Interacting With Hypertext: A Meta-Analysis of Experimental Studies

The meta-analysis compared and synthesized the results of 23 experimental studies on hypertext. The analysis was based on 56 pairs of effect sizes and significance levels of the impact of users, tasks, and tools on interactions with hypertext. This analysis focused on three factors that prevailingly influence the use of hypertext: the cognitive styles and spatial ability of users; the complexity of tasks; and the structure of information organization and the visualization of the structure. The meta-analysis found that this group of experimental studies reported significantly discrepant findings, indicating that substantial differences exist among individual experiments. Individual differences in cognition did not yield enough evidence to conclude that the effect sizes are significantly apart from zero. The meta-analysis showed that the overall performance of hypertext users tended to be more effective than that of nonhypertext users, but the differences in efficiency measures were consistently in favor of nonhypertext users. Users benefited more from hypertext tools for open tasks. Overall, the complexity of tasks has the largest combined effect sizes. Graphical maps that visualize the organization of hypertext have significant impact on the usefulness of a hypertext system. This meta-analysis raised two issues concerned with the present hypertext literature: (a) the absence of a taxonomy of tasks for analyzing and comparing hypertext usability across studies, and (b) the weaknesses of the connections between abstract hypertext reference models and specific hypertext systems. These weaknesses may considerably undermine the significance of individual findings on hypertext usability. Results of the meta-analysis suggest that the discrepancies among empirical findings are related to these weaknesses. Future work on hypertext usability should emphasize task taxonomies along with longitudinal and ethnographic studies for a deep understanding of the interactions between users and hypertext. Recommended research issues for the future are highlighted in Section 5.     

Regulation retrieval using industry specific taxonomies

Increasingly, taxonomies are being developed and used by industry practitioners to facilitate information interoperability and retrieval. Within a single industrial domain, there exist many taxonomies that are intended for different applications. Industry specific taxonomies often represent the vocabularies that are commonly used by the practitioners. Their jobs are multi-faceted, which include checking for code and regulatory compliance. As such, it will be very desirable if industry practitioners are able to easily locate and browse regulations of interest. In practice, multiple sources of government regulations exist and they are often organized and classified by the needs of the issuing agencies that enforce them rather than the needs of the communities that use them. One way to bridge these two distinct needs is to develop methods and tools that enable practitioners to browse and retrieve government regulations using their own terms and vocabularies, for example, via existing industry taxonomies. The mapping from a single taxonomy to a single regulation is a trivial keyword matching task. We examine a relatedness analysis approach for mapping a single taxonomy to multiple regulations. We then present an approach for mapping multiple taxonomies to a single regulation by measuring the relatedness of concepts. Cosine similarity, Jaccard coefficient and market basket analysis are used to measure the semantic relatedness between concepts from two different taxonomies. Preliminary evaluations of the three relatedness analysis measures are performed using examples from the civil engineering and building industry. These examples illustrate the potential benefits of regulatory usage from the mapping between various taxonomies and regulations.     

Learning to integrate web taxonomies

We investigate machine learning methods for automatically integrating objects from different taxonomies into a master taxonomy. This problem is not only currently pervasive on the Web, but is also important to the emerging Semantic Web. A straightforward approach to automating this process would be to build classifiers through machine learning and then use these classifiers to classify objects from the source taxonomies into categories of the master taxonomy. However, conventional machine learning algorithms totally ignore the availability of the source taxonomies. In fact, source and master taxonomies often have common categories under different names or other more complex semantic overlaps. We introduce two techniques that exploit the semantic overlap between the source and master taxonomies to build better classifiers for the master taxonomy. The first technique, Cluster Shrinkage, biases the learning algorithm against splitting source categories by making objects in the same category appear more similar to each other. The second technique, Co-Bootstrapping, tries to facilitate the exploitation of inter-taxonomy relationships by providing category indicator functions as additional features for the objects. Our experiments with real-world Web data show that these proposed add-on techniques can enhance various machine learning algorithms to achieve substantial improvements in performance for taxonomy integration.     

