9,070 research outputs found

    Land Reform in Sri Lanka

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    Smart Search: A Firefox Add-On to Compute a Web Traffic Ranking

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    Search engines results are typically ordered according to some notion of importance of a web page as well as relevance of the content of a web page to a query. Web page importance is usually calculated based on some graph theoretic properties of the web. Another common technique to measure page importance is to make use of the traffic that goes to a particular web page as measured by a browser toolbar. Currently, there are some traffic ranking tools available like www.alexa.com, www.ranking.com, www.compete.com that give such analytic as to the number of users who visit a web site. Alexa provides the traffic rank for a website based on two factors: The number of users that view a website and the number of pages viewed. The Alexa toolbar is not open-source.The main goal of our project was to create a Smart Search Firefox add-on for the Yioop search engine, an open source search engine developed by my project advisor, Dr. Chris Pollett. This add-on would provide similar analytic data to the Yioop search engine, but in a transparent and open-source way. With the results received from the Smart Search toolbar extension, the Yioop search engine refines the search results as well as provides user centric-search results. Eventually, users would benefit from these better search results

    Extending Yioop! With Geographical Location Local Search

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    It is often useful when doing an internet search to get results based on our current location. For example, we might want such results when we search on restaurants, car service center, or hospitals. Current open source search engines like those based on Nutch do not provide this facility. Commercial engines like Google and Yahoo! provide this facility so it would be useful to incorporate it in an open source alternative. The goal of this project is to include location aware search in Yioop!(Pollett, 2012) by using geographical data from OpenStreetMap(“Open Street map wiki”, 2012) and hostip.info (“DMOZ”, n.d.) database to geolocate IP addresses

    Finding kk Simple Shortest Paths and Cycles

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    The problem of finding multiple simple shortest paths in a weighted directed graph G=(V,E)G=(V,E) has many applications, and is considerably more difficult than the corresponding problem when cycles are allowed in the paths. Even for a single source-sink pair, it is known that two simple shortest paths cannot be found in time polynomially smaller than n3n^3 (where n=Vn=|V|) unless the All-Pairs Shortest Paths problem can be solved in a similar time bound. The latter is a well-known open problem in algorithm design. We consider the all-pairs version of the problem, and we give a new algorithm to find kk simple shortest paths for all pairs of vertices. For k=2k=2, our algorithm runs in O(mn+n2logn)O(mn + n^2 \log n) time (where m=Em=|E|), which is almost the same bound as for the single pair case, and for k=3k=3 we improve earlier bounds. Our approach is based on forming suitable path extensions to find simple shortest paths; this method is different from the `detour finding' technique used in most of the prior work on simple shortest paths, replacement paths, and distance sensitivity oracles. Enumerating simple cycles is a well-studied classical problem. We present new algorithms for generating simple cycles and simple paths in GG in non-decreasing order of their weights; the algorithm for generating simple paths is much faster, and uses another variant of path extensions. We also give hardness results for sparse graphs, relative to the complexity of computing a minimum weight cycle in a graph, for several variants of problems related to finding kk simple paths and cycles.Comment: The current version includes new results for undirected graphs. In Section 4, the notion of an (m,n) reduction is generalized to an f(m,n) reductio

    Sentiment analysis on online social network

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    A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis

    An examination into the role of knowledge management and computer security in organizations

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    Organisations develop their computer security procedures based on external guidelines such as ISO 17799 with very little provision to incorporate organisational knowledge in their security procedures. While these external guidelines make recommendations as to how an organisation should develop and implement best practices in computer security they often fail to provide a mechanism that links the security process to the organisational knowledge. The result is that often, security policies, procedures and controls are implemented that are neither strong nor consistent with the organisation's objectives. This study has examined the role of Knowledge Management in organisational Computer Security in 19 Australian SMEs. The study has determined that although the role of knowledge management in organisational computer security is currently limited, there appears to be evidence to argue that the application of knowledge management systems to organisational computer security development and management processes will considerably enhance performance and reduce costs. The study supports that future research is warranted to focus on how existing computer security standards and practices can be improved to allow for a stronger integration with organisational knowledge through the application of knowledge management systems

    Bounding Cache Miss Costs of Multithreaded Computations Under General Schedulers

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    We analyze the caching overhead incurred by a class of multithreaded algorithms when scheduled by an arbitrary scheduler. We obtain bounds that match or improve upon the well-known O(Q+S(M/B))O(Q+S \cdot (M/B)) caching cost for the randomized work stealing (RWS) scheduler, where SS is the number of steals, QQ is the sequential caching cost, and MM and BB are the cache size and block (or cache line) size respectively.Comment: Extended abstract in Proceedings of ACM Symp. on Parallel Alg. and Architectures (SPAA) 2017, pp. 339-350. This revision has a few small updates including a missing citation and the replacement of some big Oh terms with precise constant
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