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Many ranking algorithms and metrics have been proposed to identify high-impact papers. Both the direct citation counts and the network-based PageRank-like algorithms are commonly used. Ideally, the more complete the data on the citation network, the more informative the ranking. However, obtaining more data on citation relations is often costly and challenging. In some cases, obtaining the citation counts can be relatively simple. In this paper, we look into using the additional citation counts but without additional citation relations to form more informative metrics for identifying high-impact papers. As an example, we propose enhancing the original PageRank algorithm by combining the local citation network with the additional citation counts from a more complete data source. We apply this enhanced method to American Physical Society (APS) papers to verify its effectiveness. The results indicate that the proposed ranking algorithm is robust against missing data and can improve the identification of high-quality papers. This shows that it is possible to enhance the effectiveness of a network-based metric calculated on a relatively small citation network by including only the additional data of the citation counts, without the additional citation relations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to positively impact library and archives collections and services--enhancing reference, instruction, metadata creation, recommendations, and more. However, AI also has ethical implications. This paper presents an extensive literature and review analysis that examines AI projects implemented in library and archives settings, asking the following research questions: RQ1: How is artificial intelligence being used in libraries and archives practice? RQ2: What ethical concerns are being identified and addressed during AI implementation in libraries and archives? The results of this literature review show that AI implementation is growing in libraries and archives and that practitioners are using AI for increasingly varied purposes. We found that AI implementation was most common in large, academic libraries. Materials used in AI projects usually involved digitized and born digital text and images, though materials also ranged to include web archives, electronic theses and dissertations (ETDs), and maps. AI was most often used for metadata extraction and reference and research services. Just over half of the papers included in the literature review mentioned ethics or values related issues in their discussions of AI implementation in libraries and archives, and only one-third of all resources discussed ethical issues beyond technical issues of accuracy and human-in-the-loop. Case studies relating to AI in libraries and archives are on the rise, and we expect subsequent discussions of relevant ethics and values to follow suit, particularly growing in the areas of cost considerations, transparency, reliability, policy and guidelines, bias, social justice, user communities, privacy, consent, accessibility, and access. As AI comes into more common usage, it will benefit the library and archives professions to not only consider ethics when implementing local projects, but to publicly discuss these ethical considerations in shared documentation and publications. [ABSTRACT FROM AUTHOR]

This research evaluates a number of hybrid recurrent neural network (RNN) architectures for classifying sequential sentences in biomedical abstracts. The architectures include long short-term memory (LSTM), bidirectional LSTM (BI-LSTM), gated recurrent unit (GRU), and bidirectional GRU (BIGRU) models, all of which are combined with the universal sentence encoder (USE). The investigation assesses their efficacy in categorizing sentences into predefined classes: background, objective, method, result, and conclusion. Each RNN variant is used with the pre-trained USE as word embeddings to find complex sequential relationships in biomedical text. Results demonstrate the adaptability and effectiveness of these hybrid architectures in discerning diverse sentence functions. This research addresses the need for improved literature comprehension in biomedicine by employing automated sentence classification techniques, highlighting the significance of advanced hybrid algorithms in enhancing text classification methodologies within biomedical research. [ABSTRACT FROM AUTHOR]

We consider several types of internal queries, that is, questions about fragments of a given text T specified in constant space by their locations in T. Our main result is an optimal data structure for internal pattern matching (IPM) queries, which, given two fragments x and y, ask for a representation of all fragments contained in y and matching x exactly. This problem can be viewed as an internal version of the fundamental exact pattern matching problem: We are looking for exact occurrences of one substring of T within another substring of T. Our data structure answers IPM queries in time proportional to the quotient |y|/|x| of the fragments' lengths, which is required due to the worst-case information content of the output. If T is a text of length n over an integer alphabet of size σ, then our data structure occupies O(n/logσ n) machine words (that is, O(n log σ) bits) and admits an O(n/logσn)-time construction algorithm. We also show how to use IPM queries for answering internal queries corresponding to other classic string processing problems. Among others, we derive optimal data structures reporting the periods of a fragment and testing the cyclic equivalence of two fragments. Since the publication of the conference version of this paper [Kociumaka et al., Internal pattern matching queries in a text and applications, SODA 2015], IPM queries have found numerous further applications, following the path paved by the classic longest common extension (LCE) queries of Landau and Vishkin [J. Comput. System Sci., 37 (1988), pp. 63-78]. In particular, IPM queries have been implemented in grammar-compressed and dynamic settings and, along with LCE queries, constitute elementary operations of the PILLAR model, developed by Charalampopoulos, Kociumaka, and Wellnitz [Faster approximate pattern matching: A unified approach, FOCS 2020] to design approximate pattern matching algorithms that work in multiple settings. All our algorithms are deterministic, whereas the data structure in the conference version of the paper only admits a randomized construction in O(n) expected time. To achieve this, we provide a novel construction of string synchronizing sets of Kempa and Kociumaka [String synchronizing sets: Sublinear-time BWT construction and optimal LCE data structure, STOC 2019]. Our method, based on a new restricted version of the recompression technique of Jez [J. ACM, 63 (2016), pp. 4:1-4:51], yields a hierarchy of O(log n) string synchronizing sets covering the whole spectrum of the fragments' lengths. [ABSTRACT FROM AUTHOR]

