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Security Risk Assessments (SRA) play a key role in the Security Development Lifecycle (SDL). At an early stage of the project, the SRA helps allocate security resources and identifies SDL requirements and activities. In this paper, we present key findings from a machine learning approach toward the SRA that seeks to learn from a database of previous product security risk assessments and associated...
The research interest in fetal heart rate (FHR) monitoring dates back to the 1960s, and the breakthrough on fetal surveillance has been seen during the 1990s with computerized systems. Notwithstanding the general use of cardiotocography (CTG) in fetal monitoring, the assessment of fetal well-being exhibits a significant inter- and even intraobserver variability. Computerized CTG analysis has seen...
The overall goal of our Software Engineering Teamwork Assessment and Prediction (SETAP) project is to develop effective machine-learning-based methods for assessment and early prediction of student learning effectiveness in software engineering teamwork. Specifically, we use the Random Forest (RF) machine learning (ML) method to predict the effectiveness of software engineering teamwork learning based...
Configuration bugs are among the dominant causes of software failures. Software organizations often use bug tracking systems to manage bug reports collected from developers and users. In order for software developers to understand and reproduce configuration bugs, it is vital for them to know whether a bug in the bug report is related to configuration issues, this is not often easily discerned due...
GitHub is one of the most commonly used web-based code repository hosting service. Majority of projects hosted on GitHub are really small but, on the other hand, developers spend most of their time working in medium to large repositories. Developers can freely join and leave projects following their current needs and interests. Based on real data collected from GitHub we have tried to predict which...
This paper proposed a novel hybrid transactional memory(HyTM) that exploits the benefits of both Haswell's RTM(restricted transactional memory) and software transactional memory(STM). Unlike the existing HyTMs, the proposed HyTM can predict and resolve conflicts between transaction running concurrently by using a prediction matrix and transaction metadata. Also the proposed HyTM can provide the optimal...
Predictive Analytics analyze the present and the historical informations and make future predictions utilizing data mining or machine learning techniques. Predictive models usually check for some patterns and relationships leading to certain behaviours based on the dependent variables. This paper proposes a mechanism named Analysis and Prediction of Application Usage (APAU) in Android Phones for providing...
Every day numerous new vulnerabilities and exploits are reported for a wide variety of different software configurations. There is a big need to be able to quickly assess associated risks and sort out which vulnerabilities that are likely to be exploited in real-world attacks. A small percentage of all vulnerabilities account for almost all the observed attack volume. We use machine learning to make...
Effective teaching of teamwork skills in local and globally distributed Software Engineering (SE) teams is recognized as an important part of the education of current and future software engineers. Effective methods for assessment and early prediction of learning effectiveness in SE teamwork are not only a critical part of teaching but also of value in industrial training and project management. This...
Machine learning has been applied in image processing system for object recognition, inspection and measurement. It assumes that the provided training objects are representative enough to the real objects. However in real application, new (unlearned) objects always emerge over time, which may deviate from the trained (learned) objects. The conventional image processing system using machine learning...
Maintenance costs can be substantial for large organizations (several hundreds of programmers) with very large and complex software systems. By large we mean lines of code in the range of hundreds of thousands or millions. Our research objective is to improve the process of handling anomaly reports for large organizations. Specifically, we are addressing the problem of the manual, laborious and time...
This paper presents a software system that generates a database for power system dynamic security assessment. The generated database is intended to be used in machine learning techniques. The development of algorithms to generate data is a very time consuming task. This software tool is aimed at facilitating faster generation of the appropriate database. Further, the system allows the user to plug-in...
A key problem of relational database is to aggregate different values of the same object and find the first k objects with highest overall values. Many sequential algorithms have been proposed to solve this problem. In this paper, we propose a new parallel algorithm using symmetry breaking strategy. New algorithm is proved to be instance optimal. Experiment results on both synthetic and real data...
Most of the existing Chinese chess programs improved only through optimizing the data structure and improving the search strategy. Game-tree search is one of the key technologies in Chinese Chess computer programs and the required memory storage is huge. Therefore, a new method is introduced. In each match, the movement of the opponent and the position value are stored in database. Then it can avoid...
Acquiring a private computing cloud is the first step that an enterprise would choose to enable the cloud model and get its considerable benefits while keeping the control within the enterprise. The enterprise level applications that provide the infrastructure enabling cloud computing services are typically built by integrating inter-related complex software components. Critical challenges of these...
Planning and scheduling of testing activities play an important role for any independent test team that performs tests for different software systems, developed by different development teams. This work studies the application of machine learning tools and variable selection tools to solve the problem of estimating the execution effort of functional tests. An analysis of the test execution process...
Specification mining is a machine learning approach for discovering specifications of the protocols that code must obey when interacting with an application program interface or abstract data type. Two major concerns in engineering software systems are high maintenance costs and reliability of systems. To reduce maintenance efforts, there is a need for automated tools to help software developers understand...
Requirements gathering, analysis, and specification are human-intensive activities that rely upon finding and engaging a relevant set of informed stakeholders. In many projects initial requirements are captured through the use of wikis or forums, or through initial face-to-face brainstorming meetings. In this paper we introduce a technique for analyzing stakeholders' contributions, extracting domain...
The implications of collaborative theory have been far-reaching and pervasive. In this position paper, we prove the simulation of forward-error correction. In this position paper we argue not only that vacuum tubes can be made classical, game-theoretic, and adaptive, but that the same is true for SMPs. Through experiments and results discussion, it is concluded that the usability in the application...
The three sides Chinese chess is a novel game which is developed from the traditional Chinese chess. There are not any research results on the three sides Chinese chess computer game. In this paper, the three sides Chinese game is firstly compared with the traditional Chinese chess. Then an algorithm of three sides game searching with pruning is put forward, which is based on a vector evaluation function...
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