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  • 基于深度强化学习的移动边缘计算资源分配策略

    分类: 计算机科学 >> 计算机科学技术其他学科 提交时间: 2023-01-17

    摘要:云计算可解决移动设备计算资源不足的问题,但无法满足低时延的服务需求,边缘计算作为云计算技术的延伸,可通过增强边缘网络计算能力从而为用户提供低时延高质量服务。边缘计算中,需要将服务部署于资源受限的边缘服务器,并根据需求合理分配计算资源,以提高边缘服务器资源利用率,为此,本文提出了一种基于深度强化学习的服务资源分配方法,利用反正切函数两次映射建立计算资源分配函数,并实现分配比例的动态调整,最后基于真实数据集进行仿真实验,实验结果表明,本文提出的方法能够在保证低时延的情况下,合理分配计算资源。 云计算可解决移动设备计算资源不足的问题,但无法满足低时延的服务需求,边缘计算作为云计算技术的延伸,可通过增强边缘网络计算能力从而为用户提供低时延高质量服务。边缘计算中,需要将服务部署于资源受限的边缘服务器,并根据需求合理分配计算资源,以提高边缘服务器资源利用率,为此,本文提出了一种基于深度强化学习的服务资源分配方法,利用反正切函数两次映射建立计算资源分配函数,并实现分配比例的动态调整,最后基于真实数据集进行仿真实验,实验结果表明,本文提出的方法能够在保证低时延的情况下,合理分配计算资源。

  • 人与聊天机器人关系发展对用户参与度的影响

    分类: 心理学 >> 应用心理学 分类: 计算机科学 >> 自然语言理解与机器翻译 提交时间: 2023-01-10

    摘要:

    随着人工智能(Artificial Intelligence, AI)技术的迅猛发展,AI聊天机器人可模拟人类指导以改善在线自助干预(Internet-based Self-help Interventions, ISIs)中用户的参与度及疗效。然而,学界对聊天机器人作用机制的探索尚处初期阶段。因此,为加深对这一问题的理性认识,文章基于人机关系的视角提出了适应ISIs情境的理论模型:聊天机器人可与用户经历拟人化启动、功利性价值判断、发展依恋关系、建立数字治疗联盟(Digital Therapeutic Alliance, DTA)这4个阶段来逐步发展人与聊天机器人关系(Human–Chatbot Relationships, HCRs),并通过HCRs提高用户参与度。未来研究可继续丰富HCRs的相关理论并检验其内在机制,基于HCRs理论来设计聊天机器人,深入考察影响HCRs的额外变量,统一参与度的操作定义并开发适合的参与度测量工具。

  • Disentangled Representation Transformer Network for 3D Face Reconstruction and Robust Dense Alignment

    分类: 计算机科学 >> 计算机科学技术其他学科 提交时间: 2023-01-06

    摘要:在本文中,我们提出了一种基于分解表示转换网络,该网络能够在无约束环境下恢复细节丰富的三维人脸,并能更精确地进行密集对齐。传统的3DMM参数回归的方法由于目标参数,即形状、表情、姿态参数都是单独估计的,没有考虑它们之间的直接影响,虽然它们是联合优化的,我们的DRTN方法目的在于通过学习不同3D人脸属性参数的相关性来强化语义上有意义的人脸属性表示。为此,我们提出并设计了一种新颖的分解三维人脸属性表示策略,它将给定的人脸属性分解为身份,表情和姿态部分。具体来说,回归网络中三维人脸参数的估计并不是独立的,还取决于其他人脸属性参数的关联性。身份部分的分支旨在通过保留整体的人脸几何形状结构并在身份不变的基础上强化表情和姿态属性的学习。相应的,表情和姿态部分的分支分别在保留表情和姿态属性一致性的前提下,耦合其他各个参数,以帮助细化人脸细节重建及大姿态下的人脸关键点对齐。在广泛评估的基准数据集上的大量定性和定量实验结果表明,与最先进的方法相比,我们的方法获得了具有竞争力的性能。

