Reviewerly/exHarmony huggingface dataset:
Research on Path Planning and Evaluation Method of Urban Logistics UAV

Aiming at the problem of path planning and evaluation of logistics UAV (Unmanned Aerial Vehicle) in urban area, the paper plans the flight path of logistics UAV by using the improved A-star algorithm. The improved A-star algorithm considers urban airspace restrictions and UAV performance constraints from the perspectives of delivery efficiency and cost. At the same time, the paper deeply analyzes the influencing factors of path evaluation from four aspects. These four aspects are the external environment, route conditions, logistics distribution, and urban influence. Then, the paper constructs the path evaluation index system according to the characteristics of the urban airspace. Finally, the planned path is evaluated with AHP (the Analytic Hierarchy Process)-entropy method and fuzzy comprehensive evaluation method. The simulation results show that the improved A-star algorithm designed in this paper can quickly plan the feasible path. And the path evaluation model can evaluate the flight path of logistics UAV objectively and effectively.
Signal acquisition system based on wireless transmission for environmental sound monitoring system

Abstract In today’s technological era, the ability to access information through digital signals, especially voice, requires sophisticated and comprehensive applications that can convert physical signals into electrical signals. Its purpose is to assist humans in displaying and analysing structured and automated data obtained from tools with their unique set of features. In this study, we used signal data for different sounds, such as rock songs, birdsong, acoustic sounds, and conversational sounds. The data is checked using software and undergoes a data acquisition process. The results of the study are expected to reinforce that environmental sound processing is predicted to assist the development of more sophisticated automated monitoring systems capable of combining voice and visual data in a complementary way.
Identification of Radar Active Interference Types Based on Three-dimensional Residual Network

The complex and changeable radar jamming environment causes the pattern of radar jamming to change, and the recognition of jamming is the prerequisite and key to anti-jamming. This paper proposes a method to classify and discriminate the time series of interference time-frequency images by using a three-dimensional convolution residual network, and realize the classification and discrimination of five new types of active interference. Through simulation experiments and comparison with traditional methods, the effectiveness of the algorithm is verified.
Public Education

This chapter presents evidence that already before the pandemic, therewere large differences between members of the Emerging European Economies (EEE) in the effectiveness of their public education, and accordingly, their stock of human capital. While in the majority of the EEE, in terms of expected years of schooling, young people get the same or even more public education than the EU15 average, the quality measures of public education are very variable in these countries. In most of the EEE, the level of basic skills of young people is below that in more advanced economies. There is growing evidence that the inability of middle-income countries to improve the quality of public education is an important factor in the emergence and persistence of the middle-income trap. The education systems, although of very different quality in the EEE, were equally severely affected by the pandemic during the last two academic years (the second half of 2019-2020 and 2020-2021). Schools were closed, and on average, only one-fifth of schooling took place under ‘normal’ conditions in the EEE. This has resulted in large learning losses, the extent of which this chapter examines via published statistics and the authors’ own estimates and calculations. On this basis, the claim is made that not only did the pandemic heighten pre-existing disparities, but it also exposed the EEE with previously successful public education policies to the risk that their former advantage could disappear if they fail to offset the effects of the pandemic. This, in turn, has the potential to contribute to these countries falling into a middle-income trap. This, however, can be avoided through appropriate educational policy responses, to which the chapter also offers some suggestions.
Framework Proposal to Measure the Stress as Adversarial Factor on Cyber Decision Making

There are several factors that make cyber operations stressful, which include their complexity, unpredictability, and a continuum of decisions involving high risk and fast cost-benefit reasoning. Thus, in this context, defining a working methodology or framework to assess and quantify the impact of stress during decision making can be extremely useful both in real operations and in cyber exercises (to enhance decision making skills). Defining this framework is not a trivial task due to the complexity of stress and the innumerable subjective nuances associated with it, which correspond to the characteristics of each individual. This paper presents stress understood as a disease and introduces the main current biometric systems that allow inferring and quantifying the stress level of an individual. Secondly, the main methodologies that allow to evaluate the stress level of a person are presented. Thirdly, a framework composed of five stages (monitoring, visualization, risk management, evaluation, decision making) is defined to model and standardize a methodology for assessing the level of stress in cyber-operations. Fourthly, a validation scenario is proposed to test the proposed framework. Finally, the procedure for the use of the methodology is defined and future directions for the continuation of this research are proposed.
Assessing technological trends through patent landscaping: The case of Bitcoin

