Search

Detecting plagiarism in academic papers is crucial for maintaining academic integrity, preserving the originality of published work, and safeguarding intellectual property. While existing applications excel at text plagiarism detection, they fall short when it comes to image plagiarism. This paper introduces a novel algorithm, named "VIBRANT-WALK," designed to detect image plagiarism in academic manuscripts. The challenge of identifying plagiarized images is formidable, requiring a unique approach. Traditional Computer Vision algorithms, proficient in image similarity tasks, face limitations in determining whether an image has been previously used in an article. To address this, the proposed algorithm leverages a repository of all published article pages, focusing on absolute identicality rather than image similarity. The algorithm comprises two stages. In the first stage, a "Vibrancy Matrix" is created through image preprocessing, aiding in contour determination. The second stage involves pixel-by-pixel comparison with images from published manuscripts. To enhance efficiency, the algorithm initiates comparisons from the pixel with the highest score in the Vibrancy Matrix, followed by pixel comparisons through random walks, significantly reducing complexity. To conduct the study, a custom dataset was compiled from 69 research articles, capturing snapshots of each page and figure. Overall, we present 485 unique test cases where we can test the accuracy and efficiency of the algorithm. The lack of publicly available datasets necessitated this approach. The proposed algorithm outperformed the existing models and algorithms in this field by achieving an overall accuracy of 94.8% on the collated dataset, identifying 460 instances of plagiarism out of the 485 test cases. The algorithm also demonstrated a 100% accuracy rate in avoiding false positives. [ABSTRACT FROM AUTHOR]

Autism Spectrum Disorder (ASD) diagnostics requires specialized clinical expertise, posing accessibility and affordability barriers for many. To widen the availability of precise screening, this paper examines ensemble machine learning models that combine multiple algorithms for improved accuracy and generalizability. Specifically, this paper compares the performance of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and a Voting Classifier that integrates Logistic Regression, Decision Trees, and KNN. The comparison was conducted on separate datasets of pediatric and adult ASD questionnaire responses. The ensemble model significantly outperforms individual techniques, achieving higher accuracy for both pediatrics and adults with balanced sensitivity and specificity maintained across groups, indicating the viability of accessible community-available screening to alleviate diagnostic bottlenecks. Before scale-up, further model optimization for interpretability and testing on more diverse multi-site data are warranted. Overall, findings demonstrate the feasibility of mobile distributed pre-screening systems leveraging optimized ensembles to predict ASD with high precision across ages, opening possibilities for explainable AI to lower costs and widen access compared to in-person evaluation. [ABSTRACT FROM AUTHOR]

Target maneuver recognition is a prerequisite for air combat situation awareness, trajectory prediction, threat assessment and maneuver decision. To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data, and automatically and adaptively complete the task of extracting the target maneuver pattern, in this paper, an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder, G-G clustering algorithm and the selective ensemble clustering analysis algorithm. Firstly, the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension; Then, taking the time information into account, the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm, and a large number of maneuver primitives are extracted; Finally, the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm, which can prove that each class represents a maneuver action. The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3% of maneuver actions，indicating that the method is effective and satisfies the requirements for engineering accuracy. In addition, this method can provide data support for various target maneuvering recognition methods proposed in the literature, greatly reduce the workload and improve the recognition accuracy. [ABSTRACT FROM AUTHOR]

Carbon credit is known as one of the most effective market-based tools to control the generation of carbon emissions. In order to mitigate carbon intensity and enhance energy efficiency within product systems, the utilization of carbon credits emerges as a valuable tool for companies striving to achieve ambitious targets in reducing greenhouse gas (GHGs) emissions. This study aims to investigate the impact of employing carbon credits in contrast to traditional inventory models. To overcome these shortcomings, a two-stage inventory model to design a green supply chain in a carbon trading environment is presented in this paper. The model solves a pricing problem and determines the optimal production run time of component and the carbon credit price. The presented model is employed in a real-world case study, and the numerical results are meticulously analysed and interpreted. The results of the model showed that there exists an optimal solution or optimizer. We have presented an algorithm to compute the value of an optimal solution. After the computation of an algorithm, there was more than sufficient evidence to show the beneficial effects of Taiwan bike manufacturer's model. The findings of sensitivity analysis also have implications for this proposed model. As an illustration, the outcomes achieved through pollution prevention can be juxtaposed with those derived from non-investment operations, considering various parameter settings. This comparison aids in identifying an opportune moment for implementing pollution prevention measures. [ABSTRACT FROM AUTHOR]

