Development of Mobile Cloud Applications using UML

With the proliferation of cloud computing technologies, smartphone users are able to use a variety of cloud computing-based mobile services such as games, education, entertainment, and social networking. Despite the popularity of such a mobile cloud computing, the complicated multi-tier system configuration of the mobile application must be one of the major impediments to develop mobile cloud applications. This paper presents development processes and procedures for developing mobile cloud applications by effectively applying Unified Modeling Language (UML), a representative object-oriented modeling language. The paper is intended to enhance the development productivity of the mobile cloud application and to improve the effectiveness of communication between software developers. In addition, we used the Android mobile platform and Amazon Web Service for cloud computing in order to demonstrate the applicability of the proposed approach to systematically apply the UML profiles and diagrams for cloud-based mobile applications

Finding Bad Code Smells with Neural Network Models

Code smell refers to any symptom introduced in design or implementation phases in the source code of a program. Such a code smell can potentially cause deeper and serious problems during software maintenance. The existing approaches to detect bad smells use detection rules or standards using a combination of different object-oriented metrics. Although a variety of software detection tools have been developed, they still have limitations and constraints in their capabilities. In this paper, a code smell detection system is presented with the neural network model that delivers the relationship between bad smells and object-oriented metrics by taking a corpus of Java projects as experimental dataset. The most well-known object-oriented metrics are considered to identify the presence of bad smells. The code smell detection system uses the twenty Java projects which are shared by many users in the GitHub repositories. The dataset of these Java projects is partitioned into mutually exclusive training and test sets. The training dataset is used to learn the network model which will predict smelly classes in this study. The optimized network model will be chosen to be evaluated on the test dataset. The experimental results show when the modelis highly trained with more dataset, the prediction outcomes are improved more and more. In addition, the accuracy of the model increases when it performs with higher epochs and many hidden layers

Enhancing code clone detection using control flow graphs

Code clones are syntactically or semantically equivalent code fragments of source code. Copy-and-paste programming allows software developers to improve development productivity, but it could produce code clones that can introduce non-trivial difficulties in software maintenance. In this paper, a code clone detection framework is presented with a feature extractor and a clone classifier using deep learning. The clone classifier is trained with true and false clones and then is tested with a test dataset to evaluate the performance of the proposed approach to clone detection. In particular, the proposed approach to clone detection uses Control Flow Graphs (CFGs) to extract features of a given code snippet. The selected features are used to compute similarity scores for comparing two code fragments. The clone classifier is trained and tested with similarity scores that quantify the degree of how similar two code fragments are. The experimental results demonstrate that using CFG features is a viable methodology in terms of the effectiveness of clone detection for both syntactic and semantic clones

무선 센서 네트워크에서 에너지 효율적인 자가 치료 연구

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 이용환.One of key issues in the construction of wireless sensor network (WSN) is how efficiently sensor nodes re-subscribe to the network after networking failure. ZigBee has been considered as an attractive solution for the construction of cluster-tree structured WSNs due to its low-power and low-complexity features. However, it may be able to re-subscribes to the network through network rejoining which may require for large signaling overhead and time delay. In this thesis, we consider the design of a cluster-wise self-healing (CS) in a beacon-enabled cluster-tree structured WSN. When a router experience networking failure from its parent node, the proposed CS makes it maintain synchronization with its child nodes, preventing from orphan propagation to its child nodes. Meanwhile, it makes only the orphaned router initiate the re-subscription to the network on behalf of its child nodes. Thus, the proposed CS allows the network re-subscription through one re-subscription process of the orphaned cluster head, significantly reducing the recovery time and energy consumption for the recovery as well. We also design a backup link-aided self-healing (BL) where nodes select a parent node for the network subscription and also a back-up parent node for network re-subscription. The proposed BL can reduce the recovery time since it can minimizes the process for the selection of a new parent node and associated message exchanges for network re-subscription. Computer simulation and experimental results show that the proposed schemes can significantly reduce the energy consumption, recovery time and signaling overhead for network re-subscription.Abstract Contents List of Figures List of Tables 1. Introduction 2. System model 3. Previous works 3.1. Self-healing in ZigBee 3.2. Efficient self-healing process (ESP) 4. Proposed self-healing 4.1. Energy-efficient neighbor scan 4.2. Cluster-wise self-healing (CS) 4.3. Backup link aided self-healing (BL) 4.4. Messages for the proposed self-healing 5. Performance evaluation 6. Conclusions References 초 록Maste

