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Multivariate multipoint evaluation is the problem of evaluating a multivariate polynomial, given as a coefficient vector, simultaneously at multiple evaluation points. In this work, we show that there exists a deterministic algorithm for multivariate multipoint evaluation over any finite field \(\mathbb {F}\) that outputs the evaluations of an m-variate polynomial of degree less than d in each variable at N points in time, \(\begin{equation*} (d^m+N)^{1+o(1)}\cdot {{\sf poly}}(m,d,\log |\mathbb {F}|), \end{equation*}\) for all \(m\in \mathbb {N}\) and all sufficiently large \(d\in \mathbb {N}\). A previous work of Kedlaya and Umans (FOCS 2008 and SICOMP 2011) achieved the same time complexity when the number of variables m is at most \(d^{o(1)}\) and had left the problem of removing this condition as an open problem. A recent work of Bhargava, Ghosh, Kumar, and Mohapatra (STOC 2022) answered this question when the underlying field is not too large and has characteristic less than \(d^{o(1)}\). In this work, we remove this constraint on the number of variables over all finite fields, thereby answering the question of Kedlaya and Umans over all finite fields. Our algorithm relies on a non-trivial combination of ideas from three seemingly different previously known algorithms for multivariate multipoint evaluation, namely the algorithms of Kedlaya and Umans, that of Björklund, Kaski, and Williams (IPEC 2017 and Algorithmica 2019), and that of Bhargava, Ghosh, Kumar, and Mohapatra, together with a result of Bombieri and Vinogradov from analytic number theory about the distribution of primes in an arithmetic progression. We also present a second algorithm for multivariate multipoint evaluation that is completely elementary and, in particular, avoids the use of the Bombieri–Vinogradov theorem. However, it requires a mild assumption that the field size is bounded by an exponential tower in d of bounded height. More specifically, our second algorithm solves the multivariate multipoint evaluation problem over a finite field \(\mathbb {F}\) in time, \(\begin{equation*} (d^m+N)^{1+o(1)}\cdot {{\sf poly}}(m,d,\log |\mathbb {F}|), \end{equation*}\) for all \(m\in \mathbb {N}\) and all sufficiently large \(d\in \mathbb {N}\) , provided that the size of the finite field \(\mathbb {F}\) is at most \((\exp (\exp (\exp (\cdots (\exp (d)))))\) , where the height of this tower of exponentials is fixed. [ABSTRACT FROM AUTHOR]

Multipoint evaluation is the computational task of evaluating a polynomial given as a list of coefficients at a given set of inputs. Besides being a natural and fundamental question in computer algebra on its own, fast algorithms for this problem are also closely related to fast algorithms for other natural algebraic questions such as polynomial factorization and modular composition. And while nearly linear time algorithms have been known for the univariate instance of multipoint evaluation for close to five decades due to a work of Borodin and Moenck [7], fast algorithms for the multivariate version have been much harder to come by. In a significant improvement to the state-of-the-art for this problem, Umans [25] and Kedlaya & Umans [16] gave nearly linear time algorithms for this problem over field of small characteristic and over all finite fields, respectively, provided that the number of variables n is at most do(1) where the degree of the input polynomial in every variable is less than d. They also stated the question of designing fast algorithms for the large variable case (i.e., n x do(1)) as an open problem. In this work, we show that there is a deterministic algorithm for multivariate multipoint evaluation over a field Fq of characteristic p, which evaluates an n-variate polynomial of degree less than d in each variable on N inputs in time provided that p is at most do(1), and q is at most (exp(exp(exp(· · · (exp(d))))), where the height of this tower of exponentials is fixed. When the number of variables is large (e.g., n x do(1)), this is the first nearly linear time algorithm for this problem over any (large enough) field. Our algorithm is based on elementary algebraic ideas, and this algebraic structure naturally leads to the following two independently interesting applications: --We show that there is an algebraic data structure for univariate polynomial evaluation with nearly linear space complexity and sublinear time complexity over finite fields of small characteristic and quasipolynomially bounded size. This provides a counterexample to a conjecture of Miltersen [21] who conjectured that over small finite fields, any algebraic data structure for polynomial evaluation using polynomial space must have linear query complexity. We also show that over finite fields of small characteristic and quasipolynomially bounded size, Vandermonde matrices are not rigid enough to yield size-depth tradeoffs for linear circuits via the current quantitative bounds in Valiant's program [26]. More precisely, for every fixed prime p, we show that for every constant e > 0, and large enough n, the rank of any n × n Vandermonde matrix V over the field Fpa can be reduced to (n/exp(O(poly(e)log0.53 n))) by changing at most nT(e) entries in every row of V, provided a = poly(logn). Prior to this work, similar upper bounds on rigidity were known only for special Vandermonde matrices. For instance, the Discrete Fourier Transform matrices and Vandermonde matrices with generators in a geometric progression [9]. [ABSTRACT FROM AUTHOR]

This study investigates longitudinal changes in linguistic complexity and holistic scores in EFL timed argumentative writing among beginning-level learners using Complex Dynamic Systems Theory. It also explores relationships among linguistic complexity over time and with writing scores. Over nine months, 42 Japanese EFL learners completed six timed essays. Linguistic complexity was assessed through lexical, phraseological, and syntactic features. Findings indicate linear growth in writing scores, lexical sophistication, and noun phrase (NP) complexity, alongside stable phrasal complexity and non-linear subordination. Interrelated growth patterns included connections between lexical sophistication and phrasal complexity, and phraseological complexity and subordination. Competitive dynamics were observed between lexical diversity and NP complexity, and phraseological complexity and phrasal sophistication. Linear associations emerged between writing scores and lexical sophistication or syntactic complexity, while a non-linear relationship occurred with phraseological sophistication. This study illuminates the intricate dynamics of linguistic complexity and L2 writing scores among beginning-level learners in argumentative writing. [ABSTRACT FROM AUTHOR]

