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The retransmission timeout (RTO) timer used in TCP has long been standardized by the IETF in RFC2988, referred to as the TCP-RFC in this paper. Over the years, various deficiencies have been identified. In this paper, we focus on the implicit RTO offset problem, where the exact timeout limit of each packet is stretched by restarting the timer using the current timer value on the arrival of each acknowledgement...
This paper proposed a personal authentication system using small resources for home use. The following three goals were achieved at the same time by this system: (1) identification of each individual, (2) unique existence of each individual in the specified time and space, and (3) no contradiction in the existence of each individual in view of the space and the time before and after an event. Considering...
In this research, we introduce a stratified random sampling technique that guides the selection mechanism to select the events (exams) for the integrated two-stage multi-neighbourhood tabu search (ITMTS) in solving examination timetabling problem. This technique is used during the timetable improvement phase especially when dealing with the exhaustive search mechanism in order to reduce the possibilities...
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are significantly less than those of negative (majority) class leading to severe class imbalance. Constructing high quality classifiers for such imbalanced training data sets is one of the major challenges in machine learning, since...
In recommender systems, the task of automatically deriving user profiles, encoding the actual preferences of users, covers a fundamental role. In this paper, we propose a strategy for learning and updating user profiles by using fuzzy sets that reveal to be a valid tool to model the vague and imprecise nature of preferences as well as the items to be recommended. The proposed adaptation strategy resembles...
In the work we consider the situation with exact classes and fuzzy information of object features. The classification error is presented for the two-class Bayes classifier. The results are received for the full probabilistic information. The new upper bound of the probability of an error is precise twice as much as the bound based on the information energy of fuzzy events.
Due to the huge product assortments and complex descriptions of mobile products/services, it is a great challenge for new customers to select appropriate products. To solve this issue, a fuzzy matching based recommendation approach for mobile products/services is proposed in this paper. In this approach, a new customer's requirements are obtained through asking a set of questions and represented by...
Security problems arise in software systems are very challenging. Using program analysis techniques and some language based security rules can help in enforcing application-level security through control access to program resources and verification of control flow of the information inside the program based on some security properties. This paper presents a new job analyzer component for an intrusion...
In business analysis, models are sometimes oversimplified. We pragmatically approach many problems with a single financial objective and include monetary values for non-monetary variables. We enforce constraints which may not be as strict in reality. Based on a case in distributed energy production, we illustrate how we can avoid simplification by modeling multiple objectives, solving it with an NSGA-II...
Due to the development of internet rapidly, the secure transmission of information has become more and more importance. Until now, there are many scholars study in the topic of data hiding. Specially, the reversible data hiding scheme catch the researcher's attention. No matter how the researchers use the different technology to embed the secret information, they always try to increase the hide space...
Paper deals with the problem of designing efficient classifiers for a special case of incremental concept drift. We focus on its classification based on the multiple classifier system. For the problem under consideration we propose four simple methods of combining classification and evaluate them via computer experiments.
In the past few years, online social data visualization has emerged as a new platform for users to construct, share, and comment on data visualizations online. The most well known online data visualization tools include Many Eyes, Swivel, and Tableau Public. In this paper, we report our analysis of Many Eyes - an IBM research project. By analyzing all the data visualizations constructed by users from...
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered...
Linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. In this paper, a new linearization method is proposed for discrete time T-S fuzzy system based on the properties of T-S fuzzy theorem. The local linear models of a T-S fuzzy model are transformed to a controllable canonical...
In this paper, we apply classification system denoted Belief Rough Set Classifier (BRSC) based on the hybridization of belief functions and rough sets to learn decision rules from uncertain data consisting of web usage. The uncertainty appears only in decision attributes and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. The web usage mining dataset...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
In this paper, we present an approach to automatically extract and classify opinions in texts. We propose a similarity measurement calculating semantically distances between a word and predefined subgroups of seed words. We have evaluated our algorithm on the semantic evaluation company “SemEval 2007” corpus, and we obtained the best value of Precision and F1 62% and 61%. As an improvement of 20 %...
The necessity of lowering the execution of system tests' cost is a consensual point in the software development community. The present study presents an optimization of the regression tests' activity, by adapting a test cases prioritization technique called Failure Pursuit Sampling-previously used and validated for the prioritization of tests in general-improving its efficiency for the exclusive execution...
Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space...
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