A data mining based method for discovery of web services and their compositions

Due to the availability of huge number of web services, finding an appropriate Web service according to the requirements of a service consumer is still a challenge. Moreover, sometimes a single web service is unable to fully satisfy the requirements of the service consumer. In such cases, combinations of multiple inter-related web services can be utilised. This paper proposes a method that first utilises a semantic kernel model to find related services and then models these related Web services as nodes of a graph. An all-pair shortest-path algorithm is applied to find the best compositions of Web services that are semantically related to the service consumer requirement. The recommendation of individual and composite Web services composition for a service request is finally made. Empirical evaluation confirms that the proposed method significantly improves the accuracy of service discovery in comparison to traditional keyword-based discovery methods.

How everyday language can and will boost effective information retrieval

Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice. We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning. The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.

Do Students Not Care about Extra Credit Opportunities? Investigating Student Perceptions of Extra Credit Value vs. Effort

Giving “extra credit” work to students has been a controversial and hotly debated pedagogical issue for the last 20 years (Blood et al. 1993; Groves 2000; Muztaba Fuad and Jones 2012; Norcross et al. 1989; Weimer 2011). Previous work has focused on the faculty perspective discussing benefits and drawbacks associated with extra credit work (e.g. Hill et al. 1993; Norcross et al. 1989). Other scholars have investigated the use and effects of pop quizzes and other extra credit assignments on students’ final grades (Thorne 2000; Oley 1993). Some authors have criticized that the empirical exploration of understanding students’ motivational and performance efforts remains scarce and “rarely appears in the literature” (Mays and Bower 2005, p. 1). Besides a gap of empirical work it further appears that most existing studies stem from Psychology or Information Science. Yet it is surprising that, even though the topic of extra credit is considered a common practice in marketing education (Ackerman and Kiesler 2007), there is a wide gap within the marketing education literature. For example, a quick search in the Journal of Marketing Education for the keyword “extra credit” shows only 25 search results; yet none of those papers address motivational or performance effects of extra credit. A further search in Marketing Education Review yielded no results at all. To the authors’ knowledge, the topic has only been addressed once by Ackerman and Kiesler in the 2007 MEA Proceedings who conclude that for “such a common part of the marketing education curriculum, we know surprisingly little about its impact on students” (p. 123).

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

Twitter hashtags from ad hoc to calculated publics

As the use of Twitter has become more commonplace throughout many nations, its role in political discussion has also increased. This has been evident in contexts ranging from general political discussion through local, state, and national elections (such as in the 2010 Australian elections) to protests and other activist mobilisation (for example in the current uprisings in Tunisia, Egypt, and Yemen, as well as in the controversy around Wikileaks). Research into the use of Twitter in such political contexts has also developed rapidly, aided by substantial advancements in quantitative and qualitative methodologies for capturing, processing, analysing, and visualising Twitter updates by large groups of users. Recent work has especially highlighted the role of the Twitter hashtag – a short keyword, prefixed with the hash symbol ‘#’ – as a means of coordinating a distributed discussion between more or less large groups of users, who do not need to be connected through existing ‘follower’ networks. Twitter hashtags – such as ‘#ausvotes’ for the 2010 Australian elections, ‘#londonriots’ for the coordination of information and political debates around the recent unrest in London, or ‘#wikileaks’ for the controversy around Wikileaks thus aid the formation of ad hoc publics around specific themes and topics. They emerge from within the Twitter community – sometimes as a result of pre-planning or quickly reached consensus, sometimes through protracted debate about what the appropriate hashtag for an event or topic should be (which may also lead to the formation of competing publics using different hashtags). Drawing on innovative methodologies for the study of Twitter content, this paper examines the use of hashtags in political debate in the context of a number of major case studies.