Over the years the number of research publications per year is growing exponentially. Finding research papers of quality from the massive literature of relevant articles is a challenging and time-consuming task. The approaches in the latest literature address citation recommendation by utilizing large bibliographic information and use machine learning and deep learning methods for the task. These techniques clearly require a large amount of training data as well as machines with high processing power. To overcome these issues, we propose a novel method by modifying the popular hyperlink induced topic search (HITS), a web page ranking algorithm, as citation recommendation using hyperlink induced topic search (CR-HITS) that works on a directed and weighted heterogeneous bibliographic network containing diverse types of nodes and edges. We define effective scoring schemes for nodes and edges based on basic bibliographic information like citations of papers, number of publications of an author, etc. Given a few seed papers, the citation recommendation algorithm CR-HITS is run on small neighborhoods of the seed papers and hence the time taken by the execution is very small to yield the final recommendations. To the best of our knowledge, HITS has been used for the first time for the citation recommendation problem. We perform extensive experimentation on DBLP (version-11) and ACM (version-9) datasets and compare the results with many baseline methods in terms of MAP, MRR, and recall@N measures. The performance of the proposed algorithms is superior with respect to the MAP metric and matches the second best for the other two metrics. Since the top two algorithms use deep learning methods and use much larger bibliographic information including abstracts of the papers, we claim that our approach utilizes very low resources, yet yields recommendations that are very close to the top recommendations. [ABSTRACT FROM AUTHOR]

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

This paper studies the constrained two-dimensional non-guillotine cutting problem with defects, in which a set of items of a specific size is cut from a large rectangular sheet with defective areas, with the number of each type of cut item cannot exceed a specified quantity. The objective is to maximise the total value of the cut items. We propose a decomposition approach to address the problem. The process involves decomposing the original problem into a master problem and a subproblem. The master problem is formulated as a one-dimensional contiguous bin packing problem, while the subproblem is an x-Check problem to identify a two-dimensional packing that does not lead to any overlap. The x-Check problem is effectively addressed by using an integer linear programming model. When the x-Check problem proves infeasible, cuts are added to the master problem, and the iteration is repeated until the x-Check finds a feasible solution. Furthermore, we introduce several novel techniques, including valid inequalities, preprocessing techniques, and lifting the cut methods to improve the performance of the algorithm. Extensive computational results show that our method can quickly find the optimal solution for the 5450 instances in the literature. [ABSTRACT FROM AUTHOR]

Purpose: The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden. Methods: We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category. Results: We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods. Conclusion: Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents. [ABSTRACT FROM AUTHOR]

This document is a special issue of the journal Algorithms, featuring selected papers from the journal's editorial board members from 2022 and 2023. The issue includes 16 research papers covering a range of topics such as game theory, fault detection in cellular networks, optimization algorithms, machine learning, cryptocurrency trading, and more. Each paper presents its own unique research findings and methodologies. The issue aims to showcase the diverse research interests and expertise of the journal's editorial board members. [Extracted from the article]

The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object's appearance, the algorithm exhibits issues such as insufficient tracking accuracy and drift, which directly affect the stability of the algorithm. In order to overcome this problem, this paper demonstrates how to expand scene information at the scale level during the fusion process and, on this basis, achieve accurate recognition and positioning. The predicted results are promptly fed back to the subsequent tracking process, from which temporal templates are embedded. Starting from both location and time can effectively improve the adaptive ability of the tracking model. In the final experimental comparison results, the algorithm proposed in this paper can adapt well to the situation of object deformation, and the overall performance of the tracking model has also been improved. [ABSTRACT FROM AUTHOR]

High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction methods with high computational complexity and poor generalization ability. In this paper, we propose a high-precision temperature field reconstruction algorithm based on deep learning, using an efficient adaptive feature extraction method for temperature field reconstruction. We design an improved temperature field reconstruction algorithm based on the ResNet18 neural network; introduce the CBAM attention mechanism in the model; and design a feature pyramid, using M-FPN, a multi-scale feature aggregation network fusing PAN and FPN, to make the extracted feature information propagate multi-dimensionally among different layers to improve the feature characterization ability. Finally, the mean square error is used to guide the model to optimize the training so that the model pays more attention to the data and reduces the large error to ensure that the gap between the predicted value and the real value is small. The experimental results show that the reconstruction accuracy of the improved algorithm presented in this paper is significantly better than that of the original algorithm in the case of typical peaked temperature field distributions. [ABSTRACT FROM AUTHOR]

