Miracle-dz/UniFAR-FTUs huggingface dataset:
Bearing Fault Detection of a Single-phase Induction Motor Using Acoustic and Vibration Analysis Through Hilbert-Huang Transform

Bearings are widely used as a low friction component for rotating machines. Engaging research on a bearing is vital to increase life span and improve the reliability of a motor. The main objective of this study is to design a bearing fault detection system for a single-phase induction motor using acoustic and vibration analysis through Hilbert-Huang Transform (HHT). An experimental se-tup was developed to measure the vibration and acoustic signal of a motor rated at 230V and with 125W nominal power. This study introduced an advanced approach to optimizing signals using MATLAB software based on the Hilbert-Huang Transform (HHT) technique. HHT can be used to describe nonlinear distorted waves in detail. Empirical Mode Decomposition (EMD) is the one that deals with the nonlinear and non-steady-state processes to extract complex signals into a finite number of Intrinsic Mode Functions (IMF) which should be achieved for Hilbert Transform (HT) to illustrate the energy time-frequency response of a system. This study successfully developed a single-phase induction motor fault detection system using HHT. The results showed that the inner race fault could be detected with 69% accuracy, outer race fault has 75%, Ball bearing fault has 87%, and contaminated bearing fault lubrication has 68%. The overall accuracy of the detection system could be achieved up to 74.75% accuracy. )
Implementing a Wireless Sensor Network with Multiple Arduino-Based Farming Multi-Sensor Tool to Monitor a Small Farm Area Using ESP32 Microcontroller Board

The Philippines is an agricultural country and in today’s commercial agriculture, technology plays an important role in the development of different sectors of farm management, especially in resource utilization. This paper focuses on implementing a deployable wireless sensor network with multiple nodes centered in an ESP32 microcontroller board, wherein each sensor node has its own multiple sensors, using the ESP-WIFI-MESH protocol, which arranges the sensor nodes in a scalable, self-forming, self-healing, and autonomous mesh network topology for small farm-area monitoring. The input needed for this research are the plant growth parameters which include soil moisture, temperature, relative humidity, and ambient sunlight intensity; they will be obtained by the various sensors connected to a sensor node. The output is a collection of historical data to be displayed to and analyzed by farmers or other relevant groups. A node separation distance tested up to 100 meters can be achieved with RSSI of −88dB. The paper also includes calibrations and testing to improve the capabilities of each node for 10 trials each, wherein the soil moisture has an $\text{RMSE}=2.55\%$ and $\text{SD}=1.30\%$, the ambient sunlight intensity has $\text{RMSE}=4.12$ lux and $\text{SD}=4.84$ lux, the temperature has $\text{RMSE}=1.31{\circ}\text{C}$ and $SD=0.50{\circ}\text{C}$, and lastly, for the relative humidity have an $\text{RMSE}=1.73\%$ and $\text{SD}=0.96\%$. The power consumption of each of the nodes in the system is 0.865W, wherein an average current draw of 173mA drawn from a power supply with a rated capacity of 10,000mAh. The sensor nodes can last for up to 57.3 hours, but the power supply is charged during daytime with the help of solar panels.
A Fast Forest Reverberator Using Single Scattering Cylinders

Simulating forest acoustics has important applications for rendering forest sound scenes in mixed and virtual reality, developing wildlife monitoring systems that use microphone arrays distributed in a forest, or as an artistic effect. Previously proposed methods for forest impulse response (IR) synthesis are limited to small or sparse forests because of their cubic asymptotic complexity with respect to the number of trees. Here we propose a simple and efficient parametric forest IR generation algorithm that relies on a multitude of single scattering cylinders to approximate scattering caused by tree trunks. The proposed method was compared to measured forest IRs in terms of the IR echo density, energy decay, reverberation time (T60), and clarity (C50). Experimental results indicate that the proposed algorithm generates forest reverb with acoustic characteristics similar to real forest IRs at a low computational cost.
Partial Offloading MEC Optimization Scheme using Deep Reinforcement Learning for XR Real-Time M&S Devices

With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (M&S) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.
Split Rendering of the Transparent Channel for Cloud AR

We are the first to apply split rendering to AR to improve the quality of the transparent channel. The proposed method evaluates a cloud-based AR streaming system that offloads photorealistic rendering to a cloud server and splits the rendering between the cloud server and the mobile device (split rendering). Server-side rendering is capable of rendering photorealistic images, but the quality of the image is degraded by coding when the video is compressed and transmitted. In particular, degradation of the transparent channel significantly reduces the subjective image quality. The proposed method avoids this degradation by rendering the transparent channel at the mobile terminal. In addition, the server improves the coding efficiency by padding the transparent areas. Nonlinear quantization of the difference images contributes to improved subjective image quality. Experimentally, we confirmed that the proposed method has a SSIM gain of 0.022 compared to the conventional method.
A scale modeling method of simulation explosion test of soil-structure impactive interaction due to exploding

