A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

A combinatorial procurement auction for QoS-aware web services

Business processes and application functionality are becoming available as internal web services inside enterprise boundaries as well as becoming available as commercial web services from enterprise solution vendors and web services marketplaces. Typically there are multiple web service providers offering services capable of fulfilling a particular functionality, although with different Quality of Service (QoS). Dynamic creation of business processes requires composing an appropriate set of web services that best suit the current need. This paper presents a novel combinatorial auction approach to QoS aware dynamic web services composition. Such an approach would enable not only stand-alone web services but also composite web services to be a part of a business process. The combinatorial auction leads to an integer programming formulation for the web services composition problem. An important feature of the model is the incorporation of service level agreements. We describe a software tool QWESC for QoS-aware web services composition based on the proposed approach.

A pilot study of the Social Water Assessment Protocol in a mining region of Ghana

The Social Water Assessment Protocol (SWAP) is a tool consisting of a series of questions on fourteen themes designed to capture the social context of water around a mine site. A pilot study of the SWAP, conducted in Prestea-Huni Valley, Ghana, showed that some communities were concerned about whether the groundwater was potable. The mining company’s concern was that there was a cycle of dependency amongst communities that received treated water from the mining company. The pilot identified potential data sources and stakeholder groups for each theme, gaps in themes and suggested refinements to questions to improve the SWAP.

Water management capacity building to support rapidly developing mining economies

Many developing countries are experiencing rapid expansion in mining with associated water impacts. In most cases mining expansion is outpacing the building of national capacity to ensure that sustainable water management practices are implemented. Since 2011, Australia's International Mining for Development Centre (IM4DC) has funded capacity building in such countries including a program of water projects. Five projects in particular (principally covering experiences from Peru, Colombia, Ghana, Zambia, Indonesia, Philippines and Mongolia) have provided insight into water capacity building priorities and opportunities. This paper reviews the challenges faced by water stakeholders, and proposes the associated capacity needs. The paper uses the evidence derived from the IM4DC projects to develop a set of specific capacity-building recommendations. Recommendations include: the incorporation of mine water management in engineering and environmental undergraduate courses; secondments of staff to suitable partner organisations; training to allow site staff to effectively monitor water including community impacts; leadership training to support a water stewardship culture; training of officials to support implementation of catchment management approaches; and the empowerment of communities to recognise and negotiate solutions to mine-related risks. New initiatives to fund the transfer of multi-disciplinary knowledge from nations with well-developed water management practices are called for.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Hybrid learning scheme for data mining applications

Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach integrating the activities of data abstraction, frequent item generation, compression, classification and use of rough sets.

Data mining approaches to software fault diagnosis

Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper, we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automatic identification of fault causes. Spectrum kernels and SVM are used for the former while latent semantic analysis is used for the latter The techniques are demonstrated for the intrusion dataset containing system call traces. The results show that kernel techniques are as accurate as the best available results but are faster by orders of magnitude. We also show that latent semantic indexing is capable of revealing fault-specific features.

Techno Teacher Moms: Web 2.0 Connecting Mothers in the Home Education Community

Home education is on the rise in Australia. However, unlike parents who choose mainstream schooling, these parents often lack the support of a wider community to help them on their educational and parenting journey. This support is especially lacking as many people in the wider community find the choice to home education confronting. As such, these parents may feel isolated and alienated in the general population as their choice to home educate is questioned at best, and ridiculed at worst. These parents often find sanctuary online in homeschool groups on Facebook. This chapter explores the ways that Facebook Groups are used by marginalized and disenfranchised families who home educate to meet with others who are likeminded and aligned with their beliefs and philosophies. It is through these groups that parents, in relation to schooling it is especially mothers, are able to ask for advice, to vent, to explore options and find connections that may be lacking in the wider community.

Developing Peer-To-Peer Web Applications

As the virtual world grows more complex, finding a standard way for storing data becomes increasingly important. Ideally, each data item would be brought into the computer system only once. References for data items need to be cryptographically verifiable, so the data can maintain its identity while being passed around. This way there will be only one copy of the users family photo album, while the user can use multiple tools to show or manipulate the album. Copies of users data could be stored on some of his family members computer, some of his computers, but also at some online services which he uses. When all actors operate over one replicated copy of the data, the system automatically avoids a single point of failure. Thus the data will not disappear with one computer breaking, or one service provider going out of business. One shared copy also makes it possible to delete a piece of data from all systems at once, on users request. In our research we tried to find a model that would make data manageable to users, and make it possible to have the same data stored at various locations. We studied three systems, Persona, Freenet, and GNUnet, that suggest different models for protecting user data. The main application areas of the systems studied include securing online social networks, providing anonymous web, and preventing censorship in file-sharing. Each of the systems studied store user data on machines belonging to third parties. The systems differ in measures they take to protect their users from data loss, forged information, censorship, and being monitored. All of the systems use cryptography to secure names used for the content, and to protect the data from outsiders. Based on the gained knowledge, we built a prototype platform called Peerscape, which stores user data in a synchronized, protected database. Data items themselves are protected with cryptography against forgery, but not encrypted as the focus has been disseminating the data directly among family and friends instead of letting third parties store the information. We turned the synchronizing database into peer-to-peer web by revealing its contents through an integrated http server. The REST-like http API supports development of applications in javascript. To evaluate the platform’s suitability for application development we wrote some simple applications, including a public chat room, bittorrent site, and a flower growing game. During our early tests we came to the conclusion that using the platform for simple applications works well. As web standards develop further, writing applications for the platform should become easier. Any system this complex will have its problems, and we are not expecting our platform to replace the existing web, but are fairly impressed with the results and consider our work important from the perspective of managing user data.

