2020年

作者： Li, Lei Xue, Minghe H., Chen X., Wu The School of Computer Science and Technology University of Science and Technology of China China Mininglamp Academy of Sciences Mininglamp Technologies Beijing China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei China

As from time to time it is impractical to ask agents to provide linear orders over all alternatives, for these partial rankings it is necessary to conduct preference completion. Specifically, the personalized preference of each agent over all the alternatives can be estimated with partial rankings from neighboring agents over subsets of alternatives. However, since the agents’ rankings are nondeterministic, where they may provide rankings with noise, it is necessary and important to conduct the trustworthy preference completion. Hence, in this paper firstly, a trust-based anchor-kNN algorithm is proposed to find k-nearest trustworthy neighbors of the agent with trust-oriented Kendall-Tau distances, which will handle the cases when an agent exhibits irrational behaviors or provides only noisy rankings. Then, for alternative pairs, a bijection can be built from the ranking space to the preference space, and its certainty and conflict can be evaluated based on a well-built statistical measurement Probability-Certainty Density Function. Therefore, a certain common voting rule for the first k trustworthy neighboring agents based on certainty and conflict can be taken to conduct the trustworthy preference completion. The properties of the proposed certainty and conflict have been studied empirically, and the proposed approach has been experimentally validated compared to state-of-arts approaches with several data sets. © 2020, CC BY.

关键词： Nearest neighbor search

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