An initial state of design and development of intelligent knowledge discovery system for stock exchange database

Data mining is a challenging matter in research field for the last few years.Researchers are using different techniques in data mining.This paper discussed the initial state of Design and Development Intelligent Knowledge Discovery System for Stock Exchange (SE) Databases. We divide our problem in two modules.In first module we define Fuzzy Rule Base System to determined vague information in stock exchange databases.After normalizing massive amount of data we will apply our proposed approach, Mining Frequent Patterns with Neural Networks.Future prediction (e.g., political condition, corporation factors, macro economy factors, and psychological factors of investors) perform an important rule in Stock Exchange, so in our prediction model we will be able to predict results more precisely.In second module we will generate clustering algorithm. Generally our clustering algorithm consists of two steps including training and running steps.The training step is conducted for generating the neural network knowledge based on clustering.In running step, neural network knowledge based is used for supporting the Module in order to generate learned complete data, transformed data and interesting clusters that will help to generate interesting rules

Adolescent Literacy and Textbooks: An Annotated Bibliography

A companion report to Carnegie's Time to Act, provides an annotated bibliography of research on textbook design and reading comprehension for fourth through twelfth grade, arranged by topic. Calls for a dialogue between publishers and researchers

The use of data-mining for the automatic formation of tactics

This paper discusses the usse of data-mining for the automatic formation of tactics. It was presented at the Workshop on Computer-Supported Mathematical Theory Development held at IJCAR in 2004. The aim of this project is to evaluate the applicability of data-mining techniques to the automatic formation of tactics from large corpuses of proofs. We data-mine information from large proof corpuses to find commonly occurring patterns. These patterns are then evolved into tactics using genetic programming techniques

Analytic and constructive processes in the comprehension of text

This thesis explores the process of comprehension as a purposeful interaction between a reader and the information in a text. The review begins by discussing the difference between educational and psychological perspectives on comprehension. Approaches to the analysis of text structure are then described and models and theories of the representation of knowledge are evaluated. It is argued that these are limited in that they tend to focus either on the text or the reader: they either examine those procedures that are necessary for text analysis or the knowledge structures required for comprehension, storage and retrieval. Those that come nearest to examining the interaction between text and knowledge structures tend to be limited in terms of the texts they can deal with and they do not deal adequately with the predictive aspects of comprehension.Experiments are reported which look at the ongoing predictions made by readers, and how these are affected by factors such as text structure and ‘interestingness’. The experiments provided the opportunity for examining the potential of alternative methodologies (such as the content analysis of open-ended questions). It is felt that it is necessary to examine comprehension using methods which are direct but not intrusive. The studies reported demonstrate that it is possible to obtain reliable measures of a reader's predictions and that these are systematically affected by the structure and content of the text

An association rule dynamics and classification approach to event detection and tracking in Twitter.

Twitter is a microblogging application used for sending and retrieving instant on-line messages of not more than 140 characters. There has been a surge in Twitter activities since its launch in 2006 as well as steady increase in event detection research on Twitter data (tweets) in recent years. With 284 million monthly active users Twitter has continued to grow both in size and activity. The network is rapidly changing the way global audience source for information and influence the process of journalism [Newman, 2009]. Twitter is now perceived as an information network in addition to being a social network. This explains why traditional news media follow activities on Twitter to enhance their news reports and news updates. Knowing the significance of the network as an information dissemination platform, news media subscribe to Twitter accounts where they post their news headlines and include the link to their on-line news where the full story may be found. Twitter users in some cases, post breaking news on the network before such news are published by traditional news media. This can be ascribed to Twitter subscribers' nearness to location of events. The use of Twitter as a network for information dissemination as well as for opinion expression by different entities is now common. This has also brought with it the issue of computational challenges of extracting newsworthy contents from Twitter noisy data. Considering the enormous volume of data Twitter generates, users append the hashtag (#) symbol as prefix to keywords in tweets. Hashtag labels describe the content of tweets. The use of hashtags also makes it easy to search for and read tweets of interest. The volume of Twitter streaming data makes it imperative to derive Topic Detection and Tracking methods to extract newsworthy topics from tweets. Since hashtags describe and enhance the readability of tweets, this research is developed to show how the appropriate use of hashtags keywords in tweets can demonstrate temporal evolvements of related topic in real-life and consequently enhance Topic Detection and Tracking on Twitter network. We chose to apply our method on Twitter network because of the restricted number of characters per message and for being a network that allows sharing data publicly. More importantly, our choice was based on the fact that hashtags are an inherent component of Twitter. To this end, the aim of this research is to develop, implement and validate a new approach that extracts newsworthy topics from tweets' hashtags of real-life topics over a specified period using Association Rule Mining. We termed our novel methodology Transaction-based Rule Change Mining (TRCM). TRCM is a system built on top of the Apriori method of Association Rule Mining to extract patterns of Association Rules changes in tweets hashtag keywords at different periods of time and to map the extracted keywords to related real-life topic or scenario. To the best of our knowledge, the adoption of dynamics of Association Rules of hashtag co-occurrences has not been explored as a Topic Detection and Tracking method on Twitter. The application of Apriori to hashtags present in tweets at two consecutive period t and t + 1 produces two association rulesets, which represents rules evolvement in the context of this research. A change in rules is discovered by matching every rule in ruleset at time t with those in ruleset at time t + 1. The changes are grouped under four identified rules namely 'New' rules, 'Unexpected Consequent' and 'Unexpected Conditional' rules, 'Emerging' rules and 'Dead' rules. The four rules represent different levels of topic real-life evolvements. For example, the emerging rule represents very important occurrence such as breaking news, while unexpected rules represents unexpected twist of event in an on-going topic. The new rule represents dissimilarity in rules in rulesets at time t and t+1. Finally, the dead rule represents topic that is no longer present on the Twitter network. TRCM revealed the dynamics of Association Rules present in tweets and demonstrates the linkage between the different types of rule dynamics to targeted real-life topics/events. In this research, we conducted experimental studies on tweets from different domains such as sports and politics to test the performance effectiveness of our method. We validated our method, TRCM with carefully chosen ground truth. The outcome of our research experiments include: Identification of 4 rule dynamics in tweets' hashtags namely: New rules, Emerging rules, Unexpected rules and 'Dead' rules using Association Rule Mining. These rules signify how news and events evolved in real-life scenario. Identification of rule evolvements on Twitter network using Rule Trend Analysis and Rule Trace. Detection and tracking of topic evolvements on Twitter using Transaction-based Rule Change Mining TRCM. Identification of how the peculiar features of each TRCM rules affect their performance effectiveness on real datasets

