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The Bender-Gestalt Test (BGT) is a psychological assessment used to understand the characteristics of a person by evaluating the drawings of the examinees when asked to copy provided shapes. The evaluation results in numerical scores for each shape, which describe the level of psychological issues. This test has been conducted using a pencil and a paper by a human counsellor traditionally, but the results can be contaminated by unconscious bias or mistakes. This paper proposed a digitalized BGT system which utilizes a stylus pen and a tablet to assist a human counsellor. The proposed system records the responses of examinees, and the responses were evaluated by computer algorithms. The proposed system was evaluated by comparing the scores of the proposed system and human experts, with the BGT responses of 28 participants. Both scores were similar in general, but it was found that the scores of the proposed system were more accurate for the ambiguous drawings. The results indicate that the proposed system can be utilized to assist counsellors for the diagnosis of the abnormalities precisely by accurately detecting small differences in the drawings. [ABSTRACT FROM AUTHOR]

In image processing, computer vision algorithms are applied to regions bounded by closed contours. These contours are often irregular, poorly defined, and contain holes or unavailable areas inside. A common problem in computational geometry includes finding the k-sided polygon (k-gon) of maximum area or maximum perimeter inscribed within a contour. This paper presents a generic method to obtain user-defined polygons within a region. Users can specify the number k of sides of the polygon to obtain. Additionally, users can also decide whether the calculated polygon should be the largest in area or perimeter. This algorithm produces a polygon or set of polygons that can be used to segment an image, allowing only relevant areas to be processed. In a real-world application, the validity and versatility of the proposed method are demonstrated. In addition, the source code developed in Java and Python is available in a GitHub repository so that researchers can use it freely. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

Dementia, not a particular disease but rather a gathering of conditions which ascribes the debilitation of atleast two cerebrum capacities, cognitive decline and memory judgment has became ubiquous all over the world. Achieving accuracy and timely diagnosis is a major challenge in dementia detection. The recent advancements in neuroimaging techniques has setup some benchmarks. The amalgamation of computer aided algorithms with various imaging modalities proved their significance in dementia detection and classification. In this paper, a rigrous review of existing work in dementia detection and classification is presented based on the existing literature. Additionally this paper surveys the most important AI (Artificial Intellligence) and profound learning approaches for early identification of dementia. [ABSTRACT FROM AUTHOR]

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice. [ABSTRACT FROM AUTHOR]

In the design of experiments, an optimal design should minimize the confounding between factorial effects, especially main effects and two-factor interaction effects. The general minimum lower-order confounding (GMC) criterion can be used to choose optimal regular designs based on the aliased component-number pattern. This paper aims to study the confounding properties of lower-order effects and provide several computer algorithms to calculate the lower-order confounding in regular designs. We provide a search algorithm to obtain GMC designs. Through python software, we conduct these algorithms. Some examples are analyzed to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Facial recognition is a rapidly growing field with applications in security, surveillance, and human-computer interaction. The performance of the Histogram of Oriented Gradients (HOG) algorithm, a popular feature extraction method for facial recognition, heavily relies on its parameter settings. Optimizing these parameters is crucial for achieving high accuracy. In this paper, we propose a novel approach to optimize HOG algorithm parameters and image size for facial recognition using a Tree-structured Parzen Estimator (TPE)-based Bayesian optimization technique. This optimization process effectively harnesses machine learning performance metrics as the objective function, enabling the algorithm to adapt and enhance its feature extraction capabilities. Extensive experiments conducted across multiple databases, including the AR Face Database, Extended Yale B Database, ORL Database, Yale Face Database, and Facepix, have yielded an exceptional recognition accuracy of 100%. Our optimized handcrafted HOG algorithm highlights the potential and continued relevance of parameter-based handcrafted feature extraction algorithms in computer vision, particularly in resource-constrained environments or applications requiring interpretability. [ABSTRACT FROM AUTHOR]

Blockchain technology finds widespread application across various fields due to its key features such as immutability, reduced costs, decentralization, and transparency. The security of blockchain relies on elements like hashing, digital signatures, and cryptography. However, the emergence of quantum computers and supporting algorithms poses a threat to blockchain security. These quantum algorithms pose a significant threat to both public-key cryptography and hash functions, compelling the redesign of blockchain architectures. This paper investigates the status quo of the post-quantum, quantum-safe, or quantum-resistant cryptosystems within the framework of blockchain. This study starts with a fundamental overview of both blockchain and quantum computing, examining their reciprocal influence and evolution. Subsequently, a comprehensive literature review is conducted focusing on Post-Quantum Distributed Ledger Technology (PQDLT). This research emphasizes the practical implementation of these protocols and algorithms providing extensive comparisons of characteristics and performance. This work will help to foster further research at the intersection of post-quantum cryptography and blockchain systems and give prospective directions for future PQDLT researchers and developers. [ABSTRACT FROM AUTHOR]

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil. [ABSTRACT FROM AUTHOR]