India has produced several drafts of data policies. In this work, they are referred to [1] JBNSCR 2018, [2] DPDPR 2018, [3] NSAI 2018, [4] RAITF 2018, [5] PDPB 2019, [6] PRAI 2021, [7] JPCR 2021, [8] IDAUP 2022, [9] IDABNUP 2022. All of them consider Artificial Intelligence (AI) a social problem solver at the societal level, let alone an incentive for economic growth. However, these policy drafts warn of the social disruptions caused by algorithms and encourage the careful use of computational technologies in various social contexts. Hence, the emerging data society and its implications in India's social contexts demand immense social science attention, which needs to be improved in the policy drafts, primarily because they are creations of industry stakeholders, technocrats, bureaucrats, and experts from tech schools. In the larger social milieu of digital infrastructure emerging, the fundamental question is whether India's national philosophy envisioned in the Indian constitution is reflected in the policy papers. The paper enquires whether the national data policy upholds the core values dispersed through the philosophy of the Indian constitution, which, among other things, is not confined only to inclusion, diversity, rights, liberty, justice and equality. By focusing on constitutional values, the paper seeks to offer a broader and more critical understanding of India's approach to AI policy by bringing together analyses of a wide array of policy documents available in the public realm. [ABSTRACT FROM AUTHOR]

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

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

In the process of dense vehicles traveling fast, there will be mutual occlusion between vehicles, which will lead to the problem of deterioration of the tracking effect of different vehicles, so this paper proposes a research method of virtual simulation video vehicle target tracking based on you only look once (YOLO)v5s and deep simple online and realtime tracking (DeepSort). Given that the DeepSort algorithm is currently the most effective tracking method, this paper merges the YOLOv5 algorithm with the DeepSort algorithm. Then it adds the efficient channel attention networks (ECA-Net) focusing mechanism at the back for the cross-stage partial bottleneck with 3 convolutions (C3) modules about the YOLOv5 backbone network and before the up-sampling of the Neck feature pyramid. The YOLOv5 algorithm adopts expected intersection over union (EIOU) instead of complete intersection over union (CIOU) as the loss function of the target frame regression. The improved YOLOv5 algorithm is named YOLOv5-Block. The experimental results show that in the special vehicle target detection (TD) and tracking in the virtual simulation environment, The YOLOv5-Block algorithm has an average accuracy (AP) of 99.5%, which significantly improves the target recognition correctness for typical occlusion cases, and is 1.48 times better than the baseline algorithm. After the virtual simulation video sequence test, multiple objects tracking accuracy (MOTA) and various objects tracking precision (MOTP) improved by 10.7 and 1.75 percentage points, respectively, and the number of vehicle target identity document (ID) switches decreased. Compared with recent mainstream vehicle detection and tracking models, the YOLOv5-Block+Deepsort algorithm can accurately and continuously complete the detection and tracking tasks of special vehicle targets in different scenes. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative image encryption algorithm that leverages partial face recognition and DNA diffusion, building upon advancements in chaotic image encryption and face recognition technologies. The key is generated using the secure SHA-512 algorithm, while the chaotic sequence for the encryption algorithm is derived from the Chen system. This paper begins by employing facial recognition technology to identify and focus on specific facial regions within an image. Subsequently, DNA diffusion is applied to encrypt the recognized face segment via a diffusion-scrambling encryption process. Following this, the entire image undergoes diffusion utilizing the devised diffusion rules. Finally, a uniform scrambling operation based on Fisher-Yeats is performed on the entire image. The experimental results and safety analysis show that the information entropy can exceed 7.9973, the average correlation can be less than 0.0023, the pixel number change rate can reach 99.6052%, and the unified average change intensity can reach 33.4644%, all of which are very close to the ideal values. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method to improve loop closure detection within the domain of Simultaneous Localization And Mapping (SLAM) by integrating a Multi-Scale Attention and Semantic Guidance (MSA-SG) framework. In SLAM systems, accurate loop closure detection is essential for minimizing localization errors over time and ensuring the reliability of the constructed maps in robotics navigation through uncharted environments. Our proposed method utilizes EfficientNet-EA for robust feature extraction and introduces MSA-SG, a novel mechanism that synergizes multiscale attention with semantic guidance to focus on critical semantic features essential for loop closure detection. This approach ensures the prioritization of static environmental landmarks over transient and irrelevant objects, significantly enhancing the accuracy and efficiency of loop closure detection in complex and dynamic settings. Experimental validations on recognized datasets underscore the superiority of our approach, demonstrating marked improvements in precision, recall, and overall SLAM performance. This research highlights the significant benefits of leveraging semantic insights and attentional focus in advancing the capabilities of loop closure detection for SLAM applications. [ABSTRACT FROM AUTHOR]

