Reviewerly/exHarmony huggingface dataset:
Serverless Computing: Advantages, Limitations and Use Cases

Serverless computing, also known simply as "serverless," represents a significant transformation in how applications are designed, deployed, and executed. Contrary to traditional methods where developers manage servers, serverless allows them to focus only on coding, trusting cloud providers with infrastructure responsibilities. This paradigm shift results in dynamic resource allocation, where users are billed based on actual resource consumption rather than pre-allocated capacities. The article elucidates serverless computing's nature, highlighting its core components - Function as a Service (FaaS) and Backend as a Service (BaaS). The benefits of serverless are underscored, including cost efficiency, inherent scalability, rapid development, and reduced operational demands. Yet, it is not without limitations. Concerns such as "cold starts," potential vendor lock-in, restricted customization, and specific security vulnerabilities are discussed. Practical serverless applications include web applications, data processing, IoT backends, chatbots, and ephemeral tasks. In conclusion, while serverless computing heralds a new age in cloud technology, businesses are encouraged to discerningly evaluate its pros and cons, mainly as the landscape evolves. The future is serverless, prompting organizations to determine their readiness for this revolution.
Nondeterministic and nonconcurrent computational semantics for BB+ and related logics

Abstract In this paper, we provide a semantics for a range of positive substructural logics, including both logics with and logics without modal connectives. The semantics is novel insofar as it is meant to explicitly capture the computational flavor of these logics, and to do so in a way that builds in both nondeterministic and nonconcurrent computational processes.
Stringy scaling of n-point Regge string scattering amplitudes

A bstract We discover a stringy scaling behavior for a class of n -point Regge string scattering amplitudes (RSSA). The number of independent kinematics variables is found to be reduced by dim $$ \mathcal{M} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>M</mml:mi> </mml:math> .
Local propagation of visual stimuli in focus of attention

Fast reactions to changes in the surrounding visual environment require efficient attention mechanisms to reallocate computational resources to the most relevant locations in the visual field. In this paper, we present a biologically-plausible computational model of focus of attention that exhibits spatiotemporal locality and that is very well-suited for parallel and distributed implementations. Attention emerges as a wave propagation process originated by visual stimuli corresponding to details and motion information. The resulting field obeys the principle of “inhibition of return” so as not to get stuck in potential holes. The proposed model is obtained as a hyperbolic regularization of the Poisson equation to which it reduces in the limit of high speed of propagation. According to the MultiMatch algorithm for scanpaths comparison, the proposed model achieves very competitive results when considering dynamical input stimuli.
Using multi-sine excitation and rigid body motion compensation in randomly sampled camera-based experimental modal analysis to improve SNR

Camera-based Experimental Modal Analysis (EMA) is able to measure full-field vibration modes contactlessly. However, camera measurements still suffer from a lower sampling frequency and lower Signal-to-Noise Ratio (SNR) compared to accelerometers and laser vibrometers. Since regular sampling confines the results to the Nyquist frequency (i.e., half the frame rate of a camera), in a previous paper we have proposed an approach that exploits images randomly sampled in time, allowing to measure modes beyond the Nyquist frequency. In this paper we employ such an approach and we propose a novel excitation scheme to increase the SNR, i.e., the specimen under investigation is excited at selected resonance frequencies, which results in high amplitudes of the structural responses. These frequencies are measured by accelerometers during a pre-test, and a multi-sine signal containing these frequency components is generated as structural excitation through a shaker. Furthermore, as Rigid Body Motion (RBM) due to pseudo-free suspension is not desired for EMA, in this paper we also propose a novel optimization workflow for RBM compensation. The accuracy of the proposed approach with multi-sine excitation is first evaluated numerically by reconstructing steady-state response signals. Secondly, the whole workflow (i.e., multi-sine excitation and RBM compensation) is validated on an experimental case with a 3D component and a stereo camera setup. From a sequence of randomly sampled images equivalent to a frame rate below 50 fps (corresponding to an equivalent Nyquist frequency below 25 Hz), four modes up to 250 Hz (i.e., ten times higher than the Nyquist frequency) are extracted, which correlate well with the modes predicted through a finite element simulation.
Using Local Large Language Models to Simplify Requirement Engineering Documents in the Automotive Industry

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Online learning during the COVID-19 era: the antecedents of continuance intention and communication channel satisfaction from learners’ perspective

