Description:
The Internet and portability of technologies are changing the way we are doing business. Electronic business and commerce (e-commerce) platforms purportedly provide companies of all sizes with opportunities for economies of scale, process efficiencies and mechanisms for electronic advertising, retailing, trading and transaction. Australian small and medium size enterprises (SME) have, however, been reluctant to embrace information and communication technologies (ICT) and e-commerce because they fail to see the value of such tools. While SME are still coming to terms with core ICT adoption and strategic e-commerce directions, individual SME connectivity and e-commerce adoption inertia has been upstaged by collaborative network trends. Competitive advantage is becoming embedded in collaboration, networking and (virtual) clustering. This paper discusses the pathways towards online collaboration that address both industry or network needs and the capabilities, needs and attitudes of individual SME.
Description:
The Internet and portability of technologies are changing the way we are doing business. Electronic business and commerce (e-commerce) platforms purportedly provide companies of all sizes with opportunities for economies of scale, process efficiencies and mechanisms for electronic advertising, retailing, trading and transaction. Australian small and medium size enterprises (SME) have, however, been reluctant to embrace information and communication technologies (ICT) and e-commerce because they fail to see the value of such tools. While SME are still coming to terms with core ICT adoption and strategic e-commerce directions, individual SME connectivity and e-commerce adoption inertia has been upstaged by collaborative network trends. Competitive advantage is becoming embedded in collaboration, networking and (virtual) clustering. This paper discusses the pathways towards online collaboration that address both industry or network needs and the capabilities, needs and attitudes of individual SME.
Description:
Text Classification is the task of mapping a document into one or more classes based on the presence or absence of words (or features) in the document. It is intensively being studied and different classification techniques and algorithms have been developed. This thesis focuses on classification of online documents that has become more critical with the development of World Wide Web. The WWW vastly increases the availability of on-line documents in digital format and has highlighted the need to classify them. From this background, we have noted the emergence of “automatic Web Classification”. These mainly concentrate on classifying HTML-like documents into classes or categories by not only using the methods that are inherited from the traditional Text Classification process, but also utilizing the extra information provided only by Web pages. Our work is based on the fact that, Web documents, contain not only ordinary features (words) but also extra information, such as meta-data and hyperlinks that can be used to advantage the classification process. The aim of this research is to study various ways of using the extra information, in particularly, hyperlink information provided by HTML-documents (Web pages). The merit of the approach, developed in this thesis, is its simplicity, compared with existing approaches. We present different approaches of using hyperlink information to improve the effectiveness of web classification. Unlike other work in this area, we will only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets, and then apply classifiers on them for classification. In the numerical experiments we adopted two wellknown Text Classification algorithms, Support Vector Machines and BoosTexter. The results obtained show that classification accuracy can be improved by using mixtures of ordinary and linked-class features. Moreover, out-links usually work better than in-links in classification. We also analyse and discuss the reasons behind this improvement.
Description:
Text Classification is the task of mapping a document into one or more classes based on the presence or absence of words (or features) in the document. It is intensively being studied and different classification techniques and algorithms have been developed. This thesis focuses on classification of online documents that has become more critical with the development of World Wide Web. The WWW vastly increases the availability of on-line documents in digital format and has highlighted the need to classify them. From this background, we have noted the emergence of “automatic Web Classification”. These mainly concentrate on classifying HTML-like documents into classes or categories by not only using the methods that are inherited from the traditional Text Classification process, but also utilizing the extra information provided only by Web pages. Our work is based on the fact that, Web documents, contain not only ordinary features (words) but also extra information, such as meta-data and hyperlinks that can be used to advantage the classification process. The aim of this research is to study various ways of using the extra information, in particularly, hyperlink information provided by HTML-documents (Web pages). The merit of the approach, developed in this thesis, is its simplicity, compared with existing approaches. We present different approaches of using hyperlink information to improve the effectiveness of web classification. Unlike other work in this area, we will only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets, and then apply classifiers on them for classification. In the numerical experiments we adopted two wellknown Text Classification algorithms, Support Vector Machines and BoosTexter. The results obtained show that classification accuracy can be improved by using mixtures of ordinary and linked-class features. Moreover, out-links usually work better than in-links in classification. We also analyse and discuss the reasons behind this improvement.
