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The cost of deleting a software bug increases ten times as it is floated onto the next phase of software development lifecycle (SDLC). This makes the task of the project managers difficult and also degrades the quality of the output software product. Software defect prediction (SDP) was proposed as a solution to the problem which could anticipate the defective modules and hence, deal with them in...
Current research of emotion recognition from electroencephalogram (EEG) signals rarely considers common patterns embodied in multiple subjects and individual patterns for each subject simultaneously. Therefore, in this paper, we propose a novel emotion recognition approach using subjects or subject groups as privileged information, which is only available during training. First, five frequency features...
Automated or semi-automated analysis of requirements specification documents, expressed in Natural Language (NL), has always been desirable. An important precursor to this goal is the identification and correction of potentially ambiguous requirements statements. Pronominal Anaphora ambiguity is one such type of pragmatic or referential ambiguity in NL requirements, which needs attention. However,...
Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd...
This paper proposes a novel segmentation-free approach using deep neural network based hidden Markov model (DNN-HMM) for offline handwritten Chinese text recognition. In the general Bayesian framework, three key issues are comprehensively investigated, namely feature extraction, character modeling, and language modeling. First, as for the feature extraction on the basis of each frame or sliding window,...
In this work, we propose a metric adaptation method for set-based face verification and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version, the Janus Challenging Set 2 (CS2). A template-specific metric is trained to adaptively learn the discriminative information in test templates and the negative training set, which contains subjects that are mutually...
In ensemble learning, ensemble pruning is a procedure that aims at removing the unnecessary base classifiers and retaining the best subset of the base classifiers. We presented a two-step ensemble pruning framework, in which the optimal size of the pruned ensemble is first decided, and then with the optimal size as input, the optimal ensemble is selected. For the first step to find the optimal ensemble...
This work asks the question as to whether ‘novelty as an objective’ is still beneficial under tasks with a lot of ambiguity, such as Poker. Specifically, Poker represents a task in which there is partial information (public and private cards) and stochastic changes in state (what card will be dealt next). In addition, bluffing plays a fundamental role in successful strategies for playing the game...
A new approach for indoor positioning is presented, aimed at designing a WiFi positioning system that is feasible and convenient for both service providers and end users. In the proposed approach, only access points (APs) need to collect the received signal strengthes (RSS) of mobile devices, and use these RSS samples to jointly estimate the devices' locations. To enhance the accuracy of positioning,...
Learning user preferences from their implicit feedbacks is crucial to enable recommendations in various online applications. The Bayesian personalized ranking (BPR) with pairwise preference learning has been reported as one of the most promising algorithms for this problem. It follows a fundamental assumption that a user prefers interacted items to the unobserved items, which feedbacks have not happened...
In this paper, the best irradiation technology for improving the quality of liquor by 60Co-γ irradiation was studied. Different doses of rays on liquor quality Law model was built by Bayesian regularization BP neural network method. This model was used for prediction and verification, and then the particle swarm optimization algorithm was used to predict the process parameters of the irradiation process...
This research proposes a novel Bayesian sparse representation (BSR) method along with extracting facial parameters of SIFT to create sparse dictionaries, which are invariant to rotation, scale, and shift. By using K-means and information theory, a new dictionary called extended dictionary is developed. Compared with conventional orthogonal matching pursuit (OMP) algorithm, the proposed system that...
Breast cancer is one of the most widespread diseases among women in the UAE and worldwide. Correct and early diagnosis is an extremely important step in rehabilitation and treatment. However, it is not an easy one due to several uncertainties in detection using mammograms. Machine Learning (ML) techniques can be used to develop tools for physicians that can be used as an effective mechanism for early...
Data mining techniques and multi criteria decision making techniques have been used widely in many areas, such as customer relationship management, medicine, engineering, education, geographic information systems, and recommendation systems. The present study aims to design a hybrid approach based on Deep Neural Networks (DNNs) and multi criteria decision making. DNNs and multi criteria decision making...
Bias classification is a overuse and efficient classification method in data analysis, but attribute independence assumption affects its performance[1]. In view of these issues, this paper proposes a weighted naive Bayesian algorithm based on MLRM (multiple linear regression model). First, through MLRM to analyze the correlation between attributes, then, this correlation value as the weight coefficient,...
We attempt to formulate Bayesian speaker adaptation for deep models and explore two different solutions. In the first “indirect” approach, Bayesian adaptation is applied to context-dependent, Gaussian-mixture-model based hidden Markov models (CD-GMM-HMMs) with bottleneck (BN) features derived from deep neural networks (DNNs). The second method directly formulates Bayesian adaptation for CD-DNN-HMMs...
We describe an emerging application of data mining in the context of computer networks. This application concerns the problem of predicting the size of a flow and detecting elephant flows (very large flows). Flow size is a very important statistic that can be used to improve routing, load balancing and scheduling in computer networks. Flow size prediction is particularly challenging since flow patterns...
Since Web 2.0 emerges, users became very active in attending Web forum and Q&A Community. For the community about technology, engineering and science, it is likely that most of the professionals follow the same general path to study specific knowledge and this path would be between topics from basic one to specific one or from topic about old technology to a topic about new technology. Our work...
In cloud computing and high performance computing, a large job is typically divided into many small tasks for parallel execution in a distributed environment. Due to different reasons, some tasks (so-called ‘stragglers’) are considerably slower than the others, delaying the completion of the job. We propose a new machine learning approach to automatically identify and diagnose the stragglers. To first...
Researchers have designed a decision support system for granting discounts by using the Naïve Bayes method. Naïve Bayes can be used for decisions to grant discounts within a multi-criteria system. The criteria are determined by the company, the purchase of some items, the status of the product, the big day, price ranges. The management system helps the company in providing discounts accordingly. The...
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