Reviewerly/exHarmony huggingface dataset:
Antenna System for Trilateral Drone Precise Vertical Landing

This paper presents a radio frequency system that can be used to perform precise vertical landings of drones. The system is based on the three-way phase shift detection of a signal transmitted from the landing point. The antenna system is designed by taking into account parameters such as landing tracking area, analog to digital converter (ADC) resolution, phase detector output range, antenna polarization and the effect of antenna axial ratio. The fabricated prototype consists of a landing point antenna that transmits a signal at 2.46 GHz, as well as a drone tri-antenna system that includes a phase shift detection circuitry, ADC and a simple control program that provides the correction instructions for landing. The prototype provides an averaged output data rate (ODR) suitable for landing maneuvers (&#x003E;300 Hz). A simple system calibration procedure (detector output zeroing) is performed by aligning the antenna system. The measurements performed at different altitudes demonstrate both the correct operation of the proposed solution and its viability as an instrument for precision vertical landings.
spherepc: An R Package for Dimension Reduction on a Sphere

Dimension reduction is a technique that can compress given data and reduce noise. Recently, a dimension reduction technique on spheres, called spherical principal curves (SPC), has been proposed. SPC fits a curve that passes through the middle of data with a stationary property on spheres. In addition, a study of local principal geodesics (LPG) is considered to identify the complex structure of data. Through the description and implementation of various examples, this paper introduces an R package [spherepc](https://CRAN.R-project.org/package=spherepc) for dimension reduction of data lying on a sphere, including existing methods, SPC and LPG.
Annihilating and creating nonlocality without entanglement by postmeasurement information

Nonlocality without entanglement (NLWE) is a nonlocal quantum phenomenon that arises in separable state discrimination. We show that the availability of the postmeasurement information about the prepared subensemble can affect the occurrence of NLWE in discriminating nonorthogonal nonentangled states. We provide a two-qubit state ensemble consisting of four nonorthogonal separable pure states and show that the postmeasurement information about the prepared subensemble can annihilate NLWE. We also provide another two-qubit state ensemble consisting of four nonorthogonal separable states and show that the postmeasurement information can create NLWE. Our result can provide a useful method to share or hide information using nonorthogonal separable states.
Exploring English Teachers’ Attitudes and Perceptions of Online Classes: Based on the Teachers’ Experiences

The purpose of this study was to explore Korean English teachers’ attitudes and perceptions of online classes during the pandemic, based on their experiences with online classes. The following questions were constructed; 1) What skills should the English teachers be equipped for online classes? 2) How have they perceived online classes during the pandemic? 3) What kinds of online class can be most effective for learning? Six middle and high school English teachers participated in this study. The survey questionnaire, in-depth interviews, and the researcher’s journal were collected and analyzed. A thematic analysis approach was employed to explore the characteristics of online classes. According to the findings, the teachers considered computer-handling technology the most important skill to provide online classes effectively. Also they expressed mixed attitudes toward online classes, commenting the advantages and disadvantages of online classes. Lastly, they turned out to have similar opinions on which method would be the most effective online class, pointing out real-time classes would be the most effective one. Based on these results, implications for language teaching and learning are suggested.
A Novel Two-Dimensional Histogram Shifting Video Steganography Algorithm for Video Protection in H.265/HEVC

Video security is becoming a significant issue in multimedia industry while considering the increasingly prosperous TikTok and video conference. The video steganography technique which plays a key role in protecting video security has been a hot research area in recent years. In this paper, we provide a novel two-dimension histogram shifting video steganography algorithm for H.265/HEVC based video applications. In this two-dimension histogram shifting scheme, we can achieve embedding 4-bits metadata (secret data) while at most modifying only one-bit QDST coefficient. In order to eliminate the embedding error for carrier videos, the embedded carrier is limited to 4 × 4 block QDST coefficients. First, the high frequency coefficients in 4 × 4 blocks are selected to form an embedded coefficient pair, which are divided into 19 non-intersecting sets according to the value of the coefficient pair. Second, according to the set of embedding coefficient pair, the metadata is embedded by provided two-dimensional histogram shifting rules. Finally, the embedded carrier block is encoded into bitstream by entropy encoding, and the metadata is extracted by inverse process corresponding to embedding. The experimental results have been proven the superiority of efficiency and performance about the proposed algorithm.
Geo-Storylines: Integrating Maps Into Storyline Visualizations

