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Today, computer graphics and graphic image processing techniques have been widely used in daily life and industrial production. Due to the development of computers, computer graphics has brought more convenience to our daily life. In order to give full play to the value of computers, this paper takes the Hakka paper-cut art with local characteristics as the starting point, first of all its development history, artistic characteristics, compositional forms, expression techniques, cultural connotations, Hakka paper-cut patterns, and the symbolic meaning of folk customs, and then we design a visualization system for the paper-cut works of Gannan Hakka based on computer graphics. In addition, the system provides a solution for the integration of Gannan Hakka paper-cut art and Jiangxi native product packaging design and provides a reference for the theory and practice of modern native product packaging design. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities. [ABSTRACT FROM AUTHOR]

This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification. [ABSTRACT FROM AUTHOR]

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]

This article explores the applicability of DNA Computing in recyclable waste paper sorting. The primary challenge in the recycling of paper is to obtain raw material with the highest purity. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams w ill facilitate high quality end product, and save processing chemicals and energy. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. Automated paper sorting systems offer significant advantages over manual paper sorting systems in terms of fatigue, throughput, speed, and accuracy. In this research, a new DNA computing algorithm has been designed. It is based on the template matching technique for segregating recyclable waste paper according to its grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method for real time inspection. The algorithm is implemented using silicon-based computer to verify the success rate in paper grade identification. [ABSTRACT FROM AUTHOR]

One important reason for the use of field categorization in bibliometrics is the necessity to make citation impact of papers published in different scientific fields comparable with each other. Raw citations are normalized by using field-categorization schemes to achieve comparable citation scores. There are different approaches to field categorization available. They can be broadly classified as intellectual and algorithmic approaches. A paper-based algorithmically constructed classification system (ACCS) was proposed which is based on citation relations. Using a few ACCS field-specific clusters, we investigate the discriminatory power of the ACCS. The micro study focusses on the topic ‘overall water splitting’ and related topics. The first part of the study investigates intellectually whether the ACCS is able to identify papers on overall water splitting reliably and validly. Next, we compare the ACCS with (1) a paper-based intellectual (INSPEC) classification and (2) a journal-based intellectual classification (Web of Science, WoS, subject categories). In the last part of our case study, we compare the average number of citations in selected ACCS clusters (on overall water splitting and related topics) with the average citation count of publications in WoS subject categories related to these clusters. The results of this micro study question the discriminatory power of the ACCS. We recommend larger follow-up studies on broad datasets. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) paper records are used commonly for diagnosing heart abnormalities. The stored ECG paper records are recorded on thermal paper and may face ink evaporation problem over the time. Generally, to overcome this problem ECG paper record is scanned and stored as an image. However, the addition of noise during scanning such as low resolution scan, blurring, folding of paper, nonuniform lighting, orientation etc. can create difficulty in information retrieval. Current work robustly handles various degradation problems encountered in ECG paper scanning using modified k-fill algorithm. The proposed algorithm is tested with 836 ECG paper recordings with different types of degradations like aging effect, folding effect, ink evaporation effect, blurring effect and low resolution effect. We extracted clinically important parameters such as heart rate etc. with accuracy of 97.33% and abnormalities such as bradycardia, tachycardia, and atrial flutter from the ECG paper records using perceptual spectral centroid method. Overall accuracy of our prediction algorithm was found out to be 98.6%. We assume our work would be low cost, preliminary expert mechanism at rural places in the absence of expert cardiologist. [ABSTRACT FROM AUTHOR]

Abstract: A method for the comparison of paper structure using light transmission images and frequency analysis was developed. The resolution of the light transmission images and the algorithm for the feature extraction were greatly improved to enhance the visibility of peaks in the 2D power spectrum that results from frequency analysis. A comparison method based on correlation measures how well the spectra match as a function of the orientation of the paper, yielding an objective and quantitative measure of similarity between 0 and 1. A technical validation was carried out with 25 different papers showing the potential of this method with common copy papers. Finally, the method was applied in a case. [Copyright &y& Elsevier]

