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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]

With the exponential growth in the number of academic researchers, it is crucial for editors of scientific journals to identify the highest-quality papers. While several measures exist to evaluate a paper's impact post-publication, the challenge of determining the potential impact of a manuscript during the review process remains an understudied issue. In this paper, we propose a reviewer-reputation ranking algorithm to identify high-quality papers based on paper citations, where a reviewer's reputation is computed from the correlation between their past ratings and the current number of citations received by the papers they have evaluated. During the review process, reviewers with high reputation scores are given more weight to determine the quality of papers. We test the algorithm on an artificial network with 200 reviewers and 600 papers, as well as on the American Physical Society (APS) data set, including in the analysis 308,243 papers and 274,154 mutual citations. We compare our approach with two existing methods, demonstrating that our algorithm significantly outperforms the others in identifying manuscripts with the highest quality. Our findings can help improve the impact of scientific journals, thereby contributing to academic and scientific progress. • We propose an algorithm to identify the papers with the highest quality from a large number of submissions. • We compare our new algorithm with other existing methods of aggregating user ratings in various online services. • We test our algorithm both with an artificial network and with the empirical data of the APS data set. • We show that our algorithm outperforms the other methods in identifying the papers with the highest quality. [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]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.10.019 Byline: Kazuki Matsumoto (a), Hiroyoshi Miwa (b), Toshihide Ibaraki (b) Keywords: Scheduling; Corrugated paper; Complexity; Contiguous set; Integer programming Abstract: Corrugated paper is produced by gluing three types of papers of the same breadth. Given a set of orders, we first assign each order to one of the standard breadths, and then sequence those assigned to each standard breadth so that they are continuously manufactured from the three rolls of the specified standard breadth equipped in the machine called corrugator. Here we are asked to achieve multi-goals of minimizing total length of roll papers, total loss of papers caused by the differences between standard breadths and real breadths of the orders, and the number of machine stops needed during production. We use integer programming to assign orders to standard breadths, and then develop a special purpose algorithm to sequence the orders assigned to each standard breadth. This is a first attempt to handle scheduling problems of the corrugator machine. Author Affiliation: (a) Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan (b) Department of Informatics, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen Sanda, Hyogo 669-1337, Japan Article History: Received 24 March 2007; Accepted 5 October 2007

Accurate characterization is an important issue in paper currency recognition system. This paper proposes a robust paper currency recognition method based on Hidden Markov Model (HMM). By employing HMM, the texture characteristics of paper currencies are modeled as a random process. The proposed algorithm can be used for distinguishing paper currency from different countries. A similarity measure has been used for the classification in the proposed algorithm. To evaluate the performance of the proposed algorithm, experiments have been conducted on more than 100 denominations from different countries. The results indicate 98% accuracy for recognition of paper currency. [Copyright &y& Elsevier]

Abstract: More than just being a substrate, paper can also provide evidence for the provenance of documents. An earlier paper described a method to compare paper structure, based on the Fourier power spectra of light transmission images. Good results were obtained by using the 2D correlation of images derived from the power spectra as a similarity score, but the method was very computationally intensive. Different comparison algorithms are evaluated in this paper, using information theoretical criteria. An angular invariant algorithm turned out to be as effective as the original one but 4 orders of magnitude faster, making the use of much larger databases possible. [Copyright &y& Elsevier]

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: 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: 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]

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]

Abstract: We introduce an algorithm for approximating a 2-manifold 3D mesh by a set of developable surfaces. Each developable surface is a generalized cylinder represented as a strip of triangles not necessarily taken from the original mesh. Our algorithm is automatic, creates easy-to-assemble pieces, and provides global error bounds. The algorithm consists of three stages. In the first stage, the mesh is segmented into meaningful components. The second stage approximates each of the mesh components in 3D by a set of triangle strips with smooth (non-jagged) boundaries that guarantee a maximal user-defined error relative to the original mesh. Finally, these strips are unfolded to the plane, resulting in flat patterns, which may be cut and assembled from paper. The approximation quality is controlled by a user-supplied parameter specifying the allowed Hausdorff distance between the input mesh and its piecewise-developable approximation. The strips generated by our algorithm may be parameterized to conform with the parameterization of the original mesh, if given, to facilitate texture mapping. We demonstrate this by physically assembling paper-craft models from the strips generated by our algorithm when run on several polygonal 3D mesh data sets. [Copyright &y& Elsevier]

