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SSRN 2024年

作者： Li, Weiyue Zhu, Zhiguo Gao, Ming Zhang, Jun Liu, Zilong Fan, Weiguo School of Management Science and Engineering Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province Dongbei University of Finance and Economics China Department of Business Analytics Tippie College of Business University of Iowa United States

Social recommender systems are crucial in the context of social commerce platforms. Despite recent advancements in this field, we have identified two key limitations in existing research. Firstly, current social recommenders are hindered by the over-smoothing issue, making it challenging to effectively identify influential social friends within the higher-order neighborhood and provide corresponding explanations. Secondly, existing social recommenders overlook the multi-aspect hidden features of items and social relationships. To tackle the problems, the study introduces the Neighborhood Extension enhanced Graph focused Attention networks (short for NEGA). In contrast to soft attention, our NEGA model adopts focused attention to find the attractive products and the trusted social friends. To tackle the challenges posed by sparse social networks, the neighborhood extension method is proposed to further enhance recommendation performance. Additionally, we incorporate a parallel attention mechanism to capture the multi-aspect rating preferences and the multi-aspect impact of social relationships. Compared with twelve representative social recommenders on three real-world datasets, the baseline experiment demonstrates the competitive performance of our model. Finally, we conduct a series of visualized case studies to show the explainability of our model. The code is available at: https://***/usernameAI/NEGA. © 2024, The Authors. All rights reserved.

关键词： Social aspects

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Dual flow immunochromatographic assay for rapid and simultaneous quantitative detection of ochratoxin A and zearalenone in corn, wheat, and feed samples

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2018年第11期19卷 871-883页

作者： Xian ZHANG Ke HE Yun FANG Tong CAO Narayan PAUDYAL Xiao-feng ZHANG Hou-hui SONG Xiao-liang LI Wei-huan FANG China-Australian Joint Laboratory for Animal Health Big Data Analytics Zhejiang Provincial Engineering Laboratory for Animal Health Inspection and Internet TechnologyCollege of Animal Science and TechnologyZhejiang A&F UniversityLin'an 311300China Zhejiang University Institute of Preventive Veterinary Medicine Zhejiang Provincial Key Laboratory of Preventive Veterinary MedicineHangzhou 310058China Zhejiang Academy of Science and Technology for Inspection and Quarantine Hangzhou 310012China

A one-step dual flow immunochromatographic assay （DICGA）, based on a competitive format, was de- veloped for simultaneous quantification of ochratoxin A （OTA） and zearalenone （ZEN） in corn, wheat, and feed sam- ples. The limit of detection for OTA was 0.32 ng/ml with a detection range of 0.53-12.16 ng/ml, while for ZEN it was 0.58 ng/ml with a detection range of 1.06-39.72 ng/ml. The recovery rates in corn, wheat, and feed samples ranged from 77.3% to 106.3% with the coefficient of variation lower than 15%. Naturally contaminated corn, wheat, and feed samples were analyzed using both DICGA and liquid chromatography-tandem mass spectrometry （LC-MS/MS） and the correlation between the two methods was evaluated using a regression analysis. The DICGA method shows great potential for simple, rapid, sensitive, and cost-effective quantitative detection of OTA and ZEN in food safety control.

关键词： Immunochromatographic assay Gold nanoparticles Ochratoxin A Zearalenone Quantification

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Transfer and Alignment Network for Generalized Category Discovery

arXiv

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arXiv 2023年

作者： An, Wenbin Tian, Feng Shi, Wenkai Chen, Yan Wu, Yaqiang Wang, Qianying Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Lenovo Research China Department of Engineering University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize both known and novel categories from an unlabeled dataset by leveraging another labeled dataset with only known categories. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. The former leads to unreliable estimation of learning targets for novel categories and the latter hinders models from learning discriminative features. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding

关键词： Alignment

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An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis

An Interactive Attention Mechanism Fusion Network for Aspect...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Yuan Gao Haokang Li College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China Hebei Polytechnic Institute College of Artificial Intelligence and Big Data Shijiazhuang China

