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For the security problem of the spread of digital image and the digital image encryption from plaintext and too depend on the chaotic systems, this paper proposed a digital image encryption and monitoring algorithm combined with plaintext-related chaotic mapping and SHA-256. The algorithm used the SHA-256 algorithm to calculate the hash value as record to monitor the spread of digital image. Lorenz chaotic mapping generated the corresponding cryptogram by using forward diffusion, associated plaintext scrambling and backward diffusion to encrypt the digital image. The results show that the algorithm can resist many attacks and achieve the security and concealment of image propagation. [ABSTRACT FROM AUTHOR]

Letter to the Editor concerning "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab. Reply to "Describing center of pressure movement in stabilometry by ellipse area approximation" from Agnieszka Golab concerning the paper "A Review of Center of Pressure (COP) Variables to Quantify Standing Balance in Elderly People: Algorithms and Open Access Code" Our choice was actually to present the formula of the prediction ellipse area in the article, as it indeed does not strongly depend on the sample size as the confidence ellipse area does. [Extracted from the article]

In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches. [ABSTRACT FROM AUTHOR]

A novel algorithm has been developed and validated to isolate individual papermaking fibres in micro-computed tomographic images of paper handsheets as a first step to characterize the structure of the paper. The three-step fibre segmentation algorithm segments the papermaking fibres by (i) tracking the hollow inside the fibres via a modified connected component methodology, (ii) extracting the fibre walls using a distance transform and (iii) labelling the fibres through collapsed sections by a final refinement step. Furthermore, postprocessing algorithms have been developed to calculate the length and coarseness of the segmented fibres. The fibre segmentation algorithm is the first ever reported method for the automated segmentation of the tortuous three-dimensional morphology of papermaking fibres within microstructural images of paper handsheets. The method is not limited to papermaking fibres, but can be applied to any material consisting of tortuous and hollow fibres. [ABSTRACT FROM AUTHOR]

In this paper we present the Markovian Multiagent Monte-Carlo Second Order Self-Consistent Field Algorithm (M3-SOSCF). This algorithm provides a highly reliable methodology for converging SCF calculations in single-reference methods using a modified differential evolution approach. Additionally, M3 is embarrassingly parallel and modular in regards to Newton–Raphson subroutines. We show that M3 is able to surpass contemporary SOSCFs in reliability, which is illustrated by a benchmark employing poor initial guesses and a second benchmark with SCF calculations which face difficulties using standard SCF algorithms. Furthermore, we analyse inherent properties of M3 and show that in addition to its robustness and efficiency, it is more user-friendly than current SOSCFs. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle operations are quickly gaining ground due to rapid global market penetration. While on one hand, novel technologies that bridge communication networks to aviation industry are yet to be explored, on the other hand, their development requires highly scalable systems to enable beyond visual line‐of‐sight missions. This requirement imposes a big bottleneck in terms of computation complexity. This paper presents a method for fast computation of multiple diffraction of radio waves over knife‐edge obstacles based on the Deygout technique and some offline computation steps, including a ground profile analysis. We prove that this algorithm is equivalent to the original Deygout algorithm for all non‐line‐of‐sight points, show heuristics confirming that it is mostly applicable in the line‐of‐sight case. The computational and memory complexity of our algorithm is approximately O(N), compared to O(N) for the original Deygout algorithm. Finally we discuss how to apply the approach to the Epstein‐Peterson technique and the Giovanelli technique, and how to use it to compute clutter‐loss. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new approach for optimizing traffic management in multiple access networks (MANETs) by utilizing the stream‐enabled routing (SER) algorithm. The SER algorithm is used to determine which routing path is the most time‐ and resource‐efficient. The proposed approach makes use of multipath routing in a manner that is consistent with the SER method. By combining the states of flows, queues, and links, a graph neural network (GNN)‐based model attempts to break the circular dependencies that are described by these functions. The simulation is setup with joint parameters consisting of residual energy, packet delivery rate (PDR), and end‐to‐end delay. The results of the experiments show that the proposed protocol provides a significant improvement in terms of network efficiency when compared to using some baseline protocols designed for MANETs. [ABSTRACT FROM AUTHOR]

Automatic face recognition has made significant progress in recent decades, particularly in controlled environments. However, recognizing faces across different modalities, known as Heterogeneous Face Recognition, presents challenges due to variations in modality gaps. This paper addresses the problem of HFR by proposing a two-stage algorithm. In the first stage, a deep stacked PCA descriptor (DSPD) is introduced to extract domain-invariant features from face images of different modalities. The DSPD utilizes multiple convolution layers of domain-trained PCA filters, and the features extracted from each layer are concatenated to obtain a final feature representation. Additionally, pre-processing steps are applied to input images to enhance the prominence of facial edges, making the features more distinctive. The obtained DSPD features can be directly used for recognition using nearest neighbour algorithms. To further improve recognition robustness, a coupled subspace called coupled discriminant neighbourhood embedding (CDNE) is proposed in the second stage. CDNE is trained with a limited number of data samples and can project DSPD features from different modalities onto a common subspace. In this subspace, data points representing the same subjects from different modalities are positioned closely, while those of different subjects are positioned apart. This spatial arrangement enhances the recognition of heterogeneous faces using nearest neighbour algorithms. Experimental results demonstrate the effectiveness of the proposed algorithm on various HFR scenarios, including VIS-NIR, VIS-Sketch, and VIS-Thermal face pairs from respective databases. The algorithm shows promising performance in addressing the challenges posed by the modality gap, providing a potential solution for accurate and robust Heterogeneous Face Recognition. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

