为了科学,还是为了公众?——论公众科学的两种进路

自20世纪90年代中期以来,“公众科学”的概念逐渐受到学界关注并形成了一个新的研究领域,但目前学界对于“公众科学”的内涵仍没有共识。为此本文追溯了实践进路和政治进路两种不同内涵的公众科学的源起、内容与影响,并对两种公众科学的形成背景、理论预设、政策影响等方面的差异进行了系统比较;同时阐述了已有研究对于融合两种公众科学进路的尝试。最后,文章提出公众科学是一种以公众为主体的新知识生产方式,并结合国内公众科学情况提出了几点发展建议。     

SLTFNet: A spatial and language-temporal tensor fusion network for video moment retrieval

This paper focuses on temporal retrieval of activities in videos via sentence queries. Given a sentence query describing an activity, temporal moment retrieval aims at localizing the temporal segment within the video that best describes the textual query. This is a general yet challenging task as it requires the comprehending of both video and language. Existing research predominantly employ coarse frame-level features as the visual representation, obfuscating the specific details (e.g., the desired objects “girl”, “cup” and action “pour”) within the video which may provide critical cues for localizing the desired moment. In this paper, we propose a novel Spatial and Language-Temporal Tensor Fusion (SLTF) approach to resolve those issues. Specifically, the SLTF method first takes advantage of object-level local features and attends to the most relevant local features (e.g., the local features “girl”, “cup”) by spatial attention. Then we encode the sequence of the local features on consecutive frames by employing LSTM network, which can capture the motion information and interactions among these objects (e.g., the interaction “pour” involving these two objects). Meanwhile, language-temporal attention is utilized to emphasize the keywords based on moment context information. Thereafter, a tensor fusion network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. Therefore, our proposed two attention sub-networks can adaptively recognize the most relevant objects and interactions in the video, and simultaneously highlight the keywords in the query for retrieving the desired moment. Experimental results on three public benchmark datasets (obtained from TACOS, Charades-STA, and DiDeMo) show that the SLTF model significantly outperforms current state-of-the-art approaches, and demonstrate the benefits produced by new technologies incorporated into SLTF.     

The graph-theoretic field model—I. Modelling and formulations

The Graph-Theoretical Field Model provides a unifying approach for developing numerical models of field and continuum problems. The methodology examines the field problem from the first stages of conceptualization without recourse to the governing differential equations of the field problem; this is accomplished by deriving discrete statements of the physical laws which govern the field behaviour. There are generally three laws, and these are modelled by the “cutset equations”, the “circuit equations”, and the “terminal equations”. In order to establish these three sets of equations it is expedient first to spatially discretize the field in a manner similar to the finite difference method and then to associate a linear graph (denoted as the field graph) with the spatial discretization. The concept of “through” and “across” variables, which underlies the cutset and circuit equations respectively, enables one to define the graph in an unambiguous manner such that each “edge” of the graph identifies a pair of complementary variables. From a knowledge of the constitutive properties and the boundary conditions of the field it is possible to associate terminal equations with sets of edges. Since the resulting sets of equations represent the field equations, these equations provide the basis for a complete (but approximate) solution to the field or continuum problem. In fact, this system approach uses a two part model: one for the components and another for the interconnection pattern of the components which renders the formulation procedures totally independent of the solution procedure.This paper presents the theoretical basis of the model and several graph-theoretic formulations for steady-state problems. Examples from heat conduction and small- deformation elasticity are included.     

Elements of a semantic theory of information retrieval—I. The concepts of relevance and information language

The primary aim of this study is to suggest a formalized definition (“explication”) of “relevance relationship” between texts, including the explication of the concept of “degree of relevance”. The concept of information language (IL), its vocabulry and syntax and the notion of the “semantic power” of an information language are defined. The concept of ideally functioning information retrieval systems (IRS) is suggested and different kinds of deviations from such IRS are considered.     

Cultural barriers to the international transfer of information

The recent debates on such issues as the transborder data flow or the new information order have brought to light the cultural implications of information activities. This is fortunate since information science used to consider them only as aspects of language barrier.Information has recently been qualified as a humankind's heritage. Sharing it requires not only the international flow of information, but also its unimpeded creation and use. Considering that humankind is not limited to the inhabitants of Europe and North America, this paper discusses the various ways in which objective and subjective cultural traits affect the generation, presentation, transfer and use of information in different societies.It emphasizes that all presently available information products and services are culture dependent, and primarily dependent upon the “northern” culture, what drastically limits their international value, but for “occidentalized” elite groups.     