ABSTRACTBuilding on the Flow theory and Stimulus-Organism-Response framework, this study investigates the impact of online platforms’ technological environment and communicator competence on learners’ continuance intentions and communication channel satisfaction mediated by virtual experience. Data collected from undergraduate and postgraduate students in North Cyprus and Lithuania were used to assess the proposed relationships utilizing structural equation modeling. The results indicated that the platforms’ technological environment has a significant impact on virtual experience while the communicator competence has a significant impact on learners’ virtual experience in the Lithuanian context only. Moreover, the findings revealed that virtual experience has a significant impact on learners’ continuance intention and communication channel satisfaction. Furthermore, the results indicated that virtual experience mediates the relationship between the technological environment and learners’ continuance intention and communication channel satisfaction in both contexts; however, virtual experience mediates the relationship between communicator competence and learners’ responses in the Lithuanian context only.KEYWORDS: Technological environmentcommunicator competencevirtual experiencecontinuance intentioncommunication channel satisfactiononline learning Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data that support the findings of the study are available from the corresponding author upon reasonable request.Correction StatementThis article has been republished with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThis research received no external funding.
An Improved Multi-Radar Track Weighted Data Fusion Algorithm

To improve the anti-jamming capability of multi-radar systems in complex marine environments, a comprehensive adaptive weighted data fusion algorithm based on multi-radar is proposed in this paper. Firstly, the algorithm develops an improved nearest neighbor outlier suppression method to measure the degree of mutual support between multi-radar data to eliminate the outliers appearing in the radars at each moment. Secondly, the previous first and the current measurements are fused as the new measurement data by assigning the corresponding weight to them based on the intrinsic relationship that exists between the previous first measurement and the current measurement and the specific fusion principles. Finally, an adaptive weighting fusion method is used to fuse the multi-sensor data. The simulation results show that the proposed algorithm has higher fusion accuracy than the existing algorithms.
Feature Space Augmentation and Old Class Space Preservation for Class Incremental Learning

The ability of continuously learning new knowledge without forgetting old ones is crucial to adapt to an ever-changing world. This scenario becomes more challenging when the previous data are not available. Current class incremental learning (CIL) approaches tend to incorporate new classes with backward compatibility, i.e., maintaining discriminability of old ones. By contrast, we focus on the extensibility and compatibility for future new classes in the early stage. Our proposed method achieves this by augmenting feature space. In detail, a large number of pseudo-new classes are generated via real example mixture and then train the initial spatially augmented model using pseudo-new classes and the base classes. Besides, considering the fact that it is impossible to maintain backward compatibility with only one prototype for each old class, the updated model also needs to preserve old class space in incremental stages. We employ a certain perturbation to the old class prototypes to effectively avoid old class space from being over-squeezed by the samples of new classes. The experiments on CIFAR-100 and ImageNet-Subset (100 classes) demonstrate that our method substantially reduces the overlap of old and new classes, outperforming state-of-the-art various baselines.
Pix2Pix Based RAT Algorithm For Denoising of Forward-Looking Sonar Imagery

Forward-looking sonar is widely used in various underwater tasks, but the complex noise and monotonic color of sonar images make it challenging for operators to identify objects from noise. To address this issue, we propose a ROI Based Adaptive Threshold (RAT) algorithm that can effectively suppress noise in background and highlight regions of interest (ROI). However, its slow inference speed makes practical implementation difficult. Therefore, we utilize the Pix2Pix algorithm to accelerate RAT and simplify the training process. As a result, we propose the Pix2Pix Based RAT (PBR) algorithm, which achieves the best denoising performance in our experiments.
An Evaluation System for DeFi Lending Protocols

With the development of decentralized finance (DeFi), lending protocols have been increasingly proposed in the market. A comprehensive and in-depth evaluation of lending protocol is essential to the DeFi market participants. Due to the short development time of DeFi, the current evaluation is limited to the single evaluation indicator, such as total locked value (TVL). To our knowledge, we are the first to study the evaluation model for DeFi lending protocol. In this paper, we build a four-layer lending protocol evaluation model based on Analytic Hierarchy Process (AHP). The model contains three first-level indicators and ten second-level indicators, covering various aspects of the performance of lending protocol. We calculated these indicators by obtaining on-chain and off-chain data of lending protocol. Then we evaluated and ranked six mainstream lending protocols utilizing the proposed model during the period 2021/8/20-2022/11/10. Through comparative analysis of evaluation result, we found that the violent decline in the price of ETH increase the market share of stablecoin in the lending protocols. In addition, we also revealed the reasons for various fluctuations.
Remote Sensing Image Target Detection Model Based on Improved YOLOX