The fog computing model is a new computing model that has been proposed in recent years by increasing the number of requests sent to the cloud to reduce the delay and workload of the cloud computing model. In addition to its advantages, the fog computing model also has challenges, among which we can mention the issue of service placement in this computing model, which is very effective in the performance of the computing model. So far, many works have been presented to solve the problem of service deployment by considering different goals such as energy consumption, end-to-end delay, load balancing, resource efficiency, etc. Considering the importance of all the mentioned parameters, it is very important to provide a multi-objective method. In multi-objective problems, the method of evaluating the generated solutions is a separate challenge. Therefore, in this paper, a service placement method is presented by considering end-to-end delay criteria and energy consumption based on the modified Harris hawks algorithm to solve multi-objective problems. To increase accuracy, in the proposed method called multi-objective Harris hawks optimization, a multi-objective problem is modeled as several single-objective problems. The simulation results in CloudSim show that the proposed method has achieved better results than other algorithms in terms of reducing energy consumption, end-to-end delay, and network utilization. [ABSTRACT FROM AUTHOR]

Chaotic systems are often used as random sequence generators due to their excellent pseudo-randomness, but there is limitation that the discretization of complex chaotic systems requires a long computational time. Therefore, a parallel discretization method for chaotic system, and an accelerated chaotic image secure communication system based on the Zynq-7000 platform are proposed in this paper. Firstly, a 3-dimensional (3-D) chaotic system is constructed to generate random sequence, which has high Shannon entropy (SE) complexity. Then, chaotic system is parallelly discretized through finite state machine, which sequences are combined with scrambling and diffusion algorithms to construct an accelerated chaotic image secure communication system. Finally, the secure communication process based on the Zynq-7000 platform is completed, and the analysis of hardware experimental results shows that the system has safe performances, simple structure and excellent operational efficiency. [ABSTRACT FROM AUTHOR]

Let G = (V , E) be a graph. A set S ⊆ V is a total restrained dominating set if every vertex is adjacent to a vertex in S , and every vertex in V − S is adjacent to a vertex in V − S. The total restrained domination number of G , denoted γ t r (G) , is the smallest cardinality of a total restrained dominating set of G. In this paper we show that if G is a K 1 , ℓ -free graph with δ ≥ ℓ ≥ 3 and δ ≥ 5 , then γ t r (G) ≤ n 1 − (2 δ − 3) (2 δ) δ δ − 1 + o δ (1) . [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel keyframe extraction extraction method based on the gray wolf optimization (GWO) algorithm, addressing the challenge of information loss in traditional methods due to redundant and similar frames. The proposed method GWOKConvLSTM prioritizes speed, accuracy, and compression efficiency while preserving semantic information. Inspired by wolf behavior, we construct a fitness function that minimizes reconstruction error and achieves optimal compression ratios below 8%. Compared to traditional methods, our GWO method achieves the lowest reconstruction error for a given compression rate, providing a concise and visually coherent summary of keyframes while maintaining consistency across similar motions. Additionally, we propose a template-based method for video classification tasks, achieving the highest accuracy when combined with pre-trained CNNs and ConvLSTM. Our method effectively prevents dynamic background noise from affecting keyframe selection, leading to significantly improve video classification performance using deep neural networks. [ABSTRACT FROM AUTHOR]

Efficient real-time power optimization of direct methanol fuel cell (DMFC) system is crucial for enhancing its performance and reliability. The power of DMFC system is mainly affected by stack temperature and circulating methanol concentration. However, the methanol concentration cannot be directly measured using reliable sensors, which poses a challenge for the real-time power optimization. To address this issue, this paper investigates the operating mechanism of DMFC system and establishes a system power model. Based on the established model, reinforcement learning using Q-learning algorithm is proposed to control methanol supply to optimize DMFC system power under varying operating conditions. This algorithm is simple, easy to implement, and does not rely on methanol concentration measurements. To validate the effectiveness of the proposed algorithm, simulation comparisons between the proposed method and the traditional perturbation and observation (P&O) algorithm are implemented under different operating conditions. The results show that proposed power optimization based on Q-learning algorithm improves net power by 1% and eliminates the fluctuation of methanol supply caused by P&O. For practical implementation considerations and real-time requirements of the algorithm, hardware-in-the-loop (HIL) experiments are conducted. The experiment results demonstrate that the proposed methods optimize net power under different operating conditions. Additionally, in terms of model accuracy, the experimental results are well matched with the simulation. Moreover, under varying load condition, compared with P&O, proposed power optimization based on Q-learning algorithm reduces root mean square error (RMSE) from 7.271% to 2.996% and mean absolute error (MAE) from 5.036% to 0.331%. • Proposed power optimization method considers the overall power of the DMFC system. • Methanol supply control can be achieved without requiring concentration information. • Application of Q-learning algorithm is simple, real-time, and greatly reduce the complexity. [ABSTRACT FROM AUTHOR]

