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In this paper we propose an auction-based offloading market in the HMC (Heterogeneous Mobile Cloud) environment where mobile devices, Cloudlets, and public cloud form a shared resource pool. In this market, users can register their service offers or submit their offloading requests to the auction manager. Then, the auction manger makes buyer_seller threads as the source–destination of task offloading process. In any made buyer_seller thread a trade-off between conflicting utility functions of buyer and seller has to be guaranteed. Furthermore, auction manager should impose various policies in order to maintain market stability, increase market competition, and reduce monopolies. A stable market is a market where the chance of uncommitted sellers to be considered in the made buyer_seller thread is kept low. Also, almost equal opportunities are made for the users in a competitive and non-monopolized market. To address these issues, first of all, a new task offloading approach based on Hill Climbing Algorithm is designed to assure a trade-off between buyers' and sellers' utilities. Then, a new factor in name reputation is defined to make competitive market while maintain market stability. Furthermore, a Cloudlet selection algorithm is developed to find the Cloudlet as a new offloading destination in a case of reneging on the offloading commitment, leading to market stability improvement. Finally, awarding bonus to new participants is another strategy imposed by auction manager to make competitive and non-monopolized market. Extensive amounts of simulation were carried out to evaluate the performance of our proposed offloading market against (Zhou et al. in J Syst Softw 152:151–164, 2019). The results show that our designed market outperforms (Zhou et al. 2019). [ABSTRACT FROM AUTHOR]

The identification of both closed frequent high average utility itemsets (CFHAUIs) and generators of frequent high average utility itemsets (GFHAUIs) has substantial significance because they play an essential and concise role in representing frequent high average utility itemsets (FHAUIs). These concise summaries offer a compact yet crucial overview that can be much smaller. In addition, they allow the generation of non-redundant high average utility association rules, a crucial factor for decision-makers to consider. However, difficulty arises from the complexity of discovering these representations, primarily because the average utility function does not satisfy both monotonic and anti-monotonic properties within each equivalence class, that is for itemsets sharing the same subset of transactions. To tackle this challenge, this paper proposes an innovative method for efficiently extracting CFHAUIs and GFHAUIs. This approach introduces novel bounds on the average utility, including a weak lower bound called wlbau and a lower bound named auvlb . Efficient pruning strategies are also designed with the aim of early elimination of non-closed and/or non-generator FHAUIs based on the wlbau and auvlb bounds, leading to quicker execution and lower memory consumption. Additionally, the paper introduces a novel algorithm, CG-FHAUI, designed to concurrently discover both GFHAUIs and CFHAUIs. Empirical results highlight the superior performance of the proposed algorithm in terms of runtime, memory usage, and scalability when compared to a baseline algorithm. [ABSTRACT FROM AUTHOR]

Event cameras, characterized by their low power consumption, expansive dynamic range, and high temporal resolution, have attracted great attentions in various computer vision tasks. Compared to frame-based cameras, event cameras exemplify a marked paradigmatic transition in data formation and output. However, the quality of event streams is compromised by background activity and hot pixels, leading to increased computational overheads and sub-optimal outcomes in subsequent applications, notably in recognition, video reconstruction, and target detection tasks. In this paper, a two-step denoising algorithm (referred as GMCM) is proposed to counteract these challenges. The GMCM algorithm comprises two steps: Gaussian denoising preprocessing and motion denoising. The former incorporates Gaussian temporal distribution and adaptive thresholding mechanisms to discern the inclusion of motion-related information within the event streams. Experimental results demonstrate that Gaussian denoising preprocessing can not only adeptly discern whether the event data stream contains motion information but also enhance computational efficiency. Conclusively, the GMCM algorithm achieves state-of-the-art performance, yielding SNR scores of 37.22 and 26.79 on the DVSCLEAN dataset at the noise ratios of 50% and 100%, respectively. [ABSTRACT FROM AUTHOR]

Inferring multilayer diffusion networks from observed cascades is both crucial and realistic. To infer multilayer diffusion networks, constructing continuous-time diffusion models that capture diffusion dynamics is a prerequisite. However, developing such models faces two main challenges: (1) reducing the number of learnable parameters for precise optimization with limited cascades while effectively modeling for accurate inference and (2) adapting the models to more realistic scenarios. In this paper, we propose a novel continuous-time diffusion model, namely the Embedding-based Continuous-time Diffusion (ECD) model, which employs an embedding method while modeling symmetric relationship strength, asymmetric relationship strength, and trust strength. Specifically, by leveraging the embedding method, the number of learnable parameters is significantly reduced compared with previous models. Then, by modeling symmetric relationship strength, our model can be used in scenarios where the relationships between nodes are symmetric. Subsequently, the trust strength can be inferred by our proposed efficient heuristic algorithm, making our model suitable for scenarios where time information is unavailable. Furthermore, we develop an optimization algorithm to optimize the proposed model and infer multilayer diffusion networks. The experimental results on synthetic and real datasets show that our model and algorithms outperform the comparison methods. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. Wildfire spread is a highly heterogeneous process with non-uniform spreading speed and fireline intensity in both space and time. When planning the path of a UAS for monitoring a dynamically growing wildfire, it is important to consider the uneven spread of the fire because different parts of the fire boundary would need different levels of monitoring attention. This paper presents a real-time path planning algorithm for a UAS to autonomously monitor the perimeter of a spreading wildfire with uneven importance. The proposed path planning algorithm allows the UAS to focus on the most active regions of a wildfire while still covering the entire fire perimeter. The design and implementation of this algorithm are described, and an in-depth analysis of the performance of the algorithm is provided. Experiment results based on simulated wildfires demonstrate the effectiveness and robustness of the proposed algorithm for monitoring dynamic wildfires. [ABSTRACT FROM AUTHOR]

