A comparison between e-government practices in Taiwan and New Zealand.

Few studies have focused on comparing the state of e-government in Western- and Non-Western settings, where the political, social, economic, and cultural environments can be markedly different. This paper compares the views of local authority policymakers in Taiwan and New Zealand, in order to judge the sophistication of their e-government initiatives via the formal and informal policies underpinning website development. Good level of agreement were observed between the Taiwanese and New Zealander respondents for the high levels of significance they attached to 3 key issues, which the authors argue are critical for successful e-government: Accessibility, Security and Privacy. Similarly, the policymakers agreed on a medium level of significance for the 7 key issues: E-procurement, Digital Divide, Private Sector, Taxation, Cultural Obstacles, IT Workforce, and Social Effects (and on a low level of significance for E-Tailing). It was concluded that government policymakers in both countries, in an era of commercial online social networking, are continuing to favour pushing(what they deem to be important) information to citizens, rather than creating collaborative service channels with citizens, contractors and suppliers or integrating separate service processes to satisfy all stakeholders. An attendant lack of commitment to promoting heightened (e-)democracy was also noted, especially in New Zealand

Burst Denoising with Kernel Prediction Networks

We present a technique for jointly denoising bursts of images taken from a handheld camera. In particular, we propose a convolutional neural network architecture for predicting spatially varying kernels that can both align and denoise frames, a synthetic data generation approach based on a realistic noise formation model, and an optimization guided by an annealed loss function to avoid undesirable local minima. Our model matches or outperforms the state-of-the-art across a wide range of noise levels on both real and synthetic data.Comment: To appear in CVPR 2018 (spotlight). Project page: http://people.eecs.berkeley.edu/~bmild/kpn

Modeling the dynamics of web-based service and resource-oriented digital ecosystems

The notion of digital species is broadened to include services and resources, special issues arise in modeling the dynamics and workflows with representations associated with these services and resources. To address these issues, this paper explores two different yet related approaches: the traditional BPEL-based workflow modeling approach and the Mashupbased Web approach. In this paper, we first demonstrate two examples of service-oriented and resource-oriented digital ecosystems on the Web. We then identify key issues pertinent to both types of DES. We discuss formal definition, specifications and issues of BPEL-based approach and Mashup-based modeling techniques with computational formalisms. Finally, we propose a hybrid approach to deal with modeling the dynamicsin processes associated with such Digital Ecosystems

Polyostotic osteoid osteoma: A case report.

Osteoid osteomas are common, benign osteoblastic tumors that can occur in any bone in the body. They are almost always solitary, with only rare reports of multiple tumors in the same patient. When multiple, they typically are found within the same bone. We present a unique case of a young female athlete who presented initially at 16 years old with a right tibial osteoid osteoma and later at 18 years old with a right acetabular osteoid osteoma. Our case demonstrates the rare entity of polyostotic osteoid osteoma, the potential limitations of MRI in the diagnosis of osteoid osteoma, and the utility of radiofrequency ablation in the treatment of osteoid osteoma

Research Synthesis for the California Zero Traffic Fatalities Task Force

This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety

Intelligent matching for public internet web services ? towards semi-automatic internet services mashup

In this paper, we propose an Internet public Web service matching approach that paves the way for(semi-)automatic service mashup. We will first provide the overview of the solution, which requires a detailed review of two fundamental models ? schema/graph matching and semantic space. Based on the conceptual model and the literature study, the complete service matching approach is then provided with four essential steps ? semantic space, parameter tree, similarity measures, and WSDL operation matching. The system demonstration that proves the concept proposed in this approach is finally presented. The solution has the potential to facilitate the Internet services mashup

Towards the mental health ontology

Lots of research have been done within the mental health domain, but exact causes of mental illness are still unknown. Concerningly, the number of people being affected by mental conditions is rapidly increasing and it has been predicted that depression would be the world's leading cause of disabilityby 2020. Most mental health information is found in electronic form. Application of the cutting-edge information technologies within the mental health domain has the potential to greatly increase the value of the available information. Specifically, ontologies form the basis for collaboration between researchteams, for creation of semantic web services and intelligent multi-agent systems, for intelligent information retrieval, and for automatic data analysis such as data mining. In this paper, we present Mental Health Ontology which can be used to underpin a variety of automatic tasks and positively transform the way information is being managed and used within the mental health domain

Response time for cloud computing providers

Cloud services are becoming popular in terms of distributed technology because they allow cloud users to rent well-specified resources of computing, network, and storage infrastructure. Users pay for their use of services without needing to spend massive amounts for integration, maintenance, or management of the IT infrastructure. This creates the need for a reliable measurement methodology of the scalability for this type of new paradigm of services. In this paper, we develop performance metrics to measure and compare the scalability of the resources of virtualization on the cloud data centres. First, we discuss the need for a reliable method to compare the performance of cloud services among a number of various services being offered. Second, we develop a different type of metrics and propose a suitable methodology to measure the scalability using these types of metrics. We focus on the visualization resources such as CPU, storage disk, and network infrastructure. Finally, we compare well-known cloud providers using the proposed approach and conclude the recommendations. This type of research will help cloud consumers, before signing any official contract to use the desired services, to ascertain the ability and capacity of the cloud providers to deliver a particular service

Mining frequent sequences using itemset-based extension

In this paper, we systematically explore an itemset-based extension approach for generating candidate sequence which contributes to a better and more straightforward search space traversal performance than traditional item-based extension approach. Based on this candidate generation approach, we present FINDER, a novel algorithm for discovering the set of all frequent sequences. FINDER is composed oftwo separated steps. In the first step, all frequent itemsets are discovered and we can get great benefit from existing efficient itemset mining algorithms. In the second step, all frequent sequcnces with at least two frequent itemsets are detected by combining depth-first search and item set-based extension candidate generation together. A vertical bitmap data representation is adopted for rapidly support counting reason. Several pruning strategies are used to reduce the search space and minimize cost of computation. An extensive set ofexperiments demonstrate the effectiveness and the linear scalability of proposed algorithm