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With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias. • We transform citation networks into influence propagation networks by reversing the citation relationships. • We propose a novel diffusion-based metric for ranking papers, combining influence propagation and game theory. • Experiments on APS and DBLP datasets demonstrate the effectiveness of our metric in identifying milestone papers. [ABSTRACT FROM AUTHOR]

Differentiation/ranking of authors in the scientific sphere is carried out according to their influence on the particular field(s) of science. The impact of the contribution is measured by the impact a text has on the development of the field/issue. It can be measured by the usage of ideas from other researchers works given as citations. Nowadays, citations are tracked by specific algorithms and citation management systems that have access to various databases, catalogues and bibliography systems through metadata. In the presented article, I discuss problematic issues that authors are facing while writing texts in different languages (other than the English language) and publishing them in periodicals. The most popular (often indicative) texts/authors still fail to be captured/cached by the algorithms, therefore creating an imbalance between the actual number of citations performed by other scholars and cached h-index displayed by the algorithm. The paper discusses the causes of the problem and suggests solutions for both authors and publishers. [ABSTRACT FROM AUTHOR]

Highlights • We evaluate paper-level impact metrics on field bias, time bias, and ranking performance. • We evaluate mean-based, percentile-based, co-citation-based, and post hoc rescaling approaches to normalise citation scores. • Percentile-based citation scores are less field and time biased than mean-normalised citation counts. • No significant difference in ranking performance exists between percentile- and mean-normalised citation scores. • Citation counts are always less time biased but always more field biased than PageRank. Abstract We evaluate article-level metrics along two dimensions. Firstly, we analyse metrics' ranking bias in terms of fields and time. Secondly, we evaluate their performance based on test data that consists of (1) papers that have won high-impact awards and (2) papers that have won prizes for outstanding quality. We consider different citation impact indicators and indirect ranking algorithms in combination with various normalisation approaches (mean-based, percentile-based, co-citation-based, and post hoc rescaling). We execute all experiments on two publication databases which use different field categorisation schemes (author-chosen concept categories and categories based on papers' semantic information). In terms of bias, we find that citation counts are always less time biased but always more field biased compared to PageRank. Furthermore, rescaling paper scores by a constant number of similarly aged papers reduces time bias more effectively compared to normalising by calendar years. We also find that percentile citation scores are less field and time biased than mean-normalised citation counts. In terms of performance, we find that time-normalised metrics identify high-impact papers better shortly after their publication compared to their non-normalised variants. However, after 7 to 10 years, the non-normalised metrics perform better. A similar trend exists for the set of high-quality papers where these performance cross-over points occur after 5 to 10 years. Lastly, we also find that personalising PageRank with papers' citation counts reduces time bias but increases field bias. Similarly, using papers' associated journal impact factors to personalise PageRank increases its field bias. In terms of performance, PageRank should always be personalised with papers' citation counts and time-rescaled for citation windows smaller than 7 to 10 years. [ABSTRACT FROM AUTHOR]

Digital automation in reconstruction of apictorial hand torn paper document increases efficacy and reduces human effort. Reconstruction of torn document has importance in various fields like archaeology, art conservation and forensic sciences. The devised novel technique for hand torn paper document, consists of pre-processing, feature extraction and reconstruction phase. Torn fragment's boundaries are simplified as polygons using douglas peucker polyline simplification algorithm. Features such as Euclidean distance and number of sudden changes in contour orientation are extracted. Our matching criteria identify the matching counterparts. Proposed features curtail ambiguity and enriches efficacy in reconstruction. Reconstructed results of hand torn paper document favour the proposed methodology. [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]

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]

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]

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]

The 5G technology will become a truly integrated technology. It's becoming the most famous technology because of its optimal system computing complex and transforming a group of individual network components. Adequate resources must be provided for the various devices that are typically on the same network. The various functions in the data system also require resource allocation to suit its needs. This will ensure that the various devices on that network perform different types of work effectively. It also represents the various device functionalites to ensure the estimated resources onetime manner. Only then will it be convenient to carry out a variety of jobs depending on their speed and selection. And its various data requirements vary according to the functionality of the various devices featured in this series segment dynamic configuration. In this paper a smart resource utilization scheme was proposed. Its main purpose is to better manage the off-the-shelf resources available here. And provide it where it is needed and streamline data delivery to users on the network. This ensures that all data goes to the users in the correct manner. The proposed method getting 49% energy consumption, 90% resource utilization, 92% resource reservation and 91% Quality of services. [ABSTRACT FROM AUTHOR]