Discovering new relationships between entities in networked data is essential in various applications such as sociology, security, physics, and biology. This paper introduces a novel approach to directed link prediction, filling a notable research gap by acknowledging the importance of the directionality of relationships often overlooked in traditional methods. We present three algorithms: an asymmetric similarity-popularity algorithm, which applies the similarity-popularity paradigm specifically to directed networks; a path exploration algorithm, which utilizes path patterns, closure probabilities, and paths' exploratory potential to predict new links formation; and a hybrid algorithm that merges the strengths of both approaches. The effectiveness of these methods is rigorously evaluated on real-life networks, demonstrating their robust performance across various types and sizes of networked data. In addition to predictive power and runtime performance assessments, we study the impact of predicted links on network spreading capacity. This perspective provides invaluable insights into the broader implications of our algorithms on network behavior and dynamics. [ABSTRACT FROM AUTHOR]

Maximizing the advantages of different views and mitigating their respective disadvantages in fine-grained segmentation tasks are an important challenge in the field of point cloud multi-view fusion. Traditional multi-view fusion methods ignore two fatal problems: 1. the loss of depth and quantization information due to mapping and voxelization operations, resulting in "anomalies" in the extracted features; 2. how to pay attention to the large differences in object sizes among different views during point cloud learning, and fine-tune the fusion efficiency in order to improve the performance of network. In this paper, we propose a new algorithm that uses channel self-attention to fuse range-point-voxel, abbreviated as RPV-CASNet. RPV-CASNet integrates the three different views: range, point and voxel in a more subtle way through an interactive structure (range-point-voxel cross-adaptive layer known as RPVLayer for short), to take full advantage of the differences among them. The RPVLayer contains two key designs: the Feature Refinement Module (FRM) and the Multi-Fine-Grained Feature Self-Attention Module(MFGFSAM). Specifically, the FRM allows for a re-inference representation of points with entrained anomalous features, correcting the features. The MFGFSAM addresses two challenges: efficiently aggregating tokens from distant regions and preserving multiscale features within a single attention layer. In addition, we design a Dynamic Feature Pyramid Extractor (DFPE) for network deployment, which is used to extract rich features from spherical range images. Our method achieves impressive mIoU scores of 69.8% and 77.1% on the SemanticKITTI and nuScenes datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a "quasi-other"; when that happens, users anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. [ABSTRACT FROM AUTHOR]

In this paper we propose a new algorithm, named NICE, to generate counterfactual explanations for tabular data that specifically takes into account algorithmic requirements that often emerge in real-life deployments: (1) the ability to provide an explanation for all predictions, (2) being able to handle any classification model (also non-differentiable ones), (3) being efficient in run time, and (4) providing multiple counterfactual explanations with different characteristics. More specifically, our approach exploits information from a nearest unlike neighbor to speed up the search process, by iteratively introducing feature values from this neighbor in the instance to be explained. We propose four versions of NICE, one without optimization and, three which optimize the explanations for one of the following properties: sparsity, proximity or plausibility. An extensive empirical comparison on 40 datasets shows that our algorithm outperforms the current state-of-the-art in terms of these criteria. Our analyses show a trade-off between on the one hand plausibility and on the other hand proximity or sparsity, with our different optimization methods offering users the choice to select the types of counterfactuals that they prefer. An open-source implementation of NICE can be found at https://github.com/ADMAntwerp/NICE. [ABSTRACT FROM AUTHOR]

Email phishing is a social engineering scheme that uses spoofed emails intended to trick the user into disclosing legitimate business and personal credentials. Many phishing email detection techniques exist based on machine learning, deep learning, and word embedding. In this paper, we propose a new technique for the detection of phishing emails using word embedding (Word2Vec, FastText, and TF-IDF) and deep learning techniques (DNN and BiLSTM network). Our proposed technique makes use of only four header based (From, Returnpath, Subject, Message-ID) features of the emails for the email classification. We applied several word embeddings for the evaluation of our models. From the experimental evaluation, we observed that the DNN model with FastText-SkipGram achieved an accuracy of 99.52% and BiLSTM model with FastText-SkipGram achieved an accuracy of 99.42%. Among these two techniques, DNN outperformed BiLSTM using the same word embedding (FastText-SkipGram) techniques with an accuracy of 99.52%. [ABSTRACT FROM AUTHOR]

A lot of discussion is done these days regarding the actual novelty of newcomer nature-inspired approaches. Crucial role on that matter is played by the mechanisms included in these approaches, where many of these mechanisms have been previously introduced as part of another algorithm. On the other hand, a good practice would be to use the mechanisms of a nature-inspired algorithm to enhance the performance or to overcome the drawbacks of another one. This paper investigates this issue, where four mechanisms have been isolated and studied. Furthermore, the well-known Particle Swarm Optimization and Firefly Algorithm were used to test the effect of the studied mechanisms on the exploration and exploitation of established approaches that suffer from premature convergence or mostly explore the search space, respectively. [ABSTRACT FROM AUTHOR]

Short-step methods are an important class of algorithms for solving convex constrained optimization problems. In this short paper, we show that under very mild assumptions on the self-concordant barrier and the width of the ℓ 2 -neighbourhood, any short-step interior-point method is not strongly polynomial-time. [ABSTRACT FROM AUTHOR]

