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Machine learning techniques have been earnestly explored by many software engineering researchers. At present state of art, there is no conclusive evidence on the kind of machine learning techniques which are most accurate and efficient for software defect prediction but some recent studies suggest that combining multiple machine learners, that is, ensemble learning, may be a more accurate alternative...
With social media services becoming more and more popular, there now exists a constant stream of opinions publicly available on the Internet. In crisis situations, analysis of social media data can improve situation awareness and help authorities to provide better assistance to the affected population. The large amount of activity on social media services makes manual analysis infeasible. Thus, an...
Trust in Online Social Networks is an increasing popular issue in the field of social computing. It has been widely used in scenarios such as e-commerce, friend recommendation and trust-based access control system. Several trust computing methods have been proposed from different perspectives, however, most of them just quantify certain trust related factors and integrate them into a trust value by...
In this paper we present a methodology as a proof-of-concept for recognizing fundamental movements of the human arm (extension, flexion and rotation of the forearm) involved in 'making-a-cup-of-tea', typical of an activity of daily-living (ADL). The movements are initially performed in a controlled environment as part of a training phase and the data are grouped into three clusters using k-means clustering...
Nowadays, aging society is a big problem and demand for monitoring systems is becoming higher. Under this circumstance, a fall is a main factor of accidents at home. From this point of view, we need to detect falls expeditiously and correctly. However, usual methods like using a video camera or a wearable device have some issues in privacy and convenience. In this paper, we propose a system of fall...
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold...
This paper presents our experimental work on machine classification of Nepali texts. We have implemented a Naive Bayes classifier for the task and then augmented it through a multinomial lexicon pooling. The lexicon-pooled Naive Bayes Classifier obtains better results on classification task as compared to a normal Naive Bayes implementation. This hybrid approach also helps in dealing with the unavailability...
Nowadays the World Wide Web has evolved into a leading communication channel and information exchange medium. Especially after the introduction of the so-called web 2.0 and the explosion that followed regarding user generated content, the amount of data available over the internet has attracted the interest of both the scientific and business community. Their efforts focus on identifying the inner...
Recognizing a person from a distance is important to establish meaningful social interaction and to provide additional cues regarding the situations experienced by a robot. To do so, face recognition and speaker identification are biometrics commonly used, with identification performance that are influenced by the distance between the person and the robot. This paper presents a system that combines...
Study of reciprocity helps to find influential factors for users building relationships, which greatly facilitates the social behavior understanding in trust networks. In the previous literature, the dynamics of both network structure and user generated content are rarely considered. Our investigation of the available timing information from a real-world network demonstrates that time delay has significant...
In order to promote the utilization of lifelog videos, an effective retrieval framework of the emotional scenes, which are considered to be important scenes, is proposed in this paper. The proposed method is based on facial expression recognition since the emotional scenes can be detected by taking the facial expressions into consideration. Most of conventional facial expression recognition methods...
This paper presents a novel sparse representation-based classifier for landcover mapping of hyperspectral image data. Each image patch is factorized into segmentation patterns, also called shapelets, and patch-specific spectral features. The combination of both is represented in a patch-specific spatial-spectral dictionary, which is used for a sparse coding procedure for the reconstruction and classification...
Coreference resolution is the process of determining whether two expressions refer to the same entity. We adopt machine learning approach to coreference resolution. Feature selection of entity is the key of coreference resolution. This paper presents analysis methods for features which are used commonly in coreference resolution, proposes two features, entity density and antecedent characteristics...
In this paper, we propose a novel algorithm for Single-hidden Layer Feed forward Neural networks training which is able to exploit information coming from both labeled and unlabeled data for semi-supervised action classification. We extend the Extreme Learning Machine algorithm by incorporating appropriate regularization terms describing geometric properties and discrimination criteria of the training...
In this paper a method for Early Recognition (ER) of Motion Templates (MTs) is presented. We define ER as an algorithm to provide recognition results before a motion sequence is completed. In our experiments we apply Long Short-Term Memory (LSTM) and optimize the training for the task of recognizing the motion template as early as possible. The evaluation has shown that the recognition accuracy for...
Due to the simplicity and firm mathematical foundation, Support Vector Machines (SVMs) have been intensively used to solve classification problems. However, training SVMs on real world large-scale databases is computationally costly and sometimes infeasible when the dataset size is massive and non-stationary. In this paper, we propose an incremental learning approach that greatly reduces the time...
Classification is one of the most important tasks in machine learning. The ensemble classifier which consists of a number of basic classifiers is an efficient classification technique and has shown its effectiveness in many applications. The diversity and strength of the basic ones are two main elements which influence the performance of the ensemble classifier. Since different classification methods...
Learning representations that disentangle hidden explanatory factors in data has proven beneficial for effective pattern classification. Slow feature analysis (SFA) is a nonlinear dimensionality reduction technique that provides a useful representation for classification if the training data is sequential and transitions between classes are rare. The pattern discrimination ability of SFA has been...
Myoelectric control of prosthetic devices tend to rely on classification schemes of extracted features of EMG data. Those features however, may be sensitive to arm position resulting in decreased performance in real-world applications. The effect of varying limb position in a pattern recognition system have been illustrated by documenting the change in classification accuracy as the user achieves...
The accuracy of prediction is required to conduct modeling the groundwater flow. This research represents the application of extreme learning machine (ELM) that can be used to model the groundwater flow in tidal lowland reclamation. The accuracy is measured using the hold-out and k-fold cross validation methods. The study will be implemented in the Delta Telang I Lowlands area, Banyuasin District,...
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