Clinicians, researchers, regulators, and other decision-makers increasingly rely on evidence from real-world data (RWD), including data routinely accumulating in health and administrative databases. RWD studies often rely on algorithms to operationalize variable definitions. An algorithm is a combination of codes or concepts used to identify persons with a specific health condition or characteristic. Establishing the validity of algorithms is a prerequisite for generating valid study findings that can ultimately inform evidence-based health care. In this paper, we aim to systematize terminology, methods, and practical considerations relevant to the conduct of validation studies of RWD-based algorithms. We discuss measures of algorithm accuracy, gold/reference standards, study size, prioritization of accuracy measures, algorithm portability, and implications for interpretation. Information bias is common in epidemiologic studies, underscoring the importance of transparency in decisions regarding choice and prioritizing measures of algorithm validity. The validity of an algorithm should be judged in the context of a data source, and one size does not fit all. Prioritizing validity measures within a given data source depends on the role of a given variable in the analysis (eligibility criterion, exposure, outcome, or covariate). Validation work should be part of routine maintenance of RWD sources. This article is part of a Special Collection on Pharmacoepidemiology. [ABSTRACT FROM AUTHOR]

In this paper, we first examine the properties of the matrices of the generalized m -step Jacobsthal sequence, and we develop a public key cryptosystem using key exchange based on the relationship between inner product and orthogonality. Then we create encryption and decryption algorithms using this key exchange, self-adjoint operator, and generalized m -step Jacobsthal sequence, and we provide a detailed example of how encryption and decryption algorithms work. Finally, a security strength and complexity analysis of these algorithms will be made. [ABSTRACT FROM AUTHOR]

In a sweep cover problem, points of interest (PoIs) are required to be visited by mobile sensors periodically. The time span between two consecutive visits of a PoI is the sweep-period of that PoI. In this paper, we study the min sweep-period sweep cover problem with a distance constraint (MinSPSC DC ). Given M mobile sensors with speed a , K base stations and a distance constraint D , the goal of MinSPSC DC is to design routes for the mobile sensors such that the maximum sweep-period is minimized under the condition that every mobile sensor has to visit a base station before traveling distance D. We present an M M − K + 1 ⋅ 2 D D − 2 d max -approximation algorithm for MinSPSC DC , where d max is the maximum distance between a PoI and a base station. [ABSTRACT FROM AUTHOR]

In this paper, exploiting the structure of tensors, the so called refined Geršhgorin disks for H-eigenvalues of tensors are introduced, which are always tighter than the classical Geršhgorin-type theorem for H-eigenvalues of tensors introduced by Qi (J Symb Comput 40:1302–1324, 2005). A sufficient condition for the positivity of even-order tensors is also given. We introduce the definition of G H -tensors, which can be viewed as a generalization of H -tensors. Moreover, an algorithm for identifying G H -tensors is also obtained. We prove that the tensor equation A x m - 1 = b has a unique positive solution if A is a strong G H + -tensor and b ∈ R n is a positive vector. [ABSTRACT FROM AUTHOR]

A vertex v of a simple graph G = (V , E) ve-dominates every edge incident to v as well as every edge adjacent to these incident edges. A set D ⊆ V is a total vertex-edge dominating set if every edge of E is ve-dominated by a vertex of D and the subgraph induced by D has no isolated vertex. The total vertex-edge domination problem is to find a total vertex-edge dominating set of minimum cardinality. In this paper, we first show that the total vertex-edge domination problem is NP-complete for chordal graphs. Then we provide a linear-time algorithm for this problem in trees. Moreover, we show that the minimum total vertex-edge domination problem cannot be approximated within (1 −) ln | V | for any > 0 unless NP ⊆ DTIME (| V | O (log log | V |) ). Finally, we prove that the minimum total vertex-edge domination problem is APX-complete for bounded-degree graphs. [ABSTRACT FROM AUTHOR]

The connectivity is one of the crucial parameters of network used to transport network flow, routing problems and bandwidth allocation problems. The increased connectivity makes a network more stable. In this paper, a new parameter called connectivity status of a vertex is introduced in the intuitionistic fuzzy graph. The related definitions and propositions of connectivity status of a vertex are proposed and investigated in an intuitionistic fuzzy graph. Specifically, connectivity status and status sequence are defined and analysed with various examples. After deleting a vertex, we classify the vertices of an intuitionistic fuzzy graph as connectivity status enhancing vertices, connectivity status neutral vertices and connectivity status reducing vertices because of the change of connectivity status. Finally, we establish two algorithms for these concepts and give an application to illustrate feasibility of algorithms. [ABSTRACT FROM AUTHOR]

In low voltage distribution systems, series arc fault current is small and hidden, and traditional circuit protection devices cannot effectively identify it. To address this problem, a series arc fault detection method based on multi-domain depth feature association is proposed in this paper. By building an experimental platform, the current signal data on normal and arc fault states under different loads are obtained. The time-domain features, frequency-domain features and wavelet packet energy features of the current signal are extracted. To enhance the quality of the data, the importance of each domain characteristic is ranked using four distinct tree techniques, and the most useful features are chosen. A one-dimensional stacked neural network (1D-SNN) fault detection model is constructed to further extract the depth features of each domain. To achieve series arc defect detection, the depth features are combined and fed into a fully connected neural network. The Radam algorithm is used to optimize the detection model. It is then compared with Adam, SGD, and RMSprop optimization algorithms, which verifies that Radam has a better effect on the optimization of the arc detection model. Experimental results show that the average detection accuracy is 99.63%. [ABSTRACT FROM AUTHOR]

For high-dimensional generalized linear models (GLMs) with massive data, this paper investigates a unified optimal Poisson subsampling scheme to conduct estimation and inference for prespecified low-dimensional partition of the whole parameter. A Poisson subsampling decorrelated score function is proposed such that the adverse effect of the less accurate nuisance parameter estimation with slow convergence rate can be mitigated. The resultant Poisson subsample estimator is proved to enjoy consistency and asymptotic normality, and a more general optimal subsampling criterion including A- and L-optimality criteria is formulated to improve estimation efficiency. We also propose a two-step algorithm for implementation and discuss some practical issues. The satisfactory performance of our method is validated through simulation studies and a real dataset. [ABSTRACT FROM AUTHOR]