In the Brazilian state of São Paulo, contaminated sites (CSs) constitute threats to health, environment and socioeconomic situation of populations. Over the past two decades, the Environmental Agency of São Paulo (CETESB) has monitored these known CSs. This paper discusses the produced dataset through digitising the CETESB reports and making them accessible to the public in English. The dataset reports on qualitative aspects of contamination within the registered sites (e.g., contamination type and spread) and their management status. The data was extracted from CETESB reports using a machine-learning computer vision algorithm. It comprises two components: an optical character recognition (OCR) engine for text extraction and a convolutional neural network (CNN) image classifier to identify checked boxes. The digitisation was followed by harmonisation and quality assurance processes to ensure the consistency and validity of the data. Making this dataset accessible will allow future work on predictive analysis and decision-making and will inform the required policy-making to improve the management of the CSs in Brazil. [ABSTRACT FROM AUTHOR]

This paper tackles the asynchronous signature-free Byzantine consensus. One way to circumvent the FLP impossibility result consists in adding a Time-free assumption. This assumption is based on the pattern of messages that are exchanged. In the context of authenticated asynchronous Byzantine systems where at most t processes may exhibit a Byzantine behavior, Moumen and Mostéfaoui provide the main result. They assume at least one correct process p i , called ⋄ ⟨ t + 1 ⟩ -winning , and a set Q of t correct processes such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The main contribution of this paper is to show that a deterministic solution for the Signature-free Byzantine consensus problem is possible if the system model satisfies an additional assumption that relies on the pattern of exchanged messages. To solve the Consensus problem, we assume a correct process p i , called ⋄ ⟨ 2 t + 1 ⟩ -winning , and a set Q of (2 t + 1) correct processes (including p i itself) such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The processes of the set Q may change over time. Based on this assumption, a Signature-free Multivalued Byzantine consensus protocol is proposed. Whereas many time-free protocols have been designed for the consensus problem in the crash model and in the Byzantine Authenticated model, this is, to our knowledge, the first time-free deterministic solution to the Signature-free Byzantine consensus Problem. [ABSTRACT FROM AUTHOR]

We present a mixed reality-based assistive system for shading paper sketches. Given a paper sketch made by an artist, our interface helps inexperienced users to shade it appropriately. Initially, using a simple Delaunay-triangulation based inflation algorithm, an approximate depth map is computed. The system then highlights areas (to assist shading) based on a rendering of the 2.5-dimensional inflated model of the input contour. With the help of a mixed reality system, we project the highlighted areas back to aid users. The hints given by the system are used for shading and are smudged appropriately to apply an artistic shading to the sketch. The user is given flexibility at various levels to simulate conditions such as height and light position. Experiments show that the proposed system aids novice users in creating sketches with impressive shading. [ABSTRACT FROM AUTHOR]

Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or algorithm-assisted human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment. [ABSTRACT FROM AUTHOR]

The human visual system unconsciously determines the color of the objects by "discounting" the effects of the illumination, whereas machine vision systems have difficulty performing this task. Color constancy algorithms assist computer vision pipelines by removing the effects of the illuminant, which in the end enables these pipelines to perform better on high-level vision tasks based on the color features of the scene. Due to its benefits, numerous color constancy algorithms have been developed, and existing techniques have been improved. Combining different strategies and investigating new methods might help us design simple yet effective algorithms. Thereupon, we present a color constancy algorithm based on the outcomes of our previous works. Our algorithm is built upon the biological findings that the human visual system might be discounting the illuminant based on the highest luminance patches and space-average color. We find the illuminant estimate based on the idea that if the world is gray on average, the deviation of the brightest pixels from the achromatic value should be caused by the illuminant. Our approach utilizes multi-scale operations by only considering the salient pixels. It relies on varying surface orientations by adopting a block-based approach. We show that our strategy outperforms learning-free algorithms and provides competitive results compared to the learning-based methods. Moreover, we demonstrate that using parts of our strategy can significantly improve the performance of several learning-free methods. We also briefly present an approach to transform our global color constancy method into a multi-illuminant color constancy approach. [ABSTRACT FROM AUTHOR]

With the continuous progress of UAV (unmanned aerial vehicle) flight technology, more and more outdoor vision tasks begin to rely on UAV to complete, many of which require computer vision algorithms to analyze the information captured by the camera. However, it is difficult to deploy detectors on embedded devices due to the challenges among energy consumption, accuracy, and speed. In this paper, we propose an end-to-end object detection model running on a UAV platform that is suitable for real-time applications. Through the research of shufflenetv2 and mobilenetv3, a new feature extraction network structure is proposed. In order to improve the detection accuracy without losing the detection efficiency, a multi-scale fusion module based on deconvolution is added. Experiments show when deployed on our onboard Nvidia Jetson TX2 for testing and inference, our model combined with a modified focal loss function, produced a desirable performance of 21.7% mAP for object detection with an inference time of 17 fps. [ABSTRACT FROM AUTHOR]

For the first time, this paper carried out a comprehensive study on static bending, free vibration, and dynamic responses of non-uniform-thickness piezoelectric nanoplates resting on variable elastic foundations with the flexoelectric effect, where the thickness of the structure varies in both length and width directions. The whole structure is supported by a two-parameter elastic foundation, the stiffness of which varies in the direction of the plate's length. The overall equation governing the static bending, free vibration, and forced vibration of the plate is established using four-unknown high-order shear deformation theory and the Lagrange principle. A collection of Matlab-coded computer algorithms is developed for determining the nanoplate's response to any boundary condition. The approach and program's correctness are proved numerically against the dependable assertions in special instances acquired from the article's model. The effect of geometrical and material characteristics on the mechanical responses of nanoplates is investigated in detail. The results reveal that the static bending, specific vibration, and forced vibration responses of the nanoplate change significantly when compared to a conventional plate due to the influence of variable thickness, altering elastic matrix stiffness, and especially the flexoelectric effect. [ABSTRACT FROM AUTHOR]