In this paper, the implementation of artificial neural networks (multilayer perceptron [MLP] and radial base functions [RBF]) and the upgraded Markov chain model have been studied and performed to identify the human behavior patterns during rock, paper, and scissors game. The main motivation of this research is the design and construction of an intelligent robot with the ability to defeat a human opponent. MATLAB software has been used to implement intelligent algorithms. After implementing the algorithms, their effectiveness in detecting human behavior pattern has been investigated. To ensure the ideal performance of the implemented model, each player played with the desired algorithms in three different stages. The results showed that the percentage of winning computer with MLP and RBF neural networks and upgraded Markov model, on average in men and women is 59%, 76.66%, and 75%, respectively. Obtained results clearly indicate a very good performance of the RBF neural network and the upgraded Markov model in the mental modeling of the human opponent in the game of rock, paper, and scissors. In the end, the designed game has been employed in both hardware and software which include the Zana intelligent robot and a digital version with a graphical user interface design on the stand. To the best knowledge of the authors, the precision of novel presented method for determining human behavior patterns was the highest precision among all of the previous studies. [ABSTRACT FROM AUTHOR]

Abstract: Recommendation algorithms have been researched extensively to help people deal with abundance of information. In recent years, the incorporation of multiple relevance criteria has attracted increased interest. Such multi-criteria recommendation approaches are researched as a paradigm for building intelligent systems that can be tailored to multiple interest indicators of end-users – such as combinations of implicit and explicit interest indicators in the form of ratings or ratings on multiple relevance dimensions. Nevertheless, evaluation of these recommendation techniques in the context of real-life applications still remains rather limited. Previous studies dealing with the evaluation of recommender systems have outlined that the performance of such algorithms is often dependent on the dataset – and indicate the importance of carrying out careful testing and parameterization. Especially when looking at large scale datasets, it becomes very difficult to deploy evaluation methods that may help in assessing the effect that different system components have to the overall design. In this paper, we study how layered evaluation can be applied for the case of a multi-criteria recommendation service that we plan to deploy for paper recommendation using the Mendeley dataset. The paper introduces layered evaluation and suggests two experiments that may help assess the components of the envisaged system separately. [Copyright &y& Elsevier]

Abstract: A new approach of paper defect recognition is proposed. To implement this method, computer is used to acquire paper image and the subtraction algorithm is used to determine if the image contains defect at first. For the image with paper defect, wavelet packet decomposition is applied to extracting the feature vector of the defect. A BP neural network is designed for recognizing the defect type according to the feature vector. This approach provides a unified detection algorithm for different types of paper defects. The experiment data prove this method is valid and can be applied in modern paper defect inspection system. [Copyright &y& Elsevier]

Abstract: Combining previous image mosaicing algorithms, this paper puts forward a kind of damaged text restore image processing algorithm. The algorithm can restore the shredder pieces tape. A series of tapes which are obtained by scanner are treated through image processing such as binary, segmentation, denoising and enhancement and so on, Then we can get the ideal original text image from some leaflet tapes image. Experimental results show that such algorithm has certain effectiveness. [Copyright &y& Elsevier]

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]

Internet of Things (IoT) powered by high level synthesis (HLS) provides huge opportunity of progress in the area of hardware design. However, the present era of hardware design involves globalization which poses security threat to the design integrators that rely on third party intellectual property (IP) cores for increasing design productivity at reduced design time. This paper presents for the first time in the literature, a low cost optimized hardware Trojan secured HLS approach for hardware (or application specific core) designs that is based on single or nested loop control data flow graphs (CDFG) applications. The paper presents multiple novel vendor allocation schemes (each with its own attribute of delay and area) as security constraint that yield Trojan secured schedules at behavioral level. Demonstration of the proposed approach on a nested loop case study asserts our proposed theory. Results on standard benchmarks indicate significant reduction in final solution cost compared to a similar approach. [ABSTRACT FROM AUTHOR]