In the work presented in this paper, we analyse ranking algorithms that can be applied to bibliographic citation networks and rank academic entities such as papers and authors. We evaluate how well these algorithms identify important and high-impact entities. The ranking algorithms are computed on the Microsoft Academic Search (MAS) and the ACM digital library citation databases. The MAS database contains 40 million papers and over 260 million citations that span across multiple academic disciplines, while the ACM database contains 1.8 million papers from the computing literature and over 7 million citations. We evaluate the ranking algorithms by using a test data set of papers and authors that won renowned prizes at numerous computer science conferences. The results show that using citation counts is, in general, the best ranking metric to measure high-impact. However, for certain tasks, such as ranking important papers or identifying high-impact authors, algorithms based on PageRank perform better. [ABSTRACT FROM AUTHOR]

To improve the accuracy of defect detection in bank note sorting and decrease the effects of cracks and scratches of bank note on detecting, a new algorithm based on wavelet decomposition is proposed, in which affine transform and wavelet transform are applied to bank note image registration and the edge information is extracted by Kirsch operator, while the defect feature is extracted from edge intensity differential. The bank note image is divided into several fixed size subzones, in each of which the defect feature is calculated to judge the degree of contamination. The experimental results reveal that the proposed feature extraction method is robust to the gray intensity change in each subbone, and obtains high recognition rate and high stability. This method has already been used in practical bank note sorting system. [ABSTRACT FROM AUTHOR]

Abstract: There are a number of issues which are involved with organizing a conference. Among these issues, assigning conference-papers to reviewers is one of the most difficult tasks. Assigning conference-papers to reviewers is automatically the most crucial part. In this paper, we address this issue of paper-to-reviewer assignment, and we propose a method to model the reviewers, based on the matching degree between the reviewers and the papers by combining a preference-based approach and a topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with the Hungarian algorithm. [Copyright &y& Elsevier]

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: The paper describes a system for executable papers for publishers enabling them to reuse content and to generate further advances of science and engineering. The executable algorithmic descriptions within a paper are presented in natural language sentences and basic code, thereby making long term compatibility absolute. Authors are required to use publicly numerical libraries on the Internet or references to publications with executable papers. As used by authors the system automatically creates a web of algorithmic knowledge on the Internet. Novelty of new algorithms in publications can be evaluated by automated tools available to authors, reviewers and readers of scientific papers published. [Copyright &y& Elsevier]

Abstract: This paper studies how missing data in the PageRank algorithm influences the result of papers ranking and proposes PrestigeRank algorithm on that basis. We make use of PrestigeRank to give the ranking of all papers in physics in the Chinese Scientific and Technology Papers and Citation Database (CSTPCD) published between 2004 and 2006. We compared PrestigeRank result with PageRank and citation ranking. We found PrestigeRank is significantly correlated with PageRank and citation counts. We also used paper citation networks to rank journals, and compared the result with that of journal citation networks. We proposed PR sum , PR ave , and compared both of them with citation counts and impact factor. It indicates PR sum , PR ave can reflect journal''s authority favorably. We also discuss the advantages and disadvantages, application scope and application prospects of PrestigeRank in the evaluation of papers and journals. [Copyright &y& Elsevier]

Technical terms are vital elements for understanding the techniques used in academic research papers, and in this paper, we use focused technical terms to explore technical trends in the research literature. The major purpose of this work is to understand the relationship between techniques and research topics to better explore technical trends. We define this new text mining issue and apply machine learning algorithms for solving this problem by (1) recognizing focused technical terms from research papers; (2) classifying these terms into predefined technology categories; (3) analyzing the evolution of technical trends. The dataset consists of 656 papers collected from well-known conferences on ACM. The experimental results indicate that our proposed methods can effectively explore interesting evolutionary technical trends in various research topics. [ABSTRACT FROM AUTHOR]