  • Copula熵:理论和应用

    分类: 统计学 >> 数理统计学 分类: 数学 >> 统计和概率 分类: 计算机科学 >> 计算机应用技术 分类: 信息科学与系统科学 >> 信息科学与系统科学基础学科 提交时间: 2022-12-13

    摘要:统计独立性是统计学和机器学习领域的基础性概念,如何表示和度量统计独立性是该领域的基本问题。Copula理论提供了统计相关关系表示的理论工具,而Copula熵理论则给出了度量统计独立性的概念工具。本文综述了Copula熵的理论和应用,概述了其基本概念定义、定理和性质,以及估计方法。介绍了Copula熵研究的最新进展,包括其在统计学的六个基本问题(结构学习、关联发现、变量选择、因果发现、域自适应和正态性检验等)上的理论应用。讨论了前四个理论应用之间的关系,以及其对应的深层次的相关性和因果性概念之间的联系,并将Copula熵的(条件)独立性度量框架与基于核函数和距离相关的同类框架进行了对比。简述了Copula熵在理论物理学、理论化学、化学信息学、水文学、气候学、气象学、生态学、动物形态学、农学、认知神经学、运动神经学、计算神经学、心理学、系统生物学、生物信息学、临床诊断学、老年医学、精神病学、公共卫生学、经济学、社会学、教育学、新闻传播学、法学、政治学,以及能源工程、土木工程、制造工程、可靠性工程、化学工程、航空航天、电子工程、通信工程、高性能计算、测绘遥感和金融工程等领域的实际应用。

  • 基于模范系统同步实现辨识对象与优化控制的方法

    分类: 信息科学与系统科学 >> 控制科学与技术 分类: 计算机科学 >> 计算机应用技术 分类: 工程与技术科学 >> 工程控制论 提交时间: 2022-12-07

    摘要:摘要: 【目的】实践中95 %以上的工业过程控制问题都可以通过PID 控制算法解决. 本文在继承并创新地运用二自由度PID 内模控制技术的基础上, 建立了一种通用控制器优化整定新方法. 【方法】方法中引入模范系统, 离线仿真该模范系统后获得优化模板; 在信号激励实际控制系统的过程中, 用该模板引导特定算法整定PID内模控制参数. 【结果】在无精准对象参数的情况下, 经数次循环迭代后, 可同步地实现对象参数的辨识与控制性能优化. 【结论】此法整定效率高, 便于后期系统在线维护, 减少了对实施人员技术与经验的要求. 所用控制器继承内模控制大时滞控制效果好、鲁棒性强的特点, 兼顾 " 目标跟踪" 与 " 干扰抑制" 两种性能优化. 控制算法结构简单、直观, 易于在原PID 控制系统升级改造或用嵌入式系统软、硬件实现, 便于在生产中推广应用. 【局限与改进方向】受限于传统内模控制的设计要求,对于具有负响应特性的受控对象, 该方法需采用预补偿处理机制方能适用或者寻找新的模范系统模型并研究改进算法.

  • HS-ES-DE: HS-ES Followed by L-SHADE-EpSin for Real Parameter Single Objective Optimization

    分类: 计算机科学 >> 计算机软件 提交时间: 2022-12-07

    摘要:

    For real parameter single objective optimization, Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) both perform powerfully. Nevertheless, in the field of real parameter single objective optimization, it is impossible for a given algorithm to perform well in all fitness landscapes. Practice has proved that ensemble of different algorithms may lead to improvement in solution. In this paper, based on two famous population-based metaheuristics - LSHADE-EpSin and HS-ES, we propose ensemble with successively executed constituent algorithms - HS-ES-DE. In our algorithm, HS-ES is replaced by L-SHADE-EpSin after stagnation is detected. Beside our HS-ES-DE, 12 population-based metaheuristics are involved in our experiments in which three benchmark test suites are employed. Experimental results show that our algorithm is very competitive.

