Task-based language teaching has received increasing attention in second language acquisition research over the past decade (Révész, 2007). However, the target form comprises conveyance of meaning in task-based classroom to some degree. In the area of Chinese acquisition as a second language the role of recasts and explicit feedback has not been the subject of much investigation, as two types of technique to compensate learners’ attention on form. Few empirical studies have explored their short-term and long-term effects on Chinese language acquisition in task-based classroom. To test the conclusions of some research about corrective feedback in the area of SLA, the present study examined the effects of these two techniques respectively. The study employed an immediate-test and posttest design. The participants were 53 adult, beginning level Chinese language learners who study Chinese as a second language, naturally assigned to one of the two comparison groups and a control group. The comparison groups differed as to whether they received recasts or explicit feedback while completing communicative tasks. The control group also practiced the tasks; however, they received neither recasts nor explicit feedback from their instructors in the process and they participated in the testing sessions. Results analysis of collected data yielded three main findings. First, learners receiving explicit feedback immediately outperformed those who received recasts for certain structures. Second, learners receiving explicit feedback sometimes yielded some long-term advantages over those who did not receive any feedback, followed by the recast group even after a period of time. Third, the performance of the participants varied according to the complexity of the target forms. The results imply that explicit feedback and recasts can facilitate the production of certain target language forms in beginning Chinese communicative class. The effects of explicit feedback and recasts depend on the chosen forms. It is congruent with Long’s (1998) and Ellis’s (2007) speculation that the roles of various feedbacks differ according to different linguistic features. It also provides further evidence for Leeman’s (2000) conclusion that recasts may be differentially effective when the learnabililty of the target linguistic feature vary. Finally, the findings lend some support to the insight derived from Long that focus-on-form should be integrated into task-based language teaching (Long, 1996, 2000; Long & Robinson, 1998).

Recently, there has been a significant surge of interest towards the design and development of a new global-scale communication network that can overcome the limitations of the current Internet. Among the numerous directions of improvement in networking technology, recent pursuit to do better flow control of network traffic has led to the emergence of several explicit-feedback congestion control methods. As a first step towards understanding these methods, we analyze the stability and transient performance of Rate Control Protocol (RCP).We find that RCP can become unstable in certain topologies and may exhibit very high buffering requirements at routers. To address these limitations, we propose a new controller called Proportional Integral Queue Independent RCP (PIQI-RCP), prove its stability under heterogeneous delay, and use simulations to show that the new method has significantly lower transient queue lengths, better transient dynamics, and tractable stability properties. As a second step in understanding explicit congestion control, we experimentally evaluate proposed methods such as XCP, JetMax, RCP, and PIQI-RCP using their Linux implementation developed by us. Our experiments show that these protocols are scalable with the increase in link capacity and round-trip propagation delay. In steady-state, they have low queuing delay and almost zero packet-loss rate. We confirm that XCP cannot achieve max-min fairness in certain topologies. We find that JetMax significantly drops link utilization in the presence of short flows with long flow and RCP requires large buffer size at bottleneck routers to prevent transient packet losses and is slower in convergence to steady-state as compared to other methods. We observe that PIQI-RCP performs better than RCP in most of the experiments.

The increasing reliance of people on computers for daily tasks has resulted in a vast number of digital documents. Search engines were once luxury tools for quickly scanning a set of documents but are now quickly becoming the only practical way to navigate through this sea of information. Traditionally, search engine results are based upon a mathematical formula of document relevance to a search phrase. Often, however, what a user deems to be relevant and what a search engine computes as relevant are not the same. User feedback regarding the utility of a search result can be collected in order to refine query results. Additionally, user feedback can be used to identify queries that lack high quality search results. A content author can then further develop existing content or create new content to improve those search results. The most straightforward way of collecting user feedback is to add a graphical user interface component to the search interface that asks the user how much he or she liked the search result. However, if the feedback mechanism requires the user to provide feedback before he or she can progress further with his or her search, the user may become annoyed and provide incorrect feedback values out of spite. Conversely, if the feedback mechanism does not require the user to provide feedback at all then the overall amount of collected feedback will be diminished as many users will not expend the effort required to give feedback. This research focused on the collection of explicit user feedback in both mandatory (a user must give feedback) and voluntary (a user may give feedback) scenarios. The collected data was used to train a set of decision tree classifiers that provided user satisfaction values as a function of implicit user behavior and a set of search terms. The results of our study indicate that a more accurate classifier can be built from explicit data collected in a voluntary scenario. Given a limited search domain, the classification accuracy can be further improved.

