Reviewerly/exHarmony huggingface dataset:
A GAN-based Domain Adaptation Method for Glaucoma Diagnosis

Domain adaptation is an important research topic in the field of computer vision, where the goal is to solve the difference of data distribution between different scenarios of the same task. In recent times, adversarial learning method becomes a mainstream approach to generate complicated images across diverse domains through optimizing deep networks, and it can also improve the recognition accuracy rate of deep networks despite existing domain shift or dataset bias. However, there are few effective efforts of domain adaptation for the disease diagnosis on fundus images. Fundus images are normally captured on different medical devices with different rules. When diagnosing glaucoma, there is a serious homogeneous domain shift, which means feature spaces between target domain and source domain images have a distribution shift although they are very similar. We propose a unified framework to solve this problem. Previous studies have shown that glaucoma can be monitored by analyzing the optic disc/cup and its surroundings. So we exploit a novel reconstruction loss which not only leverages unsupervised data to bring the source and target distributions closer but also keeps original target domain images label unchanged. The experimental results on several public and private datasets demonstrate that our method could increase the classification accuracy of glaucoma diagnosis.
Scene Attention Mechanism for Remote Sensing Image Caption Generation

Remote sensing images play an important role in various applications. To make it easier for humans to understand remote sensing images, the task of remote sensing image captioning attracts more and more researchers' attention. Inspired from the way human receives visual information, attention mechanism has been widely used in remote sensing image understanding. To catch more scene information and improve the stability of the generated sentences, a new attention mechanism called scene attention is proposed. Except for the current attention via the current hidden state of the long shortterm memory network (LSTM), our proposed method simultaneously explores the global visual information from the mean feature of all convolutional features. The effectiveness of the proposed method is evaluated on UCM-captions, Sydney-captions and RSICD datasets. The results of our experiment show that comparing with some other captioning methods, our method is more stable and obtains a better performance.
Interaction Graphs for Object Importance Estimation in On-road Driving Videos

A vehicle driving along the road is surrounded by many objects, but only a small subset of them influence the driver's decisions and actions. Learning to estimate the importance of each object on the driver's real-time decision-making may help better understand human driving behavior and lead to more reliable autonomous driving systems. Solving this problem requires models that understand the interactions between the ego-vehicle and the surrounding objects. However, interactions among other objects in the scene can potentially also be very helpful, e.g., a pedestrian beginning to cross the road between the ego-vehicle and the car in front will make the car in front less important. We propose a novel framework for object importance estimation using an interaction graph, in which the features of each object node are updated by interacting with others through graph convolution. Experiments show that our model outperforms state-of-the-art baselines with much less input and pre-processing.
Histogram Specification-Based Image Enhancement for Backlit Image

In this study, focusing on the fact that the intensity histogram of a backlit image shows a characteristic bimodal distribution, we propose an image enhancement method for single backlit images using histogram specification. In general, well-known histogram equalization-based techniques are powerful in image enhancement, but when simply applied to backlit images, they often change the overall appearance and also highlight unnaturalness. In the proposed method, a target shape of the intensity histogram that improves the bimodal distribution is determined, and then intensity conversion by histogram specification is conducted while preserving hue and saturation. The effectiveness of the proposed method is verified through a series of comparative experiments.
An Iterative Quadratic Method for General-Sum Differential Games with Feedback Linearizable Dynamics

Iterative linear-quadratic (ILQ) methods are widely used in the nonlinear optimal control community. Recent work has applied similar methodology in the setting of multi-player general-sum differential games. Here, ILQ methods are capable of finding local equilibria in interactive motion planning problems in real-time. As in most iterative procedures, however, this approach can be sensitive to initial conditions and hyperparameter choices, which can result in poor computational performance or even unsafe trajectories. In this paper, we focus our attention on a broad class of dynamical systems which are feedback linearizable, and exploit this structure to improve both algorithmic reliability and runtime. We showcase our new algorithm in three distinct traffic scenarios, and observe that in practice our method converges significantly more often and more quickly than was possible without exploiting the feedback linearizable structure.
DeepTemporalSeg: Temporally Consistent Semantic Segmentation of 3D LiDAR Scans