Developing Peer-To-Peer Web Applications

As the virtual world grows more complex, finding a standard way for storing data becomes increasingly important. Ideally, each data item would be brought into the computer system only once. References for data items need to be cryptographically verifiable, so the data can maintain its identity while being passed around. This way there will be only one copy of the users family photo album, while the user can use multiple tools to show or manipulate the album. Copies of users data could be stored on some of his family members computer, some of his computers, but also at some online services which he uses. When all actors operate over one replicated copy of the data, the system automatically avoids a single point of failure. Thus the data will not disappear with one computer breaking, or one service provider going out of business. One shared copy also makes it possible to delete a piece of data from all systems at once, on users request. In our research we tried to find a model that would make data manageable to users, and make it possible to have the same data stored at various locations. We studied three systems, Persona, Freenet, and GNUnet, that suggest different models for protecting user data. The main application areas of the systems studied include securing online social networks, providing anonymous web, and preventing censorship in file-sharing. Each of the systems studied store user data on machines belonging to third parties. The systems differ in measures they take to protect their users from data loss, forged information, censorship, and being monitored. All of the systems use cryptography to secure names used for the content, and to protect the data from outsiders. Based on the gained knowledge, we built a prototype platform called Peerscape, which stores user data in a synchronized, protected database. Data items themselves are protected with cryptography against forgery, but not encrypted as the focus has been disseminating the data directly among family and friends instead of letting third parties store the information. We turned the synchronizing database into peer-to-peer web by revealing its contents through an integrated http server. The REST-like http API supports development of applications in javascript. To evaluate the platform s suitability for application development we wrote some simple applications, including a public chat room, bittorrent site, and a flower growing game. During our early tests we came to the conclusion that using the platform for simple applications works well. As web standards develop further, writing applications for the platform should become easier. Any system this complex will have its problems, and we are not expecting our platform to replace the existing web, but are fairly impressed with the results and consider our work important from the perspective of managing user data.

Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains

Owing to high evolutionary divergence, it is not always possible to identify distantly related protein domains by sequence search techniques. Intermediate sequences possess sequence features of more than one protein and facilitate detection of remotely related proteins. We have demonstrated recently the employment of Cascade PSI-BLAST where we perform PSI-BLAST for many 'generations', initiating searches from new homologues as well. Such a rigorous propagation through generations of PSI-BLAST employs effectively the role of intermediates in detecting distant similarities between proteins. This approach has been tested on a large number of folds and its performance in detecting superfamily level relationships is similar to 35% better than simple PSI-BLAST searches. We present a web server for this search method that permits users to perform Cascade PSI-BLAST searches against the Pfam, SCOP and SwissProt databases. The URL for this server is http://crick.mbu.iisc.ernet.in/similar to CASCADE/CascadeBlast.html.

The Value of Being on the Net- the effects of Web sites of companies on consumer decision making

The unique characteristics of marketspace in combination with the fast growing number of consumers interested in e-commerce have created new research areas of interest to both marketing and consumer behaviour researchers. Consumer behaviour researchers interested in the decision making processes of consumers have two new sets of questions to answer. The first set of questions is related to how useful theories developed for a marketplace are in a marketspace context. Cyber auctions, Internet communities and the possibilities for consumers to establish dialogues not only with companies but also with other consumers make marketspace unique. The effects of these distinctive characteristics on the behaviour of consumers have not been systematically analysed and therefore constitute the second set of questions which have to be studied. Most companies feel that they have to be online even though the effects of being on the Net are not unambiguously positive. The relevance of the relationship marketing paradigm in a marketspace context have to be studied. The relationship enhancement effects of websites from the customers’ point of view are therefore emphasized in this research paper. Representatives of the Net-generation were analysed and the results show that companies should develop marketspace strategies while Net presence has a value-added effect on consumers. The results indicate that the decision making processes of the consumers are also changing as a result of the progress of marketspace