Reviewerly/exHarmony huggingface dataset:
Overview of Research on 3D Path Planning Methods for Rotor UAV

With the continuous development of UAV technology, autonomous path planning methods suitable for UAVs are also evolving. In complex environments, UAVs not only need to consider the dynamics and time constraints, but also find the best and collision-free path. Although many researchers have conducted research on UAV path planning, there is a lack of comprehensive investigation and intuitive display of UAV path planning in a three-dimensional complex unknown environment. This article comprehensively analysis the path search algorithm and trajectory optimization method for UAVs in a three-dimensional environment, classifies them and compares them for each category, analyses the advantages and disadvantages of these methods.
HCI and deep time: toward deep time design thinking

To see objects not just through the lens of human agency but through the lives of nonhuman beings that both shape and are shaped by relationships and processes embodied in material forms is to invi...
Genetic Algorithm-Based Two-Tiered Load Balancing Scheme for Cloud Data Centers

The highly customizable cloud server interface has seen a growing demand in availing the services over the years. So to ensure proper servicing of user requests and due profit, load balancing is of utmost importance for proper resource utilization in cloud data centers. A robust and scalable system that can grow and shrink according to the number and size of application saves both power of data center and availability commitment of vendor. The proposed algorithm is designed in a modular fashion where modules work with resources divided in clusters and take a hierarchical approach to delegate tasks to virtual machines. The clusters are load sensitive and scale up or down accordingly. Tasks submitted to the data center are forwarded to a suitable cluster. Inside each cluster, tasks are mapped to resources and the process is optimized using genetic algorithm. Since each cluster receives a separate task set, this resource mapping can be done parallelly in data center. As a result, the overall response time for a task can get reduced. The forwarding of a task set to a suitable cluster is however centrally controlled for faster response time. The system has a backup central node to avoid a single-point-of-failure situation and thus availability of the system is increased.
Blockchain-Based Address Alias System

In recent years, blockchains systems have seen massive adoption in retail and enterprise environments. Cryptocurrencies become more widely adopted, and many online businesses have decided to add the most popular ones, like Bitcoin or Ethereum, next to Visa or Mastercard payments. Due to the decentralized nature of blockchain-based systems, there is no possible way to revert confirmed transactions. It may result in losses caused by human error or poor design of the user interface. We created a cryptocurrency wallet with a full on-chain solution for aliasing accounts and tokens to improve user experience and avoid unnecessary errors. The aliasing system consists of a number of smart contracts deployed on top of the blockchain network that give the ability to register aliases to accounts and tokens and use them instead of opaque addresses. Our solution shows how performant modern blockchains are and presents a way of building fully decentralized applications that can compete with centralized ones in terms of performance.
Recognition of Augmented Frontal Face Images Using FFT-PCA/SVD Algorithm

In spite of the differences in visual stimulus of human beings such as ageing, changing conditions of a person, and occlusion, recognition can even be done at a glance by the human eye many years after the previous encounter. It has been established that facial differences like the hairstyle changes, growing of one’s beard, wearing of glasses, and other forms of occlusions can hardly hinder the power of the human brain from making a face recognition. However, the same cannot easily be said about automated intelligent systems which have been developed to mimic the skill of the human brain to aid in recognition. There have been growing interests in developing a resilient and efficient recognition system mainly because of its numerous application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). Although there have been numerous researches on face recognition under varying pose, illumination, expression, and image degradations, problems caused by occlusions are mostly ignored. This study thus focuses on facial occlusions and proposes an enhancement mechanism through face image augmentation to improve the recognition of occluded face images. This study assessed the performance of Principal Component Analysis with Singular Value Decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing face recognition algorithm on face images with missingness and augmented face image database. It was found that the average recognition rates for the FFT-PCA/SVD algorithm were the same ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>90</mn> <mo>%</mo> </math> ) when face images with missingness and augmented face images were used as test images, respectively. The statistical evaluation revealed that there exists a significant difference in the average recognition distances for the face images with missingness and augmented face images when FFT-PCA/SVD is used for recognition. Augmented face images tend to have a relatively lower average recognition distance when used as test images. This finding is contrary to the equal performance assessment by the adopted numerical technique. The MICE algorithm is therefore recommended as a suitable imputation mechanism for enhancing/improving the performance of the face recognition system.
Vision-Based Target Detection and Tracking System for a Quadcopter