This paper describes a method for solving smooth nonconvex minimization problems subject to bound constraints with good worst-case complexity guarantees and practical performance. The method contains elements of two existing methods: the classical gradient projection approach for bound-constrained optimization and a recently proposed Newton-conjugate gradient algorithm for unconstrained nonconvex optimization. Using a new definition of approximate second-order optimality parametrized by some tolerance ϵ (which is compared with related definitions from previous works), we derive complexity bounds in terms of ϵ for both the number of iterations required and the total amount of computation. The latter is measured by the number of gradient evaluations or Hessian-vector products. We also describe illustrative computational results on several test problems from low-rank matrix optimization. [ABSTRACT FROM AUTHOR]

This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms. [ABSTRACT FROM AUTHOR]

In practical engineering applications, factors like dust adhesion and environmental changes can cause photovoltaic arrays to exhibit multiple peaks in output power. An optimization algorithm with global optimization capability is needed to track its maximum power. In this regard, this paper proposes an improved marine predator algorithm (IMPA) to extract the maximum power point of photovoltaic system under complex solar irradiation conditions. To overcome the issues in the traditional marine predator algorithm (MPA), the opposition-based learning(OBL) strategy is introduced in IMPA, and the sine cosine algorithm (SCA) is integrated into the iteration stage to enhance the search ability of the algorithm. Furthermore, the low-order converter in the traditional MPPT control system is replaced by the Zeta converter, which increases the operating voltage range. Ultimately, simulation results demonstrate that the MPPT based on IMPA has higher tracking efficiency and shorter response time.The experimental results also indicate the practical feasibility of this method, as well as its high level of stability and robustness. [ABSTRACT FROM AUTHOR]

The visible light communication (VLC) technology has been gathering attention recently and as the emerging systems and appliances in this technology are still new, their behavior and performance issues must be taken into the consideration as much as possible. This paper aims to fully model the noise that is emanating from the usage of the Single-photon avalanche diodes (SPADs) in the VLC systems. This noise is either modeled with the sub-Poisson or Gaussian plus sub-Poisson distributions. These noise models are then applied to the diffusion adaptive networks to show the real-world impact of the VLC noisy links on their performances. The radio noisy link impacts have been investigated on the performance of the diffusion adaptive networks, however, the effects of the optical link noise on their performances is the contribution of this paper. Also, using the realistic and precise models for the optical noise is another novelty in this paper. The results show that the diffusion network can be implemented using VLC appliances and the existence of the SPAD noise will not hinder the convergence of this network. [ABSTRACT FROM AUTHOR]

In this paper, an enhanced equilibrium optimization (EO) version named Levy-opposition-equilibrium optimization (LOEO) is proposed to select effective features in network intrusion detection systems (IDSs). The opposition-based learning (OBL) approach is applied by this algorithm to improve the diversity of the population. Also, the Levy flight method is utilized to escape local optima. Then, the binary rendition of the algorithm called BLOEO is employed to feature selection in IDSs. One of the main challenges in IDSs is the high-dimensional feature space, with many irrelevant or redundant features. The BLOEO algorithm is designed to intelligently select the most informative subset of features. The empirical findings on NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets demonstrate the effectiveness of the BLOEO algorithm. This algorithm has an acceptable ability to effectively reduce the number of data features, maintaining a high intrusion detection accuracy of over 95%. Specifically, on the UNSW-NB15 dataset, BLOEO selected only 10.8 features on average, achieving an accuracy of 97.6% and a precision of 100%. [ABSTRACT FROM AUTHOR]

This paper describes a randomized algorithm for computing mixed Nash equilibrium (NE) in bimatrix games. The new algorithm transforms the NE problem of bimatrix games into a new form by using a fuzzification technique, it calculates mixed NE points in the new bimatrix games. This paper proves that the solution of the new bimatrix games is also a solution of the original bimatrix games. It proves that the computational complexity of the new algorithm for computing mixed NE points in bimatrix games is in the class of randomized polynomial time (RP). Examples are given that show the new algorithm can find two times more mixed NE points than Lemke–Howson algorithm. Numerical experiments indicate that the average computational time of the new algorithm is four hundred time faster than Lemke–Howson algorithm for calculating the first mixed NE point. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a class of horizontal tensor complementarity problems (HTCPs). By introducing the block representative tensor, we show that finding a solution of HTCP is equivalent to finding a nonnegative solution of a related tensor equation. We establish the theory of the existence and uniqueness of solution of HTCPs under the proper assumptions. In particular, in the case of the concerned block representative tensor possessing the strong M-property, we propose an algorithm to solve HTCPs by efficiently exploiting the beneficial properties of block representative tensor, and show that the iterative sequence generated by the algorithm is monotone decreasing and converges to a solution of HTCPs. The final numerical experiments verify the correctness of the theory in this paper and show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an implicit family of sub-step integration algorithms grounded in the explicit singly diagonally implicit Runge–Kutta (ESDIRK) method. The proposed methods achieve third-order consistency per sub-step, and thus, the trapezoidal rule is always employed in the first sub-step. This paper demonstrates for the first time that the proposed s-sub-step implicit method with s ≤ 6 can reach sth-order accuracy when achieving dissipation control and unconditional stability simultaneously. Hence, this paper develops, analyzes, and compares four cost-optimal high-order implicit algorithms within the present s-sub-step method using three, four, five, and six sub-steps. Each high-order implicit algorithm shares identical effective stiffness matrices to achieve optimal spectral properties. Unlike the published algorithms, the proposed high-order methods do not suffer from the order reduction for solving forced vibrations. Moreover, the novel methods overcome the defect that the authors' previous algorithms require an additional solution to obtain accurate accelerations. Linear and nonlinear examples are solved to confirm the numerical performance and superiority of four novel high-order algorithms. [ABSTRACT FROM AUTHOR]

