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33rd ACM Web Conference, WWW 2024

作者： Li, Jiatong Liu, Qi Wang, Fei Liu, Jiayu Huang, Zhenya Yao, Fangzhou Zhu, Linbo Su, Yu University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China Hefei Normal University Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei China

ISBN: (纸本)9798400701719

Personalized learner modeling using cognitive diagnosis (CD), which aims to model learners' cognitive states by diagnosing learner traits from behavioral data, is a fundamental yet significant task in many web learning services. Existing cognitive diagnosis models (CDMs) follow theproficiency-response paradigm that views learner traits and question parameters as trainable embeddings and learns them through learner performance prediction. However, we notice that this paradigm leads to the inevitable non-identifiability and explainability overfitting problem, which is harmful to the quantification of learners' cognitive states and the quality of web learning services. To address these problems, we propose an identifiable cognitive diagnosis framework (ID-CDF) based on a novelresponse-proficiency-response paradigm inspired by encoder-decoder models. Specifically, we first devise the diagnostic module of ID-CDF, which leverages inductive learning to eliminate randomness in optimization to guarantee identifiability and captures the monotonicity between overall response data distribution and cognitive states to prevent explainability overfitting. Next, we propose a flexible predictive module for ID-CDF to ensure diagnosis preciseness. We further present an implementation of ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on four real-world datasets with different characteristics demonstrate that ID-CDF can effectively address the problems without loss of diagnosis preciseness. Our code is available at https://***/CSLiJT/ID-CDF. © 2024 ACM.

关键词： Learning systems

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Cooperative Classification and Rationalization for Graph Generalization

arXiv

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arXiv 2024年

作者： Yue, Linan Liu, Qi Liu, Ye Gao, Weibo Yao, Fangzhou Li, Wenfeng Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Douyin Vision Co. Ltd Beijing China

Graph Neural Networks (GNNs) have achieved impressive results in graph classification tasks, but they struggle to generalize effectively when faced with out-of-distribution (OOD) data. Several approaches have been proposed to address this problem. Among them, one solution is to diversify training distributions in vanilla classification by modifying the data environment, yet accessing the environment information is complex. Besides, another promising approach involves rationalization, extracting invariant rationales for predictions. However, extracting rationales is difficult due to limited learning signals, resulting in less accurate rationales and diminished predictions. To address these challenges, in this paper, we propose a Cooperative Classification and Rationalization (C2R) method, consisting of the classification and the rationalization module. Specifically, we first assume that multiple environments are available in the classification module. Then, we introduce diverse training distributions using an environment-conditional generative network, enabling robust graph representations. Meanwhile, the rationalization module employs a separator to identify relevant rationale subgraphs while the remaining non-rationale subgraphs are de-correlated with labels. Next, we align graph representations from the classification module with rationale subgraph representations using the knowledge distillation methods, enhancing the learning signal for rationales. Finally, we infer multiple environments by gathering non-rationale representations and incorporate them into the classification module for cooperative learning. Extensive experimental results on both benchmarks and synthetic datasets demonstrate the effectiveness of C2R. Code is available at https://***/yuelinan/Codes-of-C2R. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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TimeMAE: Self-Supervised Representations of Time Series with Decoupled Masked Autoencoders

arXiv

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arXiv 2023年

作者： Cheng, Mingyue Liu, Qi Liu, Zhiding Zhang, Hao Zhang, Rujiao Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science University of Science and Technology of China Anhui Hefei230027 China

