Kecelaruan personaliti antisosial di kalangan pelajar politeknik : satu kajian awal

Kajian ini adalah bertujuan untuk mengenalpasti kecelaruan personalis antisosial (KPA) yang berlaku di kalangan remaja atau muda-mudi terutama di Politeknik Malaysia yang mungkin mengakibatkan berlakunya masalah sosial di kalangan mereka. Kajian ini berbentuk kuantitatif. Sampel kajian telah dipilih di empat buah politeknik. Politeknik yang terlibat adalah politeknik zon selatan. Responden kajian ini terdiri daripada 340 orang pelajar pengambilan bam semester satu yang memasuki institusi berkenaan. Responden juga terdiri daripada pelajar peringkat sijil dan diploma daripada pelbagai pengkhususan. Instrumen yang digunakan adalah borang soal selidik. Data yang telah dikumpulkan dianalisis menggunakan Statistical Package for Social Science (SPSS). Statistik yang digunakan adalah statistik deskriptif. Dapatan kajian menunjukkan di antara 10 jenis kecelaruan, kecelaruan avoidant mencatatkan skor min tertinggi iaitu dengan skor min 3.24 (a = 1.055). Selain itu, pengkaji mendapati personaliti antisosial yang berlaku di kalangan pelajar politeknik adalah pada tahap yang sederhana iaitu skor min 2.35 (a =0.933). Hasil daripada kajian juga mendapati faktor sosial mencatatkan skor min tertinggi iaitu 2.07 (a = 0.851). Faktor keluarga pula hanya mencatatkan skor min 2.03 (g = 0.887). Pengkaji juga mendapati responden lebih gemar kepada konsep keagamaan berbanding konsep-konsep yang lain sekiranya mereka menghadapi masalah. Oleh itu diharapkan kajian ini dapat memberikan penjelasan sedikit sebanyak mengenai kecelaruan personaliti antisosial yang berlaku di kalangan pelajar politeknik di masa kini

Pembangunan kerangka transferable skills bagi perlaksanaan penyelidikan dalam kalangan pelajar pascasiswazah di Malaysia

Malaysia berhasrat menjadi negara maju dan berpendapatan tinggi maka keperluan sumber manusia profesional iaitu graduan pascasiswazah adalah semakin mendesak. Namun demikian, timbul isu tentang tekanan yang dihadapi pelajar dalam menjalankan penyelidikan, seperti putus asa, hilang minat, hilang keyakinan diri, tidak fokus, mengalami tekanan mental, ketandusan idea, tidak mencapai target yang diinginkan, hilang komitmen dan gagal dalam menamatkan pengajian. Terdapat keperluan terhadap peranan transferable skills untuk melakukan pelbagai aktiviti, untuk mencapai sasaran dan menyelesaikan masalah yang timbul sepanjang proses penyelidikan. Oleh itu, kajian ini dilaksanakan untuk membangunkan kerangka transferable skills bagi perlaksanaan penyelidikan dalam kalangan pelajar pascasiswazah di Malaysia. Dalam kajian ini, pengkaji menggunakan reka bentuk penerokaan bercampur berurutan yang melibatkan kajian kualitatif dan kajian kuantitatif. Peserta temu bual iaitu seramai 11 orang pakar dan peserta kajian Fuzzy Delphi iaitu 13 orang pakar, yang telah dipilih menggunakan kaedah persampelan bertujuan. Sampel bagi kajian tinjauan pula iaitu seramai 483 pelajar pascasiswazah dalam bidang sains sosial dan kemanusiaan di universiti awam yang terdapat di Malaysia, telah dipilih menggunakan kaedah pensampelan rawak berlapis mengikut kadar. Dapatan kajian ini menunjukkan bahawa terdapat enam domain transferable skills dan 22 elemen transferable skills. Kajian ini mendapati bahawa pelajar Sarjana dan Doktor Falsafah memberikan tahap persetujuan yang tinggi terhadap enam domain dan 22 elemen transferable skills. Hasil dapatan kajian ini menunjukkan bahawa tidak terdapat perbezaan kesesuaian domain dan elemen transferable skills untuk menjalankan proses penyelidikan berdasarkan pelajar Sarjana dan Doktor Falsafah (PhD). Kajian ini juga mendapati bahawa kerangka transferable skills yang dibangunkan adalah sah dan boleh dipercayai untuk menjadi panduan bagi perlaksanaan penyelidikan dalam kalangan pelajar pascasiswazah di Malaysia. Oleh yang demikian, pengkaji berharap kerangka transferable skills yang dibangunkan melalui kajian ini dapat menjadi panduan bagi pelajar pascasiswazah untuk mencapai target yang diinginkan dan dapat menyelesaikan penyelidikan sebagaimana tempoh yang ditetapkan sehingga berjaya menamatkan pengajian

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Object Recognition from very few Training Examples for Enhancing Bicycle Maps

In recent years, data-driven methods have shown great success for extracting information about the infrastructure in urban areas. These algorithms are usually trained on large datasets consisting of thousands or millions of labeled training examples. While large datasets have been published regarding cars, for cyclists very few labeled data is available although appearance, point of view, and positioning of even relevant objects differ. Unfortunately, labeling data is costly and requires a huge amount of work. In this paper, we thus address the problem of learning with very few labels. The aim is to recognize particular traffic signs in crowdsourced data to collect information which is of interest to cyclists. We propose a system for object recognition that is trained with only 15 examples per class on average. To achieve this, we combine the advantages of convolutional neural networks and random forests to learn a patch-wise classifier. In the next step, we map the random forest to a neural network and transform the classifier to a fully convolutional network. Thereby, the processing of full images is significantly accelerated and bounding boxes can be predicted. Finally, we integrate data of the Global Positioning System (GPS) to localize the predictions on the map. In comparison to Faster R-CNN and other networks for object recognition or algorithms for transfer learning, we considerably reduce the required amount of labeled data. We demonstrate good performance on the recognition of traffic signs for cyclists as well as their localization in maps.Comment: Submitted to IV 2018. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualization and Social Computing