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

Template Generation from Postmarks Using Cascaded Unsupervised Learning

Information in historical datasets comes in many forms. We are working with a set of World War I era postcards that contain hand written text, some preprinted text, postage stamps and postmark/cancellation stamps. The postmarks are of considerable interest to collectors looking for images of samples they had not previously seen. The postmarks also provide information on the originating location of the card that complements the information in the address block. The postmarks vary considerably with towns and dates, but also styles. The styles can be grouped into categories. A method for automatically extracting templates for each category of these postmark stamps is described. The problem is complicated by the high levels of degradation present in the cards. The approach uses a cascade of unsupervised learning steps separated with image cleaning. This introduces averaging steps, which reduces noise. It also provides a reduction in the number of comparisons between samples. While merges happen at each stage, the number of times merges are needed within each stage is reduced. The templates once extracted can be used to group the postmarks, and will contribute information about the postmark content to better separate the postmark from the paper and other interfering marks to extract further information about the postmarks and postcards

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.Comment: 12 pages, 8 figures, published in IEEE Transactions in Medical Imagin

Ensemble Machine Learning Model Generalizability and its Application to Indirect Tool Condition Monitoring

A practical, accurate, robust, and generalizable system for monitoring tool condition during a machining process would enable advancements in manufacturing process automation, cost reduction, and efficiency improvement. Previously proposed systems using various individual machine learning (ML) models and other analysis techniques have struggled with low generalizability to new machining and environmental conditions, as well as a common reliance on expensive or intrusive sensory equipment which hinders their industry adoption. While ensemble ML techniques offer significant advantages over individual models in terms of performance, overfitting reduction, and generalizability improvement, they have only begun to see limited applications within the field of tool condition monitoring (TCM). To address the research gaps which currently surround TCM system generalizability and optimal ensemble model configuration for this application, nine ML model types, including five heterogeneous and homogeneous ensemble models, are employed for tool wear classification. Sound, spindle power, and axial load signals are utilized through the sensor fusion of practical external and internal machine sensors. This original experimental process data is collected through tool wear experiments using a variety of machining conditions. Four feature selection methods and multiple tool wear classification resolution values are compared for this application, and the performance of the ML models is compared across metrics including k-fold cross validation and leave-one-group-out cross validation. The generalizability of the models to data from unseen experiments and machining conditions is evaluated, and a method of improving the generalizability levels using noisy training data is examined. T-tests are used to measure the significance of model performance differences. The extra-trees ensemble ML method, which had never before been applied to signal-based TCM, shows the best performance of the nine models.M.S

Efficient Neuromorphic Computing Enabled by Spin-Transfer Torque: Devices, Circuits and Systems

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this thesis demonstrates the encoding of biological neural and synaptic functionalities in the underlying physics of electron spin. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing neuro-mimetic device structures is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations

Vehicle make and model recognition for intelligent transportation monitoring and surveillance.

Vehicle Make and Model Recognition (VMMR) has evolved into a significant subject of study due to its importance in numerous Intelligent Transportation Systems (ITS), such as autonomous navigation, traffic analysis, traffic surveillance and security systems. A highly accurate and real-time VMMR system significantly reduces the overhead cost of resources otherwise required. The VMMR problem is a multi-class classification task with a peculiar set of issues and challenges like multiplicity, inter- and intra-make ambiguity among various vehicles makes and models, which need to be solved in an efficient and reliable manner to achieve a highly robust VMMR system. In this dissertation, facing the growing importance of make and model recognition of vehicles, we present a VMMR system that provides very high accuracy rates and is robust to several challenges. We demonstrate that the VMMR problem can be addressed by locating discriminative parts where the most significant appearance variations occur in each category, and learning expressive appearance descriptors. Given these insights, we consider two data driven frameworks: a Multiple-Instance Learning-based (MIL) system using hand-crafted features and an extended application of deep neural networks using MIL. Our approach requires only image level class labels, and the discriminative parts of each target class are selected in a fully unsupervised manner without any use of part annotations or segmentation masks, which may be costly to obtain. This advantage makes our system more intelligent, scalable, and applicable to other fine-grained recognition tasks. We constructed a dataset with 291,752 images representing 9,170 different vehicles to validate and evaluate our approach. Experimental results demonstrate that the localization of parts and distinguishing their discriminative powers for categorization improve the performance of fine-grained categorization. Extensive experiments conducted using our approaches yield superior results for images that were occluded, under low illumination, partial camera views, or even non-frontal views, available in our real-world VMMR dataset. The approaches presented herewith provide a highly accurate VMMR system for rea-ltime applications in realistic environments.\\ We also validate our system with a significant application of VMMR to ITS that involves automated vehicular surveillance. We show that our application can provide law inforcement agencies with efficient tools to search for a specific vehicle type, make, or model, and to track the path of a given vehicle using the position of multiple cameras