Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-AUV cooperative target hunting based on improved potential field in underwater environment

In this paper, target hunting aims to detect target and surround the detected target using multi-AUV (Multiple Autonomous Underwater Vehicles) in a given area. The main challenge in multi-AUV target hunting is the design of AUV's motion path and coordination mechanism. To conduct the cooperative target hunting by multi-AUV in underwater environment, an integrated algorithm based on improved potential field (IPF) is proposed. Firstly, a potential field function is established according to the information of underwater environment. Then, the dispersion degree, the homodromous degree and district-difference degree are introduced to increase the cooperation of the multi-AUV system. Lastly, the target hunting is solved by embeds the three kinds of degree into the potential field function. The simulation results show that the proposed approach is applicable and feasible for multi-AUV cooperative target hunting.
Uplink resource allocation for trade-off between throughput and fairness in C-RAN-based neighborhood area network

Wireless-based neighborhood area network (NAN) plays an increasingly important role in smart grid (SG) since the rapidly emerging smart services and rising number of terminals in grid put forward higher demand for NAN. Considering the differential business demands in NAN, we focus on the wirelessly uplink resource allocation, which allows a trade-off between network throughput and service fairness. For more flexible and coordinated allocation, this paper introduces the cloud-radio ac­cess network infrastructure into NAN with orthogonal frequency division multiplexing passive optical network (OFDM-PON) as the fronthaul link, and proposes a corresponding uplink resource allocation method that balances the network throughput and allocation fairness. By utilizing a hybrid intelligent optimization algorithm, composed by adaptive genetic algorithm and binary particle swarm optimization, the optimal throughput-fairness trade-off solution can be obtained with a good convergence ability. Simulation results demonstrate the advantages of our proposed method in both improving network throughput and achieving the trade-off between throughput and fairness.
Robust output formation control for swarm systems with nonlinear uncertainties in directed networks

In this study, output formation control problems for high-order time-invariant swarm systems with unknown nonlinear uncertainties and directed interaction topologies are studied using a robust controller. First, the dimensions of the observation matrix are extended to be non-singular. Then the output formation problem is transformed into consensus problem. Based on observability decomposition, the consensus problem is transformed into stability problem. Further more, A robust controller which consists of a nominal controller and a robust compensator is applied to achieve formation control. The nominal controller based on the output feedback is designed to achieve desired output formation properties for the nominal system and the robust compensator based on the robust signal compensator technology is to restrain the nonlinear uncertainties. The proposed controller is linear, time invariant and easy to implement. Based on partial stability theory, sufficient and necessary conditions for output formation feasibility are given and the formation errors can be made as small as desired with expected convergence rate.
Test Item Taxonomy Based on Functional Criteria

There are many taxonomies that try to classify and apply some consistency to the very many item types currently in existence. They all have various limitations, however, such as ambiguous classification criteria, little discrimination between format types, and referring almost exclusively to pen-and-paper or screen-based items. This paper aims to overcome these limitations by proposing a new item format taxonomy based on functional criteria. Current classifications are reviewed, the criteria they are based on are examined and their limitations are identified. The proposed alternative classification identifies four essential components of items according to function: the structure of the included content, the device used for transmission of the question to the examinee, the device for receiving the response, and the instructions to the examinee about how to understand and respond to the item. The combination of different facets of these four components allows any format of item to be classified, both existing formats and those that may appear in the future. In addition to systematically and coherently classifying items, this new taxonomy may also be of great utility in the construction and research of new items. The proposed model is illustrated by examples showing how specific items are classified, using a checklist as a guide.
Asynchronous Byzantine Machine Learning

Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, corrupt data, or even malicious attacks. We introduce \emph{Kardam}, the first distributed asynchronous stochastic gradient descent (SGD) algorithm that copes with Byzantine workers. Kardam consists of two complementary components: a filtering and a dampening component. The first is scalar-based and ensures resilience against $\frac{1}{3}$ Byzantine workers. Essentially, this filter leverages the Lipschitzness of cost functions and acts as a self-stabilizer against Byzantine workers that would attempt to corrupt the progress of SGD. The dampening component bounds the convergence rate by adjusting to stale information through a generic gradient weighting scheme. We prove that Kardam guarantees almost sure convergence in the presence of asynchrony and Byzantine behavior, and we derive its convergence rate. We evaluate Kardam on the CIFAR-100 and EMNIST datasets and measure its overhead with respect to non Byzantine-resilient solutions. We empirically show that Kardam does not introduce additional noise to the learning procedure but does induce a slowdown (the cost of Byzantine resilience) that we both theoretically and empirically show to be less than $f/n$, where $f$ is the number of Byzantine failures tolerated and $n$ the total number of workers. Interestingly, we also empirically observe that the dampening component is interesting in its own right for it enables to build an SGD algorithm that outperforms alternative staleness-aware asynchronous competitors in environments with honest workers.
Soft sucker shoe for anti-slippage application

