Reviewerly/exHarmony huggingface dataset:
Development of an Augmented Reality Based Educational Game to Aid Elementary School Learning Using Scrum

To comply with health protocols during the COVID-19 pandemic, studies were conducted online. This has had a significant impact on students’ motivation to learn, particularly among elementary school children. This study aims to produce an educational game with the theme of science-based augmented reality on Android devices to aid elementary-level students in their studies. By using the Augmented Reality system, students will be able to engage and interact with a 3D model of the material to be explained. The data collection and game design research method was used by implementing Scrum in the development process. This study used Scrum roles such as Product Owner, Scrum Master, Project Manager, and Development team. This study resulted in the development of an interactive augmented reality app that has successfully increased user’s enthusiasm for learning. Students’ interest in learning science subjects has also increased as a result of the application. This research concludes with the successful development of an augmented reality-based application on Android devices that includes quiz components and augmented reality.
Web Page Blocker using Machine Learning Classifier and Chrome Plugin

In 2020, Most Filipinos are using the internet due to COVID-19 pandemic lockdowns. The internet is not limited to adults and children might be exposed to online adult content and abuse. The Philippine Internet Service providers fail to capture pornographic web pages that are not for child viewing. A Web Page classifier would help in detecting and classifying web pages. In this study, a total of 12000 web pages with adult content and academic web pages were collected using scrapy and existing datasets from DMOZ were used to create a Support Vector Machine (SVM) multi-class classifier. To improve the accuracy of the SVM model, data preprocessing was performed to remove noisy and irrelevant data from the dataset. The text in the web pages was used to train the SVM classifier by using Term Frequency and Inverse Document Frequency, Count vectorizer, and Word2vec Skip-gram embedding with TF-IDF as a multiplier. A series of experiments were conducted using multiple word embedding techniques. The SVM model built using word2vec with TF-IDF multiplier outperforms the SVM model built using TF-IDF and Count Vectorizer. The word embedding generated using word2vec was generated with a window size of 9 and a vector dimension of 900. The SVM model built using word2vec shows an S6% accuracy. The SMV model is deployed in the Django framework and a chrome plugin was created to use the SVM model using REST API.
Understanding Social Media Behavior in Philippines Presidential Election using Natural Language Processing

Philippines Presidential election was conducted last May 9, 2022, and the voter turns out is 83% which is unprecedented. In this study, students at Cebu Technological University were asked for social media accounts and profiles and gathered Facebook posts 1 week before the Philippines May 9, 2022 election using a beautifulsoup and selenium scraper. A total of 300 student Facebook profiles were gathered and a total of 5321 Facebook posts were collected. Data pre-processing was performed to delete irrelevant data such as emoticons, images, and URL. After data pre-processing, a total of 3210 Facebook posts were processed using Latent Dirichlet Allocation (LDA) and K-means clustering. A total of 10 topic models were generated using LDA and 5 clusters were captured using K-means clustering. Open Coding technique was used to analyze the result of each algorithm and the result shows that the topic models are focused on the following narrative: {Marcos Golden Era, VP Leni Angat Buhay, Dilawan J}. For the clusters, the following narratives: { Marcos tops in Surveys, Presidential Debates, Unity of Candidates, Religious leaders endorsing, Marcos disqualification case}. The topic models and clusters were evaluated using human annotators and the result is 50.01% which denotes that the quality of topic and cluster models are on the average level. Sentiment analysis was also performed using Support Vector Machine with 70% training set and 30% evaluation. Each Facebook post was manually labeled in the category of “Negative”, “Neutral” and “Positive”. The accuracy of the model is 73%. All Facebook posts were classified and the result shows that 60% were negative.
Augmented Reality in Chemistry Learning: A Systematic Literature Review

This study reviews factors affecting AR-based chemistry learning with the BIM model. Although there have been many kinds of research explaining the application of augmented reality (AR) and building information modeling (BIM) in learning, no research has been found that discusses the factors affecting the integration of BIM in AR-based learning. This study adopted the SLR method with the PRISMA protocol by scanning articles written in English or articles written in Indonesian from accredited online databases, and fully accessible by the authors. The articles reviewed were related to AR, BIM, Virtual Reality (VR), and chemistry learning. The predefined strings were used to extract the 156 articles. Only 41 articles met the inclusion criteria that were used as the reference for this study. Six factors were found that affect the AR-based chemistry learning with the BIM model. They are AR work performance, accessibility, user interaction with the system, presentation of material, understanding, and models. This study implies that the factors extracted can be considered as a model in developing learning media to improve chemistry learning. The limitation of this study is the absence of further analysis of which critical factors should be addressed. Researchers recommend further research by reviewing more literature.
Camera-wise Training for Enhanced Omni-directional 2D Object Detection