Abstract: In this paper we provide a computational approach to the shape of curves which are rational in polar coordinates, i.e. which are defined by means of a parametrization where both , are rational functions. Our study includes theoretical aspects on the shape of these curves, and algorithmic results which eventually lead to an algorithm for plotting the “interesting parts” of the curve, i.e. the parts showing the main geometrical features. [Copyright &y& Elsevier]

Abstract: In a recent paper (Computer Aided Geometric Design 23 (3), 243–260), Yang presented a novel kind of nonlinear and geometry driven subdivision scheme for curve interpolation. The author declared that this scheme was shape preserving when all free parameters were explicitly selected by some specific rules. In this note, we demonstrate that the author (a) ignored the consistency of the definition to straight edges, and also (b) did not give a perfect scheme and proof for the shape preserving subdivision. [Copyright &y& Elsevier]

Most classification algorithms are “passive”, in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can—at some cost—obtain the values of some unspecified attributes, before deciding upon a class label. This can be useful, for instance, when deciding whether to gather information relevant to a medical procedure or experiment. The expected utility of using an active classifier depends on both the cost required to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification. This paper analyzes the problem of learning optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework, we show that this task can be achieved efficiently when the active classifier is allowed to perform only (at most) a constant number of tests. We then show that, in more general environments, this task of learning optimal active classifiers is often intractable. [Copyright &y& Elsevier]

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]

Abstract: In this paper we present a novel approximate algorithm to calculate the top-k closest pairs join query of two large and high dimensional data sets. The algorithm has worst case time complexity and space complexity and guarantees a solution within a factor of the exact one, where t ∈{1,2,…,∞} denotes the Minkowski metrics L t of interest and d the dimensionality. It makes use of the concept of space filling curve to establish an order between the points of the space and performs at most d +1 sorts and scans of the two data sets. During a scan, each point from one data set is compared with its closest points, according to the space filling curve order, in the other data set and points whose contribution to the solution has already been analyzed are detected and eliminated. Experimental results on real and synthetic data sets show that our algorithm behaves as an exact algorithm in low dimensional spaces; it is able to prune the entire (or a considerable fraction of the) data set even for high dimensions if certain separation conditions are satisfied; in any case it returns a solution within a small error to the exact one. [Copyright &y& Elsevier]

There are two well known transformations from non-binary constraints to binary constraints applicable to constraint satisfaction problems (CSPs) with finite domains: the dual transformation and the hidden (variable) transformation. We perform a detailed formal comparison of these two transformations. Our comparison focuses on two backtracking algorithms that maintain a local consistency property at each node in their search tree: the forward checking and maintaining arc consistency algorithms. We first compare local consistency techniques such as arc consistency in terms of their inferential power when they are applied to the original (non-binary) formulation and to each of its binary transformations. For example, we prove that enforcing arc consistency on the original formulation is equivalent to enforcing it on the hidden transformation. We then extend these results to the two backtracking algorithms. We are able to give either a theoretical bound on how much one formulation is better than another, or examples that show such a bound does not exist. For example, we prove that the performance of the forward checking algorithm applied to the hidden transformation of a problem is within a polynomial bound of the performance of the same algorithm applied to the dual transformation of the problem. Our results can be used to help decide if applying one of these transformations to all (or part) of a constraint satisfaction model would be beneficial. [Copyright &y& Elsevier]

Abstract: For positive integers , an -labeling of a digraph D is a function f from into the set of nonnegative integers such that if x is adjacent to y in D and if x is of distance two to y in D. Elements of the image of f are called labels. The -labeling problem is to determine the -number of a digraph D, which is the minimum of the maximum label used in an -labeling of D. This paper studies -numbers of digraphs. In particular, we determine -numbers of digraphs whose longest dipath is of length at most 2, and -numbers of ditrees having dipaths of length 4. We also give bounds for -numbers of bipartite digraphs whose longest dipath is of length 3. Finally, we present a linear-time algorithm for determining -numbers of ditrees whose longest dipath is of length 3. [Copyright &y& Elsevier]

