Reviewerly/exHarmony huggingface dataset:
The Development and Evaluation of a Certification Reviewer-Based System Using a Technology Acceptance Model

Getting a certification is one of the most complicated academic journeys for students in the world of technology. Industry certification has evolved into a distinct worldwide industry. The certification is an examination to evaluate students’ technical knowledge and competency in their chosen areas. Students and recent graduates pursue certification because it gives them an advantage in the job market and allows them to concentrate on relevant, meaningful, and in-demand computing subjects. Giving students the best academic support is essential to help them pass a certification exam evaluating their knowledge in courses. The goal of this study was to create a certification reviewer-based system that would let the reviewee provide a platform for review so that appropriate academic interventions could be automatically given before the examination process. The paper has applied the Technology Acceptance Model (TAM) to various information systems. The paper used the Rapid Application Development Model to develop the reviewer-based system. Document analysis, survey questions, and focus group conversations were used to gather the data. The validated survey questionnaire was administered to students, instructors, and practitioners during the School Year 2021-2022. The three groups of respondents were purposively selected as they complied with the requirements of the study. The data was analyzed quantitatively using the frequency distribution, weighted mean, and one-way variance analysis of variance.
Filipino Sign Language to Text Converter using K-Nearest Neighbor Algorithm

The Philippines recognizes Filipino Sign Language (RA 11106) as the national sign language of the Filipino deaf and the official sign language in all government transactions involving the deaf in schools, in broadcast media, and in workplaces. With that, non-signers need a translator from FSL to words to be able to communicate with people with hearing disability. This project develops a glove that will translate Filipino Sign Language (FSL) to text using the K-Nearest Neighbor (KNN) algorithm. The device will only translate the alphabet instead of words. The worn device will do a real-time language translation by capturing hand movements and gestures. The portability of the device is due to the use of android phone to view translated gesture from Bluetooth connected gloves. Test results show an accuracy of 89.63% when translating FSL to text.
Masked Face Recognition Using Multi-Task Cascaded Convolutional Networks and FaceNet

Face masks have been widely used since the start of the COVID-19 pandemic. Facial detection and recognition technologies, such as the iPhone’s Face ID, heavily rely on seeing the facial features that are now obscured due to wearing a face mask. Currently, the only way to utilize Face ID with a mask on is by having an Apple Watch as well. As such, this paper intends to find initial means of a reliable personal facial recognition system while the user is wearing a face mask without having the need for an Apple Watch. This may also be applicable to other security systems or measures. Through the use of Multi-Task Cascaded Convolutional Networks or MTCNN, a type of neural network which identifies faces and facial landmarks, and FaceNet, a deep neural network utilized for deriving features from a picture of a face, the masked face of the user could be identified and more importantly be recognized. Utilizing MTCNN, detecting the masked faces and automatically cropping them from the raw images are done. The learning phase then takes place wherein the exposed facial features are given emphasis while the masks themselves are excluded as a factor in recognition. Data in the form of images are acquired from taking multiple pictures of a certain individual’s face as well as from repositories online for other people’s faces. Images used are taken in various settings or modes such as different lighting levels, facial angles, head angles, colors and designs of face masks, and the presence or absence of glasses. The goal is to recognize whether it is the certain individual or not in the image. The training accuracy is 99.966% while the test accuracy is 99.921%.
Characterization and Optimization of Trajectory Errors on Remote-Controlled Differential Drive Robot through Speed Calibration

An increase in interest in research projects which involves the design of robotic systems that minimizes interactions between humans has been caused by the COVID-19 outbreak, as such technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. The utilization of remote-controlled robots in many different fields, especially in the medical field is becoming more and more necessary. However, mobile robots are susceptible to both systematic and nonsystematic errors that cause deviations in its trajectory. In view thereof, the researchers explored the possibility of minimizing the trajectory errors through speed calibration. The differential drive robot was navigated to finish a five-meter linear path of forward and backward motion. The test was conducted with a default linear speed of 0.5 m/s in which a high trajectory error was observed. Upon changing the speed of the robot, the same trajectory test was conducted at four additional different speeds, namely; 0.25 m/s, 0.35 m/s, 0.65m/s and 0.75 m/s. With the gathered data, the researchers conducted a linear least-squares regression model using MATLAB wherein there is only one predictor variable (speed of the robot) and one response variable (deviation). Based on the results, the researchers concluded that the speed of 0.35 m/s is the optimal speed in which the trajectory error of the robot is minimal. The researchers recommend improving the design of the caster wheels to minimize the effects of nonsystematic errors.
A Heuristic Decision Tree Algorithm for Filipino Sign Language (FSL) Translator Implemented Using Accelerometers with Bluetooth Connectivity to a Host PC