Storyline visualizations are a powerful way to compactly visualize how the relationships between people evolve over time. Real-world relationships often also involve space, for example the cities that two political rivals visited together or alone over the years. By default, Storyline visualizations only show implicitly geospatial co-occurrence between people (drawn as lines), by bringing their lines together. Even the few designs that do explicitly show geographic locations only do so in abstract ways ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.,</i> annotations) and do not communicate geospatial information, such as the direction or extent of their political campains. We introduce Geo-Storylines, a collection of visualisation designs that integrate geospatial context into Storyline visualizations, using different strategies for compositing time and space. Our contribution is twofold. First, we present the results of a sketching workshop with 11 participants, that we used to derive a design space for integrating maps into Storylines. Second, by analyzing the strengths and weaknesses of the potential designs of the design space in terms of legibility and ability to scale to multiple relationships, we extract the three most promising: Time Glyphs, Coordinated Views, and Map Glyphs. We compare these three techniques first in a controlled study with 18 participants, under five different geospatial tasks and two maps of different complexity. We additionally collected informal feedback about their usefulness from domain experts in data journalism. Our results indicate that, as expected, detailed performance depends on the task. Nevertheless, Coordinated Views remain a highly effective and preferred technique across the board.
What’s on Your Mind, NICO?

Abstract Explainable AI has become an important field of research on neural machine learning models. However, most existing methods are designed as tools that provide expert users with additional insights into their models. In contrast, in human-robot interaction scenarios, non-expert users are frequently confronted with complex, embodied AI systems whose inner workings are unknown. Therefore, eXplainable Human-Robot Interaction (XHRI) should leverage the user’s intuitive ability to collaborate and to use efficient communication. Using NICO, the Neuro-Inspired COmpanion, as a use-case study, we propose an XHRI framework and show how different types of explanations enhance the interaction experience. These explanations range from (a) non-verbal cues for simple and intuitive feedback of inner states via (b) comprehensive verbal explanations of the robot’s intentions, knowledge and reasoning to (c) multimodal explanations using visualizations, speech and text. We revisit past HRI-related studies conducted with NICO and analyze them with the proposed framework. Furthermore, we present two novel XHRI approaches to extract suitable verbal and multimodal explanations from neural network modules in an HRI scenario.
Cyber Security Perspective of Top Future Technologies

In last period, the technology development has experienced accelerated and extreme growth resulting in many concepts such as: blockchain, quantum technology, Robotic Process Automation (RPA), Artificial Intelligence (AI) and Machine Learning (ML), Internet of Things (IoT), edge computing, Virtual Reality (VR) and Augmented Reality (AR), and 5th (5G) and 6th (6G) generation networks. This trend is increased by the SARS-CoV-2 pandemic, given that the technology enabled continuance of affairs. In parallel, the threats and attacks on cyber security have dramatically increased as well, causing many losses of all kinds. These two trends need to be treated combined for the purpose of timely and proper addressing and solving of security issues and challenges in the context of future technologies. Therefore, this work contributes by providing a brief overview of cyber security perspective, i.e. challenges and issues for top future technologies. The conducted analysis has shown that security issues for future technologies are numerous and need to be treated in systematic and organized manner during the design of technologies and products in order to minimize the risk and damage and maximize the utilization and benefit.
Public Opinion Influence Through Electronic Propaganda Activities

The chapter considers future foresees of electronic propaganda and digital media transformation challenges with public opinion evolution dynamics. Special accent is given to the analytical perspective of the problem, combining both morphological scenario landscape establishment with system-of-system joint analysis. Results mixed assessment from human factor biometric and simulated multicriteria stochastic evaluation perspectives are further presented. Final wrap-up discussion is provided, trying to generalize the outlined findings and uncertainties towards the future from media and technological perspective, giving additional accents to the new smart digital society and people progress.
Simulator sickness and cybersickness as significant indicators in a primary selection of candidates for FPV drone piloting

With the rise in popularity and availability of drones, the side effects of that technology have been noted among the users. A significant accumulation of such information comes from institutes and training centers equipped with First Person View (FPV) drone pilot training, the most common use of drones. The unmanned aerial vehicle (UAV) has a visualization system that ensures a clear, high-resolution view of the spatial position outside the drone.The symptoms of simulator sickness/cybersickness/visually induced motion sickness are similar to symptoms of motion sickness. Dizziness, nausea, spatial disorientation, and limited capacity for concentration may be observed. However, those symptoms depend on individual user predispositions.Research [1], [2] shows that even 80% of civilian users of FPV system could experience dizziness, whereas, in the case of military users, lower susceptibility to those symptoms have been noted.This work aims to prove that the lack of resistance to simulator sickness/cybersickness may be a disqualifying factor in selecting FPV drone pilots.
Number Needed to Quarantine and Proportion of Prevented Infectious Days by Quarantine: Evaluating the Effectiveness of COVID-19 Contact Tracing

