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Journal of Network Intelligence 2021年第1期6卷 16-28页

作者： Xue, Xingsi Zhang, Jie Yang, Chaofan Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou Fujian350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin City Guanxi Province537000 China School of Computer Science and Mathematics Intelligent Information Processing Research Center Fujian University of Technology No.33 Xueyuan Road University Town Minhou Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology No.1 Jinji Road Guilin Guangxi541004 China

Although Inquiry-based Learning (IBL) offers compelling opportunities for the engineering education, one of the challenges to the implementation of it is that the learner always lacks of the background knowledge. To provide him with the necessary knowledge when making the inquiry, this paper proposes an Ontology Supported Inquiry Learning technique (OSIL), which works on the basis of two ontologies, i.e. curriculum ontology and the learner’s ontology. The experimental results show that the constructed curriculum ontology and learner ontology is of high-quality, and the answers recommended by OSIL almost perfectly match with the true answers. From the experimental results, we can see that it is a feasible way of using the software engineering and prototype evolution method to construct the curriculum and learner ontology, and the individual inquiry based on two ontologies is able to provide the learners with high-quality personalized services. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Curricula

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Semimetal Contacts to Monolayer Semiconductor: Weak Metalization as an Effective Mechanism to Schottky Barrier Lowering

arXiv

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arXiv 2022年

作者： Su, Tong Li, Yueyan Wang, Qianqian Zhao, Weiwei Cao, Liemao Ang, Yee Sin Sauvage Laboratory for Smart Materials School of Materials Science and Engineering Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China Science Mathematics and Technology Singapore University of Technology and Design Singapore487372 Singapore School of Information Management Nanjing University Nanjing210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing210023 China College of Physics and Electronic Engineering Hengyang Normal University Hengyang421002 China

Recent experiment has uncovered semimetal bismuth (Bi) as an excellent electrical contact to monolayer MoS2 with ultralow contact resistance. The contact physics of the broader semimetal/monolayer-semiconductor family beyond Bi/MoS2, however, remains largely unexplored thus far. Here we perform a comprehensive first-principle density functional theory investigation on the electrical contact properties between six archetypal two-dimensional (2D) transition metal dichalcogenide (TMDC) semiconductors, i.e. MoS2, WS2, MoSe2, WSe2, MoTe2 and WTe2, and two representative types of semimetals, Bi and antimony (Sb). As Bi and Sb work functions energetically aligns well with the TMDC conduction band edge, Ohmic or nearly-Ohmic n-type contacts are prevalent. The interlayer distance of semimetal/TMDC contacts are significantly larger than that of the metal/TMDC counterparts, which results in only weak metalization of TMDC upon contact formation. Intriguingly, such weak metalization generates semimetal-induced gap states (MIGS) that extends below the conduction band minimum, thus offering an effective mechanism to reduce or eliminate the n-type Schottky barrier height (SBH) while still preserving the electronic structures of 2D TMDC. A modified Schottky-Mott rule that takes into account SMIGS, interface dipole potential, and Fermi level shifting is proposed, which provides an improved agreement with the DFT-simulated SBH. We further show that the tunneling-specific resistivity of Sb/TMDC contacts are generally lower than the Bi counterparts, thus indicating a better charge injection efficiency can be achieved through Sb contacts. Our findings reveal the promising potential of Bi and Sb as excellent companion electrode materials for advancing 2D semiconductor device technology. Copyright © 2022, The Authors. All rights reserved.

关键词： Molybdenum compounds

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Unsupervised vehicle re-identification with progressive adaptation 29

Unsupervised vehicle re-identification with progressive adap...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Peng, Jinjia Wang, Yang Wang, Huibing Zhang, Zhao Fu, Xianping Wang, Meng College of Information and Science Technology Dalian Maritime University Liaoning Dalian China Pengcheng Laboratory Shenzhen Guangdong China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes;worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel progressive adaptation learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as "pseudo target samples". These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Vehicles

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Multi-View Cost-Sensitive Kernel Learning for Imbalanced Classification Problem

SSRN

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SSRN 2023年

作者： Tang, Jingjing Hou, Zhaojie Yu, Xiaotong Fu, Saiji Tian, Yingjie School of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Institute of Big Data Southwestern University of Finance and Economics Chengdu611130 China School of Economics and Management Beijing University of Posts and Telecommunications Beijing100876 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation UCAS Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