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective. [ABSTRACT FROM AUTHOR]

The Internet of things (IoT) has a variety of application domains including smart homes. A smart home is an automated intelligent home where IoT technologies are used to remotely control home appliances, manage home energy, enhance home security, and increase the comfort of home residents. On the other hand, renewable energy resources are highly speared to cover huge electricity demand. To save more energy and maximize energy efficiency, smart homes are engaged with energy management systems. Energy management and optimization solutions can help in decreasing the overall cost of energy while optimizing all operational performance. This paper presents an IoT-based optimal energy management approach depending on the Harmony Search optimization technique to save energy usage in smart homes. PV and wind renewable energy systems are used to feed the home with electricity. These on-site energy sources help smart homes to decrease their dependence on power from the electricity grid. Energy storage systems are used to maintain energy system reliability due to the inherent randomness and intermittence of solar radiation and wind speed. The proposed algorithm depends on-demand response to the price variations in the electric energy over time and applies the Time-of-Use pricing principle for controlling household appliances. IoT technology is applied to exchange data between household appliances and the control center, in addition, to communicating both energy management systems and security systems with the control center. In this paper, the IoT system is based on ZigBee wireless technology which is described as the lowest power consumed wireless technology. The algorithm is applied to a proposed building consisting of five floors, each floor contains two apartments with a total area of 200 meters for one apartment. The obtained results prove the efficacy of the proposed Harmony Search-based optimization method in saving energy and reducing electricity bills in smart homes while satisfying the required constraints. Additionally, the performance of the proposed algorithm is validated against four AI algorithms including Genetic Algorithm, Artificial Immune System, Ant Lion Optimization, and Bat Algorithm. In addition to its simplicity in programming and formulation, the conducted comparison demonstrates a similarity in the results with an advantage in the proposed algorithm in improving both the electricity cost-saving and elapsed runtime. [ABSTRACT FROM AUTHOR]

The sharing of data between financial institutions is widely recognised as a key component in the industry's efforts to combat fraud. Broader access to multiple sources of financial data is also critical to the development of high-quality fraud detection mechanisms based on artificial intelligence (AI). Given the challenges relating to sharing real financial data across countries and institutions, the use of synthetic data has recently become critical to enabling the exploration of broader data sharing and supporting open collaboration in AI model development. To generate synthetic data that can substitute for real data, computer algorithms closely mimic the key statistical properties of genuine data, while strictly preserving the privacy and sovereignty of the source data. This paper presents the results of an ongoing exploration into the generation of high-utility synthetic datasets of cross-border payment transactions using transformer models and discusses its application to the development of AI-based fraud prevention solutions. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to enhance the performance of the stochastic gradient descent (SGD) algorithm by incorporating a modified decay step size based on .... The proposed step size integrates a logarithmic term, leading to the selection of smaller values in the final iterations. Our analysis establishes a convergence rate of O ... for smooth non-convex functions without the Polyak-Łojasiewicz condition. To evaluate the effectiveness of our approach, we conducted numerical experiments on image classification tasks using the Fashion-MNIST and CIFAR10 datasets, and the results demonstrate significant improvements in accuracy, with enhancements of 0:5% and 1:4% observed, respectively, compared to the traditional p1 t step size. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose a new machine learning paradigm, federated machine learning. This method produces accurate predictions without revealing private data. It requires less network traffic, reduces communication costs and enables private learning from device to device. Federated machine learning helps to build models and further the models are moved to the device. Applications are particularly prevalent in healthcare, finance, retail, etc., as regulations make it difficult to share sensitive information. Note that this method creates an opportunity to build models with huge amounts of data by combining multiple databases and devices. There are many algorithms available in this area of machine learning and new ones are constantly being created. Our paper presents a comparative analysis of algorithms: FedAdam, FedYogi and FedSparse. But we need to keep in mind that FedAvg is at the core of many federated machine learning algorithms. Data testing was conducted using the Flower and Kaggle platforms with the above algorithms. Federated machine learning technology is usable in smartphones and other devices where it can create accurate predictions without revealing raw personal data. In organizations, it can reduce network load and enable private learning between devices. Federated machine learning can help develop models for the Internet of Things that adapt to changes in the system while protecting user privacy. And it is also used to develop an AI model to meet the risk requirements of leaking client's personal data. The main aspects to consider are privacy and security of the data, the choice of the client to whom the algorithm itself will be directed to process the data, communication costs as well as its quality, and the platform for model aggregation. [ABSTRACT FROM AUTHOR]

