A Logic for Reasoning About Knowledge of Unawareness

In the most popular logics combining knowledge and awareness, it is not possible to express statements about knowledge of unawareness such as “Ann knows that Bill is aware of something Ann is not aware of”—without using a stronger statement such as “Ann knows that Bill is aware of \(p\) and Ann is not aware of \(p\) ”, for some particular \(p\) . In Halpern and Rêgo (Proceedings of KR 2006; Games Econ Behav 67(2):503–525, 2009b) Halpern and Rêgo introduced a logic in which such statements about knowledge of unawareness can be expressed. The logic extends the traditional framework with quantification over formulae, and is thus very expressive. As a consequence, it is not decidable. In this paper we introduce a decidable logic which can be used to reason about certain types of unawareness. Our logic extends the traditional framework with an operator expressing full awareness, i.e., the fact that an agent is aware of everything, and another operator expressing relative awareness, the fact that one agent is aware of everything another agent is aware of. The logic is less expressive than Halpern’s and Rêgo’s logic. It is, however, expressive enough to express all of the motivating examples in Halpern and Rêgo (Proceedings of KR 2006; Games Econ Behav 67(2):503–525, 2009b). In addition to proving that the logic is decidable and that its satisfiability problem is PSPACE-complete, we present an axiomatisation which we show is sound and complete.     

On designing interactivity awareness for ambient displays

In the not-so-distant world in which ambient displays will likely become prevalent, immediate awareness of their interaction affordances to passerbies will be decisive for their usability. However, how to address awareness at user perception level represents a challenge for which little progress has been made so far in contrast with considerable advances in designing interaction techniques for such displays. Even though many interactive ambient displays exist with properly designed interfaces, people may not always be aware of their interactivity. This work addresses the problem of interactivity awareness by focusing on two important questions: How can people tell whether a public display is interactive or not? and, assuming interactivity, How can people tell what the interface is? A study was conducted in order to investigate factors potentially related to perceived interactivity. Results show that people’s evaluations are correct in most cases but they also tend to form incorrect perceptions in many other situations. We found that location, installation, reachability, and displayed content are factors that can influence people’s perceptions. Our findings are complemented with a discussion of techniques that can be used in the practice of designing for interactivity awareness.     

基于TAP的上下文知晓系统体系结构

由于知识的异构性和动态性,在网上查找知识需要花费大量的时间和精力。为了提高效率和准确性,我们提出了面向语义网的上下文知晓模型。在分析上下文、上下文知晓和TAP的概念的基础上,构建了基于TAP的上下文知晓系统的体系结构,重点阐述了上下文知晓模型。     

The good, the bad, the complex: Computers and Composition in transition

In this article I use Albert Borgmann’s (1984) four-part theory of technology to analyze various contributions to Computers and Composition—initially enthusiastic, sometimes fearful, and later aware of the complexities of technology and the need for anchoring discussions of technology in theory and pedagogy. By applying Borgmann’s theory, I show how readers of Computers and Composition can follow the 20-year development of technological awareness in the journal, and I also show possibilities for future directions the journal can take.     

Good edit similarity learning by loss minimization

Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. However, these methods offer no theoretical guarantee as to the generalization ability and discriminative power of the learned similarities. In this paper, we propose an approach to edit similarity learning based on loss minimization, called GESL. It is driven by the notion of (?,??,??)-goodness, a theory that bridges the gap between the properties of a similarity function and its performance in classification. Using the notion of uniform stability, we derive generalization guarantees that hold for a large class of loss functions. We also provide experimental results on two real-world datasets which show that edit similarities learned with GESL induce more accurate and sparser classifiers than other (standard or learned) edit similarities.     

Knowledge awareness in CSCL: A psychological perspective

In this paper, a specific group awareness approach for CSCL settings, namely knowledge awareness, is presented. We classify knowledge awareness in relation to already well-established concepts like shared mental models, common ground, and transactive memory system in order to provide a comprehensive definition of this approach. The functionality of knowledge awareness tools aiming at fostering knowledge awareness is conceptualized in a cyclical model. Three application scenarios are presented to illustrate how knowledge awareness tools work in three different tasks. All these knowledge awareness tools have been found to be effective, that is all tools could foster knowledge awareness and led to increased learning performance.     

