Public bicycle systems (PBSs) that connect end users’ houses to public mass transportation are typically viewed as the "last-mile" of public transportation. Because of the limited capacity of stations in a PBS, the PBS operator must dispatch bicycles between stations to ensure that there are always bicycles/spaces available for bicycle borrowing/returning. However, because the variances in flows among stations are large, bicycle dispatch is difficult without a precise flow forecasting approach. In this paper, we propose an innovative approach to forecast bicycle flow on the basis of a graph neural network (GNN). Instead of processing the temporal information using RNN, or 1D-CNN, our approach integrates both spatial and temporal information into graphs, and analyzes them using graph convolution. Our approach works well on NYCitibike open dataset in terms of prediction accuracy. From the experiment, our approach shows it capability in accurate forecasting of peak flows and self-adjustment while perceiving abnormal flows caused by sporadic situations.

Convolutional neural networks (CNNs) are known to be vulnerable to adversarial attacks. Well-crafted perturbations to the inputs can mislead a state-of-the-art CNN to make wrong decisions. Therefore, there is a pressing need for the development of methods that can test or detect the vulnerability of CNNs. In this study, we propose an adversarial attack method, called Dual Iterative Fusion (DIF) with potential critical pixels, for CNN testing to reveal the vulnerability of CNNs. DIF modifies as few as 5 pixels out of 32x32 images in this study and achieves faster, less noticeable, and more targeted attacks to a CNN. Testing CNNs with DIF, we observed that some classes are more vulnerable than the others within many classical CNNs for image classification. In other words, some classes are susceptible to misclassification due to adversarial attacks. For example, in VGG19 trained with CIFAR10 data set, the vulnerable class is "Cat". The successfully-targeted attack rate of class "Cat" in VGG19 is obviously higher than the others, 57.01% versus 25%. In the ResNet18, the vulnerable class is "Plane", with a successfully-targeted attack rate of 37.08% while the other classes are lower than 12%. These classes should be considered as vulnerabilities in the CNNs, and are pinpointed by generating test images using DIF. The issues can be mitigated through retraining the CNNs with the adversarial images generated by DIF, and the misclassification rate of the vulnerable classes declines at most from 61.67% to 6.37% after the retraining.

Agile methods advocating early and frequent delivery, incremental and iterative development, and adaptive planning gradually becomes a popular software development methodology around the world. Many top software companies adopt agile methods and develop lots of remarkable software products. Although many researchers have studied agile methods using various approaches, whether and how agile methods help developers establish better software is still controversial. In this paper, we study the business data, development, and human resources records provided by Titansoft, a Singapore IT service corporation which is well-known in its successful Agile experiences. From analysis of the data, three primary impacts brought by agile adoption are revealed: (1) A temporary business stagnation; (2) Improvement in product quality; (3) Positive evolution in corporate culture. To the best of our knowledge, this paper is one of the few studies discussing how agile methods influence a software company in software development, business, and corporate culture aspects throughout a relatively long duration (more than 5 years).

In this paper, we present DroneFace, an open dataset for testing how well face recognition can work on drones. Because of the high mobility, drones, i.e. unmanned aerial vehicles (UAVs), are appropriate for surveillance, daily patrol or seeking lost people on the streets, and thus need the capability of tracking human targets' faces from the air. Under this context, drones' distances and heights from the targets influence the accuracy of face recognition. In order to test whether a face recognition technique is suitable for drones, we establish DroneFace composed of facial images taken from various combinations of distances and heights for evaluating how a face recognition technique works in recognizing designated faces from the air. Since Face recognition is one of the most successful application in image analysis and understanding, and there exist many face recognition database for various purposes. To the best of our knowledge, DroneFace is the only dataset including facial images taken from controlled distances and heights within unconstrained environment, and can be valuable for future study of integrating face recognition techniques onto drones.

Cloud gaming, i.e., real-time game playing via thin clients, relieves users from being forced to upgrade their computers and resolve the incompatibility issues between games and computers. As a result, cloud gaming is generating a great deal of interests among entrepreneurs, venture capitalists, general publics, and researchers. However, given the large design space, it is not yet known which cloud gaming system delivers the best user-perceived Quality of Service (QoS) and what design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the QoS of current cloud gaming systems? Answering the question is challenging because most cloud gaming systems are proprietary and closed, and thus their internal mechanisms are not accessible for the research community. In this paper, we propose a suite of measurement techniques to evaluate the QoS of cloud gaming systems and prove the effectiveness of our schemes using a case study comprising two well-known cloud gaming systems: OnLive and StreamMyGame. Our results show that OnLive performs better, because it provides adaptable frame rates, better graphic quality, and shorter server processing delays, while consuming less network bandwidth. Our measurement techniques are general and can be applied to any cloud gaming systems, so that researchers, users, and service providers may systematically quantify the QoS of these systems. To the best of our knowledge, the proposed suite of measurement techniques have never been presented in the literature.

We present the first open source cloud gaming system, called GamingAnywhere. In addition to its openness, we have designed, GamingAnywhere for high extensibility, portability, and reconfigurability. We implemented it on Windows, Linux, OS X, and Android. We conducted extensive experiments to evaluate its performance. Our experimental results indicate that GamingAnywhere is efficient, scalable, adaptable to network conditions, and achieves high responsiveness and streaming quality. GamingAnywhere can be employed by researchers, game developers, service providers, and end users for setting up cloud gaming testbeds, which we believe, will stimulate more research into innovations for cloud gaming systems and applications.

Pervasive applications, like smart living, geriatrics care, agricultural development, etc, are rapidly developed recently. Situation-awareness and workflow-centric are important characteristics of the applications. Service-oriented architecture which provides flexibility in integrating heterogeneous devices and communication networks might improve the execution of pervasive applications. To integrate these techniques, we present an application model composed of a situation model, workflows, and required services for development of pervasive applications. A service-based framework supporting situation-awareness and workflow management is sketched to support execution of pervasive applications. This paper also presents the framework and its pervasive applications development. Finally, a case study is used for demonstration.

Workflow is a set of tasks which are systematized to achieve certain business goal(s), where the tasks run in a particular order under automatic control. Artifacts, collections of data items, are necessary for task (process) executions in a workflow. However, an execution may yield unexpected results due to improper artifact manipulation: e.g., activities miss artifact or artifact conflict occur at an activity dynamically. Therefore, the analyses on artifact usage in design phase are very important for quality insurance. This paper presents a model of artifact flow diagrams to state the artifact usage anomalies in processes transmitting artifacts based on DCDC, distinct control and data channel, model. There are four types of artifact usage anomalies identified in DCDC and the formal statement of each anomaly is described correspondingly for future study of anomaly search.