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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective

Group-Level Cognitive Diagnosis: A Multi-Task Learning Persp...

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IEEE International Conference on data Mining (ICDM)

作者： Jie Huang Qi Liu Fei Wang Zhenya Huang Songtao Fang Runze Wu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Fuxi AI Lab NetEase Inc. Hangzhou China IFLYTEK Research

ISBN: (纸本)9781665423991

Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.

关键词： Knowledge engineering Conferences Education Neural networks Multitasking data models data mining

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An Orbital Angular Momentum Mode Estimation Method with an Unknown Beam Axis

An Orbital Angular Momentum Mode Estimation Method with an U...

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IEEE SENSORS

作者： Gaofeng Shu Bingxu Chen Ning Li School of Computer and Information Engineering Henan University Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

Orbital angular momentum (OAM) beam is very difficult to receive due to the divergence of the beam. The position of the beam axis must be known by the existing receiving methods, including the single point method and the phase gradient method. This article proposes an OAM mode estimation method with an unknown beam axis. This method first estimates the position of the beam axis according to the phase characteristics and then uses the phase gradient method to estimate the OAM mode. The simulation results verify the effectiveness of the method.

关键词： Gradient methods Simulation Estimation Orbits Sensors

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Exploiting cognitive structure for adaptive learning

arXiv

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arXiv 2019年

作者： Liu, Qi Tong, Shiwei Liu, Chuanren Zhao, Hongke Chen, Enhong Ma, Haiping Wang, Shijin Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology School of Data Science University of Science and Technology of China Business Analytics and Statistics University of Tennessee College of Management and Economics Tianjin University IFLYTEK Research IFLYTEK CO. LTD. State Key Laboratory of Cognitive Intelligence

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL. Copyright © 2019, The Authors. All rights reserved.

关键词： Markov processes

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Estimation Soil Organic Matter Using Airborne Hyperspectral Imagery 13

Estimation Soil Organic Matter Using Airborne Hyperspectral ...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Chen, Lihan Tan, Kun Wang, Xue Pan, Chen China University of Mining and Technology Key Laboratory for Land Environment and Disaster Monitoring of Nasg Xuzhou221116 China Shanghai200241 China East China Normal University Key Lab. of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai200241 China East China Normal University School of Geographic Sciences Shanghai200241 China Shanghai Municipal Institute of Surveying and Mapping Shanghai200063 China

ISBN: (纸本)9798350395570

Soil organic matter (SOM) content plays an important part in soil environmental quality definition and should be estimated necessarily. The conventional methods for the SOM concentration assessment are mainly based on laboratory physicochemical analysis, which is costly and time consuming. Visible and near-infrared (Vis-NIR) spectroscopy offers the potential to quantify the SOM over large areas based on the soil spectral characteristics. Therefore, an innovative methodology using visible and near-infrared reflectance spectra are proposed in this work to monitoring the SOM rapidly and economically. A total of 91 soil samples and their spectral data collected in Yitong of China were utilized to characterize the relationship between the soil reflectance spectrum and SOM. First, continuum removal (CR) and competitive adaptive reweighted sampling (CARS) are introduced as the pretreatment method and wavebands selection method respectively, which can amplify the weak spectral characteristic. After the preprocessing phases, Partial Least Squares (PLS), Random Forest (RF) and XGBoost are carried out to estimate the SOM and the results show that XGBoost yields the best performance with R2 of 0.9968 on training set and 0.6831 on testing set. Finally, the distribution trend of SOM in the whole study area is mapped using the optimal CR-CARS-XGBoost model. © 2023 IEEE.

关键词： Soils

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A Framework for Passengers Demand Prediction and Recommendation

A Framework for Passengers Demand Prediction and Recommendat...

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IEEE International Conference on Services Computing (SCC)

作者： Kai Zhang Zhiyong Feng Shizhan Chen Keman Huang Guiling Wang Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China School of Computer Science and Technologv Tianjin University Tianjin China School of Computer Software Tianjin University Tianjin China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing China

With the rapid development of mobile internet and wireless network technologies, more and more people use the mobile app to call a taxicab to pick them up. Therefore, understanding the passengers' travel demand becomes crucial to improve the utilization of the taxicabs and reduce their cost. In this paper, based on spatio-temporal clustering, we propose a demand hotspots prediction framework to generate recommendation for taxi drivers. Specially, an adaptive prediction approach is presented to demand hotspots and their hotness, and then combing the driver's location and the hotness, top candidates are recommended and visually presented to drivers. Based on the dataset provided by CAR INC., the experiment shows that our approach gains a significant improvement in hotspots prediction and recommendation, with 15.21% improvement on average f-measure for prediction and 79.6% hit ratio for recommendation.