德育渗透视角下的初二物理教材分析

研究教材是提高教学效果、实现教学目标的前提。八年级《物理》（上海版）教材围绕实现“情感态度与价值观”课程目标,通过正文和“信息窗”、插图、“本章练习”、“作业”等栏目将德育素材渗透其中。这些素材可以划分为爱国主义教育、科学精神教育、研究方法教育、成长成才教育、综合素养教育等五个方面。     

医学院校健康科普实施思考

2019年通过的《中华人民共和国基本医疗卫生与健康促进法》,提出了各级人民政府应当普及健康科学知识,向公众提供科学、准确的健康信息。医疗卫生、教育等机构应当开展健康知识的宣传和普及工作。《中国健康科普作品创作与传播专家共识》指出,要构建覆盖“诊前—诊中—诊后”医疗全流程、贯穿“出生—成长—衰老—死亡”生命全周期的健康科普教育体系,全方位保障公众健康。因此,该文从医学院校实施健康科普的必要性、医学院校实施健康科普的作用、医学院校实施健康科普的目的、医学院校实施健康科普的条件以及医学院校健康科普效果评价等几个方面阐述了医学院校实施健康科普的思考。     

An information processing constraints approach to the conjunction of macroeconomic and macropolitical theory

Human information processing is a fundamental and critically important resource for political life. It is moreover a constrained resource. Using this fact connections can be established between ideas employed in political science and concepts in macroeconomic theory. A concept of “interest rates” can be derived from information processing constraints. These “interest rates” are analogous to the “arousal level” construct of motivational psychology, suggesting avenues for social research. A generalized Kynesian “general equilibrium” model, derived largely from macropolitical considerations, is presented.     

Studies on a multidimensional public opinion network model and its topic detection algorithm

“We the Media” networks are real time and open, and such networks lack a gatekeeper system. As netizens’ comments on emergency events are disseminated, negative public opinion topics and confrontations concerning those events also spread widely on “We the Media” networks. Gradually, this phenomenon has attracted scholarly attention, and all social circles attach importance to the phenomenon as well. In existing topic detection studies, a topic is mainly defined as an "event" from the perspective of news-media information flow, but in the “We the Media” era, there are often many different views or topics surrounding a specific public opinion event. In this paper, a study on the detection of public opinion topics in “We the Media” networks is presented, starting with the characteristics of the elements found in public opinions on “We the Media” networks; such public opinions are multidimensional, multilayered and possess multiple attributes. By categorizing the elements’ attributes using social psychology and system science categories as references, we build a multidimensional network model oriented toward the topology of public opinions on “We the Media” networks. Based on the real process by which multiple topics concerning the same event are generated and disseminated, we designed a topic detection algorithm that works on these multidimensional public opinion networks. As a case study, the “Explosion in Tianjin Port on August 12, 2015″ accident was selected to conduct empirical analyses on the algorithm's effectiveness. The theoretical and empirical research findings of this paper are summarized along the following three aspects. 1. The multidimensional network model can be used to effectively characterize the communication characteristics of multiple topics on “We the Media” networks, and it provided the modeling ideas for the present paper and for other related studies on “We the Media” public opinion networks. 2. Using the multidimensional topic detection algorithm, 70% of the public opinion topics concerning the case study event were effectively detected, which shows that the algorithm is effective at detecting topics from the information flow on “We the Media” networks. 3. By defining the psychological scores of single and paired Chinese keywords in public opinion information, the topic detection algorithm can also be used to judge the sentiment tendencies of each topic, which can facilitate a timely understanding of public opinion and reveal negative topics under discussion on “We the Media” networks.     

Temporal Document Retrieval Model for business news archives

Temporal expressions occurring in business news, such as “last week” or “at the end of this month,” carry important information about the time context of the news document and were proved to be useful for document retrieval. We found that about 10% of these expressions are difficult to project onto the calendar due to the uncertainty about their bounds. This paper introduces a novel approach to representing temporal expressions. A user study is conducted to measure the degree of uncertainty for selected temporal expressions and a method for representing uncertainty based on fuzzy numbers is proposed. The classical Vector Space Model is extended to the Temporal Document Retrieval Model (TDRM) that incorporates the proposed fuzzy representations of temporal expressions.     