Freehand sketching on a paper is commonly found to be used as it is a natural and intuitive communication channel for portraying ideas. Additional strokes are often drawn over the existing ones to make the sketch clearer. For its usefulness in subsequent steps, a sketch is transformed to be a 3D CAD model that is typically performed manually by a designer due to a lack of technology support. Geometrical reconstruction from an offline scanning of a sketch has been a challenge because enriched information obtained during sketching is reduced to a batch of data points. This paper presents an algorithm for identifying a single-line drawing from a paper-based overtraced sketch as it is the first important step towards geometrical reconstruction. Key activities in this approach are thick-line sketch creation, contour boundary extraction, segments identification, and line drawing creation. The algorithm has been successfully implemented on LabVIEW and tested with several samples. [ABSTRACT FROM AUTHOR]

This research studied the strategies that players use in sequential adversarial games. We took the Rock-Paper-Scissors (RPS) game as an example and ran players in two experiments. The first experiment involved two humans, who played the RPS together for 100 times. Importantly, our payoff design in the RPS allowed us to differentiate between participants who used a random strategy from those who used a Nash strategy. We found that participants did not play in agreement with the Nash strategy, but rather, their behavior was closer to random. Moreover, the analyses of the participants' sequential actions indicated heterogeneous cycle-based behaviors: some participants' actions were independent of their past outcomes, some followed a well-known win-stay/lose-change strategy, and others exhibited the win-change/lose-stay behavior. To understand the sequential patterns of outcome-dependent actions, we designed probabilistic computer algorithms involving specific change actions (i.e., to downgrade or upgrade according to the immediate past outcome): the Win-Downgrade/Lose-Stay (WDLS) or Win-Stay/Lose-Upgrade (WSLU) strategies. Experiment 2 used these strategies against a human player. Our findings show that participants followed a win-stay strategy against the WDLS algorithm and a lose-change strategy against the WSLU algorithm, while they had difficulty in using an upgrade/downgrade direction, suggesting humans' limited ability to detect and counter the actions of the algorithm. Taken together, our two experiments showed a large diversity of sequential strategies, where the win-stay/lose-change strategy did not describe the majority of human players' dynamic behaviors in this adversarial situation. [ABSTRACT FROM AUTHOR]

To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and then proposes a particle swarm optimization (PSO)-based algorithm to solve the problem. The proposed algorithm exploits an imbalance mutation operator and a task rescheduling approach to improve the performance of PSO. The proposed algorithm concerns the resource heterogeneity by correlating the probability that a computing node is decided to process a task with its capacity, by the imbalance mutation. The task rescheduling approach improves the acceptance ratio for a task offloading solution, by reassigning rejected tasks to computing nodes with available resources. Extensive simulated experiments are conducted. And the results show that the proposed offloading algorithm has an 8.93%-37.0% higher acceptance ratio than ten of the classical and up-to-date algorithms, and verify the effectiveness of the imbalanced mutation and the task rescheduling. [ABSTRACT FROM AUTHOR]

An automatic computer vision algorithm that detects individual paper fibres from an image, assesses the possibility of grasping the detected fibres with microgrippers and detects the suitable grasping points is presented. The goal of the algorithm is to enable automatic fibre manipulation for mechanical characterisation, which has traditionally been slow manual work. The algorithm classifies the objects in images based on their morphology, and detects the proper grasp points from the individual fibres by applying given geometrical constraints. The authors test the ability of the algorithm to detect the individual fibres with 35 images containing more than 500 fibres in total, and also compare the graspability analysis and the calculated grasp points with the results of an experienced human operator with 15 images that contain a total of almost 200 fibres. The detection results are outstanding, with fewer than 1% of fibres missed. The graspability analysis gives sensitivity of 0.83 and specificity of 0.92, and the average distance between the grasp points of the human and the algorithm is 220 µm. Also, the choices made by the algorithm are much more consistent than the human choices. [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]

