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arXiv 2022年

作者： Alizamir, Saed Bandara, Kasun Eshragh, Ali Iravani, Foaad School of Management Yale University New HavenCT United States School of Computing and Information Systems Melbourne Centre for Data Science University of Melbourne VIC Australia Carey Business School Johns Hopkins University MD United States International Computer Science Institute BerkeleyCA United States Uber Freight San FranciscoCA United States

We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers’ behavior for a particular product category at the largest online retailer in China, that is JD. While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output. This analysis involves identifying what features and characteristics have the most significant impact on customers’ purchase behavior, thereby enabling us to predict future sales with a high level of accuracy, and identify the most impactful variables. Our results reveal that customers’ product choice is insensitive to the promised delivery time, but this factor significantly impacts customers’ order quantity. We also show that the effectiveness of various discounting methods depends on the specific product and the discount size. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools. Customers’ choice behavior across different product classes is mostly driven by price, and to a lesser extent, by customer demographics. The former finding asks for exercising care in deciding when and how much discount should be offered, whereas the latter identifies opportunities for personalized ads and targeted marketing. Further, to curb customers’ batch ordering behavior and avoid the undesirable Bullwhip effect, JD should improve its logistics to ensure faster delivery of orders. © 2022, CC BY.

关键词： Sales

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Visible-Thermal Multiple Object Tracking: Large-scale Video dataset and Progressive Fusion Approach

arXiv

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arXiv 2024年

作者： Zhu, Yabin Wang, Qianwu Li, Chenglong Tang, Jin Huang, Zhixiang The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Public Safety and Emergency Management Anhui University of Science and Technology Hefei231131 China School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Security Artificial Intelligence School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Center for Big Data and Population Health of IHM China

The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://***/wqw123wqw/PFTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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ATHENA: An In-vehicle CAN Intrusion Detection Framework Based on Physical Characteristics of Vehicle Systems

arXiv

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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Copula Guided Parallel Gibbs Sampling for Nonparametric and Coherent Topic Discovery (Extended Abstract)

Copula Guided Parallel Gibbs Sampling for Nonparametric and ...

引用

International Conference on data Engineering

作者： Lihui Lin Yanghui Rao Haoran Xie Raymond Y. K. Lau Jian Yin Fu Lee Wang Qing Li School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Lingnan University New Territories Hong Kong SAR Department of Information Systems City University of Hong Kong Kowloon Hong Kong SAR Guangdong Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou China School of Science and Technology Hong Kong Metropolitan University Kowloon Hong Kong SAR Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong SAR

In terms of the generative process, the Gamma-Gamma-Poisson Process (G2PP) is equivalent to the nonparametric topic model of Hierarchical Dirichlet Process (HDP). Considering the high computational cost of estimating parameters in HDP, a parallel G2PP was developed to generate topics efficiently via multi-threading. Unfortunately, the above model needs to predefine the number of topics. To address this issue, we first propose a Topic Self-Adaptive Model (TSAM) for nonparametric and parallel topic discovery. In TSAM, a monitor-executor mechanism is developed to manage the global topic information using a hierarchical structure of threads. Based on the apparatus of copulas, we further extend our TSAM to TSAMcop for coherent topic modeling by exploiting a copula guided parallel Gibbs sampling algorithm. Extensive experiments validate the effectiveness of both TSAM and TSAMcop.

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Semi-supervised meibomian gland segmentation via mutual consistency constraints and uncertainty rectification

Semi-supervised meibomian gland segmentation via mutual cons...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jiawen Lin Feng Lu Li Li Lingjie Lin Dongqi Li Glen M. Borchert Jingshan Huang College of Computer and Data Science Fuzhou University Fuzhou China Department of Ophthalmology Fujian Provincial Hospital South Branch Fuzhou China Department of Economics & School of Information and Computer Sciences University of California Irvine Irvine U.S.A Department of Pharmacology College of Medicine University of South Alabama Mobile U.S.A School of Computing & College of Medicine University of South Alabama Mobile U.S.A

To solve the label scarcity of meibomian gland segmentation in infrared meibography images, a novel framework for semi-supervised meibomian gland segmentation is firstly presented in this paper. Extra mutual feature consistency constraint is added along with the cross pseudo supervision , guiding the model more robustness and discriminative. Meanwhile, cross uncertainty rectification is introduced to avoid noisy labels, further improving the pseudo supervision. Experimental results on an internal dataset reveals that our method yields significant performances using only 10% of the labeled data compared to the fully supervised segmentation, and outperforms the state-of-the art semi-supervised segmentation methods. Combination of mutual consistency regularization and cross uncertainty rectifi-cation guides model to distinguish glands from background well with limited labeled data.

