Constructing abstract maps from spatial descriptions for goal-directed exploration

This paper describes ongoing work on a system using spatial descriptions to construct abstract maps that can be used for goal-directed exploration in an unfamiliar office environment. Abstract maps contain membership, connectivity, and spatial layout information extracted from symbolic spatial information. In goal-directed exploration, the robot would then link this information with observed symbolic information and its grounded world representation. We demonstrate the ability of the system to extract and represent membership, connectivity, and spatial layout information from spatial descriptions of an office environment. In the planned study, the robot will navigate to the goal location using the abstract map to inform the best direction to explore in.

Building instance knowledge network for word sense disambiguation

In this paper, a new high precision focused word sense disambiguation (WSD) approach is proposed, which not only attempts to identify the proper sense for a word but also provides the probabilistic evaluation for the identification confidence at the same time. A novel Instance Knowledge Network (IKN) is built to generate and maintain semantic knowledge at the word, type synonym set and instance levels. Related algorithms based on graph matching are developed to train IKN with probabilistic knowledge and to use IKN for probabilistic word sense disambiguation. Based on the Senseval-3 all-words task, we run extensive experiments to show the performance enhancements in different precision ranges and the rationality of probabilistic based automatic confidence evaluation of disambiguation. We combine our WSD algorithm with five best WSD algorithms in senseval-3 all words tasks. The results show that the combined algorithms all outperform the corresponding algorithms.

Evaluating business process compliance management frameworks

This research contributes a formal framework to evaluate whether existing CMFs can model and reason about various types of normative requirements. The framework can be used to determine the level of coverage of concepts provided by CMFs, establish mappings between CMF languages and the semantics for the normative concepts and evaluate the suitability of a CMF for issuing a certification of compliance. The developed framework is independent of any specific formalism and it has been formally defined and validated through the examples of such mappings of CMFs.

An imagery-based road map to tackle maladaptive motivation in clinical disorders

There is now a widespread recognition of the importance of mental imagery in a range of clinical disorders (1). This provides the potential for a transdiagnostic route to integrate some aspects of these disorders and their treatment within a common framework. This opinion piece argues that we need to understand why imagery is such a central and recurring feature, if we are to progress theories of the origin and maintenance of disorders. This will aid us in identifying therapeutic techniques that are not simply targeting imagery as a symptom, but as a manifestation of an underlying problem. As papers in this issue highlight, imagery is a central feature across many clinical disorders, but has been ascribed varying roles. For example, the involuntary occurrence of traumatic memories is a diagnostic criterion for PTSD (2), and it has been suggested that multisensory imagery of traumatic events normally serves a functional role in allowing the individual to reappraise the situation (3), but that this re-appraisal is disabled by extreme affective responses. In contrast to the disabling flashbacks associated with PTSD, depressed adults who experience suicidal ideation often report “flash forward” imagery related to suicidal acts (4), motivating them to self-harm. Socially anxious individuals who engage in visual imagery about giving a talk in public become more anxious and make more negative predictions about future performance than others who engage in more abstract, semantic processing of the past event (5). People with Obsessive Compulsive Disorder (OCD) frequently report imagery of past adverse events, and imagery seems to be associated with severity (6). The content of intrusive imagery has been related to psychotic symptoms (7), including visual images of the catastrophic fears associated with paranoia and persecution. Imagery has been argued (8) to play a role in the maintenance of psychosis through negative appraisals of imagined voices, misattribution of sensations to external sources, by the induction of negative mood states that trigger voices, and through maintenance of negative schemas. In addiction and substance dependence, Elaborated Intrusion (EI) Theory (9, 10) emphasizes the causal role that imagery plays in substance use, through its role in motivating an individual to pursue goals directed toward achieving the pleasurable outcomes associated with substance use...