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

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks (MANETs) from dynamic topologies that pose great challenges because of the mobility of nodes. The main objective was to delve into and refine the application of the Dijkstra's algorithm in this context, a method conventionally esteemed for its efficiency in static networks. Thus, this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm, considering adaptation to the dynamic network conditions that are typical for MANETs. This paper has shown through detailed algorithmic analysis that Dijkstra's algorithm, when adapted for dynamic updates, yields a very workable solution to the problem of real-time routing in MANETs. The results indicate that with these changes, Dijkstra's algorithm performs much better computationally and 30% better in routing optimization than Bellman-Ford when working with configurations of sparse networks. The theoretical framework adapted, with the adaptation of the Dijkstra's algorithm for dynamically changing network topologies, is novel in this work and quite different from any traditional application. The adaptation should offer more efficient routing and less computational overhead, most apt in the limited resource environment of MANETs. Thus, from these findings, one may derive a conclusion that the proposed version of Dijkstra's algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods. This paper, therefore, operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness. [ABSTRACT FROM AUTHOR]

In 3D building models, a large number of texture maps with different sizes increase the number of model data loading and drawing batches, which greatly reduces the drawing efficiency of the model. Therefore, this paper proposes a texture set mapping method based on vertex importance. Firstly, based on the 2D space boxing algorithm, the texture maps are merged and a series of Mipmap texture maps are generated, and then the vertex curvature, texture variability and location information of each vertex are calculated, normalized, and weighted to get the importance of each vertex, and then finally, different Mipmap-level textures are remapped according to the importance of the vertices. The experiment proves that the algorithm in this paper can reduce the amount of texture data on the one hand, and avoid the rendering pressure brought by the still large amount of data after merging on the other hand, so as to improve the rendering efficiency of the model. [ABSTRACT FROM AUTHOR]

The AUD2IT-algorithm is a tool to structure the data, which is collected during an emergency treatment. The goal is on the one hand to structure the documentation of the data and on the other hand to give a standardised data structure for the report during handover of an emergency patient. AUD2IT-algorithm was developed to provide residents a documentation aid, which helps to structure the medical reports without getting lost in unimportant details or forgetting important information. The sequence of anamnesis, clinical examination, considering a differential diagnosis, technical diagnostics, interpretation and therapy is rather an academic classification than a description of the real workflow. In a real setting, most of these steps take place simultaneously. Therefore, the application of the AUD2IT-algorithm should also be carried out according to the real processes. A big advantage of the AUD2IT-algorithm is that it can be used as a structure for the entire treatment process and also is entirely usable as a handover protocol within this process to make sure, that the existing state of knowledge is ensured at each point of a team-timeout. PR-E-(AUD2IT)-algorithm makes it possible to document a treatment process that, in principle, does not have to be limited to the field of emergency medicine. Also, in the outpatient treatment the PR-E-(AUD2IT)-algorithm could be used and further developed. One example could be the preparation and allocation of needed resources at the general practitioner. The algorithm is a standardised tool that can be used by healthcare professionals of any level of training. It gives the user a sense of security in their daily work. [ABSTRACT FROM AUTHOR]

This paper presents an improved two-dimensional logistic map. Relative to the original logistic map, the improved chaos map has better performance, e.g., larger chaotic range, higher complexity, and boosting sequence. Based on these good performances, a novel encipherment strategy is designed according to the hybrid coding of DNA and S-box algorithm. During the design of the algorithm, the primary operations include chaotic stream generation, permutation, and diffusion. The chaotic streams are constructed from the improved two-dimensional logistic map. The diffusion stage is achieved by the spatiotemporal chaos algorithm. The security test reflects that the design algorithm can effectively defend against external attacks. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

In this paper, we report a new algorithm for creating an adaptive basis set in the Multiconfigurational Ehrenfest (MCE) method, which is termed Full Cloning (FC), and test it together with the existing Multiple Cloning (MC) using the spin-boson model at zero-temperature as a benchmark. The zero-temperature spin-boson regime is a common hurdle in the development of methods that seek to model quantum dynamics. Two versions of MCE exist. We demonstrate that MC is vital for the convergence of MCE version 2 (MCEv2). The first version (MCEv1) converges much better than MCEv2, but FC improves its convergence in a few cases where it is hard to converge it with the help of a reasonably small size of the basis set. [ABSTRACT FROM AUTHOR]

We have developed a "Kirameki display" that can represent a real texture of materials. ("Kirameki" means a shining/glittering sense in Japanese.) In addition, we have investigated an improvement of "texture representation" by optimization of both an optical design and a software algorithm. Lastly, we discuss the future technical fields for texture representation on displays. [ABSTRACT FROM AUTHOR]