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To solve the problem of identifying similar questions in examination database, an algorithm for question similarity identification is proposed in this paper. By introducing domain words to the improvement of the word similarity model in HowNet, a model for question similarity identification is proposed to make the same or similar questions be identified and cut off automatically. This method improves the accuracy of identification compared with other methods. By combining merits of the random selection with those of the backtracking method, a new algorithm of generating papers automatically based on question similarity identification is proposed. It can guarantee the quality of papers. Test results show that the accuracy of question similarity identification of this method is 96% , which approaches to that of manual identification. This method can cut off similar questions not only of the same type, but also of different types. Finally, this method has been applied to the on-line examination of C programming language. [ABSTRACT FROM AUTHOR]

Abstract: A lot of work has been done in the field of multi-valued model checking home and abroad. But some research works are not perfect. Some problems in the semantic definitions of temporal operators and corresponding model checking algorithms are found in the paper “Model Checking Using Partial Kripke Structure with 3-Valued Temporal Logic”. Some counterexamples are given to show these errors. On the basis of the discussion, some sound semantic definitions of temporal operators and correct algorithms of model checking for some temporal operators are given. [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 compare the performance of global metrics and their local variants in the identification of expert-selected milestone papers on two distinct citation datasets. • We obtain a family of local variants of PageRank (LeaderRank) with a tunable degree of locality and find they can generate highly correlated scores with the scores by the original metrics. • The local variants of PageRank (LeaderRank) perform similarly well as the original global PageRank (LeaderRank) algorithms in the identification of seminal papers. • The local variants of PageRank (LeaderRank) perform significantly better than local metrics such as citation count, h-index and semi-local centrality. • Compared to network-based global metrics, the proposed local estimates provide a better trade-off between performance and computational efficiency. Recent works aimed to understand how to identify "milestone" scientific papers of great significance from large-scale citation networks. To this end, previous results found that global ranking metrics that take into account the whole network structure (such as Google's PageRank) outperform local metrics such as the citation count. Here, we show that by leveraging the recursive equation that defines the PageRank algorithm, we can propose a family of local impact metrics. Our results reveal that the obtained PageRank-based local metrics outperform the citation count and other local metrics in identifying the seminal papers. Compared with global metrics, these local metrics can reach similar performance in the identification of seminal papers in shorter computational time, without requiring the whole network topology. Our findings could help to better understand the nature of groundbreaking research from citation network analysis and find practical applications in large-scale data. [ABSTRACT FROM AUTHOR]

Electric bicycle datasets for lift control have played a crucial role in the development of electric bicycle lift entry detection algorithms. However, existing datasets often encounter issues, including a small dataset size, a high false detection rate, and the absence of a publicly available benchmark dataset. This paper addresses these challenges by constructing a new electric bicycle detection dataset for lift control based on the research demands of the electric bicycle detection task for lift control. XHNet_EB, a car scene image dataset covering various types of electric bicycles, was constructed based on images taken by a camera from above. Segmented annotation was employed, framing the front and rear of the electric bicycle independently. This approach effectively addressed the problems of many irrelevant features and a high false detection rate caused by the use of whole-electric-bicycle annotations in mainstream datasets. AP_50, AP_75, the mAP, and the number of false detections in the images were used for quantitative analysis of the dataset. The experimental results indicated that the object detection accuracy of the XHNet_EB dataset was excellent. Quantitative analysis and evaluation of the number of false detections in images were conducted using four mainstream lightweight detection model algorithms. The results demonstrated that segmented annotation reduced the false detection rate more effectively than entire electric bicycle annotation. This study identified drawbacks in existing datasets. The dataset proposed in this paper overcomes the shortcomings of existing commercial data and solves problems such as the high false detection rate caused by the inclusion of many irrelevant features caused by the "whole-electric-bicycle annotation" method, which could help with the development of electric bicycle detection applications in lifts. [ABSTRACT FROM AUTHOR]