Integrating and evaluating neural word embeddings in information retrieval

Recent advances in neural language models have contributed new methods for learning distributed vector representations of words (also called word embeddings). Two such methods are the continuous bag-of-words model and the skipgram model. These methods have been shown to produce embeddings that capture higher order relationships between words that are highly effective in natural language processing tasks involving the use of word similarity and word analogy. Despite these promising results, there has been little analysis of the use of these word embeddings for retrieval. Motivated by these observations, in this paper, we set out to determine how these word embeddings can be used within a retrieval model and what the benefit might be. To this aim, we use neural word embeddings within the well known translation language model for information retrieval. This language model captures implicit semantic relations between the words in queries and those in relevant documents, thus producing more accurate estimations of document relevance. The word embeddings used to estimate neural language models produce translations that differ from previous translation language model approaches; differences that deliver improvements in retrieval effectiveness. The models are robust to choices made in building word embeddings and, even more so, our results show that embeddings do not even need to be produced from the same corpus being used for retrieval.

The neuroscience of inspirational leadership: the importance of collective-oriented language and shared group membership

Effective leaders are believed to inspire followers by providing inclusive visions of the future that followers can identify with. In the present study, we examined the neural mechanisms underlying this process, testing key hypotheses derived from transformational and social identity approaches to leadership. While undergoing functional MRI, supporters from the two major Australian political parties (Liberal vs. Labor) were presented with inspirational collective-oriented and noninspirational personal-oriented statements made by in-group and out-group leaders. Imaging data revealed that inspirational (rather than noninspirational) statements from in-group leaders were associated with increased activation in the bilateral rostral inferior parietal lobule, pars opercularis, and posterior midcingulate cortex: brain areas that are typically implicated in controlling semantic information processing. In contrast, for out-group leaders, greater activation in these areas was associated with noninspirational statements. In addition, noninspirational statements by in-group (but not out-group) leaders resulted in increased activation in the medial prefrontal cortex, an area typically associated with reasoning about a person’s mental state. These results show that followers processed identical statements qualitatively differently as a function of leaders’ group membership, thus demonstrating that shared identity acts as an amplifier for inspirational leadership communication.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

Measuring the expressed emotion in Chinese family caregivers of persons with dementia: Validation of a Chinese version of the Family Attitude Scale

- Background Expressed emotion (EE) captures the affective quality of the relationship between family caregivers and their care recipients and is known to increase the risk of poor health outcomes for caregiving dyads. Little is known about expressed emotion in the context of caregiving for persons with dementia, especially in non-Western cultures. The Family Attitude Scale (FAS) is a psychometrically sound self-reporting measure for EE. Its use in the examination of caregiving for patients with dementia has not yet been explored. - Objectives This study was performed to examine the psychometric properties of the Chinese version of the FAS (FAS-C) in Chinese caregivers of relatives with dementia, and its validity in predicting severe depressive symptoms among the caregivers. - Methods The FAS was translated into Chinese using Brislin's model. Two expert panels evaluated the semantic equivalence and content validity of this Chinese version (FAS-C), respectively. A total of 123 Chinese primary caregivers of relatives with dementia were recruited from three elderly community care centers in Hong Kong. The FAS-C was administered with the Chinese versions of the 5-item Mental Health Inventory (MHI-5), the Zarit Burden Interview (ZBI) and the Revised Memory and Behavioral Problem Checklist (RMBPC). - Results The FAS-C had excellent semantic equivalence with the original version and a content validity index of 0.92. Exploratory factor analysis identified a three-factor structure for the FAS-C (hostile acts, criticism and distancing). Cronbach's alpha of the FAS-C was 0.92. Pearson's correlation indicated that there were significant associations between a higher score on the FAS-C and greater caregiver burden (r = 0.66, p < 0.001), poorer mental health of the caregivers (r = −0.65, p < 0.001) and a higher level of dementia-related symptoms (frequency of symptoms: r = 0.45, p < 0.001; symptom disturbance: r = 0.51, p < 0.001), which serves to suggest its construct validity. For detecting severe depressive symptoms of the family caregivers, the receiving operating characteristics (ROC) curve had an area under curve of 0.78 (95% confidence interval (CI) = 0.69–0.87, p < 0.0001). The optimal cut-off score was >47 with a sensitivity of 0.720 (95% CI = 0.506–0.879) and specificity of 0.742 (95% CI = 0.643–0.826). - Conclusions The FAS-C is a reliable and valid measure to assess the affective quality of the relationship between Chinese caregivers and their relatives with dementia. It also has acceptable predictability in identifying family caregivers with severe depressive symptoms.