Spectral data is often required to be pre-processed prior to applying a multivariate modelling technique. Baseline correction of spectral data is one of the most important and frequently applied pre-processing procedures. This preliminary study aims to investigate the impacts of six types of baseline correction algorithms on classifying 150 infrared spectral data of three varieties of paper. The algorithms investigated were Iterative Restricted Least Squares, Asymmetric Least Squares (ALS), Low-pass FFT Filter, Median Window (MW), Fill Peaks and Modified Polynomial Fitting. Processed spectral data were then analysed using Principal Component Analysis (PCA) to visually examine the clustering among the three varieties of paper. Results show that separation among the three varieties of paper is greatly improved after baseline correction via ALS, FP and MW algorithms. [ABSTRACT FROM AUTHOR]

Paper architectures are 3D paper buildings created by folding and cutting. The creation process of paper architecture is often labor-intensive and highly skill-demanding, even with the aid of existing computer-aided design tools. We propose an automatic algorithm for generating paper architectures given a user-specified 3D model. The algorithm is grounded on geometric formulation of planar layout for paper architectures that can be popped-up in a rigid and stable manner, and sufficient conditions for a 3D surface to be popped-up from such a planar layout. Based on these conditions, our algorithm computes a class of paper architectures containing two sets of parallel patches that approximate the input geometry while guaranteed to be physically realizable. The method is demonstrated on a number of architectural examples, and physically engineered results are presented. [ABSTRACT FROM AUTHOR]

[Display omitted] • Two colorimetric paper-based sensors for creatinine determination are developed. • Signal readout is achieved with a smartphone with a self-written application. • App relies on computer-vision to enable automatic detection of the zone of interest. • The utility of the platform to determine creatinine in undiluted urine is proved. Creatinine is a clinically significant analyte used to diagnose kidney condition. However, the literature still lacks in creatinine sensors fulfilling point-of-care testing requirements. In this paper, we have developed colorimetric paper-based creatinine sensors adhering to point-of-care testing principles. The signal readout is accomplished with a smartphone modified with 3D-printed elements and processed with a self-written application compromising computer vision algorithm for automatic detection of the colored zone. Two colorimetric methods – routinely used Jaffé method and an alternative one with 3,5-dinitrobenzoic acid were both tested and compared. Hue channel intensity from HSV color space and green channel from RGB color space was used as the analytical signal in Jaffé and 3,5-dinitrobenzoate method, respectively. For both kinds of sensors the linear range of the response covered the range significant for urinary analysis, with precision, expressed as RSD, below 5%. Limit of quantification for Jaffé method was 1.05 mmol·L−1 whereas it was 0.82 mmol·L−1 for 3,5-dinitrobenzoate method. The utility of the developed sensors to selectively quantify creatinine in undiluted urine was proved using artificial urine samples and the obtained recoveries were in the range from 70 to 129 %. [ABSTRACT FROM AUTHOR]

This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance. [ABSTRACT FROM AUTHOR]

This paper introduces a new approach for improving the performance and versatility of Layered Manufacturing (LM), which is an emerging technology that makes it possible to build physical prototypes of 3D parts directly from their computer models using a “3D printer” attached to a personal computer.Current LM processes work by viewing the computer model as a single, monolithic unit. By contrast, the approach proposed here decomposes the model into a small number of pieces, by intersecting it with a suitably chosen plane, builds each piece separately using LM, and then glues the pieces together to obtain the physical prototype. This approach allows large models to be built quickly in parallel. Furthermore, it is very efficient in its use of so-called support structures that are generated by the LM process.This paper presents the first provably correct and efficient geometric algorithms to decompose polyhedral models so that the support requirements (support volume and area of contact) are minimized. Algorithms based on the plane-sweep paradigm are first given for convex polyhedra. These algorithms run in O(nlogn) time for n-vertex convex polyhedra and work by generating expressions for the support volume and contact-area as a function of the height of the sweep plane, and optimizing them during the sweep. These algorithms are then generalized to non-convex polyhedra, which are considerably more difficult due to the complex structure of the supports. It is shown that, surprisingly, non-convex polyhedra can be handled by first identifying certain critical facets using a technique called cylindrical decomposition, and then applying the algorithm for convex polyhedra to these critical facets. The resulting algorithms run in O(n2logn) time. Also given is a method for controlling the size of the decomposition, so that the number of pieces generated is within a user-specified limit. Experimental results show that the proposed approach can achieve significant reduction in support requirements in both the convex and the non-convex case. [Copyright &y& Elsevier]

