Artificial Intelligence and its Potential Adverse Impacts on the Philippine Economy

Recent developments in artificial intelligence (AI) and deep learning techniques are expected to reshape the nature of the working environment in many economic sectors through the automation of many white collar jobs. This technological breakthrough poses threats of job obsolescence in several industries, particularly for a labor abundant country such as the Philippines. With human capital as one of its largest resources, the services sector is a major contributor to the country’s economy, contributing around 60% of the total gross domestic product and employing about 22.8 million workers (Philippine Statistics Authority, 2017)

Robust estimation of depth perception and image segmentation of monocular image sequences with known camera translation

Monocular vision techniques use information taken from a single moving camera in inferring the 3-D structure of a camera observers environment. Compared to polynocular vision techniques, monocular vision techniques require less hardware and information about the camera geometry, in order to estimate relative depth. However, monocular vision is more prone to image noise and is computationally expensive. This research proposes an algorithm for depth estimation for use in mobile robotic navigation. Depth estimation in real-world image sequences of a visual scene captured by a single moving camera using optic flow information still suffer from accuracy problems due imperfection in optic flow estimates. Since the Structure from Motion problem of Monocular Vision is regarded as non-linear, the initial optic flow estimate, hence, is further enhanced using a novel approach of applying Extended Kalman Filter formulation on the corresponding divergence fields. Raw optic flow estimates of consecutive frames in any given image sequence were computed using the pyramidal Lucas-Kanade algorithm. The resulting optic flow field is then used as basis for estimating the 3-D scene structure via construction of a depth/range map. The depth maps were constructed following a Camus formulation assuming monocular image sequences captured by a camera undergoing uniform forward translation. These depth maps were refined further by application of median filtering as a post-processing mechanism. Standard tests on synthetic and real-world images indicate that the Extended Kalman Filter has been effective in making the depth estimation process consistent, most especially if the optic flow estimates of the initial frame were made very close to the ideal (57.8% and 14.7% reduction in the standard deviation of divergence magnitude error values for the Kalman-filtered divergence data with and without ground truth values, respectively, over that which used only raw optic flow data). The system developed, however, cannot still effectively apply to real-world image data due to limiting assumptions on the observer motion type and imaged surface orientation relative to the camera observers focal axis, as well as lack of textural content and ambient lighting noise

Theoretical approach of understanding impacts of team separation in global software development

© PACIS 2018. Global Software Development (GSD) refers to distributed software development in form of offshore insourcing and outsourcing. GSD promises benefits like cost-saving, time to market reduction, access to global talents, and task modularization. However, GSD encounters issues caused by team separation which has five dimensions: geographical, temporal, cultural, organizational, and work. These issues affect GSD activities. Issues reported in GSD are greater than identified solutions. Thus, it can be inferred that GSD lacks a guide on how it should be done (planned and implemented). This study aims to build a framework using open-ended inductive theory building method, to explain why GSD fail (due to team separation), and how it can succeed. The framework will be grounded in Process Virtualization, Task-Technology Fit, and Transactive Memory theories. This study will use the case study approach under phenomenological research of qualitative design to understand the phenomenon thru experiences of participants, and processes involved

A computer assisted diagnosis system for the identification/auscultation of pulmonary pathologies

Statistics show that the primary cause of morbidity and mortality among Filipinos are pulmonary illnesses. These illnesses could have been prevented if detected and treated early. With the physicians medical knowledge and experience, early detection of possible common pulmonary diseases can be performed using a stethoscope. However, with the current physician-to-population ratio in the country, early detection of respiratory diseases may not be performed on most cases especially in the rural areas, causing even benign cases to lead to mortality. In this paper, we present the development of a system that classifies lung sound for possible pulmonary pathology.Using an electronic stethoscope, lung sounds were collected from healthy individuals and patients with common pulmonary problems for the developed systems training and evaluation. The collected data were pre-processed in order to remove mechanical and other external noises. Using Support Vector Machine (SVM) for modelling and classification, the developed system was able to achieve 100% identification of the normal lung sound from the adventitious lung sound, with an average cross-validation performance of 88%. The developed system, however, has low performance in classifying specific lung sounds, that is, normal vs. crackle vs. wheeze vs. ronchi, with an average accuracy of 61.42% and an average cross-validation performance of 90%