Despite the ease of digital image distribution, storage, and replication, averting identity theft, privacy breaches, and ownership issues can be challenging. Medical image encryption plays a vital role in ensuring the confidentiality of sensitive medical data and safeguarding patient privacy. This research addresses these concerns by introducing a novel approach, RISE, to medical image security by using the fusion of chaotic keys and a secret-sharing technique. The key advancement is the use of a Rubik's cube-based bit-plane shuffling technique to reduce the complexity of strong image encryption, adding a unique dimension to the field of medical image security. Another distinguishing aspect of our approach is the strategic use of segmentation to encrypt only the sensitive part of the image and reduce the time complexity. This area is encrypted using a chaotic key with a Rubik's cube-based bit-plane shuffling algorithm, followed by the implementation of the confusion process. The encrypted image is shared using a K-N Secret sharing method, which provides authentication and high robustness. The final decrypted image is enhanced using super-resolution to provide better information outputs. The proposed technique offers excellent security and produces better outcomes while being simple. The average NPCR and UACI scores of the proposed encryption technique are 99.47, and 49.90, respectively, and the entropy is 7.995, underscoring the robustness and effectiveness of our proposed approach. It has a high key bit sensitivity and average time complexity. The result analysis further ensures resistance against crop attacks or data loss, positioning it as a formidable contender in the landscape of modern image security. [ABSTRACT FROM AUTHOR]

During the information retrieval process, individuals locate relevant web pages by entering specific keywords. Nevertheless, if users provide inaccurate keywords or if these keywords are absent from the intended page, the effectiveness of information retrieval will be significantly compromised. Thus, the role of keywords in text processing remains of utmost importance. Particularly in intricate contexts, relying on manual analysis by readers can prove to be both time-intensive and unfeasible. Most existing methods are addressed with limited accuracy, leading to elevated error rates and compromised training capabilities. To overcome these limitations, the proposed approach introduces an automated keyword extraction and ranking system based on deep learning. Several key stages, like data acquisition, pre-processing, tokenization, word-to-vector transformation, keyword classification, and ranking, are used. The effectiveness of this keyword extraction process is evaluated using 500N-KPCrowd, KPTimes, and KP20k datasets. During text pre-processing, eliminating stop words, applying Parts of Speech (PoS) tagging, stemming, and sentence segmentation are undertaken. The pre-processed text is fed into the Deep-KeywordNet model, while the pre-processed input is tokenized into individual words. The Word2Vec (W2V) Skip-gram embedding layer facilitates the categorization of distributed vector representations. The Attention Bidirectional Long Short-Term Memory Gated Convolutional Neural Network (Attn Bi-GCNN), along with the softmax layer, assign class labels, and the network's loss optimization employs the Dwarf Mongoose Algorithm (DMA). Significant keywords are ranked using the Term Frequency-Inverse Average Document Frequency (TF-IADF) model. Remarkably, the overall accuracy achieved through the implementation in PYTHON stands at 98.87%, with a minimized time complexity. [ABSTRACT FROM AUTHOR]

Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis. [ABSTRACT FROM AUTHOR]

To ensure the secure and efficient transmission of images, we propose a new image encryption algorithm based on a chaotic system with free control and zigzag scrambling. The performance of the chaotic system is analyzed using the phase trajectory, Lyapunov exponents (LEs), and bifurcation diagram, which demonstrate its good ergodicity, complex chaotic behavior, large and continuous chaotic range, and stable Lyapunov exponent spectrum. In addition, based on this chaotic system, this algorithm also provided the security for the protection of image data. The algorithm involves scrambling pixel positions using an improved zigzag algorithm and substituting pixel values. The simulation and analysis results show that the proposed algorithm has high security and low time complexity. It can resist various attacks, including statistical analysis, differential attacks, brute-force attacks, known plaintext attacks, and chosen plaintext attacks. The experimental simulations and its performance tests prove that the proposed scheme is effective and secure in image data. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new complexity computation based on the complex function. This measure exploits the dispersion Lempel–Ziv complexity (DLZC) and the dispersion statistical complexity measure based on Jensen–Shannon divergence (DCJS) of the analytic signal and constructs the binary complex function, called complex number complexity (CNC). The statistical measure depicts the complex system from different angle and mines more potential information. Through simulation experiments, we prove that the proposed method is able to detect data fluctuations more sensitively and accurately. For an application, the CNC is applied to fault detection which has different fault diameters and rotational speeds. The results deliver that the CNC measure can well represent the complexity of faulty bearings and has significant differences in any two fault types. The technique is effective to characterize different fault. [ABSTRACT FROM AUTHOR]

Image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission; it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks. [ABSTRACT FROM AUTHOR]

The manufacturing industry has been operating within a constantly evolving technological environment, underscoring the importance of maintaining the efficiency and reliability of manufacturing processes. Motor-related failures, especially bearing defects, are common and serious issues in manufacturing processes. Bearings provide accurate and smooth movements and play essential roles in mechanical equipment with shafts. Given their importance, bearing failure diagnosis has been extensively studied. However, the imbalance in failure data and the complexity of time series data make diagnosis challenging. Conventional AI models (convolutional neural networks (CNNs), long short-term memory (LSTM), support vector machine (SVM), and extreme gradient boosting (XGBoost)) face limitations in diagnosing such failures. To address this problem, this paper proposes a bearing failure diagnosis model using a graph convolution network (GCN)-based LSTM autoencoder with self-attention. The model was trained on data extracted from the Case Western Reserve University (CWRU) dataset and a fault simulator testbed. The proposed model achieved 97.3% accuracy on the CWRU dataset and 99.9% accuracy on the fault simulator dataset. [ABSTRACT FROM AUTHOR]

