Reviewerly/exHarmony huggingface dataset:
Fusion Detection via Distance-Decay Intersection over Union and Weighted Dempster–Shafer Evidence Theory

In recent years, increasing attention has been paid to object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomalies. It is a challenge to effectively utilize the cross-modality information from multisource remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Toward this end, a fast multisource fusion detection framework FusionDet is proposed in the current paper. A novel distance-decay intersection over union is employed to encode the shape properties of the targets with scale invariance. Therefore, the same target in multisource images can be paired accurately. Furthermore, the weighted Dempster–Shafer evidence theory is utilized to combine the optical and synthetic aperture radar detection, which overcomes the drawback in a feature-level fusion that requires a large amount of paired data. In addition, the paired optical and synthetic aperture radar images are taken to demonstrate our fusion algorithm. Considerable experiments and ablation studies on multiple datasets show that our method is generally effective and achieves state-of-the-art detection performance.
Single Accelerometer to Recognize Human Activities Using Neural Networks

Exoskeletons have decreased physical effort and increased comfort in activities of daily living (ADL) such as walking, squatting, and running. However, this assistance is often activity specific and does not accommodate a wide variety of different activities. To overcome this limitation and increase the scope of exoskeleton application, an automatic human activity recognition (HAR) system is necessary. We developed two deep-learning models for HAR using one-dimensional-convolutional neural network (CNN) and a hybrid model using CNNs and long-short term memory (LSTM). We trained both models using the data collected from a single three-axis accelerometer placed on the chest of ten subjects. We were able to classify five different activities, standing, walking on level ground, walking on an incline, running, and squatting, with an accuracy of 98.1% and 97.8%, respectively. A two subject real-time validation trial was also conducted to validate the real-time applicability of the system. The real-time accuracy was measured at 96.6% and 97.2% for the CNN and the hybrid model, respectively. The high classification accuracy in the test and real-time evaluation suggests that a single sensor could distinguish human activities using machine-learning-based models.
Artificial Intelligence at the Service of Medical Imaging in the Detection of Breast Tumors

Artificial intelligence is currently capable of imitating clinical reasoning in order to make a diagnosis, in particular that of breast cancer. This is possible, thanks to the exponential increase in medical images. Indeed, artificial intelligence systems are used to assist doctors and not replace them. Breast cancer is a cancerous tumor that can invade and destroy nearby tissue. Therefore, early and reliable detection of this disease is a great asset for the medical field. Some people use medical imaging techniques to diagnose this disease. Given the drawbacks of these techniques, diagnostic errors of doctors related to fatigue or inexperience, this work consists of showing how artificial intelligence methods, in particular artificial neural networks (ANN), deep learning (DL), support vector machines (SVM), expert systems, fuzzy logic can be applied on breast imaging, with the aim of improving the detection of this global scourge. Finally, the proposed system is composed of two (2) essential steps: the tumor detection phase and the diagnostic phase allowing the latter to decide whether the tumor is benign or malignant.
PDQ 2.0

Reasoning-based query planning has been explored in many contexts, including relational data integration, the SemanticWeb, and query reformulation. But infrastructure to build reasoning-based optimization in the relational context has been slow to develop. We overview PDQ 2.0, a platform supporting a number of reasoningenhanced querying tasks. We focus on a major goal of PDQ 2.0: obtaining a more modular and flexible architecture for reasoning-based query optimization.
Analysis of Blockchain Integration with Internet of Vehicles: Challenges, Motivation, and Recent Solution

Recent advances in vehicular technology, the Internet of Things, blockchain, and cyber-physical systems, have increased the importance of study in these areas. When it comes to the Internet of Vehicles (IoV), the conventional centralised management model has difficulties in coping with the demands of real-time reaction. Blockchain is being used in the IoV to ensure that transactions are permanent and can’t be rolled back once verified. In this paper, we describe IoV and how blockchain technology might be linked to it. On the basis of the current difficulties in IoV and the reason for using blockchain technology in IoV, the purpose of this chapter is to build a thorough study of the applications of blockchain technology in IoV. Additionally, the paper analyse the integration of blockchain technology and IoVs, as well as current solutions to IoV problems, with the goal of making this chapter useful as a jumping-off point for future research into IoV and blockchain-integrated solutions.
The Geologic Model: A Subjective but Core Pillar of a Recoverable Resources Model