Abstract With the appearance and the growing attention obtained by Bitcoin worldwide, the demand for cryptocurrencies based on blockchain technology is being driven forward at a great speed. Therefore, given the rapid valuation and strong price volatility of Bitcoin in combination with the huge potential for expansion that cryptocurrencies are expected to hold, the ability to develop technologies to help users and financial institutions carrying out transactions based on Bitcoins is also highly sought. To improve understanding of the technological trends associated with the recent valuation explosion of Bitcoin, this study assesses patent filing patterns associated with Bitcoin‐based technologies. More specifically, this study provides robust empirical evidence pointing out that the recent increased visibility of Bitcoin also motivated a strong increase in patent filings addressing technologies related to Bitcoin, thereby turning this particular cryptocurrency into a technological hotspot. Additionally, we present evidence that most Bitcoin technologies address data processing issues, rather than electric signal processing, or hardware technologies. Another relevant finding concerns the geographical concentration of patent applications, as the majority of applicants are based in highly heterogeneous countries, such as United States, China, and South Korea.
A Blockchain-Based Approach for Issuing Health Insurance Contracts and Claims

Health insurance provides economic coverage and medical assistance to its members. For this, the collaboration of several entities is necessary, such as: the insured party, the insurer and health service providers (medical center and pharmacy). A set of rules generates a flow of information from two activities: policy issuance and claims management. However, these have the following drawbacks: human supervision errors and lack of access for data visibility. Given these challenges, this document describes how the characteristics of blockchain-based smart contracts can support these tasks without requiring trusted third parties. For this, a model and layered overview are described using smart contracts. Conclusions and future work are explained at the end of the document.
Unsupervised domain adaptation with hyperbolic graph convolution network for segmentation of X-ray breast mass

Deep learning has been widely used in medical image segmentation, such as breast tumor segmentation, prostate MR image segmentation, and so on. However, the labeling of the data set takes a lot of time. Although the emergence of unsupervised domain adaptation fills the technical gap, the existing domain adaptation methods for breast segmentation do not consider the alignment of the source domain and target domain breast mass structure. This paper proposes a hyperbolic graph convolutional network architecture. First, a hyperbolic graph convolutional network is used to make the source and target domains structurally aligned. Secondly, we adopt a hyperbolic space mapping model that has better expressive ability than Euclidean space in a graph structure. In particular, when constructing the graph structure, we added the completion adjacency matrix, so that the graph structure can be changed after each feature mapping, which can better improve the segmentation accuracy. Extensive comparative and ablation experiments were performed on two common breast datasets(CBIS-DDSM and INbreast). Experiments show that the method in this paper is better than the most advanced model. When CBIS-DDSM and INbreast are used as the source domain, the segmentation accuracy reaches 89.1% and 80.7%.
Blockchain Technology for Security and Privacy in Energy Internet

Blockchain is an emerging technology, which can solve a series of problems of the traditional energy industry and promote its development, such as peer-to-peer transaction of distributed energy, tracking carbon emissions et al. At the same time, the application of blockchain technology brings some challenges, such as regulatory difficulties and the privacy problems. In this work, the blockchain technology in the application status of Energy Internet was briefly summarized, and the security issue and the lack of privacy protection were pointed out. By analyzing the results of other industries, some methods were summarized and suggested to meet the challenge of blockchain privacy protection in the energy Internet.
Advanced Lanchester Combat Model for Inhomogeneous Armies in RTS Games

This article introduces an approach describing the battle dynamics between two inhomogeneous (consisting of units of different types) armies in real-time strategy games, such as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Starcraft 2</i> . This approach is based on representing the armies as distributions of densities of damage per second and attributing the strengths to the armies, which in a homogeneous case match the well-known Lanchester strengths. The proposed approach can be used to optimize the army composition in various conditions: in an open flat space, in narrow passages, or in ground versus air battles.
Coming Together of Bayesian Inference and Skew Spherical Data

This paper presents Bayesian directional data modeling via the skew-rotationally-symmetric Fisher-von Mises-Langevin (FvML) distribution. The prior distributions for the parameters are a pivotal building block in Bayesian analysis, therefore, the impact of the proposed priors will be quantified using the Wasserstein Impact Measure (WIM) to guide the practitioner in the implementation process. For the computation of the posterior, modifications of Gibbs and slice samplings are applied for generating samples. We demonstrate the applicability of our contribution via synthetic and real data analyses. Our investigation paves the way for Bayesian analysis of skew circular and spherical data.
MultiPneu: Inflatable Deformation of Multilayered Film Materials