In this paper, we propose a method to perform training and inference with multiple instances of the same deep neural network architecture on images taken from cameras of different directions. Across multiple cameras, depending on each of their directional characteristics, objects viewed from the cameras can form slightly different distributions in visual features. Regarding this, we emphasize the importance of camera-wise training on multiple instances of a given deep neural network for object detection. Given the Waymo Open Perception Dataset, we used multiple instances of the YOLOv5x6 architecture and trained each of them per camera. Such a training scheme on the Training Set achieves better training progression, and the inference results are shown to have AP/L1 as high as 0.6679 on the Testing Set.
Machine Learning-Based Agoraphilic Navigation Algorithm

This paper presents a novel machine learning-based Agoraphilic (free space attraction) navigation algorithm. The proposed algorithm is capable of undertaking local path planning for mobile robots in unknown dynamic environments with a moving goal. The inability to track and reach a moving goal is one of the common weaknesses of most existing navigation algorithms operating in dynamic environments. High uncertainty involved in dynamic environments is also another major challenge. The novel machine learning-based approach helps the proposed algorithm to successfully overcome these challenges. This paper also introduces the integrated modular-based architecture for free-space attraction-based algorithms. This allows the algorithm to incorporate ten different modules with miscellaneous algorithms to perform sub-tasks such as tracking, prediction, map generation, machine learning-based free space attraction force generation and robot motion command generation. The new modular-based architecture integrates those sub-modules to create the robot’s driving force. This driving force is the single attractive force to pull the robot towards the moving goal via current free space leading to future free space passages. The proposed algorithm was experimentally tested under a dynamic environment. The experiment was focused on testing the behaviour of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that the Agoraphilic algorithm is successful in reaching a moving goal in an unknown dynamically cluttered environment.
A Simple Model for Sharing Knowledge Among Heterogeneous Sensor Data

This work presents a simple model that expose the information embedded into a sensor signal allowing to share it independently of the signal nature. Today's highly interconnected world requires a representation of sensor signals that let efficient sharing of embedded information. The proposed model is a state matrix that combine two important aspects of any signal: Its value inside a range and its behavior over the time. From this state matrix is possible to obtain a self-learning model observing the state transition probabilities and the time lapse in each state to deduce signal normality- abnormality that allows to infer a better perception of reality.
How to improve human-robot collaborative applications through operation recognition based on human 2D motion

Human-robot collaborative applications are generally based on some kind of co-working of the human operator and the robot in the execution of a given task. A disruptive change in the collaborative modalities would be given by the capability of the robot to anticipate how it could be of help for the operator. In case of an Autonomous Mobile Robot (AMR), this would imply not only a safe navigation in presence of a human operator, but the automatic adaptation of its motion to the specific operation carried out by the operator. This paper investigates the possibility of achieving operation recognition by monitoring the human motion on a 2D map and classifying his/her path on the map, taken as an image data sample. Deep learning state-of-the-art libraries and architectures are exploited with the aim of making the robotic system aware of the ongoing process. The reported results, relative to a small training dataset, are nonetheless promising.
NaviSaf: A safe navigation system for road anomalies detection

Road anomalies, such as potholes and speed bumps, are a nuisance and a source of annoyance to drivers and road users. This nuisance can affect the mobility and fluidity of road transport and can even lead to road accidents. In order to avoid such issues, drivers should be able to notice and be warned of any upcoming anomaly on their path. This paper proposes a safe navigation system called "NaviSaf" that warns drivers of road anomalies on their paths. To stay informed of every new anomaly, NaviSaf utilizes smartphones built-in sensors, machine learning techniques, crowdsourcing and data fusion techniques. Obtained results demonstrate the capacities of NaviSaf to accurately detect and report road anomalies with a precision of around 97%.
H∞ Model Reduction for Takagi–Sugeno Fuzzy Systems via Space Projection