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA–Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA–disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 ± 0.0043, 0.9449 ± 0.022, 0.9375 ± 0.0331 and 0.9556 ± 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA–disease associations. TLDAEXC utilizes disease semantic similarity, lncRNA expression similarity, and Gaussian interaction profile kernel similarity of lncRNAs and diseases for feature construction. The constructed features are fed to a deep autoencoder to extract reduced features, and an XGBoost classifier is used to predict the lncRNA–disease associations based on the reduced features. The fivefold and tenfold cross-validation experiments on a benchmark dataset showed that LDAEXC could achieve AUC scores of 0.9676 and 0.9682, respectively, significantly higher than other state-of-the-art similar methods. [ABSTRACT FROM AUTHOR]

Detection and measurement of cracks in asphalt pavement is one of the important tasks in transport industry to determine the quality of the pavement and submit repair requirements. In recent years, computer vision algorithms have been increasingly used to automate the solution of this problem. Therefore, researchers are faced with the acute issue of improving the accuracy of segmentation algorithms, since the safety of people depends on the timely detection of defects on the road. In this paper, ensemble methods based on Choquet and Sugeno fuzzy integrals are proposed to combine the scores of three pre-trained deep learning models: ResNet50, DenseNet169, and InceptionV3. We tested the proposed methods on a public dataset and compared the results with already-used popular ensemble methods. [ABSTRACT FROM AUTHOR]

High-throughput materials synthesis methods, crucial for discovering novel functional materials, face a bottleneck in property characterization. These high-throughput synthesis tools produce 104 samples per hour using ink-based deposition while most characterization methods are either slow (conventional rates of 101 samples per hour) or rigid (e.g., designed for standard thin films), resulting in a bottleneck. To address this, we propose automated characterization (autocharacterization) tools that leverage adaptive computer vision for an 85x faster throughput compared to non-automated workflows. Our tools include a generalizable composition mapping tool and two scalable autocharacterization algorithms that: (1) autonomously compute the band gaps of 200 compositions in 6 minutes, and (2) autonomously compute the environmental stability of 200 compositions in 20 minutes, achieving 98.5% and 96.9% accuracy, respectively, when benchmarked against domain expert manual evaluation. These tools, demonstrated on the formamidinium (FA) and methylammonium (MA) mixed-cation perovskite system FA1−xMAxPbI3, 0 ≤ x ≤ 1, significantly accelerate the characterization process, synchronizing it closer to the rate of high-throughput synthesis. While materials synthesis methods have seen advancements in speed and efficiency, characterization techniques have lagged behind. Here, authors design automated computer vision algorithms to accurately characterize hundreds of materials in minutes. [ABSTRACT FROM AUTHOR]

Vehicles controlled by intelligent technologies, whose goal is to reduce human error and ease congestion, do not solely rely on human resources. Cities worldwide use camera systems to monitor the traffic, which collects the images and processes them through different computer vision algorithms. It is challenging for traffic monitoring systems to maintain their accuracy during the day and night lighting conditions, camera location relative to objects, video quality, traffic light position relative to the crossing line, and object angle from the surveillance camera. In this paper, we propose an improved traffic light violation detection method that concurrently streams videos through Apache Kafka and processes them with Apache Spark. It continues to operate for long periods without human intervention and adjusts automatically to changes in the environment. The violation detection algorithm utilizes a modified YOLOv5 and Hough space to efficiently capture the violation. YOLOv5 is a lightweight, fast, and efficient algorithm for real-time object detection. The improved YOLOv5 retrieves the object coordinates relative to the traffic lights, and Hough space analysis is employed to determine the violation region during the red traffic light. Hough space considers the object's location and angle relative to the traffic lights. The model performs well in various situations of the input video datasets, as validated by performance metrics. The outcomes of extensive experiments show that the approach is well suited for deployment in real-time traffic violation detections. The outcomes are compared to a number of performance measures for object identification and traffic violations. In terms of traffic light violations, the model had 88.24% accuracy. The model is scalable enough, and it can deal effectively with real-world traffic video data at large scales. [ABSTRACT FROM AUTHOR]

The complexity of searching algorithms in classical computing is a classic problem and a research area. Quantum computers and quantum algorithms can efficiently compute some classically hard problems. In addition, quantum machine learning algorithms could be an important avenue to boost existing and new quantum-based technology, reducing the supercomputing requirements for executing such problems. This paper reviews and explores topics such as variational quantum algorithms, kernel methods, and Grover's algorithm (GA). GA is a quantum search algorithm that achieves a quadratic speed improvement as a quantum classifier. We exploit GA or amplitude amplification to simulate rudimentary classical logical gates into quantum circuits considering AND, XOR, and OR gates. Our experiments in our review suggest that the algorithms discussed can be implemented and verified with relative ease, suggesting that researchers can investigate problems in the areas discussed related to quantum machine learning and more. [ABSTRACT FROM AUTHOR]

