ISSN 1000-1239 CN 11-1777/TP

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    Journal of Computer Research and Development    2021, 58 (12): 2571-2572.   DOI: 10.7544/issn1000-1239.2021.qy1201
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    Interpretable Few-Shot Learning with Contrastive Constraint
    Zhang Lingling, Chen Yiwei, Wu Wenjun, Wei Bifan, Luo Xuan, Chang Xiaojun, Liu Jun
    Journal of Computer Research and Development    2021, 58 (12): 2573-2584.   DOI: 10.7544/issn1000-1239.2021.20210999
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    Different from deep learning with large scale supervision, few-shot learning aims to learn the samples characteristics from a few labeled examples. Apparently, few-shot learning is more in line with the visual cognitive mechanism of the human brain. In recent years, few-shot learning has attracted more researchers attention. In order to discover the semantic similarities between the query set (unlabeled image) and support set (few labeled images) in feature embedding space, methods which combine meta-learning and metric learning have emerged and achieved great performance on few-shot image classification tasks. However, these methods lack the interpretability, which means they could not provide a reasoning explainable process like human cognitive mechanism. Therefore, we propose a novel interpretable few-shot learning method called INT-FSL based on the positional attention mechanism, which aims to reveal two key problems in few-shot classification: 1)Which parts of the unlabeled image play an important role in classification task; 2)Which class of features reflected by the key parts. Besides, we design the contrastive constraints on global and local levels in every few-shot meta task, for alleviating the limited supervision with the internal information of the data. We conduct extensive experiments on three image benchmark datasets. The results show that the proposed model INT-FSL not only could improve the classification performance on few-shot learning effectively, but also has good interpretability in the reasoning process.
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    Spatio-Clock Synchronous Constraint Guided Safe Reinforcement Learning for Autonomous Driving
    Wang Jinyong, Huang Zhiqiu, Yang Deyan, Xiaowei Huang, Zhu Yi, Hua Gaoyang
    Journal of Computer Research and Development    2021, 58 (12): 2585-2603.   DOI: 10.7544/issn1000-1239.2021.20211023
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    Autonomous driving systems integrate complex interactions between hardware and software. In order to ensure the safe and reliable operations, formal methods are used to provide rigorous guarantees to satisfy logical specifications and safety-critical requirements in the design stage. As a widely employed machine learning architecture, deep reinforcement learning (DRL) focuses on finding an optimal policy that maximizes a cumulative discounted reward by interacting with the environment, and has been applied to autonomous driving decision-making modules. However, black-box DRL-based autonomous driving systems cannot provide guarantees of safe operation and reward definition interpretability techniques for complex tasks, especially when they face unfamiliar situations and reason about a greater number of options. In order to address these problems, spatio-clock synchronous constraint is adopted to augment DRL safety and interpretability. Firstly, we propose a dedicated formal properties specification language for autonomous driving domain, i.e., spatio-clock synchronous constraint specification language, and present domain-specific knowledge requirements specification that is close to natural language to make the reward functions generation process more interpretable. Secondly, we present domain-specific spatio-clock synchronous automata to describe spatio-clock autonomous behaviors, i.e., controllers related to certain spatio- and clock-critical actions, and present safe state-action space transition systems to guarantee the safety of DRL optimal policy generation process. Thirdly, based on the formal specification and policy learning, we propose a formal spatio-clock synchronous constraint guided safe reinforcement learning method with the goal of easily understanding the safe reward function. Finally, we demonstrate the effectiveness of our proposed approach through an autonomous lane changing and overtaking case study in the highway scenario.
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    NeuroSymbolic Task and Motion Planner for Disassembly Electric Vehicle Batteries
    Ren Wei, Wang Zhigang, Yang Hua, Zhang Yisheng, Chen Ming
    Journal of Computer Research and Development    2021, 58 (12): 2604-2617.   DOI: 10.7544/issn1000-1239.2021.20211002
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    Establishing a perfect electric vehicle battery recycling system is one of the bottlenecks that need to be broken through in pursuit of high-quality development of new energy vehicles in our country. Disassembly technology will play an important role in research and development of intelligent, flexible, and refined high-efficiency. Due to its unstructured environment and high uncertainties, disassembling batteries is primarily accomplished by humans with a fixed robot-assisted battery disassembly workstation. This method is highly inefficient and in dire need of being upgraded to an automated and intelligent one to exempt humans from being exposed to the high voltage and toxic working conditions. The process of removing and sorting electric vehicle batteries represents a significant challenge to the automation industry since used batteries are of distinctive specifications that renders pre-programming impossible. A novel framework for NeuroSymbolic based task and motion planning method to automatically disassemble batteries in unstructured environment using robots is proposed. It enables robots to independently locate and loose battery bolts, with or without obstacles. This method has advantages in its autonomy, scalability, explicability, and learnability. These advantages pave the way for more accurate and robust system to disassemble electric vehicle battery packs using robots. This study not only provides a solution for intelligently disassembling electric vehicle batteries, but also verifies its feasibility through a set of test results with the robot accomplishing the disassemble task in a complex and dynamic environment.
