Reviewerly/exHarmony huggingface dataset:
Radar Velocity Measurements Aided Navigation System for UAVs

In this paper, we propose a radar/inertial navigation system (RINS) integrated system for solving the problem of global navigation satellite system (GNSS)-outage. Due to the relatively compact size and low power consumption with reliable measurement, radar can be one of the alternative solutions for the weakness of GNSS. Radar transmits signals to the ground and obtains information such as the target's range, velocity, azimuth, and elevation angle from the reflected echo signal. However, the velocity of multiple targets may include incorrect information from multi-paths or ghost targets. Therefore, outlier mitigation method is required to improve the quality of the velocity measurements from the radar. Calculated velocity then can be used for an extended Kalman filter (EKF) to correct the estimation errors. We also propose an adaptation algorithm, where measurement covariance is updated per time step, based on the standard deviation of the resulted velocity from the eliminated outliers. Simulations were performed using virtual UAV simulator data, and performance is verified compared to EKF. The simulation results confirm that the radar/INS integration system gives high accuracy and robust estimation, and it can be implemented in real-time applications.
Heuristic Trajectory Planning of Robot Manipulator

In this study, different Heuristic Algorithms (HAs) are used for optimizing the point-to-point trajectory planning for a robot manipulator. The objective function for the designed algorithms minimizes trajectory error, traveling time and space. In addition, the algorithms can obtain the optimum trajectory that connects the points avoiding impacts, as the robot manipulator moves from one position to another. Non-dominated sorting genetic algorithm-II (NSGA-II), genetic algorithm (GA), artificial bee colony (ABC) optimization and particle swarm optimization (PSO) are used to optimize the point-to-point motion planning for the robot arm motion studies in the trajectory planning. For each method, the optimal position trajectories towards a start point to target point have been shown. The best cost plots for all algorithms have been introduced. The results of the proposed trajectory planning optimization are compared by means of the travel distance and computation time. In order to compare the simulation results obtained by implementing the HAs, a robot manipulator has been used.
Effective Airplane Detection in High Resolution Satellite Images using YOLOv3 Model

Airplane detection is an artificial intelligence problem that can be solved either by the classical machine learning methods or by the cutting-edge deep learning processes. Generally, object detection is a highly challenging task as it requires recognizing the desired objects in the image in addition to localizing their positions by drawing a rectangular box with a confidence percentage. To obtain a high level of detection accuracy, the developed models are to be trained with hundreds or even thousands of satellite images; however, these images are limited, with low spatial resolution and high expenses. In recent days, deep learning algorithms have proved to excel in numerous object detection tasks due to the availability of huge amount of data along with the continuous advances in computers&#x0027; power technology. This study investigates the problem of airplane detection from satellite imagery by proposing a simple, but an effective tool to achieve excellent detection rates. Our proposed method is mainly based on You Only Look Only (YOLOv3) model, a cutting-edge object detection technique, which is faster and more accurate than most of the traditional and well-known approaches such as Region-based Convolutional Neural Network (R-CNN). According to the experimental results, the overall accuracy of airplanes&#x0027; detection, using KhalifaSat images, is 97.64&#x0025; with a reasonable computational time. Our findings prove that the proposed YOLOv3 model is robust and efficient in detecting airplanes of different dimensions in high resolution satellite imagery.
OCR for Data Retrieval :An analysis and Machine Learning Application model for NGO social volunteering

With the increase in amount of information being made available in digital format, information retrieval is a challenging task. Currently there exists a gap between organizations, volunteers and NGOs for volunteering work. There has been an upsurge of NGOs, non-profit events and corresponding independent volunteers or organizations willing to interconnect especially during these pandemic times. There is a need to fill this gap and connect the stakeholders minimizing the emergency response times. This paper proposes a novel design and implementation of an OCR based application for Automated NGO connect using machine learning. Phases implemented include image de-noising, binarization, data extraction and data conversion. The framework integrates deep learning based Tesseract OCR with image processing module and Data visualization module. The proposed model can be extended to other application domains as well for research purposes.
On a Lower Bound to Classification Error Probability in an Ensemble of Data Sources