Background: Multidisciplinary team meetings (MDTMs) are crucial in the ICU. However, daily rounds may not address all sensitive issues due to time constraints and the complexity of cases. This study aimed to describe detailed information and characteristics of ad hoc MDTMs in the ICU. Methods: This single-center, retrospective study analyzed adult emergency ICU admissions at Okayama University Hospital from 1 January 2019 to 31 December 2023. During this period, weekly regular multidisciplinary team ICU rounds were introduced in June 2020, and regular weekday morning MDTMs began in April 2022. A multiple logistic regression analysis was applied to determine the impact of these changes on the frequency of ad hoc MDTMs, adjusting for variables including annual changes. Results: The study analyzed 2487 adult EICU patients, with a median age of 66, and 63.3% of them male. MDTMs were held for 168 patients (6.8%), typically those with severe conditions, including higher COVID-19 prevalence and APACHE II scores, and longer ICU stays. Despite a constant total number of MDTMs, the likelihood of conducting ad hoc MDTMs increased annually (adjusted OR 1.19; 95% CI, 1.04–1.35). Of the 329 MDTMs conducted for these patients, 59.0% addressed end-of-life care, involving an average of 11 participants, mainly nurses and emergency and critical-care physicians. Conclusions: Changes in ICU round and meeting structures might be associated with a higher frequency of conducting ad hoc MDTMs, highlighting their evolving role and importance in patient care management. [ABSTRACT FROM AUTHOR]

Hill Cipher is one of the classic symmetric encryption algorithms widely used in cloud data security. Although the hill cipher principle is relatively simple, its key matrix must be invertible, and all elements must be integers. However, the inverses of randomly generated matrix does not always exist and it is time-consuming to test whether the higher-order matrix is reversible. In this paper, we propose Random Key Matrix Generation Method (RKMGM), a novel algorithm to randomly generate a high order hill key matrix based on the modular multiplicative inverse of a triangular matrix. We prove that RKMGM extends the selection of key matrices from finite field to the rational number field and has no constraints on matrix order, and then analyze the time complexity of RKMGM. Compared to alternative hill key generation methods based on the involutory matrix, self-inversion matrix, and single mode, RKMGM has the advantages of simplicity, fewer constraints, one-time random generation, and high key space complexity. [ABSTRACT FROM AUTHOR]

With the increasing number of terminal devices in the sensor network and the Internet of things, non-orthogonal multiple access (NOMA) will be widely used in the future to improve spectrum resource utilization. In this paper, we considered how to control massive devices in the downlink in of scenario of space-based Internet of Things (S-IoT). In the case of multiple base stations (BSs), user fairness is a difficult problem, which usually requires a traversal algorithm to achieve the optimal solution. However, the time complexity caused by this method is the product of the complexity of user grouping and power allocation, which is unbearable. We split the problem into two sub-problems changing complexity from product form to additive form, and solve them by using low time complexity algorithms respectively. Firstly, we use a threshold-based algorithm to implement user grouping, and then use a bisection iterative algorithm for power allocation. In the simulation, we compared design with several benchmarks and results show that the minimum user rate is significantly improved and closed to optimal value by using proposed algorithm in this paper. [ABSTRACT FROM AUTHOR]

Based on well-known complexity theory conjectures, any polynomial-time kernelization algorithm for the NP-hard Line-Cover problem produces a kernel of size Ω (k 2) , where k is the size of the sought line cover. Motivated by the current research in massive data processing, we study the existence of kernelization algorithms with limited space and time complexity for Line-Cover. We prove that every kernelization algorithm for Line-Cover takes time Ω (n log k + k 2 log k) , and present a randomized kernelization algorithm for Line-Cover that produces a kernel of size bounded by k 2 , and runs in time O (n log k + k 2 (log k log log k) 2) and space O (k 2 log 2 k) . Our techniques are also useful for developing deterministic kernelization algorithms for Line-Cover with limited space and improved running time, and for developing streaming kernelization algorithms for Line-Cover with near-optimal update-time. [ABSTRACT FROM AUTHOR]

To equip convolutional neural networks (CNNs) with explainability, it is essential to interpret how opaque models make specific decisions, understand what causes the errors, improve the architecture design, and identify unethical biases in the classifiers. This paper introduces ADVISE, a new explainability method that quantifies and leverages the relevance of each unit of the feature map to provide better visual explanations. To this end, we propose using adaptive bandwidth kernel density estimation to assign a relevance score to each unit of the feature map with respect to the predicted class. We also propose an evaluation protocol to quantitatively assess the visual explainability of CNN models. Our extensive evaluation of ADVISE in image classification tasks using pretrained AlexNet, VGG16, ResNet50, and Xception models on ImageNet shows that our method outperforms other visual explainable methods in quantifying feature-relevance and visual explainability while maintaining competitive time complexity. Our experiments further show that ADVISE fulfils the sensitivity and implementation independence axioms while passing the sanity checks. The implementation is accessible for reproducibility purposes on https://github.com/dehshibi/ADVISE. [ABSTRACT FROM AUTHOR]

The fast growth of the web generates a significant amount of heterogeneous information such as images, text, audio, and video through various applications. These applications use different layouts to represent significant information. The layouts of table information are overloaded with anomalies that have given rise to intensive research into the semantification of web content and organizing tabular data for knowledge sharing and acquisition. Moreover, there are many anomalies present in tabular layouts that lead to the lack of semantic representation in tabular form and new challenges in data modeling. In this paper, we have discussed the various anomalies present in the tabular data that pertain to ontology learning and population tasks and provide the semantification of tabular data. To complete this task, (1) we provide the list of anomalies that pertain to semantification and provide the resolution to anomalies along with the semantification of tabular data, and (2) we have established the algorithm to interpret the table structure into a formal representation to analyze anomalies and provide the resolution. Furthermore, the proposed approach has been compared with existing approaches using ontology elements, the ability to resolve the anomalies, and the time complexity of the ontology population. [ABSTRACT FROM AUTHOR]