Understanding the semantic characteristics of the environment is a key enabler for autonomous robot operation. In this paper, we propose a deep convolutional neural network (DCNN) for semantic segmentation of a LiDAR scan into the classes car, pedestrian and bicyclist. This architecture is based on dense blocks and efficiently utilizes depth separable convolutions to limit the number of parameters while still maintaining the state-of-the-art performance. To make the predictions from the DCNN temporally consistent, we propose a Bayes filter based method. This method uses the predictions from the neural network to recursively estimate the current semantic state of a point in a scan. This recursive estimation uses the knowledge gained from previous scans, thereby making the predictions temporally consistent and robust towards isolated erroneous predictions. We compare the performance of our proposed architecture with other state-of-the-art neural network architectures and report substantial improvement. For the proposed Bayes filter approach, we shows results on various sequences in the KITTI tracking benchmark.
Distributed Charging-Record Management for Electric Vehicle Networks via Blockchain

The deep penetration of electric vehicles (EVs) into the transportation section and the associated charging management has yielded a critical issue, namely, how to efficiently store the generated charging records. In this article, we investigate the cost-efficient charging-record storage scheme by exploiting blockchain (BC). Accounting for the operational cost due to the consensus process via the practical Byzantine fault tolerance (PBFT) protocol, we model the associated cost for storing the charging records via an ideal multiblockchain system and formulate a joint optimization of the storage selection (i.e., either storing the charging record locally or selecting one of the BCs for storing the charging record) and server-node allocation for each BC, with the objective of minimizing a systemwise cost. Despite the nature of the complicated mixed binary and integer programming problem, we exploit the decomposition structure and propose a layered algorithm (i.e., the bottom subproblem for determining the optimal storage selection and the top problem for finding the server-node allocation) to solve it. For the bottom subproblem, we exploit the nature of minimum weighted matching of the problem and propose a distributed auction-based algorithm for computing the optimal storage selection. With the optimal solution from the subproblem, we further propose an annealing-based algorithm to determine the server-node allocation for each BC. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our cost-efficient charging-record storage scheme via BC.
HealthyBlock: Blockchain-Based IT Architecture for Electronic Medical Records Resilient to Connectivity Failures

The current information systems for the registration and control of electronic medical records (EMR) present a series of problems in terms of the fragmentation, security, and privacy of medical information, since each health institution, laboratory, doctor, etc. has its own database and manages its own information, without the intervention of patients. This situation does not favor effective treatment and prevention of diseases for the population, due to potential information loss, misinformation, or data leaks related to a patient, which in turn may imply a direct risk for the individual and high public health costs for governments. One of the proposed solutions to this problem has been the creation of electronic medical record (EMR) systems using blockchain networks; however, most of them do not take into account the occurrence of connectivity failures, such as those found in various developing countries, which can lead to failures in the integrity of the system data. To address these problems, HealthyBlock is presented in this paper as an architecture based on blockchain networks, which proposes a unified electronic medical record system that considers different clinical providers, with resilience in data integrity during connectivity failure and with usability, security, and privacy characteristics. On the basis of the HealthyBlock architecture, a prototype was implemented for the care of patients in a network of hospitals. The results of the evaluation showed high efficiency in keeping the EMRs of patients unified, updated, and secure, regardless of the network clinical provider they consult.
FPGA-based implementation of the SHA-256 hash algorithm

SHA-2 is one of the most popular hash functions since it ensures the integrity and the authenticity of information. However, this efficiency is verified at the cost of increasing the computational complexity and the power consumption. To overcome these drawbacks, the hardware acceleration is adopted as a solution in this work to guarantee the best trade-off between rapidity and power consumption. Indeed, this paper focuses on the SW/HW implementation of the SHA-256 hash function using the high level synthesis (HLS) under Xilinx Zynq 7000 based-FPGA. The synthesis results of the SW/HW proposed solution report a gain up to 17% and 73% in execution and power consumption respectively compared to SW case. Moreover, the hardware HLS architecture can reach a gain of 66% in throughput relative to previous work.
Convolutional Neural Network for Medical Image Classification using Wavelet Features

Automatic classification algorithms are an important component of expert decision support systems that are used in a number of medical applications including diagnostic radiology and disease detection. This study proposes a deep learning-based framework for medical image classification using wavelet features. Convolutional neural networks are incorporated to discover informative latent patterns and features from a set of X-ray images pertaining to human body parts. The features are then passed to a classifier for labelling the respective X-ray images. The experimental results show that the low-pass filter wavelet-based convolutional model outperforms the original convolutional network and some models for classifying X-ray images. The performance of the proposed method implies that it can be implemented effectively in practice for disease detection using radiological images.
Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference

