Affective, linguistic and topic patterns in online autism communities

Online communities offer a platform to support and discuss health issues. They provide a more accessible way to bring people of the same concerns or interests. This paper aims to study the characteristics of online autism communities (called Clinical) in comparison with other online communities (called Control) using data from 110 Live Journal weblog communities. Using machine learning techniques, we comprehensively analyze these online autism communities. We study three key aspects expressed in the blog posts made by members of the communities: sentiment, topics and language style. Sentiment analysis shows that the sentiment of the clinical group has lower valence, indicative of poorer moods than people in control. Topics and language styles are shown to be good predictors of autism posts. The result shows the potential of social media in medical studies for a broad range of purposes such as screening, monitoring and subsequently providing supports for online communities of individuals with special needs.

Supporting emotion communication in information systems

Society is becoming increasingly reliant on Information Systems to meet its everyday communication requirements, yet many current implementations lack support for important conversational cues. One such cue is emotion communication. Emotion communication carries with it many signals that affect our behaviour, the interpretation of the message and provide a catalyst to other forms of communication such as empathy and the formation of social ties. Emotion itself can affect the very decision to communicate, or the way in which one may respond to a given communication. To explore the ways in which systems may better support emotion communication between members of a social group, a cloud-based information system was developed and trialled which both large and small groups. This paper presents results on how Information Systems can best support emotion communication in social groups.

Affect computing in film through sound energy dynamics

We develop an algorithm for the detection and classification of affective sound events underscored by specific patterns of sound energy dynamics. We relate the portrayal of these events to proposed high level affect or emotional coloring of the events. In this paper, four possible characteristic sound energy events are identified that convey well established meanings through their dynamics to portray and deliver certain affect, sentiment related to the horror film genre. Our algorithm is developed with the ultimate aim of automatically structuring sections of films that contain distinct shades of emotion related to horror themes for nonlinear media access and navigation. An average of 82% of the energy events, obtained from the analysis of the audio tracks of sections of four sample films corresponded correctly to the proposed affect. While the discrimination between certain sound energy event types was low, the algorithm correctly detected 71% of the occurrences of the sound energy events within audio tracks of the films analyzed, and thus forms a useful basis for determining affective scenes characteristic of horror in movies.

A cluster operating system supporting parallel computing

The single factor limiting the harnessing of the enormous computing power of clusters for parallel computing is the lack of appropriate software. Present cluster operating systems are not built to support parallel computing – they do not provide services to manage parallelism. The cluster operating environments that are used to assist the execution of parallel applications do not provide support for both Message Passing (MP) or Distributed Shared Memory (DSM) paradigms. They are only offered as separate components implemented at the user level as library and independent servers. Due to poor operating systems users must deal with computers of a cluster rather than to see this cluster as a single powerful computer. A Single System Image of the cluster is not offered to users. There is a need for an operating system for clusters. We claim and demonstrate that it is possible to develop a cluster operating system that is
able to efficiently manage parallelism, support Message Passing and DSM and offer the Single System Image. In order to substantiate the claim the first version of a cluster operating system, called GENESIS, that manages parallelism and offers the Single System Image has been developed.

Comparing alternate teaching styles to teach computing skills to girls in their English classes

Low female participation rates in computing are a current concern of the education sector. To address this problem an intervention was developed — computing skills were introduced to girls in their English classes using three different teaching styles: peer tutoring, cross-age tutoring and teacher instruction (control). The sample comprised 136 girls from Years 8 and 10 from a single-sex government school. A pre-test post-test quantitative design was used. To describe the students perspectives, qualitative data were collected from six focus groups conducted with 8–10 students — one from each of the six classes. It was predicted that cross-age tutoring would yield more positive effects than peer tutoring which, in turn, would yield more positive effects than traditional teacher instruction as assessed by achievement on class tasks and attitudes towards computing. The hypothesis was not supported by the quantitative analysis, however in the qualitative data cross-age tutoring was appraised more favourably than peer tutoring or teacher instruction. The latter was the least preferred condition due to: (1) inefficiency; (2) difficulty understanding teachers' explanations; and (3) lack of teacher knowledge. Problems with the implementation of the intervention identified in the focus groups were teacher differences, system failures, missed classes, lack of communication, and selection of computing activities. Practical suggestions were provided relevant to the introduction of cross-age tutoring and the use of computers within secondary level English classes.

Fault detection service architecture for grid computing systems

The ability to tolerate failures while effectively exploiting the grid computing resources in an scalable and transparent manner must be an integral part of grid computing infrastructure. Hence, fault-detection service is a necessary prerequisite to fault tolerance and fault recovery in grid computing. To this end, we present an scalable fault detection service architecture. The proposed fault-detection system provides services that monitors user applications, grid middlewares and the dynamically changing state of a collection of distributed resources. It reports summaries of this information to the appropriate agents on demand or instantaneously in the event of failures.

Dynamic parallel job scheduling in multi-cluster computing systems

Job scheduling is a complex problem, yet it is fundamental to sustaining and improving the performance of parallel processing systems. In this paper, we address an on-line parallel job scheduling problem in heterogeneous multi-cluster computing systems. We propose a new space-sharing scheduling policy and show that it performs substantially better than the conventional policies.

Large-scale biological sequence assembly and alignment by using computing grid

Large-scale sequence assembly and alignment are fundamental parts of biological computing. However, most of the large-scale sequence assembly and alignment require intensive computing power and normally take very long time to complete. To speedup the assembly and alignment process, this paper parallelizes the Euler sequence assembly and pair-wise/multiple sequence assembly, two important sequence assembly methods, and takes advantage of Computing Grid which has a colossal computing capacity to meet the large-scale biological computing demand.

A model of the affective and congnitive events in expatriate assignments

In this paper, we take a step toward explaining how events occurring within the expatriate assignment are perceived and interpreted. In particular, we consider the conditions under which individuals perceive hassles and uplifts during intercultural interactions as a cognitive or affective event and, how the type of event influences the outcomes associated with a given event. In doing so, we develop a model to propose how characteristics of the perceiver and the event influence whether interactions in the expatriate assignment are perceived as a cognitive or affective event and, how the type of event influences the outcomes associated with a given event.

Autonomic job scheduling policy for grid computing

Autonomic middleware services will play an important role in the management of resources and distributed workloads in emerging distributed computing environments. In this paper, we address the problem of autonomic grid resource scheduling and propose a scheduling infrastructure that is capable of self-management in the face of dynamic behavior inherent to this kind of systems.

Job scheduling policy for high throughput grid computing

The growing computational power requirements of grand challenge applications has promoted the need for merging high throughput computing and grid computing principles to harness computational resources distributed across multiple organisations. This paper identifies the issues in resource management and scheduling in the emerging high throughput grid computing context. We also survey and study the performance of several space-sharing and time-sharing opportunistic scheduling policies that have been developed for high throughput computing.

Robust parallel job scheduling infrastructure for service-oriented grid computing systems

Recent trends in grid computing development is moving towards a service-oriented architecture. With the momentum gaining for the service-oriented grid computing systems, the issue of deploying support for integrated scheduling and fault-tolerant approaches becomes paramount importance. To this end, we propose a scalable framework that loosely couples the dynamic job scheduling approach with the hybrid replications approach to schedule jobs efficiently while at the same time providing fault-tolerance. The novelty of the proposed framework is that it uses passive replication approach under high system load and active replication approach under low system loads. The switch between these two replication methods is also done dynamically and transparently.