For a given ensemble of data sources, we study a classification model in terms of error probability as a function of information quantity in a set of the submitted objects relative to their classes. An analytical lower bound to the average error probability depending on the average mutual information between the ensemble and a set of class-label decisions about the objects is found. For any collection of the discriminant functions, a redundancy of the average error probability relative to the lower bound is defined. Using some weak collections of the discriminant functions in the datasets of face and signature images as well as the compositions of these discriminant functions in the ensemble of the datasets, the comparative estimates of the error probability and the redundancy are calculated.
An Ontological Approach to Detecting Irrelevant and Unreliable Information on Web-Resources and Social Networks

This paper considers an important scientific and applied task of identifying irrelevant and unreliable information on web resources, which is an important area of development and implementation of methods of data mining. The analysis of modern methods and means of estimation of irrelevant and unreliable information from the point of view of estimation of information sources is carried out and the basic problem directions which arise in the course of their functioning are allocated. A system of indicators for filtering unreliable and irrelevant information, which is obtained on the basis of several sources, is proposed. Based on this system, a method of checking information from web resources for relevance and reliability has been implemented. This approach is based on the possibility of using a predefined resource, the data from which are only reliable. A method of detecting inaccurate and irrelevant information has been developed, taking into account the peculiarities of its distribution through relevant pages in social networks and the use of multitasking classification of information obtained from various data sources. The proposed intelligent data processing methods together with other methods of intellectual analysis used to evaluate information obtained from the Internet, will significantly increase the efficiency of the process of establishing irrelevance and inaccuracy of information, and will build an assessment of a particular web resource for publishing and disseminating such information.
Sipster: Settling IOU Privately and Quickly with Smart Meters

Cyber-physical systems revolutionize how we interact with physical systems. Smart grid is a prominent example. With new features such as fine-grained billing, user privacy is at a greater risk than before. For instance, a utility company () can infer users’ (fine-grained) usage patterns from their payment. The literature only focuses on hiding individual meter readings in bill calculation. It is unclear how to preserve amount privacy when the needs to assert that each user has settled the amount as calculated in the bill.
Robust Drive Controlling System Based on Road Sign Detection for Low-Resolution Images

A driver assistance system monitors and alerts the driver continuously about the traffic signs, reducing the chance of accidents due to neglecting or misinterpreting traffic signs. It is a computerized approach with a minimal error rate. The proposed model continuously searches for a traffic sign, estimates its predefined meaning, and alerts the driver to make the decision accordingly. The model architecture processed the German Traffic Sign dataset using 2D-Convolution layers combined with dropout, max pooling and got 99.022% test accuracy. This proposed model can be integrated with advanced driver-assistance systems to aid the driver and prevent accidents due to neglecting or misinterpreting traffic signs.
Novel convolutional neural networks for efficient classification of rotated and scaled images

Abstract This paper presents a novel method for improving the invariance of convolutional neural networks (CNNs) to selected geometric transformations in order to obtain more efficient image classifiers. A common strategy employed to achieve this aim is to train the network using data augmentation. Such a method alone, however, increases the complexity of the neural network model, as any change in the rotation or size of the input image results in the activation of different CNN feature maps. This problem can be resolved by the proposed novel convolutional neural network models with geometric transformations embedded into the network architecture. The evaluation of the proposed CNN model is performed on the image classification task with the use of diverse representative data sets. The CNN models with embedded geometric transformations are compared to those without the transformations, using different data augmentation setups. As the compared approaches use the same amount of memory to store the parameters, the improved classification score means that the proposed architecture is more optimal.
Improved BM3D for Real Image Denoising

Block-matching and 3D filtering (BM3D) and most of its improved algorithms assume that the intensity of the noise image is known, while the real image does not know the specific noise intensity. Moreover, many noise estimation methods have the problem that the estimated value is smaller than the true value. In this case, there will be a problem that the denoising effect of BM3D is not up to expectations. Therefore, we propose an improved BM3D algorithm. A local noise variance estimation algorithm combined with block matching is added to the traditional BM3D to obtain the noise variance of the target block and let it participate in subsequent calculations. The experimental results show that the improved algorithm solves the shortcoming that BM3D cannot directly denoise the real image. At the same time, the algorithm solves the problem that the overall noise variance estimate of the image is too small, which leads to the poor denoising effect of BM3D.
Learning Regexes to Extract Network Names from Hostnames