Topography can strongly affect ground motion, and studies of the quantification of hill surfaces' topographic effect are relatively rare. In this paper, a new quantitative seismic topographic effect prediction method based upon the BP neural network algorithm and three-dimensional finite element method (FEM) was developed. The FEM simulation results were compared with seismic records and the results show that the PGA and response spectra have a tendency to increase with increasing elevation, but the correlation between PGA amplification factors and slope is not obvious for low hills. New BP neural network models were established for the prediction of amplification factors of PGA and response spectra. Two kinds of input variables' combinations which are convenient to achieve are proposed in this paper for the prediction of amplification factors of PGA and response spectra, respectively. The absolute values of prediction errors can be mostly within 0.1 for PGA amplification factors, and they can be mostly within 0.2 for response spectra's amplification factors. One input variables' combination can achieve better prediction performance while the other one has better expandability of the predictive region. Particularly, the BP models only employ one hidden layer with about a hundred nodes, which makes it efficient for training. [ABSTRACT FROM AUTHOR]

The synthetic control method (SCM) represents a notable innovation in estimating the causal effects of policy interventions and programs in a comparative case study setting. In this paper, we demonstrate that the data-driven approach to SCM requires solving a bilevel optimization problem. We show how the original SCM problem can be solved to the global optimum through the introduction of an iterative algorithm rooted in Tykhonov regularization or Karush–Kuhn–Tucker approximations. [ABSTRACT FROM AUTHOR]

Asymmetric image retrieval methods have drawn much attention due to their effectiveness in resource-constrained scenarios. They try to learn two models in an asymmetric paradigm, i.e., a small model for the query side and a large model for the gallery. However, we empirically find that the mutual training scheme (learning with each other) will inevitably degrade the performance of the large gallery model, due to the negative effects exerted by the small query one. In this paper, we propose Central Similarity Consistency Hashing (CSCH), which simultaneously learns a small query model and a large gallery model in a mutually promoted manner, ensuring both high retrieval accuracy and efficiency on the query side. To achieve this, we first introduce heuristically generated hash centers as the common learning target for both two models. Instead of randomly assigning each hash center to its corresponding category, we introduce the Hungarian algorithm to optimally match each of them by aligning the Hamming similarity of hash centers to the semantic similarity of their classes. Furthermore, we introduce the instance-level consistency loss, which enables the explicit knowledge transfer from the gallery model to the query one, without the sacrifice of gallery performance. Guided by the unified learning of hash centers and the distilled knowledge from gallery model, the query model can be gradually aligned to the Hamming space of the gallery model in a decoupled manner. Extensive experiments demonstrate the superiority of our CSCH method compared with current state-of-the-art deep hashing methods. The open-source code is available at https://github.com/dubanx/CSCH. [ABSTRACT FROM AUTHOR]

Formally verified compilers and formally verified static analyzers are a solution to the problem that certain industries face when they have to demonstrate to authorities that the object code they run truly corresponds to its source code and that it satisfies certain properties. From a scientific and technological point of view, they are a challenge: not only a number of nontrivial invariants and algorithms must be proved to be correct, but also the implementation must be reasonably effective so that the tools operate within reasonable time. Many optimizations in compilers rely on static analysis, and thus a formally verified compiler entails formally verified static analyses. In this paper, we explain some difficulties, possible solutions, design choices, and trade-offs pertaining to verified static analysis, in particular, when the solution of the analysis is expressed as some form of tree, map, or set. [ABSTRACT FROM AUTHOR]

As an important indicator of the surface quality of workpieces, surface roughness has a great impact on production costs and the quality performance of the finished components. Effective surface roughness prediction can not only increase productivity but also reduce costs. However, the current methods for surface roughness prediction have some limitations. On the one hand, the prediction accuracy of classical experimental and statistical-based surface roughness prediction methods is low. On the other hand, the results of deep learning-based surface roughness prediction methods are uninterpretable due to their black-box learning mechanism. Therefore, this paper presents an ensemble learning with a differential evolution algorithm, applies it to the prediction of surface roughness of abrasive water jet polishing (AWJP), and conducts an interpretability analysis to identify key factors contributing to the prediction accuracy of surface roughness. First, we proposed automatically constructing features by an Evolution Forest algorithm to train the base regression models. The differential evolution algorithm with a simplified encoding mechanism was then used to search for the best weighted-ensemble to integrate the base regression models for obtaining highly accurate prediction results. Extensive experiments have been conducted on AWJP to validate the effectiveness of our proposed methods. The results show that the prediction accuracy of our proposed method is higher than the existing machine learning algorithms. In addition, this is the first of its time for the contributions of machining parameters (i.e., features) on surface roughness prediction by using interpretable analysis methods. The analysis results can provide a reference basis for subsequent experiments and studies. [ABSTRACT FROM AUTHOR]

Animation compression is a key process in replicating and streaming animated 3D models. Linear Blend Skinning (LBS) facilitates the compression of an animated sequence while maintaining the capability of real-time streaming by deriving vertex to proxy bone assignments and per frame bone transformations. We introduce a innovative deep learning approach that learns how to assign vertices to proxy bones with persistent labeling. This is accomplished by learning how to correlate vertex trajectories to bones of fully rigged animated 3D models. Our method uses these pretrained networks on dynamic characteristics (vertex trajectories) of an unseen animation sequence (a sequence of meshes without skeleton or rigging information) to derive an LBS scheme that outperforms most previous competent approaches by offering better approximation of the original animation sequence with fewer bones, therefore offering better compression and smaller bandwidth requirements for streaming. This is substantiated by a thorough comparative performance evaluation using several error metrics, and compression/bandwidth measurements. In this paper, we have also introduced a persistent bone labeling scheme that (i) improves the efficiency of our method in terms of lower error values and better visual outcome and (ii) facilitates the fusion of two (or more) LBS schemes by an innovative algorithm that combines two arbitrary LBS schemes. To demonstrate the usefulness and potential of this fusion process, we have combined the outcome of our deep skinning method with that of Rignet—which is a state-of-the-art method that performs rigging on static meshes—with impressive results. [ABSTRACT FROM AUTHOR]

Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

The significance of facial anti-spoofing algorithms in enhancing the security of facial recognition systems cannot be overstated. Current approaches aim to compensate for the model's shortcomings in capturing spatial information by leveraging spatio-temporal information from multiple frames. However, the additional branches to extract inter-frame details increases the model's parameter count and computational workload, leading to a decrease in inference efficiency. To address this, we have developed a robust and easily deployable facial anti-spoofing algorithm. In this paper, we propose Central Difference Convolution UNet++ (UCDCN), which takes advantage of central difference convolution and improves the characterization ability of invariant details in diverse environments. Particularly, we leverage domain knowledge from image segmentation and propose a multi-level feature fusion network structure to enhance the model's ability to capture semantic information which is beneficial for face anti-spoofing tasks. In this manner, UCDCN greatly reduces the number of model parameters as well as achieves satisfactory metrics on three popular benchmarks, i.e., Replay-Attack, Oulu-NPU and SiW. [ABSTRACT FROM AUTHOR]

In k-Digraph Coloring we are given a digraph and are asked to partition its vertices into at most k sets, so that each set induces a DAG. This well-known problem is NP-hard, as it generalizes (undirected) k-Coloring, but becomes trivial if the input digraph is acyclic. This poses the natural parameterized complexity question of what happens when the input is "almost" acyclic. In this paper we study this question using parameters that measure the input's distance to acyclicity in either the directed or the undirected sense. In the directed sense perhaps the most natural notion of distance to acyclicity is directed feedback vertex set. It is already known that, for all k ≥ 2, k-Digraph Coloring is NP-hard on digraphs of directed feedback vertex set of size at most k + 4. We strengthen this result to show that, for all k ≥ 2, k-Digraph Coloring is already NP-hard for directed feedback vertex set of size exactly k. This immediately provides a dichotomy, as k-Digraph Coloring is trivial if directed feedback vertex set has size at most k − 1. Refining our reduction we obtain three further consequences: (i) 2-Digraph Coloring is NP-hard for oriented graphs of directed feedback vertex set at most 3; (ii) for all k ≥ 2, k-Digraph Coloring is NP-hard for graphs of feedback arc set of size at most k2; interestingly, this leads to a second dichotomy, as we show that the problem is FPT by k if feedback arc set has size at most k2 − 1; (iii) k-Digraph Coloring is NP-hard for graphs of directed feedback vertex k, even if the maximum degree Δ is at most 4k − 1; we show that this is also almost tight, as the problem becomes FPT for digraphs of directed feedback vertex set of size k and Δ ≤ 4k − 3. Since these results imply that the problem is also NP-hard on graphs of bounded directed treewidth, we then consider parameters that measure the distance from acyclicity of the underlying graph. On the positive side, we show that k-Digraph Coloring admits an FPT algorithm parameterized by treewidth, whose parameter dependence is (tw!)ktw. Since this is considerably worse than the ktw dependence of (undirected) k-Coloring, we pose the question of whether the tw! factor can be eliminated. Our main contribution in this part is to settle this question in the negative and show that our algorithm is essentially optimal, even for the much more restricted parameter treedepth and for k = 2. Specifically, we show that an FPT algorithm solving 2-Digraph Coloring with dependence tdo(td) would contradict the ETH. Then, we consider the class of tournaments. It is known that deciding whether a tournament is 2-colorable is NP-complete. We present an algorithm that decides if we can 2-color a tournament in O ∗ ( 6 3 n) time. Finally, we explain how this algorithm can be modified to decide if a tournament is k-colorable. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed weighted regularized least squares algorithm (DWRLS) with radial basis functions to tackle spherical data that are stored across numerous local servers and cannot be shared with each other. Via developing a novel integral operator approach based on spherical quadrature rules, we succeed in deriving optimal approximation rates for DWRLS and theoretically demonstrate that DWRLS performs similarly as running a weighted regularized least squares algorithm on the whole data stored on a large enough machine. This interesting finding implies that distributed learning is capable of sufficiently exploiting potential values of distributively stored spherical data, even though local servers cannot access the whole data. [ABSTRACT FROM AUTHOR]

This paper presents a comparative efficiency analysis of hashing algorithms from a perspective of their use in systems based on the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). Hash functions sha256, sha3, poseidon, mimc, and blake2, most commonly used in modern distributed ledgers, are considered. For parameter measurement experiments, an infrastructure using the ZoKrates toolbox is developed. Based on experimental data, the limits of practical applicability of the algorithms to the problem of proving knowledge of a hash preimage with zk-SNARK circuits in distributed ledgers are determined, and related efficiency problems are considered. [ABSTRACT FROM AUTHOR]

A d-dimensional framework is a pair (G, p), where G = (V , E) is a graph and p is a map from V to R d . The length of an edge x y ∈ E in (G, p) is the distance between p(x) and p(y). A vertex pair { u , v } of G is said to be globally linked in (G, p) if the distance between p(u) and p(v) is equal to the distance between q(u) and q(v) for every d-dimensional framework (G, q) in which the corresponding edge lengths are the same as in (G, p). We call (G, p) globally rigid in R d when each vertex pair of G is globally linked in (G, p). A pair { u , v } of vertices of G is said to be weakly globally linked in G in R d if there exists a generic framework (G, p) in which { u , v } is globally linked. In this paper we first give a sufficient condition for the weak global linkedness of a vertex pair of a (d + 1) -connected graph G in R d and then show that for d = 2 it is also necessary. We use this result to obtain a complete characterization of weakly globally linked pairs in graphs in R 2 , which gives rise to an algorithm for testing weak global linkedness in the plane in O (| V | 2) time. Our methods lead to a new short proof for the characterization of globally rigid graphs in R 2 , and further results on weakly globally linked pairs and globally rigid graphs in the plane and in higher dimensions. [ABSTRACT FROM AUTHOR]