Sairaanhoitajaksi kasvattaminen : Sairaanhoitajakoulutus ja siinä käytetyt oppikirjat Suomessa vuoteen 1967 asti

The historical development of Finnish nursing textbooks from the late 1880s to 1967: the training of nurses in the Foucauldian perspective. This study aims, first, to analyse the historical development of Finnish nursing textbooks in the training of nurses and in nursing education: what Foucauldian power processes operate in the writing and publishing processes? What picture of nursing did early nursing books portray and who were the decision makers? Second, this study also aims to analyse the processes of power in nurse training processes. The time frame extends from the early stages of nurse training in the late 1880s to 1967. This present study is a part of textbook research and of the history of professional education in Finland. This study seeks to explain how, who or what contributed the power processes involved in the writing of nursing textbooks and through textbooks. Did someone use these books as a tool to influence nursing education? The third aim of this study is to define and analyse the purpose of nurse training. Michel Foucault´s concept of power served as an explanatory framework for this study. A very central part of power is the assembling of data, the supplying of information and messages, and the creation of discourses. When applied to the training of nurses, power dictates what information is taught in the training and contained in the books. Thus, the textbook holds an influential position as a power user in these processes. Other processes in which such power is exercised include school discipline and all other normalizing processes. One of most powerful ways of adapting is the hall of residence, where nursing pupils were required to live. Trained nurses desired to separate themselves from their untrained predecessors and from those with less training by wearing different uniforms and living in separate housing units. The state supported the registration of trained nurses by legislation. With this decision the state made it illegal to work as a nurse without an authorised education, and use these regulations to limit and confirm the professional knowledge and power of nurses. Nurses, physicians and government authorities used textbooks in nursing education as tools to achieve their own purposes and principles. With these books all three groups attempted to confirm their own professional power and knowledge while at the same time limit the power and expertise of others. Public authorities sought to unify the training of nurses and the basis of knowledge in all nursing schools in Finland with similar and obligatory textbooks. This standardisation started 20 years before the government unified nursing training in 1930. The textbooks also served as data assemblers in unifying nursing practices in Finnish hospitals, because the Medical Board required all training hospitals to attach the textbooks to units with nursing pupils. For the nurses, and especially for the associations of Finnish nurses, making and publishing their own textbooks for the training of nurses was a part of their professional projects. With these textbooks, the nursing elite and the teachers tended to prepare nursing pupils’ identities for nursing’s very special mission. From the 1960s, nursing was no longer understood as a mission, but as a normal vocation. Nurses and doctors disputed this view throughout the period studied, which was the optimal relationship between theory and practice in nursing textbooks and in nurse education. The discussion of medical knowledge in nursing textbooks took place in the 1930s and 1940s. Nurses were very confused about their own professional knowledge and expertise, which explains why they could not create a new nursing textbook despite the urgency. A brand new nursing textbook was published in 1967, about 30 years after the predecessor. Keyword: nurse, nurse training, nursing education, power, textbook, Michel Foucault

Bibulocystis gloriosa sp nov (Pucciniales) on Caesalpinia scortechinii in Queensland, with comments on Spumula caesalpiniae

A rust causing leaf spotting and distortion of twigs and branches of Caesalpinia scortechinii in Queensland is described as the new species Bibulocystis gloriosa. Uredinia and telia occur on spotted pinnules, and pycnia, aecial uredinia and telia on galled and twisted leaf rachides, twigs and branches. B. gloriosa is similar to Bibulocystis viennotii on Albizia granulosa in New Caledonia in having a macrocyclic life cycle with all spore states, and teliospores with two fertile cells and two cysts. It differs in having aecial urediniospores and urediniospores with uniformly thickened walls and several scattered germ pores, rather than the apically thickened walls and equatorial germ pores of B. viennotii. Teliospores in the two species are similar in size, but those of B. gloriosa have proportionally larger fertile cells and smaller cysts than in B. viennotii. To date, B. gloriosa is known from only two localities in south-eastern Queensland. Comparison with the type specimen of Spumula caesalpiniae on Caesalpinia nuga from Indonesia has shown that the two rusts are generically distinct.

Reducing skin damage and improving efficiency of Calypso mango

Calypso mango is a relatively new variety owned by DEEDI and managed/marketed by One Harvest (Queensland-based). It is a major mango variety for the retail chains. Its main limitation is a sensitive skin, which results in lenticel spotting and skin browning.

Commercial products from bio-active extractives in cypress milling residues.