The Accelerometer-based Filipino Sign Language (FSL) Translator is an innovation to bridge the communication gap between signers and non-signers. The static acceleration-sensing feature of SCA2010 accelerometers, which are infixed in the fingertips of the glove, allowed the recognition of the gestures formed. Data inputs coming from the accelerometers were passed to the ATMega2560 and compared to a gesture library, i.e. Decision Tree classifier. The Decision Tree algorithm is based on the data performed in 30 trials and is implemented to improve the speed recognition of the system. The design is capable of rendering fingerspelled alphabets into a readable text form displayed on a host PC. The sign language translator system converts the 26 alphabets of the FSL alphabets, "Mahal Kita", and two user-defined Backspace and Clear gestures and yields 39.35 seconds average recognition speed in performing all the gestures.
GECS: Grocery Electronic Card System

Purchasing items in supermarkets and shopping malls is time-consuming. When a person purchases various objects, people put them in a shopping cart. After choosing the items, one approaches the counter for billing purposes. The cashier prepares the bill using a barcode reader to scan each product. In this case, customers wait in long queues at the billing counter. This study aims to develop a system that lessens the time consumed during the payment and speeds up the checking-out process of the items on the cashier. The system is divided into the cart and central billing units to achieve this. The cart has a barcode scanner, a Liquid Crystal Display (LCD) screen, and a Zigbee module. It also has a graphical user interface that the user interacts with. The customer must scan the product on the barcode scanner before putting the item into the cart. The item's information and total cost displayed on the LCD screen allowed the user to monitor the product list. If the user wishes to remove a product, the user must click the remove button on the Graphical user interface (GUI) and scan the item. The price automatically deducted from the total cost, and the item removed from the products list. Once the customer wants to check out the items, the final products list sent to the central billing unit through the Zigbee module. The information is then viewed on the counter. The central billing unit has a radio frequency identification (RFID) scanner for the payment of items. The customer then pays the total cost by using and swiping an electronic card to the reader. The total cost is deducted from the card balance and is updated automatically in the system database. The receipt then printed afterward, making the billing process faster and lessening the time and effort required in shopping.
Vehicle Tracking in Low Frame Rate Scenes using Instance Segmentation

Multiple object tracking is a necessary component in traffic surveillance systems. It is a requirement for today’s embedded visual surveillance systems to process videos at low frame rates. This paper aims to track objects at different frame rates focusing on improving the tracking at low frame rates. In the proposed model, an instance segmentation model was used. The mask prediction, together with the IOU of previous tracks and new observations, was utilized for data association. The proposed method was evaluated using evaluation metrics such as HOTA, MOTA, and IDF1 and achieved better results in tracking objects at low frame rates.
Disaster and Crime Reporting and Monitoring System for Hagonoy Bulacan with Decision Support Mechanism

Disaster strikes in unexpected times. The first few hours after are critical to the survival of the victims directly hit by the event. This paper explores the utilization of the Monitoring and Reporting System with Decision Support Mechanism using web and mobile applications. The citizen of Hagonoy can use the mobile application to report a crime or a disaster, they can also access the official website to view important announcements regarding safety protocols or events. The Department of Police, Fire and MDRRMO of Hagonoy will take full responsibility for managing the emergency reports and updating the website’s content. From the experiment, the mobile application was able to show that the system can provide a feasible emergency and incident report that will help both the citizen and LGU of Hagonoy. The researcher used the prototyping model as a software development model. Overall, the system was evaluated 4.86 with an interpretation result of Very Acceptable. This means that the system is ready for deployment.
Technology Adoption of Augmented and Virtual Reality in a Progressive Philippines: A Socio-techno-economical Analysis

Living in the information technology era amidst the rapid digital transformation that we are experiencing, Virtual Reality (VR) and Augmented Reality (AR) are regarded to have great potential in changing the landscape of many fields such as gaming and entertainment, education and training, tourism and cultural heritage, architecture, and engineering, automotive, defense and security, and healthcare. While it is true that AR and VR technologies brings tremendous growth and potential, these technologies face a wide range of challenges. The most common challenges and issues among the AR/VR applications as observed in this study are the privacy and security concerns of users where personal and sensitive information can be gathered and collected and are vulnerable to unauthorized intrusion. Affordability, latency, health concerns, perceived usability challenges, and ethical issues are among the other issues that are being addressed in recent research to improve adoption of these technologies. Most of the identified concerns lies upon the responsibility of manufacturers and industry actors, but policymakers should maintain balance and promulgate regulations to allow for the growth and adoption of these technologies. This study highlights several policy recommendations for the technology adoption of augmented and virtual reality.
Development of Convolutional Neural Network Model for Abaca Fiber Stripping Quality Classification System