The exploration of transformable materials and pneumatic actuation is attracting increasing interest in the field of human–computer interaction. In this study, we introduce a do-it-yourself method of creating inflatable deformation of multilayered film materials. We used a commercial FDM 3D printer and a hand-pressure plastic packaging machine to heat-seal and pattern hinges to achieve self-folding structures in multiple chambers. This technique is very flexible and allows for a variety of pneumatic deformations in 3D. We also propose a design space of creating more than one chambers as well as multi-layered deformations that we hope this approach can benefit for shape-changing interfaces.
Document Image Retrieval: Issues and Future Directions

Document image retrieval is an interesting and popular area of research that has been evolved into many stages. The current trends show that it is still an emerging area due to the availability of document scanner app with mobiles and handheld devices. The objective of this paper is to provide an insight into document retrieval, classification, a general architecture, related issues and new opportunities for research in the domain.
An Attention-Based Predictive Agent for Static and Dynamic Environments

Real-world applications of intelligent agents demand accuracy and efficiency, and seldom provide reinforcement signals. Currently, most agent models are reinforcement-based and concentrate exclusively on accuracy. We propose a general-purpose agent model consisting of proprioceptive and perceptual pathways. The agent actively samples its environment via a sequence of glimpses. It completes the partial propriocept and percept sequences observed till each sampling instant, and learns where and what to sample by minimizing prediction error, without reinforcement or supervision (class labels). The model is evaluated by exposing it to two kinds of stimuli: images of fully-formed handwritten numerals and alphabets, and videos of gradual formation of numerals. It yields state-of-the-art prediction accuracy upon sampling only 22.6% of the scene on average. The model saccades when exposed to images and tracks when exposed to videos. This is the first known attention-based agent to generate realistic handwriting with state-of-the-art accuracy and efficiency by interacting with and learning end-to-end from static and dynamic environments.
The Virtual Persona Triad

Each person projects behavioral patterns through actions. Even in a virtual environment we express our way of seeing, feeling, and reacting to the world. The analysis of the data generated allows the identification of pattern behaviors associated with users. Therefore, it is possible to obtain a better understanding of the user, the image he/she decided to express, and how he/she behaves in social media, which we name here the virtual persona. Machine learning techniques make it possible to develop a framework that allows to infer psychological and behavioral aspects of the virtual persona. The goal of this chapter is to introduce the virtual persona concept as a mechanism to understand social media users. Emphasis is given to the self-presentation of virtual persona, the perception of physical persona over virtual persona, and the description of three computational frameworks to study virtual persona, what we call here the virtual persona triad.
Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP)

The demand for better health services has resulted in the advancement of remote monitoring health, i.e., virtual nursing systems, to watch and support the elderly with innovative concepts such as being patient-centric, easier to use, and having smarter interactions and more accurate conclusions.While virtual nursing services attempt to provide consumers and medical practitioners with continuous medical and health monitoring services, access to allied healthcare experts such as nurses remains a challenge.In this research, we present Virtual Nursing Using Deep Belief Networks for Elderly People (DBN-EP), a new framework that provides a virtual nurse agent deployed on a senior citizen's home, workplace, or care centre to help manage their health condition on a continuous basis.Using this method, healthcare providers can assign various jobs to nurses by utilizing a general task definition mechanism, in which a task is defined as a combination of medical workflow, operational guidelines, and data gathered from a remotely monitored virtual nursing system.Practitioners are in charge of DBN-EP and make treatment decisions for patients.This allows a DBN-EP to act as a personalized full-time nurse for a client by carrying out practitioner support activities based on information gathered about the client's health.An electronic Personal Health Record (ePHR) system, such as a specialized web portal and mobile apps, could provide such patient information to elderly person family members and care centres.We created a prototype system using a DBN-EP system that allows traditional client applications and healthcare provider systems to collaborate.Finally, we demonstrate how this system may benefit the elderly through a result and debate.
STRisk: A Socio-Technical Approach to Assess Hacking Breaches Risk

