Miracle-dz/UniFAR-FTUs huggingface dataset:
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

Ant Credit Pay is a consumer credit service in Ant Financial Service Group. Similar to credit card, loan default is one of the major risks of this credit product. Hence, effective algorithm for default prediction is the key to losses reduction and profits increment for the company. However, the challenges facing in our scenario are different from those in conventional credit card service. The first one is scalability. The huge volume of users and their behaviors in Ant Financial requires the ability to process industrial-scale data and perform model training efficiently. The second challenges is the cold-start problem. Different from the manual review for credit card application in conventional banks, the credit limit of Ant Credit Pay is automatically offered to users based on the knowledge learned from big data. However, default prediction for new users is suffered from lack of enough credit behaviors. It requires that the proposal should leverage other new data source to alleviate the cold-start problem.Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem. In this paper, we propose an industrial-scale distributed network representation framework, termed NetDP, for default prediction in Ant Credit Pay. The proposal explores network information generated by various interaction between users, and blends unsupervised and supervised network representation in a unified framework for default prediction problem. Moreover, we present a parameter-server-based distributed implement of our proposal to handle the scalability challenge. Experimental results demonstrate the effectiveness of our proposal, especially in cold-start problem, as well as the efficiency for industrial-scale dataset.
Decentralized Estimation of Distribution Algorithms for Large Scale Networked Systems*

In order to solve optimization problems in large scale networked systems, this paper proposes a method to implement estimation of distribution algorithms (EDA) in a decentralized way. The main point of decentralized EDA is that each subsystem solves its own optimization problems based on local and its neighbors' information. Numerical examples illustrate the effectiveness of the algorithm.
Low-Complexity Generator Polynomial Search for Turbo Trellis-Coded Spatial Modulation Using Symbol-based EXIT Charts

A low-complexity generator polynomial (GP) search method is proposed for spatial modulation relying on turbo trellis coding (SM-TTC), which achieves a high coding gain over independent and identically distributed (i.i.d.) fading channels and is robust against spatial correlation among receiving antenna elements. The proposed GP search method invokes symbol-based extrinsic information transfer (EXIT) charts and does not need to execute iterative decoding. It is demonstrated that our SM-TTC scheme with the best GP obtained by the proposed method outperforms one with conventional codes.
An Intelligent and Acceleration ASIC Design for High Precision Positioning and Posture Surveillance System

This paper presents a novel chip design for higher precision positioning and posture surveillance system. The ASIC can be divided into MCU part and DSP part which can accelerate the calculation for navigation unit. Also, the intelligent chip will switch its working mode based on different input. Basically, we define five different inputs for navigation and posture surveillance calculation including GPS, acceleration, angle velocity, velocity and magnetic force. There are many research results concerning with the combinations for these 5 basic parameters and they gave the different applications for navigation or posture determine. In this paper, we define an intelligent filter that can switch different mode for navigation and posture calculation. We use Kalman filter for GPS combining with IMU, switch to velocity combining AHRS when GPS lost, switch to multi AHRS mode when velocity lost. This adaptive filter gives us a smart way for continuous calculation for navigation and posture update with relative high accuracy. Also, the ASIC design will accelerate the algorithm calculation, which guarantee the real time update and the refresh time is less than 10 milliseconds.
A Wearable Sensor for Arterial Stiffness Monitoring Based on Machine Learning Algorithms

Arterial stiffness is strongly associated with cardiovascular events. Existing devices for evaluating arterial stiffness based on ultrasound or pulse wave velocity suffer a lot from complexity and inconvenience in home-care settings. This paper proposed a wearable sensor for arterial stiffness monitoring via machine learning techniques. The proposed sensor is comprised of one electrocardiogram (ECG) and one photoplethysmogram (PPG) module. The ECG and PPG signals were first simultaneously collected by the wearable sensor, and 21 features were extracted from two signals for arterial stiffness evaluation. A genetic algorithm-based feature selection method was then used to select the important indicators. Multivariate linear regression (MLR), decision tree, and back propagation (BP) neural network were employed to develop the model. Vascular age and 10-year cardiovascular disease risk from OMRON arteriosclerosis instrument were deemed as the gold standard to evaluate arterial stiffness. Experimental results based on 501 diverse subjects showed that the MLR approach exhibited the best accuracy in vascular age estimation (correlation coefficient, 0.89; mean of the residual, 0.2136; and standard deviation of the residual, 6.2432). While the BP neural networks-based approach was best in cardiovascular disease risk estimation (correlation coefficient, 0.9488; mean of the residual, - 0.3579%; and standard deviation of the residual, 3.7131%). The results indicate that the proposed learning-based sensor has great potential in arterial stiffness monitoring in home-care settings.
A Machine Learning Based Approach for the Prediction of Road Traffic Flow on Urbanised Arterial Roads

