A product line enhanced unified process

The Unified Process facilitates reuse for a single system, but falls short handling multiple similar products. In this paper we present an enhanced Unified Process, called UPEPL, integrating the product line technology in order to alleviate this problem. In UPEPL, the product line related activities are added and could be conducted side by side with other classical UP activities. In this way both the advantages of Unified Process and software product lines could co-exist in UPEPL. We show how to use UPEPL with an industrial mobile device product line in our case study

Product line based ontology reuse in context-aware E-business environment

Improving the reusability of ontology is recognized as increasingly important due to the prevalence of OWL research and applications. But there exists no convincing methodology and tool support in this direction yet. In this paper, we apply ideas from the research and practice with software product lines in order to explore this issue. The ontology is developed and managed according to the commonalities and variabilities underlying a specific problem domain. Meta-ontology is used in order to improve the reusability, evolve-ability and customizability of ontology. Another advantage is being able to generate needed ontology with the created meta-ontology implemented with XVCL (XML based Variant Configuration Language) technology. We demonstrate our product line based reuse approach with an example B2C application

Object-orientation is evil to mobile game: Experience from industrial mobile RPGs

Mobile gaming is playing an important role in the entertainment industry. Good performance is a critical requirement for mobile games in order to achieve acceptable running speed although mobile devices are limited by scarce resources. Object-oriented programming is the prevalent programming paradigm and this is true for mobile game development as well. As the origin of object-orientation (OO) is not targeting the embedded software domain, there is suspicion as to OO's usability for embedded software, especially with respect to mobile games. Questions arise like how OO and to what degree OO will affect the performance, executable file size, and how optimization strategies can improve the qualities of mobile game software. In this paper we investigate these questions within the mobile Role-Playing-Game (RPG) domain using five industrial mobile games developed with OO. We re-implemented these five RPGs with a structural programming style, by reducing the inheritance relationships, removing excessive classes and interfaces. Some additional optimizations are also applied during the re-implementation, such as the tackling of performance bottleneck methods, using more efficient algorithms. New games after optimizations run on average almost 25% faster than the corresponding original games, with a maximum of 34.62% improvement; the memory usage is decreased by more than 10% on average and 17.56% as a maximum; we also achieved a 59% code reduction and a 71% Jar file decrease after optimization. Therefore if developers are aiming for mobile game performance, we conclude that they should use as few OO features as possible. Structural programming can be a very competitive alternative

Enhancing intelligence and dependability of a product line enabled pervasive middleware

To provide good support for user-centered application scenarios in pervasive computing environments, pervasive middleware must react to context changes and prepare services accordingly. At the same time, pervasive middleware should provide extended dependability via self-management capabilities, to conduct self-diagnosis of possible malfunctions using the current runtime context, and self-configuration and self-adaptation when there are service mismatches. In this article, we present an approach to combine the power of BDI practical reasoning and OWL/SWRL ontologies theoretical reasoning in order to improve the intelligence of pervasive middleware, supported by a set of Self-Management Pervasive Service (SeMaPS) ontologies featuring dynamic context, complex context, and self-management rules modeling. In this approach, belief sets are enriched with the results of OWL/SWRL theoretical reasoning to derive beliefs that cannot be obtained directly or explicitly. This is demonstrated with agents negotiating sports appointments. To cope with self-management, the corresponding monitoring, configuration, adaptation and diagnosis rules are developed based on OWL and SWRL utilizing SeMaPS ontologies. Evaluations show this combined reasoning approach can perform well, and that Semantic Web-based self-management is promising for pervasive computing environments

Product line enabled intelligent mobile middleware

Current mobile middleware is designed according to a 'one-size-fits-all ' paradigm, which lacks the flexibility for customization and adaptation to different situations, and does not support user-centered application scenarios well. In this paper we describe an ongoing intelligent mobile middleware research project called PLIMM that focuses on user-centered application scenarios. PLIMM is designed based on software product line ideas which make it possible for specialized customization and optimization for different purposes and hardware/software platforms. To enable intelligence, the middleware needs access to a range of context models. We model these contexts with OWL, focusing on usercentered concepts. The basic building block of PLIMM is the enhanced BDI agent where OWL context ontology logic reasoning will add indirect beliefs to the belief sets. Our approach also addresses the handling of ontology evolutions resulting from the timely adaptation of ontology to changes and the consistent propagation of these changes to all related artifacts, using Frame based product line configuration techniques

Mobile game development: Object-orientation or not

Mobile games are one of the primary entertainment applications at present. Limited by scarce resources, such as memory, CPU, input and output, etc, mobile game development is more difficult than desktop application development, with performance as one of the top critical requirements. As object-oriented technology is the prevalent programming paradigm, most of the current mobile games are developed with object-orientation (OO) technologies. Intuitively OO is not a perfect paradigm for embedded software. Questions remain such as how OO and to what degree OO will affect the performance, executable file size, and how optimization strategies can improve the qualities of mobile game software. These questions are investigated in this paper within the mobile Role-Playing-Game (RPG) domain using five industrial mobile games developed with OO. We analyzed them and found excessive usage of OO features used for the development of mobile device applications (but normal for usual desktop applications). We then apply some optimization strategies along the way of structural programming. The experiment shows that the total jar file size of these five optimized games decreases 71%, the lines of codes decreases 5

A scalable and efficient multi-label cnn-based license plate recognition on spark

Abstract Surveillance cameras are being rapidly deployed for facilitating smart transportation. Recognizing the vehicle license plate from massive videos becomes a challenge in context of system scalability and efficiency. This paper proposes a novel algorithm for scalable and efficient license plate recognition (SELPR). The SELPR algorithm first locates the license plate using a YOLO (You Look Only Once) network and recognizes the license plate using multi-label convolutional neural network (Multi-label CNN). We deploy the SELPR algorithm to the Apache Spark framework to evaluate its scalability and efficiency in parallel processing. The results demonstrates that SELPR can achieve synthesized performance with 95% recognition accuracy, better processing efficiency and scalability on a Spark cluster

Emotion recognition from Chinese speech for smart affective services using a combination of SVM and DBN

Abstract Accurate emotion recognition from speech is important for applications like smart health care, smart entertainment, and other smart services. High accuracy emotion recognition from Chinese speech is challenging due to the complexities of the Chinese language. In this paper, we explore how to improve the accuracy of speech emotion recognition, including speech signal feature extraction and emotion classification methods. Five types of features are extracted from a speech sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate and short-term energy. By comparing statistical features with deep features extracted by a Deep Belief Network (DBN), we attempt to find the best features to identify the emotion status for speech. We propose a novel classification method that combines DBN and SVM (support vector machine) instead of using only one of them. In addition, a conjugate gradient method is applied to train DBN in order to speed up the training process. Gender-dependent experiments are conducted using an emotional speech database created by the Chinese Academy of Sciences. The results show that DBN features can reflect emotion status better than artificial features, and our new classification approach achieves an accuracy of 95.8%, which is higher than using either DBN or SVM separately. Results also show that DBN can work very well for small training databases if it is properly designed

Fully convolutional network based ship plate recognition

Abstract Ship plate recognition is challenging due to variations of plate locations and text types. This paper proposes an effcient Fully Convolutional Network based Plate Recognition approach FCNPR, which uses a CNN (Convolutional Neural Network) to locate ships, then detects plate text lines with the fully convolutional network (FCN). The recognition accuracy is improved with integrating the AIS (Automatic Identification System) information. The actual FCNPR deployment demonstrates that it can work reliably with a high accuracy for satisfying practical usages