Network attacks are becoming more complex, making it more difficult to detect intrusions. Various research have been done over the years, employing different categorization techniques of Data Mining (DM) and Machine Learning (ML) inspired hybrid approaches to develop robust IDS. Almost all researchers suggested to improve accuracy in intrusion detection with low computational cost. Authors observed that dissimilar sets of features were picked for different classifiers to get the highest accuracy. This paper is dedicated to a review of relevant research, where an in-depth investigation was carried out with two emphasis points of IDS, which contain distinct pre-processing techniques in the form of feature selection and a diversity of classification algorithms. In addition, this paper presents a comparative algorithmic assessment of the DM and ML techniques applied to create an intelligent IDS. A novel feature selection method based on the CART algorithm has also introduced which provides optimal feature subset of the dataset so perfectly that it has made various existing DM and ML classifying algorithms more performant than earlier, this makes classifiers independent of feature selection. To validate the performance of proposed work, experiments have performed using the 'Python' programming language and 'corrected' & '10_percent' of 'Kddcup99' datasets used as benchmark. As an outcome proposed work, we found that feature reduction and selecting a classifier had a significant influence on the rate of intrusion detection accuracy. The results of simulation and comparison analysis of proposed work with existing DM and ML based classification approaches show that the suggested work is more competent in true prediction and attaining maximum intrusion detection accuracy with minimal computing cost of prediction. [ABSTRACT FROM AUTHOR]

In recent years, computer vision algorithms have become more powerful, which enabled technologies such as autonomous driving to evolve rapidly. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e. g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently (denoted as provident vehicle detection). For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively (react before the oncoming vehicle is directly visible). Using this experimental setting, the provident vehicle detection system's time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently (detection based on observable visual cues the objects cause before they are visible) and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward. [ABSTRACT FROM AUTHOR]

Increasing knowledges in the computer algorithms for calculating morphometric properties of the Earth's surface has provided in the last decades many methods of semi-automated extraction and classification of landforms. The use of digital elevation models (DEMs) and specific algorithms in GIS applications has made easier and faster the semi-automated recognition of landform classes. In this paper, we propose a new toolbox, implemented from the original version of the Jenness's landform classification, which takes into account a revision of the standard landform classes extracted by GIS. The new toolbox allowed us to extract 48 landform classes, and refined the typology of landform features in different natural landscapes, from a geomorphological point of view. The landforms classification and the morphometry of six different selected sites pertained to volcanic areas, mountain ridges, karst endorheic basins, hilly areas, fluvial sectors, and coastal plain areas were analysed and discussed. The selected areas are located in Southern Italy, where many different geological and geomorphological landscapes exist. [ABSTRACT FROM AUTHOR]

The fingerprinting-based positioning has great potential for indoor location estimation where GPS signals are mostly blocked. However, fingerprinting-based methods need a calibration step for establishing a fingerprint map. The site survey process should be performed to record fingerprints which is a labor-intensive task essentially in large buildings. In this paper, we address the pedestrian trajectory reconstruction problem for fingerprint map creation. Where the goal is to predict and refine users' trajectories obtained from smartphone sensor measurements. Our proposed spatial–temporal matching mechanism consists of three stages. First, the initial trajectory is calculated using the PDR algorithm. Then, landmarks error is eliminated using the proposed forward/backward error correction (FEC/BEC) algorithm. Afterward, the proposed dynamic time warping-based path-matching (DTW-PM) method applies to handle map-related errors. The evaluation results show positioning accuracy improves up to 53.69%. Finally, a traditional KNN algorithm is performed to evaluate the positioning efficiency over generated radio map, which validates the quality of the obtained radio map. [ABSTRACT FROM AUTHOR]

Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output image that presents more effective and useful information from two images. This paper proposes a multi-modal medical image fusion algorithm based on the nonsubsampled contourlet transform (NSCT) and pulse coupled neural networks (PCNN) methods. The input images are decomposed using the NSCT method into low- and high-frequency subbands. The PCNN is a fusion rule for integrating both low- and high-frequency subbands. The inverse of the NSCT method is to reconstruct the fused image. The results of medical image fusion help doctors with disease diagnosis and patient treatment. The proposed algorithm is tested on six groups of multi-modal medical images using 100 pairs of input images. The proposed algorithm is compared with eight fusion methods. We evaluate the performance of the proposed algorithm using the fusion metrics: peak signal to noise ratio (PSNR), mutual information (MI), entropy (EN), weighted edge information (Q A B / F ), nonlinear correlation information entropy (Q ncie ), standard deviation (SD), and average gradient (AG). Experimental results show that the proposed algorithm can perform better than other medical image fusion methods and achieve promising results. [ABSTRACT FROM AUTHOR]

Customization of cardiac action potential models has become increasingly important with the recognition of patient-specific models and virtual patient cohorts as valuable predictive tools. Nevertheless, developing customized models by fitting parameters to data poses technical and methodological challenges: despite noise and variability associated with real-world datasets, traditional optimization methods produce a single "best-fit" set of parameter values. Bayesian estimation methods seek distributions of parameter values given the data by obtaining samples from the target distribution, but in practice widely known Bayesian algorithms like Markov chain Monte Carlo tend to be computationally inefficient and scale poorly with the dimensionality of parameter space. In this paper, we consider two computationally efficient Bayesian approaches: the Hamiltonian Monte Carlo (HMC) algorithm and the approximate Bayesian computation sequential Monte Carlo (ABC-SMC) algorithm. We find that both methods successfully identify distributions of model parameters for two cardiac action potential models using model-derived synthetic data and an experimental dataset from a zebrafish heart. Although both methods appear to converge to the same distribution family and are computationally efficient, HMC generally finds narrower marginal distributions, while ABC-SMC is less sensitive to the algorithmic settings including the prior distribution. [ABSTRACT FROM AUTHOR]