Target-driven merging of taxonomies with Atom

The proliferation of ontologies and taxonomies in many domains increasingly demands the integration of multiple such ontologies. We propose a new taxonomy merging algorithm called Atom that, given as input two taxonomies and a match mapping between them, can generate an integrated taxonomy in a largely automatic manner. The approach is target-driven, i.e. we merge a source taxonomy into the target taxonomy and preserve the target ontology as much as possible. In contrast to previous approaches, Atom does not aim at fully preserving all input concepts and relationships but strives to reduce the semantic heterogeneity of the merge results for improved understandability. Atom can also exploit advanced match mappings containing is-a relationships in addition to equivalence relationships between concepts of the input taxonomies. We evaluate Atom for synthetic and real-world scenarios and compare it with a full merge solution.     

Learning a taxonomy from a set of text documents

We present a methodology for learning a taxonomy from a set of text documents that each describes one concept. The taxonomy is obtained by clustering the concept definition documents with a hierarchical approach to the Self-Organizing Map. In this study, we compare three different feature extraction approaches with varying degree of language independence. The feature extraction schemes include fuzzy logic-based feature weighting and selection, statistical keyphrase extraction, and the traditional tf-idf weighting scheme. The experiments are conducted for English, Finnish, and Spanish. The results show that while the rule-based fuzzy logic systems have an advantage in automatic taxonomy learning, taxonomies can also be constructed with tolerable results using statistical methods without domain- or style-specific knowledge.     

An extensible pattern-based library and taxonomy of security threats for distributed systems

Security is one of the most essential quality attributes of distributed systems, which often operate over untrusted networks such as the Internet. To incorporate security features during the development of a distributed system requires a sound analysis of potential attacks or threats in various contexts, a process that is often termed "threat modeling". To reduce the level of security expertise required, threat modeling can be supported by threat libraries (structured or unstructured lists of threats), which have been found particularly effective in industry scenarios; or attack taxonomies, which offer a classification scheme to help developers find relevant attacks more easily. In this paper we combine the values of threat libraries and taxonomies, and propose an extensible, two-level "pattern-based taxonomy" for (general) distributed systems. The taxonomy is based on the novel concept of a threat pattern, which can be customized and instantiated in different architectural contexts to define specific threats to a system. This allows developers to quickly consider a range of relevant threats in various architectural contexts as befits a threat library, increasing the efficacy of, and reducing the expertise required for, threat modeling. The taxonomy aims to classify a wide variety of more abstract, system- and technology-independent threats, which keeps the number of threats requiring consideration manageable, increases the taxonomy's applicability, and makes it both more practical and more useful for security novices and experts alike. After describing the taxonomy which applies to distributed systems generally, we propose a simple and effective method to construct pattern-based threat taxonomies for more specific system types and/or technology contexts by specializing one or more threat patterns. This allows for the creation of a single application-specific taxonomy. We demonstrate our approach to specialization by constructing a threat taxonomy for peer-to-peer systems.     

Evolutionary taxonomy construction from dynamic tag space

Collaborative tagging becomes a common feature of current web sites, facilitating ordinary users to annotate and represent online resources. The large collection of tags and their relationships form a tag space. In this kind of tag space, the popularity and correlation amongst tags capture the current social interests. Tags are freely chosen keywords and difficult to organize. As a hierarchical concept structure to represent the subsumption relationships, automatically extracted taxonomies become a viable method to manage collaborative tags. However, tags change over time, and it is also imperative to incorporate the temporal tag evolution into the extracted taxonomies. In this paper, we formalize the problem of evolutionary taxonomy generation over a large collection of tags. A line of taxonomies are generated to reflect the temporal changes of underlying tag space. The proposed evolutionary taxonomy framework consists of two novel contributions. First, we develop a context-aware edge selection algorithm for taxonomy extraction. This method is built on seminal association-rule mining algorithm. Second, we propose several strategies for evolutionary taxonomy fusion, which smooths the newly generated taxonomy with prior ones. We conduct an extensive performance study using a large real-life web page tagging dataset (i.e., Del.ici.ous). The empirical results clearly verify the effectiveness and efficiency of the proposed approach.     