Message Passing Interface (MPI) is the de facto standard for parallel programming, and collective operations in MPI are widely utilized by numerous scientific applications. The efficiency of these collective operations greatly impacts the performance of parallel applications. With the increasing scale and heterogeneity of HPC systems, the network environment has become more complex. The network states vary widely and dynamically between node pairs, and this makes it more difficult to design efficient collective communication algorithms. In this paper, we propose a method to optimize collective operations by using real-time measured network states, specifically focusing on the binomial tree algorithm. Our approach employs a low-overhead method to measure the network states, and the binomial tree with small latency is constructed based on the measurement result. Additionally, we take into account the disparities between the two underlying MPI peer-to-peer communication protocols, eager and rendezvous, and design tailored binomial tree construction algorithms for each protocol. We have implemented hierarchical MPI_Bcast, MPI_Reduce, MPI_Gather and MPI_Scatter, utilizing our network states-aware binomial tree algorithm at the inter-node level. The benchmark results demonstrate that our algorithm effectively enhances performance in small and medium message communication when compared to the default binomial tree algorithm in Open MPI. Specifically, for MPI_Bcast, we observe an average performance improvement of over 15.5% when the message size is less than 64KB. Similarly, for MPI_Reduce, there is an average performance improvement of over 12.1% when the message is below 2KB. In addition, there is an average performance improvement of over 10% for MPI_Gather when the message ranging from 64B to 512B. For MPI_Scatter, our algorithm achieved performance improvement only for certain message sizes. [ABSTRACT FROM AUTHOR]

The concept and data drift problems have received much attention in recent years. This aspect is crucial in many domains exhibiting non-stationary and cyclical patterns affecting their generative processes. Drift detection can be treated as a supervised task, with labeled data constantly used to validate the learned model. From a practical point of view, this is an impractical task because labeling is complex, costly, and time-consuming. On the other hand, unsupervised change detection techniques are cumbersome in applications because they generate many false alarms. The paper presents a new concept drift detection method based on feature analysis. Stream of data carries information about the distribution patterns that reflect different concepts that may be hidden in the data. The essential features are searched and ranked by LASSO. The rank of features and statistics are employed to feature drift detection. The proposed approach was experimentally checked based on synthetic and natural datasets. The results show that the proposed FBDD algorithm has an advantage over other solutions. [ABSTRACT FROM AUTHOR]

Multi-objective optimization problems with large-scale variables are the focus and difficulty of current research in the field of multi-objective evolutionary algorithms. In order to cope with the problem of "dimensional catastrophe", this paper proposes a large-scale multi-objective firefly algorithm based on reward and punishment mechanisms and adaptive dimensional reorganization. First, a reward and punishment mechanisms is proposed to make full use of the information of the solution itself, so that the population can break the constraint and rapidly approach the Pareto frontier, effectively improving the convergence of the population; then, an adaptive dimensional reorganization mechanism is proposed to select individuals with large differences according to different iteration periods to interact and learn from each other, effectively improving the diversity of the population. In the experimental part, to verify the effectiveness of the algorithm, the UF series and the variable expanded ZDT series, 15 test problems are selected for simulation experiments and compared with 8 advanced large-scale optimization algorithms, the results show that L-MOFA-RA has the advantages of fast convergence speed and high convergence accuracy, and the comprehensive experimental results show that compared with the comparison algorithm L-MOFA-RA has better optimization performance. The comprehensive experimental results show that L-MOFA-RA has better optimization performance compared to the comparison algorithm. [ABSTRACT FROM AUTHOR]

In many fields including aerospace, automotive, and telecommunications, MathWorks' MATLAB/Simulink is contemporary standard for model-based design. The strengths of Simulink are rapid design and algorithm exploration. Models created with Simulink are just functional. Therefore, designers cannot effortlessly consider a Simulink model's architecture. As current architectures are optimized to run on multicore processors, software running on these processors needs to be parallelized in order to benefit from their natural performance. For instance, designers need to understand how a Simulink model could be parallelized and how an adequate multicore architecture is selected. This paper focuses on the dataflow-based parallelization of Simulink models and proposes a method based on dataflow to measure the performance of parallelized Simulink models running on multicore architectures. Throughout the parallelization process, the model is converted into a Hierarchical Synchronous DataFlow Graph (HSDFG) keeping its original semantics, and each composite node in the graph is flattened. Then, the graph is mapped and scheduled into a multicore architecture with the ultimate objective that minimizes the end-to-end latency. In the experiment of applying the proposed approach to a real Simulink model, latency of the parallelized model could be reduced successfully on a various multi-core architectures. [ABSTRACT FROM AUTHOR]

C-RAN (Cloud Radio Access Network) is a 5G architecture that consists of sites and three-layer Data Centers (DCs), which include the central office DC, local DC, and regional DC. Network slicing, which enables infrastructure providers (InP) to create independent logical networks, is essential in this architecture. By utilizing this technology, InPs can maximize the utility of the network by providing slices to service providers in response to their slice requests. However, almost all of the recent research on slice admission control (SAC) schemes has only considered one or two layers of DCs, which limits the efficiency of the slicing process and decreases network utility. To address these issues, this paper proposes an intelligent SAC scheme called ISAC that considers all three-layer DCs. Instead of relying on reinforcement learning algorithms like Q-learning, which are effective in discrete environments with limited state space but give poor performance in continuous environments, ISAC employs the Approximate Reinforcement Learning (ARL) algorithm. ARL is better suited for 5G network modeling because it can adapt to continuous environments, allowing for a more accurate representation of the underlying physical processes. Extensive simulation studies demonstrate that ISAC significantly improves performance in terms of slice request rejection rates, InP revenue, accepting more slices, and optimizing resource utilization. [ABSTRACT FROM AUTHOR]