  • Toward Training and Assessing Reproducible Data Analysis in Data Science Education

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-29 合作期刊: 《数据智能(英文)》

    摘要:Reproducibility is a cornerstone of scientific research. Data science is not an exception. In recent years scientists were concerned about a large number of irreproducible studies. Such reproducibility crisis in science could severely undermine public trust in science and science-based public policy. Recent efforts to promote reproducible research mainly focused on matured scientists and much less on student training. In this study, we conducted action research on students in data science to evaluate to what extent students are ready for communicating reproducible data analysis. The results show that although two-thirds of the students claimed they were able to reproduce results in peer reports, only one-third of reports provided all necessary information for replication. The actual replication results also include conflicting claims; some lacked comparisons of original and replication results, indicating that some students did not share a consistent understanding of what reproducibility means and how to report replication results. The findings suggest that more training is needed to help data science students communicating reproducible data analysis.

  • Paving the Way to Open Data

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-29 合作期刊: 《数据智能(英文)》

    摘要:It is easy to argue that open data is critical to enabling faster and more effective research discovery. In this article, we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data (Findable, Accessible, Interoperable and Reusable) with the implementation of four data policies: “Encourages”, “Expects”, “Mandates” and “Mandates and Peer Reviews Data”. We describe the rationale for these policies and levels of adoption so far. In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations. With this information, we’ll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve, and we hope to showcase researchers from those communities leading in open research.

  • Playing Well on the Data FAIRground: Initiatives and Infrastructure in Research Data Management

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-29 合作期刊: 《数据智能(英文)》

    摘要:Over the past five years, Elsevier has focused on implementing FAIR and best practices in data management, from data preservation through reuse. In this paper we describe a series of efforts undertaken in this time to support proper data management practices. In particular, we discuss our journal data policies and their implementation, the current status and future goals for the research data management platform Mendeley Data, and clear and persistent linkages to individual data sets stored on external data repositories from corresponding published papers through partnership with Scholix. Early analysis of our data policies implementation confirms significant disparities at the subject level regarding data sharing practices, with most uptake within disciplines of Physical Sciences. Future directions at Elsevier include implementing better discoverability of linked data within an article and incorporating research data usage metrics.

  • Knowledge Graph Construction and Applications for Web Search and Beyond

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-29 合作期刊: 《数据智能(英文)》

    摘要:Knowledge graph (KG) has played an important role in enhancing the performance of many intelligent systems. In this paper, we introduce the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications. Unlike previous works that build knowledge graph with graph databases, we build the knowledge graph on top of SogouQdb, a distributed search engine developed by Sogou Web Search Department, which can be easily scaled to support petabytes of data. As a supplement to the search engine, we also introduce a series of models to support inference and graph based querying. Currently, the data of Sogou knowledge graph that are collected from 136 different websites and constantly updated consist of 54 million entities and over 600 million entity links. We also introduce three applications of knowledge graph in Sogou Inc.: entity detection and linking, knowledge based question answering and knowledge based dialogue system. These applications have been used in Web search products to help user acquire information more efficiently.

  • Building a Holistic Taxonomy Model for OGD-Related Risks: Based on a Lifecycle Analysis

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-29 合作期刊: 《数据智能(英文)》

    摘要:For many government departments, uncertainty aversion is a source of barriers in the advancement of data openness. A more active response to potential risks is needed and necessitates an in-depth examination of risks related to open government data (OGD). With a cross-case study in which three cases from the United Kingdom, the United States and China are examined, this study identifies potential risks that might emerge at different stages of the lifecycle of OGD programs and constructs a taxonomy model for them. The taxonomy model distinguishes the “risks from OGD” from the “risks to OGD”, which can help government departments make better responses. Finally, risk response strategies are suggested based on the research results.

  • Faster Zero-shot Multi-modal Entity Linking via Visual#2;Linguistic Representation

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:Multi-modal entity linking plays a crucial role in a wide range of knowledge-based modal-fusion tasks, i.e., multi-modal retrieval and multi-modal event extraction. We introduce the new ZEro-shot Multi-modal Entity Linking (ZEMEL) task, the format is similar to multi-modal entity linking, but multi-modal mentions are linked to unseen entities in the knowledge graph, and the purpose of zero-shot setting is to realize robust linking in highly specialized domains. Simultaneously, the inference efficiency of existing models is low when there are many candidate entities. On this account, we propose a novel model that leverages visual#2; linguistic representation through the co-attentional mechanism to deal with the ZEMEL task, considering the trade-off between performance and efficiency of the model. We also build a dataset named ZEMELD for the new task, which contains multi-modal data resources collected from Wikipedia, and we annotate the entities as ground truth. Extensive experimental results on the dataset show that our proposed model is effective as it significantly improves the precision from 68.93% to 82.62% comparing with baselines in the ZEMEL task.

