Second Language Writing: use of the World Wide Web to Improve Specific Writing

[EN] Different strategies should be considered when teaching writing in a second language, as successful writing involves, among other things, the ability to integrate information learnt by the writer in different kinds of contexts. Nowadays, the World Wide Web is a very useful source of information for second language students, as they can obtain information about very specific topics and practise a second language. In this paper, our main objective is to detail how the use of the World Wide Web can benefit the language skills of university students. Apart from practising a second language, students obtain useful information related to the specific subjects they study to achieve an Industrial Engineering degree in Spain. The purpose of this pilot study is twofold, to improve their performance in a second language (English) and to widen their knowledge of specific topics. The results obtained in this pilot study are shown and the benefits of the use of the World Wide Web are detailed.S235239116

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201

Reasoning with Data Flows and Policy Propagation Rules

Data-oriented systems and applications are at the centre of current developments of the World Wide Web. In these scenarios, assessing what policies propagate from the licenses of data sources to the output of a given data-intensive system is an important problem. Both policies and data flows can be described with Semantic Web languages. Although it is possible to define Policy Propagation Rules (PPR) by associating policies to data flow steps, this activity results in a huge number of rules to be stored and managed. In a recent paper, we introduced strategies for reducing the size of a PPR knowledge base by using an ontology of the possible relations between data objects, the Datanode ontology, and applying the (A)AAAA methodology, a knowledge engineering approach that exploits Formal Concept Analysis (FCA). In this article, we investigate whether this reasoning is feasible and how it can be performed. For this purpose, we study the impact of compressing a rule base associated with an inference mechanism on the performance of the reasoning process. Moreover, we report on an extension of the (A)AAAA methodology that includes a coherency check algorithm, that makes this reasoning possible. We show how this compression, in addition to being beneficial to the management of the knowledge base, also has a positive impact on the performance and resource requirements of the reasoning process for policy propagation

Automated Feature Engineering for Time Series Data

Feature engineering for time series data, a critical task in data science, involves the transformation or encoding of raw data to create more predictive input features.This paper introduces a novel web framework designed to automate the labor-intensive and expertise-demanding process of time series feature engineering. The framework comprises advanced methods for automated feature extraction and selection, providing a wide range of application possibilities. A Bayesian Optimization strategy is also integrated to identify optimal features and model parameters for specific datasets, thereby enhancing prediction performance. The paper thoroughly explores the framework\u27s design principles and operational procedures, along with validation of its effectiveness across different domains using real-world datasets

Buckets inverted lists for a search engine with BSP

Most information in science, engineering and business has been recorded in form of text. This information can be found online in the World-Wide-Web. One of the major tools to support information access are the search engines which usually use information retrieval techniques to rank Web pages based on a simple query and an index structure like the inverted lists. The retrieval models are the basis for the algorithms that score and rank the Web pages. The focus of this presentation is to show some inverted lists alternatives, based on buckets, for an information retrieval system. The main interest is how query performance is effected by the index organization on a cluster of PCs. The server design is effected on top of the parallel computing model Bulk Synchronous Parallel-BSP.Facultad de Informátic