Social engineering attacks are considered one of themost hazardous cyberattacks in cybersecurity, as human vulnerabilities are often the weakest link in the entire network. Such vulnerabilities are becoming increasingly susceptible to network security risks. Addressing the social engineering attack defense problem has been the focus of many studies. However, two main challenges hinder its successful resolution. Firstly, the vulnerabilities in social engineering attacks are unique due to multistage attacks, leading to incorrect social engineering defense strategies. Secondly, social engineering attacks are real-time, and the defense strategy algorithms based on gaming or reinforcement learning are too complex to make rapid decisions. This paper proposes a multiattribute quantitative incentive method based on human vulnerability and an improved Q-learning (IQL) reinforcement learning method on human vulnerability attributes. The proposed algorithm aims to address the two main challenges in social engineering attack defense by using a multiattribute incentive method based on human vulnerability to determine the optimal defense strategy. Furthermore, the IQL reinforcement learning method facilitates rapid decision-making during real-time attacks. The experimental results demonstrate that the proposed algorithm outperforms the traditional Qlearning (QL) and deep Q-network (DQN) approaches in terms of time efficiency, taking 9.1% and 19.4% less time, respectively. Moreover, the proposed algorithm effectively addresses the non-uniformity of vulnerabilities in social engineering attacks and provides a reliable defense strategy based on human vulnerability attributes. This study contributes to advancing social engineering attack defense by introducing an effective and efficient method for addressing the vulnerabilities of human factors in the cybersecurity domain. [ABSTRACT FROM AUTHOR]

In order to improve the performance of UAV's autonomous maneuvering decision-making, this paper proposes a decision-making method based on situational continuity. The algorithm in this paper designs a situation evaluation function with strong guidance, then trains the Long Short-Term Memory (LSTM) under the framework of Deep Q Network (DQN) for air combat maneuvering decision-making. Considering the continuity between adjacent situations, the method takes multiple consecutive situations as one input of the neural network. To reflect the difference between adjacent situations, the method takes the difference of situation evaluation value as the reward of reinforcement learning. In different scenarios, the algorithm proposed in this paper is compared with the algorithm based on the Fully Neural Network (FNN) and the algorithm based on statistical principles respectively. The results show that, compared with the FNN algorithm, the algorithm proposed in this paper is more accurate and forward-looking. Compared with the algorithm based on the statistical principles, the decision-making of the algorithm proposed in this paper is more efficient and its real-time performance is better. [ABSTRACT FROM AUTHOR]

When the target localization algorithm based on reinforcement learning is trained on few-sample data sets, the accuracy of target localization is low due to the low degree of fitting. Therefore, on the basis of deep reinforcement learning target localization algorithm, this paper proposes a target localization algorithm based on meta-reinforcement learning. Firstly, during the initial training of the model, the meta-parameters were classified and stored according to the similarity of the training tasks. Then, for the new target location task, the task feature extraction was carried out and the meta parameters with the highest similarity were matched as the initial parameters of the model training. The model dynamically updated the meta parameter pool to ensure that the optimal meta parameters of multiple different types of features were saved in the meta parameter pool, so as to improve the generalization ability and recognition accuracy of multiple types of target location tasks. Experimental results show that in a variety of single target localization tasks, compared with the original reinforcement learning target localization algorithm, under the same data set size, the model converges under a small number of training steps with the meta-parameters in the matching meta-parameter pool as the initial training parameters. Moreover, the training speed of the meta-reinforcement learning method based on MAML-RL is increased by 28.2% for random initial parameters, and that of the metareinforcement learning method based on this paper is increased by 34.9%, indicating that the proposed algorithm effectively improves the training speed, generalization performance and localization accuracy of object detection. [ABSTRACT FROM AUTHOR]