  • Uncovering Topics of Public Cultural Activities: Evidence from China

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:In this study, we uncover the topics of Chinese public cultural activities in 2020 with a two-step short text clustering (self-taught neural networks and graph-based clustering) and topic modeling approach. The dataset we use for this research is collected from 108 websites of libraries and cultural centers, containing over 17,000 articles. With the novel framework we propose, we derive 3 clusters and 8 topics from 21 provincial#2; level regions in China. By plotting the topic distribution of each cluster, we are able to shows unique tendencies of local cultural institutes, that is, free lessons and lectures on art and culture, entertainment and service for socially vulnerable groups, and the preservation of intangible cultural heritage respectively. The findings of our study provide decision-making support for cultural institutes, thus promoting public cultural service from a data-driven perspective.

  • Fuzzy-Constrained Graph Patter n Matching in Medical Knowledge Graphs

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:The research on graph pattern matching (GPM) has attracted a lot of attention. However, most of the research has focused on complex networks, and there are few researches on GPM in the medical field. Hence, with GPM this paper is to make a breast cancer-oriented diagnosis before the surgery. Technically, this paper has firstly made a new definition of GPM, aiming to explore the GPM in the medical field, especially in Medical Knowledge Graphs (MKGs). Then, in the specific matching process, this paper introduces fuzzy calculation, and proposes a multi-threaded bidirectional routing exploration (M-TBRE) algorithm based on depth first search and a two-way routing matching algorithm based on multi-threading. In addition, fuzzy constraints are introduced in the M-TBRE algorithm, which leads to the Fuzzy-M-TBRE algorithm. The experimental results on the two datasets show that compared with existing algorithms, our proposed algorithm is more efficient and effective.

  • Bi-GRU Relation Extraction Model Based on Keywords Attention

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:Relational extraction plays an important role in the field of natural language processing to predict semantic relationships between entities in a sentence. Currently, most models have typically utilized the natural language processing tools to capture high-level features with an attention mechanism to mitigate the adverse effects of noise in sentences for the prediction results. However, in the task of relational classification, these attention mechanisms do not take full advantage of the semantic information of some keywords which have information on relational expressions in the sentences. Therefore, we propose a novel relation extraction model based on the attention mechanism with keywords, named Relation Extraction Based on Keywords Attention (REKA). In particular, the proposed model makes use of bi-directional GRU (Bi-GRU) to reduce computation, obtain the representation of sentences , and extracts prior knowledge of entity pair without any NLP tools. Besides the calculation of the entity-pair similarity, Keywords attention in the REKA model also utilizes a linear-chain conditional random field (CRF) combining entity-pair features, similarity features between entity-pair features, and its hidden vectors, to obtain the attention weight resulting from the marginal distribution of each word. Experiments demonstrate that the proposed approach can utilize keywords incorporating relational expression semantics in sentences without the assistance of any high-level features and achieve better performance than traditional methods.

  • Comparative Evaluation and Comprehensive Analysis of Machine Learning Models for Regression Problems

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:Artificial intelligence and machine learning applications are of significant importance almost in every field of human life to solve problems or support human experts. However, the determination of the machine learning model to achieve a superior result for a particular problem within the wide real-life application areas is still a challenging task for researchers. The success of a model could be affected by several factors such as dataset characteristics, training strategy and model responses. Therefore, a comprehensive analysis is required to determine model ability and the efficiency of the considered strategies. This study implemented ten benchmark machine learning models on seventeen varied datasets. Experiments are performed using four different training strategies 60:40, 70:30, and 80:20 hold-out and five-fold cross-validation techniques. We used three evaluation metrics to evaluate the experimental results: mean squared error, mean absolute error, and coefficient of determination (R2 score). The considered models are analyzed, and each model's advantages, disadvantages, and data dependencies are indicated. As a result of performed excess number of experiments, the deep Long-Short Term Memory (LSTM) neural network outperformed other considered models, namely, decision tree, linear regression, support vector regression with a linear and radial basis function kernels, random forest, gradient boosting, extreme gradient boosting, shallow neural network, and deep neural network. It has also been shown that cross-validation has a tremendous impact on the results of the experiments and should be considered for the model evaluation in regression studies where data mining or selection is not performed.