Key factors in web latency savings in an experimental prefetching system

Although Internet service providers and communications companies are continuously offering higher and higher bandwidths, users still complain about the high latency they perceive when downloading pages from the web. Therefore, latency can be considered as the main web performance metric from the user's point of view. Many studies have demonstrated that web prefetching can be an interesting technique to reduce such latency at the expense of slightly increasing the network traffic. In this context, this paper presents an empirical study to investigate the maximum benefits that web users can expect from prefetching techniques in the current web. Unlike previous theoretical studies, this work considers a realistic prefetching architecture using real traces. In this way, the influence of real imple- mentation constraints are considered and analyzed. The results obtained show that web prefetching could improve page latency up to 52% in the studied traces. ©Springer Science+Business Media, LLC 2011De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Pont Sanjuan, A.; Gil Salinas, JA. (2012). Key factors in web latency savings in an experimental prefetching system. Journal of Intelligent Information Systems. 39(1):187-207. doi:10.1007/s10844-011-0188-xS187207391Balamash, A., Krunz, M., & Nain, P. (2007). Performance analysis of a client-side caching/prefetching system for web traffic. Computer Networks, 51(13), 3673–3692.Bestavros, A. (1995). Using speculation to reduce server load and service time on the www. In Proc. of the 4th ACM international conference on information and knowledge management. Baltimore, USA.Bestavros, A., & Cunha, C. (1996). Server-initiated document dissemination for the WWW. In IEEE data engineering bulletin. [Online]. Available: http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.266 . Accessed 29 November 2011.Bouras, C., Konidaris, A., & Kostoulas, D. (2004). Predictive prefetching on the web and its potential impact in the wide area. In World Wide Web: Internet and web information systems (Vol. 7, No. 2, pp. 143–179). The Netherlands: Kluwer Academic.Changa, T., Zhuangb, Z., Velayuthamc, A., & Sivakumara, R. (2008). WebAccel: Accelerating web access for low-bandwidth hosts. Computer Networks, 52(11), 2129–2147.Davison, B. D. (2002). The design and evaluation of web prefetching and caching techniques. Ph.D. dissertation, Rutgers University.de la Ossa, B., Gil, J. A., Sahuquillo, J., & Pont, A. (2007). Delfos: The oracle to predict next web user’s accesses. In Proc. of the IEEE 21st international conference on advanced information networking and applications. Niagara Falls, Canada.de la Ossa, B., Pont, A., Sahuquillo, J., & Gil, J. A. (2010). Referrer graph: A low-cost web prediction algorithm. In Proc. of the 25th ACM symposium on applied computing (pp. 831–838). doi: 10.1145/1774088.1774260 .de la Ossa, B., Sahuquillo, J., Pont, A., & Gil, J. A. (2009). An empirical study on maximum latency saving in web prefetching. In Proc. of the 2009 IEEE/WIC/ACM international conference on web intelligence (WI’09).Dom̀enech, J., Gil, J. A., Sahuquillo, J., & Pont, A. (2006a). DDG: An efficient prefetching algorithm for current web generation. In Proc. of the 1st IEEE workshop on hot topics in web systems and technologies (HotWeb). Boston, USA.Domènech, J., Gil, J. A., Sahuquillo, J., & Pont, A. (2006b). Web prefetching performance metrics: A survey. Performance Evaluation, 63(9–10), 988–1004.Domènech, J., Sahuquillo, J., Gil, J. A., & Pont, A. (2006c). The impact of the web prefetching architecture on the limits of reducing user’s perceived latency. In Proc. of the international conference on web intelligence. Piscataway: IEEE.de la Ossa, B., Gil, J. A., Sahuquillo, J., & Pont, A. (2007). Improving web prefetching by making predictions at prefetch. In Proc. of the 3rd EURO-NGI conference on next generation internet networks design and engineering for heterogeneity (NGI’07) (pp. 21–27).Duchamp, D. (1999). Prefetching hyperlinks. In Proc. of the 2nd USENIX symposium on internet technologies and systems. Boulder, USA.Fan, L., Cao, P., Lin, W., & Jacobson, Q. (1999). Web prefetching between low-bandwidth clients and proxies: Potential and performance. In Proc. of the ACM SIGMETRICS conference on measurement and modeling of computer systems (pp. 178–187).HTTP/1.1. [Online]. Available: http://www.faqs.org/rfcs/rfc2616.html . Accessed 29 November 2011.Kroeger, T. M., Long, D., & Mogul, J. C. (1997). Exploring the bounds of web latency reduction from caching and prefetching. In Proc. of the 1st USENIX symposium on internet technologies and systems. Monterrey, USA.Link prefetching in mozilla faq (2011). [Online]. Available: https://developer.mozilla.org/en/Link_prefetching_FAQ .Markatos, E., & Chronaki, C. (1998). A top-10 approach to prefetching on the web. In Proc. of INET. Geneva, Switzerland.Márquez, J., Domènech, J., Pont, A., & Gil, J. A. (2008). Exploring the benefits of caching and prefetching in the mobile web. In Second IFIP symposium on wireless communications and information technology for developing countries (WCITD 2008).Padmanabhan, V., & Mogul, J. C. (1996). Using predictive prefetching to improve World Wide Web latency. In Proc. of the ACM SIGCOMM conference. Stanford University, USA.Palpanas, T., & Mendelzon, A. (1999). Web prefetching using partial match prediction. In Proc. of the 4th international web caching workshop. San Diego, USA.Schechter, S., Krishnan, M., & Smith, M. D. (1998). Using path profiles to predict http requests. In Proc. of the 7th international World Wide Web conference. Brisbane, Australia.Teng, W., Chang, C., & Chen, M. (2005). Integrating web caching and web prefetching in client-side proxies. IEEE Transactions on Parallel and Distributed Systems, 16(5), 444–455

An Academic Search Engine for Personalized Rankings

Rapidly increasing information on the Internet and the World Wide Web can lead to information overload. Search engines become important tools to help WWW users to discover information. Exponential increases in published research papers, academic search engines become indispensable tools to search for papers in their expertise and related fields. In order to improve the quality of search, an academic search engines' capability should be enhanced. This paper proposes a search engine for personalized rankings. In order to evaluate the performance of personalized rankings, thirty-five graduate students from the Department of Web Engineering and Mobile Application Development at Dhurakij Pundit University are participants in the research experiment. Participants are asked to use a prototype of an academic search engine to find and bookmark any research papers according to their interests, which would guarantee that each participants' list of interesting research papers could be recorded. Normalized Discounted Cumulative Gain (NDCG) is used as a metric to determine the performance of the personalized rankings. The experiments suggest that the personalized rankings outperform the original search rankings. Hence, the proposed academic search engine with personalized ranking benefits research paper discovery

www.Personal_Asset_Allocation.

Today consumers demand delivery of financial services anytime and anywhere, and their needs and desires are evolving rapidly. The World Wide Web provides a rich channel for distributing customized services to a range of clients. An Internet-based system developed by Prometeia S.r.l. for Italian banks—both traditional and e-banks—supports consumers and financial advisors in planning personal finances. The system provides advice on allocating personal assets to fund consumers’ needs, such as paying for a house, children’s education, retirement, or other projects. State-of-the-art models of financial engineering—based on scenario optimization— develop plans that are consistent with clients’ goals, their attitudes towards risk, and the prevailing views on market performance. The system then helps clients to select off-the-shelf financial products, such as mutual funds, to create customized portfolios. Finally, it analyzes the risk of portfolios in terms that are intuitive for laypersons and monitors their performance in achieving the target goals. Four major banks use the system to support their networks of several thousand financial advisors and to reach tens of thousands of clients directly

Buckets inverted lists for a search engine with BSP

Most information in science, engineering and business has been recorded in form of text. This information can be found online in the World-Wide-Web. One of the major tools to support information access are the search engines which usually use information retrieval techniques to rank Web pages based on a simple query and an index structure like the inverted lists. The retrieval models are the basis for the algorithms that score and rank the Web pages. The focus of this presentation is to show some inverted lists alternatives, based on buckets, for an information retrieval system. The main interest is how query performance is effected by the index organization on a cluster of PCs. The server design is effected on top of the parallel computing model Bulk Synchronous Parallel-BSP.Facultad de Informátic