Miracle-dz/UniFAR-FTUs huggingface dataset:
Weighted Sparse Graph Non-negative Matrix Factorization based on L21 norm

Dimension reduction is widely concerned because of the rapid development of all walks of life, which leads to the exponential growth of data dimension. As a basic method of dimension reduction, non-negative matrix factorization is easily affected by noise, but its improved algorithm L21NMF is not sensitive to noise. Therefore, the paper proposes a weighted sparse graph non-negative matrix factorization based on L21 norm(LSL21NMF). In this method, L21 norm is used as the measure criterion, the non-negative matrix is decomposed into the summation of one error matrix and the product of two matrices non-negative, the weight sparse graph is applied to the regularization term to preserved geometrical structure of data. An efficient iterative approach is developed to solve the optimization problem of LSL21 NMF. On the standard noisy public dataset,the experimental results show that the method is more effective and robust than other methods.
Spatial Trajectory Tracking of a Stratospheric Airship with Constraints

This paper addresses the spatial trajectory tracking problem of a stratospheric airship in the presence of state constraint. A constrained Laguerre-based model predictive control (MPC) method integrated with sliding mode control (SMC) is proposed as the main control framework. First, by approximating the predicted control signal with Laguerre orthonormal basis functions, a linear MPC controller which is capable of dealing with the constraints explicitly is presented to track the predetermined trajectory based on the kinematic model of airship. Second, based on the dynamic model of airship, an SMC controller with a fast power rate reaching law (FPRRL) is designed to track the desired velocity calculated by the MPC controller. Furthermore, a command filter is employed to estimate the derivative of desired velocity. Stability analysis implies that the tracking error will converge to a small neighborhood of zero. Simulation validates the effectiveness of the proposed control scheme.
Neural Network Music Genre Classification

Music genre classification utilizing neural networks (NNs) has achieved some limited success in recent years. Differences in song libraries, machine learning techniques, input formats, and types of NNs implemented have all had varying levels of success. This article reviews some of the machine learning techniques utilized in this area. It also presents research work on music genre classification. The research uses images of spectrograms generated from timeslices of songs as the input into an NN to classify the songs into their respective musical genres.
Dielectric response measurement on service-aged XLPE cables: From very low frequency to power frequency

Polymeric-insulated cables, especially cross-linked polyethylene (XLPE) cables, are widely used in power system transmission and distribution networks. A cable failure can result in lengthy outages and substantial economic loss to the electrical utilities. One of the primary reasons for cable fault is insulation degradation over the service time. Various onsite electrical, thermal or mechanical stresses can accelerate the aging process of power apparatus and curtail the lifetime [1]–[3]. Additionally, different chemical processes can alter the morphological structure or chemical composition of the insulating material and degrade the dielectric properties [3], [4]. Therefore, from economic and technical points of view, regular condition monitoring and diagnostic testing have become critically important for checking the health of the insulation system before the occurrence of any complete failure. Moreover, diagnostic testing performed at appropriate intervals coupled with proper maintenance helps extend the lifespan of high voltage power equipment and components [5]–[8].
Demo Abstract: End-to-end Root Cause Analysis of a Mobile Network

In telecommunications, fault management is critical to improve network availability and user experience. To enhance reliability of their networks, operators require tools to quickly understand the cause of an outage. In particular, logs of alarms keep track of failures arising in their infrastructures. Due to the increasing size of networks and to the high diversity of technologies, these files may be verbose and noisy. That is why analyzing a log is often complex, hence delaying recovery and then degrading network availability. This demo presents a tool suite dedicated to log analysis. Our methodology is illustrated through the processing of a real alarm log issued from a 4G network. First, one can simplify the log by discarding irrelevant alarms and by clustering co-occurrent ones. Then, the underlying graph structure, called DIG-DAG, can store causal patterns by processing the input log online. Hence, experts can query the DIG-DAG to retrieve small and interpretable patterns.
Energetics of the Prairie Deer Mouse Peromyscus maniculatus bairdii