To address the problem of the low success rate caused by the lack of verification mechanism of hashed time-locked algorithm in processing blockchain cross-chain transactions, this paper proposes blockchain cross-chain protocol based on improved hashed time-locked contract. Firstly, in order to improve the success rate of cross-chain transactions, this paper introduces the virtual account verification mechanism to verify the legitimacy of the transaction before processing. Secondly, this paper also proposes the parallel processing strategy based on the virtual trading pool, which makes unqualified-transactions in virtual trading pool process in parallel, and avoids to retrieve the account information which is obtained by crossing multi-chains. Finally, the proposed algorithm is compared with hashed time-locked algorithm (HTLC) and time-locked algorithm under different chain number. The success rate of the proposed algorithm is increased to more than 90%. For large-scale transactions, compared with HTLC algorithm, time-locked algorithm, the throughput of the proposed algorithm is increased by 72 and 44 percentage points respectively. Under different transaction distribution, the proposed algorithm also outperforms the two algorithms in terms of transaction delay. The experimental results show that this algorithm can effectively improve the success rate of cross-chain transaction and shorten the delay of cross-chain transaction. [ABSTRACT FROM AUTHOR]

Underwater target detection technology is very important for Marine resources detection and underwater environment perception. The problems of low-quality underwater images, a large amount of calculation, and low accuracy of target detection models still need to be solved. Firstly, a new underwater image enhancement algorithm based on multi-scale fusion is proposed. The algorithm combines the improved white balance algorithm and the improved CLAHE (Contrast Limited Adaptive Histogram Equalization) and realizes the multi-scale fusion strategy by introducing different weight maps, aiming to improve the visual effect and quality of underwater images. Secondly, to further improve the accuracy and efficiency of underwater target detection, an improved YOLOv5s lightweight underwater target detection algorithm is proposed. The algorithm combines the Ghost convolution module, EMA(Efficient Multi-Scale Attention) mechanism, and CARAFE (Content-Aware ReAssembly of FEatures) up-sampling mode. The comparison on the URPC(Underwater Robot Perception Challenge) public dataset shows that the proposed algorithm has achieved significant improvement in terms of model size, parameter amount, calculation amount, and mAP@0.5. Thirdly, to verify the effectiveness of the image enhancement algorithm and underwater target detection algorithm proposed in this paper, the ROV(Remote Operated Vehicle) is used to conduct experiments in the experimental pool. By designing underwater target detection experiments before and after image enhancement, the results show that the enhanced image significantly improves the detection ability of underwater targets. Finally, underwater target detection software is developed to provide strong support for the application of underwater-related fields. [ABSTRACT FROM AUTHOR]

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy and society, the demand for power quality is constantly increasing. As a crucial part of grid situational awareness, distribution network state estimation plays a vital role in providing critical data support for other advanced applications, which is significant for ensuring the safe and reliable operation of the distribution network. Therefore, this paper proposes a dynamic state awareness method for distribution networks based on the robust adaptive cubature Kalman filter (RACKF). The proposed solution, grounded on the core computational concept of the cubature Kalman filter (CKF), constructs a robust noise statistical estimator (NSE) composed of a biased NSE and an unbiased NSE to adapt to the unknown and time-varying process noise parameters in the dynamic state estimation process. The proposed solution can ensure that the calculated process noise parameters always meet the constraints and guarantee the robustness of the algorithm. In addition, an estimation strategy for the fusion of multi-time scale measurement data is developed according to the RACKF-based dynamic state estimation features in order to realize system state corrections and updates. The results of simulation experiments and comparisons with traditional CKF methods demonstrate the accuracy and superiority of the proposed solution. [ABSTRACT FROM AUTHOR]

In the field of automatic bulk material loading, accurate detection of the profile of the material pile in the compartment can control its height and distribution, thus improving the loading efficiency and stability, therefore, this paper proposes a new method for pile detection based on cross-source point cloud registration. First, 3D point cloud data are simultaneously collected using lidar and binocular camera. Second, feature points are extracted and described based on 3D scale-invariant features and 3D shape contexts algorithms, and then feature points are used in progressive sample consensus algorithm to complete coarse matching. Then, bi-directional KD-tree accelerated iterative closest point is established to complete the fine registration. Ultimately, the detection of the pile contour is realized by extracting the point cloud boundary after the registration. The experimental results show that the registration errors of this method are reduced by 54.2%, 52.4%, and 14.9% compared with the other three algorithms, and the relative error of the pile contour detection is less than 0.2%. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the AAPF* algorithm, an innovative approach that synergistically integrates the A-star search algorithm (A*) with the artificial potential field (APF) method. This algorithm is designed to enhance safety and ensure smoother global path planning for autonomous vehicles, particularly addressing vehicle cornering constraints. Initially, for augmenting the safety of autonomous vehicle, we implement an obstacle expansion strategy with a factor of 2 units, enhancing environmental adaptability. The study then delves into the classical A* algorithm, examining its core principles and characteristics, leading to the development of novel heuristic functions and search strategies that address the limitations inherent in the classic A* algorithm. Subsequently, we explore the APF algorithm, recognized for its excellence in obstacle avoidance in path planning. The paper culminates in the amalgamation of the APF's repulsive field concept with the improved A* algorithm, crafting a comprehensive global planning algorithm tailored for autonomous vehicle path planning schemes. Experiments conducted in a simulated environment model validate the AAPF* algorithm's efficacy in improving both the safety and smoothness, demonstrating its potential for real-world applications. [ABSTRACT FROM AUTHOR]

