Miracle-dz/UniFAR-FTUs huggingface dataset:
Color Based Image Processing Techniques to Detect Oxide Film during Welding

From naval to aerospace and industry to household, everywhere the necessity of aluminum is felt. But oxide film can be formed on this metal and its alloys during casting or welding due to high affinity of aluminum for oxygen. This phenomenon can further lead to porosity which is a vital defect. Visual inspection is not sufficient due to different human constraints. So, many image processing techniques have been developed. For better welding, we propose here two useful and efficient techniques. Unlike most of the previous techniques, our proposed techniques are color based processing. In this manuscript, our main goal is to detect oxide film and we show that the task is successfully done by our methods.
Towards Practical Privacy-Preserving Decision Tree Training and Evaluation in the Cloud

Due to the capacity of storing massive data and providing huge computing resources, cloud computing has been a desirable platform for doing machine learning. However, the issue of data privacy is far from being well solved and thus has been a general concern in the cloud-aided machine learning. In this work, we investigate the study of how to efficiently do decision tree training and evaluation in the cloud and meanwhile achieve privacy preservation. Unlike existing cloud server-assisted model training approaches, in our proposed solution, the whole training process is mostly done by the cloud service provider who owns the machine learning model. Since the cloud cannot directly divide the encrypted dataset according to the best attributes selected, we propose a new method for decision tree training without dataset splitting. Precisely, we design three methods for decision tree training with the different tradeoff between privacy and efficiency. In all of these methods, the outsourced data are not revealed to the cloud service provider. We also propose a privacy-preserving decision tree evaluation scheme where the cloud service provider learns nothing about the user’s input and the classification result while the trained model is kept secret to the user who could only learn the classification result. Compared with previous decision tree evaluation work, our scheme achieves desirable privacy preservation against both the user and the cloud service provider, and also minimizes the user’s computation and communication costs. Moreover, besides protecting the data confidentiality, our proposed scheme also supports off-line users and thus has good scalability. The real-world dataset-based experimental results demonstrate that our system is of desirable utility and efficiency.
Voltage Control and Current Distribution for Multiple-Coil Wireless Power Transfer System

For single transmitter single receiver high-power wireless power transfer system, the coils suffer from high voltage stress and current stress. As the power requirement improves for application, the coil becomes the bottlenecks of the power improving. To solve the problem of improving the system power level, this research adopts the parallel circuits to reduce the voltage and current stress of coils as well as to improve the power level. And for multiple-transmitter multiple-receiver wireless power transfer (MTMRWPT) system, the current distribution is a problem need to be solved. In this study, a control strategy is developed to provides high system efficiency and constant output voltage, and balances the currents distribution in MTMRWPT system.
Single-Crystal Lithium Niobate Piezoelectric Disk Gyroscope

A mode-matched single-crystal lithium niobate (SC-LN) disk gyroscope has been developed, for the first time, to realize a tactical grade MEMS gyroscope. SC-LN is suitable for piezoelectric transducers because of its high Q-factor and electromechanical coupling coefficient. However, its crystallographic anisotropy prevents from making disk-shaped mode-matched gyroscope. We discovered an optimum orientation to realize uniform in-plane elastic compliance. We designed sixteen-electrode for driving and detecting two modes for operation. Brittle SC-LN disk resonators were processed using a core drill machine. The as-fabricated disk showed 95 kHz wineglass modes with less than 200 Hz frequency split. The angular rate output was successfully obtained and we demonstrated the potential for realizing high performance SC-LN gyroscope.
FPGA-Based Sparsity-Aware CNN Accelerator for Noise-Resilient Edge-Level Image Recognition

This paper presents a novel sparsity-aware CNN accelerator supporting the edge-level image recognition even for noisy images. In the proposed accelerator, we characterize the class of input noises by utilizing the FFT-based on-the-fly noise classifier. The proposed convolution engine then accesses the external memory to load the dedicated network that provides the accurate inference processing for the detected noise class. To save the energy consumed by the external DRAM accesses, in addition, we present the filter-level pruning algorithm with the memory-reduced indexing scheme, which can reduces the processing latency by utilizing the indexing method. To verify the effectiveness of the proposed methods, the proposed CNN accelerator is implemented in the commercialized FPGA-based platform, achieving the processing rate of 57.6GOPS at the speed of 100MHz. Utilizing the dedicated network for each noise type, the prototype accelerator reduces the energy consumption by 62% compared to the conventional network with a similar recognition accuracy, which is suitable for the intelligent edge-level devices subjected to the various noises in practice.
The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