Tracking a maneuvering target with a quadcopter is a challenging problem, that involves a variety of fields such as visual tracking, state estimation, and control algorithms. Most existing unmanned aerial vehicle (UAV) systems fail to track targets accurately in the long term and cannot relocate targets after target loss. This paper aims to design and implement a vision-based target tracking system for quadcopters that can steadily and accurately track the ground target as well as the air target without any prior information. We employ a vision detection algorithm to select the target quickly and precisely. To fit complex practical conditions, a target tracking algorithm is developed based on correlation filters, which is capable of tracking targets with large-scale variation and fast motion. In addition, an efficient redetection algorithm based on the support vector machine (SVM) is designed to handle target occlusions and loss. The target states are estimated from the visual information by an improved Lucas-Kanade (LK) optical flow method and an extended Kalman filter (EKF). Moreover, a double closed-loop Proportion Integral Differential (PID) controller using the estimated states is designed to follow the target. By implementing the main algorithms on an onboard NUC computer, an extensive outdoor flight is evaluated for a quadcopter platform equipped with a stereo camera. The experimental results validate the feasibility and practicability of the developed system.
Deep Reinforcement Learning for Optimal Resource Allocation in Blockchain-based IoV Secure Systems

Driven by the advanced technologies of vehicular communications and networking, the Internet of Vehicles (IoV) has become an emerging paradigm in smart world. However, privacy and security are still quite critical issues for the current IoV system because of various sensitive information and the centralized interaction architecture. To address these challenges, a decentralized architecture is proposed to develop a blockchain-supported IoV (BS-IoV) system. In the BS-IoV system, the Roadside Units (RSUs) are redesigned for Mobile Edge Computing (MEC). Except for information collection and communication, the RSUs also need to audit the data uploaded by vehicles, packing data as block transactions to guarantee high-quality data sharing. However, since block generating is critical resource-consuming, the distributed database will cost high computing power. Additionally, due to the dynamical variation environment of traffic system, the computing resource is quite difficult to be allocated. In this paper, to solve the above problems, we propose a Deep Reinforcement Learning (DRL) based algorithm for resource optimization in the BS-IoV system. Specifically, to maximize the satisfaction of the system and users, we formulate a resource optimization problem and exploit the DRL-based algorithm to determine the allocation scheme. The evaluation of the proposed learning scheme is performed in the SUMO with Flow, which is a professional simulation tool for traffic simulation with reinforcement learning functions interfaces. Evaluation results have demonstrated good effectiveness of the proposed scheme.
Conversational Semantic Role Labeling

Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every single sentence, while ellipsis and anaphora frequently occur in dialogues. To address this problem, we propose the conversational SRL task, where an argument can be the dialogue participants, a phrase in the dialogue history or the current sentence. As the existing SRL datasets are in the sentence level, we manually annotate semantic roles for 3000 chit-chat dialogues (27198 sentences) to boost the research in this direction. Experiments show that while traditional SRL systems (even with the help of coreference resolution or rewriting) perform poorly for analyzing dialogues, modeling dialogue histories and participants greatly helps the performance, indicating that adapting SRL to conversations is very promising for universal dialogue understanding. Our initial study by applying CSRL to two mainstream conversational tasks, dialogue response generation and dialogue context rewriting, also confirms the usefulness of CSRL.
Multi Angle Rotation Object Detection for Remote Sensing Image Based on Modified Feature Pyramid Networks

Remote sensing data generated by satellites have important research and application values. However, traditional detection models have poor adaptability and generalization ability. In order to solve the low accuracy problem of the existing algorithm for small object detection of remote sensing, a small object detection algorithm MARNet (multi-angle rotation network) for remote sensing images of multi-angle rotation was proposed in this study, which used ResNet101 (residual network) as the baseline network. Global attention feature pyramid networks (GA_FPN) structure was designed based on the features of the pyramid network to improve the small object detection performance in remote sensing. Then MergeNet (Merge Network) was designed to better obtain the semantic relationship between features, and the attention mechanism was introduced to enhance the feature information of the target object. Datasets of DOTA (a large-scale dataset for object detection in aerial images) and NWPU VHR-10 (northwestern polytechnical university, very-high-resolution) are used to verify the algorithm.
FedPS: A Privacy Protection Enhanced Personalized Search Framework

