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.

Incorporating visual information for spoken term detection

Spoken term detection (STD) is the task of looking up a spoken term in a large volume of speech segments. In order to provide fast search, speech segments are first indexed into an intermediate representation using speech recognition engines which provide multiple hypotheses for each speech segment. Approximate matching techniques are usually applied at the search stage to compensate the poor performance of automatic speech recognition engines during indexing. Recently, using visual information in addition to audio information has been shown to improve phone recognition performance, particularly in noisy environments. In this paper, we will make use of visual information in the form of lip movements of the speaker in indexing stage and will investigate its effect on STD performance. Particularly, we will investigate if gains in phone recognition accuracy will carry through the approximate matching stage to provide similar gains in the final audio-visual STD system over a traditional audio only approach. We will also investigate the effect of using visual information on STD performance in different noise environments.

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).

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

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.

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.

On Musical Self-Similarity : Intersemiosis as Synecdoche and Analogy

Self-similarity, a concept taken from mathematics, is gradually becoming a keyword in musicology. Although a polysemic term, self-similarity often refers to the multi-scalar feature repetition in a set of relationships, and it is commonly valued as an indication for musical coherence and consistency . This investigation provides a theory of musical meaning formation in the context of intersemiosis, that is, the translation of meaning from one cognitive domain to another cognitive domain (e.g. from mathematics to music, or to speech or graphic forms). From this perspective, the degree of coherence of a musical system relies on a synecdochic intersemiosis: a system of related signs within other comparable and correlated systems. This research analyzes the modalities of such correlations, exploring their general and particular traits, and their operational bounds. Looking forward in this direction, the notion of analogy is used as a rich concept through its two definitions quoted by the Classical literature: proportion and paradigm, enormously valuable in establishing measurement, likeness and affinity criteria. Using quantitative qualitative methods, evidence is presented to justify a parallel study of different modalities of musical self-similarity. For this purpose, original arguments by Benoît B. Mandelbrot are revised, alongside a systematic critique of the literature on the subject. Furthermore, connecting Charles S. Peirce s synechism with Mandelbrot s fractality is one of the main developments of the present study. This study provides elements for explaining Bolognesi s (1983) conjecture, that states that the most primitive, intuitive and basic musical device is self-reference, extending its functions and operations to self-similar surfaces. In this sense, this research suggests that, with various modalities of self-similarity, synecdochic intersemiosis acts as system of systems in coordination with greater or lesser development of structural consistency, and with a greater or lesser contextual dependence.

Investigating online reading with eye tracking and EEG : The influence of text format, reading task and parafoveal stimuli on reading processes

Research on reading has been successful in revealing how attention guides eye movements when people read single sentences or text paragraphs in simplified and strictly controlled experimental conditions. However, less is known about reading processes in more naturalistic and applied settings, such as reading Web pages. This thesis investigates online reading processes by recording participants eye movements. The thesis consists of four experimental studies that examine how location of stimuli presented outside the currently fixated region (Study I and III), text format (Study II), animation and abrupt onset of online advertisements (Study III), and phase of an online information search task (Study IV) affect written language processing. Furthermore, the studies investigate how the goal of the reading task affects attention allocation during reading by comparing reading for comprehension with free browsing, and by varying the difficulty of an information search task. The results show that text format affects the reading process, that is, vertical text (word/line) is read at a slower rate than a standard horizontal text, and the mean fixation durations are longer for vertical text than for horizontal text. Furthermore, animated online ads and abrupt ad onsets capture online readers attention and direct their gaze toward the ads, and distract the reading process. Compared to a reading-for-comprehension task, online ads are attended to more in a free browsing task. Moreover, in both tasks abrupt ad onsets result in rather immediate fixations toward the ads. This effect is enhanced when the ad is presented in the proximity of the text being read. In addition, the reading processes vary when Web users proceed in online information search tasks, for example when they are searching for a specific keyword, looking for an answer to a question, or trying to find a subjectively most interesting topic. A scanning type of behavior is typical at the beginning of the tasks, after which participants tend to switch to a more careful reading state before finishing the tasks in the states referred to as decision states. Furthermore, the results also provided evidence that left-to-right readers extract more parafoveal information to the right of the fixated word than to the left, suggesting that learning biases attentional orienting towards the reading direction.

Multi-Armed Bandit Mechanisms for Multi-Slot Sponsored Searth Auctions

In pay-per click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their ads. This auction is typically conducted for a number of rounds (say T). There are click probabilities mu_ij associated with agent-slot pairs. The search engine's goal is to maximize social welfare, for example, the sum of values of the advertisers. The search engine does not know the true value of an advertiser for a click to her ad and also does not know the click probabilities mu_ij s. A key problem for the search engine therefore is to learn these during the T rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced and would be referred to as multi-armed-bandit (MAB) mechanisms. When m = 1,characterizations for truthful MAB mechanisms are available in the literature and it has been shown that the regret for such mechanisms will be O(T^{2/3}). In this paper, we seek to derive a characterization in the realistic but nontrivial general case when m > 1 and obtain several interesting results.

A Nash Bargaining Approach to Retention Enhancing Bid Optimization in Sponsored Search Auctions with Discrete Bids

Bid optimization is now becoming quite popular in sponsored search auctions on the Web. Given a keyword and the maximum willingness to pay of each advertiser interested in the keyword, the bid optimizer generates a profile of bids for the advertisers with the objective of maximizing customer retention without compromising the revenue of the search engine. In this paper, we present a bid optimization algorithm that is based on a Nash bargaining model where the first player is the search engine and the second player is a virtual agent representing all the bidders. We make the realistic assumption that each bidder specifies a maximum willingness to pay values and a discrete, finite set of bid values. We show that the Nash bargaining solution for this problem always lies on a certain edge of the convex hull such that one end point of the edge is the vector of maximum willingness to pay of all the bidders. We show that the other endpoint of this edge can be computed as a solution of a linear programming problem. We also show how the solution can be transformed to a bid profile of the advertisers.

Truthful multi-armed bandit mechanisms for multi-slot sponsored search auctions

In pay-per-click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their advertisements (ads for short). A sponsored search auction for a keyword is typically conducted for a number of rounds (say T). There are click probabilities mu(ij) associated with each agent slot pair (agent i and slot j). The search engine would like to maximize the social welfare of the advertisers, that is, the sum of values of the advertisers for the keyword. However, the search engine does not know the true values advertisers have for a click to their respective advertisements and also does not know the click probabilities. A key problem for the search engine therefore is to learn these click probabilities during the initial rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced. These mechanisms, due to their connection to the multi-armed bandit problem, are aptly referred to as multi-armed bandit (MAB) mechanisms. When m = 1, exact characterizations for truthful MAB mechanisms are available in the literature. Recent work has focused on the more realistic but non-trivial general case when m > 1 and a few promising results have started appearing. In this article, we consider this general case when m > 1 and prove several interesting results. Our contributions include: (1) When, mu(ij)s are unconstrained, we prove that any truthful mechanism must satisfy strong pointwise monotonicity and show that the regret will be Theta T7) for such mechanisms. (2) When the clicks on the ads follow a certain click precedence property, we show that weak pointwise monotonicity is necessary for MAB mechanisms to be truthful. (3) If the search engine has a certain coarse pre-estimate of mu(ij) values and wishes to update them during the course of the T rounds, we show that weak pointwise monotonicity and type-I separatedness are necessary while weak pointwise monotonicity and type-II separatedness are sufficient conditions for the MAB mechanisms to be truthful. (4) If the click probabilities are separable into agent-specific and slot-specific terms, we provide a characterization of MAB mechanisms that are truthful in expectation.