In this paper, a finite difference (FD) scheme based on time two-grid algorithm is proposed for solving the two-dimensional time-fractional Fisher equation (2D-TFFE). Firstly, the Caputo fractional derivative and the spatial derivative are discretized by the $ L2-1_\sigma $ L 2 − 1 σ formula and the central difference formula, respectively. In order to improve the efficiency of computation, the time two-grid algorithm is then constructed. Secondly, based on the cut-off function technic, stability and convergence of the time two-grid FD scheme are obtained by the energy method, and the global convergence order is $ O(\tau _{F}^2 +\tau _{C}^4 +h_x^2 +h_y^2) $ O (τ F 2 + τ C 4 + h x 2 + h y 2) , where $ \tau _{F} $ τ F and $ \tau _{C} $ τ C represent time-step sizes on the fine and coarse grid, respectively, while $ h_x $ h x and $ h_y $ h y represent the space-step sizes. Finally, numerical experiments are presented to show the feasibility and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal motion planning framework that enables navigating optimally from any initial, to any final position within confined workspaces with convex, moving obstacles is presented. Our method outputs a smooth velocity vector field, which is then employed as a reference controller in order to sub-optimally avoid moving obstacles. The proposed approach leverages and extends desirable properties of reactive methods in order to provide a provably convergent and safe solution. Our algorithm is evaluated with both static and moving obstacles in synthetic environments and is compared against a variety of existing methods. The efficacy and applicability of the proposed scheme is finally validated in a high-fidelity simulation environment. [ABSTRACT FROM AUTHOR]

In this study, a general method is developed for the torsional vibration of non-circular-shaped nanorods with varying boundary conditions using second-order strain gradient theory. In most of the studies in the literature, the cross section of the rods is considered to be circular. The reason for this is that the use of warping function is inevitable when the cross section geometry is not circular. For circular cross sections after torsion, the warping is very small and is considered to be non-existent. For non-circular sections, cross section warping should be taken into account in mathematical calculations. The cross section geometry is different from circular in this study, and the boundary conditions are not rigid, contrary to most studies in the literature. In this paper, the second-order strain gradient theory and the most general solution method are discussed. In some specific cases, it is possible to transform the problem into many studies found in the literature. The correctness of the algorithm is tested by comparing the resulting solutions with closed solutions found in the literature. The influence of some variables on the torsional frequencies is illustrated by a series of graphical figures, and the superiority of the applied method is summarized. [ABSTRACT FROM AUTHOR]

In computational fluid dynamics, controlling grid scale is efficiently managed using the Advancing Front Technique (AFT). However, achieving grid generation convergence within a three-dimensional (3D) computational domain remains challenging, primarily due to excessive intersection judgments that significantly reduce efficiency. This paper addresses the non-convergence issues inherent in the 3D AFT and proposes preliminary solutions to enhance algorithm robustness while reducing intersection judgments. We introduce two neural networks trained on the backpropagation (BP) algorithms, Line-ANN and Plane-ANN, specifically designed for integration with AFT. These networks are individually combined with traditional 3D AFT to develop two enhanced methods. We assess these methods by comparing grid quality and time consumption against traditional AFT approaches. The results demonstrate that integrating Plane-ANN and Line-ANN with AFT improves overall efficiency by approximately 55% and 36%, respectively, thereby significantly enhancing grid generation efficiency. [ABSTRACT FROM AUTHOR]

The paper deals with fractional optimization problems where the objective function (ratio of two functions) is defined on a Cartesian product of two real normed spaces X and Y. Within this framework, we are interested to determine the so-called partial minimizers, i.e. points in $ X \times Y $ X × Y with the property that any of its variables minimizes the objective function, restricted to this variable, with respect to the other one. While any global minimizer is obviously a partial minimizer, the reverse implication holds true only under additional assumptions (e.g. separability properties of the involved functions). By exploiting the particularities of the objective function, we deliver a Dinkelbach type algorithm for computing partial minimizers of fractional optimization problems. Further assumptions on the involved spaces and functions, such as Lipschitz-type continuity, partial Fréchet differentiability, and coercivity, enable us to establish the convergence of our algorithm to a partial minimizer. [ABSTRACT FROM AUTHOR]

Nowadays, the use of Artificial Intelligence (AI) and Machine Learning (ML) algorithms is increasingly affecting the performance of innovative systems. At the same time, the advent of the Internet of Things (IoT) and the Edge Computing (EC) as means to place computational resources close to users create the need for new models in the training process of ML schemes due to the limited computational capabilities of the devices/nodes placed there. In any case, we should not forget that IoT devices or EC nodes exhibit less capabilities than the Cloud back end that could be adopted for a more complex training upon vast volumes of data. The ideal case is to have, at least, basic training capabilities at the IoT-EC ecosystem in order to reduce the latency and face the needs of near real time applications. In this paper, we are motivated by this need and propose a model that tries to save time in the training process by focusing on the training dataset and its statistical description. We do not dive into the architecture of any ML model as we target to provide a more generic scheme that can be applied upon any ML module. We monitor the statistics of the training dataset and the loss during the process and identify if there is a potential to stop it when not significant contribution is foreseen for the data not yet adopted in the model. We argue that our approach can be applied only when a negligibly decreased accuracy is acceptable by the application gaining time and resources from the training process. We provide two algorithms for applying this approach and an extensive experimental evaluation upon multiple supervised ML models to reveal the benefits of the proposed scheme and its constraints. [ABSTRACT FROM AUTHOR]