Enhancing the expressive capacity of deep learning-based time series models with self-supervised pre-training has become ever-increasingly prevalent in time series classification. Even though numerous efforts have been devoted to developing self-supervised models for time series data, we argue that the current methods are not sufficient to learn optimal time series representations due to solely unidirectional encoding over sparse point-wise input units. In this work, we propose TimeMAE, a novel self-supervised paradigm for learning transferrable time series representations based on transformer networks. The distinct characteristics of the TimeMAE lie in processing each time series into a sequence of non-overlapping sub-series via window-slicing partitioning, followed by random masking strategies over the semantic units of localized sub-series. Such a simple yet effective setting can help us achieve the goal of killing three birds with one stone, i.e., (1) learning enriched contextual representations of time series with a bidirectional encoding scheme;(2) increasing the information density of basic semantic units;(3) efficiently encoding representations of time series using transformer networks. Nevertheless, it is a non-trivial to perform reconstructing task over such a novel formulated modeling paradigm. To solve the discrepancy issue incurred by newly injected masked embeddings, we design a decoupled autoencoder architecture, which learns the representations of visible (unmasked) positions and masked ones with two different encoder modules, respectively. Furthermore, we construct two types of informative targets to accomplish the corresponding pretext tasks. One is to create a tokenizer module that assigns a codeword to each masked region, allowing the masked codeword classification (MCC) task to be completed effectively. Another one is to adopt a siamese network structure to generate target representations for each masked input unit, aiming at performing the mask

关键词： Time series

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Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform

arXiv

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arXiv 2024年

作者： Cheng, Mingyue Zhang, Hao Yang, Jiqian Liu, Qi Li, Li Huang, Xin Song, Liwei Li, Zhi Huang, Zhenya Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Shenzhen International Graduate School Tsinghua University Shenzhen China

Large language model evaluation plays a pivotal role in the enhancement of its capacity. Previously, numerous methods for evaluating large language models have been proposed in this area. Despite their effectiveness, these existing works mainly focus on assessing objective questions, overlooking the capability to evaluate subjective questions which is extremely common for large language models. Additionally, these methods predominantly utilize centralized datasets for evaluation, with question banks concentrated within the evaluation platforms themselves. Moreover, the evaluation processes employed by these platforms often overlook personalized factors, neglecting to consider the individual characteristics of both the evaluators and the models being evaluated. To address these limitations, we propose a novel anonymous crowd-sourcing evaluation platform, BingJian, for large language models that employs a competitive scoring mechanism where users participate in ranking models based on their performance. This platform stands out not only for its support of centralized evaluations to assess the general capabilities of models but also for offering an open evaluation gateway. Through this gateway, users have the opportunity to submit their questions, testing the models on a personalized and potentially broader range of capabilities. Furthermore, our platform introduces personalized evaluation scenarios, leveraging various forms of human-computer interaction to assess large language models in a manner that accounts for individual user preferences and contexts. The demonstration of BingJian can be accessed at https://***/Mingyue-Cheng/Bingjian. Copyright © 2024, The Authors. All rights reserved.

关键词： Crowdsourcing

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Hawk‐eye‐inspired perception algorithm of stereo vision for obtaining orchard 3D point cloud navigation map

引用

CAAI Transactions on Intelligence Technology 2023年第3期8卷 987-1001页

作者： Zichao Zhang Jian Chen Xinyu Xu Cunjia Liu Yu Han College of Engineering China Agricultural UniversityBeijingChina Jiangsu Province and Education Ministry Co‐sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiangChina Key Laboratory of Spatial‐temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghaiChina Department of Aeronautical and Automotive Engineering Loughborough UniversityLoughboroughLeicestershireUK College of Water Resources and Civil Engineering China Agricultural UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijingChina Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhenChina

The binocular stereo vision is the lowest cost sensor for obtaining 3D *** the weakness of long‐distance measurement and stability,the improvement of accuracy and stability of stereo vision is urgently required for application of precision *** address the challenges of stereo vision long‐distance measurement and stable perception without hardware upgrade,inspired by hawk eyes,higher resolution perception and the adaptive HDR(High Dynamic Range)were introduced in this *** the function from physiological structure of‘deep fovea’and‘shallow fovea’of hawk eye,the higher resolution reconstruction method in this paper was aimed at ac-curacy *** by adjustment of pupils,the adaptive HDR method was proposed for high dynamic range optimisation and stable *** various light conditions,compared with default stereo vision,the accuracy of proposed algorithm was improved by 28.0%evaluated by error ratio,and the stability was improved by 26.56%by disparity *** fixed distance measurement,the maximum improvement was 78.6%by standard *** on the hawk‐eye‐inspired perception algorithm,the point cloud of orchard was improved both in quality and *** hawk‐eye‐inspired perception algorithm contributed great advance in binocular 3D point cloud recon-struction in orchard navigation map.