In this study, we present the design and development of a shoe outsole with active soft suckers which demonstrates a better shear resistance to prevent slip/fall of the user. The proposed design of the shoe outsole is layered by a multi-material structure with a soft suckered pattern connected to a vacuum pump (−0.8 bar). The sucker function can increase the normal force at the shoe/ground interface and enhance the frictional properties of contact area, which helps assuring a secured walking both in dry and wet surface condition. The developed shoe prototypes were characterized on different ground conditions with varying vertical load assuming the applied vertical force in human locomotion. The maximum shear resistance (≥50 Kg force) was recorded for suckered outsole that was higher than the shear resistance (≥0.9 Kg force) of normal outsole of a commercial product. The experimental results are promising in the direction to have a firm grip with suction at varying surface condition to prevent fall/slip while walking with the balance of shear force and frictional force.
Online metrics prediction in monitoring systems

Monitoring thousands of machines and services in a datacenter produces a lot of time series points, giving a general idea of the health of a cluster. However, there is a lack of tools to further exploit this data, for instance for prediction purposes. We propose to apply linear regression algorithms to predict the future behavior of monitored systems and anticipate downtimes, giving system administrators the information they need ahead of the problems arising. This problem is quite challenging when dealing with a high number of monitoring metrics, given our three main constraints: a low number of false positives (thus blacklisting volatile metrics), a high availability (due to the nature of monitoring systems), and a good scalability. We implemented and evaluated such a system using production metrics from Coservit, a company specialized in infrastructure monitoring. The results we obtained are promising: sub-second latency per predicted metric per CPU core, for the entire end-to-end process. This latency is constant when scaling the system up to 125 cores on 4 machines dedicated for monitoring predictions, and the performances don't decrease with time: during 15 minutes, it is able to handle more than 100 000 monitoring metrics.
StyleBlit: Fast Example‐Based Stylization with Local Guidance

We present StyleBlit—an efficient example‐based style transfer algorithm that can deliver high‐quality stylized renderings in real‐time on a single‐core CPU. Our technique is especially suitable for style transfer applications that use local guidance ‐ descriptive guiding channels containing large spatial variations. Local guidance encourages transfer of content from the source exemplar to the target image in a semantically meaningful way. Typical local guidance includes, e.g., normal values, texture coordinates or a displacement field. Contrary to previous style transfer techniques, our approach does not involve any computationally expensive optimization. We demonstrate that when local guidance is used, optimization‐based techniques converge to solutions that can be well approximated by simple pixel‐level operations. Inspired by this observation, we designed an algorithm that produces results visually similar to, if not better than, the state‐of‐the‐art, and is several orders of magnitude faster. Our approach is suitable for scenarios with low computational budget such as games and mobile applications.
Improved face recognition based on the fusion of PCA feature extraction and sparse representation

Sparse representation is a hot research topic in the field of biometrics in recent years. Even though the image has different expression and illumination, as well as occlusion, most of the algorithms still have good recognition effect. However, when the image contains illumination and facial expression changes, sparse classification method does not have good robustness. In this paper, we present a method that combination the PCA feature extraction method and sparse representation, which is a simple and effective face recognition algorithm. Based on the sparse concentration index, we propose the index threshold. When the concentration index is lower than the threshold value, we need to choose five training samples which corresponding to the minimum residuals, then construct a new training sample dictionary, and use the reconstructed dictionary to classify the test samples. This method strengthening the verification of abnormal test samples and optimizes the classification strategy. Comparing with the traditional SRC method, the simulation results show that the improved method is better than the SRC classification.
Cluster analysis for multistatic passive combined PCL and PET fusion system

Density based search is the data mining method that can be used in PCL-PET plot level fusion. The following paper is focused on cluster parameter analysis on basis of which proper input parameter allocation to DBSCAN algorithm is discussed. What is more, a set of quality indicators is introduced along with a set of simulation results for observing algorithm performance as a function of input parameters.
Phased antenna array reconstructive diagnostics using small number of measurements

In this paper a modification of a “Compressed Sensing”-based (CS) diagnostics method is presented. The reconstructive diagnostics is carried out in two steps. First, a preliminary diagnostics using CS approach is performed to determine the positions of potentially defect elements. Second, the additional measurements are conducted to improve the diagnostics accuracy. This approach combines the advantages of CS-based and element-wise diagnostics.
Single channel clutter cancelation in mobile PCL

