Reviewerly/exHarmony huggingface dataset:
Advanced Air Mobility: Cabin of the Future Rescue Helicopters

In the summer of 2019, the Bertelsmann-Foundation caused quite some commotion in the medical world in Germany. Their study on the density of the hospitals and clinics in Germany showed that the current amount of 1900 could be reduced to 600. Even if it was considered too extreme, it did stir a change. There is a plan to gradually reduce the number of hospitals down to 1200, making the hospitals and clinics better equipped to treat a broad variety of medical issues. This has as a direct consequence that those hospitals will not be equally accessible for the entire population. People living in rural areas might end up having more than 30 minutes journey to the nearest hospital, in best case scenario when the traffic is light. As the current primary rescue helicopters are not equipped for the near future missions, there is a need for an air vehicle that will cover the requirements posed by as well the changes in the medical system as the patients.On the other side, the cities are growing bigger, causing traffic density to increase as well. Time that an ambulance needs to reach the place of medical emergency varies per city, and steadily increases over the years, due to the ever-growing traffic. Current Medical Personnel Deployment (aerial) Vehicle are off the shelf smaller helicopters, often still too big for its intended purpose. In January 2020, a new project within German Aerospace Center has started, bearing the name Chaser, as a means of answering to above challenge. Its goal is developing two different aerial vehicles with a bespoke cabin design. As the cabin is an integral part of the vehicles, its design is considered equally important to other components and will be developed parallel to the vehicle development. In order to ensure that the cabin is well fitting the needs of its users, a user centered approach will be applied according to the Design Thinking Method. There are three distinctive sorts of users in this case: medical personnel, vehicle operators and the patients. The current and future needs and desires of all three groups shall be considered through means of co-design, a method that will provide an insight in what users actually need. Considering the complexity of the vehicle, a close cooperation with other design disciplines, such as flight performance, structures and aerodynamics is required. This paper will show the mission definition of the two vehicles, the method used to gather and analyze the required data, the trend analysis as well as forth flowing requirements. The results of the co-design workshop series, expert in-depth interviews and user journey maps will be shown, as well as an example of possible design outcome. To wrap up, an outlook into the future project work will be depicted, including the conceptual design solutions for the posed challenges.
Path Quality Improvement of Sampling-Based Planners: An Efficient Optimization-Based Approach Using Analytical Gradients

Modern collaborative robotic applications require feasible robot motions that are predictable for human coworkers. We propose two path post-processing modules based on nonlinear optimization to improve path quality: the Path Length Post-Processor (PLPP) minimizes the path length; the Path Smoothness Post-Processor (PSPP) improves the path smoothness. Both keep a clearance to obstacles. We derive the analytical gradients of the optimization problem for computational efficiency and we validate our approaches in simulation.
Reversible Data Hiding Using LSB Scheme and DHE for Secured Data Transfer

There are many ways to make data secure with different processing techniques. The data is insinuated in a host and converted using encryption methods for further transfer. The host medium is mutated using some principles of alteration rules, and the genuine host medium is reclaimed back after the extraction of secret data from it. This paper adopts reversible data hiding approach to surge the security by hiding data in an image. This paper makes use of color images rather than grayscale to exaggerate the capacity of hidden data. Senders can encrypt the original image using data hiding in encryption (DHE) by using an encryption key and dynamic histogram. The LSB is then compressed to make space for the data hiding key to be used to hide the data. The receiver makes use of both encryption and hiding keys for accurate retrieval of data. If the receiver makes use of only one key, the particular functionality will respond depending on the key used.
Domain Learning Particle Swarm Optimization With a Hybrid Mutation Strategy

When traditional particle swarm optimization algorithms deal with highly complex, ultra-high-dimensional problems, traditional particle learning strategies can only provide little help. In this paper, a particle swarm optimization algorithm with a hybrid variation domain dimension learning strategy is proposed, which uses the domain dimension average of the current particle dimension to generate guiding particles. At the same time, an improved inertia weight is also used, which effectively avoids the algorithm from easily falling into local optimum. To verify the strong competitiveness of the algorithm, the algorithm is tested on nineteen benchmark functions and compared with several well-known particle swarm algorithms. The experimental results show that the algorithm proposed in this paper has a significant effect on unimodal functions, and has a better effect on multimodal functions. Guided particles, improved inertia weight and mutation strategy can effectively balance local search and global search, and can better converge to the global optimal solution.
An Insight into Load Balancing in Cloud Computing