This paper presents a new control method for surge instability of the compressor system. Due to the importance of the active control approach in improving the efficiency of the compressor system, a new scheme based on the model predictive control (MPC) technique has been considered to control surge which gives the benefits of control signal optimization by considering the constraints on states and input. Because of designing a novel MPC by using of linear matrix inequality scheme, the optimization problem is solved in less time and with less complexity. The proposed method is able to consider the limitation of the close coupled valve actuator and also covers the uncertainty on the model. In addition, the developed model describes the nonlinear behaviour of the compressor system and handles the effects of the pipe on surge instability. Using Lyapunov theory, the stability of the closed-loop system under the proposed robust active controller is shown. The simulation results show the high pressure and high efficiency operation of the system under study in different operating conditions. [ABSTRACT FROM AUTHOR]

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The heavy dependency of cryptography methods on mathematical theory and computational science necessitates the designing of a computationally secure cryptographic algorithm which is hard to break by an adversary. Matrix methods are intensively used in cryptography for their robustness and adaptability in the encryption of long messages but time complexity is a big concern while dealing with matrices of volumetric size. Further, the key generation function may limit the size of key domain which compromises the perfect security. Our work addresses these hindrances in priority to give an algorithm which is perfectly secure. This work contributes towards the matrix methods in cryptography; the two main attributes of a secured communication namely, true randomness of key selection and a liberty to extend the dimension of key space. [ABSTRACT FROM AUTHOR]

The Rabin Cryptosystem is a public-key cryptosystem known for providing security levels comparable to RSA but with reduced computational overhead. Despite these advantages, it has not been widely adopted for practical use due to its lack of a deterministic nature. This paper addresses this limitation by introducing a new Deterministic Rabin Cryptosystem (DRCS). The DRCS framework includes processes for key generation, encryption, and decryption, leveraging the concept of cubic congruence and the Chinese Remainder Theorem to ensure the decryption process is unambiguous and deterministic. This design not only retains the computational efficiency of the original Rabin Cryptosystem but also enhances its security. Our comparative analysis shows that the DRCS achieves similar performance to the traditional Rabin system in terms of computational overhead. While the encryption process in DRCS is less demanding, its decryption process is more complex, and overall, it maintains a polynomial time complexity. Furthermore, a detailed security analysis indicates that the DRCS is significantly harder to factorize compared to previous models, underscoring its improved security features. [ABSTRACT FROM AUTHOR]

Purpose: Project scheduling plays an essential role in the implementation of a project due to the limitation of resources in practical projects. However, the existing research tend to focus on finding suitable algorithms to solve various scheduling problems and fail to find the potential scheduling rules in these optimal or near-optimal solutions, that is, the possible intrinsic relationships between attributes related to the scheduling of activity sequences. Data mining (DM) is used to analyze and interpret data to obtain valuable information stored in large-scale data. The goal of this paper is to use DM to discover scheduling concepts and obtain a set of rules that approximate effective solutions to resource-constrained project scheduling problems. These rules do not require any search and simulation, which have extremely low time complexity and support real-time decision-making to improve planning/scheduling. Design/methodology/approach: The resource-constrained project scheduling problem can be described as scheduling a group of interrelated activities to optimize the project completion time and other objectives while satisfying the activity priority relationship and resource constraints. This paper proposes a new approach to solve the resource-constrained project scheduling problem by combining DM technology and the genetic algorithm (GA). More specifically, the GA is used to generate various optimal project scheduling schemes, after that C4.5 decision tree (DT) is adopted to obtain valuable knowledge from these schemes for further predicting and solving new scheduling problems. Findings: In this study, the authors use GA and DM technology to analyze and extract knowledge from a large number of scheduling schemes, and determine the scheduling rule set to minimize the completion time. In order to verify the application effect of the proposed DT classification model, the J30, J60 and J120 datasets in PSPLIB are used to test the validity of the scheduling rules. The results show that DT can readily duplicate the excellent performance of GA for scheduling problems of different scales. In addition, the DT prediction model developed in this study is applied to a high-rise residential project consisting of 117 activities. The results show that compared with the completion time obtained by GA, the DT model can realize rapid adjustment of project scheduling problem to deal with the dynamic environment interference. In a word, the data-based approach is feasible, practical and effective. It not only captures the knowledge contained in the known optimal scheduling schemes, but also helps to provide a flexible scheduling decision-making approach for project implementation. Originality/value: This paper proposes a novel knowledge-based project scheduling approach. In previous studies, intelligent optimization algorithm is often used to solve the project scheduling problem. However, although these intelligent optimization algorithms can generate a set of effective solutions for problem instances, they are unable to explain the process of decision-making, nor can they identify the characteristics of good scheduling decisions generated by the optimization process. Moreover, their calculation is slow and complex, which is not suitable for planning and scheduling complex projects. In this study, the set of effective solutions of problem instances is taken as the training dataset of DM algorithm, and the extracted scheduling rules can provide the prediction and solution of new scheduling problems. The proposed method focuses on identifying the key parameters of a specific dynamic scheduling environment, which can not only reproduces the scheduling performance of the original algorithm well, but also has the ability to make decisions quickly under the dynamic interference construction scenario. It is helpful for project managers to implement quick decisions in response to construction emergencies, which is of great practical significance for improving the flexibility and efficiency of construction projects. [ABSTRACT FROM AUTHOR]

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2 ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods. [ABSTRACT FROM AUTHOR]

In this paper, we further verify the validity of the quantum partial adiabatic search algorithm which was initialized in the previous related works by revisiting its quantum circuit model. The main results got here are as follows. When considering implementing quantum partial adiabatic evolution on a quantum circuit, a correction is given for the time slice estimation for the first stage during this approximation in the previous related works, new evidence is provided for a time complexity cost O (N / M) of quantum partial adiabatic algorithm is impossible, and the correct time complexity O (N / M) of it is emphasized once more according to its circuit correspondence, in which N is the total number of elements in the search problem of which M of them are the marked ones. The findings exposed are hopeful for revisiting quantum partial adiabatic evolution and its connection with the quantum circuit model. [ABSTRACT FROM AUTHOR]