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    Interpretable Deep Knowledge Tracing
    Liu Kunjia, Li Xinyi, Tang Jiuyang, Zhao Xiang
    Journal of Computer Research and Development    2021, 58 (12): 2618-2629.   DOI: 10.7544/issn1000-1239.2021.20211021
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    The task of knowledge tracing involves tracking users’ cognitive states by modeling their exercise-answering sequence, predicting their performance over time, and achieving an intelligent assessment of the users’ knowledge. Current works mainly model the skills related to the exercises, while ignoring the rich information contained in the contexts of exercises. Moreover, the current deep learning-based methods are agnostic, which undermines the explainability of the model. In this paper, we propose an interpretable deep knowledge tracking (IDKT) framework. First, we alleviate the data sparsity problem by using the contextual information of the exercises and skills to obtain more representative exercise and skill representations. Then the hidden knowledge states are fused with the aforementioned embeddings to learn a personalized attention, which is later used to aggregate neighbor embeddings in the exercise-skill graph. Finally, given a prediction result, an inference path is selected as the explanation based on the personalized attention. Compared with typical existing methods, IDKT exhibits its superiority by not only achieving the best prediction performance, but also providing an explanation at the inference path level for the prediction results.
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    Hierarchical Attention Network Based Interpretable Knowledge Tracing
    Sun Jianwen, Zhou Jianpeng, Liu Sannüya, He Feijuan, Tang Yun
    Journal of Computer Research and Development    2021, 58 (12): 2630-2644.   DOI: 10.7544/issn1000-1239.2021.20210997
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    Knowledge tracing is a data-driven learner modeling technology, which aims to predict learners’ knowledge mastery or future performance based on their historical learning data. Recently, with the support of deep learning algorithms, deep learning-based knowledge tracing has become a current research hotspot in the field. Aiming at the problems that deep learning-based knowledge tracing models generally have ‘black-box’ attributes, the decision-making process or results lack interpretability, and it is difficult to provide high-value education services such as learning attribution analysis and wrong cause backtracking, a Hierarchical Attention network based Knowledge Tracing model (HAKT) is proposed. By mining the multi-dimensional and in-depth semantic association between questions, a network structure containing three-layer attention of questions, semantics and elements is established, where graph attention neural network and self-attention mechanism are utilized for question representation learning, semantic fusion and questions retrieve. A regularization term to improve model interpretability is introduced into the loss function, with which a trade-off factor is incorporated to balance predictive performance and interpretability of model. Besides, we define an interpretability measurement index for the prediction results—fidelity, which can quantitatively evaluate the model interpretability. Finally, the experimental results on 6 benchmark datasets show that our method effectively improves the model interpretability.
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    Dr.Deep: Interpretable Evaluation of Patient Health Status via Clinical Feature’s Context Learning
    Ma Liantao, Zhang Chaohe, Jiao Xianfeng, Wang Yasha, Tang Wen, Zhao Junfeng
    Journal of Computer Research and Development    2021, 58 (12): 2645-2659.   DOI: 10.7544/issn1000-1239.2021.20211022
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    Deep-learning-based health status representation learning is a fundamental research problem in clinical prediction and has raised much research interest. Existing models have shown superior performance, but they fail to explore personal characteristics and provide fine-grained interpretability thoroughly. In this work, we develop a general health status
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    Reciprocal-Constrained Interpretable Job Recommendation
    Zhu Haiping, Zhao Chengcheng, Liu Qidong, Zheng Qinghua, Zeng Jiangwei, Tian Feng, Chen Yan
    Journal of Computer Research and Development    2021, 58 (12): 2660-2672.   DOI: 10.7544/issn1000-1239.2021.20211008
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    Current college student job recommendation methods based on collaborative filtering and latent factor model only consider job interests of students and ignore the requirements of employers, often leading to ‘capability mismatch’. Moreover, in most of the historical employment data, only one employment record per student is stored, which leads to unreliable negative samples and affects recommendation performance. Additionally, many methods ignore the demand for recommendation result interpretability. To this end, inspired by the idea of multi-task learning, we construct a reciprocal-constrained interpretable job recommendation method. In which, we introduce attention mechanism to extract bidirectional preferences of both students and employers, and then use fuzzy gate mechanism to adaptively aggregate them in order to alleviate the problem of capability mismatch. Next, we propose a recommendation interpretation module oriented to employer intention and employer characteristics to meet the interpretability demand. We also propose a similarity-based negative sampling method to solve the problem of incredible negative samples. The results of experiment on a real-world undergraduate employment dataset of five years, EMDAU, indicate that our method outperforms other classic and state-of-art recommendation methods and has over 6% improvement in AUC. Besides, the results of ablation experiments conducted verify the effectiveness of each module in our method.
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    Graph Matching Network for Interpretable Complex Question Answering over Knowledge Graphs
    Sun Yawei, Cheng Gong, Li Xiao, Qu Yuzhong
    Journal of Computer Research and Development    2021, 58 (12): 2673-2683.   DOI: 10.7544/issn1000-1239.2021.20211004
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    Question answering over knowledge graphs is a trending research topic in artificial intelligence. In this task, the semantic matching between the structures of a natural language question and a knowledge graph is a challenging research problem. Existing works mainly use a sequence-based deep neural encoder to process questions. They construct a semantic matching model to compute the similarity between question structures and subgraphs of a knowledge graph. However, they could not exploit the structure of a complex question, and they lack interpretability. To alleviate this issue, this paper presents a graph matching network (GMN) based method for answering complex questions of a knowledge graph, called TTQA. This method firstly constructs an ungrounded query graph which is independent of the knowledge graph via syntactic parsing. Then, based on the ungrounded query graph and the knowledge graph, this method constructs a grounded query graph which is dependent on the knowledge graph. In particular, this paper proposes a cross-graph attention GMN which combines pre-trained language model and graph neural network to learn the context representation of a query. The context representation enhances the representation of graph matching which helps to predict a grounded query. Experimental results show that TTQA achieves state-of-the-art results on LC-QuAD 1.0 and ComplexWebQuestions 1.1. Ablation studies demonstrate the effectiveness of GMN. In addition, TTQA keeps the ungrounded query and the grounded query to enhance the interpretability of question answering.
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