Achieving a balance between convergence and diversity and their mutual enhancement is a complex task in the process of algorithm improvement. This is crucial because it is directly related to the effectiveness of the algorithm in obtaining accurate and uniformly distributed Pareto frontiers. Although significant progress has been made in particle swarm algorithms, exploring new approaches is necessary. In this paper, we construct a projection plane (projection line in 2D) based on the extreme values of the non-dominated solutions, select a set of uniform reference points on the projection plane, and then project the non-dominated solutions onto the constructed projection plane to form projection points. The reference points and projection points on the projection plane are thus utilized to guide the updating of the population as well as the maintenance of the external archive, a strategy that enhances the algorithm's global exploration and local exploitation capabilities. Secondly, we aggregate the target values of particles into a single scalar value and combine the idea of particle fusion to design a scheme for the particle selection of individual optimal particles. This paper further improves the algorithm's overall performance by using the information between populations to select individual optimal particles. Lastly, it is evaluated against a number of multi-objective algorithms that are currently in use and perform well on 22 test problems. The findings demonstrate that the algorithm this paper proposes performs better when solving multi-objective problems. [ABSTRACT FROM AUTHOR]

Aiming at the problem of traditional methods which lead to low detection accuracy owing to limited sample size and imbalanced sample, this paper proposes a novel insulator self-explosion detection algorithm based on mixed samples and transfer learning. Firstly, the segmentation of insulator and background region is realized by using template matching and grabcut algorithm. Secondly, the segmented insulator image is merged with the new background image at pixel level to generate the insulator artificial sample with distinct texture. Thirdly, this paper introduces an improved Faster R-CNN model that utilizes ResNet50 for feature extraction, replacing the traditional VGG16 network. This modification significantly improves the ability of the network to extract insulator texture features. By introducing the focal loss function to address the imbalance between positive and negative samples, the detection accuracy of the network is further improved. Finally, the introduction of depthwise separable convolution helps reduce model complexity, resulting in a more lightweight architecture. The model is trained by a dataset that includes both synthetic and real samples. The real insulators are then employed for evaluation. The experimental results show that even with a reduced number of artificial samples, the algorithm proposed in this study achieves a mean average precision of 95.26%. Additionally, the average detection time is 2.31s. These results show that compared with the original Faster R-CNN method, the proposed method has improved the detection accuracy and speed. [ABSTRACT FROM AUTHOR]

Edge bundling is a technique used to improve the readability of large graph drawings by grouping edges to reduce visual complexity. This paper treats this task as a clustering problem, using compatibility metrics to evaluate solutions in an optimization pipeline combined with a clustering ensemble approach. The aim is to present the Clustering Ensemble-based Edge Bundling (CEBEB) method for solving the General-based Edge Bundling (GBEB) problem and report results for some given graphs. The CEBEB method proved very promising and generated better solutions than an existing evolutionary algorithm. Additionally, the paper introduces a new ensemble algorithm, specific for the GBEB, and reviews some previous results. [ABSTRACT FROM AUTHOR]

Cutting force is a significant indicator for in-process monitoring of cutting status. Although dynamometers are widely used in cutting force measurement, they are not suitable for nano-cutting due to insufficient sensitivities and low integrating possibilities. This paper presents a smart tool holder composed by piezoelectric ceramics and a flexible hinge, which could be integrated with a fast tool servo (FTS), for three-axial cutting force measurements in micro/nano-cutting. An algorithm was developed for enabling its quasi-static force measurement. Experiments were performed to verify the capabilities of cutting force measurement, demonstrating a high measurement sensitivity for indicating the micro/nano-cutting status. [ABSTRACT FROM AUTHOR]

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies. • The proposed framework can generate a comprehensive introduction based on users' brief inputs. • This paper demonstrates a three-testing-threshold approach to check the content quality. • The introduction generated through this method has less than 3% similarity to the original text by comparison. [ABSTRACT FROM AUTHOR]

The smart car collects the signal through the infrared sensor, and obtains the gray map in front of the car, according to the gray map, the tracking is judged.Due to the complexity of the environment, many gray-scale maps are difficult to trace, and even cannot be judged, which brings great uncertainty to the operation of the smart car.The paper designs a method to improve the infrared tracking accuracy of smart cars.First, determine the level of contrast in this graph.By improving the brightness in the grayscale image, the gray signal around the tracking line is eliminated, and then the tracking line is highlighted by increasing the contrast.Therefore, it effectively overcomes the shortcomings of poor practicality of smart cars and improves the infrared tracking accuracy of smart cars. [ABSTRACT FROM AUTHOR]

