Taylor's Q1 “Visceral” level of information need: What is it?

Taylor (1968) dramatically stated that information seekers/searchers do not use their real Q1-level of information need when formulating their query to the system. Instead, they use a compromised Q4-level form of their need. The article directly confronts what Taylor's (1968) Q1-level information need is–the “actual” or “real” information need of the searcher. The article conceptually and operationally defines what Taylor's Q1-level of information need is using Belkin's (1980) ASK concept as a basis for designing a system intervention that shifts the searcher from representing the Q4-level compromised form of the need in her query to representing instead her Q1-level real information need. The article describes the Q1 Actualizing Intervention Model, which can be built into a system capable of actualizing the uncertainty distribution of the searcher's belief ASK so that information search is directed by the searcher's real Q1-level information need. The objective of the Q1 Actualizing Intervention Model is to enable in our Knowledge Age the introduction of intervention IR systems that are organic and human-centric, designed to initiate organic knowledge production processes in the searcher.     

GTE-Rank: A time-aware search engine to answer time-sensitive queries

In the web environment, most of the queries issued by users are implicit by nature. Inferring the different temporal intents of this type of query enhances the overall temporal part of the web search results. Previous works tackling this problem usually focused on news queries, where the retrieval of the most recent results related to the query are usually sufficient to meet the user's information needs. However, few works have studied the importance of time in queries such as “Philip Seymour Hoffman” where the results may require no recency at all. In this work, we focus on this type of queries named “time-sensitive queries” where the results are preferably from a diversified time span, not necessarily the most recent one. Unlike related work, we follow a content-based approach to identify the most important time periods of the query and integrate time into a re-ranking model to boost the retrieval of documents whose contents match the query time period. For that purpose, we define a linear combination of topical and temporal scores, which reflects the relevance of any web document both in the topical and temporal dimensions, thus contributing to improve the effectiveness of the ranked results across different types of queries. Our approach relies on a novel temporal similarity measure that is capable of determining the most important dates for a query, while filtering out the non-relevant ones. Through extensive experimental evaluation over web corpora, we show that our model offers promising results compared to baseline approaches. As a result of our investigation, we publicly provide a set of web services and a web search interface so that the system can be graphically explored by the research community.     

Uncertainty and the inconvenient facts of diagnosis

One common contemporary usage of the term “diagnostic uncertainty” is to refer to cases for which a diagnosis is not, or cannot, be applied to the presenting case. This is a paradoxical usage, as the absence of diagnosis is often as close to a certainty as can be a human judgement. What makes this sociologically interesting is that it represents an “epistemic defence,” or a means of accounting for a failure of medicine’s explanatory system. This system is based on diagnosis, or the classification of individual complaints into recognizable diagnostic categories. Diagnosis is pivotal to medicine’s epistemic setting, for it purports to explain illness via diagnosis, and yet is not always able to do so. This essay reviews this paradoxical use, and juxtaposes it to historical explanations for non-diagnosable illnesses. It demonstrates how representing non-diagnosis as uncertainty protects the epistemic setting by positioning the failure to locate a diagnosis in the individual, rather than in the medical paradigm.     

主题词簇方法研究

 如何从科技文献数据中获取有效的信息,提升知识发现的能力是当前科学学研究中甚为关注的热点问题。大量相关的分析技术与方法均围绕自然语言处理技术所获取的“主题词”展开。然而,一般情况下,从科技文献数据中获取的主题词数量庞大,人工清洗几无可能,软件清洗亦缺乏可信度。本文以文献计量学方法为基础,构建了包括停词表、模糊语义处理、关联规则、词频与文档频次转换以及聚类分析在内的半自动化“主题词簇”方法体系,实现了以定量方法为主、定性方法为辅的主题词清洗、合并与聚类方案,旨在为技术竞争情报分析提供更为精准的主题词词表。本文以Derwent专利数据库中国“光伏电池”领域的科技文献为例,展开实证研究,验证了方法的科学性与有效性。     

An investigation of the levels of abstraction of tags across three resource genres

This study investigates how resource genres affect the specificity or level of abstraction of user-generated tags. This study found significant variations in frequency of assignment of superordinate, subordinate and basic level terms representing news, blog and ecommerce resource genres. Study observed users’ preferences to represent news and blog resources with basic or subordinate level tags and ecommerce resources with superordinate and basic level of tags. Study also observed multifaceted representation of resource genres, suggesting that use of genre tags is “situated” and grounded in language. This study suggests that representation of knowledge based on resource genres and levels of abstraction of user-generated tags may improve representation, organization, and findability of the resources in the distributed knowledge environments.     

试论开放源代码软件的专利风险

引言 关于“绿坝”软件的各种争论近一段时间充斥在国内外各种媒体上,其中有一则新闻值得引起人们的注意,一家外国软件开发商质疑“绿坝”软件不当使用了其所开发的开放源代码软件^1。在此姑且不论该质疑的最终真实性,假如“绿坝”软件真的使用了开放源代码软件,并且它符合最为流行的开放源代码软件许可证GPL2.0^2,     

A general model of query processing in information retrieval systems

Most current document retrieval systems require that user queries be specified in the form of Boolean expressions. Although Boolean queries work, they have flaws. Some of the attempts to overcome these flaws have involved “partial-match” retrieval or the use of fuzzy-subset theory. Recently, some generalizations of fuzzy-subset theory have been suggested that would allow the user to specify queries with relevance weights or thresholds attached to terms. The various query-processing methods are discussed and compared.     