Personalized search returns each user more accurate results by collecting the user’s historical search behaviors to infer her interests and query intents. However, it brings the risk of user privacy leakage, and this may greatly limit the practical application of personalized search. In this paper, we focus on the problem of privacy protection in personalized search, and propose a privacy protection enhanced personalized search framework, denoted with FedPS. Under this framework, we keep each user’s private data on her individual client, and train a shared personalized ranking model with all users’ decentralized data by means of federated learning. We implement two models within the framework: the first one applies a personalization model with a personal module that fits the user’s data distribution to alleviate the challenge of data heterogeneity in federated learning; the second model introduces trustworthy proxies and group servers to solve the problems of limited communication, performance bottleneck and privacy attack for FedPS. Experimental results verify that our proposed framework can enhance privacy protection without losing too much accuracy.
HPC-cloud native framework for concurrent simulation, analysis and visualization of CFD workflows

Analysis and rendering of high-resolution computational fluid dynamics (CFD) simulations often requires execution of multiple parallel data-processing pipelines. In this paper we present a hybrid cloud solution for efficient simulation and analysis of drop dispersions. The simulation component runs on a high-performance computing (HPC) cluster and cloud native framework performs the processing of CFD outputs. To facilitate some of the cloud features, we broke down the CFD data analysis pipeline into small microservice-like processes. In this way, a given resource can be set up and shared between different simulations, codes and locations to minimize idle times and dynamically adjust the capacity to the workload while producing results on the fly, independent of the simulation engine. A combination of HPC and cloud technologies allows us to create agile and highly scalable solutions for CFD purposes. We focus on HPC-cloud infrastructure; however, other arrangements such as cloud–cloud or desktop–cloud can be easily adapted depending on the needs. This enables a framework for centralizing services for collaborative use between different users as well as for automatically providing data from different sources to machine learning algorithms. We describe a proof of concept implementation of the proposed framework and provide detailed analysis of its performance applied to a real two-phase flow application.
Adaptable transmitter for discrete and continuous variable quantum key distribution

We present a versatile transmitter capable of performing both discrete variable and continuous variable quantum key distribution protocols (DV-QKD and CV-QKD, respectively). Using this transmitter, we implement a time-bin encoded BB84 DV-QKD protocol over a physical quantum channel of 47 km and a GG02 CV-QKD protocol with true local oscillator over a 10.5 km channel, achieving secret key rates of 4.1 kbps and 1 Mbps for DV- and CV-QKD, respectively. The reported transmitter scheme is particularly suitable for re-configurable optical networks where the QKD protocol is selected to optimize the performance according to the parameters of the links.
A Framework for Hexagonal Image Processing Using Hexagonal Pixel-Perfect Approximations in Subpixel Resolution

Image processing in hexagonal lattice has many advantages rather than square lattice. Researchers have addressed benefits of hexagonal structure in applications such as binarization, rotation, scaling and edge detection. Approximately all existing hardwares for capturing and displaying images are based on square lattice. Therefore, the best way for using advantages of hexagonal lattice is to find a proper software approach to convert square pixels to hexagonal ones. This paper presents a hexagonal platform based on interpolation which addresses three existing hexagonal challenges including imperfect hexagonal shape, inaccurate intensity level of hexagonal pixels and lower resolution in hexagonal space. The proposed interpolation is computed by overlaps between square and hexagonal pixels. Overlap types are formulated mathematically in 8 separate cases. Each overlap case is detected automatically and used to compute final gray-level intensity of hexagonal pixels. It is mathematically and experimentally shown that the proposed method satisfies necessary conditions for square-to-hexagonal conversion. The proposed scheme is evaluated on synthetic and real images with 10 different levels of noise in interpolation and edge detection applications. In synthetic images, the proposed method achieves the best figure of merit (FOM) 99.92% and 98.67% in high and low SNRs 100 and 20, respectively. Also, the proposed method outperforms existing state of the art hexagonal lattices with interclass correlation coefficient (ICC) 84.18% and mean rating 7.7 (out of 9) in real images.
Constructing a Comparison-based Click Model for Web Search

Extracting valuable feedback information from user behavior logs is one of the major concerns in Web search studies. Among the tremendous efforts that aim to improve search performance with user behavior modeling, constructing click models is of vital importance because it provides a direct estimation of result relevance. Most existing click models assume that whether or not users click on results only depends on the examination probability and the content of the result. However, through a carefully designed user eye-tracking study, we found that users do not make click-through decisions in isolation. Instead, they also consider the context of a result (e.g., adjacent results). This finding leads to the design of a novel click model named Comparison-based Click Model (CBCM). Different from traditional examination hypotheses, CBCM introduces the concept of an examination viewport and assumes users click results after comparing adjacent results within the same viewport. The experimental results on a publicly available user behavior dataset demonstrate the effectiveness of CBCM. We also public our code of CBCM and dataset.
Self-Supervised 3D Reconstruction and Ego-Motion Estimation Via On-Board Monocular Video

