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21.
关于智慧城市与城市应急决策情报体系 总被引：1，自引：0，他引：1
[目的/意义] 城市应急管理是当前政府机构和学术界关注的一个重要现实问题。基于智慧城市背景,从情报活动角度考察城市应急决策,对城市应急决策情报体系涉及的若干问题进行"智慧"解读,旨在为该情报体系的设计与实现提供理论指导。[方法/过程] 通过文献资料收集,总结城市应急决策情报体系的研究现状及其不足,并从"智慧"层面透视该情报体系的3个方面内容。[结果/结论] 第一,立足情报本征,情报要素是城市应急决策情报体系中的智慧"源";第二,技术理性与人文价值的整合凸显出城市应急决策情报体系的智慧"核",保证情报有效输出;第三,面向城市应急决策的快速响应情报体系的协同联动机制构建成为智慧"刃",实现情报流动与共享。三方面内容相互交叉、互为关联,并各有侧重,共同成为城市应急决策情报体系"智慧"之所在。 相似文献
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目前,对现有网络课程质量的评价多以问卷调查、访谈法、专家法等方式进行,但由于数据本身或获取的途径存在主观因素,因而难以准确、客观衡量学习者在网络课程中的学习绩效,其他评价方法(如,模糊评价、层次评价等)不易掌握,难以推广。然而,以客观数据作为量化依据的网络计量方法,为评价网络课提供了解决问题的新视角。基于此,首先在对现有网络课程评价中存在的问题进行分析之后,介绍了网络计量方法及常用工具;然后探讨运用网络计量方法评价网络课程的可行性,提出网络课程评价应以学习者学习绩效测量为核心;最后,初步构建基于网络计量方法的网络课程评价指标体系,该指标体系对视频公开课和MOOCs评价也具有借鉴意义。 相似文献
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社会网络服务的发展使得网络知识社区日渐成熟和普及,随着网络知识社区服务的发展和用户数量的累积,用户的持续使用行为已成为人们日益关注的问题。本文从网络知识社区用户行为引发机制出发,构建了基于三维结构的网络知识社区用户行为引发模型,通过实证调查分析用户持续使用行为的影响因素,并对网络知识社区服务提出优化建议,希望能对网络知识社区的发展提供有益参考。 相似文献
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《Information processing & management》2022,59(5):103063
In recent years, fake news detection has been a significant task attracting much attention. However, most current approaches utilize the features from a single modality, such as text or image, while the comprehensive fusion between features of different modalities has been ignored. To deal with the above problem, we propose a novel model named Bidirectional Cross-Modal Fusion (BCMF), which comprehensively integrates the textual and visual representations in a bidirectional manner. Specifically, the proposed model is decomposed into four submodules, i.e., the input embedding, the image2text fusion, the text2image fusion, and the prediction module. We conduct intensive experiments on four real-world datasets, i.e., Weibo, Twitter, Politi, and Gossip. The results show 2.2, 2.5, 4.9, and 3.1 percentage points of improvements in classification accuracy compared to the state-of-the-art methods on Weibo, Twitter, Politi, and Gossip, respectively. The experimental results suggest that the proposed model could better capture integrated information of different modalities and has high generalizability among different datasets. Further experiments suggest that the bidirectional fusions, the number of multi-attention heads, and the aggregating function could impact the performance of the cross-modal fake news detection. The research sheds light on the role of bidirectional cross-modal fusion in leveraging multi-modal information to improve the effect of fake news detection. 相似文献
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《Information processing & management》2022,59(6):103074
As a global health crisis, the COVID-19 pandemic has also made heavy mental and emotional tolls become shared experiences of global communities, especially among females who were affected more by the pandemic than males for anxiety and depression. By connecting multiple facets of empathy as key mechanisms of information processing with the communication theory of resilience, the present study examines human-AI interactions during the COVID-19 pandemic in order to understand digitally mediated empathy and how the intertwining of empathic and communicative processes of resilience works as coping strategies for COVID-19 disruption. Mixed methods were adopted to explore the using experiences and effects of Replika, a chatbot companion powered by AI, with ethnographic research, in-depth interviews, and grounded theory-based analysis. Findings of this research extend empathy theories from interpersonal communication to human-AI interactions and show five types of digitally mediated empathy among Chinese female Replika users with varying degrees of cognitive empathy, affective empathy, and empathic response involved in the information processing processes, i.e., companion buddy, responsive diary, emotion-handling program, electronic pet, and tool for venting. When processing information obtained from AI and collaborative interactions with the AI chatbot, multiple facets of mediated empathy become unexpected pathways to resilience and enhance users’ well-being. This study fills the research gap by exploring empathy and resilience processes in human-AI interactions. Practical implications, especially for increasing individuals’ psychological resilience as an important component of global recovery from the pandemic, suggestions for future chatbot design, and future research directions are also discussed. 相似文献
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《Information processing & management》2022,59(3):102934