Abstract Samples and geologic models are the basis of recoverable resources models. They are pillars of mineral resources and reserves estimation. The geologic model, the most subjective part, is one of the pillars for the determination of an economic block model and requires detailed knowledge of geology, including structures, lithologies, alteration, mineralization types, and genesis of the deposit. A basic conceptual error can cause significant economic losses. Given the relevance, subjectivity, and uncertainties of the information provided for the construction of the geologic model, this paper discusses some of the key variables to be considered for a practical guide to the process of interpretation and building of a robust 3-D geologic model and proposes best practices to avoid or mitigate errors and risks in grade estimation.
Reliable System for Bidding System Using Blockchain

Blockchain innovation that starts from Bitcoin, the first digital currency framework propelled in 2008, can give a viable answer for IoT protection and security. The work applies the blockchain method to the e-sale to determine the two issues. The blockchain is a shared access structure with the end goal that focuses on the structure can confide in one another focuses. Every area can safely convey, verify, and move information to any of different destinations. Therefore, in the decentralized structure, the brought together go-between can be expelled to lessen the exchange cost. The proposal is made out of the location of the auctioneer, the beginning closeout time, cutoff time, the location of the current winner, and the current most significant expense. The keen agreement is a lot of codes and digits actualized by means of the Ethereum stage. The proposal provides better reliability by 11% compared with previous contribution.
iTeach: A User-Friendly Learning Management System

Electronic learning offers various learning methodologies through leveraging the Internet and computer network across the globe. This technological incorporation in education has enabled students to learn in different styles altogether by making the process more comfortable and empowering educators by developing automation tools to create content and effective teaching. Many prevalent platforms provide electronic learning functionality, but they haven’t adopted any strategy for making the lives of content creators easy. Learning such technologies for content creation is cumbersome and discourages people from teaching online who first need to have the technical know-how for these tools. In our application, iTeach is a software application that allows students to learn online by using the content from educators and instructors worldwide, making the process a breeze. The application is updated to the demands of the modern era and uses the latest and in-demand technologies.
A Comparative Study of Machine Learning Techniques for Phishing Website Detection

In recent times, phishing has become one of the most dangerous crimes in cyberspace with the exponential increase of Internet users where an attacker steals the personal and credential information of the users. Website phishing is one of them in which attackers try to deceive the users by creating a fake website similar to the original one. In this paper, we have developed various machine learning-based phishing detection techniques to secure Internet users from a phishing website. Our proposed technique trains the phishing website detector based on structural features of the website obtained from the standard dataset to detect the phishing websites. We have compared various proposed machine learning techniques for phishing websites detection in terms of false positive, true positive, false negative, true negative, and accuracy. The experimental results show that the random forest-based phishing website detector achieves the highest accuracy in comparison with other techniques.
Source Camera Identification Using Hybrid Feature Set and Machine Learning Classifiers

Source camera identification (SCI), which is used to identify the source camera of the images/photos, plays a very important role in today’s era especially in the domain of digital image forensics. Earlier, photo response non-uniformity (PRNU)–based methods were used to identify the source camera of images, but it involves a lot of complexity as PRNU noise gets highly contaminated by image scenes which results in the wrong classification. With the growth in machine learning, researchers developed several methods that work on features extraction and then training them on classifiers which results in better results than PRNU-based methods, but due to limitations of feature space (spatial features), the performance decreases when either number of classes increases or properties of images belong to the same class are different. This chapter proposes a framework based on the frequency and spatial features by performing data augmentation to increase the diversity of images in the dataset and extracting a sufficient amount of feature vectors. These features are extracted from images by applying discrete wavelet transform techniques (DWT) and local binary pattern (LBP). Further, these are trained on multi-class classifiers such as support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbor (KNN). The comparative study of experimental results shows that the proposed method outperforms other state-of-art methods.
Fault detection in wheeled mobile robot based on extended kalman filter

This paper deals with the problem of fault detection in wheeled mobile robots. In fact, the robot is controlled in order to follow a desired trajectory given by applying a well-defined control law. However, faults when appeared may affects the actual trajectory and may lead to unacceptable results. To overcome some drawbacks, we design an Extented Kalman Filter (EKF) to diagnosis eventual faults. Then, the performance of an estimator based on an EKF is simulated based MATLAB Simulink simulations. Finally, the estimated state will then be used for system control purposes.
Extraction of image features based on the HOG method in the HSV color space