Offloading computation-intensive tasks of an application from a mobile device to a remote computation resource can improve the runtime of the application as well as improve the lifetime of the mobile device's battery. However, computation offloading is a complex endeavour: remote computation resources in the cloud and in the fog have different computation capabilities and costs, and the network bandwidth and latency between different resources need to be taken into account. In addition, the mobile device would ideally like to offload multiple applications simultaneously, further adding to the complexity of assigning different tasks of each application to available computation resources. In this paper, we introduce an open-source versatile computation offloading simulator (OVCOSim), capable of simulating and optimizing offloading multiple applications to different cloud and fog resources and visualizing the outcome for users. We demonstrate how OVCOSim can be used to optimize assignment of tasks to computation resources to accommodate different user objectives, runtime, battery life, cost, or a combination of these objectives, using a variety of assignment algorithms. We also provide several examples to demonstrate how OVCOSim can be used, as well as validate its functionality. [ABSTRACT FROM AUTHOR]

Adversarial patch attacks in the physical world are a major threat to the application of deep learning. However, current research on adversarial patch defense algorithms focuses on image pre-processing defenses, it has been demonstrated that this defense reduces the classification accuracy of clean images and is unable to defend against physically realizable attacks. In this paper, we propose a defense patch GNN (DPG), using a new perspective for defending against adversarial patch attacks. First, we extract the input image features with the feature extraction to obtain a feature set. Then downsampling the feature set by applying the global average pooling layer to reduce the perturbation of the features by the adversarial patch. Finally, this paper proposes a graph-structured feature subspace to robust the feature performance. In addition, we design an optimization algorithm based on stochastic gradient descent (SGD), which can significantly increase the mode's generalization ability. We demonstrate empirically the superior robustness of the DPG model on existing adversarial patch attacks. DPG shows without any accuracy loss in the prediction of clean images. [ABSTRACT FROM AUTHOR]

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators. [ABSTRACT FROM AUTHOR]

Ambient Intelligence deployments are very vulnerable to Cyber-Physical attacks. In these attacking strategies, intruders try to manipulate the behavior of the global system by affecting some key elements within the deployment. Typically, attackers inject false information, integrate malicious devices within the deployment, or infect communications among sensor nodes, among other possibilities. To protect Ambient Intelligence deployments against these attacks, complex data analysis algorithms are usually employed in the cloud to remove anomalous information from historical series. However, this approach presents two main problems. First, it requires all Ambient Intelligence systems to be networked and connected to the cloud. But most new applications for Ambient Intelligence are supported by isolated systems. And second, they are computationally heavy and not compatible with new decentralized architectures. Therefore, in this paper we propose a new decentralized security solution, based on a Blockchain ledger, to protect isolated Ambient Intelligence deployments. In this ledger, new sensing data are considered transactions that must be validated by edge managers, which operate a Blockchain network. This validation is based on reputation metrics evaluated by sensor nodes using historical network data and identity parameters. Through information theory, the coherence of all transactions with the behavior of the historical deployment is also analyzed and considered in the validation algorithm. The relevance of edge managers in the Blockchain network is also weighted considering the knowledge they have about the deployment. An experimental validation, supported by simulation tools and scenarios, is also described. Results show that up to 93% of Cyber-Physical attacks are correctly detected and stopped, with a maximum delay of 37 s. [ABSTRACT FROM AUTHOR]

To solve the significant performance degradation in the blind equalization for multi-level quadrature amplitude modulation (QAM) signals, an improved multimodulus algorithm based on the real part (or the imaginary part) of the transmitted signals is developed in this paper. Theoretical analysis illustrates that the proposed algorithm can roughly half the computational complexity and fundamentally suppress the well-known steady-state maladjustment of classical constant modulus algorithm and multimodulus algorithm. Moreover, the large maladjustment in the iteration process can be completely removed, thus the proposed algorithm can work well in impulsive noise environment. Finally, simulation results show the effectiveness of the proposed algorithm under both Gaussian and impulsive noise environments. [ABSTRACT FROM AUTHOR]