The goal of aspect-based multimodal sentiment analysis (ABMSA) is to classify the sentiment associated with aspect words in a given context. Most current ABMSA models focus only on general inter-modal information interactions without considering both intra-modal and inter-modal information interactions and ignoring image noise. To address these issues, this paper proposes an Interactive Attention Mechanism Fusion Network (IAMFN) model. The model first designs an image-text fusion module based on the attention, which applies the attention mechanism to a recurrent neural network to fuse text and images while filtering the noise in the images, and finally adds the fused information to the aspect information step by step to obtain the dynamic inter-modal representation. In addition, this paper proposes an aspect-text fusion module based on the attention, which learns the intra-modal contextual representation by calculating the weights of each aspect word in the context. Finally, this paper stitches the information obtained from the two modules and feeds it into the fully connected softmax layers to predict sentiment polarity. We have conducted extensive experiments on two benchmark datasets and the experimental results show that our model achieves state-of-the-art performance.

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An automated workflow for integrating environmental sustainability assessment into parametric part design through standard reference models

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Procedia CIRP 2020年 90卷 102-108页

作者： William Z. Bernstein Melissa Tensa Maxwell Praniewicz Soonjo Kwon Devarajan Ramanujan Engineering Laboratory National Institute of Standards and Technology MD USA School of Mechanical Industrial and Manufacturing Engineering Oregon State University OR USA George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GR USA Associate Engineering Laboratory National Institute of Standards and Technology MD USA Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Mechanical Engineering Section & Center for Digitalization Big Data and Data Analytics Aarhus University Aarhus 8000 Denmark

Integrating environmental sustainability assessment into parametric design presents an opportunity for enabling sustainable product design. However, this is a challenging task as quantitative methods for sustainability assessment are poorly integrated with parametric design and optimization tools. Furthermore, current streamlined approaches for computing environmental impact during the design stage often ignore manufacturing-related impacts resulting from the geometric complexity of parts. To address these gaps, we present a systematic workflow for computing environmental indicators from parametric design models. Our workflow utilizes the unit manufacturing process information model to evaluate manufacturing-phase resource consumption from process planning data, and consequently enables quantitative correlation of design parameters to the calculated environmental indicators. We demonstrate our workflow through a case study involving the design of a rigid flange coupling, wherein we evaluate the influence of geometric design parameters on the corresponding environmental impact of the design.

关键词： Life cycle assessment (LCA) Computer aided design (CAD) Computer aided manufacturing (CAM) Screening design

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Serendipity of Sharing: Large-Scale Measurement and analytics for Device-to-Device (D2D) Content Sharing in Mobile Social Networks

Serendipity of Sharing: Large-Scale Measurement and Analytic...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Xiaofei Wang Hui Wang Keqiu Li Shusen Yang Tianpeng Jiang Tianjin Key Laboratory of Advanced Networking Tianjin University Tianjin China National Engineering Laboratory for Big Data Algorithms and Analytics Technology Xi'an Jiaotong University Xi'an China Beijing Anqi Zhilian Technology Co. Ltd. Beijing China

ISBN: (纸本)9781509066001

The heavy multimedia traffic produced by mobile users poses great challenges for the mobile network operators, especially in the areas with large user densities but limited cellular network capacities (e.g. India). Recently, many studies demonstrate that exploiting the device-to- device(D2D) content sharing in offline Mobile Social Networks is a promising solution to cellular data offloading. However, such approaches are based on either unrealistic assumptions, or limited data analytics caused by small data size (e.g. hundreds of MSN users) or single-dimensional feature (e.g. human mobility only), which severely restricts their applications in practice. To address this issue, this paper performs the first large-scale data measurement and multi-feature analytics of D2D content sharing. Specifically, by using Apache Spark over a 20-server cluster, we analyze the behaviors of 30 million users (with 40 billion D2D transmissions and 16 million content files) of Xender, a leading global D2D sharing platform. Several important features are studied, including performance basics, content properties, location relations, meeting dynamics, and social characteristics. Furthermore, as a proof-of-concept study of our analytics, we also develop a multi-feature learning based framework, which demonstrates the large potentials of predicting and recommending D2D sharing activities using machine learning methods.