Data breaches have begun to take on new dimensions and their prediction is becoming of great importance to organizations. Prior work has addressed this issue mainly from a technical perspective and neglected other interfering aspects such as the social media dimension. To fill this gap, we propose STRisk which is a predictive system where we expand the scope of the prediction task by bringing into play the social media dimension. We study over 3800 US organizations including both victim and non-victim organizations. For each organization, we design a profile composed of a variety of externally measured technical indicators and social factors. In addition, to account for unreported incidents, we consider the non-victim sample to be noisy and propose a noise correction approach to correct mislabeled organizations. We then build several machine learning models to predict whether an organization is exposed to experience a hacking breach. By exploiting both technical and social features, we achieve a Area Under Curve (AUC) score exceeding 98%, which is 12% higher than the AUC achieved using only technical features. Furthermore, our feature importance analysis reveals that open ports and expired certificates are the best technical predictors, while spreadability and agreeability are the best social predictors.
Photon subtraction-based continuous-variable measurement-device-independent quantum key distribution with discrete modulation over a fiber-to-water channel

Abstract We propose a discrete-modulated continuous-variable measurement-device-independent quantum key distribution protocol over a fiber-to-water channel. Different from optical fibers, the underwater channel has more severe optical attenuation because of optical absorption and scattering, which reduces the maximum communication distance. To enhance the performance of the protocol, the photon subtraction operation is implemented at the modulator side. We carry out a performance simulation in two different kinds of seawater channel, and the result shows that the scheme with photon subtraction has a longer secure communication distance under certain conditions.
Design and Implementation of Vulnerability Attack and Utilization Platform Based on Container Virtualization

With the rapid development of the Internet, various vulnerabilities have brought great pressure to network security. However, the practitioners of network security are in short supply. One of the main reasons is that vulnerability testing is difficult to learn for beginners. Therefore, a graphical operation platform is designed and implemented. The graphical interface is designed and developed based on Web system, and the virtual environment is developed based on Docker. The attack method is implemented by encapsulating the Metasploit framework. It has the functions of virtual environment management, port scanning data management, attack mode selection and attack result output, etc. This platform can effectively reduce the amount of code that beginners need to master, and enable users to have an overall understanding of vulnerability testing in network security.
Scheduling ML training on unreliable spot instances

Cloud providers rent out surplus computational resources as spot instances at a deep discount. However, these cheap spot instances are revocable. When demand surges for higher priced on-demand instances, cloud providers can interrupt these spot instances after a brief alert. Such unreliability makes it challenging to utilize spot instances for many long-running jobs. However, with checkpoints and restoration, machine-learning (ML) training jobs are a good candidate to overcome this difficulty. In this paper, we formalize the problem of scheduling ML-training jobs on transient spot instances, especially from an ML researcher's view, who may have some grant/credit for renting cloud computing services for several ML training tasks. Such a researcher would need to partition the computational resources wisely to maximize outcome (or total expected utility of all jobs) while maintaining some fairness between jobs. We investigate the trade-off between low-cost/interruptible and high-cost/uninterruptible computation, by proposing a linear-programming (LP) rounding based polynomial time algorithm. Based on the LP solution, we also give an LP-based heuristic that performs well in practice. We implement and evaluate these algorithms, and are able to achieve the same utility with 23% to 48% of the budget needed with on-demand instances. Moreover, the total utility we get is close to the theoretical upper bound under various settings, indicating close to optimal performance.
Cardinal, a metric-based Active learning framework

<h2>Abstract</h2> In Active learning, a trained model is used to select samples to label to maximize its performance. Choosing the best sample selection strategy for a one-shot experiment is hard, but metrics have been proven to help by detecting strategies performing worse than random or detecting and avoiding noisy samples. Cardinal is a python framework that assists the practitioner in selecting a strategy using metrics and the researcher in developing those metrics. Cardinal caches experiments to compute insights costlessly, keeps track of logged metrics, and proposes extensive documentation. It also interfaces with other packages to use state-of-the-art strategies.
Artificial Dataset Generation for Automated Aircraft Visual Inspection

Aircraft visual inspection is both essential to the maintenance of an aircraft, and expensive and time-consuming to perform. Augmenting trained maintenance professionals with automated UAVs to collect and analyze images for aircraft inspection is an active research topic and a potential application of convolutional neural networks (CNNs). Training datasets for niche research topics such as aircraft visual inspection are small and challenging to produce, and the manual labeling process of these datasets produces subjective annotations. Self-driving car researchers have experimented with generating artificial datasets with modern computer graphics that can train for real-world driving scenarios. Our research borrows this idea and proposes a work-in-progress artificial data generation pipeline to create 3D rendered automatically annotated images for training CNNs for automated visual aircraft inspection.