关键词： Public transportation Trajectory Vehicles Global Positioning System Mobile communication Clustering algorithms Filtering algorithms

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An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation

arXiv

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arXiv 2024年

作者： Wang, Jun Wu, Likang Liu, Qi Yang, Yu School of Data Science City University of Hong Kong Kowloon Hong Kong College of Management and Economics Tianjin University Tianjin China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Anhui Hefei China State Key Laboratory of Cognitive Intelligence Anhui Hefei China

Sequential recommendation, where user preference is dynamically inferred from sequential historical behaviors, is a critical task in recommender systems (RSs). To further optimize long-term user engagement, offline reinforcement-learning-based RSs have become a mainstream technique as they provide an additional advantage in avoiding global explorations that may harm online users’ experiences. However, previous studies mainly focus on discrete action and policy spaces, which might have difficulties in handling dramatically growing items efficiently. To mitigate this issue, in this paper, we aim to design an algorithmic framework applicable to continuous policies. To facilitate the control in the low-dimensional but dense user preference space, we propose an Efficient Continuous Control framework (ECoC). Based on a statistically tested assumption, we first propose the novel unified action representation abstracted from normalized user and item spaces. Then, we develop the corresponding policy evaluation and policy improvement procedures. During this process, strategic exploration and directional control in terms of unified actions are carefully designed and crucial to final recommendation decisions. Moreover, beneficial from unified actions, the conservatism regularization for policies and value functions are combined and perfectly compatible with the continuous framework. The resulting dual regularization ensures the successful offline training of RL-based recommendation policies. Finally, we conduct extensive experiments to validate the effectiveness of our framework. The results show that compared to the discrete baselines, our ECoC is trained far more efficiently. Meanwhile, the final policies outperform baselines in both capturing the offline data and gaining long-term rewards. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Target Hierarchy-Guided Knowledge Tracing: Fine-Grained Knowledge State Modeling

SSRN

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SSRN 2024年

作者： Sun, Xinjie Zhang, Kai Shen, Shuanghong Wang, Fei Guo, Yuxiang Liu, Qi School of Computer Science and Technology University of Science and Technology of China Hefei China School of Data Science University of Science and Technology of China Hefei China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei China

Knowledge Tracing (KT) focuses on modeling the exercise process of students, assessing their knowledge state changes during the exercise process, and further providing targeted guidance for teaching and learning. Current KT models have made significant progress by utilizing deep learning technologies to model relevant attributes of the questions. However, in current knowledge tracing strategies, we have yet to exhaustively explore how to precisely evaluate the hierarchies of students’ knowledge mastery (encompassing Remembering, Understanding, Applying, Analyzing, Evaluating, and Creating) to better discern their current target hierarchy. To address the limitations of the KT model in accurately identifying and positioning target hierarchy, our research focuses on two key areas: first, we dynamically track students’ target hierarchies by evaluating their feedback on questions at different hierarchies;second, we assess their actual question-solving abilities within these target hierarchies, thereby optimizing our evaluation of their knowledge status. Based on these insights, we propose the Target Hierarchy-guided Knowledge Tracing (THKT) model. In this model, we first incorporate the identification of question hierarchy into the model representation. Then, to avoid homogenization of the target hierarchy, we dynamically track the appropriate hierarchy for students based on their varying feedback to questions of the same hierarchy, thereby pinpointing their learning target hierarchies. Simultaneously, we develop a KCs applied ability learning module that works in conjunction with the target hierarchies to generate interpretable prediction results. The proposed THKT model has been tested and evaluated using three public, real-world educational datasets. The findings clearly demonstrate that our approach shines in the realm of KT prediction tasks, providing significantly interpretability. For broader research, we plan to provide the source code at: https://***/xinj

关键词： Students

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GUESR: A Global Unsupervised data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation

arXiv

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arXiv 2023年

作者： Han, Yongqiang Wu, Likang Wang, Hao Wang, Guifeng Zhang, Mengdi Li, Zhi Lian, Defu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei China Huawei Technologies Co. Ltd. Hangzhou China Meituan-Dianping Group Beijing China

Sequential Recommendation is a widely studied paradigm for learning users’ dynamic interests from historical interactions for predicting the next potential item. Although lots of research work has achieved remarkable progress, they are still plagued by the common issues: data sparsity of limited supervised signals and data noise of accidentally clicking. To this end, several works have attempted to address these issues, which ignored the complex association of items across several sequences. Along this line, with the aim of learning representative item embedding to alleviate this dilemma, we propose GUESR, from the view of graph contrastive learning. Specifically, we first construct the Global Item Relationship Graph (GIRG) from all interaction sequences and present the Bucket-Cluster Sampling (BCS) method to conduct the sub-graphs. Then, graph contrastive learning on this reduced graph is developed to enhance item representations with complex associations from the global view. We subsequently extend the CapsNet module with the elaborately introduced target-attention mechanism to derive users’ dynamic preferences. Extensive experimental results have demonstrated our proposed GUESR could not only achieve significant improvements but also could be regarded as a general enhancement strategy to improve the performance in combination with other sequential recommendation methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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An Empirical Guide for Visualization Consistency in Multiple Coordinated Views

An Empirical Guide for Visualization Consistency in Multiple...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Shaocong Tan Chufan Lai Xiaolong Luke Zhang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China College of Information Sciences and Technology Pennsylvania State University USA National Engineering Laboratory for Big Data Analysis and Application Peking University China

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

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