Urbanised arterial roads connect geographically important areas and are used for commuting and the movement of goods. Prediction of traffic flow on these roads is vital to aid in the mitigation of congestion. However, there is currently a lack of research in this area. In this work we have applied machine learning models to a real dataset for the prediction of road traffic congestion on urbanised arterial road. A comparative analysis was conducted on each machine learning model, examining the prediction accuracy and time-horizon sensitivity. Furthermore, we examined different input parameter settings (various classes of vehicles such as motorcycles, cars, vans, rigid goods lorries, articulated heavy goods vehicles (HGVs), and buses) to investigate how heterogeneous traffic flow can affect prediction. The experimental results show that the Artificial Neural Network Model outperforms other models at predicting short-term traffic flow on an urbanised arterial road based on the standard performance indicator: Root Mean Squared Error (RMSE). Additionally, it is found that different classes of vehicles can aid the improvement prediction.
Teleoperation of Upper-Body Humanoid Robot Platform with Hybrid Motion Mapping Strategy

In this paper, a hybrid motion mapping strategy for the teleoperation system is proposed. The strategy is implemented on a upper-body humanoid robot platform with the Haptic Workstation. The hybrid motion mapping strategy is composed of (a) the fixed scaling factor mapping method in the free constant-orientation workspace of the slave device, (b) the virtual impedance mapping method in the constrained constant-orientation workspace of the slave device, and (c) the orientation adjustment mapping method. Among them, the fixed scaling factor mapping method and the virtual impedance mapping method are focused on the position control, by expressing and matching the workspaces of the master device and the slave device with convex hull. The expressing and matching method can provide the slave device with the largest operational space as well as the operating comfort for the operator. Compared with the fixed scaling factor mapping method, the virtual impedance mapping method is to further address the problems of obstacle avoidance and master device workspace boundary avoidance for the master-slave control, by introducing the virtual impedance to the motion mapping. The orientation adjustment is based on the incremental mapping approach, with advantages of intuitive and natural operating process. Simulation experiment results proved the feasibility of the hybrid motion mapping strategy respectively.
Refractive Index Sensor Based on a Gradient Grating Period Guided-Mode Resonance

In this letter, we introduce a novel refractive index sensor based on gradient grating period guided-mode resonance (GGP-GMR). Rather than a constant grating period, as in typical GMR, GGP-GMR comprises a grating period varying in increments of 2 nm. For grating variation from 386 to 392 nm, a detection range of 0.109 RIU with a bulk sensitivity of 1433.7 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>/RIU and theoretical and experimental detection limits of <inline-formula> <tex-math notation="LaTeX">$5.58 \times 10^{-3}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$10^{-2}$ </tex-math></inline-formula> RIU, respectively, can be achieved. Several tactics that can be readily adopted are suggested to improve the limit of detection by an order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula> RIU, sufficient for detecting many clinically relevant biomarkers. The readout system requires a narrow band of light and a simple charge-coupled device combined with the compact design of the GGP-GMR sensor, which eases the miniaturization of the system for handheld devices as well as its integration with smartphones.
Mobility Optimization on Cyber Physical System via Multiple Object Tracking and Mathematical Programming

Cyber-Physical Systems (CPSs) are attracting significant attention from a number of industries, including social infrastructure, manufacturing, retail, among others. We can easily gather big datasets of people and transportation movements by utilizing camera and sensor technologies, and create new industrial applications by optimizing and simulating social mobility in the cyberspace. In this paper, we develop the system which automatically performs a series of processes, including object detection, multiple object tracking, and mobility optimization. The mobility of humans and objects is one of the essential components in the real world. Therefore, our system can be widely applied to various application fields. Our major contributions to this paper are remarkable performance improvement of multiple object tracking and building the new mobility optimization engine. In the former, we improve the multiple object tracker using K-Shortest Paths (KSP), which achieves significant data reduction and acceleration by specifying and deleting unnecessary nodes. Numerical experiments show that our proposed tracker is over three times faster than the original KSP tracker while keeping the accuracy. We formulate the mobility optimization problem as the SATisfiability problem (SAT) and the Integer Programming problem (IP) in the latter. Numerical experiments demonstrate that the total transit time can be reduced from 30 s to 10 s. We discuss the characteristics of solutions obtained by the two formulations. We can finally select the appropriate optimization method according to the constraints of calculation time and accuracy for real applications.
Distributed Real Time Link Prediction on Graph Streams