In order to solve the problems such as complex and diverse backgrounds, dense object distribution and significant differences in target scale in high-resolution remote sensing image target detection, such as false detection and missing detection, this paper proposes a remote sensing image target detection network based on YOLOX, which realizes the high precision identification of small and medium targets in remote sensing image. Firstly, the SimAM attention module is introduced into three feature layers of YOLOX backbone network output to improve the attention of essential features and restrain the interference of complex backgrounds. Then, the loss function is optimized, and the binary cross entropy loss is replaced by Focal Loss, which increases the weight of hard-to-classify samples and the detection ability of dense targets. Finally, a comparative test is carried out on the publicly available remote sensing image target detection dataset NWPU-VHR-10, and good results are achieved. Compared with the benchmark network (YOLOX), the proposed algorithm improves the map index by 1.1%. The experimental results show that the proposed method can significantly improve the remote sensing small target detection performance and is superior to other advanced algorithms.
Secure Nonlinear Fusion Estimation Against FDI Attacks: The Completely Distributed Condition

This paper considers the problem of multi-sensors fusion estimation for cyber-physical systems (CPSs), where each sensor node sends a local estimate to the fusion center and a subset of the transmitted signals can potentially be under false data injection attacks. For the aforementioned problem, locating the compromised local estimates is of crucial importance for obtaining an accurate fusion state estimate of CPSs. In this case, a secure fusion estimation method is proposed based on a Gaussian mixture model based detection mechanism. Specifically, the proposed method first clusters the local state estimates autonomously and provides a belief for each sensor, based on which the local estimates can be fused accordingly. Finally, an illustrative example is employed to show the effectiveness of the proposed method.
A Comparative Study on k-Means Clustering with Different Cluster Representations

K-means aims at partitioning a dataset into several clusters so that samples in the same cluster are compact and samples in different clusters are well separated. The centroid point with each dimension described by a real number, is used as a modeling approach for traditional k-means to represent the cluster, which is limited in information description. Fuzzy number contains more information compared with the real number for the point. Therefore, one can try to improve the clustering analysis performance using a richer representation of the cluster. In this paper, a comparative study among the different representations of each dimension of cluster including point, interval number, triangular fuzzy number and trapezoidal fuzzy number is conducted. Experimental results on both artificial datasets and real-world datasets demonstrate that using trapezoidal fuzzy number to represent cluster outperforms other representations with respect to four metrics: accuracy, normal mutual information, rand index, and adjusted rand index.
Path Smoothing Through Learning to Optimize

In this paper, we propose a novel method based on the learning-to-optimize (L2O) framework for path smoothing. We adopt a hierarchical path-planning framework with a global collision-free guiding path generated beforehand. This path is then broken into segments to be fed into the L2O framework. We present a new cost function in L2O to indicate the smoothness of the expected path, which explicitly constrains the changes between successive path segments. The optimizer in the L20 framework takes as the input the gradient of the designed cost function and the hidden state of the last time step. It incrementally updates the path vertex coordinates and iteratively optimizes the path. The developed framework is verified through simulation experiments. It is observed that with the dedicated designed cost function, the Long Short-Term Memory (LSTM) unit in the developed L2O framework relies more on the input guiding path and releases the dependence on analyzing the environment, which further prompts the generation of the optimal path.
Low-Light Image Enhancement via Unsupervised Illumination Estimation

Retinex theory-based methods have been popularly studied in the field of low-light image enhancement these years. Supported by Retinex theory, a low-light image can be decomposed into an illumination layer and a reflectance layer, which are then utilized to produce enhancement results. Traditional Retinex-based methods typically perform an optimization to achieve the decomposition, where handcraft priors are incorporated to guarantee fine textures and structures. However, they are limited by the possible low generalization of the priors and the long runtime. To solve the above problems, this paper implements the Retinex decomposition as a lightweight convolutional neural network, which takes the low-light images as inputs and is responsible for estimating their illumination components. The reflectance components can be easily obtained and considered as the enhanced results. The model is trained in an unsupervised manner, thus getting rid of the dependence on paired training data. Experiments on a series of benchmarks demonstrate the effectiveness and superiority of our method when compared to some state-of-the-art methods.
Differential Flatness-Based Trajectory Planning and Tracking for Fixed-Wing Aircraft in Clustered Environments

