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The technology for activity classification presents new opportunities for control and monitoring of serious games players. Other than for step detection, human activity classification is normally undertaken by calculating features from a fixed interval length of sensor data and comparing them to values expected from a range of activities. It was observed that many human activities, especially vigorous...
With the current state of the art Natural language processing (NLP) technology, many researchers have proved that automatizing the requirement analysis process is possible which saves significant amount of time spent by the analysts. Numerous semi-automatic tools have been developed which assist the analyst in this process. However, a commonly used technique of using grammar in the elicited text as...
Human gesture recognition is a rather new field and many challenges, sign language recognition is a concrete example of gesture recognition. In this paper, we study the feasibility and effectiveness of vector machine learning methods, namely Support Vector Machine (SVM), Simplification of Support Vector Machine (SimpSVM) and Relevance Vector Machine (RVM) to the sign language recognition problem....
Preprocessors are a common way to implement variability in software. They are used in numerous software systems, such as operating systems and databases. Due to the ability of preprocessors to enable and disable code fragments, not all parts of the program are active at the same time. Thus, programmers and tools need to handle the interactions resulting from annotations in the program. With our Eclipse-based...
To keep up with the growing demand for customized software solutions that are tailored to specific customer requirements, techniques like Software Product Line Engineering (SPLE) or the more ad-hoc clone-and-own (where engineers do not build each product from anew, but instead maximize the reuse of the available assets in building product families) have been devised. However testing such highly variable...
To improve the accuracy and reduce the computational complexity of neural networks for vehicle type recognition, this paper proposes a novel method based on Multi-branch and Multi-layer features. First of all, each car-face image is divided into multiple sub-images according to texture features' characteristic. Secondly, global and local features are extracted using several convolutional neural networks...
Very large percentage of daily financial transactions is generally carried out on the basis of verification of signatures. Therefore signature plays an important role both for authentication and authorization of any legal documents. Signature verification is the process used to verify an individual's hand written signature is genuine or forged signature. Classification of recognition rate of genuine...
Object category and instance recognition have received much attention in this era of modern technologies. Advanced image sensing technologies provide high resolution color and depth synchronized videos such as RGB-D (Kinect style) camera. At present, various features extraction schemes are introduced to improve classification performance. Extracting useful features from both color and depth images...
This papers deals with an advanced and effective approach for testing system, by utilizing the hardware-in-the-loop (HIL) with the vision based machine learning technique to make end to end automation in the feature diagnosis and validation of automotive instrument clusters. Recently, numerous HIL systems are in practice for simulating the vehicle networks in real time, by providing necessary signals...
This paper focuses on designing an Intrusion Detection System(IDS), which detects the family of attack in a dataset. An IDS detects various types of malicious traffic and computer usage which cannot be detected by a conventional firewall. In this proposed work, the data is extracted from UNSW_NB15 dataset. To identify the data cluster centers, the k means algorithm is used. A new and one dimensional...
Texture classification is a problem that has variousapplications such as remote sensing and forest speciesrecogni- tion. Solutions tend to be custom fit to the datasetused but fails to generalize. The Convolutional NeuralNetwork (CNN) in combination with Support Vector Machine(SVM) form a robust selection between powerful invariantfeature extractor and accurate classifier. The fusion ofexperts provides...
In this paper, a work in progress towards a real-time vision-based traffic flow prediction (TFP) system is resented. The proposed method consists of three elemental operators, that are dynamic texture model based motion segmentation, feature extraction and Gaussian process (GP) regression. The objective of motion segmentation is to recognize the target regions covering the moving vehicles in the sequence...
Online shopping is one of the most comfortable ways to shop in this new era of technology. People buy online products frequently and post their reviews about the products they have used. The viewpoint of the user will be in the form of tweets or product reviews which they post in an e-commerce site. These reviews will have significant role in deciding how far the products have been placed in peoples...
We propose a method to improve speaker verification performance when a test utterance is very short. In some situations with short test utterances, performance of ivector/probabilistic linear discriminant analysis systems degrades. The proposed method transforms short-utterance feature vectors to adequate vectors using a deep neural network, which compensate for short utterances. To reduce the dimensionality...
This paper presents a new system for singing melody transcription from polyphonic songs. Instead of operating solely on polyphonic audio of each song to be processed (as most existing systems do), our system takes as inputs additionally multiple monophonic recordings of people singing the song. To transcribe the singing melody in a song, our system first tracks the singing pitch from polyphonic audio...
In this paper, a blind bandwidth extension algorithm for music signals has been proposed. This method applies the K-means algorithm to firstly cluster audio data in the feature space, and constructs multiple envelope predictors for each cluster accordingly using Support Vector Regression (SVR). A set of well-established audio features for Music Information Retrieval (MIR) has been used to characterize...
We propose a novel appearance-based gesture recognition algorithm using compressed domain signal processing techniques. Gesture features are extracted directly from the compressed measurements, which are the block averages and the coded linear combinations of the image sensor's pixel values. We also improve both the computational efficiency and the memory requirement of the previous DTW-based K-NN...
Epithelium-stroma classification is always considered as an important preprocessing step for morphological quantitative analysis in image-based histological researches of oncologic diseases. However, large-scale accurate ground-truth labeling is expensive in histopathological image analysis, thus the classification performances will still be limited with the insufficient labeled training samples....
To automatically test web applications, crawling-based techniques are usually adopted to mine the behavior models, explore the state spaces or detect the violated invariants of the applications. However, their broad use is limited by the required manual configurations for input value selection, GUI state comparison and clickable detection. In existing crawlers, the configurations are usually string-matching...
This paper proposes a new feature extraction method for synthetic aperture radar (SAR) images with application to automatic target recognition (ATR). The original SAR image is first represented by a sparse image containing only a few dominant scattering centers (SCs). According to the theory of 2D compressive sensing (CS), a sparse image can be reconstructed from a low dimensional matrix with little...
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