Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple CNN-based Tasks Scheduling across Shared GPU Platform in Research and Development Scenarios

n the scope of numerous AI enterprises and research institutes, a shared server or cluster, which are based on commodity GPU hardwares, need to process multiple diverse CNN-based tasks simultaneously which are submitted by different developers and researchers. Scheduling and processing multiple CNN-based tasks, including training and batch inference, are a significant challenge in these practical scenarios. Previous studies, which focus on either the latency of a single training task or the throughput of multiple inference tasks, cannot effectively exploit the limited system resources available for diverse CNN-based tasks. This paper, for the first time, focuses on this specific AI Research and Development scenario and conducts an series of explorations on characteration and scheduling for CNN-based tasks. In order to evaluate the qualities of processing and scheduling, we propose a series of comprehensive metrics, including user satisfaction and system efficiency. With the metrics, we characterize diverse CNN behaviors of a few typical CNN models under different application and system configurable factors. Then, a heuristic scheduling algorithm informed by our characterization is explored to better allocate computing resources for the upcoming tasks and to schedule them dynamically on the cluster or server. Compared with two baseline strategies, the results, which are evaluated on multi-GPU platforms, show that our proposed algorithm can improve system efficiency by up to 40% and decrease average response latency by around 38% for multiple CNN-based tasks.
A Gesture Recognition Device by Using FDC2214

A set of gesture recognition device is designed to realize simple gesture recognition in this paper, such as “1” - “5” and “stone”, “scissors”, “cloth” gesture determination and training. The system mainly consists of controller and the man-machine interface. The controller uses the STM32F407VGT6 micro controller with ARM Cortex-M4 kernel as the main control chip. The man-machine interface is mainly composed of FDC2214 sensor chip, OLED display screen and keypad. After detecting the value of external capacitance by two FDC2214 chips, the device obtains 8 sets of stable data through kalman filtering. The probability of each gesture is calculated by BP-neural network, and the result is finally displayed by OLED. The device can train the weights of BP-neural network by feedback, and ensure the correct rate of gesture recognition for different people. The device has the advantages of simple structure, high recognition accuracy, and learning and training.
Cylindrical Inverted Pendulum Model for Three Dimensional Bipedal Walking

Energy efficiency of biped walking is an crucial topic for humanoid robot's research. Rapid computing is also important for online planning and model transplantation. Many dynamic models for characterizing humanoids' walking have been developed, such as conventional 3 dimensional inverted pendulum (IPM), linear inverted pendulum (LIPM). This paper proposed an improved inverted pendulum model constrained on cylindrical surface (CIPM), combining the advantages of computing and energy efficiency for humanoids' walking planning. Walking patterns with different speeds can be generated by CIPM. The constraint of cylindrical surface results in low coupling between displacement variables for tested robot and the energy consumption is less than that generated based on LIPM. The advantages of CIPM over IPM and LIPM were proved by mathematic analysis, simulations of bipedal walking with different speeds.
Multi-gas detection in power transformer oil based on tunable diode laser absorption spectrum

Online dissolved gas analysis (DGA) is an effective technique to obtain the health status information of insulation oil in power transformers. The typical hydrocarbon gases (methane, ethyne, ethene, ethane) decomposed from insulation oil, are the key indicators to diagnose the fault type and severity. To realize contactless and field measurement, a multi-gas detection system based on tunable diode laser absorption spectrum (TDLAS) is proposed in this paper. Critical technique problems on the light source, long path gas cell, and the topology for multi-gas detection are investigated. Individual central wavelengths of hydrocarbon gases are set at as 1653.72 nm for methane, 1530.37 nm for ethyne, 1620.04 nm for ethene and 1679.06 nm for ethane in the near infrared band. A 10.13 m long multi-pass gas cell is developed with the advantage of anti-vibration design. To share the optical absorption path, an optical switch is adopted to merge the multi-gas detection system. The high sensitivity of the TDLAS multi-gas detection system was demonstrated in the laboratory calibrations, with ethyne detection reaching sub-parts per million level, and the other hydrocarbon gases achieving ppm level. Furthermore, the comparison detection in the real 220 kV power transformer gave evidence to the effective monitoring of DGA, proving it an alternative approach to online detection of multiple gases in power transformer oil.
Stock Price Forecasting Incorporating Market State

This paper evaluates the impact of identifying and accounting explicitly for the states of the stock market when forecasting stock prices using machine learning. Clustering methods are applied to market mood indicators (e.g. VIX) to identify various states of the stock market and forecasting models for the selected companies are developed for each market state. Tests using 85 companies from the S&P 500 indicate that, for a forecasting horizon of 126 days, accounting for the market mood improves forecasting accuracy (lower RMSE) in 47% of cases. This improvement from accounting explicitly for market states is also seen when only Technical indicators or Fundamental indicators are inputs to the forecasting models; the benefit decreases when these indicators are combined.
Ensemble One-vs-One SVM Classifier for Smartphone Accelerometer Activity Recognition