In recent years, with the popularity of smartphones, mobile sensor technologies have been widely used in indoor localization. Its advantage is that data can be collected directly from smartphones without installing any other equipment. Meanwhile, it has the disadvantages of high requirements on positioning conditions and large deviations in positioning results. A technology of crowd-sourcing landmarks-assisted smartphone in indoor localization (CLS-IL) is proposed in this paper so as to solve these disadvantages. Firstly, the accelerometer, magnetometer and gyroscope sensors are used to acquire initial positioning of users. The characteristics of magnetometer and gyroscope sensors are analyzed through a series of experiments in allusion to the key problem of heading estimation errors, then corresponding data is matched for different experimental conditions. After that, the corresponding landmark database is established for experimental environment and an adaptive optimization algorithm of landmarks is proposed to correct drift errors caused by the readings of sensors influenced by surrounding environment and long positioning time excessively during traditional pedestrian dead reckoning. Experimental results show that the location accuracy of CLS-IL system is significantly improved compared with traditional technologies, which provides strong support for subsequent studies.
Solar Position Acquisition Method for Polarized Light Navigation Based on ∞ Characteristic Model of Polarized Skylight Pattern

Inspired by the use of polarized information for navigation behaviors in nature, many researchers have carried out a lot of researches on bionic polarized light navigation. Due to the complexity of the atmospheric radiation transmission process, there is a lot of uncertainty for the bionic polarized light navigation method that accurately calculates the polarization information of the polarized skylight pattern pixel by pixel. This paper studies the distribution of the polarized skylight pattern under different weather conditions through a large number of observational experiments. It is found that the distribution of the angle of polarization shows a stable <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic, which can reflect the macro distribution characteristics and changing laws of the polarized skylight pattern. This paper extracts the <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic from the polarization angle image, then we establish a model of <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic to solve the solar position by calculating the feature similarity indexes between the measured <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic images and Rayleigh <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic images. And we propose an improved harmony search algorithm to solve the optimal solution of the model. By searching in the Rayleigh <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic image database, the solution vector corresponding to the Rayleigh <inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> characteristic image with the highest feature similarity index is the solar position. In this paper, experiments are performed on measured polarized images under sunny and cloudy weather, and the experimental results verify the effectiveness of the proposed algorithm.
Research on Application of Fire Uumanned Aerial Vehicles in Emergency Rescue

This paper studies the application of the unmanned aerial vehicles for fire-fighting in emergency rescue missions, discusses the characteristics of these drones, the ways to participate in emergency rescue missions and related technical indicators. An emergency rescue effectiveness evaluation model based on BPNN network model is proposed. Experiments show that the model has a more objective evaluation effect on various types of emergency rescue missions, which has a good guiding significance for the future fire-fighting drones to participate in emergency rescue operations.
Underwater Image Recognition Detector using Deep ConvNet

Underwater navigation and intelligent object recognition require robust machine learning algorithms to operate in turbid water. Modern life created the man-made pollution in oceans, rivers, and lakes, which contaminate our water resources. Despite environmental regulations solid waste in the form of trash, litter and garbage are thrown directly into sea spoiling the existence of underwater living creatures. The underwater vehicle can be used for survey purposes. The key challenge of underwater image-based localization comes from the unstructured nature of the seabed terrain. So, there is a need for robust detection of the features in such environments is essential. Hence, this paper proposes the automated underwater image recognition detector for submersible imagery. We train a Convolutional neural Network (ConvNet) to classify input 64 × 64 images and considered the classifier as an object feature detector. The features of the image from underwater-bed can be extracted and forward into a network. The output of the three-layer ConvNet with deeply connected network results in a probability distribution over N classes. A Stochastic gradient descent with ADAM optimizer uses the squared gradients to scale the learning rate and reduces the difference between the actual and predicted output. The evaluations are done on the precision, recall, F-Score, macro and weighted Average accuracy for both the detectors. It is observed that our proposed network, achieved an overall accuracy of 93.9 % for correct detections with a binary detector and 90.1% with a multiclass detector compared to existing detectors.
Energy Refactorings for Android in the Large and in the Wild