Abstract Cloud Computing has emerged as a High-performance computing model providing on-demand computing resources as services via the Internet. Services include applications, storage, processing power, allocation of resources and many more. It is a pay-per-use model. Despite of providing various services, it is also experiencing numerous challenges like data security, optimized resource utilization, performance management, cost management, Cloud migration and many more. Among all, Load Balancing is another key challenge faced by Cloud. Effective load balancing mechanism will optimize the utilization of resources and improve the cloud performance. Load balancing is a mechanism to identify the overloaded and under loaded nodes and then balance the load by uniformly distributing the workload among the nodes. Various load balancing mechanisms are proposed by various researchers by taking different performance metrics. However existing load balancing algorithms are suffering from various drawbacks. This paper emphasizes the comparative review of various algorithms on Load Balancing along with their advantages, shortcomings and mathematical models.
Exploration of Generous Interface Design Principles for Digital Cultural Heritage

Rapid advances in information technology have facilitated the development of cultural heritage, and digital networks have become the largest repository of cultural heritage information, containing millions of works and cultural knowledge data. The generous interface serves as the primary contact point for users to communicate with cultural heritage information, and its display form is essential for users to understand digital cultural information. Many designers have used the concept of generous interfaces for digital cultural heritage, but the design specifications of generous interfaces are still limited. Therefore, this study explores the design principles of generous interface for digital cultural heritage by case analysis and evaluation. Three design principles were identified: guiding principle, hierarchical principle, and information visualization principle. This research aims to provide a practical reference for the design of the digital cultural heritage interfaces and assess the role of the generous interface.
Organization of Information Interaction in the Medical Information System

This article is devoted to the description of information interaction in a medical information system based on advanced information technologies. It is shown that, multi-agent technologies provide the functionality of the medical information system; intelligence technologies provide a solution to various intellectual tasks in the conditions of patient examination; modeling technologies make it possible to obtain characteristics of the functional state of the patient’s body; visualization technologies make it possible to visualize the functional state of the patient’s body. The organization of information interaction on the basis of an agent-based approach ensures an increase in the efficiency of medical information systems and improves the quality of medical services.
Theoretical Analysis of Accuracy-Based Fitness on Learning Classifier Systems

Like most evolutionary algorithms, accuracy-based learning classifier systems (XCSs) use a fitness metric to recognize the superiority of rules, under a principle that a higher-quality rule has a higher fitness. However, XCS must learn the fitness values under a reinforcement learning scheme. This introduces uncertainty and asynchrony to the fitness estimation while no theoretical work formally guarantees that such a basic principle would hold. The goal of this paper is to complement this fundamental lack in the reliability of XCS by mathematically analyzing its fitness-update scheme. Our main assumption is that the fitness is updated with an absolute accuracy instead of its relative accuracy to sidestep unpredictable dynamics of XCS. Our theoretical conclusion is that the superiority of rules can be correctly recognized through the accuracy-based fitness under finite update times. We further show that recognizing the superiority among low-quality rules is a costly procedure that increases the number of necessary rule-trainings. This drawback indicates that XCS may struggle to identify good parent rules at early generations, degrading the efficiency of evolutionary propagation.
Survey of Predictive Autoscaling and Security of Cloud Resources Using Artificial Neural Networks

This paper reports the results of a survey of predictive autoscaling methods and its security practices for cloud computing resources based on machine learning models involving Artificial Neural Networks, Linear Regression and Holt-Winters forecasting model. Autoscaling makes efficient use of resources by scaling up and scaling down the compute resources according to the workload. Scaling up ensures service reliability and scaling down ensures the cloud spend stays within budget. Despite all the advantages of autoscaling, achieving its full potential is somewhat challenging in situations where the resources are needed to be scaled up or scaled down abruptly. To deal with such challenges predictive autoscaling is used which forecasts the incoming traffic and prepares the system to handle it. This paper first discusses autoscaling and predictive autoscaling, about their integration with machine learning and goes on to survey autoscaling models aided by statistical forecasting models like Holt-Winters and Linear Regression. Security practices to prevent abuse of predictive autoscaling mechanisms have also been discussed.
Memory Guided Road Segmentation