Proceedings of the 2nd Workshop on (Re)Creating Lively Cities Through Ambient Technology in Arts, Culture and Gastronomic Experiences

Digital and interactive technologies are becoming increasingly embedded in everyday lives of people around the world. Application of technologies such as real-time, context-aware, and interactive technologies; augmented and immersive realities; social media; and location-based services has been particularly evident in urban environments where technological and sociocultural infrastructures enable easier deployment and adoption as compared to non-urban areas. There has been growing consumer demand for new forms of experiences and services enabled through these emerging technologies. We call this ambient media, as the media is embedded in the natural human living environment. This workshop focuses on ambient media services, applications, and technologies that promote people’s engagement in creating and recreating liveliness in urban environments, particularly through arts, culture, and gastronomic experiences. The RelCi workshop series is organized in cooperation with the Queensland University of Technology (QUT), in particular the Urban Informatics Lab and the Tampere University of Technology (TUT), in particular the Entertainment and Media Management (EMMi) Lab. The workshop runs under the umbrella of the International Ambient Media Association (AMEA) (http://www.ambientmediaassociation.org), which is hosting the international open access journal entitled “International Journal on Information Systems and Management in Creative eMedia”, and the international open access series “International Series on Information Systems and Management in Creative eMedia” (see http://www.tut.fi/emmi/Journal). The RelCi workshop took place for the first time in 2012 in conjunction with ICME 2012 in Melbourne, Autralia; and this year’s edition took place in conjunction with INTERACT 2013 in Cape Town, South Africa. Besides, the International Ambient Media Association (AMEA) organizes the Semantic Ambient Media (SAME) workshop series, which took place in 2008 in conjunction with ACM Multimedia 2008 in Vancouver, Canada; in 2009 in conjunction with AmI 2009 in Salzburg, Austria; in 2010 in conjunction with AmI 2010 in Malaga, Spain; in 2011 in conjunction with Communities and Technologies 2011 in Brisbane, Australia; in 2012 in conjunction with Pervasive 2012 in Newcastle, UK; and in 2013 in conjunction with C&T 2013 in Munich, Germany.

Comprehending adverbs of doubt and certainty in health communication: A multidimensional scaling approach

This research explored the feasibility of using multidimensional scaling (MDS) analysis in novel combination with other techniques to study comprehension of epistemic adverbs expressing doubt and certainty (e.g., evidently, obviously, probably) as they relate to health communication in clinical settings. In Study 1, Australian English speakers performed a dissimilarity-rating task with sentence pairs containing the target stimuli, presented as "doctors' opinions". Ratings were analyzed using a combination of cultural consensus analysis (factor analysis across participants), weighted-data classical-MDS, and cluster analysis. Analyses revealed strong within-community consistency for a 3-dimensional semantic space solution that took into account individual differences, strong statistical acceptability of the MDS results in terms of stress and explained variance, and semantic configurations that were interpretable in terms of linguistic analyses of the target adverbs. The results confirmed the feasibility of using MDS in this context. Study 2 replicated the results with Canadian English speakers on the same task. Semantic analyses and stress decomposition analysis were performed on the Australian and Canadian data sets, revealing similarities and differences between the two groups. Overall, the results support using MDS to study comprehension of words critical for health communication, including in future studies, for example, second language speaking patients and/or practitioners. More broadly, the results indicate that the techniques described should be promising for comprehension studies in many communicative domains, in both clinical settings and beyond, and including those targeting other aspects of language and focusing on comparisons across different speech communities.