The main problem of target detection in passive radar placed on mobile platform is the Doppler spread of the clutter. Illumination signal can be treated as the random signal and using integration with BT gain only BT dynamin ∗strongest peaks to the noise floor can be achieved. Such dynamic range is usually not sufficient for clutter cancellation so advanced CLEAN based clutter cancelation method have to by apply to allow detection of low RCS targets. In stationary radar only the zero Doppler clutter have to be cancelled, while for Mobile PCL extended Doppler cancellation method have to be applied. The paper presents the fundamentals of single channel CLEAN method for Doppler spread clutter cancellation based on LS solution of overdetermined set of linear equations.
Red Light-Induced Systemic Resistance Against Root-Knot Nematode Is Mediated by a Coordinated Regulation of Salicylic Acid, Jasmonic Acid and Redox Signaling in Watermelon

Red light (RL) can stimulate plant defense against foliar diseases; however, its role in activation of systemic defense against root diseases remains unclear. Here, the effect of RL on root knot nematode Meloidogyne incognita (RKN) infestation was investigated in watermelon plants (Citrullus lanatus L.). Plants were exposed to 200 μmol m-2 s-1 photosynthetic photon flux density RL at the canopy level for 21 days using light-emitting photodiodes. The results showed that RL significantly suppressed gall formation and nematode development, which was closely associated with the RL-induced attenuation of oxidative stress in roots. Gene expression analysis showed that RL caused a transient upregulation of PR1 and WRKY70 transcripts at 7 days post inoculation in RKN-infected plants. Further investigation revealed that RL-induced systemic defense against RKN was attributed to increased jasmonic acid (JA) and salicylic acid (SA) content, and transcript levels of their biosynthetic genes in roots. Interestingly, while malondialdehyde content decreased, H2O2 accumulation increased in RL-treated RKN-plants, indicating a potential signaling role of H2O2 in mediating RL-induced systemic defense. Furthermore, analysis of enzymatic and non-enzymatic antidoxidants revealed that RL-induced enhanced defense agaist RKN was also attributed to increased activities of antioxidant enzymes as well as redox homeostasis. Taken together, these findings suggest that RL could enhance systemic resistance against RKN, which is mediated by a coordinated regulation of JA- and SA-dependent signaling, antioxidants, and redox homeostasis in watermelon plants.
Ground texture classification with deep learning

In this study, we investigate the use of transfer learning on various deep neural network architectures pretained on the ImageNet data set for ground texture classification purposes. We introduce a new ground texture data set collected from seven different areas. We retrain deep neural network's last layer or when possible the full set of layers on this data set. The results show that it is possible to discriminate the ground textures even when very small images are used.
ASSR: An Adjustable Scenic Spot Rating System Based on Travel Note Mining

Scenic spot rating is a helpful and straightforward metric to evaluate scenic spots and assist travel plan making. However, existing ratings from the tourism websites are always unadjustable and strongly correlated with the popularity of the spots while different tourists may have different tastes. To solve this problem, we propose an Adjustable Scenic Spot Rating System (ASSR) based on travel note mining. With the help of the information mined from numerous travel notes, which contains the sentiment of tourists towards these numerous spots, our system is able to evaluate the scenic spots properly. We implement sentiment analysis to detect sentiment polarity of the travel notes. Based on this, we bring up a Spot-Topic LDA (ST-LDA) to generate a sentiment score for each spot which reflects its quality. In this case, the popularity of spots becomes an adjustable factor to satisfy the demands of different people. Our model is validated on public databases and has been embedded in the Smart Tourism Services Platform (STSP).
An inverse kinematics method of a soft robotic arm with three-dimensional locomotion for underwater manipulation

Soft robots have several promising features for underwater manipulation, e.g., safe interaction with surroundings, lightweight, low inertia, etc. In this paper, we proposed a method for the inverse kinematics of the soft manipulator that can move in the three-dimensional space. By controlling the two bending segments to move with opposing curvatures and one elongation segment to move up and down, our method enabled the real-time solution of the inverse kinematics and allowed the tip of the manipulator executing point-point movements in three dimensions. We performed the trajectory planning ability of the soft manipulator following the straight line and circle paths. Furthermore, we investigated the hydrodynamic functions of the soft manipulator underwater including forces, and the wake flows when the soft arm stroked at different amplitudes and frequencies. We found that the hydrodynamic force (<1N) and the torques (<0.1Nm) were quite small during locomotion — which led to a negligible inertial impact on the underwater vehicle compared to the traditional rigid underwater manipulator. Finally, we demonstrated that the soft manipulator successfully picked and placed sea animals at 10m depth.
Increased Drp1-Mediated Mitochondrial Fission Promotes Proliferation and Collagen Production by Right Ventricular Fibroblasts in Experimental Pulmonary Arterial Hypertension