Design of workcards for aircraft inspection

The workcard is the primary document that controls an aircraft inspection task and serves as a major factor influencing inspection performance. The present study develops a methodology for design of workcards, based on the application of human factors knowledge to the analysis of aircraft inspection tasks. A taxonomy for design of usable documentation was developed using four basic categories: information readability, information context, information organization, and physical handling/environmental factors. Within the framework of this taxonomy two extreme representative conditions of aircraft inspection tasks, the A-check and the C-check, were analysed for usability. Issues for workcard redesign were identified within the taxonomy using data from user responses. These were then used to develop alternative design solutions offering improved usability. The increase in usability was measured using inspections of DC-9 aircraft, and proved significant. Not only does this study propose specific design solutions, it also provides a generic methodology that can be followed for design of quality documentation for other aircraft inspection tasks. This methodology is currently being extended for the design of usable information for automated workcards and hypermedia-based documentation.     

Updating generalized association rules with evolving fuzzy taxonomies

Mining generalized association rules with fuzzy taxonomic structures has been recognized as an important extension of generalized associations mining problem. To date most work on this problem, however, required the taxonomies to be static, ignoring the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced, or added into the taxonomies as new items. Additionally, the membership degrees expressing the fuzzy classification may also need to be adjusted. Under these circumstances, effectively updating the discovered generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms, called FDiff_ET and FDiff_ET*, to update the discovered generalized frequent itemsets. Empirical evaluations show that our algorithms can maintain their performance even in high degree of taxonomy evolution, and are significantly faster than applying the contemporary fuzzy generalized association mining algorithm FGAR to the database with evolving taxonomy.     

A Context-of-Use Taxonomy for Usability Studies

The interest in developing usable products has led to the inclusion of usability aspects in product development processes. Nonetheless, the fact that there is a tendency to overlook characteristics of the context in which a product is to be used means that the usability of a product in its operational environment is often diminished. One of the main reasons why this is the case is because there is no clear and sufficiently detailed model available for defining the concept of context of use. A comprehensive taxonomy that describes context of use and its attributes by means of precise definitions is proposed. This taxonomy will serve as a basis for improving the validity of usability activities by enabling an analysis of the conditions of use of a product in usability studies in a structured way.     

面向可生存性的网络攻击分类方法

网络攻击分类是研究网络攻击特点及其防护方法的前提,介绍了网络攻击分类的研究现状,对已有网络攻击分类方法进行了分析比较,提出了一种面向网络可生存性研究的网络攻击分类方法,并用实例说明了该分类方法的分类过程。该方法对网络防护人员进行网络可生存性提升很有帮助,为他们有针对性地研究网络可生存性技术提供了依据。     

基于复合结构的知识库分类体系匹配方法

近年来,分类体系匹配由于其在知识库构建和融合等方面的广泛应用,已成为国内外工业界和学术界的研究热点.然而,随着网络大数据的不断发展,分类体系变得越来越庞大和复杂,构造一种通用有效的分类体系匹配器以适应大规模、异构分类体系匹配的扩展性仍然面临很大的挑战.为此,提出了一种基于复合结构的分类体系匹配方法BiMWM,该方法利用分类体系中分类的复合结构信息：微观结构和宏观结构,将分类体系匹配问题转化为二部图上的优化问题进行求解.首先,创建赋权的二部图建模分类体系之间候选的匹配类对关系;然后,通过计算二部图上的最大权匹配剪枝选择最优的分类体系的匹配类对.BiMWM方法可以在多项式时间内为2个分类体系产生最优匹配.实验结果表明:与当前先进的基准方法相比,该方法能够有效提升大规模、异构分类体系匹配的性能.     

Taxonomy alignment for interoperability between heterogeneous virtual organizations

Resources in virtual organizations are classified based on their local taxonomies. However, heterogeneity between these taxonomies is a serious problem for efficient cooperation processes (e.g., knowledge sharing and querying-based interactions). In order to overcome this problem, we propose a novel framework based on aligning the taxonomies of virtual organizations. Thereby, the best mapping between two organization taxonomies has to be discovered to maximize the summation of a set of partial similarities between concepts in the taxonomies. We can consider two levels of alignment processes; (i) intra-alignment in a virtual organization for building an organizational taxonomy and (ii) inter-alignment between organizational taxonomies. Particularly, for intra-alignment, features extracted from resources are exploited to enhance the precision of similarity measurement between concepts. For experimentation, twelve virtual organizations have been built with different local taxonomies. The proposed inter-alignment method has shown about 76% of precision and 68% of recall. Also, feature-based intra-alignment improved those performance, during resource retrieval by query transformation. In addition, we found out that alignment results are dependent on some characteristics of taxonomies (e.g., depth and number of classes).     