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the distributed quantization and sparsity aware zero attracting least mean square (DQA‐ZA‐LMS) and its reweighted version (DQA‐RZA‐LMS) algorithms that can perform sparse spectrum sensing with the lowest power possible. The usage of the quantization aware diffusion adaptive networks has recently been proposed and they can be used in many possible mobile communicative applications. The sparsity aware feature of the proposed algorithm can help the network to track and estimate sparse random vectors that are shown to be the case with the spectrum of the new generation wireless communication systems such as 4G, 5G, 6G, and beyond. The spectrum sensing is considered in this paper to be performed by small cell eNode Bs (SC‐eNBs) for the 4th generation long term evolution (LTE) and the next generation eNB (ng‐eNB) networks for the 5th and 6th generation mobile communication systems that are scattered in an area collecting distributed quantized data from the environment and working collaboratively to estimate the sparse spectrum vectors. Our findings show that in comparison with the nonquantized version of the distributed ZA‐LMS (DZA‐LMS) and distributed regularized ZA‐LMS (DRZA‐LMS) algorithms, our proposed schemes perform considerably well using the quantized data and also reduce power consumption. [ABSTRACT FROM AUTHOR]

Archaeological illustration is a graphic recording technique that delineates the shape, structure, and ornamentation of cultural artifacts using lines, serving as vital material in archaeological work and scholarly research. Aiming at the problems of low line accuracy in the results of current mainstream image generation algorithms and interference caused by severe mural damage, this paper proposes a mural archaeological illustration generation algorithm based on multi-branch feature cross fusion (U2FGAN). The algorithm optimizes skip connections in U2Net through a channel attention mechanism, constructing a multi-branch generator consisting of a line extractor and an edge detector, which separately identify line features and edge information in artifact images before fusing them to generate accurate, high-resolution illustrations. Additionally, a multi-scale conditional discriminator is incorporated to guide the generator in outputting high-quality illustrations with clear details and intact structures. Experiments conducted on the Dunhuang mural illustration datasets demonstrate that compared to mainstream counterparts, U2FGAN reduced the Mean Absolute Error (MAE) by 10.8% to 26.2%, while also showing substantial improvements in Precision (by 9.8% to 32.3%), Fβ-Score (by 5.1% to 32%), and PSNR (by 0.4 to 2.2 dB). The experimental results show that the proposed method outperforms other mainstream algorithms in archaeological illustration generation. [ABSTRACT FROM AUTHOR]

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently. [ABSTRACT FROM AUTHOR]

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation. [ABSTRACT FROM AUTHOR]

As unmanned vehicle technology advances rapidly, obstacle recognition and target detection are crucial links, which directly affect the driving safety and efficiency of unmanned vehicles. In response to the inaccurate localization of small targets such as pedestrians in current object detection tasks and the problem of losing local features in the PointPillars, this paper proposes a three-dimensional object detection method based on improved PointPillars. Firstly, addressing the issue of lost spatial and local information in the PointPillars, the feature encoding part of the PointPillars is improved, and a new pillar feature enhancement extraction module, CSM-Module, is proposed. Channel encoding and spatial encoding are introduced in the new pillar feature enhancement extraction module, fully considering the spatial information and local detailed geometric information of each pillar, thereby enhancing the feature representation capability of each pillar. Secondly, based on the fusion of CSPDarknet and SENet, a new backbone network CSE-Net is designed in this paper, enabling the extraction of rich contextual semantic information and multi-scale global features, thereby enhancing the feature extraction capability. Our method achieves higher detection accuracy when validated on the KITTI dataset. Compared to the original network, the improved algorithm's average detection accuracy is increased by 3.42%, it shows that the method is reasonable and valuable. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive assessment procedure for evaluating Ensemble-based Machine Learning algorithms in the context of exoplanet classification. Each of the algorithm hyperparameter values were tuned. Deployments were carried out using the cross-validation method. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using confusion matrices generated from each implementation. Machine Learning (ML) algorithms were trained and used to identify exoplanet data. Most of the current research deals with traditional ML algorithms for this purpose. The Ensemble algorithm is another type of ML technique that combines the prediction performance of two or more algorithms to obtain an improved final prediction. Few studies have applied Ensemble algorithms to predict exoplanets. To the best of our knowledge, no paper that has exclusively assessed Ensemble algorithms exists, highlighting a significant gap in the literature about the potential of Ensemble methods. Five Ensemble algorithms were evaluated in this paper: Adaboost, Random Forest, Stacking, Random Subspace Method, and Extremely Randomized Trees. They achieved an average performance of more than 80% in all metrics. The results underscore the substantial benefits of fine tuning hyperparameters to enhance predictive performance. The Stacking algorithm achieved a higher performance than the other algorithms. This aspect is discussed in this paper. The results of this work show that it is worth increasing the use of Ensemble algorithms to improve exoplanet identification. [ABSTRACT FROM AUTHOR]