Multi-view imbalanced learning concentrates on recognizing valuable patterns from multi-view imbalanced data. There exist numerous algorithm-level multi-view imbalanced learning methods including multi-view ensemble learning methods and multi-view cost-sensitive learning methods. However, these approaches have two drawbacks. Firstly, from the multi-view representation perspective, either ignore the agreement among different views, or fail to fully exploit the complementarity information contained in multi-view data. Secondly, from the class imbalanced perspective, multi-view ensemble learning first needs to design a specific data pre-processing scheme to generate views and then fuse the local and global information with multi-view ensemble schemes. So such models are closely related to practical problems and lacks universality. In contrast, multi-view cost-sensitive learning methods possess concise formulations. But the success of such methods depends on imposing appropriate misclassification cost to the majority and minority classes. Inspired by elegant merits of asymmetric LINEX loss function, we propose a multi-view costsensitive kernel learning method with LINEX loss termed as MVCSKL. It not only fully exploits the consensus and complementary information of multiple views, but also adaptively learns and adjusts the misclassification cost of different classes through the asymmetric LINEX loss function. Besides, the introduction of the kernel function enables the MVCSKL model to handle complex nonlinear problem and further guarantees the prediction performance of the model. Furthermore, we adopt the alternating direction method of multipliers and gradient descent algorithm to solve MVCSKL. Based on the Rademacher complexity, we analyze the generalization capability of MVCSKL. Extensive experiments are conducted to verify the effectiveness of the proposed model. © 2023, The Authors. All rights reserved.

关键词： Support vector machines

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Personalized Recommendation Based On Entity Attributes and Graph Features

Personalized Recommendation Based On Entity Attributes and G...

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IEEE International Conference on Big knowledge (ICBK)

作者： Yi Zhu Bingbing Dong Zhiqing Sha School of Information Engineering Yangzhou University Yangzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Instituse of Big Knowledge Science Hefei University of Technology Hefei China

ISBN: (纸本)9781665438599

With the rapid increase in the amount of website data, it has been a more difficult task for users to get the infor-mation they are interested in. Personalized recommendation is an important bridge to find the information which users really need on the website. Many recent studies have introduced additional attribute information about users and/or items to the rating matrix for alleviating the problem of data sparsity. In order to make full use of the attribute information and scoring matrix, deep learning based recommendation methods are proposed, especially the autoencoder model has attracted much attention because of its strong ability to learn hidden features. However, most of the existing autoencoder- based models require that the dimension of the input layer is equal to the dimension of the output layer, which may increase model complexity and certain information loss when using attribute information. In addition, as users' awareness of privacy protection increases, user attribute information is difficult to obtain. To address the above problems, in this paper, we propose a hybrid personalized recommendation model, which uses a semi-autoencoder to jointly embed the item's score vector and internal graph features (short for Co-Agpre). Specifically, we regard the user-item historical interaction matrix as a bipartite graph, and the Laplacian of the user-item co-occurrence graph is utilized to obtain the graph features of the item for solving the problem of sparse attributes. Then a semi-autoencoder is introduced to learn the hidden features of the item and perform rating prediction. The proposed model can flexibly use information from different sources to reduce the complexity of the model. Experiments on two real-world datasets demonstrate the effectiveness of the proposed Co-Agpre compared with state-of-the-art methods.

关键词： Deep learning Privacy Laplace equations Conferences Predictive models Feature extraction Complexity theory

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A Solution for Cascade Hydropower Station Reservoirs Optimal Operation Based on Improved Bat Algorithm 6th

A Solution for Cascade Hydropower Station Reservoirs Optimal...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Dao, Thi-Kien Chang, Kuo-Chi Chu, Kai-Chun Nguyen, Thi-Thanh-Tan Ngo, Truong-Giang Nguyen, Trong-The Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China School of Management Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University Hanoi Viet Nam Haiphong University of Management and Technology Haiphong Viet Nam

ISBN: (纸本)9783030697167

This study suggests a solution to the optimal reservoir operation's typical nonlinear optimization problem by an improved Bat algorithm (IBA). The reservoir operations issue is modeled as the objective function according to the runoff data and comprehensive utilization requirements dealing with IBA’s optimization method. The IBA is implemented by introducing adaptive weight into the bat algorithm (BA) for updating mechanism. The bat's position is adjusted dynamically according to the actual situation to improve the bat's searching performance. The mechanism Tabu search (TS) diversity is also used to enhance the bat population diversity for preventing the situation that circuitous search does not leap out of the local optimum. The simulation results show that IBA can obtain a better reservoir operation scheme with higher operating efficiency than other algorithms, which offers a new way to maximize reservoir power generation operation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tabu search

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Thermodynamic Characteristics of Methane Adsorption on Coal Molecular with Selected Metamorphism Degree: Considering the Influence of Temperature, Moisture Contents, and In-Situ Modification

