Teaching workload in 21st century higher education learning setting

A standard equation on teaching workload calculation in the previous academic setting only includes the contact hours with students through lecture, tutorial, laboratory and in-person consultation (i.e. one-to-one final year project consultation). This paper discusses teaching workload factors according to the current higher-education setting. Devising a teaching workload equation that includes all teaching and learning strategies in the 21st century higher education learning setting is needed. This is indeed a challenging task for the academic administrators to scrutinize every single parameter that accounted for teaching and learning. In this work, we have discussed the parameters which are significant in teaching workload calculation. For instance, the conventional in-person contact with the students, type of delivery, type of assessment, the preparation of materials for flipped classroom as well as MOOC, to name a few. Teaching workload also affects quality teaching and from the academic perception, the higher workload means lower-quality teaching

Who Danced Better? Ranked TikTok Dance Video Dataset and Pairwise Action Quality Assessment Method

Video-based action quality assessment (AQA) is a non-trivial task due to the subtle visual differences between data produced by experts and non-experts. Current methods are extended from the action recognition domain, where most are based on temporal pattern matching. AQA has additional requirements where order and tempo matter for rating the quality of an action. We present a novel dataset of ranked TikTok dance videos and a pairwise AQA method for predicting which video of a same-label pair was sourced from the better dancer. Exhaustive pairings of same-label videos were randomly assigned to 100 human annotators, ultimately producing a ranked list per label category. Our method relies on a successful detection of the subject’s 2D pose inside successive query frames where the order and tempo of actions are encoded inside a produced String sequence. The detected 2D pose returns a top-matching Visual word from a Codebook to represent the current frame. Given a same-label pair, we generate a String value of concatenated Visual words for each video. By computing the edit distance score between each String value and the Gold Standard’s (i.e., the top-ranked video(s) for that label category), we declare the video with the lower score as the winner. The pairwise AQA method is implemented using two schemes, i.e., with and without text compression. Although the average precision for both schemes over 12 label categories is low, at 0.45 with text compression and 0.48 without, precision values for several label categories are comparable to past methods (median: 0.47, max: 0.66)

Analysis of Gate Poly Delayering in SOI Wafer

The advantages of silicon-on-insulator (SOI) technology are reduced parasitic device capacitance, improved performance as well as smaller build area. Despite the gains of SOI technology to manufacturers, new challenges arise in Physical Failure Analysis (PFA). The process of delayering polysilicon or active layer becomes impossible without harming the top silicon. This study discussed the challenges of the current fastest, reliable and reproducible method to delayer polysilicon and divulge active layer. Current delayering method using 49% Hydrofluoric (HF) concentration and SC1 solution is proven to be a faster way to reveal polysilicon layer for Bulk Commentary Metal-Oxide Semiconductor (Bulk CMOS). Thus, this method was tested on SOI Wafer to analyze the effect. The experiment was conducted by selecting small, thin and dense gate polysilicon such as in Static Random Access Memory (SRAM) cells. The result shows that high concentration of HF is not suitable for SOI since HF will etch Interlayer Dielectric (ILD) all the way down to Buried Oxide (BOX) and leave top silicon unattached. As a result, top silicon structure was peeled off or damaged. The result was not promising since the top silicon is crucial part as it holds information to discover physical cause of failure

Development of Hand Grip Assistive Device Control System for Old People through Electromyography (EMG) Signal Acquisitions

The hand grip assistive device is a glove to assist old people who suffer from hand weakness in their daily life activities. The device earlier control system only use simple on and off switch. This required old people to use both hand to activate the device. The new control system of the hand grip assistive device was developed to allow single hand operation for old people. New control system take advantages of electromyography (EMG) and flex sensor which was implemented to the device. It was programmed into active and semi-active mode operation. EMG sensors were placed on the forearm to capture EMG signal of Flexor Digitorum Profundus muscle to activate the device. Flex sensor was used to indicate the finger position and placed on top of the finger. The signal from both sensors then used to control the device. The new control system allowed single hand operation and designed to prevent user from over depended on the device by activating it through moving their fingers

Development of Hand Grip Assistive Device Control System for Old People through Electromyography (EMG) Signal Acquisitions

The hand grip assistive device is a glove to assist old people who suffer from hand weakness in their daily life activities. The device earlier control system only use simple on and off switch. This required old people to use both hand to activate the device. The new control system of the hand grip assistive device was developed to allow single hand operation for old people. New control system take advantages of electromyography (EMG) and flex sensor which was implemented to the device. It was programmed into active and semi-active mode operation. EMG sensors were placed on the forearm to capture EMG signal of Flexor Digitorum Profundus muscle to activate the device. Flex sensor was used to indicate the finger position and placed on top of the finger. The signal from both sensors then used to control the device. The new control system allowed single hand operation and designed to prevent user from over depended on the device by activating it through moving their fingers