A codec is the most important element of any streaming application like Netflix or YouTube, but its complexity is increasing with its advancement. The codec, High Efficiency Video Coding (HEVC), selects the intra mode out of a total of 35 intra modes using the brute-force method. Therefore, the Spiral Optimization Algorithm (SOA) is utilized in this article to overcome the aforementioned limitation of HEVC. Firstly, the elements of SOA are efficiently mapped to the elements of HEVC. Secondly, the current best intra mode becomes the center of the spiral of SOA. Thirdly, the current best intra mode is considered optimal if the spiral completes three spiral-loops and the current intra mode still remains the best. Otherwise, the new best intra modes becomes the center of the spiral. Results show that SOA based fast intra-mode selection reduces the time complexity of HEVC by 34.09% while BD-BR (Bjontegaard Delta - Bit Rate) overhead is only 1.56%. [ABSTRACT FROM AUTHOR]

A wireless Sensor Network (WSN) is made up of many sensor nodes which gather and transmit data to a central location. The limited resources of the nodes create significant security challenges when deploying and communicating WSNs. The detection of unauthorized access is a crucial aspect of enhancing the security measures of WSNs. The utilization of network intrusion detection systems (IDS) has become an essential aspect of any communication network, as they offer valuable services to the network. Several studies in the field of machine learning have been conducted to explore the potential of utilizing this technology for intrusion detection in WSNs, yielding promising outcomes. These efforts still need to be more precise and efficient against network traffic unbalanced data issues. The paper presents a new model for detecting intrusion attacks that utilize a hybrid multilayer perceptron (MLP) and CatBoost classifier, as well as feature selection techniques. The proposed approach aims for good performance in identifying different forms of threats. The system performs data preprocessing on various datasets and reduces the dataset size using a feature selection algorithm. Pelican Optimization Algorithm (POA) has been proposed for tuning the hyper-parameters of the classifier designs and selecting the relevant features from the dataset. The CSE-CIC-IDS2018, AWID, and UNSW-NB15 databases reutilized for conducting performance evaluations on the proposed framework. The tests included accuracy, precision, recall, FAR, DR and complexity time. The proposed model has a low FPR and high accuracy in binary classification, as shown. [ABSTRACT FROM AUTHOR]

We address the problem of finding the nearest graph Laplacian to a given matrix, with the distance measured using the Frobenius norm. Specifically, for the directed graph Laplacian, we propose two novel algorithms by reformulating the problem as convex quadratic optimization problems with a special structure: one based on the active set method and the other on direct computation of Karush–Kuhn–Tucker points. The proposed algorithms can be applied to system identification and model reduction problems involving Laplacian dynamics. We demonstrate that these algorithms possess lower time complexities and the finite termination property, unlike the interior point method and V‐FISTA, the latter of which is an accelerated projected gradient method. Our numerical experiments confirm the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Capturing dynamic preference features from user historical behavioral data is widely applied to improve the accuracy of recommendations in sequential recommendation tasks. However, existing deep neural network-based sequential recommendation methods often ignore the noise information in user behavioral data that influence recommendation effectiveness, making recommendation models sensitive to noisy data. Additionally, these deep learning-based recommendation models often require a large number of parameters to capture user behavior patterns, leading to higher time complexity and susceptibility to overfitting due to noise fluctuations. To address these issues, in this paper, we apply the moving average concept from the field of time-series analysis to sequential recommendation tasks. The approach smooths sequence data, removes noise, making the data more stable and reliable, increases the model's insensitivity to noise information, and better understands the evolution of user interests and behavioral patterns. Specifically, in our experiments, the moving average algorithm effectively denoises sequential data with low time complexity. Therefore, we propose a Sequential Recommendation with Exponential Moving Average with MLP architecture that makes the model more competitive in terms of time complexity. Experimental results conducted on two datasets demonstrate that EMARec outperforms state-of-the-art sequential recommendation methods across various common evaluation metrics. [ABSTRACT FROM AUTHOR]

Recently, finger vein recognition has become integral to biometric technology that uses vein patterns for highly secure personal recognition. Many existing studies reported accurate finger vein recognition systems using recent techniques. However, the existing techniques face high complexity in analyzing vein patterns and increase recognition errors. Hence, this article proposes a novel deep learning (DL) technique for automatically recognizing finger veins at low time complexity. Initially, image de-noising and contrast enhancement are performed in the preprocessing stage using Extended Frost Filtering (Ex-FF) and Entropy Enriched Histogram Equalization (En-HE) to enhance the image's visual quality. The preprocessed images are then fed into the Kernel Attentive Resnet152v2 (KAtt-ResNet152V2) technique to extract the vein features efficiently. The extracted vein features are then fed to the Hybridized Golden Archerfish Optimizer (HyG-ArfO) to select the optimal vein features to reduce the feature dimensionalities. Finally, the optimal features are fed into the Residual Pyramid based Depthwise Separable MobileNetV3 (PyDS-MV3) technique to accurately recognize the person's finger veins. The proposed work is implemented in MATLAB, and a publicly available finger vein dataset is utilized. The performance measures such as accuracy, precision, false discovery rate (FDR), and Mathew's correlation coefficient (MCC) are analyzed and compared with existing studies. The proposed method obtains an overall accuracy of 99.6%, precision of 99.42%, FDR of 0.005 and MCC of 0.994. [ABSTRACT FROM AUTHOR]