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]

Software requirement is become more important in recent because the development which witness in projects, badly executed requirements engineering steps can result in bad quality software and more cost for expensive maintenance. Manual classification of requirements is difficult, time-consuming, and expensive, especially in large projects and is written as a Software Requirements Specification (SRS) document. For this reason, automating software requirements classification helps in obtaining higher accuracy and saving time and effort. Most of researcher applied Intelligence techniques algorithms to avoid erroneous requirements and human intervention, as well as analyze, classify, and priority of requirements. In this paper illustrated modern of artificial techniques algorithm to classify RT approaches. It is surveyed that existing techniques like machine learning algorithms such as K-Nearest Neighbor (K-NN), decision tree (DT),.. etc. Many other technical how ensemble learning and deep learning algorithm results in classification of RF. Researchers have proposed automated techniques to classify functional and non-functional requirements using several machine learning (ML) algorithms with a combination of different vector techniques. However, using the best method in classifying functional and non-functional requirements still needs clarification, and through many studies and research by researchers. [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]

This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures. [ABSTRACT FROM AUTHOR]

Copyright of Kirkuk Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a novel multi-modal adaptive feature extraction algorithm considering both time-domain and frequency-domain modalities (AFETF), coupled with a Bidirectional Long Short-Term Memory (Bi-LSTM) network based on the Grey Wolf Optimizer (GWO) for early-stage weak fault diagnosis in bearings. Singular Value Decomposition (SVD) was employed for noise reduction, while Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was utilized for signal decomposition, facilitating further signal processing. AFETF algorithm proposed in this paper was employed to extract weak fault features. The adaptive diagnostic process was further enhanced using Bi-LSTM network optimized with GWO, ensuring objectivity in the hyperparameter optimization. The proposed method was validated for datasets containing weak faults with a 0.2 mm crack and strong faults with a 0.4 mm crack, demonstrating its effectiveness in early-stage fault detection. [ABSTRACT FROM AUTHOR]

To address the issue of the lack of specialized data filtering algorithms for dataset production, we proposed an image filtering algorithm. Using feature fusion methods to improve discrete wavelet transform algorithm (DWT) and enhance the robustness of image feature extraction, a weighted hash algorithm was proposed to hash features to reduce the complexity and computational cost of feature comparison. To minimize the time cost of image filtering as much as possible, a fast distance calculation method was also proposed to calculate the similarity of images. The experimental results showed that compared with other advanced methods, the algorithm proposed in this paper had an average accuracy improvement of 3% and a speed improvement of at least 30%. Compared with traditional manual filtering methods, while ensuring accuracy, the filtering speed of a single image is increased from 9.9s to 0.01s, which has important application value for dataset production. [ABSTRACT FROM AUTHOR]

This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy. [ABSTRACT FROM AUTHOR]

