The Development of Learning Regions in New Zealand: An ICT Perspective

<p>The term "Learning Region" is used to identify a region which is innovative, economically successful, and inhabited by citizens who are active members of their local community. Such regions are characterised by strong links between local businesses, community groups, and education providers. Within a regional area interaction and exchange of information is easier and cheaper than in a national or international context. The success of an individual organisation is directly related to the quality of information available locally. Information technology can be an important tool in improving the flow of knowledge between the stakeholders within a region. The study examines the role that information and communication technologies (ICTs) play in the development of learning regions in New Zealand, and how they can be used to improve the quality of information flows both within the region itself, and between the region and the outside world. In particular the research considers what contribution ICTs make to organisational learning and innovation. Historical methods are used to build up a picture of the significant changes that have taken place within two contrasting regions of New Zealand between 1985 and 2005. The two selected regions are Southland and Wellington. Data was collected by searching regional newspapers, and conducting interviews with key figures in each region. A "6-I" framework of the "ideal" features of a learning region was developed from the literature review and this was used to analyse the data. The findings show a clear linear progression in terms of the development of hard ICT based networks, but a less clear pattern in terms of soft social networks where the same issues were revisited a number of times over the years. Though there was evidence of a relationship between the soft networks that existed at the regional level and the utilisation of hard ICT networks within a region it was difficult to quantify. Hard and soft networks evolve differently over time and the relationship between the two is nuanced. Both regions were successful in setting up high quality ICT networks. However, with the exception of the education sector, both regions struggled to co-ordinate their soft networks. Though good social capital existed in each region, especially in Southland, it was located in different interest groups and was not easy to bring together. This lack of co-ordination meant that the possibilities opened up by ICT infrastructure in terms of increasing innovation were not fully realised. Both regions demonstrated many of the characteristics of learning regions but neither region was able to bring all aspects together to reach their full potential. The thesis demonstrates the important role that soft social networks play in the successful utilisation of ICT networks within a regional setting.</p>

The Development of Learning Regions in New Zealand: An ICT Perspective

<p>The term "Learning Region" is used to identify a region which is innovative, economically successful, and inhabited by citizens who are active members of their local community. Such regions are characterised by strong links between local businesses, community groups, and education providers. Within a regional area interaction and exchange of information is easier and cheaper than in a national or international context. The success of an individual organisation is directly related to the quality of information available locally. Information technology can be an important tool in improving the flow of knowledge between the stakeholders within a region. The study examines the role that information and communication technologies (ICTs) play in the development of learning regions in New Zealand, and how they can be used to improve the quality of information flows both within the region itself, and between the region and the outside world. In particular the research considers what contribution ICTs make to organisational learning and innovation. Historical methods are used to build up a picture of the significant changes that have taken place within two contrasting regions of New Zealand between 1985 and 2005. The two selected regions are Southland and Wellington. Data was collected by searching regional newspapers, and conducting interviews with key figures in each region. A "6-I" framework of the "ideal" features of a learning region was developed from the literature review and this was used to analyse the data. The findings show a clear linear progression in terms of the development of hard ICT based networks, but a less clear pattern in terms of soft social networks where the same issues were revisited a number of times over the years. Though there was evidence of a relationship between the soft networks that existed at the regional level and the utilisation of hard ICT networks within a region it was difficult to quantify. Hard and soft networks evolve differently over time and the relationship between the two is nuanced. Both regions were successful in setting up high quality ICT networks. However, with the exception of the education sector, both regions struggled to co-ordinate their soft networks. Though good social capital existed in each region, especially in Southland, it was located in different interest groups and was not easy to bring together. This lack of co-ordination meant that the possibilities opened up by ICT infrastructure in terms of increasing innovation were not fully realised. Both regions demonstrated many of the characteristics of learning regions but neither region was able to bring all aspects together to reach their full potential. The thesis demonstrates the important role that soft social networks play in the successful utilisation of ICT networks within a regional setting.</p>