Existence metabolism (EM) of caged mice was measured by subtracting the caloric value of measured fecal and estimated urine losses from food consumed over periods of 2 days when mice maintained constant weight (summer, 18.4 g; winter, 18.1 g). EM (Mkcal = kcal animal^day-1 ) increased significantly with drop in temperature (T = C) and was higher in summer (Mkcal = 19.8 ? 0.447T) than in winter (Mkcal = 18.0 ? 0.447T). Winter-acclimatized animals tolerated temperature extremes of ? 5 and +40 C; summer-acclimatized animals, extremes of ? 2 and +44 C. Maximum potential metabolism (EM at lower limit of temperature tolerance) averaged 20.5 kcal animal-1 day1. Treadles placed at entrances of burrows showed that free-living mice were active aboveground about 4.3 hr per night in the winter and 2.7 hr during the summer. Using the mean temperatures to which the mice were exposed above ground (winter, ? 3; summer, 17.5 C) and in their burrows (winter, 10.7; summer, 20.2 G), we found that energy requirements of free-living adult mice not undergoing reproduction, molting or weight change averaged about 14.3 kcal mouse-1day-1 in the winter and 10.9 kcal in the summer. Body insulation against heat loss was better in winter than summer, as indicated by greater weight and length of hairs and lower rates of metabolism. No significant differences occurred between winter and summer in water or lipid contents of carcasses. Productive energy available for reproduction (maximum potential minus EM at prevailing temperatures) was 5.9 kcal animal^day-1 in winter and 9.8 kcal during tfye
Comparison Between Airborne LiDAR Data and Digital Image Matching for Surface Reconstruction

Nowadays some aerial surveying projects (both manned or not) might integrate imagery and ranging sensor technology, thus allowing both the reconstruction of the surface from airborne laser scanning (ALS) and from the Photogrammetric pipeline through digital image matching (DIM). DIM algorithms have been continuously improved, therefore comparisons to other techniques such as ALS must be conducted. Despite the scientific community efforts, there are few evaluations regarding the extraction of building roofs using point clouds derived from both ALS and imagery. In this sense, this study provides a brief comparison between ALS and DIM point clouds and the accuracy that can be achieved in surface reconstruction and building roofs extraction. The experiments indicated that in some situations such as the building roof shape extraction, the accuracy is similar between these techniques, however, this is not valid for vertical accuracy, where larger differences were observed.
Comprehensive Detection of Vulnerable Personal Information Leaks in Android Applications

Privacy is important and valued by individuals in our society. However, the wide use of the Internet and networked applications has introduced new complexities to how privacy can be maintained. Many notorious mobile applications have taken away user privacy by transmitting personal information without users' awareness. In this paper, we introduce VULPIX, an approach towards comprehensive detection of privacy leaks in Android mobile applications. We define a comprehensive list of data elements that are considered to be personal information and evaluate the comprehensiveness of detecting leaked personal information on a set of Android applications.
Exploiting Visual-Outer Shape for Tactile-Inner Shape Estimation of Objects Covered with Soft Materials

In this letter, we consider the problem of inner-shape estimation of objects covered with soft materials, e.g., pastries wrapped in paper or vinyl, water bottles covered with shock-absorbing fabrics, or human bodies dressed in clothes. Due to the softness of the covered materials, tactile information obtained through physical touches can be useful to estimate such inner shape; however, using only tactile information is inefficient since it can collect local information at around the touchpoint. Another approach would be taking visual information obtained by cameras into account; however, it is not straightforward since the visual information only captures the outer shape of the covered materials, and it is unknown how much such visual-outer shape is similar/dissimilar to the tactile-inner shape. We propose an active tactile exploration framework that can utilize the visual-outer shape to efficiently estimate the inner shape of objects covered with soft materials. To this end, we propose the Gaussian Process Inner-Outer Implicit Surface model (GPIOIS) that jointly models the implicit surfaces of inner-outer shapes with their similarity by Gaussian processes. Simulation and real-robot experimental results demonstrated the effectiveness of our method.
Learning Class Prototypes Via Anisotropic Combination of Aligned Modalities for Few-Shot Learning