In distributed edge storage, data storage data is allocated to network edge devices to achieve low latency, high security, and flexibility. However, traditional systems for distributed edge storage only consider individual factors, such as node capacity, while overlooking the network status and the load states of the storage nodes, thereby impacting the system's read and write performance. Moreover, these systems exhibit inadequate scalability in widely adopted wireless terminal application scenarios. To overcome these challenges, this paper introduces a software-defined edge storage model and a distributed edge storage architecture grounded in software-defined networking (SDN) and the Server Message Block (SMB) protocol. A data storage node selection and distribution algorithm is formulated based on a maldistributed decision model that comprehensively considers the network and storage node load states. A system prototype is implemented in combination with 5G wireless communication technology. The experimental results demonstrate that, in comparison to conventional distributed edge storage systems, the proposed wireless distributed edge storage system exhibits significantly enhanced performance under high load conditions, demonstrating superior scalability and adaptability. This approach effectively addresses the scalability limitation, rendering it suitable for edge scenarios in mobile applications and reducing hardware deployment costs. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (EfficientDP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded by the client during the training process. Specifically, in the federated learning framework, this article uses an adaptive DP-FL method for gradient descent training, which makes the model converge faster than traditional stochastic gradient descent. In addition, due to model convergence, noise should be reduced accordingly. This paper introduces an early stopping mechanism to improve data availability. This paper demonstrates the performance improvement of the Efficient DP-FL algorithm through simulation experiments on real MNIST and Fashion-MNIST datasets. Experimental showthat the efficient DP-FL algorithm is significantly superior to other algorithms. [ABSTRACT FROM AUTHOR]

For the autonomous and intelligent operation of robots in unknown environments, simultaneous localization and mapping (SLAM) is essential. Since the proposal of visual odometry, the use of visual odometry in the mapping process has greatly advanced the development of pure visual SLAM techniques. However, the main challenges in current monocular odometry algorithms are the poor generalization of traditional methods and the low interpretability of deep learning-based methods. This paper presented a hybrid self-supervised visual monocular odometry framework that combined geometric principles and multi-frame temporal information. Moreover, a post-odometry optimization module was proposed. By using image synthesis techniques to insert synthetic views between the two frames undergoing pose estimation, more accurate inter-frame pose estimation was achieved. Compared to other public monocular algorithms, the proposed approach showed reduced average errors in various scene sequences, with a translation error of 2.211 % and a rotation error of 0.418 ∘ / 100 m. With the help of the proposed optimizer, the precision of the odometry algorithm was further improved, with a relative decrease of approximately 10 % intranslation error and 15 % in rotation error. [ABSTRACT FROM AUTHOR]

Predicting operational performance enables organizations to develop operational effectiveness goals considering different combinations of resources. Measuring performance is consolidated with advances in relative efficiency analysis techniques, including data envelopment analysis (DEA) and stochastic frontier analysis (SFA), albeit these methods lack predictive capability. This paper proposes an approach for performance prediction by integrating relative efficiency measurement models with machine learning algorithms. Data analyses were conducted using data provided by the energy assessment project offered to small and medium-sized manufacturing companies in the United States (n 7,548) using sales as the output, with the inputs being the number of employees, hours of operation, electricity, natural gas, cost of electricity, and cost of natural gas. Performance was estimated differently, employing parametric (SFA) and non-parametric (DEA) methods. The prediction benchmarking process occurred by adopting machine learning algorithms: regression (LM), support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), random forest (RF), and decision tree (DT). The findings showed that it is possible to identify the best prediction algorithm associated with a performance model. However, the performance prediction may differ if different strategies for measuring performance or machine learning model configurations are used. In addition, SFA-LOG and SVM had the best performance for regression, and DEA-VRS/IRS excelled with random forest; the RF algorithm was the best fit across all performance approaches. The error rate depends on the algorithm and the performance model, and the number of classes must be reduced to obtain a higher success rate. [ABSTRACT FROM AUTHOR]

In this paper, we study the finite-time stability and applications of positive switched linear delayed systems under synchronous impulse control, which includes two types of random switching and average dwell time switching. By constructing a type of linear timevarying co-positive Lyapunov functional, we first propose several new finite-time stability criteria. It should be emphasized that the linear term coefficient of the linear vector of the Lyapunov functional is adjusted to the difference between the weighting vector and the given vector. Then, we apply the obtained stability criteria to the linear time-varying delayed systems with impulsive effects. At last, three examples are given to demonstrate the validity of the obtained results, which includes the specific linear programming algorithm process. [ABSTRACT FROM AUTHOR]