Link prediction refers to estimating the likelihood of a link appearing in the future based on the current status of a graph. Link prediction problem applications in various domains such as bioinformatics, social network analysis, cybersecurity and e-commerce. Some of these graphs are massive and are constantly evolving. Many applications require these graph streams to be processed them in real-time, to predict the link based on the most recent information as the graph features may change over time. Existing approaches to process large graphs for link prediction is non-trivial due to the following reasons: 1) Graphs required to predict the links are too large to be stored in a single RAM. Link prediction on these large graphs is expensive in terms of computation resources and time required to perform link prediction, 2) Sketch-based approaches are not suitable in applications where accuracy is critical (such as analyzing criminal social networks or supply chain networks) and 3) Sketch-based approaches also fail to handle dynamic graphs, where edges are not only added, but also removed. This results in changes to the graph topology, making the features previously computed to be obsolete. Distributed data stream frameworks such as Apache Flink could be potentially used for distributed graph processing. However, there are no techniques to handle link prediction on distributed graph streams. In this paper, we consider three fundamental, neighborhood-based link prediction measures, Jaccard coefficient, Preferential attachment, and common neighbors and enable an accurate measurement of them to address link prediction problem in dynamic graph streams. We propose a neighborhood-centric graph processing approach to handle graphs that exploits the locality, parallelism, and incremental computation of existing distributed frameworks to calculate these graph features with exact results. We perform experimental studies on various real-world graph streams. The results demonstrate that our graph measures are accurate and are more efficient than the existing vertex-centric approaches to graph processing.
DLIDX: A Double Level Index for Cloud Storage Environment

As data grows in amount, the researchers focus on the multiple condition query problem in cloud store data. The traditional index structures which are used in the relationship database. They have defects in maintenance and query efficiency in the cloud storage environment. This paper proposes a double level index framework(DLIDX). In DLIDX, the index of cloud storage is constructed by global index and local indexes. Each property of cloud data forms a dimension. For each dimension, the global index establishes the mapping relationship between the property and the node devices by inverting the hash table. The local indexes, a kind of bitmap index, are stored in the node devices. The bitmap indexes support local query of nodes. This framework can reduce the unnecessary retrieval cost. Meanwhile, the executive efficiency of the framework is less affected by the dataset. The experimental result proves that the performance of DLIDX is better than other methods.
Design and Implementation of Intelligent Vehicle Control System Based on ROS

intelligent vehicles, especially autonomous ones, have been studied in recent years. This paper proposes an intelligent vehicle control system solution. An upper-computer is employed to process sensor information detecting road conditions, while a STM32F103 microprocessor is used to implement vehicle motion control. The upper computer and the lower-MCU communicate with each other through the Modbus/RTU protocol. All the software in the designed is open source, scalable and at low cost. Experimental results show that the control system is feasible and effective.
Using HOG Descriptors and UAV for Crop Pest Monitoring

One of the major agricultural disasters, which depressed crop production and lower-quality, is pest in China. The lack of technical and scientific knowledge to prevent pest diseases is the main reason for low productivity of these crop commodities. Traditional methods based on artificial judgment have the disadvantages of large workload, low efficiency, poor working environment, and caused damage to crops. In response to the above problems, a HOG + SVM-based crop pest monitoring drone (PM-VAV) system was proposed, which utilizes computer vision combined with the flexibility of the drone for non-contact measurement. Firstly, the real aircraft working platform of this PM-UAV system was constructed. Then, through self-built data sets satisfy the requirements of the model training for pest detection task. Secondly, the on-line monitoring task of crop pests was accomplished through the use of the Histogram of Oriented Gradient (HOG) feature and Support Vector Machine (SVM) classifier, the algorithm was ported to the airborne embedded platform NVIDIA TK1. Finally, experimental tests show that the designed monitoring aircraft can effectively implement on-line monitoring for the crop pest which contained in the self-built data set.
A Sound Source Localization Method Based on Microphone Array for Mobile Robot