The network pricing problem (NPP) is a bilevel problem, where the leader optimizes its revenue by deciding on the prices of certain arcs in a graph, while expecting the followers (also known as the commodities) to choose a shortest path based on those prices. In this paper, we investigate the complexity of the NPP with respect to two parameters: the number of tolled arcs, and the number of commodities. We devise a simple algorithm showing that if the number of tolled arcs is fixed, then the problem can be solved in polynomial time with respect to the number of commodities. In contrast, even if there is only one commodity, once the number of tolled arcs is not fixed, the problem becomes NP-hard. We characterize this asymmetry in the complexity with a novel property named strong bilevel feasibility. Finally, we describe an algorithm to generate valid inequalities to the NPP based on this property, whose numerical results illustrate its potential for effectively solving the NPP with a high number of commodities. [ABSTRACT FROM AUTHOR]

We consider the oracle complexity of computing an approximate stationary point of a Lipschitz function. When the function is smooth, it is well known that the simple deterministic gradient method has finite dimension-free oracle complexity. However, when the function can be nonsmooth, it is only recently that a randomized algorithm with finite dimension-free oracle complexity has been developed. In this paper, we show that no deterministic algorithm can do the same. Moreover, even without the dimension-free requirement, we show that any finite-time deterministic method cannot be general zero-respecting. In particular, this implies that a natural derandomization of the aforementioned randomized algorithm cannot have finite-time complexity. Our results reveal a fundamental hurdle in modern large-scale nonconvex nonsmooth optimization. [ABSTRACT FROM AUTHOR]

The Set Packing problem is, given a collection of sets S over a ground set U, to find a maximum collection of sets that are pairwise disjoint. The problem is among the most fundamental NP-hard optimization problems that have been studied extensively in various computational regimes. The focus of this work is on parameterized complexity, Parameterized Set Packing (PSP): Given parameter r ∈ N , is there a collection S ′ ⊆ S : | S ′ | = r such that the sets in S ′ are pairwise disjoint? Unfortunately, the problem is not fixed parameter tractable unless W [ 1 ] = FPT , and, in fact, an "enumerative" running time of | S | Ω (r) is required unless the exponential time hypothesis (ETH) fails. This paper is a quest for tractable instances of Set Packing from parameterized complexity perspectives. We say that the input (U , S) is "compact" if | U | = f (r) · poly (log | S |) , for some f (r) ≥ r . In the Compact PSP problem, we are given a compact instance of PSP. In this direction, we present a "dichotomy" result of PSP: When | U | = f (r) · o (log | S |) , PSP is in FPT, while for | U | = r · Θ (log (| S |)) , the problem is W[1]-hard; moreover, assuming ETH, Compact PSP does not admit | S | o (r / log r) time algorithm even when | U | = r · Θ (log (| S |)) . Although certain results in the literature imply hardness of compact versions of related problems such as Set r -Covering and Exact r -Covering, these constructions fail to extend to Compact PSP. A novel contribution of our work is the identification and construction of a gadget, which we call Compatible Intersecting Set System pair, that is crucial in obtaining the hardness result for Compact PSP. Finally, our framework can be extended to obtain improved running time lower bounds for Compact r -VectorSum. [ABSTRACT FROM AUTHOR]

We study the approximability of the maximum size independent set (MIS) problem in bounded degree graphs. This is one of the most classic and widely studied NP-hard optimization problems. It is known for its inherent hardness of approximation. We focus on the well known minimum-degree greedy algorithm for this problem. This algorithm iteratively chooses a minimum degree vertex in the graph, adds it to the solution and removes its neighbors, until the remaining graph is empty. The approximation ratios of this algorithm have been widely studied, where it is augmented with an advice that tells the greedy algorithm which minimum degree vertex to choose if it is not unique. Our main contribution is a new mathematical theory for the design of such greedy algorithms for MIS with efficiently computable advice and for the analysis of their approximation ratios. Using this theory we obtain the ultimate approximation ratio of 5/4 for greedy algorithms on graphs with maximum degree 3, which completely solves an open problem from the paper by Halldórsson and Yoshihara (in: Staples, Eades, Katoh, Moffat (eds) Algorithms and computations—ISAAC '95, in 2026 LNCS, Springer, Berlin, Heidelberg, 1995). Our algorithm is the fastest currently known algorithm for MIS with this approximation ratio on such graphs. We also obtain a simple and short proof of the (Δ + 2) / 3 -approximation ratio of any greedy algorithms on graphs with maximum degree Δ , the result proved previously by Halldórsson and Radhakrishnan (Nord J Comput 1:475–492, 1994). We almost match this ratio by showing a lower bound of (Δ + 1) / 3 on the ratio of a greedy algorithm that can use an advice. We apply our new algorithm to the minimum vertex cover problem on graphs with maximum degree 3 to obtain a substantially faster 6/5-approximation algorithm than the one currently known. We complement our algorithmic, upper bound results with lower bound results, which show that the problem of designing good advice for greedy algorithms for MIS is computationally hard and even hard to approximate on various classes of graphs. These results significantly improve on the previously known hardness results. Moreover, these results suggest that obtaining the upper bound results on the design and analysis of the greedy advice is non-trivial. [ABSTRACT FROM AUTHOR]

