Dynamic Face Video Segmentation via Reinforcement Learning

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.Comment: CVPR 2020. 300VW with segmentation labels is available at: https://github.com/mapleandfire/300VW-Mas

A Nested Attention Neural Hybrid Model for Grammatical Error Correction

Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography

Network layer access control for context-aware IPv6 applications

As part of the Lancaster GUIDE II project, we have developed a novel wireless access point protocol designed to support the development of next generation mobile context-aware applications in our local environs. Once deployed, this architecture will allow ordinary citizens secure, accountable and convenient access to a set of tailored applications including location, multimedia and context based services, and the public Internet. Our architecture utilises packet marking and network level packet filtering techniques within a modified Mobile IPv6 protocol stack to perform access control over a range of wireless network technologies. In this paper, we describe the rationale for, and components of, our architecture and contrast our approach with other state-of-the- art systems. The paper also contains details of our current implementation work, including preliminary performance measurements

BPMN++ to support managing organisational, multiteam and systems engineering aspects in cyber physical production systems design and operation

Interdisciplinary engineering of cyber physical production systems (CPPS) are often subject to delay, cost overrun and quality problems or may even fail due to the lack of efficient information exchange between multiple interdisciplinary teams working in complex networks within and across companies. We propose a direct integration of multiteam and organisational aspects into the graphical notation of the systems engineering workflow. BPMN++, with eight new notational elements and two subdiagrams, enables the modelling of the required cooperation aspects. BPMN++ provides an improved overview, uniform notation, more compact presentation and easier modifiability from an engineering point of view. We also included a first set of empirical studies and historical qualitative and quantitative data in addition to subjective expert-based ratings to increase validity. The use case introduced to explain the procedure and the notation is derived from surveys in plant manufacturing focussing on the start-up phase and decision support at site. This, in particular, is one of the most complex and critical phases with potentially high economic impact. For evaluation purposes, we compare two alternative solutions for a short-term management decision in the start-up phase of CPPS using the BPMN++ approach

Research on Wireless Multi-hop Networks: Current State and Challenges

Wireless multi-hop networks, in various forms and under various names, are being increasingly used in military and civilian applications. Studying connectivity and capacity of these networks is an important problem. The scaling behavior of connectivity and capacity when the network becomes sufficiently large is of particular interest. In this position paper, we briefly overview recent development and discuss research challenges and opportunities in the area, with a focus on the network connectivity.Comment: invited position paper to International Conference on Computing, Networking and Communications, Hawaii, USA, 201

Continuous Improvement in Education

In recent years, 'continuous improvement' has become a popular catchphrase in the field of education. However, while continuous improvement has become commonplace and well-documented in other industries, such as healthcare and manufacturing, little is known about how this work has manifested itself in education.This white paper attempts to map the landscape of this terrain by identifying and describing organizations engaged in continuous improvement, and by highlighting commonalities and differences among them. The findings classify three types of organizations engaged in continuous improvement: those focused on instructional improvement at the classroom level; those concentrating on system-wide improvement; and those addressing collective impact. Each type is described in turn and illustrated by an organizational case study. Through the analysis, six common themes that characterize all three types of organizations (e.g., leadership and strategy, communication and engagement, organizational infrastructure, methodology, data collection and analysis, and building capacity) are enumerated. This white paper makes four concluding observations. First, the three case studies provide evidence of organizations conducting continuous improvement work in the field of education, albeit at different levels and in different ways. Second, entry points to continuous improvement work are not mutually exclusive, but are nested and, hence, mutually informative and comparative. Third, continuous improvement is not synonymous with improving all organizational processes simultaneously; rather, research and learning cycles are iterative and gradual in nature. Fourth, despite being both iterative and gradual, it is imperative that improvement work is planned and undertaken in a rigorous, thoughtful, and transparent fashion

Knowledge Management As an Economic Development Strategy

The United States is shifting to an information economy. Productive capability is no longer completely dependent on capital and equipment; information and knowledge assets are increasingly important. The result is a new challenge to the practice of local economic development. In this information economy, success comes from harnessing the information and knowledge assets of a community and from helping local businesses succeed in the new environment. Knowledge Management (KM) can provide the tools to help economic development practitioners accomplish that task. KM is a set of techniques and tools to uncover and utilize information and knowledge assets -- especially tacit knowledge. Economic development organizations can use KM tools to enhance external communications of local companies including marketing and to promote internal communications within local businesses and help companies capture tacit knowledge. More importantly, they can use those tools to uncover and develop local intellectual assets, including helping develop information products, and helping identify entrepreneurial and business opportunities. KM tools are also useful in developing local economic clusters. Finally, these tools can be used to enhance external knowledge sharing among the economic development community and to capture and share tacit knowledge within an economic development organization

The Promise of Citywide Charter Strategies

Charter school enrollment is on the rise in many urban areas. In fact, 56% of all public charter schools are located in urban areas, and 10 of our nation's largest school districts now have 20,000 students enrolled in public charter schools. With this growth in the charter movement, there is an increasing need for local infrastructure support through technical services, advocacy, and coordination. This report examines the potential for citywide charter strategies as a key leverage point for increasing charter school quality