Knowledge sharing by entrepreneurs in a virtual community of practice (VCoP)

PurposeThis paper examines how entrepreneurs engage in a Virtual Community of Practice (VCoP) to share knowledge. Intensity of engagement is taken as a proxy to measure the strength of knowledge sharing.Design/methodology/approachThe archival data spanning over a three-year period from ‘Start-up-Nation©’ (a VCoP purposefully setup for entrepreneurs) is used for analysis. A set of indices are introduced to measure participants’ intensity of engagement in terms of message length, message frequency and reciprocity in the knowledge sharing process. Content analysis is employed to test a sample of ‘highly engaged’, ‘moderately engaged’, ‘low engaged’ and ‘not engaged’ discussion topics as part of the on-line discourse.FindingsWe find that entrepreneurs normally use short (fewer than 100 words) or medium (fewer than 250 words) message size to contribute to the discussions. In addition, we find that senior members and discussion moderators play important roles in igniting the ‘reciprocity’ behaviour in stimulating the interest of the community with the topic discussion. We also findthat highly engaged topics usually lead to further discussion threads.Originality/valueThis is the first study of its kind to explore how entrepreneurs engage in a VCoP to share their knowledge and experiences. The set of measurement indices tested here provide a tool for the owner, designer and moderator of the VCoP to measure the utility of their website in terms of its members’ participation. In addition, the set of textual and subjective interventions identified here enable the moderator (administrator) of a VCoP to design effective interventions to facilitate on-line discourse and augment the knowledge sharing process amongst its community members

Accelerating the parsing process with an Application Specific VLSI RISC processor

This thesis investigates the topic of the design, implementation and potential use of specialised hardware used to accelerate the recognition and translation of computer programs expressed in a range of computer languages. This investigation focuses specifically on the twin processes of parsing and lexical analysis. The research described was carried out in two areas namely, the feasibility of designing a specialised instruction set for a RISC like processor able to accelerate the parsing and lexical analysis process, and the physical implementation of a RISC processor in CMOS VLSI technology able to execute the designed instruction set. The feasibility of mapping the process of language recognition onto the instruction set of a RISC processor is investigated. This involves an assessment of the suitability of the LL(1) and LALR(1) algorithms, both of which are used for parsing, and other associated algorithms, used for lexical analysis, as a basis for an appropriate instruction set architecture. The feasibility of an instruction set design which uses fixed size instructions with variable size data fields to ensure scaleable operation is also investigated. The appropriate software mechanisms used to validate the instruction set architecture are outlined. The practical implementation using CMOS technology of a RISC processor able to execute the new instruction set is investigated. In particular the feasibility of using bit-slice technology to implement the processor having fixed size instructions with variable size data-paths and address ranges is investigated. The combination of novel instruction set with variable data-widths and the fabricated devices able to activate semantic actions directly from hardware together form an original contribution to the field of parsing and lexical analysis

Distributed resource discovery using a context sensitive infrastructure

Distributed Resource Discovery in a World Wide Web environment using full-text indices will never scale. The distinct properties of WWW information (volume, rate of change, topical diversity) limits the scaleability of traditional approaches to distributed Resource Discovery. An approach combining metadata clustering and query routing can, on the other hand, be proven to scale much better. This paper presents the Content-Sensitive Infrastructure, which is a design building on these results. We also present an analytical framework for comparing scaleability of different distribution strategies

Computing Web-scale Topic Models using an Asynchronous Parameter Server

Topic models such as Latent Dirichlet Allocation (LDA) have been widely used in information retrieval for tasks ranging from smoothing and feedback methods to tools for exploratory search and discovery. However, classical methods for inferring topic models do not scale up to the massive size of today's publicly available Web-scale data sets. The state-of-the-art approaches rely on custom strategies, implementations and hardware to facilitate their asynchronous, communication-intensive workloads. We present APS-LDA, which integrates state-of-the-art topic modeling with cluster computing frameworks such as Spark using a novel asynchronous parameter server. Advantages of this integration include convenient usage of existing data processing pipelines and eliminating the need for disk writes as data can be kept in memory from start to finish. Our goal is not to outperform highly customized implementations, but to propose a general high-performance topic modeling framework that can easily be used in today's data processing pipelines. We compare APS-LDA to the existing Spark LDA implementations and show that our system can, on a 480-core cluster, process up to 135 times more data and 10 times more topics without sacrificing model quality.Comment: To appear in SIGIR 201

LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar

When Gender Meets Sex: An Exploratory Study of Women Who Seduce Adolescent Boys

This article describes the origins, design, and implications of a new study exploring female-perpetrated statutory rape against adolescent boys in the United States. In contrast to both legal frameworks, which typically regard statutory rape as a male-on-female phenomenon, and existing literature from the fields of psychology and psychiatry derived from clinical samples and sex offender registries, this study examines the incidence of female-perpetrated statutory rape using data from electronic news reports covering the period 1990-2008. In this short article, the author explains the advantages of her approach over those taken by prior scholars, in terms of the size of the data set and the scope of coverage, as well as her decision to focus on statutory rape exclusively, rather than on female sex abuse more generally. The article also discusses the projected implications of the study for understanding not only the crime of statutory rape, but also the gender assumptions implicit in conventional works on this topic