Deep reinforcement learning (RL) is being actively studied for robot navigation due to its promise of superior performance and robustness. However, most existing deep RL navigation agents are trained using fixed parameters, such as maximum velocities and weightings of reward components. Since the optimal choice of parameters depends on the use-case, it can be difficult to deploy such existing methods in a variety of real-world service scenarios. In this paper, we propose a novel deep RL navigation method that can adapt its policy to a wide range of parameters and reward functions without expensive retraining. Additionally, we explore a Bayesian deep learning method to optimize these parameters that requires only a small amount of preference data. We empirically show that our method can learn diverse navigation skills and quickly adapt its policy to a given performance metric or to human preference. We also demonstrate our method in real-world scenarios.
Comparison of Augmented Reality Display Techniques to Support Medical Needle Insertion

Needle navigation aid visualized using different display techniques.a) Video see-through visualization on a stationary tablet computer.b) Visualization displayed by an optical see-through head-mounted display.c) Spatial augmented reality visualization directly projected onto a phantom.d) Visualization shown on a monitor.Abstract-Augmented reality (AR) may be a useful technique to overcome issues of conventionally used navigation systems supporting medical needle insertions, like increased mental workload and complicated hand-eye coordination.Previous research primarily focused on the development of AR navigation systems designed for specific displaying devices, but differences between employed methods have not been investigated before.To this end, a user study involving a needle insertion task was conducted comparing different AR display techniques with a monitor-based approach as baseline condition for the visualization of navigation information.A video see-through stationary display, an optical see-through head-mounted display and a spatial AR projector-camera-system were investigated in this comparison.Results suggest advantages of using projected navigation information in terms of lower task completion time, lower angular deviation and affirmative subjective participant feedback.Techniques requiring the intermediate view on screens, i.e. the stationary display and the baseline condition, showed less favorable results.Thus, benefits of providing AR navigation information compared to a conventionally used method could be identified.Significant objective measures results, as well as an identification of advantages and disadvantages of individual display techniques contribute to the development and design of improved needle navigation systems.
Tracking Multiple Surface Vessels With an Autonomous Underwater Vehicle: Field Results

This article describes the development and testing of a passive sonar, multitarget tracker, and adaptive behavior that enable an autonomous underwater vehicle (AUV) to detect and actively track nearby surface vessels. A planar hull-mounted hydrophone array, originally designed for active sonar, is repurposed for passive sonar use and provides acoustic data to a time-delay-and-sum beamformer that generates multiple angle-only contacts. A particle filter tracker assimilates these contacts with a single-hypothesis data association strategy to estimate the position and velocity of targets. Summary statistics of each track are periodically reported to an onboard database along with qualitative labels. To improve tracking performance, detections trigger an adaptive behavior that maneuvers the AUV to maintain multiple targets in the field of view by minimizing the worst case aspect angle deviation from broadside (across all targets). The tracking system is demonstrated through at-sea experiments in which a Bluefin-21 AUV adaptively tracks multiple surface vessels, including another autonomous platform, in the approaches to Boston Harbor.
Occlusion detection and drift-avoidance framework for 2D visual object tracking

This paper presents a long-term 2D tracking framework for the coverage of live outdoor (e.g., sports) events that is suitable for embedded system application (e.g. Unmanned Aerial Vehicles). This application scenario requires 2D target (e.g., athlete, ball, bicycle, boat) tracking for visually assisting the UAV pilot (or cameraman) to maintain proper target framing, or even for actual 3D target following/localization when the drone flies autonomously. In these cases, it should be expected that the target to be tracked/followed, may disappear from the UAV camera field of view, due to fast 3D target motion, illumination changes, or due to visual target occlusions by obstacles, even if the actual UAV continues following it (either autonomously, by exploiting alternative target localization sensors, or by pilot maneuvering). Therefore, the 2D tracker should be able to recover from such situations. The proposed framework solves exactly this problem. Target occlusions are detected from the 2D tracker responses. Depending on the occlusion immensity, the proposed framework decides whether to not update the tracking model, or to employ target re-detection in a broader window. As a result, the proposed framework allows continued target tracking once the target re-appears in the video stream, without tracker re-initialization.
egoTEB: Egocentric, Perception Space Navigation Using Timed-Elastic-Bands

