Linking software testing results with a machine learning approach

Software testing techniques and criteria are considered complementary since they can reveal different kinds of faults and test distinct aspects of the program. The functional criteria, such as Category Partition, are difficult to be automated and are usually manually applied. Structural and fault-based criteria generally provide measures to evaluate test sets. The existing supporting tools produce a lot of information including: input and produced output, structural coverage, mutation score, faults revealed, etc. However, such information is not linked to functional aspects of the software. In this work, we present an approach based on machine learning techniques to link test results from the application of different testing techniques. The approach groups test data into similar functional clusters. After this, according to the tester's goals, it generates classifiers (rules) that have different uses, including selection and prioritization of test cases. The paper also presents results from experimental evaluations and illustrates such uses.     

Estimating city-level poverty rate based on e-commerce data with machine learning

Electronic Commerce Research - There are many big data sources in Indonesia, for example, data from social media, financial transactions, transportation, call detail records, and e-commerce. These...     

A study on effectiveness of extreme learning machine

Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM.     

A reasoning-based approach to machine learning

This paper describes a novel approach to machine learning, based on the principle of learning by reasoning. Current learning systems have significant limitations such as brittleness, i.e., the deterioration of performance on a different domain or problem and lack of power required for handling real-world learning problems. The goal of our research was to develop an approach in which many of these limitations are overcome in a unified, coherent and general framework. Our learning approach is based on principles of reasoning, such as the discovery of the underlying principle and the recognition of the deeper basis of similarity, which is somewhat akin to human learning. In this paper, we argue the importance of these principles and tie the limitations of current systems to the lack of application of these principles. We then present the technique developed and illustrate it on a learning problem not directly solvable by previous approaches.     

Develop a personalized intelligent music selection system based on heart rate variability and machine learning

Music often plays an important role in people’s daily lives. Because it has the power to affect human emotion, music has gained a place in work environments and in sports training as a way to enhance the performance of particular tasks. Studies have shown that office workers perform certain jobs better and joggers run longer distances when listening to music. However, a personalized music system which can automatically recommend songs according to user’s physiological response remains absent. Therefore, this study aims to establish an intelligent music selection system for individual users to enhance their learning performance. We first created an emotional music database using data analytics classifications. During testing, innovative wearable sensing devices were used to detect heart rate variability (HRV) in experiments, which subsequently guided music selection. User emotions were then analyzed and appropriate songs were selected by using the proposed application software (App). Machine learning was used to record user preference, ensuring accurate and precise classification. Significant results generated through experimental validation indicate that this system generates high satisfaction levels, does not increase mental workload, and improves users’ performance. Under the trend of the Internet of Things (IoT) and the continuing development of wearable devices, the proposed system could stimulate innovative applications for smart factory, home, and health care.     

Biometrics from brain electrical activity: a machine learning approach

The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis     

Two-tier network anomaly detection model: a machine learning approach

Network anomaly detection is one of the most challenging fields in cyber security. Most of the proposed techniques have high computation complexity or based on heuristic approaches. This paper proposes a novel two-tier classification models based on machine learning approaches Naïve Bayes, certainty factor voting version of KNN classifiers and also Linear Discriminant Analysis for dimension reduction. Experimental results show a desirable and promising gain in detection rate and false alarm compared with other existing models. The model also trained by two generated balance training sets using SMOTE method to evaluate the chosen similarity measure for dealing with imbalanced network anomaly data sets. The two-tier model provides low computation time due to optimal dimension reduction and feature selection, as well as good detection rate against rare and complex attack types which are so dangerous because of their close similarity to normal behaviors like User to Root and Remote to Local. All evaluation processes experimented by NSL-KDD data set.     

Semi-supervised machine learning approach for DDoS detection

Even though advanced Machine Learning (ML) techniques have been adopted for DDoS detection, the attack remains a major threat of the Internet. Most of the existing ML-based DDoS detection approaches are under two categories: supervised and unsupervised. Supervised ML approaches for DDoS detection rely on availability of labeled network traffic datasets. Whereas, unsupervised ML approaches detect attacks by analyzing the incoming network traffic. Both approaches are challenged by large amount of network traffic data, low detection accuracy and high false positive rates. In this paper we present an online sequential semi-supervised ML approach for DDoS detection based on network Entropy estimation, Co-clustering, Information Gain Ratio and Exra-Trees algorithm. The unsupervised part of the approach allows to reduce the irrelevant normal traffic data for DDoS detection which allows to reduce false positive rates and increase accuracy. Whereas, the supervised part allows to reduce the false positive rates of the unsupervised part and to accurately classify the DDoS traffic. Various experiments were performed to evaluate the proposed approach using three public datasets namely NSL-KDD, UNB ISCX 12 and UNSW-NB15. An accuracy of 98.23%, 99.88% and 93.71% is achieved for respectively NSL-KDD, UNB ISCX 12 and UNSW-NB15 datasets, with respectively the false positive rates 0.33%, 0.35% and 0.46%.     

Intelligent web-based learning system with personalized learning path guidance

Personalized curriculum sequencing is an important research issue for web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line web-based learning and adaptively provide learning paths in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smooth learning paths enhance the linked strength between learning concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, compared to the freely browsing learning mode without any personalized learning path guidance used in most web-based learning systems, this paper assesses whether the proposed genetic-based personalized e-learning system, which can generate appropriate learning paths according to the incorrect testing responses of an individual learner in a pre-test, provides benefits in terms of learning performance promotion while learning. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can conduct personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support web-based learning. Experimental results indicated that applying the proposed genetic-based personalized e-learning system for web-based learning is superior to the freely browsing learning mode because of high quality and concise learning path for individual learners.     