With the development of Computer-aided Diagnosis (CAD) and image scanning techniques, Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis. Therefore, WSI analysis has become the key to modern digital histopathology. Since 2004, WSI has been used widely in CAD. Since machine vision methods are usually based on semi-automatic or fully automatic computer algorithms, they are highly efficient and labor-saving. The combination of WSI and CAD technologies for segmentation, classification, and detection helps histopathologists to obtain more stable and quantitative results with minimum labor costs and improved diagnosis objectivity. This paper reviews the methods of WSI analysis based on machine learning. Firstly, the development status of WSI and CAD methods are introduced. Secondly, we discuss publicly available WSI datasets and evaluation metrics for segmentation, classification, and detection tasks. Then, the latest development of machine learning techniques in WSI segmentation, classification, and detection are reviewed. Finally, the existing methods are studied, and the application prospects of the methods in this field are forecasted. [ABSTRACT FROM AUTHOR]

Recent state-of-the-art Face Detection algorithms in the field of Computer Vision focus greatly on real-time processing and results. The applications using these algorithms deal with low quality video feeds having less Pixels Per Inch (ppi) and/or low frame rate. The algorithms perform well with such video feeds, but their performance deteriorates towards high quality, high data-per-frame videos. Such video files mostly exist in offline mode, that could be used for post processing by the Computer Vision applications. This paper focuses on developing such an algorithm that gives faster results on high quality videos, at par with the algorithms working on live low quality video feeds. The proposed algorithm uses Convolutional-MTCNN as base algorithm, and speeds it up for high definition videos. This paper also presents a novel solution to the problem of occlusion and detecting partial or fully hidden faces in the videos. This is achieved by using probabilistic approaches, given that the face has been identified in first few frames, to give the algorithm an estimate of where the face should be in the occluded region. [ABSTRACT FROM AUTHOR]

Enhancements in the structural and operational aspects of transportation are important for achieving high-quality mobility. Toll plazas are commonly known as a potential bottleneck stretch, as they tend to interfere with the normality of the flow due to the charging points. Focusing on the automation of toll plazas, this research presents the development of an axle counter to compose a free-flow toll collection system. The axle counter is responsible for the interpretation of images through algorithms based on computer vision to determine the number of axles of vehicles crossing in front of a camera. The You Only Look Once (YOLO) model was employed in the first step to identify vehicle wheels. Considering that several versions of this model are available, to select the best model, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 were compared. The YOLOv5m achieved the best result with precision and recall of 99.40% and 98.20%, respectively. A passage manager was developed thereafter to verify when a vehicle passes in front of the camera and store the corresponding frames. These frames are then used by the image reconstruction module which creates an image of the complete vehicle containing all axles. From the sequence of frames, the proposed method is able to identify when a vehicle was passing through the scene, count the number of axles, and automatically generate the appropriate charge to be applied to the vehicle. [ABSTRACT FROM AUTHOR]

Mask wearing has been required in various settings since the outbreak of COVID-19, and research has shown that identity judgements are difficult for faces wearing masks. To date, however, the majority of experiments on face identification with masked faces tested humans and computer algorithms using images with superimposed masks rather than images of people wearing real face coverings. In three experiments we test humans (control participants and super-recognisers) and algorithms with images showing different types of face coverings. In all experiments we tested matching concealed or unconcealed faces to an unconcealed reference image, and we found a consistent decrease in face matching accuracy with masked compared to unconcealed faces. In Experiment 1, typical human observers were most accurate at face matching with unconcealed images, and poorer for three different types of superimposed mask conditions. In Experiment 2, we tested both typical observers and super-recognisers with superimposed and real face masks, and found that performance was poorer for real compared to superimposed masks. The same pattern was observed in Experiment 3 with algorithms. Our results highlight the importance of testing both humans and algorithms with real face masks, as using only superimposed masks may underestimate their detrimental effect on face identification. [ABSTRACT FROM AUTHOR]

Advanced photodetectors with intelligent functions are expected to take an important role in future technology. However, completing complex detection tasks within a limited number of pixels is still challenging. Here, we report a differential perovskite hemispherical photodetector serving as a smart locator for intelligent imaging and location tracking. The high external quantum efficiency (~1000%) and low noise (10−13 A Hz−0.5) of perovskite hemispherical photodetector enable stable and large variations in signal response. Analysing the differential light response of only 8 pixels with the computer algorithm can realize the capability of colorful imaging and a computational spectral resolution of 4.7 nm in a low-cost and lensless device geometry. Through machine learning to mimic the differential current signal under different applied biases, one more dimensional detection information can be recorded, for dynamically tracking the running trajectory of an object in a three-dimensional space or two-dimensional plane with a color classification function. Differential spectrometers recognise different wavelength via their differential photodetector responsivity. The authors combine an 8-pixel hemispherical perovskite photodetector with neural network algorithms to realise 3D trajectory tracking and 2D location tacking with colour classification. [ABSTRACT FROM AUTHOR]