关键词： adaptive high dynamic range binocular stereo vision hawk‐eye‐inspired perception point cloud of orchard super‐resolution generative adversarial network

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Dependency and Association Enhanced Finding Similar Exercises in Online Education System 8

Dependency and Association Enhanced Finding Similar Exercise...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Tang, Zihang Wang, Hao Gao, Weibo Huang, Zhenya Liu, Qi Yu, Jun University of Science and Technology of China School of Data Science China University of Science and Technology of China Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology China IFLYTEK CO. LTD. IFLYTEK Research

ISBN: (纸本)9781665477352

Finding Similar Exercises (FSE) is a tough nut to crack in online education systems, especially among large scale exercise corpus. Given exercises with associated attributes, contents (texts and images) and knowledge concepts, most solutions learn exercise representations in isolation, where only contents or concepts are exploited. Though a few works try to combine concept-exercise associations for capturing long-range similarity among exercises, they just utilize a single relation among concepts but neglect the multiple dependencies. In this paper, we propose a novel Dependency and Association enhanced Finding Similar Exercises framework, namely DAFSE, which can consider both the concept-exercise associations and the multiple dependency relations among concepts, simultaneously. Specifically, we first build a concept-exercise-content multi-layer graph and embed each concept, exercise and content node through an Embedding Layer. Then we introduce Attentive Combination Layer to effectively preserve the long-range similarity as well as rich domain knowledge to exercise representations by propagating on the multi-layer graph. Finally, we use a Similarity Score Layer to assess the similarity of exercise pairs. Extensive experiments on real-world datasets demonstrate the effectiveness of DAFSE. © 2022 IEEE.

关键词： Online systems

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DARE: disentanglement-augmented rationale extraction 22

DARE: disentanglement-augmented rationale extraction

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Linan Yue Qi Liu Yichao Du Yanqing An Li Wang Enhong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence and ByteDance

ISBN: (纸本)9781713871088

Rationale extraction can be considered as a straightforward method of improving the model explainability, where rationales are a subsequence of the original inputs, and can be extracted to support the prediction results. Existing methods are mainly cascaded with the selector which extracts the rationale tokens, and the predictor which makes the prediction based on selected tokens. Since previous works fail to fully exploit the original input, where the information of non-selected tokens is ignored, in this paper, we propose a Disentanglement-Augmented Rationale Extraction (DARE) method, which encapsulates more information from the input to extract rationales. Specifically, it first disentangles the input into the rationale representations and the non-rationale ones, and then learns more comprehensive rationale representations for extracting by minimizing the mutual information (MI) between the two disentangled representations. Besides, to improve the performance of MI minimization, we develop a new MI estimator by exploring existing MI estimation methods. Extensive experimental results on three real-world datasets and simulation studies clearly validate the effectiveness of our proposed method.

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Communication-Efficient Distributed Learning with Local Immediate Error Compensation

arXiv

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arXiv 2024年

作者： Cheng, Yifei Shen, Li Xu, Linli Qian, Xun Wu, Shiwei Zhou, Yiming Zhang, Tie Tao, Dacheng Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application State Key Laboratory of Cognitive Intelligence School of Data Science School of Computer Science University of Science and Technology China The JD Explore Academy China The Shanghai Artificial Intelligence Laboratory China