SSRN

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SSRN 2022年

作者： Zhang, Xiaoyu Cai, Yongbo Zhou, Tianbai Chen, Jian Zhao, Guofei Kang, Jianting Zhang, Liankun Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China State Key Laboratory of Coal Mining and Clean Utilization Beijing100013 China Key Laboratory of In-situ Property Improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan030024 China Emergency Science Research Institute China Coal Research Institute Beijing100013 China Security Engineering Shanxi Institute of Energy Jinzhong030600 China College of Safety and Emergency Management Engineering Taiyuan University of Technology Taiyuan030024 China

A better understanding of the methane (CH4) adsorption characteristic and microscopic mechanism on coal surface cannot be overemphasized for safe coal mining and high-efficiency coalbed methane (CBM) extraction. In this work, the atomistic representations of long flame coal, coking coal, and anthracite coal obtained from China were created, and the CH4 adsorption isotherms considering temperature, moisture contents, and microwave in-situ modification was simulated and analyzed. The influence law and mechanism of these parameters and modification on methane adsorption were quantitatively explored by methane adsorption heat. The results of molecular structure parameters showed that with the coal metamorphism degree increased from long flame coal to coking coal and anthracite coal, the functional groups in the molecular structure of the coal decreased while the degree of graphite increased. It is worth mentioning that the action of in-situ modification on coal facilitates this process. The saturation adsorption capacity of CH4 decreased and then increased with the increase of coal metamorphism degree, decreased linearly with the increase of temperature, and when water molecular was present, the adsorbed CH4 showed different degrees of decreasing trend with the increase of moisture content. The isosteric heat of CH4 adsorption is coking coal > anthracite coal > long flame coal, the isosteric heat gradually decreased with the increase of methane adsorption, and the presence of water molecules has a limited effect on the isosteric heat. The in-situ modification used in this experiment disrupted the self-conjugated hydrogen bonds on the coal surface, which reduced the oxygen-containing functional groups. Functional groups like methyl and methylene were broken off and released as gases, as a result, the aromatic carbon rate, aromaticity, and aromatic ring condensation increased, and the alkane side chain length and branching degree decreased. At the same time, the associate

关键词： Moisture

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An integrated classification model for massive short texts with few words 19

An integrated classification model for massive short texts w...

引用

2019 International Conference on Robotics Systems and Vehicle Technology, RSVT 2019

作者： Tang, Xuetao Zhu, Yi Hu, Xuegang Li, Peipei Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology China

ISBN: (纸本)9781450362429

The excellent performance of short texts classification has emerged in the past few years. However, massive short texts with few words like invoice data are different with traditional short texts like tweets in its no contextual and less semantic information, which hinders the application of conventional classification algorithms. To address these problems, we propose an integrated classification model for massive short texts with few words. More specifically, the word embedding model is introduced to train the word vectors of massive short texts with few words to form the feature space, and then the vector representation of each instance in texts is trained based on sentence embedding. With this integrated model, higher level representations are learned from massive short texts with few words. It can boost the performance of the base subsequent classifiers such as K-Nearest Neighbor. Extensive experiments conducted on dataset including 16 million real data demonstrate the superior classification performance of our proposed model compared with all competing state-of-the-art models. © 2019 Association for Computing Machinery.

关键词： Embeddings

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Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection

arXiv

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arXiv 2022年

作者： Xu, Shaoqing Li, Fang Song, Ziying Fang, Jin Wang, Sifen Yang, Zhi-Xin The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China School of Mechanical Engineering Beijing Institute of Technology China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China School of Transportation Science and Engineering Beihang University Beijing100083 China Inceptio United States

LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy. Nevertheless, the restricted resolution of 2D feature maps impedes accurate re-projection and often induces a pronounced boundary-blurring effect, which is primarily attributed to erroneous semantic segmentation. To well handle this limitation, we propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results. Specifically, we employ 2D/3D semantic segmentation methods to generate the parsing results for 2D images and 3D point clouds. The 2D semantic information is further reprojected into the 3D point clouds with calibration parameters. To handle the misalignment between the 2D and 3D parsing results, we propose an Adaptive Attention-based Fusion (AAF) module to fuse them by learning an adaptive fusion score. Then the point cloud with the fused semantic label is sent to the following 3D object detectors. Furthermore, we propose a Deep Feature Fusion (DFF) module to aggregate deep features at different levels to boost the final detection performance. The effectiveness of the framework has been verified on two public large-scale 3D object detection benchmarks by comparing them with different baselines. The experimental results show that the proposed fusion strategies can significantly improve the detection performance compared to the methods using only point clouds and the methods using only 2D semantic information. Most importantly, the proposed approach significantly outperforms other approaches and sets state-of-the-art results on the nuScenes testing benchmark. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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