Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences. Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities

A method of determining culture related users using computation of correlation

The provision of security on most of computer networks is based on the obtaining and exchange of a unique key between the communicating parties. It is, however, difficult to come up with a truly unique and random secret between two parties with the help from physical randomness. In this work, we focus on the problem of unique random number generation or derivation between users in online social networks. As a result of rapid development of Internet, online social networks provide a vast set of different user comments on different products and services. Such comments can inherently reflect the mindset or cultural background of those people who wrote them and it is possible to derive some unique randomness from such texts with some maneuver. We select movie reviews as the sandbox for our investigation. To manage textual content and search for certain hidden relations, the methodologies of text matching are studied. By looking the similarities of movie reviews from different users, we can refer insights into the cultural background and even predict future preferences from past comments. We present all of our findings here to aspire further investigation. We have investigated the correlation of movie reviews and studied the values of different weight assignments to the sentence and word relation. According to our results, synonym relations are the dominant positive association that impacts correlation value. We calculate correlation between review sets containing multiple reviews to avoid randomness. These correlations have then been used to evaluate and derive a unique random number. We target at a single review, and put it together with other reviews to obtain correlation values from different pairs of reviewers. Then the correlation value is binning to a 1-bit binary number. Through such a simplified extraction, a unique random number can be generated by repeating the process of binning. Such unique random number is able to facilitate to secure information exchanges between the users. In our future work, we will explore such correlations to generate a practically usable unique secret for secret keys

Term Weighting Based on Positive Impact Factor Query for Arabic Fiqh Document Ranking

Query becomes one of the most decisive factor on documents searching. A query contains several words, where one of them will become a key term. Key term is a word that has higher information and value than the others in query. It can be used in any kind of text documents, including Arabic Fiqh documents. Using key term in term weighting process could led to an improvement on result\u27s relevancy. In Arabic Fiqh document searching, not using the proper method in term weighting will relieve important value of key term. In this paper, we propose a new term weighting method based on Positive Impact Factor Query (PIFQ) for Arabic Fiqh documents ranking. PIFQ calculated using key term\u27s frequency on each category (mazhab) on Fiqh. The key term that frequently appear on a certain mazhab will get higher score on that mazhab, and vice versa. After PIFQ values are acquired, TF.IDF calculation will be done to each words. Then, PIFQ weight will be combine with the result from TF.IDF so that the new weight values for each words will be produced. Experimental result performed on a number of queries using 143 Arabic Fiqh documents show that the proposed method is better than traditional TF.IDF, with 77.9%, 83.1%, and 80.1% of precision, recall, and F-measure respectively

Finding similarity using metadata of clinical trials using Natural Language Processing in DataBridge

Information explosion in every field in this age creates one big challenge - how to extract meaningful information from massive amounts of data. Currently there are millions of datasets available as a result of research by various scientists. It is necessary to find the hidden potential of this data and identify different ways data can be related to each other. The purpose of the project is to identify the level of similarity between the metadata of any two clinical trials that have been completed in Databridge application. The dataset being considered is the entire metadata of all the concluded clinical trials as updated on clinicaltrials.gov. Only the trials which have been completed and have results updated are being considered. This paper discusses four different techniques employed for finding similarity between any two particular clinical trials and their corresponding results.Master of Science in Information Scienc

Using text analytics to discover organizational congruences: A study of the Thai IT industry

Organizational congruence is a leading indicator for organizational adaptation and increasing relevant in technological disruptive environments. However, the congruence perspective is often investigated through another lens. Information technology (IT) literature is less familiar with this perspective. This study aims to raise awareness of the perspective among IT literature by strictly investigating constructs under the perspective. It postulated an investigation akin to a measure development under the congruence perspective. Data was collected from Thai IT industry and a combination of computeraided text analysis and traditional measure development were implemented. The data was preprocessed to ensure high quality and entered to measure modeling techniques. The results unveil four organizational congruence constructs. Three are first-level constructs: strategy consensus, operational congruence, and competitive congruence. One is second-level construct: organizational ambidexterity. Implications of this discovery are discussed. Limitations and future directions are recognized in the last section

How Unique and Traceable are Usernames?

Suppose you find the same username on different online services, what is the probability that these usernames refer to the same physical person? This work addresses what appears to be a fairly simple question, which has many implications for anonymity and privacy on the Internet. One possible way of estimating this probability would be to look at the public information associated to the two accounts and try to match them. However, for most services, these information are chosen by the users themselves and are often very heterogeneous, possibly false and difficult to collect. Furthermore, several websites do not disclose any additional public information about users apart from their usernames (e.g., discus- sion forums or Blog comments), nonetheless, they might contain sensitive information about users. This paper explores the possibility of linking users profiles only by looking at their usernames. The intuition is that the probability that two usernames refer to the same physical person strongly depends on the "entropy" of the username string itself. Our experiments, based on crawls of real web services, show that a significant portion of the users' profiles can be linked using their usernames. To the best of our knowledge, this is the first time that usernames are considered as a source of information when profiling users on the Internet