We present the design, implementation, evaluation, and validation of a system that automatically learns regular expressions (regexes) to extract network names from Internet hostnames assigned by operators using their own conventions. Our fully automated method does not rely on a human to provide a starting regex, labeled examples of valid extractions, or a dictionary of network names. Our method first learns the dictionary of network names, and then automatically generates and evaluates regexes that extract these names. We validate our dictionary against ground truth, finding that 97.3% of the names our regexes extract are valid names for the networks.
A Cloud QoS-driven Scheduler based on Deep Reinforcement Learning

Task scheduling is one of the most challenging issues in cloud management. Because it is a decision-making problem, Reinforcement Learning has been utilized by several works to build an intelligent and efficient cloud task scheduler. However, while resources utilization was used as the key factor to schedule tasks in most studies, another crucial requirement for cloud task scheduling which is tasks' Quality-of-Service (QoS) guarantee has not been approached yet. Therefore, in this paper, we present a Deep Reinforcement Learning-based task scheduler which focuses on QoS guarantee.
Machine Learning-Based Advertisement Banner Identification Technique for Effective Piracy Website Detection Process

In the contemporary world, digital content that is subject to copyright is facing significant challenges against the act of copyright infringement. Billions of dollars are lost annually because of this illegal act. The current most effective trend to tackle this problem is believed to be blocking those websites, particularly through affiliated government bodies. To do so, an effective detection mechanism is a necessary first step. Some researchers have used various approaches to analyze the possible common features of suspected piracy websites. For instance, most of these websites serve online advertisement, which is considered as their main source of revenue. In addition, these advertisements have some common attributes that make them unique as compared to advertisements posted on normal or legitimate websites. They usually encompass keywords such as click-words (words that redirect to install malicious software) and frequently used words in illegal gambling, illegal sexual acts, and so on. This makes them ideal to be used as one of the key features in the process of successfully detecting websites involved in the act of copyright infringement. Research has been conducted to identify advertisements served on suspected piracy websites. However, these studies use a static approach that relies mainly on manual scanning for the aforementioned keywords. This brings with it some limitations, particularly in coping with the dynamic and ever-changing behavior of advertisements posted on these websites. Therefore, we propose a technique that can continuously fine-tune itself and is intelligent enough to effectively identify advertisement (Ad) banners extracted from suspected piracy websites. We have done this by leveraging the power of machine learning algorithms, particularly the support vector machine with the word2vec word-embedding model. After applying the proposed technique to 1015 Ad banners collected from 98 suspected piracy websites and 90 normal or legitimate websites, we were able to successfully identify Ad banners extracted from suspected piracy websites with an accuracy of 97%. We present this technique with the hope that it will be a useful tool for various effective piracy website detection approaches. To our knowledge, this is the first approach that uses machine learning to identify Ad banners served on suspected piracy websites.
Autonomous Unbiased Study Group Formation Algorithm for Rapid Knowledge Propagation

Knowledge propagation is a necessity, both in academics and in the industry.The focus of this work is on how to achieve rapid knowledge propagation using collaborative study groups.The practice of knowledge sharing in study groups finds relevance in conferences, workshops, and class rooms.Unfortunately, there appears to be only few researches on empirical best practices and techniques on study groups formation, especially for achieving rapid knowledge propagation.This work bridges this gap by presenting a workflow driven computational algorithm for autonomous and unbiased formation of study groups.The system workflow consists of a chronology of stages, each made of distinct steps.Two of the most important steps, subsumed within the algorithmic stage, are the algorithms that resolve the decisional problem of number of study groups to be formed, as well as the most effective permutation of the study group participants to form collaborative pairs.This work contributes a number of new algorithmic concepts, such as autonomous and unbiased matching, exhaustive multiplication technique, twisted round-robin transversal, equilibrium summation, among others.The concept of autonomous and unbiased matching is centered on the constitution of study groups and pairs purely based on the participants' performances in an examination, rather than through any external process.As part of practical demonstration of this work, study group formation as well as unbiased pairing were fully demonstrated for a collaborative learning size of forty (40) participants, and partially for study groups of 50, 60 and 80 participants.The quantitative proof of this work was done through the technique called equilibrium summation, as well as the calculation of inter-study group Pearson Correlation Coefficients, which resulted in values higher than 0.9 in all cases.Real life experimentation
Modular Dynamic Neural Network: A Continual Learning Architecture