In this paper, the discrete octonion linear canonical transform (DOCLCT) is presented. According to the definition of DOCLCT, its shift transform and modulation transform are explored. In addition, based on a new convolution operator, we obtain the convolution theorem of DOCLCT. The DOCLCT can be derived by the three-dimensional (3D) linear canonical transform. So, the fast algorithm of 3D DOCLCT is discussed. Finally, Heisenberg's uncertainty principle associated with DOCLCT is established. [ABSTRACT FROM AUTHOR]

This paper presents a gradient-based optimisation method of antenna array for null controlling in specific regions and sidelobe suppression. The optimisation problem is formulated as optimising the electronic control parameters (excitation amplitude and phase) of the array to achieve that the maximum value of the peak sidelobe levels (PSLLs) is minimised and the depths of the nulls in specific regions are not greater than a threshold. The Kreisselmeier – Steinhauser (KS) function is used to describe the maximum value of the PSLLs, in this way, the optimisation problem becomes differentiable and can be solved efficiently by gradient-based algorithm. The sensitivities of the objective with respect to the design variables are analytically derived. Typical numerical examples and detailed evaluations are provided to validate the effectiveness of the proposed method. The results show that the null controlling in the predefined specific single, single-wide, dual, multiple, and annular regions is realised, and the PSLL suppression is performed comparing to the uniform planar arrays. In the 28 GHz base station transmitting antenna-array example, the proposed method effectively suppresses the PSLL and greatly saves the calculation time while maintaining the various specific null regions controlling. [ABSTRACT FROM AUTHOR]

Aiming at the problem of object occlusion in the traditional assembly process, this paper proposes an assisted assembly method based on augmented reality. First, the virtual target model and the on-site assembly scene are used to achieve the initial three-dimensional positioning of the occluded object. Then, an area division method based on bounding box mapping is proposed to efficiently partition the two-dimensional target area required for follow-up tracking registration. Then, a keyframe-based LK optical flow tracking registration method is proposed for real-time pose estimation. This method combines feature matching with the tracking method and realizes smooth switching between them through the tracking failure judgment mechanism. In order to improve the accuracy of tracking registration, a forward and backward error screening mechanism based on the tracking of feature points is added in the feature tracking stage. In the feature matching stage, quadtree homogenization feature extraction and PROSAC mismatching elimination are used. In addition, a jitter correction method based on motion consistency is proposed to further ensure the stability of the algorithm. Finally, according to the tracking registration results, the virtual auxiliary information is fused and superimposed with the actual assembly operation scene, which brings the "perspective" vision beyond reality to the construction personnel and assists the construction personnel in completing the assembly task simply and efficiently. Experimental results show that the proposed method has high tracking accuracy and good stability and can reach 28 frames per second, which can meet the requirements of augmented reality–assisted assembly. [ABSTRACT FROM AUTHOR]

Complex curved composite components often rely on multiple reference curve algorithms for path planning in automated fiber placement. However, the reference curves are typically manually drawn. Moreover, designing the reference curves follows an iterative planning-analysis-improvement process, which can be inefficient. A new approach for the automatic pre-analysis and optimized generation of reference curves for fiber placement is proposed in this paper to enhance the efficiency of reference curve analysis and generation. Firstly, a pre-analysis algorithm for reference curves based on triangular meshes is proposed. This algorithm analyzes the theoretical geodesic curvature and angular deviation of the path before its planning. Subsequently, a comprehensive evaluation index for reference curve generation is formulated based on the pre-analysis algorithm, and the reference curve is optimized using genetic algorithms. The results demonstrate that the pre-analysis algorithm accurately computes the steering radius distribution of the path. Areas with over-limit steering radius can be eliminated while maintaining angular deviations within 10° by utilizing optimized reference curves for path planning. [ABSTRACT FROM AUTHOR]

In this research paper, we introduce an adaptive block-matching motion estimation algorithm to improve the accuracy and efficiency of motion estimation (ME). First, we present a block generation system that creates blocks of varying sizes based on the detected motion location. Second, we incorporate predictive tools such as early termination and variable window size to optimize our block-matching algorithm. Furthermore, we propose two distinct search patterns to achieve maximum quality and efficiency. We evaluated the proposed algorithms on 20 videos and compared the results with known algorithms, including the full search algorithm (FSA), which is a benchmark for ME accuracy. Our proposed quality-based algorithm shows an improvement of 0.27 dB in peak signal-to-noise ratio (PSNR) on average for reconstructed frames compared to FSA, along with a reduction of 71.66% in searched blocks. Similarly, our proposed efficiency-based method results in a 0.07 dB increase in PSNR and a 97.93% reduction in searched blocks compared to FSA. These findings suggest that our proposed method has the potential to improve the performance of ME in video coding. [ABSTRACT FROM AUTHOR]

Accurate measurement is an important prerequisite for sound source localization. In the enclosed environments, noise and reverberation tend to cause localization errors. To address these issues, this paper proposes a compound model particle filter algorithm based on multi-feature. Based on a multi-feature observation, the likelihood function of speaker tracking is constructed for particle filter, and multi-hypothesis and frequency selection function are adopted to establish multi-feature optimization mechanism, including time delay selection and beam output energy fusion. It is found that they effectively solved the difficulty in the simultaneous suppression of noise and reverberation by single feature. Moreover, considering the randomness of speaker motion, a compound model for sound source tracking is developed, where the stability of the speaker tracking system is improved by integrating multi-feature observation into the compound model filtering. The experimental results with both simulated and real acoustic data indicate that the proposed method has better tracking performance, compared with the existing ones with low SNR and strong reverberation as well as highly mobile conditions. [ABSTRACT FROM AUTHOR]

