Web 2.0 for Reference Services: An Overview

This paper examines what the concept of web 2.0 is all about and it attributes by using an acronym SLATES to explain what web 2.0 entails and they are as follows: Search: this is the ease of finding information through keyword search which makes the platform valuable Links: guides to important pieces of information. The best pages are the most frequently linked to.Authoring: the ability to create constantly updating content over a platform that is shifted from being the creation of a few to being the constantly updated, interlinked work. In Wikis, the content is iterative in the sense that the people undo and redo each other’s work. In blogs, content is cumulative in that posts and comments of individuals are accumulated over time.Tags: categorization of content by creating tags that are simple, one-word descriptions to facilitate searching and avoid rigid, pre-made categories.Extensions: automation of some of the work and pattern matching by using algorithms e.g. amazon.com recommendations.Signals: the use of RSS (Really Simple Syndication) technology to notify users with any changes of the content by sending e-mails to them.” The various web 2.0 tools such as social networks, wiki, blogs, microblogging etc. it also talks about the application of these tools in the provision of effective and efficient reference services. The paper alsoaddressthe effects of web 2.0 application on library and information professionals and the library users. The constraints to the application of web 2.0 for reference services

ENHANCING IMAGE FINDABILITY THROUGH A DUAL-PERSPECTIVE NAVIGATION FRAMEWORK

This dissertation focuses on investigating whether users will locate desired images more efficiently and effectively when they are provided with information descriptors from both experts and the general public. This study develops a way to support image finding through a human-computer interface by providing subject headings and social tags about the image collection and preserving the information scent (Pirolli, 2007) during the image search experience. In order to improve search performance most proposed solutions integrating experts’ annotations and social tags focus on how to utilize controlled vocabularies to structure folksonomies which are taxonomies created by multiple users (Peters, 2009). However, these solutions merely map terms from one domain into the other without considering the inherent differences between the two. In addition, many websites reflect the benefits of using both descriptors by applying a multiple interface approach (McGrenere, Baecker, & Booth, 2002), but this type of navigational support only allows users to access one information source at a time. By contrast, this study is to develop an approach to integrate these two features to facilitate finding resources without changing their nature or forcing users to choose one means or the other. Driven by the concept of information scent, the main contribution of this dissertation is to conduct an experiment to explore whether the images can be found more efficiently and effectively when multiple access routes with two information descriptors are provided to users in the dual-perspective navigation framework. This framework has proven to be more effective and efficient than the subject heading-only and tag-only interfaces for exploratory tasks in this study. This finding can assist interface designers who struggle with determining what information is best to help users and facilitate the searching tasks. Although this study explicitly focuses on image search, the result may be applicable to wide variety of other domains. The lack of textual content in image systems makes them particularly hard to locate using traditional search methods. While the role of professionals in describing items in a collection of images, the role of the crowd in assigning social tags augments this professional effort in a cost effective manner

Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

Sustainable car life cycle design, taking inspiration from natural systems and thermodynamics

This paper exposes the search for a tool and method, which from a systems approach, adopts the rules and logic that govern our physical context (biosphere) in order to provide guidelines that the car industry could use to achieve an ideal state for ecological, economical and social sustainability

Shai: Enforcing Data-Specific Policies with Near-Zero Runtime Overhead

Data retrieval systems such as online search engines and online social networks must comply with the privacy policies of personal and selectively shared data items, regulatory policies regarding data retention and censorship, and the provider's own policies regarding data use. Enforcing these policies is difficult and error-prone. Systematic techniques to enforce policies are either limited to type-based policies that apply uniformly to all data of the same type, or incur significant runtime overhead. This paper presents Shai, the first system that systematically enforces data-specific policies with near-zero overhead in the common case. Shai's key idea is to push as many policy checks as possible to an offline, ahead-of-time analysis phase, often relying on predicted values of runtime parameters such as the state of access control lists or connected users' attributes. Runtime interception is used sparingly, only to verify these predictions and to make any remaining policy checks. Our prototype implementation relies on efficient, modern OS primitives for sandboxing and isolation. We present the design of Shai and quantify its overheads on an experimental data indexing and search pipeline based on the popular search engine Apache Lucene