An essay on the past and future (?) of information science education—I: Historical overview

The rapid and rather chaotic evolution of information science has left the field's academic sector in a largely disorganized state. This essay examines the basic issues confronting information science education, issues that must be resolved if information science education and thus information science itself are to evolve in an orderly fashion. For the quality of a field's professional services and research activities depends upon the quality of its formal academic programs. The essay is organized in three parts. In this first part are considered definitions and in a historic context the emergence, evolution and current state of information science and its education. The second part considers the “externalities” of education—problems and unresolved questions in information science education that deal with: (i) academic affiliations, (ii) degree levels, (iii) admission requirements, (iv) jurisdiction and (v) financing. The third part considers the problems and unresolved questions in respect to internal aspects (“internalities”) of information science education: (i) objectives, (ii) content, (iii) teachers and (iv) teaching. It is suggested that information science cannot prosper; possibly even survive in the next decade if serious, concentrated action is not undertaken in the “externalities” and “internalities” of its education. Recommendations about the areas that need action are made.     

Supporting “Everyday Analysts” in Safety- and Time-Critical Situations

The need for quick, timely, and accurate information is critical in emergency events. During mass emergencies, people assemble information from both official and unofficial sources. As digital access expands, people will increasingly incorporate information from digital sources into decision making and assess it against the local circumstances they experience. If we extrapolate what such behavior means for the future, we can see that information management under emergency conditions will need to become increasingly socially distributed. The key question then is how to assess the quality of information: how “good” or “bad” it is; whether it is “misinformation” or “disinformation.” Borrowing from Simon's notion of satisficing, the authors argue that people's assessment of information helpfulness and credibility is a function of the “everyday analytic” skills they employ during mass emergencies. To facilitate the critical work of “everyday analysts,” we outline a research agenda for the development of analytical support tools.     

新时代背景下的数学类专业新理科人才培养协同系统构建研究

该文在新时代背景下,基于系统论、控制论、协同论的视角,探讨聚焦如何培养数学类专业新理科人才。结合大数据、人工智能、5G等新技术、新业态、新变革,分别从人才培养方案、课程体系、师资队伍建设、实践环节等方面,探寻新时代背景下数学与应用数学、信息与计算科学、金融数学等与数据科学和大数据技术专业人才培养协同机制,努力构建数学类新理科专业人才培养协同系统。     

A “Grand Challenge”: Measuring Information Societies

Even though there has been a proliferation of e-society measures in recent years, analyses of the metrics of the “information society” are still far from responsive to the needs of many stakeholders and continue to suffer from a number of serious limitations. Issues in eight critical areas are briefly presented. They include: definition of the universe to be measured; definition of the objects and phenomena to include in the universe; need to establish measurements based upon solid theories; units of measurements; data sources and collection; methods of analysis and construction of indicators; target audiences; and purpose and utilization of measurements. An organized collective effort, which could provide the impetus for the development of a coherent academic field of study, is called for to address this “grand challenge.”     

Either-or communication-based identification of FIR systems with binary-valued observations and channel uncertainty

Event-triggered mechanism can effectively save communication resources, however, when it encounters channel uncertainty, the remote receiver cannot distinguish between “the sender did not send data” and “the sender sent data but the data lost” when it does not receive data, which causes that it is difficult to make full use of the information provided by the event-triggered mechanism. This paper addresses the identification of FIR (Finite Impulse Response) systems with binary-valued observations and either-or communication mechanism when the packet loss probability is known and unknown respectively. When the packet loss probability is known, it is used for compensation in the parameter estimation. An online identification algorithm is proposed, its strong convergence is proved, and its asymptotic normality is given. Furthermore, how does the packet loss probability affect the algorithm performance is discussed. When the packet loss probability is unknown, an identification algorithm is proposed to jointly estimate it and unknown system parameters by redesigning the either-or communication mechanism. The strong convergence of the algorithm is shown. The tradeoff between the communication rate and the convergence performance of the identification algorithm is modelled as a constrained optimization problem, and its solution is obtained. The rationality of theoretical results is verified by numerical simulation.     

