History of communication in Malaysia (1940-2008)

The Second World War was, in some ways, one of the lowest points in Malaysia's history. Japanese forces landed on the northeast border of Malaya on 8 December 194 1 and, in one month, succeeded in establishing their control of both Peninsula Malaya and Sabah and Sarawak. On 15 March 1942, Singapore surrendered. Singapore was renamed Shonan and became the centre of a regional administrative headquarters that incorporated the Straits Settlements, and the Federated Malay States and Sumatra. Much like the British who had installed residents in the Malay ruling houses fifty years earlier, the Japanese appointed local governors to each state. The only difference was that this time, it was the Sultans who were placed in the positions of advisors. The Unfederated Malay States, Perlis, Kedah, Kelantan and Terengganu found themselves back under the sovereignty of Thailand in 1942, when Thailand declared war on Britain and the USA. Most large scale economic activities grounded to a halt during the period of the War. The production of tin which was already falling before the War stopped almost completely. People turned their occupation away from the cultivation of commercial crops, concentrating instead on planting rice and vegetables to ensure they did not go hungry. [1

Mobile language learning now and in the future

The widespread ownership of mobile devices such as cellphones, personal media players, personal digital assistants (PDAs), smartphones and wireless laptops means that ‘mobile learning’ is no longer in the preserve of technical experts and researchers with specialist knowledge. Teachers and learners have begun to integrate mobile technologies into everyday practices and there is evidence of efforts to invent exciting new scenarios of use. Language learning is one of the disciplines that looks set to benefit from these developments. Learners can make good use of the facilities to record and to listen to audio at any time, supported by the rising availability of podcasts and the ‘always on’ characteristics of portable devices which encourage spontaneous interactions. Mobile learning promises to deliver closer integration of language learning with everyday communication needs and cultural experiences

CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.Comment: 8 pages, Submitted in CCIA 201

An Experience-Connected e-Learning System with a Personalization Mechanism for Learners’ Situations and Preferences

This paper presents an “experience-connected” e- Learning system that facilitates users to learn practical skills of foreign language by associating knowledge and daily-life experiences. “Experience-Connected” means that the users of this system receive personalized and situation-dependent learning materials automatically. Knowledge associated to users’ daily-life has the following advantages: 1) provides opportunities to learn frequently, and 2) provides clear and practical context information about foreign language usage. The unique feature of this system is a dynamic relevance computation mechanism that retrieves learning materials according to both preference relevance and spatiotemporal relevance. Users of this system obtain appropriate learning materials, without manual and time-consuming search processes. This paper proves the feasibility of the system by showing the actual system implementation that automatically broadcasts the media-data of foreign language learning materials to smart-phones

Practitioners as innovators: Emergent practice in personal mobile teaching, learning, work and leisure

Mobile devices have become commonplace tools, yet little is known about how individuals use them in their teaching, learning, work, and leisure. We report on an investigation into personal mobile device use by students and alumni from the global master's degree in online and distance education offered by the Institute of Educational Technology at the Open University (UK). The study identified various types of activity undertaken, and focused on emerging issues in relation to innovative practices. Participants described their uses of four types of device, the frequency of specific uses, and their views on the attractions and disadvantages of mobile learning. The chapter is intended to inform those who are interested in the potential of mobile learning, designing learning for a specific type of device, or who own a mobile device and are simply looking to make better use of it in the future

COMIC: Towards A Compact Image Captioning Model with Attention

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact Image Captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39x - 99x smaller. The source code and models are available at: https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table