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Reading in English for academic purposes (EAP): the effect of background knowledge with special reference to schema-directed processes

It is my belief that the present study adds to the accumulated knowledge on EAP reading, and that in fact, some of its findings may be relevant not only to EAP reading but to L2 reading in general.The findings of the study provide supporting evidence for the advisability of maintaining or promoting EAP reading courses given that reading in one's own discipline has been shown to counterbalance to a significant extent the difficulties of reading in a foreign language.The findings of the interpretative analysis suggest however, that the beneficial effects of previous knowledge of topic do not necessarily obliterate the influence of 'lower-level' linguistic difficulties. Thus, we also conclude that ways to circumvent such influence need to be contemplated for the implementation of EAP reading courses. In other words, the characteristic nature of EAP reading as processing of a deficiently known code should not be underestimated.The second conclusion that can be drawn from the study concerns the relevancy of converging methodologies 239 of research.1 Whenever possible, EAP reading research should involve both a quantitative and a qualitative approach to data analysis. „As indicated by the findings of this study the use of only one of these approaches may lead to a partial picture or to premature conclusions of the results obtained. If we are interested in clarifying the EAP reading phenomenon we will need the complementary contributions that both kinds of methodology are capable of providing.With regard to the theoretical approach that was adopted, it is my contention that the notion of schema has a place in L2 reading research. The summaries obtained from the experiment showed how previous knowledge of topic made the readers label something as part of a certain schema and how this previous knowledge also provided the necessary details to elaborate on the interpretations that had been generated. In other words, knowledge schemata 'filter and shape'² comprehension in EAP reading just as much or perhaps more than they do in other instances of discourse comprehension

In Search of the Perfect Prompt

The study investigates the efficacy of soft and hard prompt strategies in the scientific domain, namely in the tasks of conversational abstract generation. The proposed approach incorporates two distinct methods, prompt engineering and prompt tuning, within a Conversational Recommender System (CRS). The primary objective of this system is to aid users in generating abstracts for their research. The present study employs an evaluation approach that integrates user research with objective performance criteria. This study examines the strengths and disadvantages associated with both categories of prompts, commencing with an analysis of existing literature on CRS and prompting studies, and subsequently conducting original research tests. This study makes three primary contributions. Initially, a compilation of prerequisites and hypothetical situations is formed by an examination of the issue at hand. This wish list presents a range of potential technological, user, and functional views that have the potential to contribute to future studies in this area. Furthermore, the examination of user studies is an integral element of our evaluation methodology. During this process, we analyze many factors pertaining to the 6 participants, including their cognitive load, response time, and overall happiness while applying challenging prompts within the CRS. In our investigation, we examine the behavior and needs of the target demographic, consisting of academics and researchers. Our findings suggest a tendency among this group to favor interactions that are focused on factual information and question-and-answer exchanges, as opposed to more expansive and conversational encounters. Thirdly, our study delves into the comprehensibility and relevance of the generated abstracts, utilizing well-established criteria such as Rouge and F1 scores. In our research, the anticipated effect of combining prompts with text-generation tasks is to produce scientific abstracts that are imprecise and broader in nature. However, this objective contradicts the expectations of the users. The research findings shed light on the difficulties and advantages that arise from implementing prompting techniques with a CRS. This study makes a valuable contribution by recognizing the importance of contextual comprehension and employing prompting strategies from both technical and user-centric viewpoints. One of the primary findings is that it is crucial to customize prompt tactics in accordance with user preferences and domain demands. The given findings contribute to the existing body of knowledge on conversational recommender systems and their applications in the field of natural language processing