The TEB hierarchical planner for real-time navigation through unknown environments is highly effective at balancing collision avoidance with goal directed motion. Designed over several years and publications, it implements a multi-trajectory optimization based synthesis method for identifying topologically distinct trajectory candidates through navigable space. Unfortunately, the underlying factor graph approach to the optimization problem induces a mismatch between grid-based representations and the optimization graph, which leads to several time and optimization inefficiencies. This paper explores the impact of using egocentric, perception space representations for the local planning map. Doing so alleviates many of the identified issues related to TEB and leads to a new method called egoTEB. Timing experiments and Monte Carlo evaluations in benchmark worlds quantify the benefits of egoTEB for navigation through uncertain environments.
Survey on Fake Profile Detection on Social Sites by Using Machine Learning Algorithm

To avoid the spam message, malicious and cyber bullies activities which are mostly done by the fake profile. These activities challenge the privacy policies of the social network communities. These fake profiles are responsible for spread false information on social communities. To identify the fake profile, duplicate, spam and bots account there is much research work done in this area. By using a machine-learning algorithm, most of the fake accounts detected successfully. This paper represents the review of Fake Profile Detection on Social Site by Using Machine Learning.
Optical multimodal biometric encryption that uses digital holography

Abstract In this paper, we propose an optical multimodal biometric encryption technique which uses digital holography (DH) for multiple biometric recording and successive optical encryption methods, by means of keys generated from biometric data. Physiological biometrics, such as fingerprints or iris scans, along with behavioral biometrics such as voice, are recorded simultaneously as multimodal biometrics, using multi-parameter off-axis DH. Moreover, multiple biometrics embedded in the same holograms are encrypted using the Fresnel domain double random phase encoding method, in which keys are generated from biometric data via a phase retrieval algorithm. By employing the decryption procedure for this encoding method, along with the correct keys and reconstruction parameters of DH, the original multimodal biometrics can be successfully retrieved . The proposed method provides multimodal biometrics with a higher level of security by availing itself of the advantages of optical techniques. Our proposed multimodal biometric security scheme is demonstrated and validated by the results of the optical recording and encryption of biometric data presented below.
Blockchain-Based Cyberthreat Mitigation Systems for Smart Vehicles and Industrial Automation

The smart vehicle interaction grid is susceptible to cyberthreats, which are hard to crack employing outdated centralized security methodologies. Blockchain is an absolute P2P circulated record having cryptographically protected information. Blockchain demonstrates effective use cases in economic purposes, smart communication, etc. It expands to every industry comprising protected IoT devices. The exceptional characteristic of blockchain is that its distributed, unalterable, inspect database that safeguards transactions by keeping secrecy. In this paper, we anticipate the situation of the smart vehicle interaction grid and deliver considering approaches for assembling a blockchain-oriented system among smart vehicles. The paper discusses various use cases of blockchain in smart vehicles and ongoing advancement from motorized industries in addition to educational organizations. Blockchain in smart vehicles functions as a public distributed ledger which is capable of recording dealings between two groups. Though, the acceptance and execution of blockchain technologies in numerous organizations and facilities is a difficult job. The goal of this paper is to classify the obstacles for the effective execution of blockchain technologies in the companies.
Action Recognition Using Form and Motion Modalities

Action recognition has attracted increasing interest in computer vision due to its potential applications in many vision systems. One of the main challenges in action recognition is to extract powerful features from videos. Most existing approaches exploit either hand-crafted techniques or learning-based methods to extract features from videos. However, these methods mainly focus on extracting the dynamic motion features, which ignore the static form features. Therefore, these methods cannot fully capture the underlying information in videos accurately. In this article, we propose a novel feature representation method for action recognition, which exploits hierarchical sparse coding to learn the underlying features from videos. The learned features characterize the form and motion simultaneously and therefore provide more accurate and complete feature representation. The learned form and motion features are considered as two modalities, which are used to represent both the static and motion features. These modalities are further encoded into a global representation via a pairwise dictionary learning and then fed to an SVM classifier for action classification. Experimental results on several challenging datasets validate that the proposed method is superior to several state-of-the-art methods.
A Study on Software Defect Prediction using Feature Extraction Techniques

Identification and elimination of defects in software is time and resource-consuming activity. The maintenance of a defective software system is burdensome. Software defect prediction (SDP) at an early stage of the Software Development Life Cycle (SDLC) results in quality software and reduces its development cost. In this study, a comparison is performed on nine open-source softwaresystems written in Java from PROMISE Repository using four mostly used feature extraction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel-based Principal Component Analysis (K-PCA) and Autoencoders with Support Vector Machine (SVM) as base machine learning classifier. The model validation is performed using a ten-fold cross-validation method and the efficiency of the model is evaluated using accuracy and ROCAUC. The results of this study indicate that Autoencoders is an effective method to reduce the dimensions of a software defect dataset successfully.