A recognition framework to identify six full body motion from smartphone sensory data is proposed. The proposed system relies on accelerometer, gyroscope and magnetometer data to classify user activities into six groups (sitting, standing, lying down, walking, walking up stairs and walking downstairs). The proposed solution is an improvement of a one-verse-one SVM classifier with an ensemble of different learning methods each trained to discriminate a single activity against another. The improvement presented here doesn't only focus on accuracy but also potential embedded implementation capable of performing real-time classification with mobile data from the cloud. The presented one-versus-one approach, based on a linear kernel achieved 97.50 percent accuracy on a public dataset; second best to 98.57 percent reported in literature which uses a polynomial kernel.
On the Linearity of Performance and Energy at Virtual Machine Consolidation: The CiS2 Index for CPU Workload in Server Saturation

HPC-based datacenters consume significant energy, despite of a multitude of efforts in reducing it. Virtual Machine Consolidation is an effective technique to minimize the number of physical servers in order to reduce energy consumption, and, consequently, data center costs. However, consolidating virtual machines also reduces performance of servers. Thus, an efficient number of virtual machines consolidated in a physical server is one key issue to optimize the tradeoff between performance and energy efficiency in datacenters. In this paper, we propose a new index, namely CiS2 (Consolidation index for Server CPU in Saturation), to quantify the relation of performance degradation and energy efficiency of virtual machine consolidation under parallel workload execution, that is, the tradeoff of performance and energy in consolidation. As a result, CiS2 provides an approach to help performance engineers to decide about the suitability of a number of consolidated virtual machines. CiS2 could be modified and extended to any other non-functional features for servers.
Signal Strength and Read Rate Prediction Modeling Using Machine Learning Algorithms for Vehicular Access Control and Identification

The advancement in the field of transportation has been accelerating with policymakers shifting the focus towards autonomous and connected vehicles. Being an important component of the IoT infrastructure, a majority of its deployment is still based on the results of conventional approaches and measurement practice. Factors and the best conditions are identified using trial-and-error approaches which are not entirely accurate. Therefore, a thorough understanding of the influences that affect the performance of the windshield tag and developing a prediction model based on the most influential factor has been presented in this paper. In addition, two machine learning algorithms are proposed to achieve the objective of the paper. Linear regression analysis provides a reliable estimation of the RSSI levels of the windshield tag from all the selected location. The second proposed algorithm is based on the logistic regression model which provides an accurate prediction model based on tag readability by the reader. The predicted tag detectability model coupled with the RSSI level estimation model gives a complete overview of the behavior of the RFID tag when deployed on the windshield of the vehicle. This will serve as a guideline for the RFID transportation application which may extensively rely on RFID as a part of the IoT transformation.
Brain MRI Segmentation using efficient 3D Fully Convolutional Neural Networks

Brain Magnetic Resonance Images (MRI) segmentation and analysis is a fundamental step in measuring brain anatomical structure and visualizing any change and development in the brain. Segmentation of MRI is very challenging due to low contrast of Grey Matter (GM) and White Matter (WM) tissues of the brain. We propose a 3D Fully Convolutional Neural Network (FCNN) for the brain MRI segmentation of 6 months infant into WM, GM and Cerebrospinal fluid (CSF) with the use of multimodality input of T1-weighted images and T2-weighted images. The proposed method employs careful tuning and adaptation of the architecture to drastically reduce the number of hyperparameters to increase efficiency while retaining comparable performance with the state-of-the-art. We evaluate our proposed method on the iSeg2017 infant MRI segmentation challenge, where we achieve state-of-the-art results, acquiring an average DSC score of 93%.
SMCLBRT: A Novel Load-Balancing Strategy of Multiple SDN Controllers Based on Response Time

Distributed controllers have been proposed to resolve software-defined networking issues of scalability and reliability when a centralized controller suffers. One key limitation of distributed controllers is the static mapping between a switch and a controller. It quickly causes uneven load distribution among controllers. Load-balancing via dynamic switch migration is an effective way to solve this problem. However, absolute load-balancing leads to frequent and unnecessary switch migration. Additionally, if load-balancing is necessary, the efficiency of load-balancing for multiple overloaded controllers needs to be improved. To address this issue, we present a novel load-balancing strategy based on real-time response time, called SMCLBRT. It not only can avoid unnecessary migrating costs and sufficiently utilize the controller's performance, it can also improve the processing rate of the load balancers. Simulation experiments exhibit the effectiveness of our scheme.
Destination-Aware Social Routing for Mobile Opportunistic Networks

