Reviewerly/exHarmony huggingface dataset:
Speaking in front of cartoon avatars: A behavioral and psychophysiological study on how audience design impacts on public speaking anxiety in virtual environments

Public speaking anxiety is defined as a strong difficulty in speaking in front of an audience and has been shown to impair work performance and social relationships. Virtual Reality (VR) offers an efficient tool for modulating public speaking anxiety through a wide range of customizations concerning environmental settings. However, scientific research needs to understand better what features of the simulated environment are more important to increase or reduce participants' perceived discomfort. The present study investigates the role of visual (human vs cartoon characters) and acoustic (human vs robotic voice) audience features on perceived anxiety, sense of presence, and perceived realism in an interactive VR public speaking scenario. 42 participants (mean age = 24 y.o; Females = 30) performed four public speaking sessions, characterized by different levels (high vs. low) of graphic and acoustic audience design. Both explicit (questionnaires) and implicit physiological measures (Electrodermal activity-EDA) collected during audience interaction were used to assess the participants' experiences. The results showed that the features of the simulated audience played a crucial role in perceived anxiety during a virtual public speaking. Specifically, the more realistic level of graphic and acoustic stimuli resulted in higher levels of self-reported anxiety as compared to the lower realism level. However, the experienced realism and the sense of presence seem more affected by the graphical than acoustic features of the virtual environment. By contrast, the acoustic features impact on the interaction realism with the virtual audience. Interestingly, the robotic voice (lower acoustic realism) increased electrodermal response during the interaction with the audience, interpreted as a break in the sense of presence. A positive correlation between anxiety, sense of presence, and experienced realism was found. As well, perceived anxiety is correlated with electrodermal activity during the performance. Nevertheless, physiological activity is more affected by the first experience than the realism features, suggesting the presence of a habituation effect across the repeated sessions. Taken together, the results of our study showed that multisensory features (graphical and acoustic) of the virtual environment play a fundamental role in creating realistic public speaking experiences and might be used within gamification strategies for soft skill training (e.g., for improving public speaking anxiety).
Tool for automatic labeling of objects in images obtained from Carla autonomous driving simulator

To successfully train modern object detectors in a supervised manner, a large number of labeled images is usually required. Collecting and annotating images can be an expensive and time-consuming job, especially in the field of autonomous driving. A cheaper and faster alternative can be found in computer simulations of real-world traffic scenes, where the object of interest can be automatically labeled. In this spirit, a tool for automatic labeling of the images obtained from CARLA autonomous driving simulator is proposed. The tool runs in parallel with CARLA simulator and creates a synthetic dataset with annotations in the appropriate format for the following objects of interest: traffic lights, traffic signs, vehicles, and pedestrians. The tool enables the end-user to generate a synthetic dataset with a desired number of images and with desired parameters such as weather condition, image resolution, and traffic density. In that way, large synthetic datasets can be generated in a short period of time.
Discussion Manipulation, Language and Domain Dependent Models: An Overview

The amount of antisocial online behavior (AOB) in the political field has been on the rise in recent years. In the research, we are dealing with the reason for confusing AOB with other forms of antisocial behavior. We specifically dealt with NLP in the political field in the Slovak language, with a specific number of prefixes and suffixes. In this paper, we focus on the area of detection of manipulation of political discussions. The data source consisted of previously collected data in the period from April 2018 with an update until November 19, 2022 realized within this research from the site demagog.sk, which we supplemented with new claims. We trained several classification models on preprocessed data and evaluated them. For multi-class classification, the best results were achieved using logistic regression and a support vector machine trained on the resampled dataset—both achieving an accuracy of 0.56 and a macro F1 score of 0.39. In the case of binary classification, the best results were achieved by logistic regression—accuracy 0.7 and macro F1 score 0.56. These models could help detect manipulation in online political discussions.
Drones Tracking Adaptation Using Reinforcement Learning: Proximal Policy optimization

