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The prediction precision of mathematical models and their interpretability go usually against each other. The increase of the quality of one of the features decreases the other. In this article we introduce a new mathematical model based on Perception-based Logical Deduction (see [18], [19]) which is an implicative fuzzy inference mechanism based on linguistics semantics, and which enables the users...
The sequential covering strategy has been and still is a very common way to develop rule learning algorithms. This strategy follows a greedy procedure to learn rules, where, after each step one rule is obtained. Recently, we proposed a new sequential covering strategy that allowed the review of previously learned knowledge during the learning process itself. This review of knowledge allowed the algorithm...
Hyperspectral image classification is a challenging classification problem: obtaining complete and representative training sets is costly; pixels can belong to unknown classes; and it is generally an ill-posed problem. The need to achieve high classification accuracy surpasses the need to classify the entire image. To achieve this, we use classification with rejection by providing the classifier an...
Recently, machine-learning based vulnerability prediction models are gaining popularity in web security space, as these models provide a simple and efficient way to handle web application security issues. Existing state-of-art Cross-Site Scripting (XSS) vulnerability prediction approaches do not consider the context of the user-input in output-statement, which is very important to identify context-sensitive...
Effort estimation is a project management activity that is mandatory for the execution of software projects. Despite its importance, there have been just a few studies published on such activities within the Agile Global Software Development (AGSD) context. Their aggregated results were recently published as part of a secondary study that reported the state of the art on effort estimation in AGSD...
Chronic Respiratory Disease (CRD) is a serious problem in broiler farms and food production industry. This disease cannot be observed easily during the broiler raised process. The model for predicting CRD rate is not exactly identified, because of the variation in farm environment and the development of breeding. Therefore the embedded of concept drift to the normal predictor is the possible way to...
In this research, we propose using time context to improve predictive accuracy and quality of collaborative filtering for music recommendation. We use time contextual information called micro-profiling. Thus, each user has multiple micro profiles, in particular, six-time slots instead of a single profile. The recommendation is performed depended on these micro-profiling. Our method takes into account...
This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based...
Online learning communities have become an important place serving informal learning due to the prevalence of online social networking services during the past few years. This paper proposes a social analytics framework aiming to boost recommendation service catering for the different learning demands of learners. Based on the traditional collaborative filtering approach, this study focuses on constructing...
This paper reports and discusses on a project about designing a digital tool to support Chinese undergraduate students in reflecting on their English language (L2) learning experience. The tool namely ACTIVE was developed primarily based on a classification framework called A-S-E-R and Latent Semantic Analysis. It can automatically classify reflective L2 learning skills into four elements with each...
In the proposed approach, an attempt was made to disambiguate Bengali ambiguous words using Naïve Bayes Classification algorithm. The whole task was divided into two modules. Each module executes a specific task. In the first module, the algorithm was applied on a regular text, collected from the Bengali text corpus developed in the TDIL project of the Govt. of India and the accuracy of disambiguation...
Coverage-based fault localization techniques leverage coverage information to identify the suspicious program entities for inspection. However, coincidental correctness (CC) widely occurs during software debugging, and brings negative impact to the effectiveness of CBFL techniques. In this paper, we propose a regression approach to identity CC execution with weighted clustering analysis. Based on...
In this work we present A-Wristocracy, a novel framework for recognizing very fine-grained and complex inhome activities of human users (particularly elderly people) with wrist-worn device sensing. Our designed A-Wristocracy system improves upon the state-of-the-art works on in-home activity recognition using wearables. These works are mostly able to detect coarse-grained ADLs (Activities of Daily...
In the current social, technological and economic context, customers make their decisions based mostly on the opinion of other consumers. On the other side, companies need quick feedback from their customers in order to adapt to their needs in real time. The effective connection between these two aspects relies on opinion mining tools, which automatically process consumers' reviews and opinions about...
We present a graph matching refinement framework that improves the performance of a given graph matching algorithm. Our method synergistically uses the inherent structure information embedded globally in the active association graph, and locally on each individual graph. The combination of such information reveals how consistent each candidate match is with its global and local contexts. In doing...
During the last decade, we have witnessed the prosperity of the Internet-based social platforms and mobile social applications such as Facebook, Twitter, etc. Meanwhile, due to the popularity of mobile terminals such as smart phones and variety of PADs, it is feasible to obtain relatively accurate tempo-spatial data from mobile terminal holders when they visit and upload geo-tagged messages or pictures...
The atan2 function computes the polar angle arctan(y/x) of a point given by its cartesian coordinates. It is widely used in digital signal processing to recover the phase of a signal. This article studies for this context the implementation of atan2 with fixed-point inputs and outputs. It compares the prevalent CORDIC shift-and-add algorithm to two multiplier-based techniques. The first one computes...
It is common for users to explicitly or implicitly compose on-line services to accomplish daily tasks, such as shopping for a pair of shoes on-line. However, unnecessary and repetitive data typing into the services would negatively impact the user experience and decrease the efficiency of service composition. Recent studies have proposed several approaches to help users fill in values to services...
The number of sensors deployed around the world is growing at a rapid pace when we are moving towards the Internet of Things (IoT). The widespread deployment of these sensors represents significant financial investment and technical achievement. These sensors continuously generate enormous amounts of data which is capable of supporting an almost unlimited set of high value proposition applications...
Service functionality can be provided by more than one service consumer. In order to choose the service which creates the most benefit before its consumption, a selection based on previous measurable experiences by other consumers is beneficial. In this paper, we present the results of our analysis of two machine learning approaches to predict the best service within this selection problem. The first...
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