Tumor is one of the deadliest diseases; nowadays the cases of tumors are increasing rapidly. Researchers from worldwide are doing extensive research for the diagnosis and discernment of tumors by employing machine learning algorithms and performing experiments on the basis of observations which are stored in the form of datasets. The tumor-related dataset is high-dimensional and has many genes, most of which are not prognostic. Some of them are irrelevant and redundant. Here, we proposed a methodology named IG based on IWSSr-Random Forest(RF) which selects the most relevant prognostic genes by using Information Gain for gene ranking and evaluates the importance of genes by using RF after selecting the genes in an incremental manner in a wrapper. Furthermore, we use the RF for classification purposes. Experiments are performed on nine publicly available tumor-related datasets. Accuracy, Confusion matrix, Precision, F-measure, and Recall are used as performance evaluators. The proposed methodology selects 3 most relevant genes out of 2000 genes, 5 genes out of 7129 genes, 3 genes out of 7129 genes, 5 genes out of 24,481 genes, 7 genes out of 12,601 genes, 5 genes out of 15,154 genes, 2 genes out of 4026 genes, 5 genes out of 12,582 genes and 4 genes out of 2308 genes, and produces 88.71%, 71.67%, 98.61%, 79.38%, 93.60%, 99.60%, 92.42%, 95.83% and 92.77% accurate results in case of Colon, Central Nervous System, Leukemia, Breast Cancer, Lung Cancer, Ovarian Cancer, Lymphoma, MLL and SRBCT respectively. Experimental results present that IG based on IWSSr(RF) performs well compared to the state-of-the-art algorithms' results for instance RF, Naïve Bayes, KNN, and Decision Tree. IG based on IWSSr(RF) also has nominal time complexity compared to the time complexity of the above-mentioned classification algorithms. [ABSTRACT FROM AUTHOR]

Agriculture plays a substantial part in aggregating the nation's economic growth. The production of crops had been greatly affected due to alterations in weather patterns. Recent technologies greatly assist in increasing agricultural output by changing conventional farming into precision farming. The primary issue that has yet to be resolved is growing a specific crop in certain soil and climatic circumstances. This research introduces a novel deep learning (DL) technique for the automatic recommendation of crops based on soil, fertilizers, and climatic conditions. The proposed method is trained and tested with publicly available Kaggle datasets and real-time collected Maharashtra datasets consisting of both soil and climatic parameters. Initially, data from the dataset are collected and fed into a new Concurrent Excited Gated Recurrent Unit (CEGRU) based DL approach for reliably proposing crops. In addition, a meta-heuristic algorithm named the hunter-prey optimization (HPO) technique is introduced to tune the parameters of the proposed network model. For implementation, the Python platform is utilized, and various performances like accuracy, kappa, PPV, recall, sensitivity, time complexity, and RMSE are analyzed. The proposed method obtains the overall accuracy of 99.2% and 99.3%, PPV of 97.5% and 98.3%, kappa of 95.7% and 96.6%, RMSE of 1.05 and 1.50, the time complexity of 105 s are obtained for both Maharashtra and Kaggle datasets respectively. Furthermore, sensitivity and recall scores were assessed independently for each dataset, yielding results of 98.61% and 97.54%, respectively. [ABSTRACT FROM AUTHOR]

This paper attempts to highlight the decoding capabilities of MDP convolutional codes over the erasure channel by defining them as discrete linear dynamical systems, with which the controllability property and the observability characteristics of linear system theory can be applied, in particular those of output observability, easily described using matrix language. Those are viewed against the decoding capabilities of MDS block codes over the same channel. Not only is the time complexity better but the decoding capabilities are also increased with this approach because convolutional codes are more flexible in handling variable-length data streams than block codes, where they are fixed-length and less adaptable to varying data lengths without padding or other adjustments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) have emerged as efficient tools in disaster-stricken areas, facilitating efficient data dissemination for post-disaster rescue operations. However, the limited onboard energy of UAVs imposes significant constraints on their operational lifespan, thereby presenting substantial challenges for efficient data dissemination. Therefore, this work investigates a data dissemination scheme to enhance the UAVs' bandwidth efficiency in multi-UAV-enabled Internet of Vehicles, thereby reducing UAVs' energy consumption and improving overall system performance when UAVs hover along designated flight trajectories for data dissemination. Specifically, first, we present a software-defined network-based framework for data dissemination in multi-UAV-enabled IoV. According to this framework, we formulate a problem called C2BS (Coding-based Cooperative Broadcast Scheduling) that focuses on optimizing the UAVs' bandwidth efficiency by leveraging the combined benefits of coding and caching. Furthermore, we demonstrate the NP-hardness of the C2BS problem by employing a polynomial time reduction technique on the simultaneous matrix completion problem. Then, inspired by the benefits offered by genetic algorithms, we propose a novel approach called the Genetic algorithm-based Cooperative Scheduling (GCS) algorithm to address the C2BS problem. This approach encompasses a coding scheme for representing individuals, a fitness function for assessing individuals, operators (i.e., crossover and mutation) for generating offspring, a local search technique to enhance search performance, and a repair operator employed to rectify infeasible solutions. Additionally, we present an analysis of the time complexity for the GCS algorithm. Finally, we present a simulation model to evaluate the performance. Experimental findings provide evidence of the excellence of the proposed scheme. [ABSTRACT FROM AUTHOR]

Two spanning trees T 1 and T 2 are called completely independent if there is a path P 1 ∈ T 1 from the root node r 1 ∈ T 1 to a certain node u that is disjoint with other path P 2 ∈ T 2 from the root node r 2 ∈ T 2 to a certain node u, where r 1 ≠ r 2 . Completely Independent Spanning Trees (CISTs) are used in computer networks to increase the reliability of the network through fault-tolerant communications and secure message distribution. In this work, we propose two CISTs construction algorithms in Eisenstein-Jacobi (EJ) networks, sequential and parallel construction algorithms. In a sequential construction, the root node propagates a construction message to all other nodes in the network through a certain direction, where each node continues the message propagation until the tree is constructed, whereas in a parallel construction, each node in the network has the knowledge of the network topology and all nodes connect to their neighboring nodes simultaneously using certain links to form a tree. Both constructions have time complexity O(n) where n is the total number of nodes in the network. They also have communication complexities O(d) and O(1), respectively, d is the tree depth. Further, we developed a novel fault-tolerant communication algorithm that can be used for routing, broadcasting, and secure message distribution with O(n) time complexity. Simulation results of our work illustrate the average of the maximum number of steps required to deliver a message from the root node of any CIST to any destination in all trees. CISTs show that it is more reliable than ISTs in terms of root node failure. [ABSTRACT FROM AUTHOR]