The stripe noise existing in remote sensing images badly degrades the visual quality and restricts the precision of data analysis. Therefore, many destriping models have been proposed in recent years. In contrast to these existing models, in this paper, we propose a nonconvex model with a DC function (i.e., the difference of convex functions) structure to remove the strip noise. To solve this model, we make use of the DC structure and apply an inexact proximal majorization-minimization algorithm with each inner subproblem solved by the alternating direction method of multipliers. It deserves mentioning that we design an implementable stopping criterion for the inner subproblem, while the convergence can still be guaranteed. Numerical experiments demonstrate the superiority of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

A sufficient number of participants should be included to adequately address the research interest in the surveys with sensitive questions. In this paper, sample size formulas/iterative algorithms are developed from the perspective of controlling the confidence interval width of the prevalence of a sensitive attribute under four non‐randomized response models: the crosswise model, parallel model, Poisson item count technique model and negative binomial item count technique model. In contrast to the conventional approach for sample size determination, our sample size formulas/algorithms explicitly incorporate an assurance probability of controlling the width of a confidence interval within the pre‐specified range. The performance of the proposed methods is evaluated with respect to the empirical coverage probability, empirical assurance probability and confidence width. Simulation results show that all formulas/algorithms are effective and hence are recommended for practical applications. A real example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, we consider the frictional contact model between a piezoelectric body and an electrically conductive foundation. This contact is described by a reduced normal compliance law. To address this problem, we establish a mixed variational formulation and prove the existence of a unique solution. Moreover, we present an efficient algorithm for approximating the weak solution for the contact problem, taking into account both friction and electrical contact conditions. We conclude with numerical examples that demonstrate the practicality of the model. [ABSTRACT FROM AUTHOR]

The task of a semantic community query is to obtain a subgraph S based on a given query vertex q (or vertex set) and other query parameters in an attributed graph G such that S belongs to G, contains q and satisfies a predefined community cohesiveness model. In most cases, existing community query models based on the network structure for traditional attributed networks usually lack community semantics. However, the features of vertex attributes, especially the attributes of the query vertices, which are closely related to the community semantics, are rarely considered in an attributed graph. Existing community query algorithms based on both structure cohesiveness and attribute cohesiveness usually do not take the attributes of the query vertex as an important factor of the community cohesiveness model, which leads to weak semantics of the communities. This paper proposes a semantic community query method named SCQ in a large‐scale attributed graph. First, the k‐core structure model is adopted as the structure cohesiveness of our community query model to obtain a subgraph of the original graph. Second, we define attribute cohesiveness based on the average distance between the query vertices and other vertices in terms of attributes in the community to prune the subgraph and obtain the semantic community. In order to improve the community query efficiency in large‐scale attributed graphs, SCQ applies two heuristic pruning strategies. The experimental results show that our method outperforms the existing community query methods in multiple evaluation metrics and is ideal for querying semantic communities in large‐scale attributed graphs. [ABSTRACT FROM AUTHOR]

A multi-objective solid transportation problem that includes source, destination, and mode of transport parameters may have fractional objective functions in real-life applications to maximize the profitability ratio, which could be the profit/cost or profit/time. We refer to such transportation problems as multi-objective fractional solid transportation problems. In addition, although most of the studies in the literature deal with the standard equality-constrained form of the multi-objective fractional solid transportation problem, the mixed-constrained type is addressed in this paper. With these mixed constraints, the multi-objective fractional solid transportation problem offers more flexible modeling of real-world problems that exist in many application areas, but mixed constraints also make the optimization process more challenging. This article presents an iterative fuzzy approach that combines the use of linear programming and the bisection algorithm using linear membership functions to obtain a strongly efficient solution. The algorithm's ability to convert a nonlinear problem into a set of linear problems is one of its main advantages, and it also decreases the amount of time needed to solve large-scale problems. A numerical example from the literature is adopted to illustrate the solution procedure. Moreover, large-scale instances are generated to further test the presented algorithm, and a comparison between the traditional fuzzy approach and the proposed method is presented. [ABSTRACT FROM AUTHOR]

In the construction process of prefabricated buildings, collision detection of the tower crane's lifting path is of paramount importance. During the actual operation of a tower crane, attention is not only given to whether components collide with obstacles but also to calculating the minimum distance from hoisted components to obstacles and identifying the closest obstacle's ID. This information provides operators or path planners with valuable insights to prevent accidents. This study addresses this issue by proposing a minimum distance calculation method for collision issues in lifting construction scenarios. First, based on the motion characteristics of tower crane hoisting, we employ vertically aligned oriented bounding boxes (VA-OBB) to fit the structural elements in the scene. VA-OBBs are compact bounding volumes (BV) with a rapid update rate. Subsequently, a method for calculating the minimum distance between VA-OBBs is introduced. With this approach, specific calculations are proposed for different risk scenarios in lifting construction. Using real building information modeling (BIM) data from a prefabricated construction scenario as a case study, we conducted an analysis that recorded the minimum distance information throughout the lifting path. This aids in analyzing the causes of accidents and rectifying unsafe path planning. Finally, we evaluate the accuracy and efficiency of the algorithm. Empirical results demonstrate that the algorithm can perform real-time and accurate collision calculations during the hoisting process of prefabricated construction. Additionally, this paper discusses the limitations of the research method and suggests directions for future improvements, providing a viable solution for collision issues in the field of hoisting construction. [ABSTRACT FROM AUTHOR]