Contextual Probability and Neighbourhood Counting

In this paper, we review the concept of contextual probability, the resulting notion of neighbourhood counting and the various specialisations of this notion which result in new functions for measuring similarity, such as all common subsequences. We also provide new results on the generalisation of the all common subsequences similarity. Contextual probability was originally proposed as an alternative way of reasoning. It was later found to be an alternative way of estimating probability, and it led to the introduction of the neighbourhood counting notion. This notion was then found to be a generic similarity metric that can be applied to different types of data.     

Emotion understanding and performance during computer-supported collaboration

Individuals collaborating around and through computers benefit from receiving information that helps them understand one another, which is often termed awareness. This article explores what collaborators understand about each other’s emotions and the potential benefits for performance that might come from raising this understanding. In Experiment 1 co-located collaborators judged each other’s emotions after playing a game that required cooperative data collection and analysis. Their judgements were largely inaccurate and based on their own emotions, suggesting limited emotion understanding. Experiment 2 explored if this could be overcome by making collaborators aware of each other’s emotions. Co-located and remote collaborators played a cooperative puzzle-solving game under conditions of awareness or no awareness. Awareness was manipulated by making collaborators share their self-reported emotions during key moments of their game play. Both remote and co-located collaborators improved their performance after sharing their emotions. However, unlike co-located collaborators, remote collaborators also improved their understanding of each other’s emotions and experienced more positive affect. We conclude by discussing the content of collaborators’ emotion understanding and the probable mechanisms underlying the observed effects of being made aware of a partner’s emotions.     

Computing inter-document similarity with Context Semantic Analysis

We propose a novel knowledge-based technique for inter-document similarity computation, called Context Semantic Analysis (CSA). Several specialized approaches built on top of specific knowledge base (e.g. Wikipedia) exist in literature, but CSA differs from them because it is designed to be portable to any RDF knowledge base. In fact, our technique relies on a generic RDF knowledge base (e.g. DBpedia and Wikidata) to extract from it a Semantic Context Vector, a novel model for representing the context of a document, which is exploited by CSA to compute inter-document similarity effectively. Moreover, we show how CSA can be effectively applied in the Information Retrieval domain. Experimental results show that: (i) for the general task of inter-document similarity, CSA outperforms baselines built on top of traditional methods, and achieves a performance similar to the ones built on top of specific knowledge bases; (ii) for Information Retrieval tasks, enriching documents with context (i.e., employing the Semantic Context Vector model) improves the results quality of the state-of-the-art technique that employs such similar semantic enrichment.     

Relevance measures for the creation of groups in an annotation system

The MADCOW annotation system supports a notion of group, facilitating focused annotations with respect to a domain. In previous work, we adopted ontologies to represent knowledge about domains, thus allowing more refined annotations to a group, and discussed how the use of ontologies facilitates the formulation of semantically significant queries for retrieving annotations on specific topics. We now expand on previous results and study two new types of measures to identify matches between users׳ interests and groups: Degree Centrality, developed for social networks to assess the quality of concepts in an ontology, and URL concordance, indicating the similarity of interests among users who annotate the same pages.     

Weighted-object ensemble clustering: methods and analysis

In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.     

Searching for similar trajectories in spatial networks

In several applications, data objects move on pre-defined spatial networks such as road segments, railways, and invisible air routes. Many of these objects exhibit similarity with respect to their traversed paths, and therefore two objects can be correlated based on their motion similarity. Useful information can be retrieved from these correlations and this knowledge can be used to define similarity classes. In this paper, we study similarity search for moving object trajectories in spatial networks. The problem poses some important challenges, since it is quite different from the case where objects are allowed to move freely in any direction without motion restrictions. New similarity measures should be employed to express similarity between two trajectories that do not necessarily share any common sub-path. We define new similarity measures based on spatial and temporal characteristics of trajectories, such that the notion of similarity in space and time is well expressed, and moreover they satisfy the metric properties. In addition, we demonstrate that similarity range queries in trajectories are efficiently supported by utilizing metric-based access methods, such as M-trees.     

Scaffolding information problem solving in web-based collaborative inquiry learning

This study investigated the impact of different modes of scaffolding on students who are learning science through a web-based collaborative inquiry project in authentic classroom settings and explored the interaction effects with students’ characteristics. The intervention study aimed to improve domain-specific knowledge and metacognitive awareness during online information problem solving (IPS) as part of an online inquiry project. Three experimental conditions (teacher-enhanced scaffolding, technology-enhanced scaffolding, and both forms of scaffolding) were compared with a control condition in a two-by-two factorial quasi-experimental design. Moreover, gender and prior knowledge were examined as two factors which may have a significant impact on Web-based learning. In a four-week field study in secondary science education, pretest-posttest differences were measured. In total 347 students from 18 secondary school classes were involved and the classes were randomly distributed over the 4 conditions. Our findings support the notion of multiple scaffolding as an approach to enhance both knowledge acquisition and metacognitive awareness with respect to IPS-processes and to meet a mix of students with different needs within the context of a web-based inquiry learning project.     