The longest increasing subsequence (LIS) problem aims to find the subsequence exhibiting an increasing trend in a numeric sequence with the maximum length. In this paper, we generalize the LIS problem to the longest wave subsequence (LWS) problem, which encompasses two versions: LWSt and LWSr. Given a numeric sequence A of distinct values and a target trend sequence T, the LWSt problem aims to identify the longest subsequence of A that preserves the trend of the prefix of T. And, the LWSr problem aims to find the longest subsequence of A within r segments, alternating increasing and decreasing subsequences. We propose two efficient algorithms for solving the two versions of the LWS problem. For the LWSt problem, the time complexity of our algorithm is O(nlog n), where n represents the length of the given numeric sequence A. Additionally, we propose an O(rnlog n)-time algorithm for solving the LWSr problem. In both algorithms, we utilize the priority queues for the insertion, deletion, and successor operations. [ABSTRACT FROM AUTHOR]

Kernel matching pursuit (KMP) requires every step of the searching process to be global optimal searching in the redundant dictionary of functions in order to select the best matching signal structure. Namely, the dictionary learning time of KMP is too long. To solve the above drawbacks, a rough dataset was divided into some small-sized dictionaries to substitute local searching for global searching by using the property superiority of dynamic clustering performance, which is also superior in the intuitionistic fuzzy c-means (IFCM) algorithm. Then, we proposed a novel technique for KMP based on IFCM (IFCM-KMP). Subsequently, three tests including classification, effectiveness, and time complexity were carried out on four practical sample datasets, the conclusions of which fully demonstrate that the IFCM-KMP algorithm is superior to FCM and KMP. [ABSTRACT FROM AUTHOR]

Maintaining quality in software development projects is becoming very difficult because the complexity of modules in the software is growing exponentially. Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. However, SDP faces challenges like imbalanced data, high-dimensional features, model overfitting, and outliers. Moreover, traditional SDP models lack transparency and interpretability, which impacts stakeholder confidence in the Software Development Life Cycle (SDLC). We propose SPAM-XAI, a hybrid model integrating novel sampling, feature selection, and eXplainable-AI (XAI) algorithms to address these challenges. The SPAM-XAI model reduces features, optimizes the model, and reduces time and space complexity, enhancing its robustness. The SPAM-XAI model exhibited improved performance after experimenting with the NASA PROMISE repository's datasets. It achieved an accuracy of 98.13% on CM1, 96.00% on PC1, and 98.65% on PC2, surpassing previous state-of-the-art and baseline models with other evaluation matrices enhancement compared to existing methods. The SPAM-XAI model increases transparency and facilitates understanding of the interaction between features and error status, enabling coherent and comprehensible predictions. This enhancement optimizes the decision-making process and enhances the model's trustworthiness in the SDLC. [ABSTRACT FROM AUTHOR]

Scientific data are essential to advancing scientific knowledge and are increasingly valued as scholarly output. Understanding what drives dataset downloads is crucial for their effective dissemination and reuse. Our study, analysing 55,473 datasets from 69 data repositories, identifies key factors driving dataset downloads, focusing on interpretability, reliability, and accessibility. We find that while lengthy descriptive texts can deter users due to complexity and time requirements, readability boosts a dataset's appeal. Reliability, evidenced by factors like institutional reputation and citation counts of related papers, also significantly increases a dataset's attractiveness and usage. Additionally, our research shows that open access to datasets increases their downloads and amplifies the importance of interpretability and reliability. This indicates that easy access enhances the overall attractiveness and usage of datasets in the scholarly community. By emphasizing interpretability, reliability, and accessibility, this study offers a comprehensive framework for future research and guides data management practices toward ensuring clarity, credibility, and open access to maximize the impact of scientific datasets. [ABSTRACT FROM AUTHOR]

Immunotherapy has emerged as promising treatment in sarcomas, but the high variability in terms of histology, clinical behavior and response to treatments determines a particular challenge for its role in these neoplasms. Tumor immune microenvironment (TiME) of sarcomas reflects the heterogeneity of these tumors originating from mesenchymal cells and encompassing more than 100 histologies. Advances in the understanding of the complexity of TiME have led to an improvement of the immunotherapeutic responsiveness in sarcomas, that at first showed disappointing results. The proposed immune-classification of sarcomas based on the interaction between immune cell populations and tumor cells showed to have a prognostic and potential predictive role for immunotherapies. Several studies have explored the clinical impact of immune therapies in the management of these histotypes leading to controversial results. The presence of Tumor Infiltrating Lymphocytes (TIL) seems to correlate with an improvement in the survival of patients and with a higher responsiveness to immunotherapy. In this context, it is important to consider that also immunerelated genes (IRGs) have been demonstrated to have a key role in tumorigenesis and in the building of tumor immune microenvironment. The IRGs landscape in soft tissue and bone sarcomas is characterized by the connection between several tumor-related genes that can assume a potential prognostic and predictive therapeutic role. In this paper, we reviewed the state of art of the principal immune strategies in the management of sarcomas including their clinical and translational relevance. [ABSTRACT FROM AUTHOR]