Improving the energy efficiency of mobile applications is a timely goal, as it can contribute to increase a device's usage time, which most often is powered by batteries. Recent studies have provided empirical evidence that refactoring energy-greedy code patterns can in fact reduce the energy consumed by an application. These studies, however, tested the impact of refactoring patterns individually, often locally (e.g., by measuring method-level gains) and using a small set of applications. We studied the application-level impact of refactorings, comparing individual refactorings, among themselves and against the combinations on which they appear. We use scenarios that simulate realistic application usage on a large-scale repository of Android applications. To fully automate the detection and refactoring procedure, as well as the execution of test cases, we developed a publicly available tool called Chimera. Our findings include statistical evidence that i) individual refactorings produce consistent gains, but with different impacts, ii) combining as much refactorings as possible most often, but not always, increases energy savings when compared to individual refactorings, and iii) a few combinations are harmful to energy savings, as they can actually produce more losses than gains. We prepared a set of guidelines for developers to follow, aiding them on deciding how to refactor and consistently reduce energy.
Rainbow Deep Reinforcement Learning Agent for Improved Solution of the Traffic Congestion

While traffic congestion hits severely the world economy, adaptive traffic signal systems would efficiently provide potential solutions. In this paper, we propose a deep reinforcement learning system to control the signal lights in an isolated intersection. The proposed system uses a deep convolutional neural network to extract the crucial features from the environment state that is described by raw traffic information; i.e., vehicles positions, speeds, and waiting times. Besides, the system utilizes a multi-objective reward and the Rainbow agent which provides further space of enhancements to the conventional Deep Q-Networks agent. Extensive experiments illustrate that our proposed deep framework outperforms the baseline under a number of settings and traffic measures, including trip time, waiting time, fuel consumption, and stability.
Microorganism Image Recognition based on Deep Learning Application

The application of Machine Learning for microorganism, especially bacteria and yeast, recognition becomes attractive because it can reduce the analyzing time of microorganism classification and eliminates human error compare to the classic biological techniques. Therefore, the recognition of microorganism based on Deep Learning increases the efficiency and accuracy of diagnostic process of infected patient. This research studies the possibility to use image classification and deep learning method to recognize bacteria and yeast with the comparison of cell image data-quality between our standard-resolution dataset and high-resolution dataset. We purpose this implementation method of microorganism recognition system using Python programming and the Keras API with Tensorflow Machine Learning framework. The experimental results have shown that bacteria and yeast cell images from microscope are able to be recognized. From the experimental results compare the deep learning methodology of different quality image dataset, our standard resolution dataset could be applied for obtaining more than 80% accuracy of prediction bacteria and yeast.
Using Big Data and AI to Examine Product Engagement in Social Media Influencer Posts

In this paper we explore the recent phenomenon of influencer marketing in social media. Posts of the top-75 social media influencers on Instagram were analyzed over a 12-month period. The types of products in posts were identified for 226, 801 images using an Inception V3 convolutional neural network (CNN). Products were then compared by level of engagement achieved to explore efficiency of engagement by product and influencer type. Results indicated that general influencers performed best, while travel influencers achieved greater overall engagement independent across product types, and specific types of influencers achieved better engagement for particular product types. The research points to the importance of fit between a product and influencer type in achieving impact via influencer marketing. Our findings offer help to brands in selecting influencers to endorse their products.
Development of Automatic Waste Segregator with Monitoring System

Automatic Waste Segregator and Monitoring System is an automated dustbin which can be used by all level of society. This paper proposes an Automatic Waste Segregator and Monitoring System which is designed to sort the wet waste, paper, plastic and aluminum material into each bin respectively. The main objective of this project is to automatically segregate the waste based on its material and monitor daily waste disposal rates and apply proper maintenance of the dustbin. Besides that, this project is to promote 3R (reuse, recycle, reduced) concept among citizens and prototype development that allows janitors to monitor the level of garbage in the dustbin detected by sensors inside the dustbin. When the bin reaches the threshold limit, the janitors is notified via SMS by using Icomsat 1.1 SIM900 GSM module. This project has been tested at the Faculty of Electrical Engineering UiTM Shah Alam. This dustbin will automatically open when someone is close to the dustbin and will not open if the garbage inside it has reached the threshold limit to prevent overflow and mess. Anyone who throws the rubbish in the dustbin will receive “thank you” on the LCD screen as a token of appreciation. In this paper, this project is built by using Microcontroller based platform Arduino Mega 2560 board which is interfaced with ESPS266 12E LoLin NodeMCU V3 module, Inductive proximity sensor, Laser module, Light Dependent Resistor (LDR), Liquid sensor module and Ultrasonic sensor.
Classification of Citation Sentence for Filtering Scientific References