Objectives: Information on the effectiveness of COVID-19 contact tracing is lacking. We proposed 2 measures for evaluating the effectiveness of contact tracing and applied them in a public health unit in northern Portugal. Methods: This retrospective cohort study included the contacts of people with COVID-19 diagnosed July 1–September 15, 2020. We examined 2 measures: (1) number needed to quarantine (NNQ), as the number of quarantine person-days needed to prevent 1 potential infectious person-day; and (2) proportion of prevented infectious days by quarantine (PPID), as the number of potential infectious days prevented by quarantine divided by all infectious days. We assessed these measures by sociodemographic characteristics, types of contacts, and intervention timings (ie, time between diagnosis or symptom onset and intervention). We considered 3 scenarios for infectiousness periods: 10 days before to 10 days after symptom onset, 3 days before to 3 days after symptom onset, and 2 days before to 10 days after symptom onset. Results: We found an NNQ of 19.8-41.8 person-days and a PPID of 19.7%-38.2%, depending on the infectiousness period scenario. Effectiveness was higher among cohabitants and symptomatic contacts than among social or asymptomatic contacts. NNQ and PPID changed by intervention timings: the effectiveness of contact tracing decreased with time from diagnosis to quarantine of contacts and with time from symptom onset of the index case to contacts’ quarantine. Conclusions: These proposed measures of contact tracing effectiveness of communicable diseases can be important for decision making and prioritizing contact tracing when resources are scarce. They are also useful measures for communication with the general population, policy makers, and clinicians because they are easy to understand and use to assess the impact of health interventions.
SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks

Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importance to certify the safety of these DNNs, i.e. that they comply with a formal safety specification. While safety certification tools exactly answer this question, they are of no help in debugging unsafe DNNs, requiring the developer to iteratively verify and modify the DNN until safety is eventually achieved. Hence, a repair technique needs to be developed that can produce a safe DNN automatically. To address this need, we present SpecRepair, a tool that efficiently eliminates counter-examples from a DNN and produces a provably safe DNN without harming its classification accuracy. SpecRepair combines specification-based counter-example search and resumes training of the DNN, penalizing counter-examples and certifying the resulting DNN. We evaluate SpecRepair’s effectiveness on the ACAS Xu benchmark, a DNN-based controller for unmanned aircraft, and two image classification benchmarks. The results show that SpecRepair is more successful in producing safe DNNs than comparable methods, has a shorter runtime, and produces safe DNNs while preserving their classification accuracy.
Importance of Big Data Mining: (Tools, Techniques)

We are in that era in which a substantial amount of data is being generated all the time. The need for tools and techniques is increasing day by day to evaluate and maintain this data correctly. Data analytics is beneficial in extracting important information because this data contains many types of information. There are many types of tools, techniques and algorithms for data mining, with the help of which we do data mining easily. These tools and algorithms can handle considerable data easily and also give important information from it at the time of need. Given the increasing order of data day by day, new tools and techniques are coming, with the help of which mining of data is becoming easier. Sometimes with the help of some good tools, we can only visualize the data which will be useful in future, which also saves our time and expens
QoS/QoE in Flying Ad Hoc Networks Applied in Natural Disasters

In this work, a group of mechanisms are exposed to provide quality of experience in flying ad hoc networks using a swarm of drones in a natural disaster service. End-to-end video traffic was analyzed. The metrics used to experimentally measure QoE/QoS are: delay, jitter and packet loss. The experience quality was evaluated before the disaster (C00), at the moment (B00) and after the disaster (I00). The methodology used to perform the design was experimental, and the NS simulator was used to evaluate the behavior of the swarm of drones connected through a flying ad hoc network. To perform data analysis, treatment and repetitions related to video traffic, the response surface methodology (MSR) was used, which is a set of mathematical techniques in order to optimize the obtained responses. The composite core design (DCC) was also used as it was the best fit to our experiment due to its flexibility. Since the quality of the experience was evaluated at three moments, the quality of services was also analyzed with three metrics. The main contributions of the research are a mathematical model of the quality of the experience based on the quality of the service; an experiment design using the end-to-end NS simulator; a methodology for the mathematical and statistical analysis of the data obtained; an algorithm that allows, from service quality metrics, to obtain the quality of the experience for end-to-end video traffic; and a proposal for future work for data analysis in a physical environment and applied to the environmental sector.
Automated UX Evaluation for User-Centered Design of VR Interfaces