In this paper, a fast, effective and robust features extraction system has been developed. To successfully design this hardware architecture, a conversion from RGB to HSV was applied. Likewise, hardware architecture of Histogram of Oriented Gradient (HOG) method was proposed. Vivado System Generator (VSG) tool was used to design architectures. To validate the performance of the proposed system, Benchmarks was applied. Moreover, execution time and resources utilization demonstrated a promising quality of such proposed system.
Fast Gumbel-Max Sketch and its Applications

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> in proportion to its positive weight <inline-formula><tex-math notation="LaTeX">$v_{i}$</tex-math></inline-formula> , the Gumbel-Max Trick first computes a Gumbel random variable <inline-formula><tex-math notation="LaTeX">$g_{i}$</tex-math></inline-formula> for each positive weight element <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> , and then samples the element <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> with the largest value of <inline-formula><tex-math notation="LaTeX">$g_{i}+\ln v_{i}$</tex-math></inline-formula> . Recently, applications including similarity estimation and weighted cardinality estimation require to generate <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> independent Gumbel-Max variables from high dimensional vectors. However, it is computationally expensive for a large <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> (e.g., hundreds or even thousands) when using the traditional Gumbel-Max Trick. To solve this problem, we propose a novel algorithm, <i>FastGM</i> , which reduces the time complexity from <inline-formula><tex-math notation="LaTeX">$O(kn^+)$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$O(k \ln k + n^+)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$n^+$</tex-math></inline-formula> is the number of positive elements in the vector of interest. FastGM stops the procedure of Gumbel random variables computing for many elements, especially for those with small weights. We perform experiments on a variety of real-world datasets and the experimental results demonstrate that FastGM is orders of magnitude faster than state-of-the-art methods without sacrificing accuracy or incurring additional expenses.
Combining DNA Encoding and Chaos for Medical Image Encryption

A vast volume of digital electronic health records is exchanged across the open network in this modern era. Cross all the existing security methods, encryption is a dependable method of data security. This study discusses an encryption technique for digital medical images that uses chaos combined with deoxyribonucleic acid (DNA). In fact, Rossler's and Lorenz's chaotic systems along with DNA encoding are used in the suggested medical image cryptographic system. Chaos is used to create a random key stream. The DNA encoding rules are then used to encode the key and the input original image. A hardware design of the proposed scheme is implemented on the Zedboard development kit. The experimental findings show that the proposed cryptosystem has strong security while maintaining acceptable hardware performances.
Deep Learning-Based Object Detection Approach for Autonomous Vehicles

Recently, object detection is one of the most important methods in autonomous vehicles systems. To address the issue of false recognition and missed identification of small and blocked objects in autonomous vehicle situations, an enhanced deep learning object recognition approach is proposed. This paper proposes GPU-based implementation of intelligent multi-object detection and counting methods. In our approach, the pedestrian tracking by detection task is based on the 4Th version of YOLO and Deep SORT algorithms. For accurate multi-object detection and real-time support, an Enhanced one-stage YOLOv4 is presented. To monitor and count the discovered object, the deep simple online real-time tracker (SORT) method is used. The obtained results suggest that the proposed technique surpasses prior studies, indicating its efficacy as an intelligent vision-based approach for autonomous vehicles.
Single Sample Face Recognition: A Comparative Study between Texture and Deep-Learning-based Features

Single sample face recognition (SSFR) is an active and challenging research subject in the biometric and artificial intelligence communities with the ultimate objective of recognizing individuals using only one facial image per class in the training set. Arguably, feature extraction is the crucial phase in constructing any pattern recognition system, which aims to extract the relevant information that characterizes each pattern. Texture and deep-learning-based approaches are the most prominent strategies for extracting relevant information. This paper compares several SSFR models constructed with texture and deep-learning-based features. We tested four common texture descriptors and seven pre-trained deep learning-based convolutional neural networks (CNNs) models to extract the discriminating face features. Finally, based on the K-Nearest Neighbor (K-NN) classifier, we performed the classification process. The results of the experiments on the unconstrained Alex & Robert (AR) dataset showed superior performance using texture-based approaches compared to the deep-learning-based strategies.
Design of Quantum/Reversible Ternary Adder Circuit