Compared with explicit sentiment analysis that attracts considerable attention, implicit sentiment analysis is a more difficult task due to the lack of sentimental words. The abundant information in an external sentimental knowledge base can play a significant complementary and expansion role. In this paper, a sentimental commonsense knowledge graph embedded multi-polarity orthogonal attention model is proposed to learn the implication of the implicit sentiment. We analyzed the effectiveness of different knowledge relations in the ConceptNet knowledge base in detail, and proposed a matching and filtering method to distill useful knowledge tuples for implicit sentiment analysis automatically. By introducing the sentimental information in the knowledge base, the proposed model can extend the semantic of a sentence with an implicit sentiment. Then, a bi-directional long–short term memory model with multi-polarity orthogonal attention is adopted to fuse the distilled sentimental knowledge with the semantic embedding, effectively enriching the representation of sentences. Experiments on the SMP2019-ECISA implicit sentiment dataset show that our model fully utilizes the information of the knowledge base and improves the performance of Chinese implicit sentiment analysis. 相似文献
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《Information processing & management》2022,59(4):102992
Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues. 相似文献
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《Information processing & management》2022,59(4):102994
This study investigates the positive and behavioral topic of screen golf, which is often regarded as the most commercially successful virtual reality sports game. Our team analyzed the decision-making process related to screen golf through the widely used the technology readiness and acceptance model to explain the relationships among technology readiness, belief in technology acceptance, and use intentions. The proposed model fit the data satisfactorily, and several of our hypotheses were supported. Structural equation modeling tested the nine hypotheses established based on a literature review, analyzing 350 valid responses obtained through online surveys. Perceived utility (ß = .519**) was the most influential factor in individuals’ plans to participate in the virtual sport. This means that practical considerations such as correcting individuals’ posture and improving their abilities should be prioritized when creating screen golf programs. 相似文献
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《Information processing & management》2022,59(4):102977
Information residing in multiple modalities (e.g., text, image) of social media posts can jointly provide more comprehensive and clearer insights into an ongoing emergency. To identify information valuable for humanitarian aid from noisy multimodal data, we first clarify the categories of humanitarian information, and define a multi-label multimodal humanitarian information identification task, which can adapt to the label inconsistency issue caused by modality independence while maintaining the correlation between modalities. We proposed a Multimodal Humanitarian Information Identification Model that simultaneously captures the Correlation and Independence between modalities (CIMHIM). A tailor-made dataset containing 4,383 annotated text-image pairs was built to evaluate the effectiveness of our model. The experimental results show that CIMHIM outperforms both unimodal and multimodal baseline methods by at least 0.019 in macro-F1 and 0.022 in accuracy. The combination of OCR text, object-level features, and the decision rule based on label correlations enhances the overall performance of CIMHIM. Additional experiments on a similar dataset (CrisisMMD) also demonstrate the robustness of CIMHIM. The task, model, and dataset proposed in this study contribute to the practice of leveraging multimodal social media resources to support effective emergency response. 相似文献
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《Information processing & management》2022,59(3):102902
Both node classification and link prediction are popular topics of supervised learning on the graph data, but previous works seldom integrate them together to capture their complementary information. In this paper, we propose a Multi-Task and Multi-Graph Convolutional Network (MTGCN) to jointly conduct node classification and link prediction in a unified framework. Specifically, MTGCN consists of multiple multi-task learning so that each multi-task learning learns the complementary information between node classification and link prediction. In particular, each multi-task learning uses different inputs to output representations of the graph data. Moreover, the parameters of one multi-task learning initialize the parameters of the other multi-task learning, so that the useful information in the former multi-task learning can be propagated to the other multi-task learning. As a result, the information is augmented to guarantee the quality of representations by exploring the complex constructure inherent in the graph data. Experimental results on six datasets show that our MTGCN outperforms the comparison methods in terms of both node classification and link prediction. 相似文献