User-centered design (UCD) is a continuous product development process where user feedback shapes the final product. An initial product design is iteratively refined until it provides a satisfying user experience (UX). Applying this iterative process to the development of Virtual Reality (VR) applications using traditional evaluation techniques such as questionnaires and interviews following UX testing is time-consuming. Active feedback from the users needs to be manually evaluated before it can be reflected in the subsequent refinements. In this work, we propose a framework to speed up the UCD process of VR applications by utilizing automated UX evaluation. We demonstrate it on the UCD of a VR-assisted robot programming interface. We conduct a UX study with 14 participants and report that we achieve a final design that provides a better UX, which evolved in short iteration cycles.
Managing the First Wave

Higher education institutions (HEI s) internationally have been significantly impacted by COVID-19 since March 2020. While there are many medical, social, economic, and educational dilemmas resulting from this pandemic, its specific impact, and responses of HEI s across the globe remains less interrogated. Drawing from publicly available policy statements from three HEI s in two English-speaking Caribbean territories, dated between March and September 2020, the authors interrogate the pre-existing and emerging opportunities that triggered and formed a response to ensure continuity of higher education during a public health emergency. This chapter explores issues related to communication and engagement with stakeholders, adaptations to ensure academic continuity and the management of fiscal resources. In the end, the authors make recommendations on how small universities can manage teaching and learning in times of economic and social disruptions.
VirTEE

Modern security architectures provide Trusted Execution Environments (TEEs) to protect critical data and applications against malicious privileged software in so-called enclaves. However, the seamless integration of existing TEEs into the cloud is hindered, as they require substantial adaptation of the software executing inside an enclave as well as the cloud management software to handle enclaved workloads. We tackle these challenges by presenting VirTEE, the first TEE architecture that allows strongly isolated execution of unmodified virtual machines (VMs) in enclaves, as well as secure live migration of VM enclaves between VirTEE-enabled servers. Combined with its secure I/O capabilities, VirTEE enables the integration of enclaved computing in today's complex cloud infrastructure. We thoroughly evaluate our RISC-V-based prototype, and show its effectiveness and efficiency.
GTuner

It is an open problem to compile DNN models on GPU and improve the performance. A novel framework, GTuner, is proposed to jointly learn from the structures of computational graphs and the statistical features of codes to find the optimal code implementations. A Graph ATtention network (GAT) is designed as the performance estimator in GTuner. In GAT, graph neural layers are used to propagate the information in the graph and a multi-head self-attention module is designed to learn the complicated relationships between the features. Under the guidance of GAT, the GPU codes are generated through auto-tuning. Experimental results demonstrate that our method outperforms the previous arts remarkably.
Random Walk Binary Grey Wolf Optimization for feature selection in sEMG based hand gesture recognition

In the last few years, there has been a significant on the relation between Human Machine Interface (HMI) and hand gesture recognition. Moreover, researchers are recently focusing further on the development of new methods for gesture classification and signal feature selection mainly based on proposed swarm intelligence optimization methods. This paper presents a versatile optimizer - Random Walk Binary Grey Wolf Optimization. Belonging to the category of evolutionary optimization, this meta-heuristic algorithm can solve optimization problems and perform feature selection. Firstly, the optimizer improvises searchability through random walks in the search space. Secondly, through the optimal selection of features, the optimizer improvises the prediction accuracy and reduces the computational complexity. Moreover, this paper presents a collection of surface electromyography sEMG signals from 10 subjects performing each 10 gestures for 10 observations each. For the data collection, two myoware sensors were used on the forearm of the participants. Features in time and feature domain were extracted from the collected signals and used for the RWBGWO implementation. The performance of the proposed algorithm is verified using the collected sEMG for gesture recognition. The results and observations show that RWBGWO is an efficient optimizer having a better performance in comparison with BGWO, binary particle swarm optimizer BPSO and binary hybrid grey wolf particle swarm optimizer BGWOPSO in feature selection with the least number of selected features achieving a higher classification accuracy reaching 95.5%.
Real-Time Twitter Sentiment Analytics and Visualization Using Vader

Twitter is an excellent source of real-time user-generated data. This is a platform where anyone may use Tweets to share their own views, opinions, or feedback on various topics. These themes can be from any field, including politics, business, and entertainment. Sentiment Analysis is a technique of obtaining key consumer data from text in the form of a sentiment score, and it identifies the emotional tone behind a body of text. If this information can be obtained and assessed accurately, and can be used as a vital element in making a decision. However, due to the changing and complex formats of tweets, analyzing tweets has been more challenging. With sentiment values in mind, there is a huge amount of use of emoticons and slang. In this paper, we intend to provide visual demonstration of real-time tweets by classifying and determining the polarity score of live tweets where it is positive, negative, or neutral using Vader, that is more efficient than traditional algorithms, and providing the user with a graph of the subject matter, which is updated in accordance with the tweets posted every second, trying to make decision-making a very simple process by providing information in a word cloud