In self-driving car applications, there is a requirement to predict the location of the road given an input RGB front-facing image. We propose a framework that utilizes an interleaving strategy of large and small feature extractors assisted via a propagating shared feature space allowing us to realize gains of over 2.5X in speed with a negligible loss in the accuracy of predictions. By utilizing the gist of previously observed frames, we train the network to predict the current road with greater accuracy and lesser deviation from previous frames.
Digitalization of the Educational Process as a Vector of Development of a Modern University in the Digital Economy

Purpose: The purpose of the article is to analyze the development of the educational process of the university in the conditions of active digitalization, based on the trends in the development of the world culture of education, socio-economic trends. The object of research in the article is a modern university. The subject of the research is the processes aimed at digitalization and development of the university as a subject of the digital economy. Methodology: Tasks define the following: Highlight the effectiveness of the digitalization of the university, to determine the quality of use of digital technologies in the educational process from the point of view of two main actors of the educational process, to identify priorities in the development of the university against the backdrop of rapid digitization as a construct of the modern university. The materials of the article are based on empirical research, as well as monitoring of open and accessible university information, an online survey of faculty and students of the 4th year of two universities in Rostov-on-don. Findings: The results of the study were discussed using the Zoom digital platform among the teaching staff of two universities in Rostov-on-don, summed up and formulated recommendations. Originality: The scientific novelty lies in the fact that the authors identified and specified the vector of development of a local university in the digital economy, taking into account the professional characteristics, competencies of higher education institutions, their significance for the labor market and the emerging needs of students against the background of complex economic and social phenomena.
A Taxonomy Guided Method to Identify Metaheuristic Components

A component-based view of metaheuristics has recently been promoted to deal with several problems in the field of metaheuristic research. These problems include inconsistent metaphor usage, non-standard terminology and a proliferation of metaheuristics that are often insignificant variations on a theme. These problems make the identification of novel metaheuristics, performance-based comparisons, and selection of metaheuristics difficult. The central problem for the component-based view is the identification of components of a metaheuristic. This paper proposes the use of taxonomies to guide the identification of metaheuristic components. We developed a general and rigorous method, TAXONOG-IMC, that takes as input an appropriate taxonomy and guides the user to identify components. The method is described in detail, an example application of the method is given, and an analysis of its usefulness is provided. The analysis shows that the method is effective and provides insights that are not possible without the proper identification of the components.
Skeleton Keypoints Extraction Method Combined with Object Detection

Big data is a comprehensive result of the development of the Internet of Things and information systems. Computer vision requires a lot of data as the basis for research. Because skeleton data can adapt well to dynamic environment and complex background, it is used in action recognition tasks. In recent years, skeleton-based action recognition has received more and more attention in the field of computer vision. Therefore, the keypoints of human skeletons are essential for describing the pose estimation of human and predicting the action recognition of the human. This paper proposes a skeleton point extraction method combined with object detection, which can focus on the extraction of skeleton keypoints. After a large number of experiments, our model can be combined with object detection for skeleton points extraction, and the detection efficiency is improved.
Momentum of Federalism? National, State, and Municipal Responses to the COVID-19 Pandemic in Germany

This chapter outlines the various political responses and measures to handle the COVID-19 pandemic on the federal, state, and municipal levels in Germany. Here, I take also a look at measures toward migrants and refugees as specific examples of vulnerable groups who are particularly affected by federal and state responses during the pandemic. In addition, I discuss to what extent the concept of federalism was rather beneficial or more debilitating in this political process of finding the right responses and measures. I refer to legal provisions—passed by the federal German government and by 16 member states and various selected cities and municipalities—as well as publications of leading research institutions, which had major impacts on political decisions, and media reports between the beginning of March 2020 and early March 2021. For lack of space, I focus only on the most relevant responses and measures. The findings refer to the following central points: The pandemic politicized Germany enormously and provoked an extremely active legislation of federal and state governments and local entities. While most of the political responses were successful until late summer 2020, federal and state governments struggled to find the right measures during fall and winter 2020/2021. Overall, federalism was so far more an advantage than a disadvantage to manage the pandemic crisis politically.
Towards More Clean Results in Data Visualization: A Weka Usability Experiment