  • COKG-QA: Multi-hop Question Answering over COVID-19 Knowledge Graphs

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:COVID-19 evolves rapidly and an enormous number of people worldwide desire instant access to COVID- 19 information such as the overview, clinic knowledge, vaccine, prevention measures, and COVID-19 mutation. Question answering (QA) has become the mainstream interaction way for users to consume the ever-growing information by posing natural language questions. Therefore, it is urgent and necessary to develop a QA system to offer consulting services all the time to relieve the stress of health services. In particular, people increasingly pay more attention to complex multi-hop questions rather than simple ones during the lasting pandemic, but the existing COVID-19 QA systems fail to meet their complex information needs. In this paper, we introduce a novel multi-hop QA system called COKG-QA, which reasons over multiple relations over large-scale COVID-19 Knowledge Graphs to return answers given a question. In the field of question answering over knowledge graph, current methods usually represent entities and schemas based on some knowledge embedding models and represent questions using pre-trained models. While it is convenient to represent different knowledge (i.e., entities and questions) based on specified embeddings, an issue raises that these separate representations come from heterogeneous vector spaces. We align question embeddings with knowledge embeddings in a common semantic space by a simple but effective embedding projection mechanism. Furthermore, we propose combining entity embeddings with their corresponding  schema embeddings which served as important prior knowledge, to help search for the correct answer entity of specified types. In addition, we derive a large multi-hop Chinese COVID-19 dataset (called COKG-DATA for remembering) for COKG-QA based on the linked knowledge graph OpenKG-COVID19 launched by OpenKG, including comprehensive and representative information about COVID-19. COKG-QA achieves quite competitive performance in the 1-hop and 2-hop data while obtaining the best result with significant improvements in the 3-hop. And it is more efficient to be used in the QA system for users. Moreover, the user study shows that the system not only provides accurate and interpretable answers but also is easy to use and comes with smart tips and suggestions.

  • Ensemble Making Few-Shot Learning Stronger

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:Few-shot learning has been proposed and rapidly emerging as a viable means for completing various tasks. Many few-shot models have been widely used for relation learning tasks. However, each of these models has a shortage of capturing a certain aspect of semantic features, for example, CNN on long-range dependencies part, Transformer on local features. It is difficult for a single model to adapt to various relation learning, which results in a high variance problem. Ensemble strategy could be competitive in improving the accuracy of few-shot relation extraction and mitigating high variance risks. This paper explores an ensemble approach to reduce the variance and introduces fine-tuning and feature attention strategies to calibrate relation-level features. Results on several few-shot relation learning tasks show that our model significantly outperforms the previous state-of-the-art models.

  • Knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:Temporal information is pervasive and crucial in medical records and other clinical text, as it formulates the development process of medical conditions and is vital for clinical decision making. However, providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging. In order to capture complex temporal semantics in clinical text, we propose a novel Clinical Time Ontology (CTO) as an extension from OWL framework. More specifically, we identified eight time#2; related problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time, cyclic time, irregular time, negations and other complex aspects of clinical time. Then, we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple time. Simultaneously, we provided a formulaic and graphical presentation of complex time and complex time relationships. We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets. Finally, experiment results demonstrate that CTO could faithfully represent and reason over 93% of the temporal expressions, and it can cover a wider range of time-related classes in clinical domain.

  • Analysis of Pioneering Computable Biomedical Knowledge Repositories and their Emerging Governance Structures

    分类: 计算机科学 >> 计算机科学的集成理论 提交时间: 2022-11-28 合作期刊: 《数据智能(英文)》

    摘要:A growing interest in producing and sharing computable biomedical knowledge artifacts (CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification (TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.