Higher Education for Development: The Role of University Towns in China

China’s expansion in higher education has also given rise to developing university towns in sub-cities to deal with increasing enrollments and contribute to broader socio-economic development. Taking Xiasha University Town in Hangzhou as a case study, this paper adopts a tripartite framework of teaching, research, and service to investigate the role of university towns in human capital and skills development, regional innovation, and social and community services. This paper is the first to systematically evaluate Hangzhou’s largest university town after more than two decades since its development; it also provides a more nuanced and contextual approach to university town developments similar to others in China or broader learning region integrations globally. Documentary research and interviews from relevant stakeholders were utilized to collect data. This study presents the three dimensions contextualized within Xiasha and points to issues that can further improve such through a more efficient resource-sharing scheme, a focused discipline orientation, more significant investments in research and development, and a more active role in community engagement.

Investigasi Pengaruh Skema Stride dan Step Training untuk Deteksi Jari Pada Region-based Fully Convolutional Network (R-FCN) dalam Teknologi Augmented Reality

Combining the real world with the virtual world and then modeling it in 3D is an effort carried on Augmented Reality (AR) technology. Using fingers for computer operations on multi-devices makes the system more interactive. Marker-based AR is one type of AR that uses markers in its detection. This study designed the AR system by detecting fingertips as markers. This system is designed using the Region-based Deep Fully Convolutional Network (R-FCN) deep learning method. This method develops detection results obtained from the Fully Connected Network (FCN). Detection results will be integrated with a computer pointer for basic operations. This study uses a predetermined step scheme to get the best IoU parameters, precision and accuracy. The scheme in this study uses a step scheme, namely: 25K, 50K and 75K step. High precision creates centroid point changes that are not too far away. High accuracy can improve AR performance under conditions of rapid movement and improper finger conditions. The system design uses a dataset in the form of an index finger image with a configuration of 10,800 training data and 3,600 test data. The model will be tested on each scheme using video at different distances, locations and times. This study produced the best results on the 25K step scheme with IoU of 69%, precision of 5.56 and accuracy of 96%.Keyword: Augmented Reality, Region-based Convolutional Network, Fully Convolutional Network, Pointer, Step training Abstrak Menggabungkan dunia nyata dengan dunia virtual lalu memodelkannya bentuk 3D merupakan upaya yang diusung pada teknologi Augmented Reality (AR). Menggunakan jari untuk operasi komputer pada multi-device membuat sistem yang lebih interaktif. Marker-based AR merupakan salah satu jenis AR yang menggunakan marker dalam deteksinya. Penelitian ini merancang sistem AR dengan mendeteksi ujung jari sebagai marker. Sistem ini dirancang menggunakan metode deep learning Region-based Fully Convolutional Network (R-FCN). Metode ini mengembangkan hasil deteksi yang didapat dari Fully Connected Network (FCN). Hasil deteksi akan diintegrasikan dengan pointer komputer untuk operasi dasar. Penelitian ini menggunakan skema step training yang telah ditentukan untuk mendapatkan parameter IoU, presisi dan akurasi yang terbaik. Skema pada penelitian ini menggunakan skema step yaitu: 25K, 50K dan 75K step. Presisi tinggi menciptakan perubahan titik centroid yang tidak terlalu jauh. Akurasi yang tinggi dapat meningkatkan kinerja AR dalam kondisi pergerakan yang cepat dan kondisi jari yang tidak tepat. Perancangan sistem menggunakan dataset berupa citra jari telunjuk dengan konfigurasi 10.800 data latih dan 3.600 data uji. Model akan diuji pada tiap skema dilakukan menggunakan video pada jarak, lokasi dan waktu yang berbeda. Penelitian ini menghasilkan hasil terbaik pada skema step 25K dengan IoU sebesar 69%, presisi sebesar 5,56 dan akurasi sebesar 96%.Kata kunci: Augmented Reality, Region-based Convolutional Network, Fully Convolutional Network, Pointer, Step training

Learning Region-Based Attention Network for Traffic Sign Recognition

Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).