A simulation explosion test is an important method to study the dynamic property of foundation, the soil-structure impactive interaction due to exploding, and the anti-explosion ability and destroyable mechanism of underground safeguard engineering structure under impactive load due to exploding. The design of scale model similitude is one of keys to insure that simulation explosion test can really reflect the dynamic characters of prototype under impactive load due to exploding.To aim at the safeguard effect of superstratum of saturated sand to defence tunnel of circular arch and vertical wall which isnt destroyed by impactive terra wave as a result of the explosion of bomb falling to the ground, this dissertation will base on the theorem of Bockinghamπwidely applied at present, and a method mainly used is the dimensional analysis method, also the comparabilities of the nonlinear dynamic response of soil-structure system to impactive process of exploding, the dynamic response of soil-structure interface to impactive process of exploding, and the dynamic interaction of exploding impactive terra wave and structure are considered , in order to solve the similitude relation of model design of simulation explosion test of soil-structure impactive interaction due to exploding.
A Survey of Machine Learning Technologies for COVID-19 Pandemic

Intelligent technologies including machine learning and artificial intelligence are playing significant roles in human's battle against COVID-19 pandemic. Machine learning enables the machine to learn and improve on its own without being programmed in detail. Machine learning has now penetrated into many fields to help fight the epidemic. However, a specific and representative review of the contributions of machine learning is currently lacking. The purpose of this paper is to summarize several machine learning applications against COVID-19 including: i) predicting confirmed cases and trend, ii) classifying and diagnosing using ML-based images, and iii) managing medical resources. A database related to machine learning Technologies for COVID-19 is created. Moreover, a concise review is finished on the collected information by evaluating the different uses of machine learning against COVID-19. We also assemble researches in the present COVID-19 literature focused on ML-based methods in order to demonstrate a profound insight into COVID-19 related topics. Our discoveries emphasize crucial variables and available COVID-19 resources that facilitate clinical and translational research.
Evaluating Spectral Magnitude Representation and Spectral Energy for Audio-based Activity Detection

Acoustics has received a great research interest for human activity detection in indoor and outdoor environments. Compared to vision-based approaches, microphones can achieve a high percentage of recognition accuracy in a variety of activities, while not being affected by lighting conditions. Furthermore, audio-based activity detection can be considered an unobtrusive method, as long as the data is not related to speech or other sensitive information and no data is sent on cloud. Selecting the appropriate audio features that can achieve a high recognition accuracy and generalize in multiple domestic environments is a challenging task. In this work, three of the most commonly used spectrogram representations are evaluated, based on their spectral magnitude and the spectral energies. Specifically, using multi-channel audio data, the Short-time Fourier Transform (STFT), the Mel and the Gammatone spectrograms are extracted and trained on a 2D Convolutional Neural Network (CNN). The F1-Scores of each feature representation are computed, while the McNemar tests and the Receiver Operating Characteristic (ROC) curves ensure the statistical independence between the magnitude and the energy representations. Extensive experimental results on a public database for detection of daily activities in a home environment, show that the overall highest recognition accuracy is achieved by the STFT magnitude representations.
Agreement between Powertap, Quarq and Stages power meters for cross-country mountain biking

Abstract Advances in technology have made the use of a variety of power meters ubiquitous in road cycling along with an ever-increasing popularity during mountain biking. This study compared data from one bicycle using three power meters: Stages (non-driveside crank arm); Quarq (chainring spider); and Powertap (rear-wheel hub). While no differences (p > .05) between power meters were present during treadmill riding at high or low cadences, dissimilarities for both power (W) and cadence (rpm) were apparent during actual cross-country mountain bike riding. Frequency distribution and analysis of coasting indicate that the Stages records more time (p < .001) at zero watts (6.9 ± 3.3 s) and zero cadence (6.9 ± 3.3 s) compared with Quarq (W = 3.3 ± 1.5 s, rpm = .8 ± .7 s) and Powertap (W = 1.1 ± .8 s, rpm = 3.0 ± 1.2 s). Consequently, significant interactions (power meter × terrain, p = .0351) and main effects (power meter p < .0001, and terrain p < .0001) for power output were present and included: uphill (317.5 ± 50.7, 340.8 ± 52.6, 327.3 ± 48.6 W); downhill (127.6 ± 12.3, 147.4 ± 23.8, 160.1 ± 24.0 W); and flat (201.1 ± 21.6, 225.2 ± 27.2, 224.0 ± 29.6 W) for the Stages, Quarq and Powertap, respectively. It is likely that accelerometry (Stages) compared with reed switch (Powertap and Quarq) technology to determine cadence, resulted in the discrepancies between power meters. However, while the reliability of the different methods appears acceptable for intermittent exercise such as cross-country mountain biking, the validity of each in such a situation requires confirming.
Users trust assessment based on their past behavior in large scale collaboration