In this paper, the problem of model reduction for Takagi-Sugeno (T–S) fuzzy systems is studied. The virtual inner disturbance is first constructed to map a potential link between the states of two systems before and after dimensionality reduction. Based on Lyapunov theorem, the H∞ approach is then used to analyze the augmented system with the inner disturbance. The results are unrelated to reduced-order system membership functions (MFs), which can be freely selected. Furthermore, both the number of rules and the size of the reduced-order system and the inner disturbance may be flexibly chosen. An example is adapted to demonstrate the efficiency of the proposed method.
An Online System of Detecting Anomalies and Estimating Cycle Times for Production Lines

Energy consumption data of production machines often exhibit quasi-periodicity, and anomalies are observed when deviations from the quasi-periodicity are detected. For such data, it is crucial to quickly estimate the individual cycles at each time point and detect abnormalities. In this study, we propose a system that satisfies these requirements. The proposed system trains a neural network with an attention mechanism and applies the weight vectors in the mechanism to the two tasks. Experimental results demonstrate that the proposed method outperforms benchmark methods for sensor data that mimic power consumption data of production lines.
Motion-Prediction-Based Obstacle Avoidance Method for Mobile Robots via Deep Reinforcement Learning

Obstacle avoidance is essential for mobile robot when applying to dynamic scenarios. Existing methods that based on deep reinforcement learning (DRL) use the position information as the environment states and neural network input to train the robot in a low efficiency manner, because the position information is unable to indicate obstacle’s motion trend. To address this problem, this paper proposes a new motion-prediction-based obstacle avoidance method for mobile robots based on DRL. The position information of dynamic obstacles in time domain is used to construct a motion trend vector, and together with other motion state factors to form the robot motion guidance matrix, which effectively expresses the motion change trend of dynamic obstacles in a period that provides more valuable information for the robot to choose avoidance action. The experimental results show that the safety of avoiding dynamic obstacles is effectively improved.
Robust Real-time Junction Detection Under Various Conditions Using Dark Channel Maps

The study in this paper aims to demonstrate the performance of a junction detection architecture based on a deep learning paradigm, included with dark channel transformation. We must take into account the hostile conditions when detecting such features. Many of previous papers proposed models for junction detection with hand-crafted logic, which works well under normal conditions but not under hostile conditions. This necessitates a data-driven approach for junction detection. We attempt to do so using two recently proposed deep neural networks: ResNet50 and EfficientNet-B0. Here, given a set of input images of roads with junctions or no junctions, dark channel transformation is applied to better inform the networks about the road regions prominent in the images. According to our experiments on the Oxford RobotCar Dataset, using the dark channel transformation on the ResNet50 trained from scratch can achieve a junction classification accuracy of over 94%. This numerical figure is 8% greater than when the ResNet50 pre-trained on ImageNet is directly trained on RGB Oxford dataset images. When using pre-trained weights of the deep networks, junction classification accuracy rises to 95%, and the precision increases to 99%.
A Pragmatic Framework for Mobile Redundant Manipulator Performing Sequential Tasks

In this paper, a framework combining base placement, path planning and redundancy resolution for a mobile manipulator performing sequential tasks, such as screwing, drilling or assembling tasks, is proposed. For a set of given tasks, the outputs of the proposed algorithm meet the following practical performance indicators: minimization of the number of the base positions, minimization of the number of manipulator joint configuration changes, feasibility of each task considering the force capacity of the manipulator (which takes benefit of redundancy resolution) and path planning of the end-effector motion with obstacle avoidance. The effectiveness of the proposed approach is evaluated considering a 3 DOFs mobile platform and a 7 DOFs manipulator performing screwing in a application with 42 tasks.
An Evaluation of Direct Image Based Visual Tracking System for Autonomous Manipulation

In this paper, we implement and evaluate a direct image-based visual tracking system for autonomous manipulation applications. The direct image-based visual tracking method is developed to relax complex image processing tasks from traditional image and position-based visual tracking methods. The visual signals are constructed by using multiresolution coefficients. The method compares the multiresolution image transformation of the current and desired images and uses the mismatch between them to drive the system. The method needs to design a multiresolution interaction matrix with half and details images. The matrix connects the multiresolution image transformation coefficients with the velocity of the manipulator and controller. To illustrate the effectiveness, the direct image-based tracking system design is implemented and evaluated to locate the position of the valve stem and set of wrenches for autonomous grasping and manipulation for fire fighting applications.
Automated usage control for secure data sharing based on Ricardian contracts

