A Trio Neural Model for Dynamic Entity Relatedness Ranking

Measuring entity relatedness is a fundamental task for many natural language processing and information retrieval applications. Prior work often studies entity relatedness in static settings and an unsupervised manner. However, entities in real-world are often involved in many different relationships, consequently entity-relations are very dynamic over time. In this work, we propose a neural networkbased approach for dynamic entity relatedness, leveraging the collective attention as supervision. Our model is capable of learning rich and different entity representations in a joint framework. Through extensive experiments on large-scale datasets, we demonstrate that our method achieves better results than competitive baselines.Comment: In Proceedings of CoNLL 201

Architecture for Cooperative Prefetching in P2P Video-on- Demand System

Most P2P VoD schemes focused on service architectures and overlays optimization without considering segments rarity and the performance of prefetching strategies. As a result, they cannot better support VCRoriented service in heterogeneous environment having clients using free VCR controls. Despite the remarkable popularity in VoD systems, there exist no prior work that studies the performance gap between different prefetching strategies. In this paper, we analyze and understand the performance of different prefetching strategies. Our analytical characterization brings us not only a better understanding of several fundamental tradeoffs in prefetching strategies, but also important insights on the design of P2P VoD system. On the basis of this analysis, we finally proposed a cooperative prefetching strategy called "cooching". In this strategy, the requested segments in VCR interactivities are prefetched into session beforehand using the information collected through gossips. We evaluate our strategy through extensive simulations. The results indicate that the proposed strategy outperforms the existing prefetching mechanisms.Comment: 13 Pages, IJCN

Text Summarization Using Semantic Technique

This work proposes semantic technique as a new approach for text summarization of online news/ journal articles. This text summarization project contains two part: parsing and semantic analysis. At first, for the parsing, we set up criteria to evaluate importance of sentences within text such as position, length. According to this, only sentences with high score of importance will be selected. The number of sentence selected depends on how much compact that users expect summary output would be. System combines those sentences into summary draft. Second part is semantic analysis; the technique we use here is lexical semantic approach. During this part, system will use Word Net lexical database to analyze words within summary draft. This database had words linked through semantic relations such as synonym, antonym, hyponymy, and more. Our project would end up by evaluation process. We evaluate accuracy of summary output, also compare and find differences between human summary work and system result.

Investigation of Heterogeneous Approach to Fact Invention of Web Users’ Web Access Behaviour

World Wide Web consists of a huge volume of different types of data. Web mining is one of the fields of data mining wherein there are different web services and a large number of web users. Web user mining is also one of the fields of web mining. The web users’ information about the web access is collected through different ways. The most common technique to collect information about the web users is through web log file. There are several other techniques available to collect web users’ web access information; they are through browser agent, user authentication, web review, web rating, web ranking and tracking cookies. The web users find it difficult to retrieve their required information in time from the web because of the huge volume of unstructured and structured information which increases the complexity of the web. Web usage mining is very much important for various purposes such as organizing website, business and maintenance service, personalization of website and reducing the network bandwidth. This paper provides an analysis about the web usage mining techniques. Â

Log Analysis in Cyber Threat Detection

Many organizations have good and well-trained staff that run networks that is well-designed with nicely structured procedures and security policies. However, they are still experiencing threatening situations consistently, as result of some worker\u27s defect or unfavorable circumstances and malicious intent. Hackers are consistently creating new complex way of breaching corporate information systems, and organizations need to protect their data, networks and systems in more reliable and effective ways, the most accurate, potent and effective tools in good security portfolio include incident and audit logs generated by networked devices. However, some organizations comprehend what sort of devices to screen and monitor, what data to catch, or how to appropriately assess the data. Also, a few people have the assets and resources required to keep steady over work. This paper will walk you through the basic premise of log analysis - why it is important, what it can tell you and how to do it. I will talk about the five essential elements of a successful log analysis process, its application to monitoring performance, in continuous process monitoring and security (threat hunting and detection) with commercially available solutions, and open with a lot of rules with Source Solutions and compliance law