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Stochastic Gradient Descent (SGD) is the method of choice for large scale problems, most notably in deep learning. Recent studies target improving convergence and speed of the SGD algorithm. In this paper, we equip the SGD algorithm and its advanced versions with an intriguing feature, namely handling constrained problems. Constraints such as orthogonality are pervasive in learning theory. Nevertheless...
Non-intrusive load monitoring (NILM) systems estimate the amount of energy each appliance consumes using as input the aggregate building energy consumption. Typically, NILM results are presented for a single sampling rate. To evaluate tradeoffs between end-uses and sensor costs, it is important to study the performance of NILM systems across sampling rates. In this work, we examine the performance...
Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. Although such distribution of resources can lead to benefits, it also comes with its associated problems such as rampant duplication of file transfers increasing congestion, long job completion...
In this study, we apply machine learning algorithms to predict technical failures that can be encountered in Oracle databases and related services. In order to train machine learning algorithms, data from log files are collected hourly from Oracle database systems and labeled with two classes; normal or abnormal. We use several data science approaches to preprocess and transform the input data from...
Memory leaks are one of the primary causes of software aging. Despite of recent countermeasures in C/C++ such as smart pointers, leak-related defects remain a troublesome issue in C/C++ code, especially in legacy applications.We propose an approach for automatic detection of memory leaks in C/C++ programs based on characterizing memory allocation sites via the age distribution of the non-disposed...
The amount of data circulating on the Internet is increasing day by day. With the increasing use of social media in particular, the importance of analyzing these data is increasing. The use of machine learning approaches to analyze large amounts of data is still popular today. Today, the social network Facebook is the most popular social networking sites. In this study, some data taken on Facebook...
In the era of Internet and electronic devices bullying shifted its place from schools and backyards into the cyberspace; it is now known as Cyberbullying. Children of the Arab countries are suffering from cyberbullying same as children worldwide. Thus concerns from cyberbullying are elevating. A lot of research is done for the purpose of handling this situation. The current research is focusing on...
In recent studies, researchers have developed various computation offloading frameworks for bringing cloud services closer to the user via edge networks. Specifically, an edge device needs to offload computationally intensive tasks because of energy and processing constraints. These constraints present the challenge of identifying which edge nodes should receive tasks to reduce overall resource consumption...
Low graduation rate is a significant and growing problem in U.S. higher education systems. Although previous studies have demonstrated the usefulness of building statistical models for predicting students' graduation outcomes, advanced machine learning models promise to improve the effectiveness of these models, and hone in on the “difference that makes a difference” not only on the group level, but...
Video streaming has become a main contributor in an ever increasing Internet traffic, and meets the users expectation is a challenging task for both the Network service Provider (NsP) and Content service Provider (CsP). In this context, a new metric called: Quality of Experience (QoE) is evolved to measure the user satisfaction using video service, and it becomes a key driver for achieving the business...
Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when...
Given the heterogeneity of the data that can be extracted from the software development process, defect prediction techniques have focused on associating different sources of data with the introduction of faulty code, usually relying on handcrafted features. While these efforts have generated considerable progress over the years, little attention has been given to the fact that the performance of...
A wide range of text-based artifacts contribute to software projects (e.g., source code, test cases, use cases, project requirements, interaction diagrams, etc.). Traceability Link Recovery (TLR) is the software task in which relevant documents in these various sets are linked to one another, uncovering information about the project that is not available when considering only the documents themselves...
Defect prediction has been the subject of a great deal of research over the last two decades. Despite this research it is increasingly clear that defect prediction has not transferred into industrial practice. One of the reasons defect prediction remains a largely academic activity is that there are no defect prediction tools that developers can use during their day-to-day development activities....
In recent years, network representation learning (NRL) has been increasingly applied into web data analysis, such as video, image and text. Most of NRL methods can widely pursue nodes classification, community detection and link prediction tasks. Due to the nodes in these kinds of networks mostly contain the common attributes and share the same neighbors, we identify them as homogeneous networks,...
This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for image patch representation by employing deep learning techniques. It can be achieved by taking any descriptor learning architecture for learning a leading descriptor and augmenting the architecture with an additional network stream for learning a complementary descriptor. To enforce the complementary...
Feature extraction and matching are two crucial components in person Re-Identification (ReID). The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images. To overcome these difficulties, in this work we propose a Pose-driven Deep Convolutional (PDC) model to learn...
With the ever rising amount of security and alert information, the decision process which incident to address first becomes increasingly important and prioritizing incidents is a common approach towards this problem. Meanwhile, networks and policies have a dynamic and complex nature. Machine learning techniques have successfully been applied in the area of intrusion detection systems (IDS) to cope...
The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules...
With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether...
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