The author discusses the replacement of journal and articles by algorithms that rate, filter and disseminate scholarship. He explores the transition of the peer-review system from paper-native to Web-native system that uses Web technology to enhance dissemination, wherein, web-native articles have inconsistent quality and require filtering compared to journal articles. Topics discussed include dissemination, certification, and reward structure of scholarship.

This paper presents an algorithm for generating realistic drawing strokes in real-time that can take on the appearance of pastels, charcoals, or crayons. The similarity between the pigment deposit patterns on paper surfaces produced by pastel strokes and the shadows/shades on illuminated paper surfaces have been investigated. Multiple paper textures have been prepared and the paper surfaces have been ascertained by illumination from various directions to represent strokes in arbitrary directions. These textures have been processed as if they could be used as a height field, and pigments deposited on the paper have been calculated using the height field and tablet inputs. [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]

For Printed Circuit Board (PCB) surface defect detection, traditional detection methods mostly focus on template matching-based reference method and manual detections, which have the disadvantages of low defect detection efficiency, large errors in defect identification and localization, and low versatility of detectionmethods. In order to furthermeet the requirements of high detection accuracy, real-time and interactivity required by the PCB industry in actual production life. In the current work, we improve the You-only-look-once (YOLOv4) defect detection method to train and detect six types of PCB small target defects. Firstly, the original Cross Stage Partial Darknet53 (CSPDarknet53) backbone network is preserved for PCB defect feature information extraction, and secondly, the original multi-layer cascade fusion method is changed to a single-layer feature layer structure to greatly avoid the problem of uneven distribution of priori anchor boxes size in PCB defect detection process. Then, the K-means++ clustering method is used to accurately cluster the anchor boxes to obtain the required size requirements for the defect detection, which further improves the recognition and localization of small PCB defects. Finally, the improved YOLOv4 defect detection model is compared and analyzed on PCB dataset with multi-class algorithms. The experimental results show that the average detection accuracy value of the improved defect detection model reaches 99.34%, which has better detection capability, lower leakage rate and false detection rate for PCB defects in comparison with similar defect detection algorithms. [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]

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 paper aims to develop a rapid and practical procedure that can locate the slip surface for a slope with the minimum reliability index for limit equilibrium analysis at the minimum expense of time. The comparative study on the reliability indices from different sample numbers using the Monte Carlo Simulation Method has demonstrated that the results from large enough sample number are related with those from small sample number with high correlation indices. This observation has been tested for many homogeneous and heterogeneous slopes with various conditions under parametric studies. Based on this observation, the reliability index for a potential slip surface can be calcu lated with a small sample number, and the search for the minimum reliability index and the slip surface can be determined by heuristic optimization algorithm. Based on the comparisons between the critical deterministic and probabilistic slip surfaces for many different cases, the use of the proposed fast method in locating the critical probabilistic slip surface is found to perform well, which is suitable for normal routine analysis and design works. [ABSTRACT FROM AUTHOR]

An introduction is presented which discusses various reports related to model checking and fault prediction including the SemiBoost for the prediction of software defect, the semi-supervised learning technique for software fault detection and the new algorithm for reachability analysis.

