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Software security is an important aspect of ensuring software quality. Early detection of vulnerable code during development is essential for the developers to make cost and time effective software testing. The traditional software metrics are used for early detection of software vulnerability, but they are not directly related to code constructs and do not specify any particular granularity level...
Background: Understanding and controlling the impact of change decides about the success or failure of evolving products. The problem magnifies for start-ups operating with limited resources. Their usual focus is on Minimum Viable Product (MVP's) providing specialized functionality, thus have little expense available for handling changes. Aims: Change Impact Analysis (CIA) refers to the identification...
Time series prediction techniques reduce the number of messages generated at the application level, saving energy spent in the communication and, consequently, extending the network lifetime. Trickle is a well-known time series prediction mechanism commonly used to decrease the number of transmitted messages in Wireless Sensor Networks (WSN) and thus save energy. This paper presents the Space-Time...
The number of software vulnerabilities discovered and publicly disclosed is increasing every year; however, only a small fraction of them is exploited in real-world attacks. With limitations on time and skilled resources, organizations often look at ways to identify threatened vulnerabilities for patch prioritization. In this paper, we present an exploit prediction model that predicts whether a vulnerability...
Background: Many relevancy filters have been proposed to select training data for building cross-project defect prediction (CPDP) models. However, up to now, there is no consensus about which relevancy filter is better for CPDP. Goal: In this paper, we conduct a thorough experiment to compare nine relevancy filters proposed in the recent literature. Method: Based on 33 publicly available data sets,...
Background: Software defect models can help software quality assurance teams to allocate testing or code review resources. A variety of techniques have been used to build defect prediction models, including supervised and unsupervised methods. Recently, Yang et al. [1] surprisingly find that unsupervised models can perform statistically significantly better than supervised models in effort-aware change-level...
Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate...
Purpose of this study was to develop a fall prediction model based on various variables with linear and nonlinear analysis using postural sway. The included variables in the regression model were Sample Entropy, Largest Lyapunov exponent and Hurst exponent in anterior-posterior direction, which are nonlinear variables. Accuracy of this regression model for fall prediction was 81.9%.
Bacterial small non-coding RNAs (sRNAs) play important roles in various physiological processes, and predicting sRNAs is an important task. In this paper, we develop a computational method for the sRNA prediction by using sRNA sequence-derived features. We investigate a variety of sRNA sequence-derived features, and evaluate the usefulness of features for the sRNA prediction. Then, we develop the...
Parkinson's disease is a debilitating and chronic disease of the nervous system. Traditional Chinese Medicine (TCM) is a new way for diagnosing Parkinson, and the data of Chinese Medicine for diagnosing Parkinson is a multi-label data set. Considering that the symptoms as the labels in Parkinson data set always have correlations with each other, we can facilitate the multi-label learning process by...
With the rapid adoption of smartphones and tablets, more and more remote medical diagnostic applications have mushroomed. Tongue Diagnosis (TD) is a kind of noninvasive diagnostic technique, which offers significant information for health conditions. However, it is rather tough to extract the tongue from a high-quality image, in which there is a definite large area of the tongue, to say nothing of...
Heart failure (HF) has a highly variable annual mortality rate and there is an urgent need of determining patient prognosis to enable informed decision-making about heart failure treatment strategies. Existing survival risk prediction models either require features that limit their applicability or pose difficulties for parameter estimation as physicians have to use a limited set of variables with...
The dysregulations of long intergenic non-coding RNAs (lincRNAs) have shown to be linked with a wide variety of human diseases over the past few years. However, there are only a few lincRNA-disease association inference tools available with most of them relying on very specific type of prior knowledge about the lincRNAs and the diseases. They fall short in generalized association predictions when...
Affective computing research traditionally focused on labeling a person's emotion as one of a discrete number of classes e.g. happy or sad. In recent times, more attention has been given to continuous affect prediction across dimensions in the emotional space, e.g. arousal and valence. Continuous affect prediction is the task of predicting a numerical value for different emotion dimensions. The application...
Social Media allows people to post widely and share the posted online-items. Such items gain their popularity by the amount of attention received. Thus, studies on modeling the arrival process of attention to an individual item have recently attracted a great deal of interest. In this paper, we propose, by combining a Dirichlet process with a Hawkes process in a novel way, a probabilistic model, called...
Forecasting models that utilize multiple predictors are gaining popularity in a variety of fields. In some cases they allow constructing more precise forecasting models, leveraging the predictive potential of many variables. Unfortunately, in practice we do not know which observed predictors have a direct impact on the target variable. Moreover, adding unrelated variables may diminish the quality...
As average life expectancy continuously rises, assisting the elderly population with living independently is of great importance. Detecting abnormal behaviour of the elderly living at home is one way to assist the eldercare systems with the increase of the elderly population. In this study, we perform an initial investigation to identify abnormal behaviour of household residents using energy consumption...
Randomized experiments have been critical tools of decision making for decades. However, subjects can show significant heterogeneity in response to treatments in many important applications. Therefore it is not enough to simply know which treatment is optimal for the entire population. What we need is a model that correctly customize treatment assignment base on subject characteristics. The problem...
Understanding user query intent is a crucial task to Question-Answering area. With the development of online health services, online health communities generate huge amount of valuable medical Question-Answering data, where user intention can be mined. However, the queries posted by common users have many domain concepts and colloquial expressions, which make the understanding of user intents very...
One of the most current challenging problems in Gaussian process regression (GPR) is to handle large-scale datasets and to accommodate an online learning setting where data arrive irregularly on the fly. In this paper, we introduce a novel online Gaussian process model that could scale with massive datasets. Our approach is formulated based on alternative representation of the Gaussian process under...
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