The existing forensics systems are developing in a more and more perfect direction in terms of forensic steps, legal constraints, audit supervision, etc., but they are still not fully applicable to legal forensics. A general legal forensics system must have a complete forensics process to ensure the smooth progress of the forensics work. One of the purposes of legal forensics is to use legal means to bring criminals to justice. Only by ensuring the legality of the evidence can it be accurately identified case, and therefore also to ensure the legality of the evidence collection process. In order to design a more powerful legal forensics system, this paper introduces a data mining algorithm to extract valuable information from massive data through association rules to improve the efficiency of forensics. At the same time, this paper also proposes three coding schemes for the integrity of the evidence object, and compares the forensic effects of the three schemes. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence technology, it has become convenient for people to work, study and travel and face recognition is widely used in many fields, but also brings a lot of hidden dangers to security. Aiming at the problem of reduced accuracy of face detection algorithm caused by local occlusion factors, The paper proposes a partial occlusion face recognition method based on DCGAN face repair combined with ResNet. The experimental results show that the algorithm proposed in the paper has better face recognition effect when there is partial occlusion in the face. [ABSTRACT FROM AUTHOR]

In this paper, we construct an enterprise economic management sharing platform based on the feature recognition grid system, firstly, we use the fuzzy matching recognition algorithm to extract the grid features of economic management and map them into the fuzzy set of affiliation features, and then we extract the feature vectors from each grid to get the feature matrix, and finally, according to the similarity degree of the feature sequences, we set a threshold to give the recognition result. The results show that the recognition rate of the platform constructed in this paper reaches more than 99%, the classification accuracy is about 90% on average, and the optimization time is only about 19 s. It is verified that the method of this paper improves the accuracy of enterprise economic management, and has a positive role in promoting the healthy development of enterprises. [ABSTRACT FROM AUTHOR]

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

The unstoppable trend of globalization has made the integration of voice recognition with science and technology increasingly close, and has called for the emergence of more powerful oral voice recognition systems. At present, most speech recognition research uses Hidden Markov Model-Gaussian Mixture Modell (HMM-GMM) and onnectionist Temporal Classification (CTC) end-to-end method, which has obvious limitations. Therefore, based on neural network, this paper introduces deep neural network posterior probability algorithm into speech recognition system, and constructs the spoken speech recognition system based on deep neural network posterior probability algorithm. The design principle of the constructed speech system is discussed and the performance analysis and effectiveness evaluation are carried out. The experimental results show that the performance of the neural network-based speech recognition system constructed in this paper has made a breakthrough compared with the traditional technology, and the Character Error Rate (CER) is only 4.6%, and the End-to-End Latency and Equal Error Rate (EER) have reached a low level. This study points out the future development direction of speech recognition and provides the impetus to promote its further development [ABSTRACT FROM AUTHOR]

To find the area of an irregular shape, we break the shape into common shapes. Then we find the area of each shape and add them, but this approach does not always work. In this paper we investigate Gauss’s area formula (for irregular shapes), also known as the shoelace formula or the shoelace algorithm. This theorem is outside the scope of school program. Nevertheless, we provide several applications that emphasize the importance and usefulness of this theorem. Most of the applications provided in this paper are created by the authors. [ABSTRACT FROM AUTHOR]

As a crucial development direction for the sixth generation of mobile communication networks (6G), Low Earth Orbit (LEO) satellite networks exhibit characteristics such as low latency, seamless coverage, and high bandwidth. However, the frequent changes in the topology of LEO satellite networks complicate communication between satellites, and satellite power resources are limited. To fully utilize resources on satellites, it is essential to determine the association between satellites before power allocation. To effectively address the satellite association problem in LEO satellite networks, this paper proposes a satellite associationbased resource allocation algorithm. The algorithm comprehensively considers the throughput of the satellite network and the fairness associated with satellite correlation. It formulates an objective function with logarithmic utility by taking the logarithm and summing the satellite channel capacities. This aims to maximize the sum of logarithmic utility while promoting the selection of fewer associated satellites for forwarding satellites, thereby enhancing the fairness of satellite association. The problems of satellite association and power allocation are solved under constraints on resources and transmission rates, maximizing the logarithmic utility function. The paper employs an improved Kuhn-Munkres (KM) algorithm to solve the satellite association problem and determine the correlation between satellites. Based on the satellite association results, the paper uses the Lagrangian dual method to solve the power allocation problem. Simulation results demonstrate that the proposed algorithm enhances the fairness of satellite association, optimizes resource utilization, and effectively improves the throughput of LEO satellite networks. [ABSTRACT FROM AUTHOR]