The development of Information Technologies has contributed to the increase and complexity of data, implying a greater diversity of mechanisms for knowledge extraction. This high data availability has made citizens increase their interest in analyzing data and making more informed decisions. Data mining is an intrinsically complex process that expert users generally use. The non-expert users are overwhelmed because they lack relevant techniques for analyzing and understanding these results. This proposal presents a usability experiment to evaluate the level of understanding of the results when applying classification techniques. The users worked with decision trees, one of the “friendliest” of existing patterns. We need to start focusing on new patterns for non-expert users from the results exposed.
Improving Access to Information While Also Revitalizing Interest in Books or Their Authors

This paper explores a path to rapidly expand information access to all while avoiding the politicized fights over copyright reform. That path is the establishment of a centralized distribution engine that starts to work once the commercial life a book is over. Where rightsholders opt-in, a government (or other non-profit) engine will produce and distribute copies of a work to every public library, ensuring that every child and every adult has access to the same materials, regardless of status, wealth, or other identity. By relieving the publisher of the costs of production and distribution, this engine overcomes the financial disincentive to continue investing in a work after it no longer has commercial value and instead shifts efforts to further the non-profit purposes of copyright (e.g., education and learning). In doing so, it will also put the work in front of audiences who have never had the opportunity to use it, creating the potential to reignite interest in the work or the author.
Ore Fluid Types as Recorded in Fluid Inclusions

Minute inclusions in quartz have trapped ore fluids while the gold deposits formed. Study of these fluid inclusions is a direct way to determine the ore fluid composition. The nature of the ore fluid as determined from the inclusions is the fifth characteristic that needs to be accommodated when deciding how gold deposits may form.
Chatbot-Enhanced Requirements Resolution for Automated Service Compositions

This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.
Fpswap: Fair and Privacy Friendly Swap of Digital Assets on Blockchain

Bitcoin introduces a new type of digital currency that does not rely on a central system to process and maintain transactions.Benefiting from the concept of a decentralized ledger, users who do not know or trust each other can still conduct transactions ina peer-to-peer manner. Recently, cryptocurrencies have been introduced to enhance the privacy provided by Bitcoin. For example,CryptoNote, which implements the “Traceable Ring Signature” approach to achieve Untraceability, and Zcash seek to hide thecontent of a crypto note to enhance privacy while allowing the public to verify the validity of a payment transaction. However, allthese schemes consider one-way transaction, and there are few mechanisms to support the two-way exchange of privacy-preservingnotes or digital assets on the chain and simultaneously protect the privacy of the exchange operation. Existing approaches for fairexchange of assets with privacy mostly rely on off-chain or sidechain exchanges, escrow, or centralized services. Instead, in thispaper, we propose a solution that supports the oblivious and private, fully on-chain fair exchange of crypto notes or confidentialdigital assets. We demonstrate this technology by extending zero-knowledge-based crypto notes. To address privacy and multiassetsupport, we built a new zero-knowledge proof system and extended the crypto note format with a new property to representvarious types of digital or “tokenized” assets. We extend the payment protocol to implement fair exchange operations throughthe orchestration of privacy-enhanced asset transfer and payment transactions. Based on possible scenarios during the exchangeoperation, we add new constraints and conditions to the zero-knowledge proof system used for public verification of transactionvalidity. We present experimental results from an implementation of our solution using the xJsnark tool.
Multi-key Encryption Based on RSA and Block Segmentation

Security is an essential part of modern-day communications; this is mainly achieved using cryptography techniques, and one of its most important types is asymmetric encryption which is used to provide both confidentiality and authentication. One of those techniques is the RSA public key algorithm. In this paper, we propose an encryption structure that uses some characteristics of the block cipher techniques and the random ordering of blocks mixed with the traditional RSA algorithm to overcome the problem of increasing security at the expense of the time that is present when using large keys in RSA algorithm; small RSA keys are prone to factorization attacks, while large keys are very slow and inefficient. By using a fixed encryption block size, segmenting and encrypting each segment with a randomly chosen RSA key, we can increase complexity and allow the use of larger block sized without sacrificing speed.