索菲亚科技园转型中的社会技术创新研究

索菲亚·安蒂波里斯是欧洲最具战略地位的科技园区,已成为欧洲的"电信谷"。基于"社会技术"分层思想,从产业模式、政策法规和社会气质层面研究索菲亚转型发展中的社会技术创新及其作用机制。索菲亚转型发展中,形成了以"园区品牌"与"地方性生产网络"建构为代表的产业模式层面社会技术创新,以"中小企业促进政策"与"产业集群发展动议"为代表的政策法规层面社会技术创新,以"分权化"为标志的自由民主社会气质的塑造。     

A novel dropout mechanism with label extension schema toward text emotion classification

Researchers have been aware that emotion is not one-hot encoded in emotion-relevant classification tasks, and multiple emotions can coexist in a given sentence. Recently, several works have focused on leveraging a distribution label or a grayscale label of emotions in the classification model, which can enhance the one-hot label with additional information, such as the intensity of other emotions and the correlation between emotions. Such an approach has been proven effective in alleviating the overfitting problem and improving the model robustness by introducing a distribution learning component in the objective function. However, the effect of distribution learning cannot be fully unfolded as it can reduce the model’s discriminative ability within similar emotion categories. For example, “Sad” and “Fear” are both negative emotions. To address such a problem, we proposed a novel emotion extension scheme in the prior work (Li, Chen, Xie, Li, and Tao, 2021). The prior work incorporated fine-grained emotion concepts to build an extended label space, where a mapping function between coarse-grained emotion categories and fine-grained emotion concepts was identified. For example, sentences labeled “Joy” can convey various emotions such as enjoy, free, and leisure. The model can further benefit from the extended space by extracting dependency within fine-grained emotions when yielding predictions in the original label space. The prior work has shown that it is more apt to apply distribution learning in the extended label space than in the original space. A novel sparse connection method, i.e., Leaky Dropout, is proposed in this paper to refine the dependency-extraction step, which further improves the classification performance. In addition to the multiclass emotion classification task, we extensively experimented on sentiment analysis and multilabel emotion prediction tasks to investigate the effectiveness and generality of the label extension schema.     

知识架构、信息筛选与群体对立认知机制

在一个组织乃至整个社会中,认知差异往往构成组织冲突的重要成因。研究基于知识管理理论与认知理论,以转基因技术产品为例,对群体对立认知的形成机制与互动模式进行探索式案例研究。研究发现持不同观点态度的群体可以追溯到其不同的知识架构基础,例如支持("挺转")群体的专业知识逻辑和方法逻辑,抵制("反转")群体的非专业知识逻辑、生活经验和职业经历,及其基于不同知识架构的信息搜寻和筛选策略,进而形成不同的认知模式和认知对象,故提出一个整合性的认知框架。研究还发现支持群体在互动中演化出正面自证和推翻质疑两种应对策略。这些结果对于消除认知偏见、企业制定市场进入战略和政府政策制定都有一定的启示意义。     

Theory building with big data-driven research – Moving away from the “What” towards the “Why”

Data availability and access to various platforms, is changing the nature of Information Systems (IS) studies. Such studies often use large datasets, which may incorporate structured and unstructured data, from various platforms. The questions that such papers address, in turn, may attempt to use methods from computational science like sentiment mining, text mining, network science and image analytics to derive insights. However, there is often a weak theoretical contribution in many of these studies. We point out the need for such studies to contribute back to the IS discipline, whereby findings can explain more about the phenomenon surrounding the interaction of people with technology artefacts and the ecosystem within which these contextual usage is situated. Our opinion paper attempts to address this gap and provide insights on the methodological adaptations required in “big data studies” to be converted into “IS research” and contribute to theory building in information systems.     

Consumer Information Requirements and Telecommunications Regulation

Consumers can be disadvantaged and suffer detriment when information is withheld, when there is deception in the information provided, or when information is too difficult for consumers to obtain or evaluate. While information provision is often favored as a policy/regulatory tool to address “market failure,” it can have its own failings and drawbacks. For instance, the volume of information presented can contain its effectiveness. While the demand-side analysis indicates that actual consumer behavior exhibits systematic departures from the “rational” behavior assumed by conventional (neoclassical) economics, behavioral economics goes further in questioning whether additional information is always useful. It challenges some of the presumptions of conventional economics that consumers make their choices coherently and rationally. This article incorporates such considerations in proposing measures (including regulatory initiatives) that could improve the information made available to telecommunications consumers.