Extractive components obtained from milling residues of white cypress were studied for chemical identity and bioactivity with a view to developing a commercial use for these components, thus increasing the value of the residues and improving the economics of cypress sawn wood production. Extracts obtained by solvent or steam extraction techniques from cypress sawdust were each fractionated by a range of techniques into groups of similar compounds. Crude extracts and fractions were screened against a range of agricultural pests and diseases, including two fungi, subterranean termites, fruit spotting bugs, two-spotted mites, thrips, heliothis, banana scab moths, silverleaf whiteflies, cattle tick adults and larvae, and ruminant gastrointestinal nematodes. Additional screening was undertaken where encouraging results were achieved, for two-spotted mites, thrips, silverleaf whiteflies, cattle tick adults and ruminant gastrointestinal nematodes. After considering degrees of efficacy against, and economic importance of, the agricultural pests, and likely production costs of extracts and fractions, the crude extract (oil) produced by steam distillation was chosen for further study against silverleaf whitefly. A useful degree of control was achievable when this oil was applied to tomato or eggplant at 0.1%, with much less harmful effects on a beneficial insect. Activity of the oil against silverleaf whitefly was undiminished 3.5 years after it was generated. There was little benefit from supplementing the extract with co-formulated paraffinic oil. From the steam distilled oil, fifty-five compounds were characterised, thirty-five compounds representing 92.478 % of the oil, with guaiol (20.8%) and citronellic acid (15.9%) most abundant. These two compounds, and a group of oxygenated compounds containing bulnesol and a range of eudesmols, were found to account for most of the activity against silverleaf whitefly. This application was recommended for first progression to commercialisation.

Deeply superficial digital media engagement? The case of Twitter and Movember 2013

Using activity generated with Twitter during Movember 2013, we interrogate the natures of superficiality running through what can be defined as a highly successful public health engagement intervention. Indeed, Movember arguably has not just been successful in one year in terms of raising funds for the causes it is concerned with, it has done this year-on-year since 2004. We tracked the keyword 'movember' (without the hash symbol) using an in-house installation of YourTwapperkeeper hosted on a NECTAR server. Data collection ran from 01 October - 04 December 2013, covering the ramp-up and wind-down periods of the event. We collected a total of 1,313,426 tweets from 759,345 unique users.

Semantic Classes in Topic Detection and Tracking

Topic detection and tracking (TDT) is an area of information retrieval research the focus of which revolves around news events. The problems TDT deals with relate to segmenting news text into cohesive stories, detecting something new, previously unreported, tracking the development of a previously reported event, and grouping together news that discuss the same event. The performance of the traditional information retrieval techniques based on full-text similarity has remained inadequate for online production systems. It has been difficult to make the distinction between same and similar events. In this work, we explore ways of representing and comparing news documents in order to detect new events and track their development. First, however, we put forward a conceptual analysis of the notions of topic and event. The purpose is to clarify the terminology and align it with the process of news-making and the tradition of story-telling. Second, we present a framework for document similarity that is based on semantic classes, i.e., groups of words with similar meaning. We adopt people, organizations, and locations as semantic classes in addition to general terms. As each semantic class can be assigned its own similarity measure, document similarity can make use of ontologies, e.g., geographical taxonomies. The documents are compared class-wise, and the outcome is a weighted combination of class-wise similarities. Third, we incorporate temporal information into document similarity. We formalize the natural language temporal expressions occurring in the text, and use them to anchor the rest of the terms onto the time-line. Upon comparing documents for event-based similarity, we look not only at matching terms, but also how near their anchors are on the time-line. Fourth, we experiment with an adaptive variant of the semantic class similarity system. The news reflect changes in the real world, and in order to keep up, the system has to change its behavior based on the contents of the news stream. We put forward two strategies for rebuilding the topic representations and report experiment results. We run experiments with three annotated TDT corpora. The use of semantic classes increased the effectiveness of topic tracking by 10-30\% depending on the experimental setup. The gain in spotting new events remained lower, around 3-4\%. The anchoring the text to a time-line based on the temporal expressions gave a further 10\% increase the effectiveness of topic tracking. The gains in detecting new events, again, remained smaller. The adaptive systems did not improve the tracking results.