With the increasing importance of multimedia and multilingual data in online encyclopedias, novel methods are needed to fill domain gaps and automatically connect different modalities for increased accessibility. For example, Wikipedia is composed of millions of pages written in multiple languages. Images, when present, often lack textual context, thus remaining conceptually floating and harder to find and manage. In this work, we tackle the novel task of associating images from Wikipedia pages with the correct caption among a large pool of available ones written in multiple languages, as required by the image-caption matching Kaggle challenge organized by the Wikimedia Foundation. A system able to perform this task would improve the accessibility and completeness of the underlying multi-modal knowledge graph in online encyclopedias. We propose a cascade of two models powered by the recent Transformer networks able to efficiently and effectively infer a relevance score between the query image data and the captions. We verify through extensive experiments that the proposed cascaded approach effectively handles a large pool of images and captions while maintaining bounded the overall computational complexity at inference time. With respect to other approaches in the challenge leaderboard, we can achieve remarkable improvements over the previous proposals (+8% in nDCG@5 with respect to the sixth position) with constrained resources. The code is publicly available at https://tinyurl.com/wiki-imcap. [ABSTRACT FROM AUTHOR]

Summary: One of the practical preferences of cloud service providers is to use specialized physical hosts. In other words, the goal is to place homogeneous virtual machines (VMs) on the physical host according to performance criteria such as energy consumption, resource wastage, and utilization. virtual machine placement (VMP) falls into NP‐hard knapsack problems. To overcome the time complexity, the use of heuristic and metaheuristic methods has attracted the attention of researchers. In this paper, we use an entropy‐based method for VMP for the first time. The proposed method tries to place the VMs on physical machines by considering the type of VMs to minimize entropy. Entropy is a measurable property that is more associated with disorder, randomness, or uncertainty. We use one of the most common entropy criteria called the Gini coefficient. In summary, among the different placement combinations of VMs, those that can minimize the Gini coefficient are preferred. We then solve the multi‐objective problem with the non‐dominated sorting genetic algorithm (NSGA‐III). We also combine this method with differential evolution methods to improve the quality of solutions. Recent research in other engineering fields has shown that combining metaheuristic methods with differential evolution methods increases the rate of convergence toward the optimal solution. The simulation results on the CloudSim simulator, along with statistical analysis, show that the entropy‐based method has a significant improvement over the state‐of‐the‐art methods in terms of significant performance criteria such as utilization, resource wastage, and energy consumption. [ABSTRACT FROM AUTHOR]

In this study, the Improved Map Generation Algorithm (IMGA) method is presented to improve traditional methods in procedural map creation. Traditional procedural map generation techniques using noise generation exhibit shortcomings in the consistent composition of a real map with its uniformly distributed features. On the other hand, procedural map creation techniques that use wave function collapse require that some map pieces already exist to create a map. The observed disadvantages were eliminated by using a hybrid technique with the designed IMGA method. The developed algorithm is similar to real maps in terms of the distribution of map regions, does not need 3D model parts, and performs map creation operations without increasing the algorithm's time complexity. The evaluation of IMGA was carried out by coding the method into the Unity game engine. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been widely used in exploratory data analysis. Contrastive PCA (Abid et al.), a generalized method of PCA, is a new tool used to capture features of a target dataset relative to a background dataset while preserving the maximum amount of information contained in the data. With high dimensional data, contrastive PCA becomes impractical due to its high computational requirement of forming the contrastive covariance matrix and associated eigenvalue decomposition for extracting leading components. In this article, we propose a geometric curvilinear-search method to solve this problem and provide a convergence analysis. Our approach offers significant computational efficiencies. Specifically, it reduces the time complexity from O ((n ∨ m) p 2) to a more manageable O ((n ∨ m) p r) , where n, m are the sample sizes of the target data and background data, respectively, p is the data dimension and r is the number of leading components. Additionally, we streamline the space complexity from O (p 2) , necessary for storing the contrastive covariance matrix, to a more economical O ((n ∨ m) p) , sufficient for storing the data alone. Numerical examples are presented to show the merits of the proposed algorithm. for this article are available online. [ABSTRACT FROM AUTHOR]

The history of repairs underscores the complexity of time through the practices of reusing and adapting objects. While this research field primarily focuses on the contemporary period, its close ties to present-day life risk obscuring the historical depth of these practices and the stakes they hold in various societies. How can we incorporate a truly historical perspective into our understanding of repairs? This article, a product of collective reflection, aims to broaden the analysis of repairs by embracing the concept of longue durée , a term coined by Fernand Braudel to describe an extensive span of historical time. Rather than striving to construct a grand narrative, this article addresses the challenge of acknowledging the diverse contexts and the discrepancies in how individuals interact with the lifespan of materials, artifacts, and their functionalities. [ABSTRACT FROM AUTHOR]

[Objectives] Identifying crops and weeds are crucial aspects of advancing intelligent and automated weeding. This article aimed to improve the accuracy of crop and weed identification, to enhance the real-time performance of detection models, and to enhance robustness. [Methods] Focusing on maize crops and their corresponding weeds in the leaf age range of 3-8 leaves, this research endeavored to devise a detection method for maize seedlings and associated grasses. The seedling detection method leveraged an improved real-time end-to-end object detection with transformers (RT-DETR) for maize and weed detection in field conditions. The novel concept of replacing large-scale deep convolution with small-scale convolution equivalence within RT-DETR was introduced, reducing training complexity and inference time while maintaining detection accuracy. Furthermore, a self-attention mechanism with contextual information was integrated to enhance target attention and improve small target detection. Additionally, a combined image enhancement strategy was employed to enhance model accuracy and generalization. [Results]The improved model effectively distinguished weeds from crops in complex field scenarios, achieving an average detection accuracy of 90.11%. In the inference stage, each image took 33.67 ms for processing, with a model size of 44.86 MB. Compared with the mainstream target detection model, the improved model had higher overall accuracy and fast speed. [Conclusions]The proposed method had excellent overall detection performance for corn seedlings and associated weeds, which could improve the accuracy and efficiency of weed identification. [ABSTRACT FROM AUTHOR]