Description:
Modern healthcare systems are undergoing a paradigm shift from in-hospital care to in-home monitoring, leveraging the emerging technologies in the area of bio-sensing, wireless communication, mobile computing, and artificial intelligence. In-home monitoring promises to significantly reduce healthcare spending by preventing unnecessary hospital admissions and visits to healthcare professionals. Most of the in-home monitoring systems, proposed in the literature, focus on monitoring a set of specific vital signs. However, from the perspective of caregivers it is infeasible to maintain a collection of specialized monitoring systems. In this paper, we view the problem of in-home monitoring from the perspective of caregivers and present a framework that supports various monitoring capabilities while making the complexity transparent to the end users. The essential idea of the framework is to define a 'general purpose architecture' where the system specifies a particular protocol for communication and makes it public. Then any bio-sensing system can communicate with the system as long as it conforms to the protocol. We then argue that as the system grows in terms of number of patients and bio-sensing systems, artificial intelligence technologies need to be employed for patients' risk assessment, prioritization, and recommendation. Finally, we present an initial prototype of the system designed according to the proposed framework.
Description:
Modern healthcare systems are undergoing a paradigm shift from in-hospital care to in-home monitoring, leveraging the emerging technologies in the area of bio-sensing, wireless communication, mobile computing, and artificial intelligence. In-home monitoring promises to significantly reduce healthcare spending by preventing unnecessary hospital admissions and visits to healthcare professionals. Most of the in-home monitoring systems, proposed in the literature, focus on monitoring a set of specific vital signs. However, from the perspective of caregivers it is infeasible to maintain a collection of specialized monitoring systems. In this paper, we view the problem of in-home monitoring from the perspective of caregivers and present a framework that supports various monitoring capabilities while making the complexity transparent to the end users. The essential idea of the framework is to define a 'general purpose architecture' where the system specifies a particular protocol for communication and makes it public. Then any bio-sensing system can communicate with the system as long as it conforms to the protocol. We then argue that as the system grows in terms of number of patients and bio-sensing systems, artificial intelligence technologies need to be employed for patients' risk assessment, prioritization, and recommendation. Finally, we present an initial prototype of the system designed according to the proposed framework.
Anxiety online-A virtual clinic: Preliminary outcomes following completion of five fully automated treatment programs for anxiety disorders and symptoms
Description:
Objectives MoodSwings 2.0 is an online self-guided intervention for bipolar disorder that includes educational modules, interactive tools, and discussion forums. The primary aim of the study was to determine if participation in MoodSwings 2.0 would result in decreased symptoms of depression and mania compared to the control condition. Secondary aims were to identify improvements in core depression symptoms, quality of life, medication adherence, functioning, and time to relapse. Methods This was a three-arm randomized controlled trial that compared two intervention arms against a peer support control group (forum). A total of 304 adults aged 21 to 65 years with a diagnosis of bipolar disorder were assigned to a forum-only control group (Group 1; n = 102), a forum plus modules treatment group (Group 2; n = 102), or a forum, modules, and tools treatment group (Group 3; n = 100), in addition to usual care. Results There was a significant intervention impact showing improvement on the primary outcome of depression for Group 2 compared to Group 1 (P = .05) with effect sizes (Cohen's d) ranging from 0.17 to 0.43. There was also a significant intervention impact showing improvement on the secondary outcome of core depression for Group 2 (P = .02) and Group 3 (P = .05), but worse physical functioning for Group 3 (P = .01), compared to Group 1. Conclusions This study provides evidence of the efficacy of internet-based psychoeducation interventions for bipolar disorder in reducing depressive symptoms. Further investigation is needed to assess effectiveness in a public program.
Description:
Background: Electronic methods are increasingly being used to manage health-related data among sporting populations. Collection of such data permits the analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical or sports science teams to monitor and advocate for their health. Employing a Web-based system, such as the AMS, May provide an avenue to increase the visibility of health problems experienced by combat athletes and deliver key information to stakeholders detailing where prevention programs May be targeted. Objective: The objectives of this paper are to (1) report on the feasibility of utilizing the AMS to collect longitudinal injury and illness data of combat sports athletes and (2) describe the type, location, severity, and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. Methods: We invited 26 elite and developing athletes from 4 Olympic combat sports (boxing, judo, taekwondo, and wrestling) to participate in this study. Engagement with the AMS was measured, and collected health problems (injuries or illnesses) were coded using the Orchard Sports Injury Classification System (version 10.1) and International Classification of Primary Care (version 2). Results: Despite >160 contacts, athlete engagement with online tools was poor, with only 13% compliance across the 12-week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries or illness were recorded across 11 athletes who entered data—22 unique injuries, 8 unique illnesses, 30 recurrent injuries, and 2 recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than boxers, yet judo players sustained more illnesses than boxers. In 97.0% (126/130) of cases, athletes in this cohort continued to train irrespective of their health problems. Conclusions: Among athletes who reported injuries, many reported multiple conditions, indicating a need for health monitoring in Australian combat sports. A number of factors May have influenced engagement with the AMS, including access to the internet, the design of the system, coach views on the system, previous experiences with the system, and the existing culture within Australian combat sports. To increase engagement, there May be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the Barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.