Prototypical networks have shown their simplicity and effectiveness in few-shot learning. Recently, some works leverage cross-modal information to enhance the class prototype in few-shot learning. However, they seldom make use of structured information from textual space to optimize class prototype representation. And they either don’t align textual modality to visual modality or align them too rigidly. We argue that proper alignment method is important to improve the performance of cross-modal methods, as query data only has visual information in the few-shot learning tasks. In this paper, we propose a cross-modal alignment method to optimize class prototypes with structured information from textual space. We further introduce an anisotropic combination method to enhance class prototypes with information from two modalities. Experiments show that the cross-modal alignment method and anisotropic combination method achieve state-of-the-art results on the miniImageNet and tieredImageNet benchmark in one-shot regime.
Environmental Factors Limiting Emergence and Longevity of Male California Red Scale (Homoptera: Diaspididae)

Temperature, light intensity, and humidity each influenced diel periodicity of emergence by male California red scale (CRS), Aonidiella aurantii (Maskell). Higher constant temperatures in the range of 15–35°C or light intensities in the range of 25–1,000 lm/ft2 shifted emergence toward midafternoon from early evening. Response of males to light was negligible at ≤10°C. Optimum humidity for male emergence was 60–65%, with extreme high and low humidity delaying emergence. Longevity of males was negatively correlated with temperature. Almost all CRS males that emerged during a given afternoon were dead the next morning. At the highest temperature tested (40°C), ca. 80% of males were dead within 3 h, and all were dead within 7 h. At the lowest temperature tested (15°C), some males lived 29 h. Longevity was also influenced by light intensity and humidity: at the lowest light intensity (1 lm/ft2), some males lived 31 h. High humidity favored survival of males; some lived for 20 h at 80–85% RH.
Robotic Grasp Detection Using Light-weight CNN Model

Manipulating objects is the premise of many robotic applications, such as assembly, sort and palletezing. Teach programming is widely used in industrial robot, it is necessary to improve the intelligent level of robot. In this paper, we propose an accurate, real-time, light-weight convolutional neural network SqueezeNet-RCM to predict a grasp configuration from a RGB and aligned depth image of graspable object. Instead of using sliding window or regional proposal network to generate potential bounding box, we use a way of end-to-end to train and test our network. On the standard Cornell Grasping Dataset, our model achieves accuracy of 90.1% and 88.6% on image-wise split and object-wise split respectively, speed at 73 frames per second(fps) on GPU inferencing, which could meet the requirement of real-time. Moreover, our model size is 2.9MB, which is able to fit the memory to limited environment such as FPGA.
A Calculation Model of Charge and Discharge Capacity of Electric Vehicle Cluster Based on Trip Chain

The rapid response characteristics and high-speed growth of electric vehicles (EVs) demonstrate its potential to provide auxiliary frequency regulation services for independent system operators through vehicle-to-grid (V2G). However, due to the spatiotemporal random dynamics of travel behavior, it is challenging to evaluate the ability of EV cluster to provide ancillary services under the premise of reaching the expected state of charge (SOC) level. To address this issue, a novel calculation model of charge and discharge capacity of EV cluster based on trip chain with excellent parallel computing performance is presented in this work. Following the introduction of the characteristic variables of the proposed trip chain model, the user’s continuous travel behavior in a time scale of several weeks is simulated. In particular, a bidirectional V2G scheduling strategy based on the five-zone map is designed to guide the charging and discharging behavior of EVs, where the expected SOC levels are guaranteed. The results of a 3-week travel simulation verify the effectiveness of the presented model in coordinating the V2G scheme and calculating the charge and discharge capacity of the EV cluster.
Amphibian Movements in Response to Forest Edges, Roads, and Streambeds in Southern New England