Detecting,classifying, and tracing non-functional software requirements

In this paper, we describe a novel unsupervised approach for detecting, classifying, and tracing non-functional software requirements (NFRs). The proposed approach exploits the textual semantics of software functional requirements (FRs) to infer potential quality constraints enforced in the system. In particular, we conduct a systematic analysis of a series of word similarity methods and clustering techniques to generate semantically cohesive clusters of FR words. These clusters are classified into various categories of NFRs based on their semantic similarity to basic NFR labels. Discovered NFRs are then traced to their implementation in the solution space based on their textual semantic similarity to source code artifacts. Three software systems are used to conduct the experimental analysis in this paper. The results show that methods that exploit massive sources of textual human knowledge are more accurate in capturing and modeling the notion of similarity between FR words in a software system. Results also show that hierarchical clustering algorithms are more capable of generating thematic word clusters than partitioning clustering techniques. In terms of performance, our analysis indicates that the proposed approach can discover, classify, and trace NFRs with accuracy levels that can be adequate for practical applications.     

Supporting on-line resource discovery in the context of ongoing tasks with proactive software assistants

We present ongoing work on systems aimed at improving a user's awareness of resources available to them on the Internet and in intranets. First, we briefly describe Watson, a system that proactively retrieves documents from on-line repositories that are potentially useful in the context of a task, allowing the user to quickly become aware of document resources available in on-line information repositories. Next, we describe I2I, an extension of Watson that builds communities of practice on the fly, based on the work that its users do, so that users with similar goals and interests can discover each other and communicate both synchronously and asynchronously. Both Watson and I2I operate given some knowledge of the user's current task, gleaned automatically from the behavior of users in software tools. As a result, the systems can provide users with useful resources in the context of the work that they are performing. We argue that the systems can foster a greater sense of awareness of the resources available, while minimizing the effort required to discover them.     

Towards an Awareness-Based Semantics for Security Protocol Analysis

We report on work-in-progress on a new semantics for analyzing security protocols that combines complementary features of security logics and inductive methods. We use awareness to model the agents' resource-bounded reasoning and, in doing so, capture a more appropriate notion of belief than those usually considered in security logics. We also address the problem of modeling interleaved protocol executions, adapting ideas from inductive methods for protocol verification. The result is an intuitive, but expressive, doxastic logic for formalizing and reasoning about attacks. As a case study, we use awareness to characterize, and demonstrate the existence of, a man-in-the-middle attack upon the Needham-Schroeder Public Key protocol. This is, to our knowledge, not only the first doxastic analysis of this attack but also the first practical application of an awareness logic. Even though defining the awareness sets of the agents, a task that is left unspecified in formal works on awareness logics, turns out to be surprisingly subtle, initial results suggest that our approach is promising for modeling, verifying and reasoning about security protocols and their properties.     

Generic screen representations for future-proof systems,is it possible?: There is more to a GUI than meets the eye

BackgroundFuture-proof EHR systems must be capable of interpreting information structures for medical concepts that were not available at the build-time of the system. The two-model approach of CEN 13606/openEHR using archetypes achieves this by separating generic clinical knowledge from domain-related knowledge. The presentation of this information can either itself be generic, or require design time awareness of the domain knowledge being employed.ObjectiveTo develop a Graphical User Interface (GUI) that would be capable of displaying previously unencountered clinical data structures in a meaningful way.MethodsThrough “reasoning by analogy” we defined an approach for the representation and implementation of “presentational knowledge”. A proof-of-concept implementation was built to validate its implementability and to test for unanticipated issues.ResultsA two-model approach to specifying and generating a screen representation for archetype-based information, inspired by the two-model approach of archetypes, was developed. There is a separation between software-related display knowledge and domain-related display knowledge and the toolkit is designed with the reuse of components in mind.ConclusionsThe approach leads to a flexible GUI that can adapt not only to information structures that had not been predefined within the receiving system, but also to novel ways of displaying the information. We also found that, ideally, the openEHR Archetype Definition Language should receive minor adjustments to allow for generic binding.