Gradient compression with error compensation has attracted significant attention with the target of reducing the heavy communication overhead in distributed learning. However, existing compression methods either perform only unidirectional compression in one iteration with higher communication cost, or bidirectional compression with slower convergence rate. In this work, we propose the Local Immediate Error Compensated SGD (LIEC-SGD) optimization algorithm to break the above bottlenecks based on bidirectional compression and carefully designed compensation approaches. Specifically, the bidirectional compression technique is to reduce the communication cost, and the compensation technique compensates the local compression error to the model update immediately while only maintaining the global error variable on the server throughout the iterations to boost its efficacy. Theoretically, we prove that LIEC-SGD is superior to previous works in either the convergence rate or the communication cost, which indicates that LIEC-SGD could inherit the dual advantages from unidirectional compression and bidirectional compression. Finally, experiments of training deep neural networks validate the effectiveness of the proposed LIEC-SGD algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Error compensation

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Queue Length Prediction Method of Short-linked Intersections Based on Long Short-Term Memory 9

Queue Length Prediction Method of Short-linked Intersections...

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9th International Conference on Systems and Informatics, ICSAI 2023

作者： Shi, Kang Shi, Kang Yang, Jun Liang, Zi Jun Jun Liang, Zi Li, Yu Qi Wang, Rui Han Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai201804 China Anhui Chaoyuan Information Technology Co. Ltd HeFei230088 China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Hefei230601 China Hefei University School of Urban Construction and Transportation Hefei230601 China

ISBN: (纸本)9798350383706

To address traffic congestion and vehicle queue overflow at short-link intersections, it is essential to conduct a predictive analysis of vehicle queue length between these intersections. This helps in proactively implementing appropriate traffic management measures to prevent traffic overflow and congestion. In this paper, we propose a short-link intersection vehicle queue length prediction method based on Long Short-Term Memory (LSTM). First, historical traffic data from the morning peak hours over the past month is collected. After comparing multiple machine learning models, LSTM is chosen for traffic flow prediction. Subsequently, mathematical models are used to predict queue length at future time intervals. Through practical validation, we found that the proposed method for predicting queue length between short-link intersections in this paper is effective, with an average prediction error rate of 11.9%. It meets the requirements for predicting vehicle queue length between intersections and proactively controlling them, holding significant practical significance for preventing traffic flow and congestion events. © 2023 IEEE.

关键词： Long short-term memory

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AdaptSSR: pre-training user model with augmentation-adaptive self-supervised ranking 23

AdaptSSR: pre-training user model with augmentation-adaptive...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yang Yu Qi Liu Kai Zhang Yuren Zhang Chao Song Min Hou Yuqing Yuan Zhihao Ye Zaixi Zhang Sanshi Lei Yu Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence OPPO Research Institute Hefei University of Technology

User modeling, which aims to capture users' characteristics or interests, heavily relies on task-specific labeled data and suffers from the data sparsity issue. Several recent studies tackled this problem by pre-training the user model on massive user behavior sequences with a contrastive learning task. Generally, these methods assume different views of the same behavior sequence constructed via data augmentation are semantically consistent, i.e., reflecting similar characteristics or interests of the user, and thus maximizing their agreement in the feature space. However, due to the diverse interests and heavy noise in user behaviors, existing augmentation methods tend to lose certain characteristics of the user or introduce noisy behaviors. Thus, forcing the user model to directly maximize the similarity between the augmented views may result in a negative transfer. To this end, we propose to replace the contrastive learning task with a new pretext task: Augmentation-Adaptive Self-Supervised Ranking (AdaptSSR), which alleviates the requirement of semantic consistency between the augmented views while pre-training a discriminative user model. Specifically, we adopt a multiple pairwise ranking loss which trains the user model to capture the similarity orders between the implicitly augmented view, the explicitly augmented view, and views from other users. We further employ an in-batch hard negative sampling strategy to facilitate model training. Moreover, considering the distinct impacts of data augmentation on different behavior sequences, we design an augmentation-adaptive fusion mechanism to automatically adjust the similarity order constraint applied to each sample based on the estimated similarity between the augmented views. Extensive experiments on both public and industrial datasets with six downstream tasks verify the effectiveness of AdaptSSR.

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