This work deals with the issue of poviding recommendations for individual users of e-shop based on the obtained user preferences. The work includes an overview of existing recommender systems, their methods of getting user preferences, the methods of using objects' content and recommender algorithms. An integral part of this work is design and implementated for independent software component for Content-based recommendation. Component is able to receive various user preferences and various forms of object's input data. The component also contains various processing methods for implicit feedback and various methods for making recommendations. Component is written in the Java programming language and uses a PostgreSQL database. The thesis also includes experiments that was carried out with usage of component designed on datasets slantour.cz and antikvariat-ichtys.cz e-shops.

Η έλλειψη της δραστηριότητας του χρήστη σχετικά με τα αποτελέσματα της αναζήτησης μέχρι πρόσφατα θεωρείτο ως ένδειξη της δυσαρέσκειας του από την απόδοση ανάκτησης, και συχνά τέτοια αδράνεια χαρακτήριζε την αναζήτηση ως αποτυχημένη (negative search abandonment). Ωστόσο, πρόσφατες μελέτες δείχνουν ότι ορισμένες αναζητήσεις μπορούν να ικανοποιηθούν από το περιεχόμενο των αποτελεσμάτων που παρουσιάζονται στον χρήστη, χωρίς να χρειάζεται να κάνει κλικ σε κάποιο από τα ανακτημένα αποτελέσματα (positive search abandonment), και έτσι τονίζεται η ανάγκη να γίνουν διακρίσεις μεταξύ των επιτυχημένων και αποτυχημένων αναζητήσεων που δεν ακολουθούνται από κλικς. Με αυτή την εργασία προτείνουμε τον σχεδιασμό και την υλοποίηση μιας μεθοδολογίας αξιολόγησης της ικανοποίησης του χρήστη από τα αποτελέσματα αναζητήσεων που δεν ακολουθούνται από επισκέψεις στο περιεχόμενο των δεδομένων ανάκτησης. Για την επίτευξη του στόχου αυτού διενεργήσαμε μελέτη χρηστών που διερευνά τις προθέσεις των χρηστών πίσω από ερωτήματα που δεν ακολουθούνται από επίσκεψη σε κάποιο από τα αποτελέσματα που επέστρεψε η αναζήτηση και εξετάζει τις εργασίες αναζήτησης που μπορούν να ολοκληρωθούν με επιτυχία βασισμένες εξ ολοκλήρου στις πληροφορίες που παρέχονται στη σελίδα με τα αποτελέσματα. Επιπρόσθετα, μελετήθηκαν και υλοποιήθηκαν εργαλεία, QWC Browser, για την καταγραφή της δραστηριότητας του χρήστη με συστήματα ανάκτησης πληροφορίας από τον Παγκόσμιο Ιστό. Στηριζόμενοι στην ευρέως αποδεχόμενη ιδέα της χρήσης της δραστηριότητας του χρήστη ως δείκτη υπονοούμενης αξιολόγησης συσχέτισης (implicit relevance judgments), εξετάσαμε την ύπαρξη σχέση μεταξύ των ρητών δηλώσεων (explicit judgments) ικανοποίησης του χρήστη και μετρικών αξιολόγησης της υπονοούμενης ανατροφοδότησης (implicit measures) του χρήστη. Τέλος, χρησιμοποιήσαμε τεχνικές μοντελοποίησης για την ανάπτυξη μοντέλων πρόβλεψης για την σύλληψη της ικανοποίησης του χρήστη από τις αναζητήσεις που δεν ακολουθούνται από κλικς. / The lack of user activity on search results was until recently perceived as a sign of user dissatisfaction from retrieval performance, often, referring to such inactivity as a failed search (negative search abandonment). However, recent studies suggest that some search tasks can be achieved in the contents of the results displayed without the need to click through them (positive search abandonment); thus they emphasize the need to discriminate between successful and failed searches without follow-up clicks. In this paper we propose to design and implement a methodology for assessing user satisfaction from the results of searches that are not followed by visits to the content of the retrieved results. To achieve this goal we conducted a user study in order to identify the search intentions of queries without follow-up clicks to any of the results returned by the search and identify the search tasks that can be accomplished successfully based entirely on information provided on the results page. Additionally, we developed an instrumented browser, QWC Browser, to collect a variety of measures of user activity after the query submittion. Moreover, we examined whether there is an association between explicit judgments of user satisfaction and implicit measures of user interest in order to understand what implicit measures were most strongly associated with user satisfaction. Finally, we used Bayesian modeling techniques to develop predictive models, to capture user satisfaction from searches that are not followed by clicks to the retrieved results.