For effective copyright protection of digital images, this paper proposes a zero-watermarking algorithm based on local image feature information. The feature matrix of the algorithm is derived from the keypoint set determined by the Speeded-Up Robust Features (SURF) algorithm, and it calculates both the gradient feature descriptors and the vortex-like texture feature (VTF) descriptors of the keypoint set. Unlike traditional texture feature descriptors, the vortex-like texture feature descriptors proposed in this paper contain richer information and exhibit better stability. The advantage of this algorithm lies in its ability to calculate the keypoints of the digital image and provide a stable vector description of the local features of these keypoints, thereby reducing the amount of erroneous information introduced during attacks. Analysis of experimental data shows that the algorithm has good effectiveness, distinguishability, security, and robustness. [ABSTRACT FROM AUTHOR]

With the launch of OpenAI's ChatGPT, large language models have garnered significant attention, and applications based on these models have proliferated. A critical challenge has emerged: how to rapidly enhance the capabilities of general LLMs in specialized domains. Compared to fine-tuning and other methods, prompt engineering has proven to be a cost-effective approach for improving the performance of LLMs on specific tasks, yielding remarkable results. However, current prompt example construction methods are numerous and lack a universally applicable approach that spans different models and tasks. Furthermore, existing research is predominantly tested and evaluated on a limited range of specific datasets, failing to explore the broader impact of these methods on a wider array of tasks. This paper proposes a prompt example construction method based on clustering and semantic similarity, which combines clustering algorithms with semantic similarity techniques to significantly improve the quality of prompt examples. In comparative tests conducted on six LLMs and seven datasets, the overall accuracy and stability of the proposed method significantly outperforms five other common methods, demonstrating broad applicability and the potential to enhance the output performance of all LLMs. Through comparative experiments, this paper also identifies that as the parameter scale of LLMs increases, the improvement effect of the prompt example construction method on LLM output performance tends to diminish. Additionally, diversified prompt example sets provide a more pronounced enhancement in LLM output performance. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

The growing popularity of JSON as the data storage and interchange format increases the availability of massive multi-structured data collections. Clustering JSON documents has become a significant issue in organising large data collections. Existing research uses various structural similarity measures to perform clustering. However, differently annotated JSON structures may also encode semantic relatedness, necessitating the use of both syntactic and semantic properties of heterogeneous JSON schemas. Using the SchemaEmbed model, this paper proposes an embedding-based clustering approach for grouping contextually similar JSON documents. The SchemaEmbed model is designed using the pre-trained Word2Vec model and a deep autoencoder that considers both syntactic and semantic information of JSON schemas for clustering the documents. The Word2Vec model learns the attribute embeddings, and a deep autoencoder is designed to generate context-aware schema embeddings. Finally, the context-based similar JSON documents are grouped using a clustering algorithm. The effectiveness of the proposed work is evaluated using both real and synthetic datasets. The results and findings show that the proposed approach improves clustering quality significantly, with a high NMI score of 75%. In addition, we demonstrate that clustering results obtained by contextual similarity are superior to those obtained by traditional semantic similarity models. [ABSTRACT FROM AUTHOR]

To tackle problems such as the destruction of topological structures and the loss of detailed features in the simplification of 3D building models, we propose a 3D building model simplification algorithm that considers detailed features and topological structures. Based on the edge collapse algorithm, the method defines the region formed by the first-order neighboring triangles of the endpoints of the edge to be collapsed as the simplification unit. It incorporates the centroid displacement of the simplification unit, significance level, and approximate curvature of the edge as influencing factors for the collapse cost to control the edge collapse sequence and preserve model details. Additionally, considering the unique properties of 3D building models, boundary edge detection and face overlay are added as constraints to maintain the model's topological structure. The experimental results show that the algorithm is superior to the classic QEM algorithm in terms of preserving the topological structure and detailed features of the model. Compared to the QEM algorithm and the other two comparison algorithms selected in this paper, the simplified model resulting from this algorithm exhibit a reduction in Hausdorff distance, mean error, and mean square error to varying degrees. Moreover, the advantages of this algorithm become more pronounced as the simplification rate increases. The research findings can be applied to the simplification of 3D building models. [ABSTRACT FROM AUTHOR]