Social characteristics which are usually long-term stable have attracted particular interest for routing in Mobile Opportunistic Networks (MONs). Although social-aware routing approaches are shown to achieve superior performance, they are usually destination-agnostic since social metrics are measured without destination information. To this end, we propose a Destination-Aware Social routing scheme for MONs, namely DAS, which combines the advantages of destination-aware betweenness centrality and spread of epidemic disease. When forwarding, DAS utilizes a destination-aware social metric to make routing decision. Only a small number of replicas for a message are disseminated based on susceptible/infective (SI) model to ensure that the overhead is small and controlled. We conduct extensive simulations using real trace data sets to show improved performance with low overhead compared to existing social-aware routing approaches in various scenarios.
Modular Integrated Longitudinal, Lateral, and Vertical Vehicle Stability Control for Distributed Electric Vehicles

There is a nonlinear coupling relationship between the vehicle in the longitudinal, lateral, and vertical directions, which brings great difficulties to the design of the controller. To tackle the chattering problem, a hierarchical integrated controller for distributed electric vehicle is proposed to improve driving safety, handling stability, ride comfort, and road tracking capabilities. The proposed algorithm has been developed to overcome a major challenge of the conventional method, which can improve only partial dynamic performance of the vehicle. The optimal pre-pointing lateral acceleration model is used to simulate the operator's expected reaction to the vehicle. The body control layer decouples complex problems to achieve multi-target independent tracking through a nonlinear sliding-mode control algorithm, and calculates the expected total body force to meet the upper level instructions. The tire force distribution layer is designed to optimize the function by reducing the tire load ratio and balancing the vertical dynamic load coefficient to improve the vehicle's driving stability and ride comfort. The lower actuator control layer controls the corresponding actuator to achieve the optimal tire force output from the middle layer. Finally, the effectiveness of the chassis integrated control system is verified in CarSim and MATLAB co-simulation.
Object Detection Using Deep Convolutional Neural Networks

Object detection is a fundamental problem in image analysis and understanding. Lots of progress have been acquired on object detection due to the introduction of deep convolutional neural networks in recent years. Most of those algorithms can be categorized into two types, the two-stage method composed of region proposals generation and object classification along with the position regression of bounding box, and the one-stage regression method directly predicting classes and anchor offsets of objects. In this paper, the typically explored methods of these two types will be discussed to illustrate their development procedures. Moreover, Faster R-CNN and SSD are chosen as representatives for comparison. Experimental results demonstrate that the detection accuracies of SSD and Faster R-CNN are close, and each has its own merits in different images.
Exploring the Subject of Simulated Practical Teaching Method in Insurance Bilingual Courses

China was the biggest contributor to global life premium growth nowadays, a large number of insurance professionals are needed, and business must operate in an increasingly complex, global environment that generates many risks, etc. Talent cultivation in universities should keep steps to the economic and social demands in China. This paper analyzes the current conditions of insurance specialty , and examines a system of simulated practice teaching methods in the bilingual courses. College students are supposed to analyze the international and domestic insurance market and apply these knowledge based on what they have learned under this method. This paper also points out the key problems that should be paid attention to in the method’s implementation. Keywords—Insurance growth; Simulated practice teaching method; Bilingual course
Semi-supervised Deep Linear Discriminant Analysis for Histopathology Image Classification

Recently, deep learning techniques achieve remarkable classification performance on histopathology images. How-ever, they usually require a large amount of labeled training images to obtain satisfactory accuracy, and manual labeling is labor expensive and time consuming. To address this issue, in this paper, we propose a novel semi-supervised deep learning framework, namely semi-supervised deep linear discriminant analysis, by taking advantage of the deep neural network (DNN) and the graph to simultaneously exploit the semantic information of labeled and unlabeled images for classification. Specifically, we first replace the loss function of the DNN with the objective function of linear discriminant analysis to produce features minimizing the intra-class distance yet maximizing the inter-class distance, in order to construct a robust and effective graph Laplacian. Afterwards, we design a new objective function via employing the graph constructed by features of labeled and un-labeled images, and then adopt this objective as the loss function of the DNN to produce features for classification. Experiments on skeletal muscle and lung cancer images demonstrate the proposed framework outperforms several recent state of the arts.
Composite Trajectory Tracking Control of Unmanned Surface Vehicles with Disturbances and Uncertainties