Introduction: Right ventricular (RV) fibrosis contributes to RV failure in pulmonary arterial hypertension (PAH). The mechanisms underlying RV fibrosis in PAH and the role of RV fibroblasts (RVfib) are unknown. Activation of the mitochondrial fission mediator dynamin-related protein 1 (Drp1) contributes to dysfunction of RV myocytes in PAH through interaction with its binding partner, fission protein 1 (Fis1). However, the role of mitochondrial fission in RVfib and RV fibrosis in PAH is unknown. Objective: We hypothesize that mitochondrial fission is increased in RVfib of rats with monocrotaline (MCT)-induced PAH. We evaluated the contribution of Drp1 and Drp1–Fis1 interaction to RVfib proliferation and collagen production in culture and to RV fibrosis in vivo. Methods: Vimentin (+) RVfib were enzymatically isolated and cultured from the RVs of male Sprague–Dawley rats that received MCT (60 mg/kg) or saline. Mitochondrial morphology, proliferation, collagen production, and expression of Drp1, Drp1 binding partners and mitochondrial fusion mediators were measured. The Drp1 inhibitor mitochondrial division inhibitor 1 (Mdivi-1), P110, a competitive peptide inhibitor of Drp1–Fis1 interaction, and siRNA targeting Drp1 were assessed. Subsequently, prevention and regression studies tested the antifibrotic effects of P110 (0.5 mg/kg) in vivo. At week 4 post MCT, echocardiography and right heart catheterization were performed. The RV was stained for collagen. Results: Mitochondrial fragmentation, proliferation rates and collagen production were increased in MCT-RVfib versus control-RVfib. MCT-RVfib had increased expression of activated Drp1 protein and a trend to decreased mitofusin-2 expression. Mdivi-1 and P110 inhibited mitochondrial fission, proliferation and collagen III expression in MCT-RVfib. However, P110 was only effective at high doses (1 mM). siDrp1 also reduced fission in MCT-RVfib. Despite promising results in cell therapy, in vivo therapy with P110 failed to prevent or regress RV fibrosis in MCT rats, perhaps due to failure to achieve adequate P110 levels or to the greater importance of interaction of Drp1 with other binding partners. Conclusion: PAH RVfib have increased Drp1-mediated mitochondrial fission. Inhibiting Drp1 prevents mitochondrial fission and reduces RVfib proliferation and collagen production. This is the first description of disordered mitochondrial dynamics in RVfib and suggests that Drp1 is a potential new antifibrotic target.
Be Stable and Fair: Robust Data Scheduling for Vehicular Networks

The stable and fair data transmission of vehicular networks can improve transport efficiency and reduce traffic accident. It is challenging to ensure the stability and fairness of data transmission in dynamic vehicular networks. However, existing works based on opportunistic scheduling cannot support reliable transmission since stability and fairness are difficult to be guaranteed at the same time. In this paper, we propose a stable scheduling scheme for dynamic vehicular networks based on fair allocation of available channel resources. We formulate the problem of stability and fairness as network utility maximization, and propose an algorithm, called SF-NUM, to solve it. SF-NUM uses the stability scheduling method based on back pressure vector for the first time in vehicular networks. In order to achieve the fairness distribution of channel resources of the vehicular networks in the distributed computing way, multiple factors are considered in the SF-NUM algorithm. The experimental results show that the proposed algorithm outperforms other algorithms.
Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians

This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to the product of a likelihood and prior for each patch. By assuming identical parameters for similar patches, our approach can be classified as a model-based non-local method. For the prior term in the potential function of the CRF model, multivariate Gaussians and multivariate scale-mixture of Gaussians are considered, with the latter being a novel prior for image patches. Our results show that the proposed approach outperforms methods based on Gaussian mixture models for image denoising and state-of-the-art methods for image interpolation/inpainting.
A patrol mobile robot for power transformer substations based on ROS

In order to release human resources from daily patrol tasks in power transformer substations, a patrol mobile robot is designed to monitor substation facilities and collect information needed, such as gauge reading and surface images. This patrol mobile robot is equipped with a platform, a laser sensor and an industrial personal computer (IPC), and programmed in Robot Operation System (ROS). ROS packages, including communication package, processor package and move control package, are designed in IPC. With a global map of the power transformer substation installed in the robot, once a patrol tasked is received, this patrol mobile robot can choose the shortest route to finish the job. And a motion control method, which uses a laser sensor to navigate, is applied in the robot. Also, an additional Kalman filter is added to the controller to help kick out unusual position information provided by navigation sensors. With all these packages, the robot can receive orders from a client computer, reach the watching points, collect information with the platform and go back automatically.