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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...
Campus card system in generated a lot of data during it's operation, and the system itself cannot analyze these data. How it can be learned from these massive, outdated data for student management to assist decision-making becomes a very realistic subject. This paper takes the transaction data of campus card as the research object, and uses the comprehensive application of data warehouse, online analysis...
Biological data is often represented as networks, as in the case of protein-protein interactions and metabolic pathways. Modeling, analyzing, and visualizing networks can help make sense of large volumes of data generated by high-throughput experiments. However, due to their size and complex structure, biological networks can be difficult to interpret without further processing. Cluster analysis is...
Many scientific experiments in Bioinformatics are executed as computational workflows. Frequently, it is necessary to re-run an experiment under the original circumstances in which it was run to recognize and validate it. Data provenance concerns the origin of data. Knowing the data source facilitates the understanding and analysis of the results, by detailing and documenting the history and the paths...
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...
Computer science solutions for molecular biology problems are often presented in the form of workflows. There is a set of activities performed by different processing entities through managed tasks. Knowledge about the data trajectory throughout a given workflow enables reproducibility by data provenance. In order to reproduce an in silico bioinformatics experiment one must consider other aspects...
Deep learning algorithms have recently produced state-of-the-art accuracy in many classification tasks, but this success is typically dependent on access to many annotated training examples. For domains without such data, an attractive alternative is to train models with light, or distant supervision. In this paper, we introduce a deep neural network for the Learning from Label Proportion (LLP) setting,...
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...
As the blooming development of data mining in social computing systems (e.g., crowdsourcing system), statistical inference from crowdsourced data severs as a powerful tool to provide diversified services. To support critical applications (e.g., recommendation), in this paper, we shall focus on the collaborative ranking problems and construct a system of which the input is crowdsourced pairwise comparisons...
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...
Global aging brings new challenges to elderly healthdata management. Existing systems such as HIS and CIS focus on the storage and management of information, the key limitations are that they lack effective mining approaches and usually cannot handle the large-scale health data, these drawbacks make them very hard to be a robust and light-weight system. In this paper, we develop a memory computing...
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...
User-generated mobile application reviews have become a gold mine for timely identifying functional defects in this type of software artifacts. In this work, we develop a hidden structural SVM model for extracting detailed defect descriptions from user reviews at the sentence level. Structured features and constraints are introduced to reduce the demand of exhaustive manual annotation at the sentence...
To improve cancer survival rates and prognosis, one of the first steps is to improve our understanding of contributory factors associated with cancer survival. Prior research has suggested that cancer survival is influenced by multiple factors from multiple levels. Most of existing analyses of cancer survival used data from a single source. Nevertheless, there are key challenges in integrating variables...
The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as the direction with the lowest Kolmogorov complexity. This notion is very powerful as it can detect any causal dependency that can be explained by a physical process. However, due to the halting problem, it is also not computable. In this paper we propose an computable...
Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning...
Motivation: Next-generation sequencing (NGS) technologies using DNA, RNA, or methylation sequencing are prevailing tools used in modern genome research. For DNA sequencing, whole genome sequencing (WGS) and whole exome sequencing (WES) are two typical applications with a different preference on the trade-off between sequencing depth and base coverage. Although sequencing costs have been greatly reduced,...
In today's era of big data, robust least-squares regression becomes a more challenging problem when considering the adversarial corruption along with explosive growth of datasets. Traditional robust methods can handle the noise but suffer from several challenges when applied in huge dataset including 1) computational infeasibility of handling an entire dataset at once, 2) existence of heterogeneously...
Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models...
We consider the fundamental problem of inferring the causal direction between two univariate numeric random variables X and Y from observational data. The two-variable case is especially difficult to solve since it is not possible to use standard conditional independence tests between the variables. To tackle this problem, we follow an information theoretic approach based on Kolmogorov complexity...
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