To attenuate the effect of uncertainties and unknown disturbances, a composite trajectory tracking control scheme using disturbance observer and neural networks (NN) is proposed for an unmanned surface vehicle (USV) in this paper. In the absence of uncertainties and unknown disturbances, by defining a nonsingular terminal sliding mode (NTSM) manifold, a NTSM-based controller is designed for the USV to guarantee the tracking errors exactly converge to zero within a finite time. In the presence of uncertainties and unknown disturbances, NN is employed to compensate uncertainties while a disturbance observer is applied to simultaneously observe NN approximation error and unknown disturbances. Simulation studies demonstrate the effectiveness of the proposed control scheme.
An Approach to 3D SLAM for a Mobile Robot in Unknown Indoor Environment towards Service Operation

In this paper, an approach of 3D SLAM (Simultaneous Localization and Mapping) is presented for mobile robot towards service operation. The scheme can be briefly concluded as follows. Firstly, various robust and stable point and line features are extracted from RGB images and their corresponding 3D information is obtained from the depth image as landmarks for robot localization. Secondly, considering the occlusion and segmentation errors of line features in RGB images, a class of optimal decision models are constructed with a kind of Huber kernel function of point errors and line errors so as to find the optimizing solutions of rotation matrix and translation vector for the pose estimation of service robot. Thirdly, a pose graph is generated as an effective reference for localization along with the selection of keyframes and loop closure based on a bag-of-words model. Moreover, the pose graph is optimized using bundle adjustment algorithm and the robot trajectory is also globally optimized to correct drift error and enhance the consistency of map. Along with the localization of robot, a 3D sparse map and a point cloud map is built simultaneously using the features in keyframes. A series of simulation experiments are carried out with public datasets to verify the performance advantages of our proposed approach.
HEART SOUNDS CLASSIFICATION WITH DEEP FEATURES AND SUPPORT VECTOR MACHINES

In this study, the heart which is one of the most important organ affecting life-sustaining function is examined whether it works properly in a certain rhythm or not. In this regard, an effective algorithm both analyzing and categorizing Phono-cardiogram signals (PCG) which is significant at diagnosis of diseases is presented. First of all in this context, as colored spectrogram images of heart sounds are established to be able to analyze PCG signals, the characteristic extraction maps of Convolutional Neural Networks (CNN) are used to educate the data of images obtained. CNN-VGG16 model educated previously is used when these maps are established and and it is categorized with Support Vector Machine (SVM) which is an effective classifier at machine education. The performance of all rating labels is evaluated separately for experimental study with two different data. While max performance improves about %8 in one data set (DATASETA), max performance is obtained at other dataset (DATASETB) for normal rating label.
3D Reconstruction of Plant Leaves for High-Throughput Phenotyping

Generating 3D digital representations of plants is indispensable for researchers to gain a detailed understanding of plant dynamics. Emerging high-throughput plant phenotyping techniques can capture plant point clouds that, however, often contain imperfections and make it a changeling task to generate accurate 3D reconstructions. We present an end-to-end pipeline to reconstruct surfaces from point clouds of maize and rice plants. In particular, we propose a two-step clustering approach to accurately segment the points of each individual plant component according to maize and rice properties. We further employ surface fitting and edge fitting to ensure the smoothness of resulting surfaces. Realistic visualization results are obtained through post-processing, including texturing and lighting. Our experimental study has explored the parameter space and demonstrated the effectiveness of our pipeline for high-throughput plant phenotyping.
A Multitask Cascaded Convolutional Neural Network Based on Full Frame Histogram Equalization for Vehicle Detection

In order to improve the accuracy of vehicle detection, a Multitask Cascaded Convolutional Neural Network(MC-CNN) based on Full Frame Histogram Equalization (FFHE) algorithm is proposed. FFHE is used to enhance the image, which can solve the problem of unclear image, low contrast, low overall gray value and uneven illumination when the imaging condition is not ideal. MC-CNN locates and classifies the object in the image. The algorithm first performs FFHE operation on the test datasets. Then the region image containing a single vehicle is obtained by the object location algorithm based on Faster R-CNN. Next, the six classification(bus, microbus, minivan, sedan, SUV, and truck) results of each vehicle are obtained through the object classification algorithm based on CNN. To verify the performance of the algorithm, MC-CNN was trained on BIT-Vehicle and tested on SYIT2018-Vehicle. Compared with Faster R-CNN, the average classification accuracy of our algorithm was improved by 11.5% on SYIT2018-Vehicle dataset.
Analytical Solution to Global Dynamic Balance Control of the Acrobot

To provide a high level dynamic stability objective for humanoid robots that takes into consideration forces due to joint coupling, we derive an analytical solution to the dynamic balance control of the Acrobot, a fixed-base underactuated inverted double pendulum. We will show that the proof for stability involves an analogy to the dynamic stabilization of a rigid pendulum through vertical vibrations of its base, thus providing physical and mathematical insights into controls and dynamic stability of underactuated, articulated systems like the humanoid robot.