Computer vision aims to produce an understanding of digital image's content and the generation or transformation of images through software. Today, a significant amount of computer vision algorithms rely on techniques of machine learning which require large amounts of data assembled in collections, or named data sets. To build these data sets a large population of precarious workers label and classify photographs around the clock at high speed. For computers to learn how to see, a scale articulates macro and micro dimensions: the millions of images culled from the internet with the few milliseconds given to the workers to perform a task for which they are paid a few cents. This paper engages in details with the production of this scale and the labour it relies on: its elaboration. This elaboration does not only require hands and retinas, it also crucially zes mobilises the photographic apparatus. To understand the specific character of the scale created by computer vision scientists, the paper compares it with a previous enterprise of scaling, Malraux's Le Musée Imaginaire, where photography was used as a device to undo the boundaries of the museum's collection and open it to an unlimited access to the world's visual production. Drawing on Douglas Crimp's argument that the "musée imaginaire", a hyperbole of the museum, relied simultaneously on the active role of the photographic apparatus for its existence and on its negation, the paper identifies a similar problem in computer vision's understanding of photography. The double dismissal of the role played by the workers and the agency of the photographic apparatus in the elaboration of computer vision foreground the inherent fragility of the edifice of machine vision and a necessary rethinking of its scale. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fault detection system by the combination of Support Vector Data Description and Squirrel Search Algorithm. This approach is capable to deal with processes or machines where the number of fault observations is small or not even available for training phase. In this work the use of classic Support Vector Data Description as well as its fast version with two kernel functions is proposed. The experimental results showed that the proposed system exhibits suitable capabilities for fault detection in complex industrial processes such as the one presented in this research. Moreover, a nonparametric statistical analysis is also included in order to compare the considered strategies and enhance the efficiency of the presented approach. Finally, a comparison with genetic algorithm approach and the one-class classifier based on support vectors is carried out which shows that the proposed algorithm outperforms traditional techniques. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today's financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from "Yahoo Finance". Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values. [ABSTRACT FROM AUTHOR]

The security of many public key cryptosystems that are used today depends on the difficulty of factoring an integer into its prime factors. Although there is a polynomial time quantum-based algorithm for integer factorization, there is no polynomial time algorithm on a classical computer. In this paper, we study how to improve the wheel factoring method using two approaches. The first approach is introducing two sequential modifications on the wheel factoring method. The second approach is parallelizing the modified algorithms on a parallel system. The experimental studies on composite integers n that are a product of two primes of equal size show the following results. (1) The percentages of improvements for the two modified sequential methods compared to the wheel factoring method are almost 47 % and 90 % . (2) The percentage of improvement for the two proposed parallel methods compared to the two modified sequential algorithms is 90 % on the average. (3) The maximum speedup achieved by the best parallel proposed algorithm using 24 threads is almost 336 times the wheel factoring method. [ABSTRACT FROM AUTHOR]

In 2019, Yonghae Lee et al. combined the circuit implementation of the Harrow–Hassidim–Lloyd (HHL) algorithm with a classical computer, and designed a hybrid HHL algorithm to reduce experimental errors caused by decoherence and so on. However, the improvement is achieved only in the auxiliary quantum coding phase, and no quantum resource reduction is done on the quantum phase estimation and inverse quantum phase estimation stages. At the same time, the circuit improvement illustration on a 2 × 2 linear system just has the result and no specific process. In this paper, based on the idea of the hybrid HHL algorithm and a generic circuit of HHL algorithm, an improved circuit implementation of the HHL algorithm is proposed. The feasibility of the improved circuit implementation of the HHL algorithm is verified by IBM's qiskit. The improved circuit illustrations on a 4 × 4 linear system show that the improved circuit implementation of the HHL algorithm can effectively reduce quantum resources without losing the fidelity of the results. Thus the improved circuit implementation of the HHL algorithm can further avoid some result errors than the existing implementation methods. [ABSTRACT FROM AUTHOR]

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a "machine" that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies. [ABSTRACT FROM AUTHOR]

Today, the use of large networks under the cloud and big data has become very common, as well as learning methods and artificial intelligence have been designed for them. One of the main issues in the banking and Big Data industry is granting bank facilities to people. The main challenge in evaluating the customers' credit in the banking industry is that, due to the large volume of the data, it is impossible to review them manually, as we have to use computer algorithms for this purpose. Hence, the accuracy of the algorithm used for this purpose is essential and influential. In this paper, several predictive algorithms are employed on the problem, so that the result would be a prediction based on the combination of the results of each of them. To integrate the results of the algorithms, the weight to each of the algorithms is allocated, which presents the amount of importance of the algorithm in the result. The Ordered weighting averaging is used in this research. By applying these techniques, the proposed algorithms are described in this paper to achieve better accuracy than the existing ones. [ABSTRACT FROM AUTHOR]