This paper considers an asynchronous grant-free non-orthogonal multiple access (NOMA) systems which can be applied in massive machine-type communications (mMTCs) and underwater IoT acoustic communication scenarios due to its high spectral efficiency and low power consumption characteristics. In particular, the system's asynchronous reception of user signals can effectively reduce the additional overhead caused by synchronous reception. We investigate the joint activity and channel estimation in the asynchronous case, where an asynchronous frame structure is considered, and a pilot sequence designed by chaotic sequence is used to reduce the pilot storage space. The joint estimations are formulated as single measurement vector (SMV) and multiple measurement vector (MMV) problems for single-antenna and multiple-antenna systems. Different from the existing estimation algorithms, where prior information is considered for estimation, an adaptive alternating direction method of multiplier (ADMM)is proposed for the SMV problem and a two-stage ADMM is proposed for the MMV problem. In particular, an index set is first estimated in each iteration of our proposed adaptive ADMM, and a linear ADMM is performed based on the index set. The first stage of our proposed two-stage ADMM is to estimate the delay and the activity, and then the channel state information is estimated. Further, we analyze the complexity of the two algorithms and their sensitivity to the initial values of chaotic sequences. Finally, simulation results reflecting the detection performance of the algorithms are given. Based on the simulation results, the proposed two algorithms are computationally efficient, providing superior signal recovery accuracy and user activity detection performance. More importantly, the signal delay has a relatively small impact on the proposed algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial IoT technology, a growing number of intelligent devices are being deployed in smart factories to digitally upgrade the manufacturing industry. The increasing number of intelligent devices brings a huge task request. Fog computing, which is an emerging distributed computing paradigm, is widely applied to process the device data generated in smart manufacturing. However, as fog nodes are resource limited and geographically widely distributed limitations, proper fog node placement strategies are critical to enhance the service performance of fog computing systems. In this paper, we study the problem of fog node placement in smart factories and divide it into two scenarios, fixed device and mobile device fog node placement, depending on the mobility of the devices. The fog node placement model and objective function are built in the two scenarios, and two improved heuristic algorithms are proposed to obtain the most optimal placement scheme. In addition, we perform simulation experiments based on existing intelligent production line prototype platforms and devices to evaluate the performance of the proposed algorithms. The IGA reduces latency by an average of 586.7 - 1089 ms over the benchmark algorithm, saving 18.3 - 39 % in energy consumption. The total latency of IMOA is reduced by 59.8 - 68.5 % , and the maximum latency is reduced by 48.8 - 69.2 % . The experimental results show that the proposed algorithms outperform other benchmark algorithms in terms of task response time and energy consumption. [ABSTRACT FROM AUTHOR]

Since non-orthogonal multiple access (NOMA) has become a promising technology to enhance network energy efficiency (EE) thanks to its low latency,high reliability and massive connectivity, the optimization of EE in NOMA-based uplink heterogeneous networks (HetNets) is considered in this paper. The optimization problem for EE maximization is formulated as a combination of user association (base station selection, sub-channel allocation) and power control, which is hardly tractable. To address it, a user association algorithm satisfied the many-to-one relationship between mobile users, base stations and sub-channels is firstly proposed based on matching theory. Then, an iterative power control algorithm evolved from fractional programming and difference of convex programming is presented by proving the concavity of the sum-rate of sub-channels. Finally, simulation results are shown that NOMA-based HetNets equipped with our designed algorithms yield much higher EE than that with the compared schemes. [ABSTRACT FROM AUTHOR]

Application problems can often not be solved adequately by numerical algorithms as several difficulties might arise at the same time. When developing and improving algorithms which hopefully allow to handle those difficulties in the future, good test instances are required. These can then be used to detect the strengths and weaknesses of different algorithmic approaches. In this paper we present a generator for test instances to evaluate solvers for multiobjective mixed-integer linear and nonlinear optimization problems. Based on test instances for purely continuous and purely integer problems with known efficient solutions and known nondominated points, suitable multiobjective mixed-integer test instances can be generated. The special structure allows to construct instances scalable in the number of variables and objective functions. Moreover, it allows to control the resulting efficient and nondominated sets as well as the number of efficient integer assignments. [ABSTRACT FROM AUTHOR]

In multi-objective mixed-integer convex optimization, multiple convex objective functions need to be optimized simultaneously while some of the variables are restricted to take integer values. In this paper, we present a new algorithm to compute an enclosure of the nondominated set of such optimization problems. More precisely, we decompose the multi-objective mixed-integer convex optimization problem into several multi-objective continuous convex optimization problems, which we refer to as patches. We then dynamically compute and improve coverages of the nondominated sets of those patches to finally combine them to obtain an enclosure of the nondominated set of the multi-objective mixed-integer convex optimization problem. Additionally, we introduce a mechanism to reduce the number of patches that need to be considered in total. Our new algorithm is the first of its kind and guaranteed to return an enclosure of prescribed quality within a finite number of iterations. For selected numerical test instances we compare our new criterion space based approach to other algorithms from the literature and show that much larger instances can be solved with our new algorithm. [ABSTRACT FROM AUTHOR]

Consensus plays a crucial role in blockchain technology, with the deleted proof of stake (DPoS) consensus mechanism commonly utilized in both public and hybrid chains. However, the current DPoS mechanism faces challenges such as low node engagement in voting and potential security risks posed by malicious nodes. In response, we propose the DL-DPoS (deep link–delegated proof of stake) mechanism, which builds upon the DPoS framework. The DL-DPoS incorporates the LINK incentive mechanism to encourage inactive nodes to participate in voting and leader selection. Furthermore, a comprehensive credit scoring system based on wealth, performance, and stability is introduced to enhance the security of elected nodes. The verification process is optimized to involve all nodes except the leader node, and mechanisms are in place to handle malicious attacks by degrading or removing offending nodes and redistributing their responsibilities to the LINK group. Performance testing of the DL-DPoS mechanism, conducted through blockchain simulation tests using the GO language, shows a 23% increase in throughput compared to DPoS, with over 95% node participation and improved distribution of rights and equity. These results indicate the enhanced performance, security, and stability of the DL-DPoS consensus mechanism. [ABSTRACT FROM AUTHOR]

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG. [ABSTRACT FROM AUTHOR]

Communication quality is compromised when wireless sensor network (WSN) operate in harsh environments, which can be improved by supplementing the nodes. This paper proposes a deployment strategy to optimize the placement of new nodes in WSN, specifically in complex environments with limited communication. To achieve this, the paper introduces the concept of strong connectivity relationships and presents a novel metaheuristic algorithm, namely double-state differential evolution (DSDE), which divides the optimization process into two states and adopts different optimization strategies. The proposed DSDE can improve the overall network communication glowing at a lower computing cost. Extensive experiments show that the proposed DSDE has better performance than state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]