The article focuses on the research flaws in the study "The Harmful Effects of Algorithms in Grades 1-4," by Constance Kamii and Ann Dominick. The research explained the benefits of "no algorithms" experience in mastering arithmetic, where the authors theorized that the approach improved the ability of children to master mental math techniques and disadvantages of standard addition algorithm. The flaws proved that standard algorithms are essential in solving arithmetic problems in simple ways.

In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

Multiple criteria evolutionary algorithms, being essentially parallel in their character, are a natural instrument of finding a representation of entire Pareto set (set of solutions and outcomes non-dominated in criteria space) for vector optimisation problems. However, it is well known that Pareto sets for problems with more than two criteria might become complicated and their representation very time-consuming. Thus, the application of such algorithms is essentially limited to bi-criteria problems or to vector optimisation problems with more criteria but of simple structure. Even in such cases, there are problems related to various important aspects of vector optimisation, such as the uniformity of representation of Pareto set, stopping tests or the accuracy of representing Pareto set, that are not fully covered by the broad literature on evolutionary algorithms in vector optimisation. These problems and related computational tests and experience are discussed in the paper. In order to apply evolutionary algorithms for decision support, it would be helpful to use them in an interactive mode. However, evolutionary algorithms are in their essence global and of batch type. Nevertheless, it is possible to introduce interactive aspects to evolutionary algorithms by focusing them on a part of Pareto set. The results of experimental tests of such modifications of evolutionary algorithms for vector optimisation are presented in the paper. Another issue related to vector optimisation problems with more than two criteria is the computational difficulty of estimating nadir points of Pareto set. The paper describes the use of diverse variants of evolutionary algorithms to the estimation of nadir points, together with experimental evidence. [ABSTRACT FROM AUTHOR]

Purpose: This study explores the potential of computational design processes in creating contextually responsive envelopes for high-rise residential buildings in the Middle East. This includes considering both physical constraints and social preferences, with a focus on balancing sunlight exposure, privacy and views. Design/methodology/approach: A two-phase simulation study analyzed various exterior envelope systems in Baghdad high-rise buildings. The first phase examined two commonly used exterior envelopes – fully glazed and window-based – to assess sunlight exposure, privacy and views. In the second phase, a multi-objective optimization process was applied to derive contextually optimized design solutions addressing the challenges identified in the first phase. Findings: The study reveals that contextually optimized design solutions significantly improved direct sunlight exposure and privacy while maintaining satisfactory views. Although fully glazed exterior envelopes provided better-uninterrupted views, the optimized solutions offered more balanced performance across all factors, demonstrating the potential of computational design processes in creating contextually responsive building envelopes. Originality/value: This paper emphasizes the importance of considering both physical and social contexts in the development of algorithms for architecture in the Middle East. This paper supports a progressive interpretation of traditional building references and demonstrates how computational design processes can create contextually responsive building envelopes that satisfy social needs and provide better-performing buildings for inhabitants. [ABSTRACT FROM AUTHOR]

A critical aspect of software engineering is Software fault prediction which aims to identify and prevent errors in software systems before their release which can cause failures or issues for its users. Various techniques and tools have been developed to detect software faults, including static code analysis, dynamic testing, and machine learning-based approaches. In past few years, the world has seen a growing interest in the use of ML models for predicting software faults, as they can effectively analyse high dimensional datasets and detect complex patterns which are difficult for human experts to detect. However, developing accurate and reliable software fault detection models requires careful selection of data, feature engineering, and model evaluation. This purpose of this paper is to present a comprehensive analysis of potential applications and future research directions in the field of software fault detection. The study emphasizes the importance of identifying and addressing software faults to ensure the reliability and efficiency of software systems. Additionally, the paper outlines various approaches and techniques that can be employed for effective software fault detection. [ABSTRACT FROM AUTHOR]

Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton–Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader. [ABSTRACT FROM AUTHOR]