Image processing and computer vision on mobile devices have a wide range of applications such as digital image enhancement and augmented reality. While images acquired by cameras on mobile devices can be processed with generic image processing algorithms, there are numerous constraints and external issues that call for customized algorithms for such devices. In this paper, we survey mobile image processing and computer vision applications while highlighting these constraints and explaining how the algorithms have been modified/adapted to meet accuracy and performance demands. We hope that this paper will be a useful resource for researchers who intend to apply image processing and computer vision algorithms to real-world scenarios and applications that involve mobile devices. [ABSTRACT FROM AUTHOR]

Teamwork skills are commonly evaluated by human assessors, which can be logistically challenging and resource intensive. Technological advancements provide an opportunity for a new assessment method – virtual behavioural simulations with self-scoring algorithms. This study explores whether a rule-based algorithm can match human assessors at evaluating teamwork skills. 206 undergraduate students completed a virtual simulation assessment, where they interacted with "teammates" (represented by chatbots) using natural language. In this study, students' teamwork skills were assessed independently by a computer algorithm and two human experts based on the transcripts of their conversations with "teammates" (chatbots). The relative accuracy of these assessments was evaluated against peer- and self-evaluations of teamwork. The assessment scores generated by the algorithm and human experts were highly correlated with each other and were comparable in their ability to predict teamwork. The scores generated by the algorithm were slightly more correlated with peer-evaluations than those generated by human experts (r =.25 and r =.17, respectively; p =.21). The results indicate that AI-based techniques offer a promising method of skill assessment to support learning and acquisition teamwork skills. [ABSTRACT FROM AUTHOR]

The move from open outcry to electronic trading added another responsibility to futures exchanges—that of matching orders between buyers and sellers. Matching systems can affect the level and speed of price discovery, the distribution of revenue, as well as the level of price efficiency of a given market. Whether the matching system is procedurally fair is another important consideration. I argue that while FIFO (First In First Out) is a fair procedure in principle and is perceived as the default matching system, it is not a fair procedure in practice. Likewise, while pro rata is a fair procedure in principle, it is not so in practice. Nevertheless, both FIFO and pro rata are relics of an open outcry system. Instead, I propose an alternative approach to matching systems that builds on the strengths of electronic trading—the ability to randomize in real time. I introduce random selection for service (RSS) as a matching system that is procedurally fair both in principle and in practice. [ABSTRACT FROM AUTHOR]

The use of virtual reality or augmented reality systems in billiards sports are useful tools for pure entertainment or improving the player's skills. Depending on the purpose of these systems, tracking algorithms based on computer vision must be used. These algorithms are especially useful in systems aiming to reconstruct the trajectories followed by the balls after a strike. However, depending on the billiard modality, the problem of tracking multiple small identical objects, such as balls, is a complex task. In addition, when an amateur or nontop professional player uses low-frame-rate and low-resolution devices, problems such as blurred balls, blurred contours, or fuzzy edges, among others, arise. These effects have a negative impact on ball-tracking accuracy and reconstruction quality. Thus, this work proposes two contributions. The first contribution is a new tracking algorithm called "multiobject local tracking (MOLT)". This algorithm can track balls with high precision and accuracy even with motion blur caused by low-resolution and low-frame-rate devices. Moreover, the proposed MOLT algorithm is compared with nine tracking methods and four different metrics, outperforming the rest of the methods in the majority of the cases and providing a robust solution. The second contribution is a whole system to track (using the MOLT algorithm) and reconstruct the movements of the balls on a billiard table in a 3D virtual world using computer vision. The proposed system covers all steps from image capture to 3D reconstruction. The 3D reconstruction results have been qualitatively evaluated by different users through a series of questionnaires, obtaining an overall score of 7.6 (out of 10), which indicates that the system is a promising and useful tool for training. Finally, both the MOLT algorithm and the reconstruction system are tested in three billiard modalities: blackball, carom billiards, and snooker. [ABSTRACT FROM AUTHOR]

GMA welding process stability and robustness, as well as weld quality, depend strongly on metal transfer behavior. Evaluating in detail the phenomenon of droplet transfer, considering the detachment of metal from the tip of the electrode until it reaches the weld pool and considering the forces acting in the process, is a field of great interest for those looking to control these parameters to achieve the desired characteristics in welding. Currently, there are better high-speed filming systems, which allow the acquisition of better-quality images and open the possibility for the application of computer vision algorithms with the aim of better understanding the dynamics of the metallic transfer. The objective of this research was to develop an algorithm that allows the detection of metal droplets in the footage of the metal transfer process. For this, exploratory work performed an algorithm that managed to detect the drops with high precision. Several methods using findContours, Canny, Laplacian, Sobel, and a weightless neural model were adapted and compared in the detection and measurement of the projected areas, as well as with regard to the processing times. The results generated by the algorithm allowed the detection and monitoring of the metal transferred in the form of drops, as well as the calculation of the projected area and the displacement, allowing for a better understanding of the metal transfer phenomena and, consequently, future conceptions of welding manufacturing control methods. [ABSTRACT FROM AUTHOR]

The knowledgeable, human–machine interaction sight system has the benefits of low interference, lower permeability, and no interface attachment. The smart vision system has been critical in human–computer interaction to grow and advance technologies and research. The Human–Computer Interaction based Visual Feedback System (HCIVFS) is very quickly relative to the conventional collaborative mode. Such challenges may also affect the smart machine's view and the general use of sensation communication. The fundamental premise of the computer's sight communication architecture requires practical stability. This article explores the quality of the intellectual computer's enabling communication. The Rule of Fitts has also been included in this paper for three points-to-clicks applications. The proposed algorithm's reliability is analyzed in operations, visualization, and computer vision algorithms. There is a fair recommendation for an immersive configuration of the input method for intellectual sight by computer. [ABSTRACT FROM AUTHOR]

