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Automatic image aesthetics rating has received a growing interest with the recent breakthrough in deep learning. Although many studies exist for learning a generic or universal aesthetics model, investigation of aesthetics models incorporating individual user’s preference is quite limited. We address this personalized aesthetics problem by showing that individual’s aesthetic preferences exhibit strong...
This work-in-progress research study examines the response patterns of first-generation college students (FGCS) to the engineering identity measures compared to non-first-generation college students (non-FGCS). This work answers the following research question, “Do FGC and non-FGC engineering students interpret the engineering identity measurement items in a conceptually different manner?” We explore...
Massive Open Online Courses (MOOCs) focus on manifold subjects, ranging from social sciences over languages to technical skills, and use different means to train the respective skills. MOOCs that are teaching programming skills aim to incorporate practical exercises into the course corpus to give students the hands-on experience necessary for understanding and mastering programming. These exercises,...
Software fault prediction is one of the significant stages in the software testing process. At this stage, the probability of fault occurrence is predicted based on the documented information of the software systems that are already tested. Using this prior knowledge, developers and testing teams can better manage the testing process. There are many efforts in the field of machine learning to solve...
When using formal verification on critical software, an important question involves whether we have we specified enough properties for a given implementation model. To address this question, coverage metrics for property-based formal verification have been proposed. Existing metrics are usually based on mutation, where the implementation model is repeatedly modified and re-analyzed to determine whether...
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...
The volume and variety of scientific data being generated at experimental facilities requires the seamless interaction of the scientist’s knowledge with the large-scale machines and software that is required to process the data. In the last few years, scientific software tools are being developed to address these increasingly complex workflow and data management needs. However, current...
Link prediction plays an important role in complex network analysis. It is to predict the existence of an unknown link or a future link in a network. Classical methods for link prediction evaluate the similarity of vertices based on common neighbors, and denote that every common neighbor makes equal contribution to the connection likelihood. However, common neighbors may play different roles depending...
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...
Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two...
Mutation testing of object-oriented programs differs from that of standard (traditional) mutation operators in accordance to the number of generated mutants and ability of tests to kill mutants. Therefore, outcomes of cost reduction analysis cannot be directly transferred from a standard mutation to an object-oriented one. Mutant sampling is one of reduction methods of the number of generated and...
Code review is a key tool for quality assurance in software development. It is intended to find coding mistakes overlooked during development phase and lower risk of bugs in final product. In large and complex projects accurate code review is a challenging task. As code review depends on individual reviewer predisposition there is certain margin of source code changes that is not checked as it should...
There are several security requirements identification methods proposed by researchers in up-front requirements engineering (RE). However, in open source software (OSS) projects, developers use lightweight representation and refine requirements frequently by writing comments. They also tend to discuss security aspect in comments by providing code snippets, attachments, and external resource links...
Ensuring similarity of user interfaces (UI) is often desirable, e.g. in software migration and redesign projects, to minimize experience disruption for regular users and increase subjective satisfaction with new versions. In our paper we explore applicability of artificial neural networks (ANNs) to support test-driven development by predicting similarity assessments without employing the actual users...
The rise of mobile smartphones and its usage of mobile applications made the human life more productive and boosted several activities in everyday routine jobs and business areas. In another side now a day, most of the business and personal applications transferred to the mobile apps. The one hand mobile application providing more features than traditional desktop applicationas and proving endless...
This paper reveals that the standard test procedures for the qualification and verification of traction battery systems are not sufficient in certain cases. In fact, realistic and application-oriented tests on the interaction among the complex battery subsystems are inevitable. To achieve these objectives, a high-performance test bench with the unique selling features of a high voltage/- and current...
A novel approach of a test environment for embedded networking nodes has been conceptualized and implemented. Its basis is the use of virtual nodes in a PC environment, where each node executes the original embedded code. Different nodes run in parallel, connected via so-called virtual channels. The environment allows to modifying the behavior of the virtual channels as well as the overall topology...
A methodology for lifetime testing of lithium-ion batteries in small SOC intervals is presented. The specific test procedure and level of control is important in producing reliable data. The methodology offers high precision without increasing the test duration. The duration time can be reduced with up to 50%, by ensuring more continuous cycling and by reducing the number of performance tests.
Today, performing life tests on power semiconductor devices is of major importance in industrial and automotive power applications. Traditionally, qualification tests, like the HTOL test, are carried out in a semi-automated manner. This paper first reviews different test methods and a recently proposed modular test system (MTS) that allows testing of individual devices as well as application specific...
The research was carried out and the Cyrillic typeface, based on the handwriting, was developed. The font-building software was investigated. Sketches of the font were drawn and converted to the vector format. Afterward, the characters and glyphs, ligatures, tracking, kerning pairs of the handwritten font were constructed. The FineReader was learned to recognize the developed font.
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