A Machine Learning based Activity Recognition for Ambient Assisted Living

Ambient assisted living (AAL) technology is of considerable interest in supporting the independence and quality of life of older adults. As such, it is a core focus of the emerging field of gerontechnology, which considers how technological innovation can aid health and well-being in older age. Human activity recognition plays a vital role in AAL. Successful identification of human activity is crucial for any assistive care services for elderly people living alone in a home. In this paper, a method for activity recognition is proposed which recognizes or classifies activities based on sensor data. The method uses most trending algorithm in deep learning domain, i.e. LSTM to build the model .The proposed method is evaluated using a well known activity sensor dataset

A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks

Generative Adversarial Networks (GANs) provide a fascinating new paradigm in machine learning and artificial intelligence, especially in the context of unsupervised learning. GANs are quickly becoming a state of the art tool, used in various applications such as image generation, image super resolutions, text generation, text to image synthesis to name a few. However, GANs potential is restricted due to the various training difficulties. To overcome the training difficulties of GANs, the use of a more powerful measure of dissimilarity via the use of the Wasserstein distance was proposed. Thereby giving birth to the GAN extension known as Wasserstein Generative Adversarial Networks (WGANs). Recognizing the crucial and central role played by both the cost function and the order of the Wasserstein distance used in WGAN, this thesis seeks to provide a comparative assessment of the effect of a various common used norms on WGANs. Inspired by the impact of norms like the L1-norm in LASSO Regression, the L2-norm Ridge Regression and the great success of the combination of the L1 and L2 norms in elastic net and its extensions in statistical machine learning, we consider exploring and investigating to a relatively large extent, the effect of these very same norms in the WGAN context. In this thesis, the primary goal of our research is to study the impact of these norms on WGANs from a pure computational and empirical standpoint, with an emphasis on how each norm impacts the space of the weights/parameters of the machines contributing to the WGAN. We also explore the effect of different clipping values which are used to enforce the k-Lipschitz constraint on the functions making up the specific WGAN under consideration. Another crucial component of the research carried out in this thesis focuses on the impact of the number of training iterations on the WGAN loss function (objective function) which somehow gives us an empirical rough estimate of the computational complexity of WGANs. Finally, and quite importantly, in keeping WGANs\u27 application to recovery of scenes and reconstruction of complex images, we dedicate a relative important part of our research to the comparison of the quality of recovery across various choices of the norms considered. Like previous researchers before us, we perform a substantial empirical exploration on both synthetic data and real life data. We specifically explore a simulated data set made up of a mixture of eight bivariate Gaussian random variables with large gaps, the likes of which would be hard task for traditional GANs but can be readily handled quite well be WGANs thanks to the inherent strength/power of the underlying Wasserstein distance. We also explore various real data sets, namely the ubiquitous MNIST datasets made up of handwritten digits and the now very popular CIFAR-10 dataset used an de facto accepted benchmark data set for GANs and WGANs. For all the datasets, synthetic and real, we provide a thorough comparative assessment of the effect and impact of the norms mentioned earlier, and it can be readily observed that there are indeed qualitative and quantitative difference from one norm to another, with differences established via measures such as (a) shape, value and pattern of the generator loss, (b) shape, value and pattern of the discriminator loss (c) shape, value and pattern of the inception score, and (d) human inspection of quality of recovery or reconstruction of images and scenes

Conceptual model of mobile augmented reality for cultural heritage site towards enjoyable informal learning (Marchsteil)

A mobile augmented reality (AR) is one of the emerging technologies that may provide interactive content to tourists at cultural heritage sites. Past studies show enjoyable informal learning experience is highly needed for tourists to broaden knowledge for tourists. Although many mobile AR applications have been developed to expose cultural heritage site information, they are still lacking in providing such experience due to lack of comprehensive models which taking into consideration the elements of enjoyable informal learning experience in the development of such applications. Therefore, this study proposes a comprehensive conceptual model of mobile AR where it considers the components of enjoyable informal learning experience at cultural heritage site. This study followed design science research methodology. The proposed conceptual model is reviewed and validated through expert review and focus group discussion The review was analysed based on frequency of the responses on each component. As a proof-of-concept, the prototype (named as AR@Melaka) was developed and then evaluated on its enjoyable informal learning aspects to 200 tourists of a renowned cultural heritage site. From user perspective, it is proven that AR@Melaka provides enjoyable informal learning. In conclusion, these findings proved that the conceptual model is useful for assisting tourists in learning at cultural heritage site in an enjoyable way. This study contributes a conceptual model to serve as guidelines for developing a mobile augmented reality that considers an enjoyable informal learning component