Grading the quality of abaca fiber is employed for its efficient and different uses in the global market. Recent studies focused on grading the abaca fiber based on its normal grade. In this study, we introduced a device that classifies abaca fiber based on it stripping quality. We applied Convolutional Neural Network (CNN) VGGNet-16 architecture to train and develop a model. In model training, we gathered 300 images of excellent fiber, 300 for good fiber, and 200 training images of a fiber with fair quality. The model training employed the 80/20 ratio wherein out of the 800 training images of gathered Abaca fiber, 80% are used as training data and the 20% are utilized to validate the efficiency and accuracy of the model. The result also shows an overall classification accuracy of 96.7 % upon its testing. In addition, to create a working prototype, we use raspberry pi 4 as the microcontroller of the device which integrated both the hardware and the software components. Although, very satisfactory classification accuracy was obtained, the model can be applied to continual learning scenario to see whether the catastrophic forgetting phenomenon will also occur in this kind of tasks.
Ubiquitous Limited Sensor-based Weather Binary Prediction Network Using Linear and Nonlinear Fittings and 14-gene Genetic Expression

Temperature and humidity are two of the many factors that play vital roles in weather, and these two factors are used in determining the present weather conditions. Not only does it concern meteorology, but especially the food science and agricultural fields. Weather monitoring pertains to an activity in which the state of the atmosphere is analyzed, which usually includes the variables such as wind speed, temperature, humidity, air moisture, pressure, and rainfall. This study uses the Arduino Uno board as a microcontroller and the DHT11 temperature (T) and humidity (H) sensor to gather information about the environment and display it in the LCD module. Simple linear, Gauss-Newton and Nernst-based non-linear, and 14-gene genetic programming regression models were developed and embedded to motes in four selected rain test areas in Metro Manila and Rizal province in predicting two weather states (no rain and raining). The expected result in this system is an approximation as to whether or not it would rain based on the data gathered throughout the project development. Weather data were automatically uploaded and stored in a ThingSpeak server using ESP32, which is viewed in the form of a graph. Based on the results, the temperature changes slightly during rainfall while humidity; on the other hand, changes much more drastically during rainfall and is a key telltale sign of rainfall. Linear regression outperformed other models in binary rain prediction based on temperature and humidity parameters only.
Microprocessor-Based Interactive Mathematics Learning Tool using Real-Time Computer-Vision

When it comes to learning, there are beneficial effects of incorporating gestures in learning mathematics especially within the early ages of life. At first, we learn Math by counting and incorporating our fingers as a guide, as well as writing numbers along on a piece of paper. These gestures are known to be examples of hands-on learning experience for children. Aside from counting, they learn basic mathematical operations as well.Teaching children mathematics is not an easy task. The child’s attentiveness plays a huge factor in the learning process. The average attention span of the child from 2 to 10 years of age is 20 to 30 minutes only. Making the learning interactive can help in maximizing the learning experience of the child within the timespan. Thus, we aim to develop a compact device that is interactive with children when it comes to learning basic mathematical operations such as addition, subtraction, and multiplication.This device has software that uses computer-vision and image detection as a way of teaching children math in an interactive way. The child has two different options of answering, by using hand gestures or premade printed numbers. The child is asked five random basic mathematical questions within a time limit. It has three levels of difficulty which the child must pass in order to progress each level. Furthermore, the software also has audio feedback as a way of letting the child know the correct answer. With these features, children are engaged in an interactive learning opportunity in mathematics.
Using Deep Learning and Adaptive Thresholding Approach for Image-based Baybayin to Tagalog Word Transliteration

The "Baybayin" writing system is a National Cultural Treasure in the Philippines of which a House bill 1022 was formulated in order to revive its use. However, as the new generations of people come, the languages have also evolved and developed making it more challenging to recognize and understand the Baybayin scripts. Thus, this study proposed an alternative approach for word-level Baybayin transliteration on text images. The method entails the following: (1) application of adaptive thresholding technique for Baybayin character extraction, (2) classification of every extracted character to its syllabic equivalent through the generated 97.62% accurate CNN-based Baybayin Optical Character Recognition model, (3) concatenation mechanism for constructing the transliterated word, and (4) word validation using Filipino word dictionary. Using 200 Baybayin word testing images, the transliteration achieved a satisfactory accuracy rate of 92.00%.
Mystic Tours: Cross-Platform Museum Virtual Tourism Application Using 360-degree Imagery and Virtual Reality Technologies