In a large scale peer-to-peer collaboration where control over data is given to users who can decide with whom to share their data, a main challenge is how to compute trust in the collaborators. In this paper we show how to automatically compute users trust according to their past behavior during the collaboration in order to be able to predict their future behavior. We focus on two use cases: contract-based multi-synchronous collaboration and trust game from game theory. We show that computing trust from a single interaction between two users depends on the application domain, but that a general methodology for aggregating trust during the successive interactions between two users can be applied for both use cases.
Highly Reliable Vehicle Detection through CNN with Attention Mechanism

This paper presents a novel approach to provide reliable vehicle detection by using CNN-based approach and lane information. We firstly propose an adaptive RoI strategy that utilizes road lane information to give focus on frontal area for vehicle detection. Then we introduce a novel attention mechanism that automatically learns an attention map to refine the features for detection. Experimental results show a large improvement (+73 % on recall rate) for long-range (30 to 60m) vehicle detection which is extremely useful for users of driving assistant systems.
Security Issue Classification for Vulnerability Management with Semi-supervised Learning

: Open-Source Software (OSS) is increasingly common in industry software and enables developers to build better applications, at a higher pace, and with better security. These advantages also come with the cost of including vulnerabilities through these third-party libraries. The largest publicly available database of easily machine-readable vulnerabilities is the National Vulnerability Database (NVD). However, reporting to this database is a human-dependent process, and it fails to provide an acceptable coverage of all open source vulnerabilities. We propose the use of semi-supervised machine learning to classify issues as security-related to provide additional vulnerabilities in an automated pipeline. Our models, based on a Hierarchical Attention Network (HAN), outperform previously proposed models on our manually labelled test dataset, with an F1 score of 71%. Based on the results and the vast number of GitHub issues, our model potentially identiﬁes about 191 036 security-related issues with prediction power over 80%.
Feature Importance and Deep Learning for Android Malware Detection

: Effective and efﬁcient malware detection is key in today’s world to prevent systems from being compromised, to protect personal user data, and to tackle other security issues. In this paper, we worked on Android malware detection by using static analysis features and deep learning methods to separate benign applications from malicious ones. Custom feature vectors are extracted from the Drebin and the AndroZoo dataset and different data science methods of feature importance are used to improve the results of Deep Neural Network classiﬁcation. Experimental results on the Drebin dataset were signiﬁcant with 99.31% accuracy in malware detection. We extended our work on more recent applications with a complete pipeline for the AndroZoo dataset, with about 40,000 APKs used from 2014 to 2021 pre-tagged as reported malicious or not. The pipeline includes static features extracted from the manifest ﬁle and bytecode such as suspicious behaviors, restricted and suspicious API calls, etc. The accuracy result for AndroZoo is 97.7%, conﬁrming the power of deep learning on Android malware detection.
A Transformer-based Attention Flow Model for Intelligent Question and Answering Chatbot

The question and answer system is a key step and core component in building an intelligent voice assistant, and traditional question and answer systems use various deep learning networks to model this task, in our task, we propose a novel neural network-based model structure that uses the Transformer layer to enhance the model semantic feature extraction capability, and to compensate for the fact that the Transformer is not good at capturing location information, we use a bidirectional LSTM layer to enhance the model's temporal feature extraction capability. In the experiments with SQUAD data, our model achieves an EM index of 68.5 and an Fl score of 77.7, which proves the effectiveness of our model.
You Only Look Once on Power Line Components: An Object Detection Using Unmanned Aerial Vehicle