Rotor-crafts are increasingly becoming capable of flying in crowded spaces, whereas conventional fixed-wing aircraft cannot. These situations present difficulties because of constrained maneuverability and crowded surroundings. We build a trajectory planner and tracking controller to meet the requirements of obstacle avoidance, kinematic limitations, and dynamical feasibility, to enable fixed-wing aircraft navigation in clustered situations. We derive a useful differential flatness of the coordinated flying model to transfer states and control inputs into the flat space. We design cost functions particular to fixed-wing aircraft and optimize trajectories spatio-temporally in the flat space using a state-of-the-art trajectory representation named MINCO. Thus, high-quality trajectories can be acquired. We design a tracking controller based on differential flatness to track the trajectory spatially and temporally. High-fidelity simulations in MATLAB/Simulink and PX4 SITL with Gazebo show the effectiveness of the proposed methods.
Recursive Filtering for Time-Varying Nonlinear Delayed Systems with Stochastic Parameter Matrices and Censored Measurements

This paper discusses the design problem of recursive filtering method for time-varying nonlinear delayed systems (NDSs) with stochastic parameter matrices (SPMs) and censored measurements. In particular, the Tobit Type I model provides a description of the censored measurements. The main objective of this paper is to construct a recursive filter for NDSs with both SPMs and censored measurements. The upper bound of the filtering error covariance is first calculated via mathematical induction, and the upper bound is then minimized by choosing proper filter gain. Besides, a sufficient condition is provided to guarantee that the filtering error is uniformly bounded in the mean-square sense. Finally, the viability and applicability of the proposed filtering method are demonstrated using a numerical simulation.
Deep Reinforcement Learning Based Mobile Robot Navigation Using Sensor Fusion

At present, mobile robot navigation usually uses the traditional SLAM method, which can accurately obtain the environment information, but it is difficult to effectively model the environment if the environment changes greatly in the later stage or in dynamic and complex scenes. In order to make the mobile robot better adaptable to the environment, a reinforcement learning method is used to control the mobile robot. This paper mainly introduces the application of reinforcement learning algorithms and data fusion to mobile robot navigation obstacle avoidance. We use the deep deterministic policy gradient(DDPG) algorithm in reinforcement learning to train neural networks. Gazebo simulation platform is adopted as the simulation environment of this paper, which can effectively simulate indoor and dynamic pedestrian environments. Considering that the depth value detected by a binocular camera may be affected by light in a real environment, we supposed the method of sensor fusion which is added to fuse the data of the laser sensor and vision sensor for 3D reconstruction and generate corresponding point cloud data. It is verified that this method performs better than the vision sensor as well as the laser sensor, which can improve the success rate of navigation by 10% in a dynamic environment. And this method can make the trajectory of the agent smoother and reach the target point faster.
Interval Adaptive Predictive Control of Nonlinear Systems Based on LSTM Network

This paper presents a formulation of interval control for nonlinear systems based on Long Short-Term Memory network (LSTM) and using predictive control to solve the nonlinear interval control problem. The gradient descent with momentum is used as the learning algorithm to train the LSTM, which is used as the nonlinear system prediction model. The set interval is introduced into the optimized performance index and the gradient descent with momentum is applied to obtain the control variable in the performance index to realize the interval control, and the LSTM online learning algorithm is used to replace the feedback correction to realize the adaptive control. Furthermore, the system stability for the proposed formulation is proved based on Lyapunov theorem. The effectiveness of the proposed algorithm is proved by simulation and comparison experiments.
A Feature Fusion Detector Based on Graph Reasoning for Object Detection of Transmission Line External Force Damage Risk

It is significant to accurately identify the risk target of external force damage to ensure the safe operation of transmission lines. The rapid development of cameras and UAVs provides a hardware basis for identifying the risk objects of external force damage. At the same time, the recent object detection networks have developed rapidly, providing methods for identifying these risk objects. However, in some networks, the fusion of features with different depths without processing will lead to the loss of small weak parts and confusion about similar features. In this paper, we propose a bidirectional stripe thinning module based on graph modeling and reasoning. It adaptively captures random spatial dependence and long-range channel dependence with directional information. Specifically, we designed a mechanism of shallow feature projection to effectively project spatial information into the graph space. The reasoning between graph nodes realizes the dynamic correlation of target-related features and improves the feature representation of small targets. In addition, we propose a new Feature Fusion Detector based on Graph Reasoning(FF-GR detector), which embeds Graph Inference modules based on Shallow Spatial Features (GISSF) in specific layers and achieves good performance. Finally, the experimental results show that the mAP0.5 of the FF-GR detector on the external risk data set reaches 80.2%, which is superior to the current advanced methods.