Abstract: In this paper, we will derive a solver for a symmetric strongly nonsingular higher order generator representable semiseparable plus band matrix. The solver we will derive is based on the Levinson algorithm, which is used for solving strongly nonsingular Toeplitz systems. In the first part an solver for a semiseparable matrix of semiseparability rank p is derived, and in a second part we derive an solver for a band matrix with bandwidth . Both solvers are constructed in a similar way: firstly a Yule–Walker-like equation needs to be solved, and secondly this solution is used for solving a linear equation with an arbitrary right-hand side. Finally, a combination of the above methods is presented to solve linear systems with semiseparable plus band coefficient matrices. The overall complexity of this solver is plus lower order terms. In the final section numerical experiments are performed. Attention is paid to the timing and the accuracy of the described methods. [Copyright &y& Elsevier]

Consistency control at the outlet of blow tanks and hi-density towers, the so-called double-dilution problem, is an area where further control development is needed. The task is to coordinate the manipulation of two input variables, coarse and fine dilution, to control the consistency, while simultaneously mid-ranging the fine dilution to keep it from saturating. This is made difficult by process nonlinearities, complex dynamics and tight manipulated variable saturation constraints. To address these problems, a model-based predictive controller (MPC) was applied to blow tank consistency control in a pulp mill. One of the unique features of the proposed MPC algorithm is that it is well suited to run on standard distributed control system (DCS) hardware of the type that already exists in most process plants. Several control strategies were tested, including MPC and the previously implemented scheme, with and without a ratio control scheme aimed at removing the dominant process nonlinearity. The results of a mill trial indicate that both controllers, when implemented as ratio controllers, give excellent disturbance rejection for production rate changes, keeping the consistency within 0.1% of target. However, MPC outperformed the conventional strategy in its response to setpoint changes and unmeasured disturbances, and was able to cope with actuator failure. [Copyright &y& Elsevier]

Objective: Most preventable adverse drug events and medication errors occur during medication ordering. Medication order entry and clinical decision support are available on paper or as computerized systems. In this post-hoc analysis we investigated frequency and clinical impact of blood glucose (BG) documentation- and user-related calculation errors as well as workflow deviations in diabetes management. We aimed to compare a paper-based protocol to a computerized medication management system combined with clinical workflow and decision support.Methods: Seventy-nine hospitalized patients with type 2 diabetes mellitus were treated with an algorithm driven basal-bolus insulin regimen. BG measurements, which were the basis for insulin dose calculations, were manually entered either into the paper-based workflow protocol (PaperG: 37 patients) or into GlucoTab(®)-a mobile tablet PC based system (CompG: 42 patients). We used BG values from the laboratory information system as a reference. A workflow simulator was used to determine user calculation errors as well as workflow deviations and to estimate the effect of errors on insulin doses. The clinical impact of insulin dosing errors and workflow deviations on hypo- and hyperglycemia was investigated.Results: The BG documentation error rate was similar for PaperG (4.9%) and CompG group (4.0%). In PaperG group, 11.1% of manual insulin dose calculations were erroneous and the odds ratio (OR) of a hypoglycemic event following an insulin dosing error was 3.1 (95% CI: 1.4-6.8). The number of BG values influenced by insulin dosing errors was eightfold higher than in the CompG group. In the CompG group, workflow deviations occurred in 5.0% of the tasks which led to an increased likelihood of hyperglycemia, OR 2.2 (95% CI: 1.1-4.6).Discussion: Manual insulin dose calculations were the major source of error and had a particularly strong influence on hypoglycemia. By using GlucoTab(®), user calculation errors were entirely excluded. The immediate availability and automated handling of BG values from medical devices directly at the point of care has a high potential to reduce errors. Computerized systems facilitate the safe use of more complex insulin dosing algorithms without compromising usability. In CompG group, missed or delayed tasks had a significant effect on hyperglycemia, while in PaperG group insufficient precision of documentation times limited analysis. The use of old BG measurements was clinically less relevant.Conclusion: Insulin dosing errors and workflow deviations led to measurable changes in clinical outcome. Diabetes management systems including decision support should address nurses as well as physicians in a computerized way. Our analysis shows that such systems reduce the frequency of errors and therefore decrease the probability of hypo- and hyperglycemia. [ABSTRACT FROM AUTHOR]

