CEST: a Cognitive Event based Semi-automatic Technique for behavior segmentation

This work introduces CEST, a Cognitive Event based Semiautomatic Technique for behavior segmentation. The technique was inspired by an everyday cognitive process. Humans, in fact, make sense of what happens to them by breaking the continuous stream of activity into smaller units, through a process known as segmentation. A cognitive theory, the Event Segmentation Theory, provides a computational and neurophysiological account of this process, describing how the detection of changes in the current situation drive boundary perception. CEST was designed with the aim of providing affective researchers with a tool to semi-automatically segment behavior. Researchers investigating behavior, as a matter of fact, often need to parse their research data into simpler units, either manually or automatically. To perform segmentation, the technique combines manual annotations and the output of change-point detection algorithms, techniques from time-series research that afford the detection of abrupt changes in time-series. CEST is inherently multidisciplinary: it is, to the best of our knowledge, the first attempt to adopt a cognitive science perspective on the issue of (semi) automatic behavior segmentation. CEST is a general-purpose technique, as it aims at providing a tool for segmenting behavior across research areas. In this manuscript, we detail the theories behind the design of CEST and the results of two experimental studies aimed at assessing the feasibility of the approach on both single and group scenarios. Most importantly, we present the results of the evaluation of CEST on a data-set of dance performances. We explore seven different techniques for change-point detection that could be leveraged to achieve semi-automatic segmentation through CEST and illustrate how two different bayesian algorithms led to the highest scores. Upon selecting the best algorithms, we measured the effect of the temporal grain of the analysis on the performance. Overall, our results support the idea of a semiautomatic segmentation technique for behavior segmentation. The output of the analysis mirrors cognitive science research on segmentation and on event structure perception. The work also tackles new challenges that may arise from our approach

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Group level influence on blog's design behaviour

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The purpose of this research is twofold. Firstly, this research aims to investigate whether the design preferences of bloggers in selected countries from different cultural backgrounds are influenced by national culture traits. The investigation involves two categories of blogs selected within a country where the bloggers share similar attributes such as language or geographical location. Secondly, simultaneously, this research intends to discover the possibility of the impact of group level influence on design preferences of bloggers who are linked together in a network through bloggers’ linkage or blogrolls. To achieve the said purposes, observations on both the global and local blogs of six selected countries are conducted using the content analysis method. This method allows this research to observe web pages and rate design preferences of bloggers via a coding system, similar to the method used to analyse documents or manuscripts to find common themes or keywords. A total of 612 blogs (306 global and 306 local) are observed for a period of nine months to identify cultural traits on design behaviour based on national culture indicators chosen from prominent literatures. To prevent a systematic error, an independent second observer was appointed and the results obtained are compared using a statistical methodology. In addition, translators were also engaged to verify that the translations are of a correct meaning and comprehension since blogs use various national languages on their web pages. The data were statistically tested using SPSS engaging in statistical analysis of frequency table, Cross-Tabulation and cluster analyses and MANOVA. Results shown that design preferences between both the global and local blogs in each country, has significant differences in most of the design indicators chosen. The findings indicate that the national culture influence on design preferences in linked networks of blogs is weakening indicating another type of influence might be in existence. The results also provide evidence that blogs in linked networks are statistically significant as a cluster or a group by themselves and are independent from one cluster to another. The research, however, studies only six countries from six different cultural dimensions. The inclusion of other countries, similar to or different from the countries under investigation, would be an added advantage. Furthermore, the use of only a single type of global blog provider (blogspot.com) in this research could be extended to other global blog providers such as wordpress.com to give fairer coverage of major and popular global blogs as well as providing a wider generalisation effect of the research findings

A Systematic Review of Music Teacher Education Research within the United States:1982-2010