Sound source localization technology is a key technology for robot hearing. People have done a lot of research in different aspects. In this paper a microphone array based sound source localization method for mobile robot is proposed. The method proposed combines the advantages of the sound source localization method based on microphone array and the one based on double microphones, simplifying the procedure of the sound source localization, reducing the algorithm complexity, and improving the real-time performance of sound source localization of the mobile robot. The principle and performance of the proposed algorithm are analyzed in detail, and the effectiveness of the proposed algorithm is verified by experiments.
Attributed Multi-layer Network Embedding

Network embedding has gained much attention in recent years. Embedding network into a low-dimensional vector space has shown promising performance in many graph mining tasks such as node classification, link prediction, and community detection. However, in many real-world applications, a variety of networks could be abstracted and presented in a multilayer fashion with rich information, such as user profiles of friendship networks and textual content of citation networks. Most existing algorithms focus on single-layer networks or homogeneous networks with a single type of nodes and edges. They fail to leverage the rich attributes and consider the rich semantic correlations of the nodes which are among within-layer or cross-layer. In this paper, we exploit the rich semantic information embedded in the multi-layer network by means of meta-path-based proximities and leverage the rich source of attributes in the multi-layer network to improve network embedding. Specifically, the semantic correlations come from both within-layer and cross-layer node connections, and attribute proximity is considered to refine the homogeneity of nodes that belong to the same type. We propose a generic Attributed Multi-layer Network Embedding framework, which learns representations for nodes by capturing both the rich semantic correlations and attribute information simultaneously in a unified optimization framework. Our extensive experimental evaluations on real-world multi-layer networks demonstrate that the proposed framework achieves better performance compared with the state-of-the-art embedding algorithms.
Automatic Acceptance Prediction for Answers in Online Healthcare Community

Predicting whether an answer of doctors would be accepted by a patient on online healthcare communities plays an important role in the development of e-Health. In this paper, we proposed a framework combining different types of features to predict the acceptance of answers. In order to extract textual features from both the questions posted by patients and answers posted by doctors, we first trained a sentence encoder on a held- out dataset, which encodes a pair of questions and answers into a co-dependent representation. and then both numerical features and textual features are combined to predict the acceptance of answers. The experimental results on our dataset demonstrates that our framework is able to extract additional features from text and make a better prediction.
Adaptive Control Strategy for Gait Rehabilitation Robot to Assist-When-Needed

Conventional assist-as-needed (AAN) control strategies utilize only the deviation of user's limb to a predefined trajectory, but individual condition is not considered. In this paper, the control strategy of assist-when-needed (AWN) is proposed for gait rehabilitation robotics. The robot only provides assistance in any one of the seven gait phases involving the lower-limb impairments but it works in zero force mode in the other phases during walking. A synchronous reference trajectory is generated based on the gait phases. The gait phases are detected with IMU sensors and a hidden Markov model (HMM). The level of assistance is decided independently with the deviations in the seven gait phases. Then the adaptive robotic assistance is given to assist the human walking only when needed. This control strategy has been implemented in an exoskeleton robot and tested on one healthy subject. Experiment results showed that with this control strategy, the robot significantly increased knee joint angle ranges with the existence of external resistance. The experiment results revealed that the proposed control strategy is feasible and effective in delivering flexible assistance to the subjects during overground walking.
Convolution Structure Sparse Coding for Fusion of Panchromatic and Multispectral Images

Recently, sparse coding-based image fusion methods have been developed extensively. Although most of them can produce competitive fusion results, three issues need to be addressed: 1) these methods divide the image into overlapped patches and process them independently, which ignore the consistency of pixels in overlapped patches; 2) the partition strategy results in the loss of spatial structures for the entire image; and 3) the correlation in the bands of multispectral (MS) image is ignored. In this paper, we propose a novel image fusion method based on convolution structure sparse coding (CSSC) to deal with these issues. First, the proposed method combines convolution sparse coding with the degradation relationship of MS and panchromatic (PAN) images to establish a restoration model. Then, CSSC is elaborated to depict the correlation in the MS bands by introducing structural sparsity. Finally, feature maps over the constructed high-spatial-resolution (HR) and low-spatial-resolution (LR) filters are computed by alternative optimization to reconstruct the fused images. Besides, a joint HR/LR filter learning framework is also described in detail to ensure consistency and compatibility of HR/LR filters. Owing to the direct convolution on the entire image, the proposed CSSC fusion method avoids the partition of the image, which can efficiently exploit the global correlation and preserve the spatial structures in the image. The experimental results on QuickBird and Geoeye-1 satellite images show that the proposed method can produce better results by visual and numerical evaluation when compared with several well-known fusion methods.