Citation sentence is able to inform readers about relation between scientific articles that cite and are cited by finding its purpose against the research. Besides giving credit to other researchers and recommendation to read other related articles, citation can help readers to know what knowledge they have obtained based on the cited scientific articles they have read. In this research, we try to define citation categories for filtering scientific references which will be initial step in guided summarization of scientific articles. Our goal is to classify citation sentence first into ‘problem’, ‘other’, ‘useModel’, ‘useTool’ and ‘useData’. This category will make it easier to classify scientific articles into more specific topics. Then we use features namely voice, tenses, citation location, meta-discourse and bag of words. Then, we employ SVM Linear for building classification model and sampling technique, namely SMOTE for imbalance dataset. The best result of f-measure for our citation classification is achieved at 61.2% when combining voice& tense, meta-discourse, bag of words and sampling the feature data of UseData category with SMOTE.
Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System

Intrusion detection is one of the most prominent and challenging problem faced by cybersecurity organizations. Intrusion Detection System (IDS) plays a vital role in identifying network security threats. It protects the network for vulnerable source code, viruses, worms and unauthorized intruders for many intranet/internet applications. Despite many open source APIs and tools for intrusion detection, there are still many network security problems exist. These problems are handled through the proper pre-processing, normalization, feature selection and ranking on benchmark dataset attributes prior to the enforcement of self-learning-based classification algorithms. In this paper, we have performed a comprehensive comparative analysis of the benchmark datasets NSL-KDD and CIDDS-001. For getting optimal results, we have used the hybrid feature selection and ranking methods before applying self-learning (Machine / Deep Learning) classification algorithmic approaches such as SVM, Naïve Bayes, k-NN, Neural Networks, DNN and DAE. We have analyzed the performance of IDS through some prominent performance indicator metrics such as Accuracy, Precision, Recall and F1-Score. The experimental results show that k-NN, SVM, NN and DNN classifiers perform approx. 100% accuracy regarding performance evaluation metrics on the NSL-KDD dataset whereas k-NN and Naïve Bayes classifiers perform approx. 99% accuracy on the CIDDS-001 dataset.
Dual Complementary Source Magneto-Electric Dipole Antenna Loaded With Split Ring Resonators

In this paper, a magneto-electric (ME) dipole antenna excited by two slots through a printed ridge gap waveguide (PRGW) is presented. This antenna, which operates in the Ka-band, is considered as a dual complementary source (DCS), and exhibits a higher gain and a wider impedance bandwidth, compared to conventional ME dipoles. The proposed antenna has an impedance matching bandwidth with <inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert < -10$ </tex-math></inline-formula> dB from 24.5 to 40 GHz with a stable gain of 10±1 dBi over the 25–35 GHz band. Moreover, by integrating horizontally three layers, each with an array of <inline-formula> <tex-math notation="LaTeX">$4\times 10$ </tex-math></inline-formula> split-ring resonator (SRR) unit cells on top of the DCS-ME dipole antenna, the realized gain is increased while maintaining the impedance bandwidth of the antenna. The antenna was fabricated and measured to confirm the simulation results. The fabricated prototype has a size of <inline-formula> <tex-math notation="LaTeX">$1.1\times 1\times 0.58\,\,\lambda ^{3}$ </tex-math></inline-formula> (at 30 GHz) and exhibits a measured impedance bandwidth in excess of 50%, from 24 to 40 GHz, a maximum measured gain of 14.2 dBi at 35 GHz, and a measured radiation efficiency of 93% at 30 GHz.
An Empirical Evaluation for Object Initialization of Member Variables in Unit Testing

Automated test case generation techniques usually aim to maximize some coverage criteria. For object oriented languages, like Java, the branches that can be reached in source code, frequently depend on the internal object state. Meaning certain branches will only be taken, if fields inside the tested class are set to specific values. It is however not obvious how much of the internal object state can be controlled. In this paper, we analyzed a corpus of 110 open source systems to evaluate how settable their classes are. Meaning we looked for ways that fields inside classes can be written. For instance, we analyzed the source code to identify setter methods that can be used to set the values of a field. Our results show that 66.5% of fields can be set to a desired value, while 31.5% of fields may be only settable to particular values or require a more in depth analysis. Only for 2% of fields, we did not find any way to set their values.
A Review on Negative Road Anomaly Detection Methods

The main limitation to obstacle avoidance nowadays has been negative road anomalies which is the term we used to refer to potholes and cracks due to their negative drop from the surface of the road. This has for long been a limitation because of the fact that they exist in different, random and stochastic shapes. Today’s technology lacks the presence of sensors capable of detecting negative road anomalies efficiently as the latter surpasses the sensor’s limitations rendering the sensing technique inaccurate. A significant amount of research has been focused on the detection of negative road anomalies due to the fact that this topic is becoming a hot research topic. In this paper, the existing techniques will be reviewed. Their limitations will be highlighted and they will be assessed via certain performance indicators and via some chosen criteria which will be introduced.