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Over the years, system calls (syscalls) have become an increasingly popular data source for host intrusion detection systems (HIDS). This is partly due to their strong security semantic implications. As syscalls conform to a program's control-flow graph, a deviation in a syscall sequence may imply a deviation in a program's control-flow graph. This is useful for detecting the control-flow hijacking...
Human factors have been increasingly recognized as one of the major driving forces of requirement changes. We believe that the requirements elicitation (RE) process should largely embrace human-centered perspectives, and this paper focuses on changing human intentions and desires over time. To support software evolution due to requirement changes, Situ framework has been proposed to model and detect...
The Internet of Things (IoT) has penetrated various domains, from smart grids to precision agriculture, facilitating remote sensing and control. However, IoT devices are target to a spectrum of reliability and security issues. Therefore, capturing the normal behavior of these devices and detecting abnormalities in program execution is key for reliable deployment. However, existing program anomaly...
In order to notify users about potentially unsafe situations and to track mistakes or efficiency performing activities, it is important to monitor the quality of performing an activity and identify the missing/wrong steps. However, the state-of-the-art activity recognition frameworks ignore such details and impose constraints on sensor values, the types of detected activities (no parallel/interleaved/joint...
Web services bring more conveniences for users and developers. However, it makes user face the problem of service information explosion. The personalized service recommendation solves the problem. This paper proposes a method to predicting the system reliability which bases on user context information in mobile computing environment. The method construct user behavior model by formatting user location...
The presence of non-standard spellings in Twitter causes challenges for many natural language processing tasks. Traditional approaches mainly regard the problem as a translation, spell checking, or speech recognition problem. This paper proposes a method that represents the stochastic relationship between words and their non-standard versions in real vectors. The method uses dynamic time warping to...
Hierarchical Multipole Methods (HMMs) are an important class of methods in scientific and engineering applications. They are challenging to parallelize for contemporary and emerging platforms using existing programming models. Asynchronous many-tasking (AMT) execution models provide abstractions suitable for HMMs and promise scalability in the context of future exascale systems. In our work we (1)...
In artificial intelligence, many tasks of speech recognition, video analysis, and language processing involve temporal processing where the outputs depend on not only spatial contents of the current sensory input frame, but also the relevant context in the attended past. It is illusive how brains use temporal contexts. Many computer methods, such as Hidden Markov chains and recurrent neural networks,...
In order to understand information transmission, it is highly important to find a hidden structure of a social network such as rumors in daily life, and leaders of the underground organizations. The information above is difficult to be forecasted because such information can only be obtained in fragments and cannot be achieved through observation itself based on the data. This research aims to clarify...
This paper studies the influence factor on HMM-based Tibetan Lhasa speech synthesis. In order to find the key factor which makes the most contribution to improve the synthesized Tibetan Lhasa speech, we synthesize Tibetan Lhasa speech by different context labeling and different number of training sentences with different speech synthesis unit, respectively. We build two Tibetan Lhasa speech corpora...
Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic...
We consider a non-stationary data stream in which the data statistics may change abruptly from one sample to another, i.e. each sample might be generated from a different (unknown) source in a mixture of K sources. The problem of identifying the models and parameters of K sources, as well as the source switching model is investigated. We proposed an algorithm based on Bayesian Information Criterion...
This paper proposes an alternative multi-level approach to duration prediction for improving prosody generation in statistical parametric speech synthesis using multiple Gaussian process experts. We use two duration models at different levels, specifically, syllable and phone. First, we individually train syllable- and phone-level duration models. Then, the predictive distributions of syllable and...
Real-time low-latency online inference and decoding in sequential probabilistic models are important in many interactive systems, including automatic speech recognition (ASR) and streaming environments. We study total inference latency (TL) in such systems, the additively combined latency of the inherent look-ahead of a deep neural network's (DNN) contextual window (CWL) in a DNN-HMM hybrid system...
To provide assistance functions in context of surgical interventions, the use of medical workflows plays an important role. Workflow models can be used to assess the progress of an on-going surgery, enabling tailored (i.e., context sensitive) support for the medical practitioner. Subsequently, this provides opportunities to prevent malpractices, to enhance the patient's outcome and to preserve a high...
The task of chord recognition in music signals is often based upon pattern matching in chromagrams. Many variants of chroma exist and quality of chord recognition is related to the feature employed. Chroma Reduced Pitch (CRP) features are interesting in this context as they were designed to improve timbre invariance for the purpose of query retrieval. Their reapplication to chord recognition, however,...
Real time detection of transitions between activities based on sensor data is a valuable but somewhat untapped challenge. Detecting these transitions is useful for activity segmentation, for timing notifications or interventions, and for analyzing human behavior. In this work, we design and evaluate real time machine learning-based methods for automatic segmentation and recognition of continuous human...
This paper presents a phonetic analysis of Arabic speech language phonemes using hidden Markov model classifiers and their confusion matrices. For this purpose, a new classical Arabic speech corpus was planned and designed. The corpus is based on recitations from The Holy Quran of specific scripts. Semi-manual labeling and segmentation of the audio files along with other language resources such as...
To avoid the introduction of false information during the fusion progress, a novel multi-focus image fusion method is proposed in quaternion wavelet transform domain. To obtain the dependency in different high frequency subbands, a quaternion wavelet contextual hidden Markov model (Q-CHMM) is established for modeling quaternion wavelet coefficients. And for better image representations, several features...
In this paper we propose a novel end-to-end framework for mathematical expression (ME) recognition. The method uses a convolutional neural network (CNN) to perform mathematical symbol detection and recognition simultaneously incorporating spatial context, and can handle multi-part and touching symbols effectively. To evaluate the performance, we provide a benchmark that contains MEs both from real-life...
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