Membina kemahiran berfikir secara kritis dan holistik dalam kalangan pelajar menggunakan modul Edward de Bono

Tujuan kajian PTS ini dilakukan adalah bagi membina kemahiran berfikir dalam kalangan pelajar tahun akhir Program Pengajian Bahasa Melayu, UKM. 2 daripada 9 HPP Program Pengajian Bahasa Melayu adalah berkait secara langsung dengan kemahiran berfikir. 2 HPP itu ialah HPP3 dan HPP6 yang menyatakan bahawa pelajar perlu mempunyai kemahiran menyelesaikan masalah, kemahiran pengurusan maklumat dan kemahiran saintifik. Walau bagaimanapun, tiada satu kursus pun, baik yang ditawarkan di peringkat universiti, fakulti, pusat atau program pengajian yang menawarkan kursus tentang aspek ini. Penyelidikan ini akan cuba mengatasi kekurangan ini. Dalam penyelidikan PTS ini, empat bengkel kemahiran berfikir akan diadakan dalam kalangan pelajar tahun ketiga Program Pengajian Bahasa Melayu, iaitu dengan tujuan untuk membentuk kemahiran berfikir dalam kalangan pelajar menggunakan modul Six Thinking Hats yang dikemukakan oleh Edward De Bono. Penyelidikan ini akan menggunakan model kajian tindakan yang dikemukakan oleh John Heron & Peter Reason sebagai kerangka penyelidikan. Model ini dikenali sebagai Model Cooperative Inquiry. Data kajian membuktikan bahawa empat bengkel yang diadakan telah berjaya membina kemahiran berfikir dalam kalangan pelajar untuk menyelesaikan masalah pembelajaran khususnya dalam penulisan latihan ilmiah. Para pelajar juga didapati berupaya menggunakan kemahiran berfikir untuk menyelesaikan permasalahan dalam kehidupan seharian mereka secara professional. Keputusan kajian memperlihatkan kepada kita bahawa kemahiran berfikir yang baik boleh dibentuk dalam kalangan pelajar menggunakan modul Six Thinking Hats yang dicadangkan oleh Edward de Bono

Moving object detection via TV-L1 optical flow in fall-down videos

There is a growing demand for surveillance systems that can detect fall-down events because of the increased number of surveillance cameras being installed in many public indoor and outdoor locations. Fall-down event detection has been vigorously and extensively researched for safety purposes, particularly to monitor elderly peoples, patients, and toddlers. This computer vision detector has become more affordable with the development of high-speed computer networks and low-cost video cameras. This paper proposes moving object detection method based on human motion analysis for human fall-down events. The method comprises of three parts, which are preprocessing part to reduce image noises, motion detection part by using TV-L1 optical flow algorithm, and performance measure part. The last part will analyze the results of the object detection part in term of the bounding boxes, which are compared with the given ground truth. The proposed method is tested on Fall Down Detection (FDD) dataset and compared with Gunnar-Farneback optical flow by measuring intersection over union (IoU) of the output with respect to the ground truth bounding box. The experimental results show that the proposed method achieves an average IoU of 0.92524

Deep-learning based single object tracker for night surveillance

Tracking an object in night surveillance video is a challenging task as the quality of the captured image is normally poor with low brightness and contrast. The task becomes harder for a small object as fewer features are apparent. Traditional approach is based on improving the image quality before tracking is performed. In this paper, a single object tracking algorithm based on deep-learning approach is proposed to exploit its outstanding capability of modelling object’s appearance even during night. The algorithm uses pre-trained convolutional neural networks coupled with fully connected layers, which are trained online during the tracking so that it is able to cater for appearance changes as the object moves around. Various learning hyperparameters for the optimization function, learning rate and ratio of training samples are tested to find optimal setup for tracking in night scenarios. Fourteen night surveillance videos are collected for validation purpose, which are captured from three viewing angles. The results show that the best accuracy is obtained by using Adam optimizer with learning rate of 0.00075 and sampling ratio of 2:1 for positive and negative training data. This algorithm is suitable to be implemented in higher level surveillance applications such as abnormal behavioral recognition

A review of automated micro-expression analysis

Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focused on the deep learning approach. Compared to traditional machine learning, which relies on manual feature processing and requires the use of formulated rules, deep learning networks produce more accurate micro-expression recognition performances through an end-to-end methodology, whereby the features of interest were extracted optimally through the training process, utilizing a large set of data. This paper reviews the developments and trends in micro-expression recognition from the earlier studies (hand-crafted approach) to the present studies (deep learning approach). Some of the important topics that will be covered include the detection of micro-expression from short videos, apex frame spotting, micro-expression recognition as well as performance discussion on the reviewed methods. Furthermore, major limitations that hamper the development of automated micro-expression recognition systems are also analyzed, followed by recommendations of possible future research directions