Abstract: The automatic control of grammage (basis weight) in paper and board production is based upon on-line grammage measurement. Furthermore, the automatic control of other quality variables such as moisture, ash content and coat weight, may rely on the grammage measurement. The integrity of Kr-85 based on-line grammage measurement systems was studied, by performing basic calibrations with traceably calibrated plastic reference standards. The calibrations were performed according to the EN ISO/IEC 17025 standard, which is a requirement for calibration laboratories. The observed relative measurement errors were 3.3% in the first time calibrations at the 95% confidence level. With the traceable basic calibration method, however, these errors can be reduced to under 0.5%, thus improving the integrity of on-line grammage measurements. Also a standardised algorithm, based on the experience from the performed calibrations, is proposed to ease the adjustment of the different grammage measurement systems. The calibration technique can basically be applied to all -radiation based grammage measurements. [Copyright &y& Elsevier]

Abstract: During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution using only basic elementary operations. The proposed approach has been developed for a UK paper mill. [Copyright &y& Elsevier]

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]

Topology optimization has seen increased interest with the rise of additive manufacturing (AM) as a fabrication method, because of its ability to exploit the geometric complexity that AM offers. However, AM still imposes some geometric restrictions on the design, most notably on minimum feature size, overhang angles, and enclosed voids. Enclosed voids are problematic because for many AM methods it is impossible to remove supports, unmelted powder or uncured liquid from them. This paper introduces a filter based on a cumulative sum flood fill algorithm to alleviate this issue in a flexible manner. This filter produces a density field where every enclosed void element is rendered solid. It successfully eliminates enclosed voids in both 2D and 3D problems, with low computational cost due to its geometric nature. In addition we demonstrate direct control over the location, amount, and size of powder removal features by varying boundary conditions for the filter, running additional flood fills, and adding morphology operators, respectively. • The cumulative sum flood fill approach is introduced in topology optimization. • The approach works as a filter and successfully eliminates enclosed voids. • Direct control can be achieved over the amount, location, and size of access channels. • Due to its geometric nature it adds little computational effort. [ABSTRACT FROM AUTHOR]

Abstract: This paper treats controller design and tuning for systems with two input signals and two output signals in the process industry. Two design methods that can be combined to form a core in an algorithm for automatic design and tuning for the considered systems are presented. The proposed controller consists of a decoupler and a diagonal PID controller. The decoupler has the property that as little dynamics as possible is introduced in the decoupler. The PID design method is based on exhaustive search. The methods are combined and tested in an industrial environment in a paper mill. [Copyright &y& Elsevier]

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals to be reviewed is the objective of the reviewer assignment problem. However, the majority of the existing studies ignore quantitative constraints with respect to the articles assigned to the reviewers during the review process. In this study, we propose a novel optimization model with several review condition constraints to address the reviewer assignment problem. In the proposed model, the expertise and research areas of the candidate reviewers and proposals are identified using semantic topic models, which are demonstrated to be effective when measuring the relevance of the reviewers with respect to the proposals to be reviewed; further, the computational efficiency is improved owing to the reduced representation dimensionality. Herein, an improved heuristic algorithm is proposed to match reviewers and papers based on specific topic areas, and candidate reviewers are assigned to each proposal under the global optimum condition based on their overall performance values. Subsequently, an empirical test is conducted using a conference reviewer dataset. The obtained results show that the proposed model can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks. • A novel optimization model is proposed to address the reviewer assignment problem. • The expertise and profiles are identified with the semantic topic models. • An improved heuristic algorithm is proposed to assign reviewer with global optimum. • The effectiveness and robustness is validated in three stages of experiments. [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]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese Algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese Algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese Algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese Algorithm problem-solving capabilities. The primary objective of the Snow Geese Algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese Algorithm is publicly available at https://github.com/stones3421/SGA-project. • Snow Geese Algorithm: A novel meta-heuristic approach inspired by snow geese flight behavior, tackling optimization problems. • Two-stage Strategy: Mimics snow geese exploration and exploitation patterns, setting it apart from traditional methods. • Performance Evaluation: Multiple algorithms are assessed on different problems. • Algorithm Features: Experimental results validate the effectiveness of the Snow Geese Algorithm. • Practical Applications: Snow Geese Algorithm shows potential in engineering optimization, aiding accurate decision-making. [ABSTRACT FROM AUTHOR]