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Machine Knowledge and Human Cognition

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Big data Mining and Analytics 2020年第4期3卷 292-299页

作者： Fashen Li Lian Li Jianping Yin Liang Huang Qingguo Zhou Ning An Yong Zhang Li Liu Jialin Zhang Kun Kuang Lei Yang Zhixi Wu Lianchun Yu the School of Physical Science and Technology Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China the School of Cyber Science and Engineering Dongguan University of TechnologyDongguan 523808China the Institute of Computational Physics and Complex Systems Lanzhou UniversityLanzhou 730000China the School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China the School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230601China the Department of Physics Xiamen UniversityXiamen 361005China the School of Big Data&Software Engineering Chongqing UniversityChongqing 400044China the Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China the College of Computer Science and Technology Zhejiang UniversityHangzhou 310000China the Institute ofModern Physics Chinese Academy of SciencesLanzhou 730000China

Intelligent machines are knowledge systems with unique knowledge structure and *** this paper,we discuss issues including the characteristics and forms of machine knowledge,the relationship between knowledge and human cognition,and the approach to acquire machine *** issues are of great significance to the development of artificial intelligence.

关键词： intelligent machine machine knowledge human cognition knowledge interpretation principle of functional similarity Probable Approximative Correction(PAC)model

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Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation

arXiv

引用

arXiv 2022年

作者： Wang, Xiangmeng Li, Qian Yu, Dianer Cui, Peng Wang, Zhichao Xu, Guandong Data Science and Machine Intelligence Lab Faculty of Engineering and Information Technology University of Technology Sydney NSW Australia The School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia The Department of Computer Science and Technology Tsinghua University Beijing100084 China

Traditional recommendation models trained on observational interaction data have generated large impacts in a wide range of applications, it faces bias problems that cover users’ true intent and thus deteriorate the recommendation effectiveness. Existing methods tracks this problem as eliminating bias for the robust recommendation, e.g., by re-weighting training samples or learning disentangled representation. The disentangled representation methods as the state-of-the-art eliminate bias through revealing cause-effect of the bias generation. However, how to design the semantics-aware and unbiased representation for users true intents is largely unexplored. To bridge the gap, we are the first to propose an unbiased and semantics-aware disentanglement learning called CaDSI (Causal Disentanglement for Semantics-Aware Intent Learning) from a causal perspective. Particularly, CaDSI explicitly models the causal relations underlying recommendation task, and thus produces semantics-aware representations via disentangling users true intents aware of specific item context. Moreover, the causal intervention mechanism is designed to eliminate confounding bias stemmed from context information, which further to align the semantics-aware representation with users true intent. Extensive experiments and case studies both validate the robustness and interpretability of our proposed model. © 2022, CC BY.

关键词： Semantics

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A Bibliometric Analysis of Artificial Intelligence and Electrical Energy Reliability 5

A Bibliometric Analysis of Artificial Intelligence and Elect...

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5th IEEE International Conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Ajibade, Samuel-Soma M. Ehineni, Kehinde Hoong, Angela Lee Siew Jasser, Muhammed Basheer Oyebode, Oluwadare Joshua Sunway University School of Engineering and Technology Department of Data Science and Artificial Intelligence Selangor Malaysia Selangor Malaysia Industrial Business Unit Littelfuse The Galleria 15 Innovation Blvd #140 SaskatoonSKS7N 2X8 Canada Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia Afe Babalola University Civil and Environmental Engineering Department Ekiti Ado Nigeria