Shortening Time-to-Discovery with Dynamic Software Updates for Parallel High Performance Applications

Despite using multiple concurrent processors, a typical high performance parallel application is long-running, taking hours, even days to arrive at a solution. To modify a running high performance parallel application, the programmer has to stop the computation, change the code, redeploy, and enqueue the updated version to be scheduled to run, thus wasting not only the programmer’s time, but also expensive computing resources. To address these inefficiencies, this article describes how dynamic software updates can be used to modify a parallel application on the fly, thus saving the programmer’s time and using expensive computing resources more productively. The net effect of updating parallel applications dynamically reduces their time-to-discovery metrics, the total time it takes from posing a problem to arriving at a solution. To explore the benefits of dynamic updates for high performance applications, this article takes a two-pronged approach. First, we describe our experience in building and evaluating a system for dynamically updating applications running on a parallel cluster. We then review a large body of literature describing the existing state of the art in dynamic software updates and point out how this research can be applied to high performance applications. Our experimental results indicate that dynamic software updates have the potential to become a powerful tool in reducing the time-to-discovery metrics for high performance parallel applications

Performance-Based Multiobjective Optimal Seismic Retrofit Method for a Steel Moment-Resisting Frame Considering the Life-Cycle Cost

This study proposes a performance-based multiobjective optimization seismic retrofit method for steel moment-resisting frames. The brittle joints of pre-Northridge steel moment-resisting frames are retrofitted to achieve ductility; the method involves determining the position and number of connections to be retrofitted. The optimal solution is determined by applying the nondominated sorting genetic algorithm-II (NSGA-II), which acts as a multiobjective seismic retrofit optimization technique. As objective functions, the initial cost for the connection retrofit and lifetime seismic damage cost were selected, and a seismic performance level below the 5% interstory drift ratio was employed as a constraint condition. The proposed method was applied to the SAC benchmark three- and nine-story buildings, and several Pareto solutions were obtained. The optimized retrofit solutions indicated that the lifetime seismic damage cost decreased as the initial retrofit cost increased. Although every Pareto solution existed within a seismic performance boundary set by a constraint function, the seismic performance tended to increase with the initial retrofit cost. Analysis and economic assessment of the relations among the initial retrofit cost, lifetime seismic damage cost, total cost, and seismic performance of the derived Pareto solution allow building owners to make seismic retrofit decisions more rationally

Controlling the Magnetic Anisotropy of the van der Waals Ferromagnet Fe3GeTe2 through Hole Doping.

Identifying material parameters affecting properties of ferromagnets is key to optimized materials that are better suited for spintronics. Magnetic anisotropy is of particular importance in van der Waals magnets, since it not only influences magnetic and spin transport properties, but also is essential to stabilizing magnetic order in the two-dimensional limit. Here, we report that hole doping effectively modulates the magnetic anisotropy of a van der Waals ferromagnet and explore the physical origin of this effect. Fe3-xGeTe2 nanoflakes show a significant suppression of the magnetic anisotropy with hole doping. Electronic structure measurements and calculations reveal that the chemical potential shift associated with hole doping is responsible for the reduced magnetic anisotropy by decreasing the energy gain from the spin-orbit induced band splitting. Our findings provide an understanding of the intricate connection between electronic structures and magnetic properties in two-dimensional magnets and propose a method to engineer magnetic properties through doping

(S)-[5-Methyl-3-(3-methyl­thio­phen-2-yl)-4,5-dihydro­isoxazol-5-yl]methanol

In the title compound, C10H13NO2S, the thio­phene and isoxazoline rings are almost coplanar, the dihedral angle between their least-squares planes being 2.08 (1)°. The O—H atoms of the methyl hy­droxy group and the N atom of the isoxazole ring are orientated in the same direction to allow for the formation of inter­molecular O—H⋯N hydrogen bonds that lead to a supra­molecular chain along the a axis