Considering the ubiquity of WiFi enabled devices and user's mobility, location privacy within WiFi networks is one of the focus points of many researchers. Preferred Network Lists (PNL), within which devices store a list of names of previously used hotspots - Service Set Identifiers (SSIDs), is transmitted in clear by a portion of devices as a part of WiFi connection protocol. PNL has proven to be one exceptionally interesting source of private data on user's previous whereabouts. However, since the PNL datasets are anonymized, most of the available work focuses on groups of users as opposed to one particular user. In this paper we work towards finding the name of the person behind the device's PNL. We introduce a novel SSID - location tag matching function, followed by an algorithm used for intersecting large PNL datasets with localization tags on Instagram social network. The algorithm enables us to match the user's MAC address and PNL with his full name, photos and activities. We find that deanonymization of a MAC address provides serious implications for potential long term tracking. We tested our work in real life conditions on a large scale music festival. To approach the ground truth we conducted hand check tests performed by 10 testers who concluded that more than 50% of the proposed matches were correct. [ABSTRACT FROM AUTHOR]

In this paper, a pipeline leakage detection algorithm based on information fusion of pressure and flow is proposed, and its core work is the construction of BPA based on discount factors. The wavelet packet decomposition is carried out for the original signals, and the processed signals are used to train different SVM classifiers to achieve the first identification results. For the samples whose initial classification results are not completely consistent, BPA is calculated according to the confusion matrixes of the initial SVM classifiers. In this paper, static discount factors and dynamic discount factors are constructed using different methods, and dynamic discount factors are modified when decisions fail. Then comprehensive discount factors are constructed based on the combination of different static discount factors and dynamic discount factors. The Shafer discount rule is used to modify BPA. Finally, D-S evidence theory is used to fuse the BPA results of all classifiers under different discount combinations. Experiments show that this algorithm can effectively use the correlation of evidences to reasonably fuse the decision results of multiple sensors, overcome the problem that the accuracy of a single sensor is not high enough for leakage identification, and improve the identification accuracy of pipeline leakage. [ABSTRACT FROM AUTHOR]

This paper investigates the convergence behavior of the Dirichlet-Neumann and Neumann-Neumann waveform relaxation algorithms for time-fractional sub-diffusion and diffusion-wave equations. The algorithms are applied to regular domains in 1D and 2D for multiple subdomains, and the impact of different constant values of the generalized diffusion coefficient on the algorithms' convergence is analyzed. The convergence rate of the algorithms is analyzed as the fractional order of the time derivative changes. The paper demonstrates that the algorithms exhibit slow superlinear convergence when the fractional order is close to zero, almost finite step convergence (exact finite step convergence for wave case) when the order approaches two, and faster superlinear convergence as the fractional order increases in between. The transitional nature of the algorithms' behavior is effectively captured through estimates with changes in the fractional order, and the results are verified by numerical experiments. [ABSTRACT FROM AUTHOR]