The traditional practices of foot patrol and manned airborne survey for inspecting electric utility assets are now deemed slow, costly, albeit subject to inaccuracy and hazard. As a result, current efforts turned to unmanned aerial vehicles (UAVs) with onboard cameras and equipped with computer vision technologies. In this paper, the authors address two main challenges of a computer vision system for unmanned aerial vehicles in utility asset inspection: (i) the scarcity of data; and (ii) the detection of six power line components, namely transformer bank, high voltage bushing, low voltage bushing, arrester, radiator fins, and cutoff fuse. In addition, the new curated dataset contains images of the subject of interest taken in pole-mounted and pad-mounted assemblies inside the university using an unmanned aerial vehicle. Furthermore, flipping, injecting different brightness adjustments, and other data augmentations to the dataset simulate the diverse environmental conditions. Finally, the authors re-trained the You Only Look Once (YOLOv5) pre-trained checkpoints on the collected custom data. The experimental results reveal that the proposed system is accurate and precise in detecting and classifying the power line components, with fewer missed detections.
Classification of Otitis Media Infections using Image Processing and Convolutional Neural Network

Developing countries still to this day suffer from misdiagnosis of otitis media infections. Various studies to solve this issue with various success rates. This study explores a different variation of convolutional neural network (CNN), YOLO V3, or Version 3 of You Only Look Once. This algorithm detects particular objects in various forms of media, and one of them is images. Considering it is designed to detect specific objects, it was the perfect candidate to test on detecting Acute Otitis Media (AOM) and Chronic Suppurative Otitis Media (CSOM). These two variations have an object to look for whenever a doctor is diagnosing a case. Inflammation of the middle ear or otitis media (OM) are separate disease entities but may overlap. Hence, it may be confusing for a newly trained doctor to diagnose it correctly. This study achieved an accuracy rate of 75% when 20 images of AOM, CSOM, and normal tympanic membrane were tested. This result can be improved by adding more images into the training datasets using the same camera used in testing. Another appealing feature of YOLOV3 is the low cost of development and the availability of documentation on using and improving it.
Motion-augmented Change Detection for Video Surveillance

The main task of change detection is to segment moving objects from the background. Recently, the deep learning-based change detection method has attracted much attention, but it tends to classify stationary objects as foreground. The reason for this phenomenon is that it identifies objects mainly based on appearance features while the same object has a similar appearance both in motion and stationary state. To address this problem, we propose a novel network called Motion-augmented Change Detection Network (MCDNet) to distinguish moving objects using both motion and appearance information. To achieve this purpose, firstly, we introduce a Motion-augmented Background Model (MBM) to simulate scene background without any foreground objects which can be dynamically updated by the predicted mask. In this way, the motion information can be implicitly highlighted by comparing the current frame and the background. Secondly, we design an Attention Memory Module (AMM) to store past features and use it to guide the segmentation of the current frame, facilitating the extraction of motion and appearance features. Experiments on two challenging public benchmarks (i.e. AGVS 1 and CDnet2014) demonstrate that our proposed method achieves compelling performance against state-of-the-art methods.
Automatic Street Parking Space Detection Using Visual Information and Convolutional Neural Networks

This paper proposes a unique real-time street parking detection scheme that utilizes visual information and the YOLOv4 convolutional neural network to accurately detect available parking spaces. We also introduce a new video dataset that is captured specifically for this task and is used for training our network. Our network being the first of its kind, successfully detects available street parking spaces. Performance evaluations of our model confirm its efficacy across all types of scenarios.
Debated Backpropagation

Dialogue has long been used in human society to explain seemingly opaque concepts. In this paper we focus on how to better explain training models for neural networks, to entertain as well as inform. We present a multi-agent argumentation-based dialogue system to generate human understandable dialogue to explain backpropagation. The system incorporates a model of agent personality and introduces social elements between agents to produce characterful discussion. Natural language templates are used to render utterances in English.
On feasibility of GAN-based fingerprint morphing

Morphing of two fingerprints is shown to be feasible when using a model-based minutia-oriented approach, in which original fingerprint images are cut to two almost equal parts. The morphed fingerprint is a result of assembling two parts of different fingerprints along a cut line. It is important that each part of an original fingerprint in the morphed fingerprint contains enough minutiae to enable the successful matching between the morphed and both original fingerprints. The major drawback of this approach is that the resulting fingerprint often does not appear realistic. Another way to morph fingerprints is exploiting neural generative models. The projections of fingerprints onto the latent space of the generator network are blended and the resulting latent vector is fed to the generator network. In contrast to the model-based approach, a morphed fingerprint almost always appears realistic, but there is no guarantee that it matches successfully both original fingerprints, unless the identity prior is included into the generation process. This paper discusses the advantages and pitfalls of fingerprint morphing using generative adversarial networks (GAN). We experimentally show that GAN-based fingerprint morphing is feasible for creating double-identity fingerprints but fails to anonymize fingerprints i.e. create new virtual identities.