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.     

Fuzzy swarm diversity hybrid model for text summarization

High quality summary is the target and challenge for any automatic text summarization. In this paper, we introduce a different hybrid model for automatic text summarization problem. We exploit strengths of different techniques in building our model: we use diversity-based method to filter similar sentences and select the most diverse ones, differentiate between the more important and less important features using the swarm-based method and use fuzzy logic to make the risks, uncertainty, ambiguity and imprecise values of the text features weights flexibly tolerated. The diversity-based method focuses to reduce redundancy problems and the other two techniques concentrate on the scoring mechanism of the sentences. We presented the proposed model in two forms. In the first form of the model, diversity measures dominate the behavior of the model. In the second form, the diversity constraint is no longer imposed on the model behavior. That means the diversity-based method works same as fuzzy swarm-based method. The results showed that the proposed model in the second form performs better than the first form, the swarm model, the fuzzy swarm method and the benchmark methods. Over results show that combination of diversity measures, swarm techniques and fuzzy logic can generate good summary containing the most important parts in the document.     

Irony detection via sentiment-based transfer learning

Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text’s ironic intent is defined by its context incongruity. For example in the phrase “I love being ignored”, the irony is defined by the incongruity between the positive word “love” and the negative context of “being ignored”. Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in “I like to think of myself as a broken down Justin Bieber – my philosophy professor.” We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.     

A conceptual framework for fuzzy query processing—A step toward very intelligent database systems

This paper is concerned with techniques for fuzzy query processing in a database system. By a fuzzy query we mean a query which uses imprecise or fuzzy predicates (e.g. AGE = “VERY YOUNG”, SALARY = “MORE OR LESS HIGH”, YEAR-OF-EMPLOYMENT = “RECENT”, SALARY ? 20,000, etc.). As a basis for fuzzy query processing, a fuzzy retrieval system based on the theory of fuzzy sets and linguistic variables is introduced. In our system model, the first step in processing fuzzy queries consists of assigning meaning to fuzzy terms (linguistic values), of a term-set, used for the formulation of a query. The meaning of a fuzzy term is defined as a fuzzy set in a universe of discourse which contains the numerical values of a domain of a relation in the system database.The fuzzy retrieval system developed is a high level model for the techniques which may be used in a database system. The feasibility of implementing such techniques in a real environment is studied. Specifically, within this context, techniques for processing simple fuzzy queries expressed in the relational query language SEQUEL are introduced.     

基于“睡美人”文献识别的高校学术论文价值挖掘方法研究——以东北大学为例

[目的/意义]研究“睡美人”文献的识别方法,对尽早发现重要科技成就及其发明人、加快科技成果转化以及完善学术评价方法等均具有重要意义。[方法/过程]针对高校学术论文成果评价这一特定场景,提出“先客观指标粗筛、后多维参数细选”的研究思路,组合使用K值算法和三指标法,对东北大学发表于Web of Science核心合集的论文样本集进行了“睡美人”文献挖掘的实证研究。[结果/结论]该方法共识别出12篇“睡美人”文献,并对其被引特征、期刊特征、睡眠特征、内容特征等因素进行了分析。实获数据处理结果表明了该方法的有效性,相关研究方法和结果可对东北大学学术论文评价提供重要参考。     

The effect of toxicity on COVID-19 news network formation in political subcommunities on Reddit: An affiliation network approach

Political polarization remains perhaps the “greatest barrier” to effective COVID-19 pandemic mitigation measures in the United States. Social media has been implicated in fueling this polarization. In this paper, we uncover the network of COVID-19 related news sources shared to 30 politically biased and 2 neutral subcommunities on Reddit. We find, using exponential random graph modeling, that news sources associated with highly toxic – “rude, disrespectful” – content are more likely to be shared across political subreddits. We also find homophily according to toxicity levels in the network of online news sources. Our findings suggest that news sources associated with high toxicity are rewarded with prominent positions in the resultant network. The toxicity in COVID-19 discussions may fuel political polarization by denigrating ideological opponents and politicizing responses to the COVID-19 pandemic, all to the detriment of mitigation measures. Public health practitioners should monitor toxicity in public online discussions to familiarize themselves with emerging political arguments that threaten adherence to public health crises management. We also recommend, based on our findings, that social media platforms algorithmically promote neutral and scientific news sources to reduce toxic discussion in subcommunities and encourage compliance with public health recommendations in the fight against COVID-19.     