• A novel combined interval prediction algorithm is proposed in this paper. • Hybrid feature selection strategy is designed in the proposed system. • Multi-objective optimization mechanism reflects strong search capabilities. • Four interval prediction models cover the inherent modes of sequence. Traditional point prediction approaches can not reflect the uncertainty, which brings greater risks to decision-makers. To fill this gap, this paper extends a feature selection strategy that relies solely on correlation and redundant feature judgment, proposes a novel combined interval prediction algorithm, 3-Mcip (Combined Interval Prediction Based on Maximize Relevancy, Minimize Redundancy and Maximize Synergy) system, and solves the tradeoff between prediction accuracy and interval width. This system first designs a hybrid feature selection strategy to optimally select candidate variables and reduce model input redundancy. Secondly, the structure of the four ANN models is improved to accommodate the results of feature selection, and an optimization mechanism is introduced to search for the Pareto optimal solution set. In order to measure the comprehensive performance of the 3-Mcip system, hourly power load data and related candidate variables from Pittsburgh and Washington, D.C are considered. The numerical results show that the 3-Mcip system has coverage rates of 53.3333, 90.1667, and 99.4479 for Site 1 at different levels of interval width coefficients, which not only achieves perfect prediction of power load but also analyzes uncertainty. It is also helpful for power system managers to better capture the fluctuation range of future load and improve the flexibility of smart grid dispatching. [ABSTRACT FROM AUTHOR]

To enhance solution accuracy and training efficiency in neural network approximation to partial differential equations, partitioned neural networks can be used as a solution surrogate instead of a single large and deep neural network defined on the whole problem domain. In such a partitioned neural network approach, suitable interface conditions or subdomain boundary conditions are combined to obtain a convergent approximate solution. However, there has been no rigorous study on the convergence and parallel computing enhancement on the partitioned neural network approach. In this paper, iterative algorithms are proposed to enhance parallel computation performance in the partitioned neural network approximation. Our iterative algorithms are based on classical additive Schwarz domain decomposition methods. For the proposed iterative algorithms, their convergence is analyzed under an error assumption on the local and coarse neural network solutions. Numerical results are also included to show the performance of the proposed iterative algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a novel non-convex fractional-order image denoising model is proposed to suppress the staircase effect produced by the TV model while maintaining a neat contour. The model combines ℓ q (0 < q < 1) quasi-norm and fractional-order regularization, and employs a diffusion coefficient with a faster convergence rate to preserve more image edges and details. Additionally, an adaptive regularization parameter is designed to adjust the denoising performance of the algorithm. To obtain the optimal approximate solution of the model, an enhanced primal-dual algorithm is adopted and the complexity and convergence of the algorithm are theoretically analyzed. Finally, the effectiveness of the proposed method is demonstrated through numerical experiments. • A new non-convex FOTV model is proposed. • A new diffusion coefficient is introduced to retain more edge details of the image. • The existing primal-dual algorithm is improved, and the convergence of this algorithm is analyzed. • An adaptive regularization parameter is designed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a second-order adaptive DLN algorithm with different subdomain variable time steps for the 3D closed-loop geothermal system. Theoretically, the stability and convergence of the algorithm in the case of variable time steps are analyzed. In numerical experiments, the DLN algorithm is combined with an adaptive algorithm to verify not only its effectiveness and second-order convergence under any arbitrary sequence of time steps but also to improve computational efficiency. We overcome the difficulty of 3D programming, and verify the applicability and accuracy of the proposed algorithm by simulating the flow characteristics and heat transfer of the closed-loop geothermal system with 3D convection. [ABSTRACT FROM AUTHOR]

This paper explains the construction of a novel augmented hunger games search algorithm using a logarithmic spiral opposition-based learning technique. The proposed algorithm (LsOBL-HGS) is used as an efficient tool for both function optimization and controller design. To assess the performance of the algorithm for function optimization, benchmark functions from the CEC2017 test suite were employed and comparisons were made with available and good performing algorithms. In terms of controller design, the proposed LsOBL-HGS algorithm was utilized to design a FOPID controlled magnetic ball suspension system. Comparative assessments were also performed for FOPID controller design, as well using other state-of-the-art methods reported for the magnetic ball suspension system. The results showed that the proposed LsOBL-HGS algorithm has good capability for FOPID controller design employed in a magnetic ball suspension system as it provided an improvement of more than 13 % in terms of the transient response-related parameters and more than 34 % in terms of bandwidth compared to the best-reported approach used for comparisons. [ABSTRACT FROM AUTHOR]