If management of landscape linkages is to be promoted as a means of conserving amphibian populations, it must be demonstrated that amphibian dispersal does not occur independently of ecosystem edges and other salient landscape features. I used drift fences and pitfall traps to intercept dispersing amphibians and examine amphibian movements relative to roads, forest edges, and streambeds in a forest tract in southern Connecticut. Capture rates of 3 species (marbled salamander, Ambystoma opacum; red-spotted newt, Notophthalmus viridescens; pickerel frog, Rana palustris) were influenced by forest borders and streambeds, whereas captures of 3 other species (spotted salamander, Ambystoma maculatum; redback salamander, Pleth-odon cinereus; wood frog, R. sylvatica) were not. Across all species, the relative permeability of forest-road edges was much reduced in comparison to the forest interior and to edges between forest and open land. The data suggest that landscape-level conservation strategies aimed at amphibians should account for such filters and conduits to amphibian movement.
Density-Based Optimal UAV Path Planning for Photovoltaic Farm Inspection in Complex Topography

In order to improve power generation efficiency and reduce land cost, many large-scale photovoltaic (PV) farms are built on the mountain. The rugged terrain of the mountains leads to an irregular distribution of the PV modules’ location. Compared with manual inspection, the efficiency of unmanned aerial vehicles (UAVs) inspection is much higher. The path planning of scattered PV modules has become a challenge. In this paper, we use improved Density-Based Spatial Clustering of Applications with Noise (DBSACN) to partition the entire area. In order to reduce the number of turns of UAV, the partitions tend to divide the same row of PV modules into the same area. For the PV modules in each area, the full coverage path planning (CPP) of the boustrophedon will be adopted, and the lateral spacing between the PV modules can fly directly. Finally, the path control points are smoothed using Bezier Curves interpolation.
Research on A New Media Access Control Protocol

A new MAC protocol has been proposed for cognitive radio networks in the paper. This protocol guarantees the QoS of network for such applications by giving priority to applications with higher time delay requirements in the stage of channel reservation. The problems of low spectrum utilization and hidden terminals in cognitive radio networks are solved. The simulation results show that the protocol design is efficient and simple. It has high spectrum efficiency and the protocol improves the throughput of the network. This MAC protocol can solves the problem of multi-channel hiding terminal effectively.
Design of Soft Sensor for Industrial Antimony Flotation Based on Deep CNN

Froth flotation is the most widely used method of mineral separation. The froth state reflects the information of mineral content in the froth. Therefore, the most common method is to evaluate the froth surface state to control the operation to improve the mineral recovery. However, it is still a challenge to evaluate the froth state in real time, efficiently, quickly and accurately. This paper uses a deep convolutional neural network (CNN) to design an online antimony flotation process detection system, instead of traditional artificial observation or on-stream analyzers (OSA), is used for real-time detection of froth grade and abnormal condition. And through transfer learning, the detection system is trained and tested using samples from the antimony flotation site. Experimental results show that the accuracy rate of froth state detection reaches 99.17%, which is higher than the accuracy rate of human classification. Therefore, the deep CNN model can identify the froth state more reliably.
Discrimination of Excessive Exhaust Emissions of Vehicles based on Catboost Algorithm

This paper proposes a classification model based on CatBoost algorithm to discriminate excessive exhaust emission of the vehicles. The carbon monoxide (CO), hydrocarbon (HC) concentrations and other related exhaust data emitted by vehicles are accumulated from the Urban Road Network Vehicle Emissions Monitoring System. The original exhaust data is pre-possessed to yield clean and accurate data. The CatBoost algorithm we applied is a new Boosting algorithm, which has the advantage of higher discrimination accuracy than traditional machine learning algorithms. We collected exhaust data for two separate districts in Beijing and Hefei, China. The experimental results confirm that the discrimination model based on CatBoost algorithm is superior to the traditional machine learning classification algorithms, which can achieve higher discrimination accuracy.
Distributed Adaptive Sliding Mode Formation Control for Multiple Unmanned Aerial Vehicles