关键词： Device-to-device communication Mobile computing Videos Multimedia communication Social network services Cellular networks

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$\rho$ -Dominant Skyline Computation on data Stream

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IEEE Access 2018年 6卷 53201-53213页

作者： Zhiqiong Wang Junchang Xin Linlin Ding Jianmin Ba Xiaosong Gao Neusoft Institute of Intelligent Healthcare Technology Co. Ltd. Key Laboratory of Big Data Management and Analytics Shenyang China School of Information Liaoning University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang China

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, p-dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional p-dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of p-dominant skyline query is proposed. First, the definitions of full-dominant relationship and p-dominance are given, after which the definitions of p-dominant skyline and p-dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based p-dominant skyline, a new data filtering method is established. Based on the method, p-Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of p-dominant skyline query. After that, we expend the p-dominant skyline query of data stream into n-of-N p-dominant skyline query and the (n1, n 2 )-of-Np-dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional p-dominant skyline query algorithms through abundant experiments.

关键词： IEEE merchandise Microsoft Windows Indexes Decision making Heuristic algorithms Computer science

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Weakly-Supervised Action Localization by Hierarchically-structured Latent Attention Modeling

Weakly-Supervised Action Localization by Hierarchically-stru...

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International Conference on Computer Vision (ICCV)

作者： Guiqin Wang Peng Zhao Cong Zhao Shusen Yang Jie Cheng Luziwei Leng Jianxing Liao Qinghai Guo School of Computer Science and Technology Xi’an Jiao Tong University School of Mathematics and Statistics Xi’an Jiao Tong University National Engineering Laboratory for Big Data Analytics Xi’an Jiao Tong University ACS Lab Huawei Technologies

Weakly-supervised action localization aims to recognize and localize action instancese in untrimmed videos with only video-level labels. Most existing models rely on multiple instance learning(MIL), where the predictions of unlabeled instances are supervised by classifying labeled bags. The MIL-based methods are relatively well studied with cogent performance achieved on classification but not on localization. Generally, they locate temporal regions by the video-level classification but overlook the temporal variations of feature semantics. To address this problem, we propose a novel attention-based hierarchically-structured latent model to learn the temporal variations of feature semantics. Specifically, our model entails two components, the first is an unsupervised change-points detection module that detects change-points by learning the latent representations of video features in a temporal hierarchy based on their rates of change, and the second is an attention-based classification model that selects the change-points of the foreground as the boundaries. To evaluate the effectiveness of our model, we conduct extensive experiments on two benchmark datasets, THUMOS-14 and ActivityNet-v1.3. The experiments show that our method outperforms current state-of-the-art methods, and even achieves comparable performance with fully-supervised methods.

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Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text

arXiv

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arXiv 2023年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Zhao, Tianzhe Zhang, Jian Zhang, Lingling School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D National Engineering lab for Big Data Analytics Xi’an China

Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of options as the input, previous methods achieve superior performances on the full-data setting. However, the current benchmark dataset has the ideal assumption that the reasoning type distribution on the train split is close to the test split, which is inconsistent with many real application scenarios. To address it, there remain two problems to be studied: (1) How is the zero-shot capability of the models (train on seen types and test on unseen types)? (2) How to enhance the perception of reasoning types for the models? For problem 1, we propose a new benchmark for generalized zero-shot logical reasoning, named ZsLR. It includes six splits based on the three type sampling strategies. For problem 2, a type-aware model TaCo is proposed. It utilizes both the heuristic input reconstruction and the contrastive learning to improve the type perception in the global representation. Extensive experiments on both the zero-shot and full-data settings prove the superiority of TaCo over the state-of-the-art methods. Also, we experiment and verify the generalization capability of TaCo on other logical reasoning dataset. © 2023, CC BY.

关键词： Benchmarking

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Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning

arXiv

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arXiv 2022年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Pan, Yudai Zhang, Lingling School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&d National Engineering Lab for Big Data Analytics Xi'an China

Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks. © 2022, CC BY.

关键词： Embeddings

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