Description:
Background: Electronic methods are increasingly being used to manage health-related data among sporting populations. Collection of such data permits the analysis of injury and illness trends, improves early detection of injuries and illnesses, collectively referred to as health problems, and provides evidence to inform prevention strategies. The Athlete Management System (AMS) has been employed across a range of sports to monitor health. Australian combat athletes train across the country without dedicated national medical or sports science teams to monitor and advocate for their health. Employing a Web-based system, such as the AMS, May provide an avenue to increase the visibility of health problems experienced by combat athletes and deliver key information to stakeholders detailing where prevention programs May be targeted. Objective: The objectives of this paper are to (1) report on the feasibility of utilizing the AMS to collect longitudinal injury and illness data of combat sports athletes and (2) describe the type, location, severity, and recurrence of injuries and illnesses that the cohort of athletes experience across a 12-week period. Methods: We invited 26 elite and developing athletes from 4 Olympic combat sports (boxing, judo, taekwondo, and wrestling) to participate in this study. Engagement with the AMS was measured, and collected health problems (injuries or illnesses) were coded using the Orchard Sports Injury Classification System (version 10.1) and International Classification of Primary Care (version 2). Results: Despite >160 contacts, athlete engagement with online tools was poor, with only 13% compliance across the 12-week period. No taekwondo or wrestling athletes were compliant. Despite low overall engagement, a large number of injuries or illness were recorded across 11 athletes who entered data—22 unique injuries, 8 unique illnesses, 30 recurrent injuries, and 2 recurrent illnesses. The most frequent injuries were to the knee in boxing (n=41) and thigh in judo (n=9). In this cohort, judo players experienced more severe, but less frequent, injuries than boxers, yet judo players sustained more illnesses than boxers. In 97.0% (126/130) of cases, athletes in this cohort continued to train irrespective of their health problems. Conclusions: Among athletes who reported injuries, many reported multiple conditions, indicating a need for health monitoring in Australian combat sports. A number of factors May have influenced engagement with the AMS, including access to the internet, the design of the system, coach views on the system, previous experiences with the system, and the existing culture within Australian combat sports. To increase engagement, there May be a requirement for sports staff to provide relevant feedback on data entered into the system. Until the Barriers are addressed, it is not feasible to implement the system in its current form across a larger cohort of combat athletes.
Description:
Street furniture such as bins, seats and bus shelters can become "smart" with the inclusion of wireless sensor nodes, which consist of environmental sensors, wireless modules, processors and microcontrollers. One of the most crucial challenges for smart street furniture is how to manage power consumption efficiently without affecting data freshness. In this work, we propose a novel Wireless Sensor Network (WSN) architecture for smart street furniture. Unlike existing WSNs which are based on a one-way communication model between wireless sensor nodes and the server, the proposed architecture employs a two-way communication model and a dynamic adaptation of the time interval of measurements to balance between power consumption and data updates. Our approach also provides a real-time low-power design for wireless sensor nodes which efficiently communicate the updated data instead of sending the same data on a regular basis. To the best of our knowledge, this is the first work in the relevant literature which extends the functionality of the wireless module in wireless sensor nodes to act not only as a station sending environmental data but also as soft Access Point (AP), sensing MAC addresses and WiFi signal strengths from surrounding WiFi-enabled devices. We have conducted experiments on the Murdoch University campus and our results show that our proposal improves lifetime of wireless sensor nodes up to 293% compared to static architectures similar to the ones that have been proposed in the literature. Moreover, network bandwidth is improved up to 38% without affecting data freshness. Finally, storage space for the database at the server is reduced up to 99%.