An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms

In this paper, we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we describe two well-known mechanisms for sponsored search auction-Generalized Second Price (GSP) and Vickrey-Clarke-Groves (VCG). We then derive a new mechanism for sponsored search auction which we call optimal (OPT) mechanism. The OPT mechanism maximizes the search engine's expected revenue, while achieving Bayesian incentive compatibility and individual rationality of the advertisers. We then undertake a detailed comparative study of the mechanisms GSP, VCG, and OPT. We compute and compare the expected revenue earned by the search engine under the three mechanisms when the advertisers are symmetric and some special conditions are satisfied. We also compare the three mechanisms in terms of incentive compatibility, individual rationality, and computational complexity. Note to Practitioners-The advertiser-supported web site is one of the successful business models in the emerging web landscape. When an Internet user enters a keyword (i.e., a search phrase) into a search engine, the user gets back a page with results, containing the links most relevant to the query and also sponsored links, (also called paid advertisement links). When a sponsored link is clicked, the user is directed to the corresponding advertiser's web page. The advertiser pays the search engine in some appropriate manner for sending the user to its web page. Against every search performed by any user on any keyword, the search engine faces the problem of matching a set of advertisers to the sponsored slots. In addition, the search engine also needs to decide on a price to be charged to each advertiser. Due to increasing demands for Internet advertising space, most search engines currently use auction mechanisms for this purpose. These are called sponsored search auctions. A significant percentage of the revenue of Internet giants such as Google, Yahoo!, MSN, etc., comes from sponsored search auctions. In this paper, we study two auction mechanisms, GSP and VCG, which are quite popular in the sponsored auction context, and pursue the objective of designing a mechanism that is superior to these two mechanisms. In particular, we propose a new mechanism which we call the OPT mechanism. This mechanism maximizes the search engine's expected revenue subject to achieving Bayesian incentive compatibility and individual rationality. Bayesian incentive compatibility guarantees that it is optimal for each advertiser to bid his/her true value provided that all other agents also bid their respective true values. Individual rationality ensures that the agents participate voluntarily in the auction since they are assured of gaining a non-negative payoff by doing so.

Towards a typology of hashtag publics: A large-scale comparative study of user engagement across trending topics

Twitter’s hashtag functionality is now used for a very wide variety of purposes, from covering crises and other breaking news events through gathering an instant community around shared media texts (such as sporting events and TV broadcasts) to signalling emotive states from amusement to despair. These divergent uses of the hashtag are increasingly recognised in the literature, with attention paid especially to the ability for hashtags to facilitate the creation of ad hoc or hashtag publics. A more comprehensive understanding of these different uses of hashtags has yet to be developed, however. Previous research has explored the potential for a systematic analysis of the quantitative metrics that could be generated from processing a series of hashtag datasets. Such research found, for example, that crisis-related hashtags exhibited a significantly larger incidence of retweets and tweets containing URLs than hashtags relating to televised events, and on this basis hypothesised that the information-seeking and -sharing behaviours of Twitter users in such different contexts were substantially divergent. This article updates such study and their methodology by examining the communicative metrics of a considerably larger and more diverse number of hashtag datasets, compiled over the past five years. This provides an opportunity both to confirm earlier findings, as well as to explore whether hashtag use practices may have shifted subsequently as Twitter’s userbase has developed further; it also enables the identification of further hashtag types beyond the “crisis” and “mainstream media event” types outlined to date. The article also explores the presence of such patterns beyond recognised hashtags, by incorporating an analysis of a number of keyword-based datasets. This large-scale, comparative approach contributes towards the establishment of a more comprehensive typology of hashtags and their publics, and the metrics it describes will also be able to be used to classify new hashtags emerging in the future. In turn, this may enable researchers to develop systems for automatically distinguishing newly trending topics into a number of event types, which may be useful for example for the automatic detection of acute crises and other breaking news events.

Intent Based Clustering of Search Engine Query Log

The keyword based search technique suffers from the problem of synonymic and polysemic queries. Current approaches address only theproblem of synonymic queries in which different queries might have the same information requirement. But the problem of polysemic queries,i.e., same query having different intentions, still remains unaddressed. In this paper, we propose the notion of intent clusters, the members of which will have the same intention. We develop a clustering algorithm that uses the user session information in query logs in addition to query URL entries to identify cluster of queries having the same intention. The proposed approach has been studied through case examples from the actual log data from AOL, and the clustering algorithm is shown to be successful in discerning the user intentions.