Analyzing the public discourse on works of fiction – Detection and visualization of emotion in online coverage about HBO’s Game of Thrones

This paper presents a Web intelligence portal that captures and aggregates news and social media coverage about “Game of Thrones”, an American drama television series created for the HBO television network based on George R.R. Martin’s series of fantasy novels. The system collects content from the Web sites of Anglo-American news media as well as from four social media platforms: Twitter, Facebook, Google+ and YouTube. An interactive dashboard with trend charts and synchronized visual analytics components not only shows how often Game of Thrones events and characters are being mentioned by journalists and viewers, but also provides a real-time account of concepts that are being associated with the unfolding storyline and each new episode. Positive or negative sentiment is computed automatically, which sheds light on the perception of actors and new plot elements.     

Predictive intelligence in harmful news identification by BERT-based ensemble learning model with text sentiment analysis

In an environment full of disordered information, the media spreads fake or harmful information into the public arena with a speed which is faster than ever before. A news report should ideally be neutral and factual. Excessive personal emotions or viewpoints should not be included. News articles ought not to be intentionally or maliciously written or create a media framing. A harmful news is defined as those explicit or implicit harmful speech in news text that harms people or affects readers’ perception. However, in the current situation, it is difficult to effectively identify and predict fake or harmful news in advance, especially harmful news. Therefore, in this study, we propose a Bidirectional Encoder Representation from Transformers (BERT) based model which applies ensemble learning methods with a text sentiment analysis to identify harmful news, aiming to provide readers with a way to identify harmful news content so as to help them to judge whether the information provided is in a more neutral manner. The working model of the proposed system has two phases. The first phase is collecting harmful news and establishing a development model for analyzing the correlation between text sentiment and harmful news. The second phase is identifying harmful news by analyzing text sentiment with an ensemble learning technique and the BERT model. The purpose is to determine whether the news has harmful intentions. Our experimental results show that the F1-score of the proposed model reaches 66.3%, an increase of 7.8% compared with that of the previous term frequency-inverse document frequency approach which adopts a Lagrangian Support Vector Machine (LSVM) model without using a text sentiment. Moreover, the proposed method achieves a better performance in recognizing various cases of information disorder.     

区间两相模糊多目标模型在种植结构优化中的应用——以辽宁省大连市为例

针对基于“Max-min”算子的区间模糊多目标规划仅采用一或两个控制变量放松所有目标和模糊约束会造成某些约束过满意而某些约束不满意的情况,本文引入两相模糊规划,构建了区间-两相模糊多目标规划模型,并以辽宁省大连市种植结构优化为例进行研究。结果表明,该模型引入多个控制变量放松每个不确定目标和约束条件,且要求它们分别不小于“Max-min”算子中相应目标和约束条件的隶属度,更充分地利用了约束资源,保证了求解的有效性,减少了农业灌溉用水量;另外区间形式的最优解及4种不同情景的优化方案为决策者提供了决策空间,更真实地反映输入参数的不确定性对配置结果的影响。     

基于SEIR模型的“反电诈”微信公众号宣传效果分析及预测

[目的/意义]研究微信公众号在反电信网络诈骗犯罪宣传中的作用和效果,对普及公众防骗意识、提高宣传效率,遏制电信网络诈骗犯罪发生具有重要意义。[方法/过程]根据微信使用群体广、消息易扩散、传播渠道多等特点,在“电诈可防”理念背景下,采用改进的SEIR模型分析甘肃省兰州市“反电信网络诈骗中心”微信公众号对涉疫情电信网络诈骗犯罪宣传效果。[结果/结论]本地政务微信的关注群体仍以本地微信用户为主,公众号新增“关注用户”和新增“取关用户”均在文章发布时段后达到峰值,且呈现正相关,较从文章读者中产生“关注用户”相比,直接吸引公众号本体“关注用户”方式将更为直接和有效。     

Shape-oriented storage and retrieval of geometric figures and chromosome images

Four advantages of storing and retrieving geometric figures and chromosome images through the use of shape-oriented similarity measures are presented. A complemented but not distributive lattice and a distributive but not complemented lattice are found. Answers to triangle related fuzzy queries such as “retrieve the triangles which are very similar to isosceles triangles but not similar to a given triangle Δx”, and chromosome related fuzzy queries such as “retrieve the chromosomes which are more or less similar to median chromosomes and very very similar to a given chromosome A” are presented and illustrated by examples. For shape-oriented storage of triangles, it is proposed to store the angles of each triangle in decreasing order of the magnitude and logically order all the triangles according to the magnitude of the angles. For shape-oriented storage of chromosomes, it is proposed to logically order all the chromosomes individually and independently according to the angular sums of its exterior biangles and interior biangles. The results may have useful applications in information storage and retrieval, artificial intelligence and pattern recognition.     

Why do urban legends go viral?

Urban legends are a genre of modern folklore, consisting of stories about rare and exceptional events, just plausible enough to be believed, which tend to propagate inexorably across communities. In our view, while urban legends represent a form of “sticky” deceptive text, they are marked by a tension between the credible and incredible. They should be credible like a news article and incredible like a fairy tale to go viral. In particular we will focus on the idea that urban legends should mimic the details of news (who, where, when) to be credible, while they should be emotional and readable like a fairy tale to be catchy and memorable. Using NLP tools we will provide a quantitative analysis of these prototypical characteristics. We also lay out some machine learning experiments showing that it is possible to recognize an urban legend using just these simple features.