Learning to recognize a new object after having learned to recognize other objects may be a simple task for a human, but not for machines. The present go-to approaches for teaching a machine to recognize a set of objects are based on the use of deep neural networks (DNN). So, intuitively, the solution for teaching new objects on the fly to a machine should be DNN. The problem is that the trained DNN weights used to classify the initial set of objects are extremely fragile, meaning that any change to those weights can severely damage the capacity to perform the initial recognitions; this phenomenon is known as catastrophic forgetting (CF). This paper presents a new (DNN) continual learning (CL) architecture that can deal with CF, the modular dynamic neural network (MDNN). The presented architecture consists of two main components: (a) the ResNet50-based feature extraction component as the backbone; and (b) the modular dynamic classification component, which consists of multiple sub-networks and progressively builds itself up in a tree-like structure that rearranges itself as it learns over time in such a way that each sub-network can function independently. The main contribution of the paper is a new architecture that is strongly based on its modular dynamic training feature. This modular structure allows for new classes to be added while only altering specific sub-networks in such a way that previously known classes are not forgotten. Tests on the CORe50 dataset showed results above the state of the art for CL architectures.
Evaluation of urban and rural population flow and spatial planning effect under the background of big data

In order to improve the effect of urban and rural regional spatial planning and resource allocation, we need to focus on the factor of urban and rural population flow. Therefore, this paper evaluates the effect of urban and rural population mobility and spatial planning under the background of big data. Based on the evaluation of the effect of urban-rural population migration, according to the evaluation principle of the effect of urban-rural population migration after the reform and opening up, the evaluation index system is constructed, the dimensionless processing of the evaluation index system is carried out, the evaluation index weight is calculated, and the evaluation index is quantified, so as to complete the effect evaluation of spatial planning. Finally, taking Tianjin as an example, the application effect of this design method is verified.
Cowrie Honeypot Data Analysis and Predicting the Directory Traverser Pattern during the Attack

Honeypots are recent innovation in intrusion detection technology. They are the traps designed to basically entrap potential intruders and log their activities. The main objective of such systems is to collect the information about the intruders, deviate them from accessing critical systems, push them to stay on top of the system for some time so their behavior can be observed. We have used Cowrie Honeypot to achieve the above objectives. The log of intruder activities is maintained which is processed and graphically visualized using ELK. This intruder activity data is useful to know the intruder behavior and accordingly safety measures can be employed against that. In extension to data visualization, we have also implemented the probabilistic approach to predict the directory traverser pattern of the intruder. This will help us to understand the next traverser step in advance so that we can take precautionary measures to avoid it.
Practical Deanonymization Attack in Ethereum Based on P2P Network Analysis

Ethereum is the second-largest cryptocurrency, which is an open-source public blockchain platform with smart contract functionality. With the increasing popularity of Ethereum, considerable attention has been paid to its privacy and anonymity. Previous work in Ethereum deanonymization mostly focused on the analysis of its transaction graph and user behaviors. In this paper, for the first time we explored the feasibility of deanonymizing Ethereum users based on P2P network analysis. By measurement and analysis, we observed that the attacker can make connections with approximately 90% mainnet synced full nodes. Based on the well-connected supernode, the deanonymization experiments with basic estimators preliminarily indicate that the anonymity of Ethereum P2P network is pretty limited. To further improve the effect of deanonymization, we implemented and evaluated a machine learning based estimator, which reduces the influence of network delay on deanonymization and thus increases the success rate to 88%. At last, we provide the discussion about the anonymity and efficiency of the propagation mechanisms.
The role of Pronunciation English Learning Applications in Vietnam: An experimental study

English pronunciation teaching and learning are essential in international integration and globalization. Speaking applications were born to support teaching and learning English pronunciation. Combined with the Task-Technology Fit theory, this study used an experimental method to evaluate three methods of English pronunciation teaching and learning for university students including, learning directly with teachers in class, self-study using the mobile app, and a combination of both of them. In addition, the study also built a questionnaire based on two characteristics mentioned in the TTF, namely task characteristics and technology characteristics, specifically, pronunciation English learning applications characteristics and English pronunciation learning characteristics. The research results pointed out that a combination of teaching in class and application adoption for pronunciation is the best method. Finally, this research has contributed to English pronunciation teaching methods in countries using English as a second language and proposed managerial implications for companies producing English-speaking applications on mobile devices.