This article compares two optimization methods considering random variations in design parameters. One is reliability-based design optimization, which depends on the availability of the joint probability density function. A more practical alternative is robust optimization, which does not require the estimation of failure probability. It accounts for the random response of the structure through definitions of objective functions and constraints, incorporating mean values and response variances. An important element of the algorithm involves approximating unknown responses of the structures and employing efficient statistical moment estimation methods. The kriging method was used in this paper. Additionally, the article evaluates two experimental plan techniques: the classical random sampling plan and the OLH plan. [ABSTRACT FROM AUTHOR]

Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications. [ABSTRACT FROM AUTHOR]

This methods paper describes the methodological and statistical underpinnings of the highly adaptive testing design (HAT), which was developed for the Programme for International Student Assessment (PISA). The aim of HAT is to allow for a maximum of adaptivity in selecting items while taking the constraints of PISA into account with appropriate computer algorithms. HAT combines established methods from the area of computerized adaptive testing (a) to improve item selection when items are nested in units, (b) to make use of the correlation between the dimensions measured, (c) to efficiently accomplish constraint management, (d) to control for item position effects, and (e) to foster students' test-taking experience. The algorithm is implemented using the programming language R and readers are provided with the necessary code. This should facilitate future implementations of the HAT design and inspire other adaptive testing designs that aim to maximize adaptivity while meeting constraints. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere. [ABSTRACT FROM AUTHOR]

Batch scheduling is a crucial part of pipeline enterprise operation management, especially in the context of market-oriented operation. It involves 3 main tasks: quickly preparing batch plans, accurately tracking interface movement, and operation condition in real time. Normally, the completion of multi-product pipeline batch scheduling depends on simulation models or optimization models and corresponding conventional solving algorithm. However, this approach becomes inefficient when applied to large-scale systems. The rapid development of machine learning has brought new ideas to batch scheduling research. This paper first reviews the current state of batch scheduling technology, and suggests that applying machine learning to it is a promising development direction. Then, we summarize the progress of machine learning applications in batch planning, interface movement tracking, and operational condition monitoring, and point out their limitations. Finally, considering the separation of refined oil production, transportation, and sales processes, 5 recommendations are put forward: oil supply and demand prediction and pipeline capacity prediction, batch planning, batch interface movement tracking, mixed oil development monitoring, and pipeline operation condition identification. [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]

Effective algorithm visualization in computer science education greatly enhances the level of understanding and interest in the subject. This paper presents a development and evaluation of a web-based visualizer of data structures intended for learning sorting algorithms. Our system allows interactive and dynamic visualization of various sorting techniques, including Bubble Sort, Merge Sort, Quick Sort, and Heap Sort. We provide a real-time graphical display for the user to view step-by-step executions of such algorithms. This provides insight into their operational mechanics and performance characteristics. We present the architecture of the visualizer, methodologies for algorithm visualization, and some feedback from users during testing phases. Both qualitative and quantitative metrics are used for assessing the improvement in algorithmic understanding. Our findings indicate that the visualizer significantly enhances students' understanding of the concepts in sorting algorithms and that it is indeed a very important tool for computer science educators and learners alike. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and −3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy. [ABSTRACT FROM AUTHOR]

The increasing impact of algorithmically driven processes on human societies, which can exacerbate political, economic, and cultural asymmetries, raises questions about reducing human agency by constraining platform structures. We draw on the theoretical concept of algorithmic imaginary, which captures users' appropriations and ideas of these processes. In this paper, we focus on the dynamics between agency and structure in algorithmic imaginaries regarding the future of digital media platforms in Europe. The paper takes structuration theory as a theoretical starting point and employs methods of futures studies to analyze how the future is constructed in scenarios developed by a diversity of experts participating in a series of workshops. The future scenarios analysis is mapped around four actors, namely platform users, platform corporations, algorithms and institutions. By considering the role of various actors, particularly institutions, and their interdependencies this paper contributes to more balanced conceptualizations of algorithmic imaginaries, which tend to be centered around users' perspectives. [ABSTRACT FROM AUTHOR]

Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solving methods, often drawing from existing knowledge in multi-objective optimization based on decomposition (MOO/D). Yet, a clear categorization based on both RL and MOO/D is lacking in the existing literature. Consequently, MORL researchers face difficulties when trying to classify contributions within a broader context due to the absence of a standardized taxonomy. To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization. Moreover, a flexible framework derived from the taxonomy is introduced. This framework accommodates diverse instantiations using tools from both RL and MOO/D. Its versatility is demonstrated by implementing it in different configurations and assessing it on contrasting benchmark problems. Results indicate MORL/D instantiations achieve comparable performance to current state-of-the-art approaches on the studied problems. By presenting the taxonomy and framework, this paper offers a comprehensive perspective and a unified vocabulary for MORL. This not only facilitates the identification of algorithmic contributions but also lays the groundwork for novel research avenues in MORL. [ABSTRACT FROM AUTHOR]

Personalized PageRank, as a basic algorithm in large graph analysis, has a wide range of applications in search engines, social recommendation, community detection, and other fields and it has been a hot problem of interest to researchers. The existing distributed personalized PageRank algorithms assume that all data are located in the same geographic location and the network environment is the same among the computing nodes where the data are located. However, in the real world, these data may be distributed in multiple data centers across continents, and these geo-distributed data centers are connected to each other through WANs, which are characterized by heterogeneous network bandwidth, huge hardware differences, and high communication costs. Moreover, the distributed personalized PageRank algorithm requires multiple iterations and random walk on the global graph. Therefore, the existing distributed personalized PageRank algorithms are not applicable to the geo-distributed environment. To address this problem, the GPPR (Geo-distributed Personalized PageRank) algorithm is proposed in this paper. The algorithm first preprocesses the big graph data in the geo-distributed environment and maps the graph data by using a heuristic algorithm to reduce the impact of network bandwidth heterogeneity on the iteration speed of the algorithm. Secondly, GPPR improves the random walk approach and proposes a probability-based push algorithm to further lower the number of iterations required by the algorithm by reducing the bandwidth load of data transmission between working nodes. We implement the GPPR algorithm based on the Spark framework and build a real geo-distributed environment in AliCloud to conduct experiments comparing the GPPR algorithm with several existing representative distributed personalized PageRank algorithms on eight open-source big graph datasets. The results show that the communication data volume of GPPRis reduced by 30% on average in the geo-distributed environment compared with that of other algorithms. In terms of algorithm running efficiency, GPPR improves by an average 2.5 factor compared with other algorithms. [ABSTRACT FROM AUTHOR]

Bi-mobile planar mechanisms are commonly used in various fields of robotics. By adding additional degrees of mobility (rotation and/or translation) to the basis, multi-mobile systems for manipulators or pedipulators (legs) found in various fields are obtained. The paper aims to highlight these mechanisms using bi-mobile planar kinematic linkages described in the classical literature of mechanism theory [1-5,16,17,23,25] in which the basis and the effector are nominated by inverse modelling [8-12,13,15]. The effector extremity of such mechanism may describe any trajectory in its field. This is equivalent to introduce a pair with two constrains which determines the structure to have zero degree of mobility, its inverse structural model could have only passive modular groups. All basis-effector solutions for planar bimobile mechanisms obtained from linkages with five degrees of mobility and two and three independent contours are mentioned. The mechanism mobility is given by two active kinematic pairs suitably placed in the same chain [8-15]. The structural algorithm included in the paper offers the possibility to create various optimal solution for bi-mobile mechanisms. At the same time, the base-effector structural solutions mentioned in the paper are useful in designing these systems, and can be used to verify or adopt appropriate solutions. [ABSTRACT FROM AUTHOR]

Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components. Additionally, the calculus operator is performed to strengthen weak fault features and make them easier to extract, and the singular value decomposition (SVD) weighted by power spectrum entropy (PSE) can be utilized as the sample feature representation. Finally, AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification. The superior performance of the proposed method has been validated by various experiments. [ABSTRACT FROM AUTHOR]