This paper presents a distributed multiple unmanned aerial vehicles formation control strategy based on adaptive sliding mode control. The Lagrange method is used to model the subsystem based on the actual quadrotor dynamics, and the formation consensus protocol is designed under the communication graph containing a directed spanning tree. Moreover, the distributed sliding mode surface and adaptive sliding mode controller are designed to make the state of the system reach the sliding mode surface in a finite time. At the same time the controller estimates the uncertainty and compensates the external bounded disturbance in the system. It is shown that the presented controller has the capability to achieve the stability of each subsystems via Lyapunov stability theory. Finally, the simulation results demonstrate the effectiveness of the proposed method.
Classication Of Land-Cover Through Machine Learning Algorithms For Fusion Of Sentinel-2a And Planetscope Imagery

To monitor and manage the changes in the land use and land cover, is vital the process of classification; machine learning offers the potential for effective and efficient classification of remotely sensed imagery. However, not many articles have explicitly dealt with the effects of image fusion on land-cover classification accuracy. Although some studies have compared thematic mapping accuracy produced using different classification algorithms, there are no currently many studies that utilize image fusion for assessing different machine learning algorithms for classification purposes. The main aim of this study is to compare different machine learning algorithm for pixel classification of imagery fused with sensors Sentinel-2A and PlanetScope. The method used for image fusion is a variational model, the high spectral resolution of Sentinel-2A imagery and the high spatial resolution of PlanetScope imagery was fused; the machine learning algorithms evaluated are six that have been widely used in the remote sensing community: DT (Decision Tree), Boosted DT, RF (Random Forest), SVM radial base (Support Vector Machine), ANN (Artificial Neural Networks), KNN (k-Nearest Neighbors), for the classification four spectral indices (NDVI, NDMI, NDBI, MSAVI) were included, derived of the image fusion. The results show that the highest accuracy was produced by SVM radial base (OA: 87.8%, Kappa: 87%) respect to the other methods, nevertheless the methods RF, Boosted DT and KNN shown to be very powerful methods for classification of the study area.
Impulse Response Modeling of Underwater Optical Scattering Channels for Wireless Communication

Despite the fact that underwater optical wireless communication (UOWC) systems are able to provide high-data rate links with high security, the performance of these systems presents several limitations related to the maximum achievable distance due to attenuation, and scattering effects. Hence, quantifying the signal attenuation, and the time-dispersion produced by such effects represents a crucial work in channel modeling. Motivated by this, we present, for the first time, a novel, and unified impulse response modeling of underwater optical scattering channels based on the superposition of one impulsive component, and one dispersive component with two degrees of freedom. We provide analytical results for channel path loss, and channel impulse response (CIR) which are validated through Monte-Carlo simulations based on photon-tracing for clear ocean, coastal, and harbor waters. In order to provide a physical insight, the developed CIR is used to compute the root-mean-square (RMS) delay spread as a function of distance, and type of water, as well as to analyze in greater detail the impact of inter-symbol interference (ISI) on the data rate. These outcomes can be used for high-speed systems design, and optimization.
Reachable set synthesis of singular systems

In this paper, the reachable set estimation and control problems of singular systems are investigated. By utilizing a novel system decomposition method , a sufficient condition is proposed in terms of linear matrix inequalities (LMIs) to find an ellipsord. Then a state feedback controller is designed to guarantee all the reachable set of the considered closed-loop singular systems to be contained by the "smallest" ellipsoid. Lastly, a numerical example is given to demonstrate the effectiveness of the obtained results.
Adaptive tracking control of stochastic nonlinear systems with unknown powers

This paper investigates the adaptive tracking problem for a class of high-order stochastic nonlinear systems with unknown powers and nonlinear parameterization. By using the parameter separation technique and adding a power integrator design method, an adaptive controller with upper and lower bounds of the unknown powers is successfully designed to guarantee that all the states of the closed-loop system are bounded in probability and the tracking error can be regulated into a small neighborhood of the origin in probability. Finally, a simulation example is provided to illustrate the effectiveness of the designed controllers.