ORGB: Offset Correction in RGB Color Space for Illumination-Robust Image Processing

Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second, we present a simple and effective way to detect and remove the offset. The resulting images, named ORGB, have almost the same appearance as the original RGB images while are more illumination-robust for color space conversion. Besides, image processing using ORGB instead of RGB is free from the interference of shadows. Finally, the proposed offset correction method is applied to road detection task, improving the performance both in quantitative and qualitative evaluations.Comment: Project website: https://baidut.github.io/ORGB

The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoost

Privacy has raised considerable concerns recently, especially with the advent of information explosion and numerous data mining techniques to explore the information inside large volumes of data. In this context, a new distributed learning paradigm termed federated learning becomes prominent recently to tackle the privacy issues in distributed learning, where only learning models will be transmitted from the distributed nodes to servers without revealing users' own data and hence protecting the privacy of users. In this paper, we propose a horizontal federated XGBoost algorithm to solve the federated anomaly detection problem, where the anomaly detection aims to identify abnormalities from extremely unbalanced datasets and can be considered as a special classification problem. Our proposed federated XGBoost algorithm incorporates data aggregation and sparse federated update processes to balance the tradeoff between privacy and learning performance. In particular, we introduce the virtual data sample by aggregating a group of users' data together at a single distributed node. We compute parameters based on these virtual data samples in the local nodes and aggregate the learning model in the central server. In the learning model upgrading process, we focus more on the wrongly classified data before in the virtual sample and hence to generate sparse learning model parameters. By carefully controlling the size of these groups of samples, we can achieve a tradeoff between privacy and learning performance. Our experimental results show the effectiveness of our proposed scheme by comparing with existing state-of-the-arts

Improvement and Performance Evaluation for Multimedia Files Transmission in Vehicle-Based DTNs

In recent years, P2P file sharing has been widely embraced and becomes the largest application of the Internet traffic. And the development of automobile industry has promoted a trend of deploying Peer-to-Peer (P2P) networks over vehicle ad hoc networks (VANETs) for mobile content distribution. Due to the high mobility of nodes, nodes’ limited radio transmission range and sparse distribution, VANETs are divided and links are interrupted intermittently. At this moment, VANETs may become Vehicle-based Delay Tolerant Network (VDTNs). Therefore, this work proposes an Optimal Fragmentation-based Multimedia Transmission scheme (OFMT) based on P2P lookup protocol in VDTNs, which can enable multimedia files to be sent to the receiver fast and reliably in wireless mobile P2P networks over VDTNs. In addition, a method of calculating the most suitable size of the fragment is provided, which is tested and verified in the simulation. And we also show that OFMT can defend a certain degree of DoS attack and senders can freely join and leave the wireless mobile P2P network. Simulation results demonstrate that the proposed scheme can significantly improve the performance of the file delivery rate and shorten the file delivery delay compared with the existing schemes

ACHIKO-M Database for high myopia analysis and its evaluation

Myopia is the leading public health concern with high prevalence in developed countries. In this paper, we present the ACHIKO-M fundus image database with both myopic and emmetropic cases for high myopia study. The database contains 705 myopic subjects and 151 normal subjects with both left eye and right eye images for each subject. In addition, various clinical data is also available, allowing correlation study of different risk factors. We evaluated two state-of-the-art automated myopia detection algorithms on this database to show how it can be used. Both methods achieve more than 90% accuracy for myopia diagnosis. We will also discuss how ACHIKO-M can be a good database for both scientific and clinical research of myopia

Research progress on risk factors for dermatomyositis with malignancy

Malignant tumors are one of the major causes of mortality in patients with dermatomyositis. But the majority of these tumors are diagnosed after the diagnosis of dermatomyositis. Because the timing of diagnosis of tumor is associated with the survival of patients with dermatomyositis, early screening for malignant tumors is crucial to improve the prognosis of these patients. This review summarizes the epidemiological features, clinical symptoms and laboratory findings of malignancy in patients with dermatomyositis. The highest incidence of malignant tumors is observed within the first year after the diagnosis of dermatomyositis, and patients are still at a higher risk of developing cancers up to five years following the diagnosis of dermatomyositis. Risk factors for malignancy in patients with dermatomyositis include male gender, age over 40 years, and the presence of Gottron's sign, purplish-red macules on the face and sun-exposed areas, and dysphagia. Further screening of malignant cancers, especially nasopharyngeal cancer in Asians, should be performed in patients with dermatomyositis when malignant signs and symptoms appear during the examination of myositis-specific autoantibodies, skin and muscle tissue pathology, and electromyography. Early detection, diagnosis, and the treatment of malignancies are crucial for improving the prognosis of patients with dermatomyositis

Structure Identification of Uncertain Complex Networks Based on Anticipatory Projective Synchronization.

This paper investigates a method to identify uncertain system parameters and unknown topological structure in general complex networks with or without time delay. A complex network, which has uncertain topology and unknown parameters, is designed as a drive network, and a known response complex network with an input controller is designed to identify the drive network. Under the proposed input controller, the drive network and the response network can achieve anticipatory projective synchronization when the system is steady. Lyapunov theorem and Barbǎlat's lemma guarantee the stability of synchronization manifold between two networks. When the synchronization is achieved, the system parameters and topology in response network can be changed to equal with the parameters and topology in drive network. A numerical example is given to show the effectiveness of the proposed method

Adaptive cluster synchronization of directed complex networks with time delays.

This paper studied the cluster synchronization of directed complex networks with time delays. It is different from undirected networks, the coupling configuration matrix of directed networks cannot be assumed as symmetric or irreducible. In order to achieve cluster synchronization, this paper uses an adaptive controller on each node and an adaptive feedback strategy on the nodes which in-degree is zero. Numerical example is provided to show the effectiveness of main theory. This method is also effective when the number of clusters is unknown. Thus, it can be used in the community recognizing of directed complex networks

An Efficient and QoS Supported Multichannel MAC Protocol for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) employ multichannel to provide a variety of safety and non-safety (transport efficiency and infotainment) applications, based on the IEEE 802.11p and IEEE 1609.4 protocols. Different types of applications require different levels Quality-of-Service (QoS) support. Recently, transport efficiency and infotainment applications (e.g., electronic map download and Internet access) have received more and more attention, and this kind of applications is expected to become a big market driver in a near future. In this paper, we propose an Efficient and QoS supported Multichannel Medium Access Control (EQM-MAC) protocol for VANETs in a highway environment. The EQM-MAC protocol utilizes the service channel resources for non-safety message transmissions during the whole synchronization interval, and it dynamically adjusts minimum contention window size for different non-safety services according to the traffic conditions. Theoretical model analysis and extensive simulation results show that the EQM-MAC protocol can support QoS services, while ensuring the high saturation throughput and low transmission delay for non-safety applications