In this paper, the researchers developed a cross-platform virtual tourism application named Mystic Tours that utilizes 360-degree imagery and Virtual Reality (VR) Technologies. Mystic Tours has three (3) main features: navigation, interaction, and perception. For navigation, users can pan, tilt, and zoom the environment to explore the 3D scene, including a map that can easily teleport the user to other parts of the museum. For interaction, users can click on the information icon to open the information canvas consisting of text and images with narration support available in both English and Filipino. For perception, users can view the museum using different perspective modes. For android devices, users can turn on the virtual reality mode and explore the area and interact with objects using the gaze control feature. The proponents of the study implemented data analytics using Unity Analytics to understand the behavioral pattern of the users and make data-driven adjustments and recommendations to museums. For testing, the User Acceptance Test (UAT) was divided into five (5) sections: personal data, web application assessment, android touch assessment, virtual reality mode assessment, and general features assessment. In compliance with ISO 9126, the result showed that the respondents rated the application Excellent Quality (EQ) in terms of functionality, reliability, usability, efficiency, maintainability, and portability. In conclusion, the developed application was highly accepted by the test respondents.
Hardware Performance Evaluation of Different Computing Devices on YOLOv5 Ship Detection Model

Over the years, edge devices have a large impact in terms of processes for artificial intelligence. The advancement quickly created a more accessible technology for the development of AI. Different studies have applied their own ship detection model using a single device. This prevents the maximization of the performance of the model since the best hardware option is not determined. Therefore, in developing a custom detection model, it is necessary to determine the best hardware to be used in the deployment of the model. This study aims to determine the best hardware for deployment. The average pre-processing time is 33.86s, 50.33s, and 8.47s with an average FPS of 4.86, 1.25, and 27.2 for Jetson Nano, Raspberry Pi 4, and desktop computer, respectively. It is observed that the desktop computer performed significantly better, followed by Jetson Nano, and lastly, Raspberry Pi under the same input, configuration, and model. The deciding factor in these results are dependent on the component of the device, which are the OS, CPU, GPU, and memory.
Mask Wearing Detection Model based on Deformable Detr

This paper presents a lightweight Deformable DETR detection method under the condition that the current wearing mask data set has a small number of samples, and the hardware conditions are limited. Traditional target detection has many problems, such as invalid and slow pixel candidate frame, too much RPN output redundancy, too few adjustable super parameters, and complex model adjustment. In this paper, a deformable DETR target detection method is proposed, and the effect of small target detection is good. Compared with other models, it achieves better performance, reduces the training time by 10 times, and greatly improves its fast convergence, computing, and storage efficiency.
Relationship Strength of Handwriting Consistency and Number of Local Features with the Accuracy of a Handwritten Forgery Detection Model

Even with today’s continuation of the digital era, there are still cases where handwriting is still required, which can cause problems when forgery occurs. The current studies did not cover a verification method focusing on handwritten letters and the correlation between the model’s final accuracy and the extracted features used. In this study, a handwriting forgery detection system is proposed, which will be able to differentiate between original and imitated handwriting. A support Vector Machine will be utilized in creating the model. The features that will be used are the aspect ratio and detected local features using Oriented FAST and rotated BRIEF. In addition, the study will also determine the relationship between handwriting consistency and the number of detected local features with the F1 score of the model using Pearson Correlation. The resulting P-value between the F1 score and consistency is 0.1405, lower than the significance level of 0.5, indicating a statistically irrelevant relationship. On the other hand, the acquired P-value between the F1 score and the number of local features is 0.0211, showing a statistically significant correlation between the variable.
Applying Association Rules Mining Approach in Skills Competition based on APRIORI Algorithm

In aid of delivering quality Information Technology Education in the Philippines and to upscale the quality of ITE in the country, organizations, and other universities have participated in extra-curricular and academic activities. The study aimed at determining the associated relationships among six hundred competitors and mentors in a skills competition using the APRIORI Algorithm. The CRISP-DM Model and Python programming language were utilized in the study. Accordingly, there were thirteen (13) predicted rules that will allow competing institutions to carefully study before joining the IT Skills Olympics Competition, as such, the rules set can guide each competing school to design a program for their competing teams and select category of the competition to compete in the future. The researchers recommend further empirical analysis using the dataset by employing other machine learning techniques and algorithms.