As the Wireless sensor network (WSN) has huge part in Internet of Things (IoT), it is used in different applications, for example, detecting climate and sending information by means of the internet. In any case, because of the issue of weighty congestion, the performance of WSN-IoT may affect. Despite the fact that machine learning calculations have been introduced by analysts for distinguishing the congested data, accuracy of detection is further to be enhanced. To control the congestion, squirrel search algorithm (SSA) based support vector machine (SVM) is proposed in this paper. To enhance on the execution of SVM, tuning parameters of SVM is improved utilizing SSA algorithm. Simulation results depict that the SVM-SSA outflanks the model, for example, SVM with Genetic Algorithm (SVM-GA), SVM and TCP dependent on throughput, energy utilization, delivery ratio, and overhead. Also, detection accuracy of SVM-SSA increase to 92%. [ABSTRACT FROM AUTHOR]

One of the most important challenges in the field of affective computing is the collection of databases from human color-based affective responses. This multimedia database has many applications in anthropology, sociology, psychology, computer, and graphics. However, researches have conducted some research on the affective aspect of monochrome images. Unfortunately, a complete and coherent database is not available to researchers right now. In this paper, data collection for multimedia affective response is tailored to images with two adjacent colors. Here, we examined the color parameters luminance, chrominance, and color hue in the images as well as the observers' characteristics such as age, gender, and education level. To this end, the observers obtained a simple and a patterned two-color image by posting images on a website and using the Self-Assessment Manikin (SAM) technique. A total of 171 simple two-color images and 105 patterned two-color images were considered. The total number of observers was 405, and finally, 4757 responses were collected. Finally, by statistically examining the effect of the mentioned parameters on the emotional valence and arousal, we found that lightness, hue, and pattern parameters had the greatest effect on the value of emotions (valence), but arousal was more related to lightness and chrominance parameters. The parameters were lightness and chrominance. This Two-Color Affective Response (TCAR) database is suitable for processing using advanced computer algorithms such as clustering and classification. The entire data collection process, including color selection to make images, getting emotional responses, and evaluation, was fully described. [ABSTRACT FROM AUTHOR]

The computational difficulty of factoring large integers forms the basis of security for RSA public-key cryptography. The best-known factoring algorithms for classical computers run in sub-exponential time. The integer factorization problem can be reduced to the Boolean Satisfiability problem (SAT). While this reduction has proved to be useful for studying SAT solvers, large integers have not been factored via such a reduction. Shor's quantum factoring algorithm factors integers in expected polynomial time. Large-scale fault-tolerant quantum computers capable of implementing Shor's algorithm are not yet available, preventing relevant benchmarking experiments. Recently, several authors have attempted quantum factorizations via reductions to SAT or similar NP-hard problems. While this approach may shed light on algorithmic approaches for quantum solutions to NP-hard problems, in this paper we study and question its practicality. We find no evidence that this is a viable path toward factoring large numbers, even for scalable fault-tolerant quantum computers, as well as for various quantum annealing or other special purpose quantum hardware. [ABSTRACT FROM AUTHOR]

Visual effects such as shadows and highlights appear in photographs due to variations in lighting conditions. Although these effects add more meaning to the images, they pose various problems to specific computer vision algorithms. Hence, the removal of shadows and highlights is often considered as a prerequisite to such algorithms. This paper presents an interactive technique for shadow elimination from images. Our method requires user input in the form of rough strokes on the shadow region and its corresponding non-shadow region in the image. We further use quaternion rotation in the YCbCr color space to derive an image that is invariant to shadows. The actual colors of the image are finally recovered by color transfer from the original shadow image. The proposed method takes less time to generate the shadow-free image and does not necessitate the detection of shadows prior to its removal. Also, unlike the existing shadow-removal techniques, our method generates invariant image with minor texture loss. Experimental findings are reported to demonstrate the performance of our shadow-removal technique. [ABSTRACT FROM AUTHOR]

Biological interaction databases accommodate information about interacted proteins or genes. Clustering on the networks formed by the interaction information for finding regions highly connected could reveal the functional affinities or structural similarities between protein or gene entities. With the ever-increasing amounts of information in these databases, the runtime of a clustering task is more and more unaffordable. In this paper, we propose a heterogeneous parallel algorithm focusing on accelerating clustering tasks using distributed CPU–GPU clusters. Our parallel implementation is based on the original serial algorithm of the Markov clustering (MCL). In our parallel implementation, we utilize both the CPUs and GPUs to exploit the power of heterogeneous platforms. With the BioGRID biological interaction database, we have tested the proposed algorithm on a computer cluster equipped with NVIDIA Tesla P100 GPU accelerators. The result shows that, the algorithm is efficient in GPU memory usage and inter-node data transmission, and it can complete the clustering task in 3.2 minutes with the best speedup of 70.02 times compared to the serial counterpart.We believe our work can provide key insights for realizing fast MCL analyses on large-scale biological data, with distributed CPU–GPU computer clusters. [ABSTRACT FROM AUTHOR]