Disaster resilience is an emerging concept for managing the risk of civil structural systems considering not only structural safety against extreme loads but also aftermath recovery efforts. The system reliability–based resilience analysis framework facilitates the quantitative evaluation of the disaster resilience performance of structural systems by assessing reliability and redundancy for numerous disruption scenarios. However, its practical application is limited due to the substantial number of structural analyses required to estimate the reliability and redundancy indices. To address the computational challenges, this paper proposes a new importance sampling algorithm, termed Importance Sampling for Noteworthy Scenarios (ISNS). The ISNS algorithm leverages a Gaussian process–based principal point search method to identify initial disruption scenarios that dominantly impact the structure's resilience. Subsequently, a mixture-based distribution is constructed to represent the near-optimal importance sampling density characterizing the failure domains of the noteworthy disruption scenarios. The two-step procedure enables the simultaneous estimation of both reliability and redundancy indices of the identified noteworthy scenarios. Furthermore, an active learning scheme is incorporated to efficiently train surrogates. Numerical examples of engineering applications are investigated to demonstrate the improved efficiency offered by the proposed method. However, the proposed method faces limitations in multihazard contexts and high-dimensional, highly nonlinear scenarios. These limitations necessitate further validation of the Gaussian process and mixture models to ensure robustness. [ABSTRACT FROM AUTHOR]

In recent years, the occurrence of task failures are becoming prevalent in cloud computing due to various factors such as the increasing complexity of cloud environments, heterogeneity of resources, resource limitations and inadequate allocation. Task failure due to insufficient allocation poses a significant challenge in cloud computing. When tasks are not allocated effectively, they may not be completed within their deadlines which ultimately leads to failure. Hence, effective allocation strategies combined with appropriate fault tolerance measures are vital for addressing these challenges and mitigating the risk of task failures. This paper proposes a fault-tolerant task allocation algorithm (FTTA) for independent tasks with deadline through preemptive migration in heterogeneous cloud environments to reduce task failure. The proposed algorithm involves three phases: the initial phase decides the priority of tasks in the ready list to minimize the execution time and meet task deadlines, the second phase includes the selection of a suitable virtual machine with minimum execution time and the last phase assigns task on available or non-available (which may available in future) virtual machines to find the best execution time within the deadline limit. During the task allocation process, the algorithm adopts fault-tolerant strategy that includes preemptive migration if necessary which allows the migration of tasks to identify the best suitable virtual machine. An analysis of the proposed algorithm reveals that the overall time complexity is O (n log n + n m 2) where n is the number of tasks and m is the number of virtual machines. Further, the performance of the algorithm is evaluated for different sets of tasks (small to large) while varying the number of virtual machines. The experimental results demonstrate that FTTA outperforms First Come First Served (FCFS), Priority based algorithm, Shortest Job First (SJF), Dynamic Maximum Minimum (Dy max min) and RADL algorithms in terms of number of rejected tasks, makespan, speedup and efficiency. [ABSTRACT FROM AUTHOR]

Developers often encounter challenges with their introductory programming tasks as part of the development process. Unfortunately, rectifying these mistakes manually can be time-consuming and demanding. Automated program repair (APR) techniques offer a potential solution by synthesizing fixes for such errors. Previous research has investigated the utilization of both symbolic and neural techniques within the APR domain. However, these approaches typically demand significant engineering efforts or extensive datasets and training. In this paper, we explore the potential of using a large language model trained on code, specifically, we assess ChatGPT's capability to detect and repair bugs in simple Python programs. The experimental evaluation encompasses two benchmarks: QuixBugs and Textbook. Each benchmark consists of simple Python functions that implement well-known algorithms and each function contains a single bug. To gauge repair performance in various settings, several benchmark variations were introduced including addition of plain English documentation and code obfuscation. Based on thorough experiments, we found that ChatGPT was able to correctly detect and fix about 50% of the methods, when code is documented. Repair performance drops to 25% when code is obfuscated, and 15% when documentation is removed and code is obfuscated. Furthermore, when compared to existing APR systems, ChatGPT considerably outperformed them. [ABSTRACT FROM AUTHOR]

In contemporary datacenter networks, various applications experience the challenge of incast occurrences. Data-intensive applications, particularly in distributed scenarios such as distributed deep learning, frequently encounter incast. While single-path transport protocols are widely deployed in datacenter networks, the available bandwidth is often severely underutilized. MP-RDMA, as a state-of-the-art multi-path transport protocol, has been implemented; however, it faces performance issues during large-scale incast communications. MP-RDMA employs insufficient congestion feedback and fixed rate adjustment for congestion management, making the scheme hardly scalable. In this paper, we introduce MPTD, an RTT-based multi-path transport protocol. MPTD encompasses two primary aspects: (1) a novel congestion control algorithm based on the setting of target delay; (2) an optimized packet scheduling mechanism. Specifically, the receiver dynamically updates the target delay in real-time based on network conditions, and the sender adjusts the sending rate accordingly. A categorized implementation for paths is added to achieve a more accurate identification of path characteristics. Our evaluation demonstrates that, in comparison to MP-RDMA, MPTD can achieve speedups of 1.35x/1.40x in flow completion time for two traffic patterns. [ABSTRACT FROM AUTHOR]