Deforestation detection using deep learning-based semantic segmentation techniques: a systematic review

Deforestation poses a critical global threat to Earth’s ecosystem and biodiversity, necessitating effective monitoring and mitigation strategies. The integration of deep learning with remote sensing offers a promising solution for precise deforestation segmentation and detection. This paper provides a comprehensive review of deep learning methodologies applied to deforestation analysis through satellite imagery. In the face of deforestation’s ecological repercussions, the need for advanced monitoring and surveillance tools becomes evident. Remote sensing, with its capacity to capture extensive spatial data, combined with deep learning’s prowess in recognizing complex patterns to enable precise deforestation assessment. Integration of these technologies through state-of-the-art models, including U-Net, DeepLab V3, ResNet, SegNet, and FCN, has enhanced the accuracy and efficiency in detecting deforestation patterns. The review underscores the pivotal role of satellite imagery in capturing spatial information and highlights the strengths of various deep learning architectures in deforestation analysis. Multiscale feature learning and fusion emerge as critical strategies enabling deep networks to comprehend contextual nuances across various scales. Additionally, attention mechanisms combat overfitting, while group and shuffle convolutions further enhance accuracy by reducing dominant filters’ contribution. These strategies collectively fortify the robustness of deep learning models in deforestation analysis. The integration of deep learning techniques into remote sensing applications serves as an excellent tool for deforestation identification and monitoring. The synergy between these fields, exemplified by the reviewed models, presents hope for preserving invaluable forests. As technology advances, insights from this review will drive the development of more accurate, efficient, and accessible deforestation detection methods, contributing to the sustainable management of the planet’s vital resources

Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach

This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques

Hegemoni ilmu dan masa hadapan bahasa Melayu

Makalah ini membahaskan peri pentingnya pembinaan tamadun ilmu dalam proses membina sebuah negara bangsa,iaitu membina negara Malaysia yang berdaulat yang di tunjangi dengan pembinaan tamadun ilmu. Pembinaan tamadun ilmu terserlah melalui penulisan dan penerbitan bahan ilmiah, khususnya penerbitan dalam bentuk buku dan jurnal.Turut disentuh dalam makalah ini adalah tentang dan kepentingan para penerbit dan editor sebagai pemain utama dalam penerbitan karya ilmiah.Fokus khusus diberikan kepada peranan dewan bahasa dab pustaka sebagai penerbit gergasi milik kerajaan dalam usaha memperbanyakan karya ilmiah berbahasa melayu.Faktor-faktor yang menyebabkan dewan bahasa dan pustaka gagal melaksanakan peranannya sebagai badan penerbitan ilmiah turut dibincangkan, termasuklah kesukaran mendapatkan manuskrip ilmiah yang bermutu untuk diterbitkan.Perbincangan seterusnya ditumpukan kepada permasalahan yang berkaitan hegemoni barat dalam mendominasi penerbitan karya-karya ilmiah. Makalah ini cuba menjelaskan mengapa barat mahu mengekalkan hegemoni dalam penerbitan karya-karya ilmiah, dan strategi yang mereka telah dan bakal gunakan untuk mengekalkan fenomena hegemoni ini.Turut disentuh adalah sikap negara bukan barat , dan strategi yang mereka gunakan dalam usaha memecahkan tembok hegemoni ini. Makalah ini turut menjelaskan mengapa hegemoni bahasa inggeris sukar dicabar dan turut dibincangkan peranan bahasa melayu dalam memecahkan tembok hegemoni ilmu ini

Attitude and acceptance towards bahasa Melayu among the speakers in the state of Pahang

This study focuses on the speakers of bahasa Melayu in the east coast of the Peninsula or more specifically in the state of Pahang Darul Makmur. Six districts in Pahang have been chosen as the area of study namely Temerloh, Maran, Jerantut, Muadzam Shah, Kuala Lipis, and Kuantan. In this study, the attitude and view of the urban and rural communities towards the capability and commercial value of bahasa Melayu will be seen and compared, whether or not they have changed. A close look at the language attitude has been made, and sociolinguistic approach has been used as a theoretical frameworks. As many as 200 questionnaire forms had been distributed to respondents aged between 10 to 61 years old. Other than the questionnaires, the data was also obtained using the interview and the observation methods. Data was analyzed using SPSS. The findings indicate that the language attitude of the East Coast community varies according to the variables that have been determined. Language attitude based on age, religion, educational level and residential location have illustrated a significant difference. However, from the income level and marital status, the language attitude does not show significant difference. All in all, based on the tests of validity and reliability, the finding shows that the attitude and acceptance of bahasa Melayu in the Malay community residing in the East Coast has been very positive