Data is important for the industry to take advantage of digitalization, realize automation, assure quality, and more. Values from data are not only created individually by companies, but also in eco-systems in which data is shared among participating organizations. Secure data sharing is essential in such eco-systems to prevent unauthorized access and use of the data. Usage control extends traditional access control with restrictions concerned with requirements that pertain to data processing contractual obligations, rather than data access provisions only. Thus, usage control is relevant in the context of intellectual property protection, compliance with regulations, and digital rights management. This paper presents a method to negotiate contractual obligations and access provisions, and automatically enforce those provisions with access control. Finalized negotiations establish Ricardian contracts at two levels; a superordinate level with a connected subordinate level. These contracts contain provisions in terms of access control attributes. Using our implementation of a negotiation engine we demonstrate the automatic creation of NIST Next Generation Access Control (NGAC) access control policies. Our negotiation engine uses a lightweight model for the storage of an unforgeable and immutable log of the established contracts based on digital signatures and hashing.
Temporal-spatial Feature Fusion for Few-shot Skeleton-based Action Recognition

Recognizing new action categories from a few reference samples is an encouraging research field because the cost of labeling data is expensive. This work presents a method for few-shot (or one-shot) skeleton-based action recognition by fusing temporal and spatial features of actions. Trajectory primitives are proposed to characterize the temporal features, which can be obtained by segmenting and clustering the trajectories of joints. After that, we modify the original dynamic time warping (DTW) algorithm and use it to measure the similarity between trajectory primitive sequences. Besides, we compute the joint angles as spatial feature vectors. Support vector machines (SVM) are used to classify the joint angle vectors. In this way, the temporal distance matrix can be calculated by modified DTW, and the spatial distance matrix can be obtained by trained SVM. Finally, we fuse temporal and spatial distance matrices by adjusting a parameter to improve recognition accuracy. Furthermore, extensive experiments are conducted on three small-scale datasets to verify the effectiveness of our proposed method.
Broadband Over-the-Air Computation for Federated Learning in Industrial IoT

We consider a broadband over-the-air computation empowered model aggregation scheme for federated learning (FL) in Industrial Internet of Things systems. Due to fading and communication noise, the received global gradient parameters inevitably become inaccurate, leading to a notable decrease of the learning performance. Instead of discarding any edge nodes to reduce the aggregation error, we propose to assign each of them a proper weight coefficient in the model aggregation procedures, i.e., amplitude alignment of the received local gradient parameters from different edge nodes is not required in this paper. We derive an upper bound on the performance loss of the proposed FL scheme, which is shown to be related to the weight coefficients of edge nodes and the mean-squared error (MSE) between the desired global gradient parameters and the actually received ones. Then, we derive a closed-form expression for MSE and use it as the objective function to formulate an optimization problem with respect to the edge nodes’ transmit equalization coefficients, their weight coefficients, and the receive scalars of the cloud server. We transform the formulated optimization problem into a convex one and solve it optimally using CVX. Last, we leverage the popular MNIST dataset and conduct experiments to evaluate the prediction accuracy of the proposed FL scheme. Simulation results demonstrate its superior performances.
Depth Estimation Using Deep Learning Guided By Ontology Reasoning-Based Monocular Cues

Monocular cues are one of the key elements considered by humans to appreciate the depth in their environment, even using only one eye. The idea of this paper is to help a deep neural network to perform the task of monocular depth estimation by integrating the so-called monocular cues into the learning process. To this end, we first propose an ontology model to define several human knowledge, mainly contextual, semantic, and geometric. Thus, thanks to ontology rules that imitate human-like reasoning, monocular cues maps are extracted and integrated into the deep neural network. The proposed approach can be applied in several domains, mainly autonomous driving, either in the urban environment or the industrial one. Our method was validated on the urban driving CityScapes dataset and have shown promising results.
MLR: An Efficient Denoising Model for Highly Corrupted Images

Internet of Things (IoT) consists of devices that generate, process, and exchange vast amounts of images. Unfortunately, these images always contain some kinds of noise, which significantly degrades data utility after the processing center receives the images. Image denoising plays a crucial role to recover the original image approximately from its noisy image according to the features of noise distribution and the structure information of the original image. However, the existing denoising algorithms are invalid when the original images are highly corrupted due to the high computational complexity. Therefore, this paper proposes a novel denoising algorithm based on the multi-low-rank model (MLR), which successively enforces similar blocks, dictionaries, and coefficient matrices approximating to low rank, thereby gradually removing noise. Extensive experimental simulations demonstrate that the MLR algorithm has the optimal denoising performance in terms of denoising quality and efficiency, especially in the case of strong salt noise.