In this paper, we proposed a method for accelerating the transmission of a series of streaming files for multi-session file delivery scenarios. When streaming files are transmitted, the throughput of data transmission can be increased and the out of order problem of file reception (i.e., file reception delay) can also be reduced at the same time. This is achieved by using a repair file generation mechanism based on AL-FEC, which is triggered while an empty file transmission session appears but there are still uncompleted files waiting to be delivered. Performance evaluation results shows that the file transmission throughput of the proposed method are 28% to 44% higher than the throughput of a traditional file transmission method that is designed for reducing file reception delay. However, the file reception delay is the same as the above-mentioned traditional method.

Summary Currently, wireless sensor networks (WSNs) have been widely developed in different domains. Generally, sensors work independently, so it is relatively easier for a WSN to add new nodes. Often, a WSN is constrained to low energy consumption. However, data encryption/decryption not only takes a long time but also consumes many network resources. Due to limited battery power and CPU capability, a sensor's encryption algorithm should not be complicated. However, a simple security algorithm will downgrade the system's security level. Hence, designing a secure wireless transmission system with high encryption/decryption performance, particularly for 5G networks, is an urgent task. Therefore, in this study, we propose an efficient and secure approach, named the Symmetric Key Matrix and Rectangular Frame Scheme (SKeMRF, for short), which involves security mechanisms to speed up encryption/decryption operations for a WSN. Further, the SKeMRF integrates global Symmetric Key Matrix (SKM, for short) and other security mechanisms to ensure the security of transmitted data and enable the system to effectively resist the forgery KDS attack, thus greatly enhancing security of the whole system. Theoretical analysis and simulation show that the SKeMRF is an efficient and highly secure WSN compared with those state‐of‐the‐art schemes.

Summary Performance problems in a cloud service are difficult to diagnose because they may be caused by various system components. This study proposes an automatic cloud service testing and bottleneck detection system that is applicable to different types of services. With the proposed test module, the user can customize test scenarios to automatically test and collect the corresponding metrics of the target service. Afterward, the proposed bottleneck detection algorithm analyzes the collected metrics and determines whether a bottleneck is presented in the target system. The bottleneck detection module also provides a scaling recommendation for the service provider to facilitate the service system reconfiguration. The experimental results reveal that the proposed system could detect a potential bottleneck in a service system accurately. In accordance with the scaling recommendation, the performance of the target cloud service can be improved efficiently after reconfiguration. Therefore, usage of the proposed system can ensure a high quality of service, and the service level objective could be fulfilled.

As the advance of technology, it is increasingly speech recognition tools on mobile devices, such as Google Voice and Apple Siri, those have been widely used and have high recognition rate of people's speech. However, these speech recognition tools cannot work well for people with the disease of "Dysphasia" and has very low recognition rate. It is important issue to develop a speech recognition toot for dysphasia, such as Cerebral Palsy(CP) and Amyotrophic Lateral Sclerosis(ALS), to assist those people communicating with others well. Recently, there are various open source programs has been announced, such as Google's Tensorflow, which is used to develop speech recognition based on Deep Neural Networks (DNNs). In this paper, we propose a Convolutional Neural Networks (CNNs) model to perform speech recognition for ALS. The model consists of two hidden CNN layers, each includes one CNN, Rectified Linear Unit (ReLU), Dropout Unit, and MaxPooling Layer. We implement the CNN for dysphasia speech recognition using Google's Tensorflow and collect 33 pronunciations from a dysphasia, each pronunciation at least has 350 training voice files. The highest accuracy is about 63% for one word. And it will be up to totally 94% for top five words. The result shows that it can effectively recognize speech for dysphasia. It can be expected to construct a speech recognition system for assisting dysphasia communicating with others.

Previous application sharing systems have no unified communication protocols. The well known application sharing protocols include RFB (remote framebuffer), RDP (remote desktop protocol) and ITU-T T.128. A generic application sharing architecture is proposed for providing the capability of connecting various application sharing servers with the same application sharing client. A communication standard for application sharing systems is defined based on the message-oriented middleware (MOM). To compare with previous application sharing systems, there are many advantages including easy to use, easy to connect, easy to integrate, higher scalability and less bandwidth consumption

Object oriented database system (OODB) supports an object oriented data model with the functionality of persistency and transaction semantics. In order to facilitate the use of OODB, the Object Database Management Group (ODMG) defined a standard for object database management systems. On the other hand, the Object Management Group (OMG) defined the Common Object Request Broker Architecture (CORBA), which is an emerging standard of distributed object technology providing the interconnection network between distributed objects. For the sake of matching these two object models, taking the advantages of merging both of them, and building a more sophisticated infrastructure, the integration of CORBA and OODB is currently an urgent and important issue in distributed object systems. Instead of using Object Database Adapter (ODA) suggested by the ODMG, we provide a novel way of reusing the Object Transaction Service (OTS) and wrapping techniques to introduce OODB into CORBA automatically. Through our design, CORBA clients or OODB object implementers do not need to learn any knowledge of each other. In addition, error recovery is also provided to guarantee the consistency of object states. The whole task for integrating CORBA and OODB is done transparently by our proposed preprocessor, which plays an important role in solving problems encountered by ORB and OODB vendors easily.