This paper presents a reinforcement learning approach for automatic adaptation of the process noise covariance (Q). The Q value plays a crucial role in estimating future state values within a Kalman filter tracking system. Proximal Policy Optimization (PPO), a state-of-the-art policy optimization algorithm, was employed to determine the optimal Q value that enhances tracking performance, as measured by Root Mean Square Error (RMSE). Our results demonstrate the successful learning capability of the PPO agent over time, enabling it to suggest the optimal Q value by effectively capturing the policy of appropriate rewards under varying environmental conditions. These outcomes were compared with those of a feed-forward neural network learning, the Castella innovation/ Q values mapping, and fixed Q values. The PPO algorithm yielded promising results. We employed the Stone Soup library to simulate ground truths, measurements, and the Kalman filter tracking process.
Understanding Advertising in Virtual Worlds and Best Practices for Metaverse Advertising

The metaverse, a virtual realm where individuals can engage in a plethora of experiences and interact with one another, is rapidly gaining traction among both consumers and corporations. As the metaverse continues to grow in popularity, advertising within virtual worlds is becoming increasingly important for businesses looking to reach new customers. However, there is a lack of research on the effectiveness of advertising in virtual worlds and best practices for creating effective campaigns. This study aims to address this research gap by exploring the current state of advertising in virtual worlds and identifying areas where further research is needed. The study will also develop best practices for creating effective advertising campaigns in virtual worlds by analyzing existing advertising campaigns and identifying successful strategies including comparison between traditional and metaverse advertising. The results of this study will contribute to the understanding of advertising in virtual worlds and help businesses to make more informed decisions about their advertising efforts in the metaverse.
Instantaneous 3D velocity estimation using coordinated OFDM Radar nodes

Recently, Joint Communication and Sensing (JCAS) gained a lot of attention due to its relevance for the upcoming 6G standard. Sensing can supplement communication. For example, when the sensing module senses the presence of an obstacle and its motion direction, it then passes the sensing result to a network orchestrator module. In this way, the communication module can take reasonable measures to maintain communication quality. This paper describes 2D and 3D instantaneous velocity estimation methodologies for a target tracked using coordinated OFDM radar nodes. With our approach, the 2D and 3D velocities of objects can be found accurately. A precise estimation of the instantaneous velocity improves the prediction of the target’s trajectory, which is crucial for many potential applications, such as automated guided vehicles and robotic arms in factories.
Runtime Verification of Self-Adaptive Systems with Changing Requirements

To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements formalized as temporal-logic properties. These requirements, however, may change due to system evolution or uncertainty in the environment, managed system, and requirements themselves. Thus, the properties under investigation by the runtime verification have to be dynamically adapted to represent the changing requirements while preserving the knowledge about requirements satisfaction gathered thus far, all with minimal latency. To address this need, we present a runtime verification approach for self-adaptive systems with changing requirements. Our approach uses property specification patterns to automatically obtain automata with precise semantics that are the basis for runtime verification. The automata can be safely adapted during runtime verification while preserving intermediate verification results to seamlessly reflect requirement changes and enable continuous verification. We evaluate our approach on an Arduino prototype of the Body Sensor Network and the Timescales benchmark. Results show that our approach is over five times faster than the typical approach of redeploying and restarting runtime monitors to reflect requirements changes, while improving the system’s trustworthiness by avoiding interruptions of verification.
DocToModel: Automated Authoring of Models from Diverse Requirements Specification Documents

Early stages of Software Development Life Cycle (SDLC) namely requirement elicitation and requirements analysis have remained document-centric in the industry for market-driven, complex, large-scale business applications and products. The documentation typically runs into hundreds of Natural Language (NL) text documents which requirements engineers need to sift looking for the relevant information and also maintain these documents in-sync over time – a time-consuming and error-prone activity. Much of this difficulty can be overcome if the information is available in a structured form that is amenable to automated processing. Purposive models offer a way out. However, for easy adoption by industry practitioners, these models must be populated from NL text documents in a largely automated manner. This task is characterized by high variability with several documents containing different information conforming to different structures and styles. As a result, purposive information extractors need to be developed for each project/ product. Moreover, being an open-ended space there is no upper bound on the information extractors that need to be developed. To overcome this difficulty, we propose a document structure agnostic and meta-model agnostic tool, DocToModel, for the automated authoring of models from NL text documents. It provides a pattern mapping language to specify a mapping of structured and unstructured document information to meta-model elements, and a pattern interpreter to automate model authoring. The configurable and extensible architecture of DocToModel makes it generic and amenable to easy repurposing for other NL documents. This paper, describes the approach and illustrates its utility and efficacy on multiple real-world case studies.
A/B Integrations: 7 Lessons Learned from Enabling A/B testing as a Product Feature