Cloud computing has gained immense popularity for delivering computing resources over the internet, enabling organizations to efficiently and cost-effectively store, process, and access data from any location worldwide. However, the security of data stored and processed outside of the organization's premises has raised concerns. In order to address the changing threat landscape and guarantee the integrity and confidentiality of data in the cloud environment, we address the latest developments and best practices in cloud security in this article, which delves into the complex world of security threats and related countermeasures for cloud computing in the contemporary era of digital transformation. Offered a thorough examination of cloud computing security risks and solutions in the age of digital transformation. The paper defines cloud computing, explores its advantages and disadvantages, identifies major security threats in cloud computing, including data breaches, insider threats, and denial-of-service attacks, and discusses the countermeasures that can be implemented to mitigate these threats. The most popular cryptographic algorithms in cloud computing environments as part of their defense are presented. Special emphasis is given on the mode of operation of these algorithms is presented so that we can choose the appropriate one in each situation. Finally, access control and monitoring are presented in order to have a thorough understanding of protection methods in cloud computing environments. [ABSTRACT FROM AUTHOR]

The necessity of parallel programming for leveraging the full potential of modern multicore processors cannot be overstated. One of the fundamental algorithms used extensively across a multitude of fields is the array sum reduction algorithm. This algorithm is pivotal in scenarios requiring the aggregation of array elements, a task that can benefit significantly from parallel processing techniques. Implementing the array sum reduction algorithm on a multiprocessor system can be approached using various parallel programming models. This paper delves into a comparative analysis of two prominent parallel programming approaches for the array sum reduction algorithm on a shared memory microprocessor environment. We explore the implementation and performance characteristics of C with OpenMP, a well-established and widely adopted parallel programming model in the C programming ecosystem, and C# with Parallel.For, a robust and flexible parallel programming framework within the .NET environment. Our analysis involves a rigorous empirical evaluation and benchmarking process to assess the execution efficiency, scalability, and ease of implementation associated with each approach. By systematically examining these factors, we aim to elucidate the specific contexts and application scenarios where each parallel programming paradigm excels or may present limitations. Furthermore, we consider the development overhead and learning curve associated with adopting these technologies, providing a holistic view of their practicality in real-world applications. In addition to performance metrics, we examine the debugging and maintenance complexities that developers may encounter when working with these paradigms. We also assess the extent to which each approach supports advanced parallelism features such as task parallelism and load balancing. Another important aspect of our study is the interoperability of these programming models with other tools and libraries commonly used in high-performance computing. Through this comparative study, we seek to provide developers and researchers with a comprehensive understanding of the trade-offs involved in selecting between C with OpenMP and C# with Parallel.For. The insights garnered from our evaluation will aid in making informed decisions, ensuring optimal performance and resource utilization in parallel programming applications tailored to modern multicore processors. Ultimately, our findings contribute to the broader discourse on parallel computing best practices and the future directions of parallel algorithm development. Our results aim to serve as a reference point for future research, highlighting potential areas for enhancement in both programming models. We hope to inspire further innovation in parallel computing techniques, fostering advancements that can lead to more efficient and scalable software solutions. [ABSTRACT FROM AUTHOR]

Industrial photogrammetry is a reliable method to achieve submillimeter accuracy when mapping 2D or 3D objects. In the field of fatigue testing of steel welded details, it can be used to find a new method of crack initiation assessment. Fatigue testing is an important method for determining and predicting the durability of structural details in service. The research presented in this paper is based on a computer vision algorithm developed using the open-source code OpenCV library and the Oriented FAST and Rotated BRIEF (ORB) method to provide a solution for the assessment of crack initiation. Within this research, a method for determining the crack initiation period using polynomial functions of a certain degree is developed. The developed algorithm fully automatically determines the test specimen displacement for all imagery and assesses the crack initialization period by polynomial interpolation with a percentage threshold. The algorithm shows us the best results based on a 26th-degree polynomial with a deviation from the critical value of 5%. The validation of the algorithm was carried out using completely independently recorded data from the hydraulic press used for fatigue tests. The results of all test specimens show that the percentage accuracy of determination crack initiation period is between −0.04% for test specimens S355-TA-AW-02 and S355-TA-HFMI-03 and −0.82% for test specimen S355-TA-HFMI-03, with the mean of all results being 0.39%. [ABSTRACT FROM AUTHOR]

