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Because of the volatility of memory, nodes in in-memory storage system crashing down would lead to data lost. One solution to this problem is backing data up. However, if we backup data to a node which is about to fail down, the data should be recopied again. That would lead to a large amount of backup data, and in turn reduce the system reliability. We first establish a correlated failure model with...
Stereotactic radiotherapy such as Cyberknife is one of the main methods of treatment for lung cancer, but tumor location change caused by human respiration has brought great difficulties to accurate radiation therapy. The main method to reduce the effect of respiratory motion in the process of radiotherapy is respiratory motion real-time tracking technology. The basis of real-time tracking is establishing...
Since the performance of educational institutions depends critically on their students, it is imperative that educational institutions deploy an efficient and reliable admission criteria. In the context of Pakistan, a variety of admission criteria has been developed—mostly in isolation—by different universities. Despite the importance of these admission criteria, limited systematic information exists...
Fixing some security failures are difficult because they cannot be easily reproduced. To address Hardly Reproducible Vulnerabilities (HRVs), security experts spend a significant amount of time, effort, and budget. Sometimes they do not succeed in the reproduction step and ignore some security failures. The exploitation of a vulnerability due to its irreproducibility may cause severe consequences....
We propose a traffic jam prediction method based on mining frequent patterns correlated to traffic jams. For traffic jam prediction at a given sensor, first, we apply a one-dimensional clustering scheme to identify automatically which sensors are and in what degree correlated to the given sensor in terms that certain volume values with a compact distribution co-occur frequently with the traffic jams...
User number prediction in cell phone base station is a very important problem for cell phone communication system design and base station location selection. Recent years, we have witnessed the encouraging potentials of deep neural networks for real-life applications of various domains. User number prediction, however, is still in its initial stage. In this paper, we propose a wavelet-based stacked...
Adverse effects, such as voice change and fatigue, are prevalent in cancer treatment duration. These adverse effects have been significant burden for patients physically and emotionally. Predicting multiple adverse effects becomes important for patients and oncologists. In this paper, we formulate the prediction of multiple adverse effects in cancer treatment as a longitudinal multiple-output regression...
Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing...
Given a health-related question (such as "I have a bad stomach ache. What should I do?"), a medical self-diagnosis Android inquires further information from the user, diagnoses the disease, and ultimately recommend best solutions. One practical challenge to build such an Android is to ask correct questions and obtain most relevant information, in order to correctly pinpoint the most likely...
Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
Link prediction in complex network often focuses on the single network in many fields and develops a lot of methods. However, the relationship between two dependent networks with specific global structure has hardly caught attention, but only the local topology analysis. In this paper, we aim to infer the correlation of two high dimensional dependent networks based on the similar nodes' attributes...
SQL injection is the most common web application vulnerability. The vulnerability can be generated unintentionally by software developer during the development phase. To ensure that all secure coding practices are adopted to prevent the vulnerability. The framework of SQL injection prevention using compiler platform and machine learning is proposed. The machine learning part will be described primarily...
In this paper, we propose algorithms for biomolecular docking sites selection problem by various machine learning approaches with selective features reduction. The proposed method can reduce the number of various amino acid features before constructing machine learning prediction models. Given frame boxes with features, the proposed method analyzes the important features by correlation coefficients...
This paper proposes a new multi-agent system to solve very short-term solar forecasting problems. The system organizes the training data into clusters using Part and Select Algorithm. These clusters are used to generate different forecasting models, where each one is performed by a different agent. Finally, another agent is responsible for deciding which model will be applied at each forecasting situation...
Cochlear implant technology gives deaf people the ability to sense sound and speech. It consists of an electrode array inserted (implanted) in the cochlea of the ear and an external device that wirelessly connects to the electrodes. Each electrode stimulates different areas of the auditory nerve based of what frequency they represent. The sensitivity of auditory nerve fibers varies from patient to...
Today's dynamic computing deployment for commercial and scientific applications is propelling us to an era where minor inefficiencies can snowball into significant performance and operational bottlenecks. Data center operations is increasingly relying on sensors based control systems for key decision insights. The increased sampling frequencies, cheaper storage costs and prolific deployment of sensors...
Content Delivery Networks (CDN) and their globally dispersed caches host a myriad of User Generated Videos (UGV) to meet end-user requests with quality of service. To efficiently utilize the limited storage of the caches, it is imperative to improve the hit ratio of UGVs. In contrast to the traditional static content, UGV popularity is highly dynamic and dependent on end-user behavior. Therefore,...
Human behavior prediction is critical to studying how healthy behavior can spread through a social network. In this work we present a novel user representation based human behavior prediction model, the User Representation-based Socialized Gaussian Process model (UrSGP). First, we present the Deep Interaction Representation Learning (Deep Interaction) model for learning latent representations of interaction...
The location of a real estate property has a considerable impact on its appraised value. Accounting for geograph-ical information eliminates some reducible errors in the accuracy of a hedonic housing regression model. An im-proved performance will benefit home buyers, sellers, government and real estate professionals. This paper investigates the spatial dependency and substitutability of submarket...
Imaging-genetic data mapping is important for clinical outcome prediction like survival analysis. In this paper, we propose a supervised conditional Gaussian graphical model (SuperCGGM) to uncover survival associated mapping between pathological images and genetic data. The proposed method integrates heterogeneous modal data into the survival model by weighted projection within the data. To obtain...
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