A Voting Algorithm for Dynamic Object Identification and Pose Estimation

While object identification enables autonomous vehicles to detect and recognize objects from real-time images, pose estimation further enhances their capability of navigating in a dynamically changing environment. This thesis proposes an approach which makes use of keypoint features from 3D object models for recognition and pose estimation of dynamic objects in the context of self-driving vehicles. A voting technique is developed to vote out a suitable model from the repository of 3D models that offers the best match with the dynamic objects in the input image. The matching is done based on the identified keypoints on the image and the keypoints corresponding to each template model stored in the repository. A confidence score value is then assigned to measure the confidence with which the system can confirm the presence of the matched object in the input image. Being dynamic objects with complex structure, human models in the COCO-DensePose dataset, along with the DensePose deep-learning model developed by the Facebook research team, have been adopted and integrated into the system for 3D pose estimation of pedestrians on the road. Additionally, object tracking is performed to find the speed and location details for each of the recognized dynamic objects from consecutive image frames of the input video. This research demonstrates with experimental results that the use of 3D object models enhances the confidence of recognition and pose estimation of dynamic objects in the real-time input image. The 3D pose information of the recognized dynamic objects along with their corresponding speed and location information would help the autonomous navigation system of the self-driving cars to take appropriate navigation decisions, thus ensuring smooth and safe driving

Going deeper than words

Art can provide kids with an easier way to express themselves since children are more naturally artistic and creative. Most children with special needs have innate visual powers. A question and answer type of format can be daunting and intimidating for a child, especially when they have to try and explain themselves with their already limited vocabulary

Openness towards mental illness in Malaysia

Mental health is an important component for well-being beside physical health. Recent years, mental illness has become a challenge in society. However, public stigma towards mental illness is very bad. This stigma makes people with mental illness have difficulty to recover and to get help from society. This paper reports the evidences gathered through the recovered patients themselves of the importance of openness towards mental illness and the need for removal of stigma towards mental illness. There were six interviewees participated in a study conducted in a non- governmental organization (NGO) that supports mental wellness at Petaling Jaya City. The permission of interview was granted by the NGO’s management and also the participants. The data gathered was open-ended answers addressing questions which are related to their feeling, public perceptions and also their expectation of the society and supports provided by external parties such as government. The results of the study show that the perception towards mental illness patients should be more open and receptive. Supports and reception given to them is crucial. Public and government should work together to build a mentally healthy and supportive environment for a better society

COMPARISON OF ANTIFUNGAL EFFECTS OF COMMERCIALLY AVAILABLE HERBAL MOUTHWASHES AND CHLORHEXIDINE AGAINST CANDIDA ALBICANS IN DIABETIC PATIENTS: AN IN VITRO STUDY

Objective: Candida albicans is a part of the normal flora of the mouth in diabetes mellitus (DM) patients. Periodontitis is one of the main complicationsin diabetic patients. Mechanical and chemical plaque control are the most productive methods in preventing periodontal diseases in the oral cavity.The objective of this study is to compare the in vitro effect of herbal mouthwashes and chlorhexidine (CHX) against C. albicans.Methods: Saliva samples were obtained from diabetic patients reporting for treatment to Saveetha medical college. C. albicans was cultured from thesalivary sample. A yeast suspension was made by sub culturing the C. albicans. The mouthwashes used in the study are HiOra regular (0.2%), HiOrasensitive (0.5%), and clohex plus (CHX gluconate 0.02%) mouthwashes. The fungal suspension was spread on Sabouraud's dextrose agar (SDA) plateswith a sterile swab. Subsequently, wells of 6 mm in diameter were made with a suitable distance using sterile cork borer on pre-inoculated agar platesand filled with 100 μl of each mouthwashes. From the zones of inhibition seen, antimicrobial activity was expressed in terms of average diameter ofthe zones of inhibition measured.Results: Using HiOra regular mouthwash, 13/18 (72%) wells were found to show zone of inhibition ≥20 mm. In HiOra sensitive mouthwash, only9/18 (50%) showed inhibition zone ≥20 mm. With effect of regular CHX mouthwash, none of the strains showed the zone of inhibition to be ≥20 mm.Most of the strains responded well with all the three mouthwashes.Conclusion: Among the 2 herbal mouthwashes, HiOra regular mouthwash was most effective in inhibiting the candidal growth when compared tothe HiOra sensitive. HiOra regular mouthwash still showed better inhibitory actions when compared to the regular CHX mouthwash and the candidalspecies showed increased sensitivity to it.Keywords: Candida albicans, Chlorhexidine mouthwash, Diabetes mellitus, Herbal mouthwashes, Zone of inhibition