A thorough examination of current developments in adaptive filtering for coherent dual-polarization optical communication systems is provided in this work. The emphasis is on high-capacity networks made possible by dual-polarization, coherent detection, variable bit-rate transceivers. The review explores the effectiveness of different adaptive algorithms in coherent receivers, the importance of dual polarization, and the function of adaptive filters in reducing channel impairments. The research also sheds light on the trade-offs and difficulties related to flexible bitrate optical transceivers. The paper comprises an extensive assessment of dynamic optical networks, categorized by network granularity and generation, in addition to the study of adaptive filtering. A comprehensive understanding of the developments in dynamic optical networking technologies is provided by the discussion of the evolution and traits of each generation. The poll also covers optical access networks, emphasizing the acceptance and advantages of optical access protocols as IEEE EPON and ITU-T GPON. The study also examines how digital filtering might be used to manage transmission constraints, highlighting the significance of segmenting digital filtering into distinct blocks. An overview of the types and applications of optical filters utilized in optical communication systems is included in the discussion. The study wraps up with a review of the literature that summarizes current research on optical communication systems, such as studies on adaptive algorithms' convergence properties, coherent dual-polarization receivers, and machine learning's use in optical fiber communication. The contributions of this paper include a thorough analysis of adaptive algorithm performance, a comparative study of dynamic optical networks, and a comprehensive overview of recent research in optical communication systems. [ABSTRACT FROM AUTHOR]

Today, the Radio frequency identification system is widely used in various applications on a large scale. The readers must be densely deployed in order to cover their entire work area. If the number of readers is not optimal, some readers will be redundant, which will reduce the efficiency of the whole system. In more detail, redundant readers increase system overhead, unnecessary tag-reader communication, tag collision, and reader collision, and also, decrease the lifetime of RFID networks. The redundant reader elimination problem is a process that finds the least number of readers to cover all system tags. It is proved that the redundant reader elimination problem is NP hard. One of the useful tasks in eliminating redundant readers is improving the performance of existing approximation algorithms. In this paper, we propose a distributed algorithm, the LWFO algorithm, which combines the count base algorithm (CBA) and a weighting algorithm. The simulation results show that the proposed algorithm reduces reader redundancy compared to existing algorithms also processing time is reasonable. [ABSTRACT FROM AUTHOR]

In this paper, Grover’s quantum search algorithm is analyzed using a classical computer by calculating the amplitudes and the probabilities of finding a single marked state for n=5, 10, 15, 20, 25, and 27 qubit states. The calculations show that the marked state can be found in O(√N) iterations, where N = 2n is the number of items. The possibility of improving Grover’s search algorithm to find a single item in N search elements is discussed by calculating the amplitudes and hence the probabilities of finding a single marked state for n=5, 10, 15, 20, 25, 30, 35, 40, 45, and 50 qubit states. The calculations showed that the marked state could be found with sufficiently high probability in O(ln(N)) iterations. This is quite a remarkable speed-up that can be achieved to find a single marked element in an unsorted N search element. [ABSTRACT FROM AUTHOR]

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system. [ABSTRACT FROM AUTHOR]

The significance of urban rail transit path connection planning lies in optimizing network efficiency, enhancing passenger mobility, and promoting sustainable urban development, ensuring seamless and efficient transportation services. In order to overcome the problems of long and time-consuming traditional path connection planning methods, this paper proposes an urban rail transit path connection planning method based on federated learning and computer immune algorithm. Firstly, design the architecture of the federated learning system, construct a travel time matrix for the transportation network, and estimate the weights for future times. Then, time-varying travel time and distance between demand points are introduced for modeling, and computer immune algorithms are used for solving to optimize path connection. The results show that the selection of vehicle models has a greater impact on demand response and connecting bus route planning. After planning, the path length is only 132km and the number of transfers is only 8, indicating that the method proposed in this paper can improve the effectiveness of traffic path connection planning. [ABSTRACT FROM AUTHOR]

Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods. [ABSTRACT FROM AUTHOR]