p , q -quasirung orthopair fuzzy set (p , q -QOFS) is an extension of the q -rung orthopair fuzzy set (q -ROFS) theory used to represent uncertainty and vagueness in decision-making processes. The paper aims to utilize robust sine-trigonometric operational laws to investigate the group decision-making process within the p , q -QOFSs framework. The p , q -QOFS possess a distinctive characteristic of handling uncertain information by utilizing a wider space for membership representation compared to q -ROFS. Consequently, the current paper has been categorized into three distinct phases. In the initial phase, novel operational laws will be presented for p , q -QOFSs. The fundamental concept behind these newly proposed operations is to integrate the properties of the sine function, which include being periodic and symmetric about the origin, into the decision-making process for objects. Then, based on these laws, several operators for aggregating information will be obtained, along with their necessary properties and relationships. Lastly, an algorithm will be presented for interpreting the problem of multi attribute group decision-making (MAGDM), utilizing the operators, and demonstrating it with an illustrative example. A comprehensive comparative analysis will be conducted with some of the existing methods to uncover their impacts. [ABSTRACT FROM AUTHOR]

I n network information mining, people must measure the influence of important nodes in complex networks, otherwise it cannot be mined. However, traditional node influence measurement methods have defects and strong application limitations. Therefore, this paper proposes a multi-scale centrality algorithm under some traditional methods, and carries out related research. This paper introduces the basic concept and application advantages of multi-scale centrality algorithm, then constructs the algorithm model, and finally verifies the effectiveness of the algorithm model in the measurement of node influence in complex networks. After verification, the algorithm is effective and the measurement result is accurate. [ABSTRACT FROM AUTHOR]

With the construction of smart cities, smart ports are developing rapidly, providing favorable conditions for enhancing port competitiveness, promoting industrial upgrading, and economic transformation. With the continuous progress of society and science and technology, the safety risks of intelligent ports are also increasing. In order to better address these risks, it is necessary to establish a stable and efficient management system. This paper applies data mining and fuzzy control theory technologies for large-volume, high-precision real-time information collection, based on the research of the BP neural network algorithm. By establishing and analyzing a safety risk management system model based on the BP multilayer neuron network, it provides a rationality evaluation method and design concept. After designing the safety risk management system, this paper conducts a performance test on the system. The test results show that through optimization, the accuracy, generalization ability, and reliability of the model can be improved. Its accuracy is as high as 92% or more, fully demonstrating the system's reliability and superiority! [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

Big data has become the mainstream of information dissemination today. Big data plays an important role in the field of intelligent network monitoring. How to improve the hidden danger identification of intelligent network monitoring system and improve its accuracy is a hot issue to be solved. Therefore, in the context of big data, this paper uses intelligent analysis algorithm to identify and analyze the hidden dangers of intelligent network monitoring system. This paper mainly applies the methods of experimental setup, fuzzy analysis and data comparison to carry out relevant research on intelligent analysis of hidden danger identification of intelligent network monitoring system. The experimental data shows that the accuracy of the data obtained by the proposed method in the hidden danger identification is more than 50%, but less than 80%. Therefore, the application of background difference method needs further improvement. [ABSTRACT FROM AUTHOR]

This study introduces a non-convex fractional-order hyper-Laplacian variational model for Gaussian noise removal. It employs first the primal-dual hybrid gradient algorithm to solve problems involving overlapping group sparse structures. Additionally, this paper designs a new algorithm leveraging the framework of the Chambolle-Pock algorithm with convergence and aims to recover high-quality images. The model, integrating the overlapping group sparse structure of the hyper-Laplacian prior with the non-convex fractional-order total variation, exhibits superior performance in reducing the staircase effect and maintaining sharp edge contours. To further improve the performance of the algorithm, a semi-adaptive p (x) non-convex penalty weight assignment mechanism is designed by introducing the structure tensor, which according to the characteristics of each region of the image and the noise level. The effectiveness and superiority of the proposed algorithm in image denoising with simulation experiments and comparative analyses are fully verified. • A new non-convex fractional-order hyper-Laplace variation model is proposed. • The PDHG algorithm is introduced to solve optimization problems involving OGS-HL for the first time. • The convergence and complexity of the new design algorithm are analyzed. • A semi-adaptive p (x) determined by the structure of the image and the noise level is designed. [ABSTRACT FROM AUTHOR]