Micro-polarizer array (MPA) is the core optical component of the Division of Focal-Plane (DoFP) imaging system, and its design is very important to the system's performance. Traditional design methods rely on theoretical analysis and simulation, which is complicated and requires designers to have profound theoretical foundations. In order to simplify the design process and improve efficiency, this paper proposes a 2 × 2 MPA reverse-design strategy based on particle swarm optimization (PSO). This strategy uses intelligent algorithms to automatically explore the design space in order to discover MPA structures with optimal optical properties. In addition, the all-pass filter is introduced to the MPA superpixel unit in the design, which effectively reduces the crosstalk and frequency aliasing between pixels. In this study, two MPA models were designed: a traditional MPA and an MPA with an all-pass filter. The Degree of Linear Polarization (DOLP) image contrast is used as the evaluation standard and compared with the traditional MPA; the results show that the contrast of the newly designed traditional MPA image is increased by 21%, and the MPA image with the all-pass filter is significantly increased by 82%. Therefore, the reverse-design method proposed in this paper not only simplifies the design process but also can design an MPA with enhanced optical performance, which has obvious advantages over the traditional method. [ABSTRACT FROM AUTHOR]

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification. [ABSTRACT FROM AUTHOR]

This research focuses on improving the Harris’ Hawks Optimization algorithm (HHO) by tackling several of its shortcomings, including insufficient population diversity, an imbalance in exploration vs. exploitation, and a lack of thorough exploitation depth. To tackle these shortcomings, it proposes enhancements from three distinct perspectives: an initialization technique for populations grounded in opposition-based learning, a strategy for updating escape energy factors to improve the equilibrium between exploitation and exploration, and a comprehensive exploitation approach that utilizes variable neighborhood search along with mutation operators. The effectiveness of the Improved Harris Hawks Optimization algorithm (IHHO) is assessed by comparing it to five leading algorithms across 23 benchmark test functions. Experimental findings indicate that the IHHO surpasses several contemporary algorithms its problem-solving capabilities. Additionally, this paper introduces a feature selection method leveraging the IHHO algorithm (IHHO-FS) to address challenges such as low efficiency in feature selection and high computational costs (time to find the optimal feature combination and model response time) associated with high-dimensional datasets. Comparative analyses between IHHO-FS and six other advanced feature selection methods are conducted across eight datasets. The results demonstrate that IHHO-FS significantly reduces the computational costs associated with classification models by lowering data dimensionality, while also enhancing the efficiency of feature selection. Furthermore, IHHO-FS shows strong competitiveness relative to numerous algorithms. [ABSTRACT FROM AUTHOR]

The paper presents a study of robot interaction algorithms. The study of algorithms that control the behavior of robots is relevant due to the development of robotic means and their wide application in various fields: in manufacturing, medicine, military. The paper considers the application of nanorobot swarms in medicine. The study is performed using simulation methods. The simulation system NetLogo is used as a toolkit. The Netlogo simulation system is agent-based system. In the first part of the article, experiments using reactive agents are performed. Next, we consider intelligent agents that can make decisions related to the optimization of the algorithms by which they act. A series of experiments were carried out with intelligent agents and the values of parameters that affect the time of destruction of "bad" cells were determined. [ABSTRACT FROM AUTHOR]

Information steganography has received more and more attention from scholars nowadays, especially in the area of image steganography, which uses image content to transmit information and makes the existence of secret information undetectable. To enhance concealment and security, the Steganography without Embedding (SWE) method has proven effective in avoiding image distortion resulting from cover modification. In this paper, a novel encrypted communication scheme for image SWE is proposed. It reconstructs the image into a multi-linked list structure consisting of numerous nodes, where each pixel is transformed into a single node with data and pointer domains. By employing a special addressing algorithm, the optimal linked list corresponding to the secret information can be identified. The receiver can restore the secret message from the received image using only the list header position information. The scheme is based on the concept of coverless steganography, eliminating the need for any modifications to the cover image. It boasts high concealment and security, along with a complete message restoration rate, making it resistant to steganalysis. Furthermore, this paper proposes linked-list construction schemes within the proposed framework, which can effectively resist a variety of attacks, including noise attacks and image compression, demonstrating a certain degree of robustness. To validate the proposed framework, practical tests and comparisons are conducted using multiple datasets. The results affirm the framework's commendable performance in terms of message reduction rate, hidden writing capacity, and robustness against diverse attacks. [ABSTRACT FROM AUTHOR]

The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration. [ABSTRACT FROM AUTHOR]