The ternary quantum circuit is very useful in arithmetic and logic operations. This circuit has several advantages as it reduces power dissipation, step time, etc. Adder circuit is the main block of various computing units and for several other complex computer designs. In this current paper, we have used generalized ternary gate (GTG) and reversible gates for the synthesis of reversible half adder and a full adder circuits. The presented designs offer reduced constant inputs and less quantum cost, compared to existing designs. Our proposed design allows a reduction of 40% of the cost and 25% of the number of gates used.
Quantum Approach to Image Data Encoding and Compression

High-resolution images are being generated due to the rapid development of image sensors. Various image processing algorithms require images of reduced sizes, given the computational constraints. Hence, preprocessing the images to reduce their size is crucial for many applications. This letter proposes a novel approach to compress images using quantum computing. A comparative study on different standard data encoding techniques used in quantum computing is undertaken. We propose four quantum compression techniques by extending the unitary operations of amplitude embedding for compressing images. The proposed methods provide exponential scaling as amplitude embedding is used, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2^{n}$</tex-math></inline-formula> classical data values are encoded into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> qubits. Compression performance, visual evaluation, and objective evaluation are carried out to assess the proposed compression techniques. Our experimental results show that the crucial patterns in images are retained in the compressed images even at 75% compression. The compressed images can be used for postprocessing tasks using classical or quantum computing algorithms.
Improved face recognition under varying illumination conditions using webface-based local descriptors

An improved face recognition-based method subjected to varying lighting is proposed. It is performed through achieving the following steps. First, Contrast Equalization (CE) is integrated into the framework of Weberface (WF) method. It reduces as much the lighting impact on the input face image as possible, so it becomes more robust than the one ensured by the standard WF method. Second, The separate implementation of the two distinct local descriptors, which are Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ), as well as the fusion of them descriptors allows enhancing the classification accuracy. They are chosen due to their ability to discriminate and their robustness against a variety of facial variations. The proposed methodology is assessed on two public face datasets. It is found that the identification accuracy of these two extended Yale B and AR datasets is quite challenging as compared to all existing methodologies. The simulation results demonstrate the superiority of the proposed method over existing ones in terms of efficiency and improvement ratio.
Analysis of Text Sentiment Analysis

We wish to ascertain the overall sentiment of a given document, which is the goal of sentiment analysis, also known as opinion mining. Natural language processing and machine learning techniques can be used to extract a document's subjective content. Categorize it as positive, neutral, or negative based on its polarity. It is a very helpful analysis since it allows us to ascertain the general consensus regarding the sale of goods or forecast stock prices for a certain firm. For example, if most people have a positive opinion of a company, its stock prices may rise. Due to the complexity of the language (objectivity/subjectivity, negation, lexicon, and grammar), sentiment analysis is still far from being fully understood.
Context Aware Back-Transliteration from English to Sinhala

The Sinhala language is widely used on social media by using the English alphabet to represent native Sinhala words. The standard script of English language is Roman script. Hence we refer to Sinhala texts transliterated using English alphabet as Romanized-Sinhala texts. This process of representing texts of one language using the alphabet of another language is called transliteration. Over the time Sinhala Natural Language Processing (NLP) researchers have developed many systems to process native Sinhala texts. However, it is impossible to use the existing Sinhala text processing tools to process Romanized-Sinhala texts as those systems can only process Sinhala scripts. Therefore these texts need to be transliterated back using their original Sinhala scripts to be processed using existing Sinhala NLP tools. Transliterating texts backwards using their native alphabet is referred to as back-transliteration. In this study, we present a Transliteration Unit (TU) based back-transliteration system for the back-transliteration of Romanized-Sinhala texts. We also introduce a novel method for converting the Romanized-Sinhala scripts into TU sequences. The system was trained using a primary data set and evaluated using an unseen portion of the same data set as well as a secondary data set which represents texts from a different context to the primary data set. The proposed model has achieved 0.81 in BLEU score and 0.78 in METEOR score on the primary data set while achieving 0.57 in BLEU score and 0.47 in METEOR score on the secondary data set.
Generalized net model of the DOBOT Magician robot functionalities

Applying the theory of Generalized Nets (GN), a GN-model of the DOBOT Magician robot is constructed. Developed GN model describes both the robot’s functionalities, as well as some results of its operation. Presented model may be considered as a basis of a more complex GN model aiming to describe a joint work of a group of robots.