An extreme learning machine approach for speaker recognition

Over the last two decades, automatic speaker recognition has been an interesting and challenging problem to speech researchers. It can be classified into two different categories, speaker identification and speaker verification. In this paper, a new classifier, extreme learning machine, is examined on the text-independent speaker verification task and compared with SVM classifier. Extreme learning machine (ELM) classifiers have been proposed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. They are extremely fast in learning and perform well on many artificial and real regression and classification applications. The database used to evaluate the ELM and SVM classifiers is ELSDSR corpus, and the Mel-frequency Cepstral Coefficients were extracted and used as the input to the classifiers. Empirical studies have shown that ELM classifiers and its variants could perform better than SVM classifiers on the dataset provided with less training time.     

A machine learning approach to reading level assessment

Reading proficiency is a fundamental component of language competency. However, finding topical texts at an appropriate reading level for foreign and second language learners is a challenge for teachers. Existing measures of reading level are not well suited to this task, where students may know some difficult topic-related vocabulary items but not have the same level of sophistication in understanding complex sentence constructions. Recent work in this area has shown the benefit of using statistical language processing techniques. In this paper, we use support vector machines to combine features from n-gram language models, parses, and traditional reading level measures to produce a better method of assessing reading level. We explore the use of negative training data to handle the problem of rejecting data from classes not seen in training, and compare the use of detection vs. regression models on this task. As in many language processing problems, we find substantial variability in human annotation of reading level, and explore ways that multiple human annotations can be used in comparative assessments of system performance.     

Document-level sentiment classification using hybrid machine learning approach

It is a practice that users or customers intend to share their comments or reviews about any product in different social networking sites. An analyst usually processes to reviews properly to obtain any meaningful information from it. Classification of sentiments associated with reviews is one of these processing steps. The reviews framed are often made in text format. While processing the text reviews, each word of the review is considered as a feature. Thus, selection of right kind of features needs to be carried out to select the best feature from the set of all features. In this paper, the machine learning algorithm, i.e., support vector machine, is used to select the best features from the training data. These features are then given input to artificial neural network method, to process further. Different performance evaluation parameters such as precision, recall, f-measure, accuracy have been considered to evaluate the performance of the proposed approach on two different datasets, i.e., IMDb dataset and polarity dataset.     

A machine learning approach to two-voice counterpoint composition

Algorithmic composition of musical pieces is one of the most popular areas of computer aided music research. Various attempts have been made successfully in the area of music composition. Artificial intelligence methods have been extensively applied in this area. Representation of musical pieces in a computer-understandable form plays an important role in computer aided music research.This paper presents a neural network-based knowledge representation schema for representing notes, melodies, and time in first species counterpoint pieces. A musical note is composed of pitch and duration in this representation schema. The proposed representation technique was tested using the back-propagation algorithm to generate two-voice counterpoint pieces.     

An approach for modeling a new single machine scheduling problem with deteriorating and learning effects

This paper presents a new approach to model a mixed-integer mathematical programming for a single machine scheduling problem with deteriorating and learning effects. Neglecting the effect of performing a job on its own processing time is mentioned as a deficiency in the literature of industrial scheduling. At first, self-influential factors are defined and then equations are presented to utilize these new factors. GAMS IDE/CPLEX software is used to validate the represented model by solving small to medium-sized sample problems, in which the associate results are compared with a hybrid meta-heuristic algorithm based on Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Imperialist Competitive Algorithm (ICA). The vitality of considering and modeling the effect of learning and deteriorating made by a job, on its own processing time is tested for several sample problems. Then a significant improvement is observed by comparing the results acquired by neglecting the self-influential factors with the results based on considering them. Finally, the computational results are analyzed and the conclusion is given.     

Classification of sentiment reviews using n-gram machine learning approach

With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.     

Applications of machine learning approach on multi-queue message scheduling

Due to limited resource contentions and deadline constraints, messages on the controller area network (CAN) are competing for service from the common resources. This problem can be resolved by assigning priorities to different message classes to satisfy time-critical applications. Actually, because of the fluctuation of network traffic or an inefficient use of resources, these static or dynamic priority policies may not guarantee flexibility for different kinds of messages in real-time scheduling. Consequently, the message transmission which cannot comply with the timing requirements or deadlines may deteriorate system performance significantly. In this paper, we have proposed a controller-plant model, where the plant is analogous to a message queue pool (MQP) and the message scheduling controller (MSC) is responsible to dispatch resources for queued messages according to the feedback information from the MQP. The message scheduling controller, which is realized by the radial basis function (RBF) network, is designed with machine learning algorithm to compensate the variations in plant dynamics. The MSC with the novel hybrid learning schemes can ensure a low and stable message waiting time variance (or a uniform distribution of waiting time) and lower transmission failures. A significant emphasis of the MSC is the variable structure of the RBF model to accommodate to complex scheduling situations. Simulation experiments have shown that several variants of the MSC significantly improve overall system performance over the static scheduling strategies and the dynamic earliest-deadline first (EDF) algorithms under a wide range of workload characteristics and execution environments.     

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.