A/B tests are the gold standard for evaluating product changes. At Microsoft, for example, we run tens of thousands of A/B tests every year to understand how users respond to new designs, new features, bug fixes, or any other ideas we might have on what will deliver value to users. In addition to testing product changes, however, A/B testing is starting to gain momentum as a differentiating feature of platforms or products whose primary purpose may not be A/B testing. As we describe in this paper, organizations such as Azure PlayFab and Outreach have integrated experimentation platforms and offer A/B testing to their customers as one of the many features in their product portfolio. In this paper and based on multiple-case studies, we present the lessons learned from enabling A/B integrations – integrating A/B testing into software products. We enrich each of the learnings with a motivating example, share the trade-offs made along this journey, and provide recommendations for practitioners. Our learnings are most applicable for engineering teams developing experimentation platforms, integrators considering embedding A/B testing into their products, and for researchers working in the A/B testing domain.
From Self-Adaptation to Self-Evolution Leveraging the Operational Design Domain

Engineering long-running computing systems that achieve their goals under ever-changing conditions pose significant challenges. Self-adaptation has shown to be a viable approach to dealing with changing conditions. Yet, the capabilities of a self-adaptive system are constrained by its operational design domain (ODD), i.e., the conditions for which the system was built (requirements, constraints, and context). Changes, such as adding new goals or dealing with new contexts, require system evolution. While the system evolution process has been automated substantially, it remains human-driven. Given the growing complexity of computing systems, human-driven evolution will eventually become unmanageable. In this paper, we provide a definition for ODD and apply it to a self-adaptive system. Next, we explain why conditions not covered by the ODD require system evolution. Then, we outline a new approach for self-evolution that leverages the concept of ODD, enabling a system to evolve autonomously to deal with conditions not anticipated by its initial ODD. We conclude with open challenges to realise self-evolution.
SUAVE: An Exemplar for Self-Adaptive Underwater Vehicles

Once deployed in the real world, autonomous underwater vehicles (AUVs) are out of reach for human supervision yet need to take decisions to adapt to unstable and unpredictable environments.To facilitate research on self-adaptive AUVs, this paper presents SUAVE, an exemplar for two-layered system-level adaptation of AUVs, which clearly separates the application and self-adaptation concerns.The exemplar focuses on a mission for underwater pipeline inspection by a single AUV, implemented as a ROS 2-based system.This mission must be completed while simultaneously accounting for uncertainties such as thruster failures and unfavorable environmental conditions.The paper discusses how SUAVE can be used with different self-adaptation frameworks, illustrated by an experiment using the Metacontrol framework to compare AUV behavior with and without selfadaptation.The experiment shows that the use of Metacontrol to adapt the AUV during its mission improves its performance when measured by the overall time taken to complete the mission or the length of the inspected pipeline.
An energy trading framework using smart contracts

The adoption of blockchain in various industries is gaining more popularity, especially in the energy industry. With the increase of distributed energy resources (DER), energy users can generate, store, and trade their resources with others. Utility companies or energy users are influenced by blockchain-based peer-to-peer (P2P) energy trading markets. Blockchain adds transparency and immutability to the involved transactions. Smart contracts in blockchain automatically execute when the conditions are met without any third-party intervention. Motivated by these benefits, in this paper an energy trading framework is developed using Ethereum smart contracts. Energy users can trade their excess energy or buy energy using the smart contract functions. Smart contract written in solidity is compiled and deployed using remix with injected metamask provider. Ganache is used to create accounts and these accounts are imported to metamask for signing transactions. We also discuss alternative methods for smart contract deployment. Computational cost analysis is performed by evaluating the gas consumption analysis for the smart contract functions.
Along the Margins: Marginalized Communities’ Ethical Concerns about Social Platforms