Optimizing the cost function of histogram of oriented gradient-based INRIA dataset

Person detection in images requires both image processing and machine learning concepts. Image processing techniques are used in extracting feature descriptor sets. The extracted features are then used as inputs for training a machine learning algorithm to perform classification of objects are persons. One of the feature description algorithms used for image classification is the Histogram of Oriented Gradients (HOG). HOG is based on gradient vectors and the use of sliding windows in order to obtain the feature descriptor sets. For machine learning, support vector machine (SVM) is used for person classification. In this paper, the images used are based on the INRIA person dataset, which contains 3542 human images with varying range of pose and backgrounds. This paper presents the finding of the optimized cost function C for each type of linear-based SVM models, for person detection in the INRIA person data set, based on the HOG feature detector set

Designing a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfaces

The detection of mirrors is a challenging task due to their lack of a distinctive appearance and the visual similarity of reflections with their surroundings. While existing systems have achieved some success in mirror segmentation, the design of lightweight models remains unexplored, and datasets are mostly limited to clear mirrors in indoor scenes. In this paper, we propose a new dataset consisting of 454 images of outdoor mirrors and reflective surfaces. We also present a lightweight edge-guided convolutional neural network based on PMDNet. Our model uses EfficientNetV2-Medium as its backbone and employs parallel convolutional layers and a lightweight convolutional block attention module to capture both low-level and high-level features for edge extraction. It registered Fβ scores of 0.8483, 0.8117, and 0.8388 on the Mirror Segmentation Dataset (MSD), Progressive Mirror Detection (PMD) dataset, and our proposed dataset, respectively. Applying filter pruning via geometric median resulted in Fβ scores of 0.8498, 0.7902, and 0.8456, respectively, performing competitively with the state-of-the-art PMDNet but with 78.20× fewer floating-point operations per second and 238.16× fewer parameters. The code and dataset are available at https://github.com/memgonzales/mirror-segmentation

Alternative feature extraction from digitized images of dye solutions as a model for algal bloom remote sensing

Digital images of methyl violet dye and methyl orange solutions were obtained under controlled contributions to simulate images of algal blooms. From those images, feature extraction based from both Red-Green-Blue (RGB) and Hue-Saturation-Value (HSV) color space were used. The independent variable C, which is the concentration value of the dye solution, is mapped independently with the R-channel, G-channel and B-channel as well as the H-channel, S-channel and V-channel. Linear regression and non-linear regression techniques were used to determine the best fit equation while Akaike Information Criterion (AIC) were used to compare which among the equations provide the best fit. © 2014 IEEE

Exploring sustainable sensor-based smart city services

The Advanced Research Institute for Informatics, Computing and Networking (ADRIC) De La Salle University in cooperation with International Association of La Salle Universities (IALU) held a La Salle Sustainability Lecture Series on Exploring Sustainable Sensor-based Smart City Service

Philippine component of the network-based ASEAN language translation public service

Communication between different nations is essential. Languages which are foreign to another impose difficulty in understanding. For this problem to be resolved, options are limited to learning the language, having a dictionary as a guide, or making use of a translator. This paper discusses the development of ASEANMT-Phil, a phrase-based statistical machine translator, to be utilized as a tool beneficial for assisting ASEAN countries. The data used for training and testing came from Wikipedia articles comprising of 124,979 and 1,000 sentence pairs, respectively. ASEANMT-Phil was experimented on different settings producing the BLEU score of 32.71 for Filipino-English and 31.15 for English-Filipino. Future Directions for the translator includes the following: improvement of data through changing or adding the domain or size; implementing an additional approach; and utilizing a larger dictionary to the approach. © 2014 IEEE