Recovering the three-dimensional structure information from a monocular camera is significant for automated driving, robot navigation, and traffic safety assessment. Recent work has solved various tight issues on self-supervised monocular depth estimation of leveraging on-board videos, such as occlusion/disocclusion, dynamic objects, and scale inconsistent. Nevertheless, rare work focuses on the model’s prediction confidence and underlying relations between depths, while they are essential for a decision-making system and performance improvement, respectively. This paper proposes a novel scheme, that of correlation-aware structure, to dig into the relations between depths, converting the independent depths into a graph-like connected depth map. Subsequently, a Gaussian estimator is devised to predict the depth map and uncertainty map concurrently. The uncertainty map can show us problematic regions where it is difficult to predict, from which we further develop uncertainty-based strategies to improve the performance. Specifically, we propose a simple image preprocessing method to overcome the gradient locality issue caused by low-texture, especially in smooth roads and shadows. Also, to avoid the influence of high-uncertainty regions, we propose a solidity-aware mask to recognize the reliable pixels for training in the image. The experiments on the KITTI dataset show that our method results in a competitive performance in both depth and ego-motion estimation tasks compared with the state-of-the-art methods. Besides, additional experiments on the Make3D and Cityscapes datasets demonstrate our method’s strong generalization capability and practicality.
Feature Matching Based on Minimum Relative Motion Entropy for Image Registration

Accurate point matching is widely used, and it is a critical and challenging process in feature-based image registration. To improve feature matching accuracy on putative matches with heavy outliers and similar local structures, an accurate and robust feature point matching algorithm based on minimum relative motion entropy (MRME) is proposed, in which the relative motion between the putative matches and their K-nearest neighbors is formulated. Based on the relative motion clustering result, the relative motion entropy is defined to find the coincident relative motions. According to relative motions with MRME, the outliers are removed in a two-stage feature match strategy. With quasi-linear time complexity, outliers with random or irregular relative motion are removed efficiently and accurately, while inliers with coincident relative motion are retained. Three data sets with repetitive patterns, viewpoint changes, low overlapping areas, and local deformations are used to demonstrate the performance of the proposed algorithm. MRME is shown to be more robust and accurate than ten state-of-the-art feature matching algorithms.
ResD-Unet Research and Application for Pulmonary Artery Segmentation

In the three-dimensional reconstruction of the pulmonary artery and the identification of pulmonary embolism, experts find it difficult to accurately estimate the severity of the embolism in the pulmonary artery, due to its irregular shape and complex adjacent tissues.In effect, segmenting the pulmonary artery accurately is the basis for assessing the severity of pulmonary embolism, and it is also a challengeable task.To solve this problem, this study proposes a ResD-Unet architecture for pulmonary artery segmentation.To begin with, the U-Net network is used as the basic structure, which allows efficient information flow and good performance in the absence of a sufficiently large dataset.In what follows, novel Residual-Dense blocks are introduced in the ResD-Unet architecture to refine image segmentation and build a deeper network while improving the gradient circulation of the network.Finally, a novel hybrid loss function is utilized to make full use of the advantages of the binary cross entropy loss, Dice loss and SSIM loss.Equipped with the hybrid loss, the proposed architecture is able to effectively segment the object areas and accurately predict the structures with clear boundaries.The experimental results show that the proposed framework can achieve high segmentation accuracy and efficiency, and the segmentation results are comparable to that of manual segmentation.
DOZEN: Cross-Domain Zero Shot Named Entity Recognition with Knowledge Graph

With the new developments of natural language processing, increasing attention has been given to the task of Named Entity Recognition (NER). However, the vast majority of work focus on a small number of large-scale annotated datasets with a limited number of entities such as person, location and organization. While other datasets have been introduced with domain-specific entities, the smaller size of these largely limits the applicability of state-of-the-art deep models. Even if there are promising new approaches for performing zero-shot learning (ZSL), they are not designed for a cross-domain settings. We propose Cross Domain Zero Shot Named Entity Recognition with Knowledge Graph (DOZEN), which learns the relations between entities across different domains from an existing ontology of external knowledge and a set of analogies linking entities and domains. Experiments performed on both large scale and domain-specific datasets indicate that DOZEN is the most suitable option to extracts unseen entities in a target dataset from a different domain.