ISBN: (纸本)9798331542559

The escalating dependence on artificial intelligence (AI) in energy and electrical power dependability systems has garnered significant interest in both academic and industrial circles. This study provides a bibliometric examination of artificial intelligence (AI) applications in the field of electrical dependability, covering the time frame from 2002 to 2023. The main aim of this study is to examine the patterns of publication, notable authors, institutions, and funding organizations that contribute to research in artificial intelligence for electrical dependability. A further objective of the endeavor is to chart the co-authorship networks, keyword frequencies, and citation trends in the field. Extraction of data from the Scopus database was carried out using an extensive search approach, and subsequently, network visualization was performed using VOSviewer. The findings indicated a substantial rise in research productivity throughout the last ten years, accompanied with a distinguishable expansion in joint research endeavors. China has established itself as the foremost nation in research on electrical reliability powered by artificial intelligence, with the united States and India following closely after. In addition, the National Natural science Foundation of China was recognized as the most productive and influential funding institution. The thematic analysis revealed many prominent subjects in the sector, such as predictive maintenance, problem detection, and energy consumption optimization via artificial intelligence. The paper offers invaluable insights into the present research landscape and emphasizes developing trends, providing a clear plan for future research objectives in the fields of artificial intelligence and electrical dependability. © 2024 IEEE.

关键词： Artificial intelligence Bibliometric Analysis Electrical Reliability Energy

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Transformer-Based Hierarchical Dynamic Decoders for Salient Object Detection

SSRN

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SSRN 2023年

作者： Zheng, Qingping Zheng, Ling Deng, Jiankang Li, Ying Shang, Changjing Shen, Qiang School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech & Image Information Processing Northewestern Polytechnical University Shaanxi Xi’an710129 China Fuzhou Institute of Data Technology Fujian350200 China Department of Computing Imperial College London LondonSW7 2AZ United Kingdom

HighlightsTransformer-Based Hierarchical Dynamic Decoders for Salient Object DetectionQingping Zheng, Ling Zheng, Jiankang Deng, Ying Li, Changjing Shang, Qiang Shen• T-HDDNet employs dynamic upsampling and fusion for data-driven salient object detection.• T-HDDNet uses a spatially sensitive loss to learn salient object structures, yielding a high-resolution map.• Our method exceeds benchmarks at 22.19G MACs and 27M Params, reducing costs compared to the top model. © 2023, The Authors. All rights reserved.

关键词： Object detection

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Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of data: Results from Clinical Neuroscience

arXiv

引用

arXiv 2023年

作者： Wang, Rongguang Erus, Guray Chaudhari, Pratik Davatzikos, Christos Department of Electrical and Systems Engineering University of Pennsylvania United States Center for AI and Data Science for Integrated Diagnostics University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania United States Department of Computer and Information Science University of Pennsylvania United States Department of Radiology Perelman School of Medicine University of Pennsylvania United States Center for Biomedical Image Computing and Analytics University of Pennsylvania 3700 Hamilton Walk 7th Floor PhiladelphiaPA19104 United States

Machine learning (ML) is revolutionizing many areas of engineering and science, including healthcare. However, it is also facing a reproducibility crisis, especially in healthcare. ML models that are carefully constructed from and evaluated on data from one part of the population may not generalize well on data from a different population group, or acquisition instrument settings and acquisition protocols. We tackle this problem in the context of neuroimaging of Alzheimer's disease (AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk minimization approach that optimally combines data from a source group, e.g., subjects are stratified by attributes such as sex, age group, race and clinical cohort to make predictions on a target group, e.g., other sex, age group, etc. using a small fraction (10%) of data from the target group. We apply this method to multi-source data of 15,363 individuals from 20 neuroimaging studies to build ML models for diagnosis of AD and SZ, and estimation of brain age. We found that this approach achieves substantially better accuracy than existing domain adaptation techniques: it obtains area under curve greater than 0.95 for AD classification, area under curve greater than 0.7 for SZ classification and mean absolute error less than 5 years for brain age prediction on all target groups, achieving robustness to variations of scanners, protocols, and demographic or clinical characteristics. In some cases, it is even better than training on all data from the target group, because it leverages the diversity and size of a larger training set. We also demonstrate the utility of our models for prognostic tasks such as predicting disease progression in individuals with mild cognitive impairment. Critically, our brain age prediction models lead to new clinical insights regarding correlations with neurophysiological tests. In summary, we present a relatively simple methodology, along with ample experimental evidence, supporting

关键词： Neuroimaging

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