In this paper, we identified marginalized communities’ ethical concerns about social platforms. We performed this identification because recent platform malfeasance indicates that software teams prioritize shareholder concerns over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. We first scraped 586 marginalized communities’ subreddits, aggregated a dataset of their social platform mentions and manually annotated mentions of ethical concerns in these data. We subsequently analyzed trends in the manually annotated data and tested the extent to which ethical concerns can be automatically classified by means of natural language processing (NLP). We found that marginalized communities’ ethical concerns predominantly revolve around discrimination and misrepresentation, and reveal deficiencies in current software development practices. As such, researchers and developers could use our work to further investigate these concerns and rectify current software flaws.In this paper, we identified marginalized communities’ ethical concerns about social platforms. We did this because recent platform wrongdoing indicates that software teams prioritize profit over user concerns. Additionally, these platform shortcomings often have devastating effects on marginalized populations. To accomplish this, we collected Reddit posts from marginalized communities’ subreddits where users mention social media platforms. Then, we labeled whether posts contained mentions of ethical concerns, like privacy or misinformation. Finally, we established trends within the resulting data and used artificial intelligence (AI) to find these ethical concerns automatically. We discovered that marginalized communities’ ethical concerns revolve around discrimination and misrepresentation, among other problems, and reveal deficiencies in current social platforms. As such, researchers and software engineers could use our work to further investigate these concerns and rectify present software flaws.
Do Software Security Practices Yield Fewer Vulnerabilities?

Due to the ever-increasing number of security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers in making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts.To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecard security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found that four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs. time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that a practice can aid in the reduction of externally-reported vulnerabilities. We suggest that vulnerability count and security score data be refined such that these measures may be used to provide actionable guidance on security practices.
A Quantum Neural Network Regression for Modeling Lithium-ion Battery Capacity Degradation

Given their high power density, low discharge rate, and decreasing cost, rechargeable lithium-ion batteries (LiBs) have found a wide range of applications such as power grid-level storage systems, electric vehicles (EVs), and mobile devices. Developing a framework to accurately model the nonlinear degradation process of LiBs, which is indeed a supervised learning problem, becomes an important research topic. This paper presents a classical-quantum hybrid machine learning approach to capture the LiB degradation model that assesses the loss of battery cell life from the operating profiles. Our work is motivated by recent advances in quantum computers as well as the similarity between neural networks and quantum circuits. Similarly to adjusting weight parameters in conventional neural networks, the parameters of the quantum circuit, namely, the degree of freedom of the qubits, can be tuned to learn a nonlinear function in a supervised learning fashion. As a proof of concept paper, our obtained numerical results with the battery dataset provided by NASA demonstrate the ability of the quantum neural networks in modeling the nonlinear relationship between the degraded capacity and the operating cycles. We also discuss the potential advantage of the quantum approach compared to conventional neural networks in classical computers in dealing with massive data, especially in the context of future penetration of EVs and energy storage.
Compact Depth-Wise Separable Precise Network for Depth Completion

Predicting a dense depth map from synchronized LiDAR scans and RGB images using compact deep neural networks presents a significant challenge.While most state-of-the-art models enhance prediction accuracy by increasing the number of parameters, leading to substantial memory consumption, depth completion tasks in areas such as autonomous driving primarily utilize edge devices powered by embedded GPUs.In this paper, we introduce a methodology for creating an efficient, high-fidelity depth completion model derived from a base model.Our proposed compact model replaces conventional convolutional encoder layers with depth-wise separable convolutions, and transposed convolutional decoders with up-sampling plus depth-wise separable convolution.We further employ random layer pruning as a stability test, guiding the design of our architecture and preventing over-parameterization.Additionally, we introduce a straightforward yet robust knowledge distillation method to enhance network performance and improve model scalability to meet higher quality requirements.Our experimental results demonstrate substantial improvement over existing compact models in terms of state-of-the-art performance, while significantly reducing the number of parameters compared to larger models.