The feasibility of the application of image/signal processing for measuring, marking, matching, and sorting vast quantities of data derived from materials typically found in artworks is presented through four case studies. Different patterns produced by canvas weave structures, surface textures of historic photographic papers, chain line intervals in Rembrandt's printing papers, and watermark variations have been subjected to different modes of computational analysis. The art-historical implications that result from computer-generated algorithms – including dating, attribution, authenticity, and workshop practices – can be considered as "computational connoisseurship." The case studies discussed point to future areas for research. Finally, because of the need for statistically meaningful datasets of images, a practical means of recording internal paper structure is introduced. [ABSTRACT FROM AUTHOR]

The traditional home product design system mainly depends on relatively shallow learning network, relatively simple embedded technology and Internet of things technology. The traditional home design system mainly depends on the traditional self-encoder technology. When combined with the deep neural network, this technology has serious defects in the computer vision algorithm, resulting in the serious waste of the corresponding system storage and computing resources, the corresponding system learning efficiency is relatively poor and the learning ability is weak. Based on this, this paper will build a home product design system based on the deep neural network of self-encoder. By improving the sparsity of self-encoder in the process of learning and training, we can further improve the sparsity of the system and further optimize the structure of self-encoder in the design system, The performance of the deep learning model of the design system is further improved through the hierarchical features continuously learned by the self-encoder in the process of home case design. Based on the optimization of the home product design system in this paper, the system effectively improves and improves the accuracy and stability of the internal feature classifier of the system, and improves the overall performance of the furniture design system. In the specific system construction part, based on ZigBee technology and embedded technology as the design carrier, and adhering to the goal of simplicity, intelligence and convenience, this paper designs and constructs the home product design system. The experimental results show that the noise processing level of the proposed home product design system is lower than 4-5db compared with the traditional design system, and the corresponding image classification accuracy is about 4% higher than the traditional design system. Therefore, the experimental results show that the home design system proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

wireless sensor network enables the regular monitoring of military, civil and other application. This paper proposes a new improved cluster algorithm of LEACH protocol which is intended to balance the energy consumption of the entire network and extend the lifetime of the network . In this paper we propose a leach protocol that works on network layer in OSI model. Leach protocol stands for Low-Energy Adaptive Clustering Hierarchy, a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network . LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.. It proides a better results than conventional protocols. [ABSTRACT FROM AUTHOR]

The article focuses on the paper "A primal-dual aggregation algorihtm for minimizing conditional value-at-risk in linear programs," by Daniel Espinoza and Eduardo Moreno which won the Computational Optimization and Applications (COAP) Best Paper Award. Topics discussed include the approximation method for stochastic optimization problems, the conditional value at risk (CvaR), and production scheduling problems.

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]

Recommender systems (RS) have been developed to make personalized suggestions and enrich users' preferences in various online applications to address the information explosion problems. However, traditional recommender-based systems act as black boxes, not presenting the user with insights into the system logic or reasons for recommendations. Recently, generating explainable recommendations with deep knowledge graphs (DKG) has attracted significant attention. DKG is a subset of explainable artificial intelligence (XAI) that utilizes the strengths of deep learning (DL) algorithms to learn, provide high-quality predictions, and complement the weaknesses of knowledge graphs (KGs) in the explainability of recommendations. DKG-based models can provide more meaningful, insightful, and trustworthy justifications for recommended items and alleviate the information explosion problems. Although several studies have been carried out on RS, only a few papers have been published on DKG-based methodologies, and a review in this new research direction is still insufficiently explored. To fill this literature gap, this paper uses a systematic literature review framework to survey the recently published papers from 2018 to 2022 in the landscape of DKG and XAI. We analyze how the methods produced in these papers extract essential information from graphbased representations to improve recommendations' accuracy, explainability, and reliability. From the perspective of the leveraged knowledge-graph related information and how the knowledge-graph or path embeddings are learned and integrated with the DL methods, we carefully select and classify these published works into four main categories: the Two-stage explainable learning methods, the Joint-stage explainable learning methods, the Path-embedding explainable learning methods, and the Propagation explainable learning methods. We further summarize these works according to the characteristics of the approaches and the recommendation scenarios to facilitate the ease of checking the literature. We finally conclude by discussing some open challenges left for future research in this vibrant field. [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]