Music education researchers have explored several issues within music teacher education (MTE) including: coursework, teacher and musicianship skills, design and implementation of undergraduate programs, and music teacher identity development. An examination and discussion of this research will assist those responsible for educating future music teacher educators with developing meaningful and effective teacher training programs. In this systematic review, I examined the research published in peer-review journals between 1982 and 2010 and defended music education dissertations between 2005 and 2010. The purpose of the current synthesis was to synthesize peer-review research relating to MTE and to recount the findings and connections of existing research for current music teacher educators. Before studies were included in the synthesis, I reviewed each one to ensure they met the following inclusion criteria: (a) relevant to the proposed research questions under consideration; (b) published in a peer-review journal or a defended dissertation between 2005-2010; (c) printed in English; (d) published between 1982 and July 2010; (e) involved subjects who were members of an undergraduate teacher preparation program in the United States; (f) detailed in the presentation of the methodology; and (g) presented the content so that relevant information could be attained. To further explore the implications of the current synthesis' findings, three practicing music teacher educators completed a two-part questionnaire designed to elicit information about their perspectives of MTE research and opinions of the current findings. I reviewed, categorized, and reported responses from each questionnaire as part of the research synthesis intending to identify the role of research in MTE, commonalities, possible concerns, and possible future research needs for meaningful research agendas specific to music teacher education

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Resource discovery in heterogeneous digital content environments

The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice.The concept of 'resource discovery' is central to our understanding of how users explore, navigate, locate and retrieve information resources. This submission for a PhD by Published Works examines a series of 11 related works which explore topics pertaining to resource discovery, each demonstrating heterogeneity in their digital discovery context. The assembled works are prefaced by nine chapters which seek to review and critically analyse the contribution of each work, as well as provide contextualization within the wider body of research literature. A series of conceptual sub-themes is used to organize and structure the works and the accompanying critical commentary. The thesis first begins by examining issues in distributed discovery contexts by studying collection level metadata (CLM), its application in 'information landscaping' techniques, and its relationship to the efficacy of federated item-level search tools. This research narrative continues but expands in the later works and commentary to consider the application of Knowledge Organization Systems (KOS), particularly within Semantic Web and machine interface contexts, with investigations of semantically aware terminology services in distributed discovery. The necessary modelling of data structures to support resource discovery - and its associated functionalities within digital libraries and repositories - is then considered within the novel context of technology-supported curriculum design repositories, where questions of human-computer interaction (HCI) are also examined. The final works studied as part of the thesis are those which investigate and evaluate the efficacy of open repositories in exposing knowledge commons to resource discovery via web search agents. Through the analysis of the collected works it is possible to identify a unifying theory of resource discovery, with the proposed concept of (meta)data alignment described and presented with a visual model. This analysis assists in the identification of a number of research topics worthy of further research; but it also highlights an incremental transition by the present author, from using research to inform the development of technologies designed to support or facilitate resource discovery, particularly at a 'meta' level, to the application of specific technologies to address resource discovery issues in a local context. Despite this variation the research narrative has remained focussed on topics surrounding resource discovery in heterogeneous digital content environments and is noted as having generated a coherent body of work. Separate chapters are used to consider the methodological approaches adopted in each work and the contribution made to research knowledge and professional practice

Intelligent Sensors for Human Motion Analysis

The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems

Modelling the dynamics of team situation awareness

For decades both industry and academia have been interested in situation awareness, from individual situation awareness to system situation awareness of dynamic collaborative systems. Several theories and definitions exist for situation awareness and although considerable research has been conducted in this domain no definitive consensus has been reached. Therefore, the purpose of the research in this thesis is not develop new theories or definitions, but to explore how situation awareness presents itself in teams and systems in terms of team cognition. The methods used in this thesis include simulating team tasks using agent-based modelling, analysing team knowledge using concept maps and analysing team processes using entropy. In order to remove the risk of intrusion on the tasks being explored, the communications of team members are recorded and used as the primary data for the analyses conducted. Visually presenting knowledge of agents using concept maps made it easier to understand how the information was stored and transferred throughout the teams. An interesting result showed that it was not important for all agents to have the same information when key decisions were made and that when information is not shared the team performed better and with greater accuracy than when there was a focus on information sharing. Visually presenting team processes using entropy and process distribution allowed for patterns of behaviour to be identified. Results show that while individuals within teams feel confident with the amount of knowledge they have they will focus on working independent up until the point they can no longer achieve results on their own, at that point the team shifts to teamworking. The differences between teamwork and taskwork are related to the theories of shared and distributed situation awareness, concluding that shifts in team processes represent shifts in the two types of situation awareness