向量模型和多源词汇分类体系相结合的词语相似性计算

现有的词语语义相似性计算主要包括基于向量模型以及基于词汇分类体系两类方法,但这两类方法都存在自身的缺点。向量模型所依赖的文本共现中的上下文信息不等同于真正意义上的语义,而词汇分类体系方法则存在构建代价大,并且在一定程度上还不够完善的问题。该文提出一种向量模型与多源词汇分类体系相结合的词语相似性计算方法,采用多源词汇分类体系的近义词关系以及向量模型得到的词向量,计算得到词语的向量表达,并探索了不同类型词汇分类体系提供的知识的选用和融合问题,弥补了单一词向量和单一词汇分类体系在词语相似性计算中的缺点。该文采用了NLPCC-ICCPOL 2016词语相似度评测比赛中的PKU 500数据集进行评测。在该数据集上,该文的方法取得了0.637的斯皮尔曼等级相关系数,比NLPCC-ICCPOL 2016词语相似度评测比赛第一名的方法的结果提高了23%。     

Usability in agile software development: A tertiary study

In the last years the interest in developing research on integration of usability and agile software development has been increasing. The number of systematic literature reviews, systematic mapping studies and non-systematic reviews, related to this thematic has also increased. Nevertheless, there is no analysis on the quality of these published secondary studies, nor is there a consolidated research that brings the answer of how to integrate these two areas. The goal of this paper is to categorize secondary studies related to the integration of usability and agile software development and present a critical analysis on the quality of the selected studies. To accomplish this goal a tertiary study was performed to categorize the related studies selected. Initially 3,065 papers were identified and further narrowed to 14 by applying exclusion criteria and analysis. We classified the selected studies as systematic literature reviews, systematic mapping studies and non-systematic literature reviews to report the data analysis. As a result of this study different forms to integrate usability and agile software development were detected as well as the various challenges that must be overcome for the integration success. Six main categories were identified to represent ways of integrating usability into agile development: processes, techniques, practices, recommendations, principles and different approaches. Regarding to the challenges for the integration seven main categories were also identified: issues related to tests, time, work balance, modularization, feedback, prioritization, and documentation. Although the interest in researching the integration of usability and agile software development has increased in the last years, mostly of the analyzed studies neglected the quality criteria and presented difficulties to use methods to synthetize the research results. Despite this, it has been realized that the integration of usability with agile software development is possible and is strongly aligned with user-centered design. The initial studies indicated a separation of activities and roles into specific tracks with parallel work to treat usability in agile software development, but the trend is no longer to manage and control these activities in separate ways, so new challenges are becoming to appear. Although we have identified several points of tension, the integration does not become unfeasible.     

Asymmetric information distances for automated taxonomy construction

A novel method for automatically constructing taxonomies for specific research domains is presented. The proposed methodology uses term co-occurrence frequencies as an indicator of the semantic closeness between terms. To support the automated creation of taxonomies or subject classifications we present a simple modification to the basic distance measure, and describe a set of procedures by which these measures may be converted into estimates of the desired taxonomy. To demonstrate the viability of this approach, a pilot study on renewable energy technologies is conducted, where the proposed method is used to construct a hierarchy of terms related to alternative energy. These techniques have many potential applications, but one activity in which we are particularly interested is the mapping and subsequent prediction of future developments in the technology and research.     

TIE algorithm: a layer over clustering-based taxonomy generation for handling evolving data

Taxonomy is generated to effectively organize and access large volume of data. A taxonomy is a way of representing concepts that exist in data. It needs to continuously evolve to reflect changes in data. Existing automatic taxonomy generation techniques do not handle the evolution of data; therefore, the generated taxonomies do not truly represent the data. The evolution of data can be handled by either regenerating taxonomy from scratch, or allowing taxonomy to incrementally evolve whenever changes occur in the data. The former approach is not economical in terms of time and resources. A taxonomy incremental evolution (TIE) algorithm, as proposed, is a novel attempt to handle the data that evolve in time. It serves as a layer over an existing clustering-based taxonomy generation technique and allows an existing taxonomy to incrementally evolve. The algorithm was evaluated in research articles selected from the computing domain. It was found that the taxonomy using the algorithm that evolved with data needed considerably shorter time, and had better quality per unit time as compared to the taxonomy regenerated from scratch.