Reviewerly/exHarmony huggingface dataset:
Vision: Usable Privacy for XR in the Era of the Metaverse

Extended Reality (XR) — an umbrella term for Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR) — has penetrated the consumer market and is prone to increasingly impact our lives in the near future. Various devices, e.g., MR glasses, AR smartphones apps, or VR headsets, are becoming more affordable, and leader tech companies are heavily investing for a more immersive, realistic, and connected future. Lately, this vision of an interconnected virtual space for people to work, learn, play and share experiences with others has been formulated as the so-called “Metaverse”. This expected paradigm shift will heavily rely on XR, and hence implies an unprecedented amount of sensible data being collected about the users. Indeed, XR devices collect large amounts of sensitive data, including biometric data (e.g., eye gaze and body movement data) that are primarily used as Natural User Interfaces (NUIs) or for the proper functioning of technologies and services. However, research has identified a number of privacy and security threats rooting from this pervasive data collection, as well as privacy threats regarding XR inputs, outputs, user interactions and devices themselves. Still, further efforts must be made to guarantee the privacy and safety of users in a usable fashion in the future, and XR must be considered as a whole rather than as the sum of its parts to match the vision of the Metaverse. In this context, we propose to analyse the gap between user privacy perceptions in XR as a whole, and their concrete behaviour. The goal of this research is to understand the differences and similarities between AR, MR and VR in terms of user privacy perceptions. This will help us to better understand the relationships between XR variants, which, we argue, is an important requirement to approach the future evolution of these technologies, and to consider usable privacy aspects that match the entire XR spectrum. Adopting this vision early on will be beneficial for future work, and will be the foundation for the implementation of a usable privacy-preserving solution in order to raise awareness and empower users by giving them more control over their privacy in the context of these new and future technologies.
Common European Data Spaces: Challenges and Opportunities

Abstract Common European data sharing spaces are essential for the implementation of the European digital market. This chapter addresses the challenges and opportunities of Data Spaces identified by the Big Data Value Association community. It brings forward five independent goals, convergence, experimentation, standardization, deployment, and awareness, each targeted toward specific stakeholders in the data sharing ecosystem and presents a timeframe when the goals should take place. Furthermore, we have proposed actions based on BDVA recommendations and mapped them over the five goals.
Content-based Recommendations Using Similarity Distance Measures with Application in the Tourism Domain

In this paper, we explore the use of similarity distance measures for Content-based recommendations for touristic attractions. First, we study ways of deploying hierarchies of points of interests (POIs) and operate upon them with well-known similarity distance measures originating in the text analysis domain. Then, we progressively build three novel, hierarchy-free, similarity measures, and discuss their strengths and weaknesses. We end up with a measure, the Weighted Extended Jaccard Similarity (WEJS) that combines information regarding the user interests (in the form of user preference-related weights applied on the items’ features) and specific items’ characteristics (in the form of particular values for the items’ features). As such, the use of WEJS allows the provision of recommendations that are effectively personalized. Interestingly, though it is a hierarchy-free measure, it is able to recommend items based on others that would naturally appear close in a features-based POIs hierarchy; while at the same time it is able to capture similarities among items that would be distant to each other in any hierarchy built solely based on the POIs’ features. Our systematic experimental evaluation on a real-world dataset showcases the benefits and limitations of the various measures, and confirms the effectiveness of WEJS in offering “rich” and personalized recommendations.
Bridging Script and Animation Utilizing a New Automatic Cinematography Model

For animated film-making, automatic cinematography is an effective approach for junior filmmakers to speed up the process. The virtual camera placement in the 3D environment can be automatically conducted by auto-cinematography algorithms. In the literature, the algorithms proposed were mainly optimized from aesthetic point of view, thus the scene captured by virtual cameras may not effectively represent the intention of the script sometimes. In this paper, a new model that incorporates the fidelity and aesthetic models in a unified framework was proposed, so that the visual presentation of the input script and the compliance of the generated video with given cinematography specifications can be jointly considered during the optimization process. In this way, the problem of virtual camera placement is translated into an optimization problem that can be solved through dynamic programming that optimizes the computational efficiency. Experimental results shows that the proposed algorithm has quality improvement up to 35% compared to the earlier methodology and the content of the video is easier to understand. More video examples can be reviewed https://youtu.be/0PUdV6OeMac.
Towards Trustworthy Artificial Intelligence in Healthcare

Healthcare informatics is an interdisciplinary area where computer science, data science, cognitive science, informatics principles, and information technology meet to address problems and support healthcare, medicine, public health, and/or everyday wellness. In many healthcare and medical applications, it is helpful to have models that can learn from historical healthcare data or instances to make predictions on future instances. For human to trust these models or to perceive these models to be trustworthy, it is equally important to build a trustworthy artificial intelligence (AI) solution. Hence, in this paper, towards trustworthy AI in healthcare, we present an explainable AI (XAI) solution that makes accurate predictions and explains the predictions. Evaluation results on real-life datasets demonstrates the effectiveness of our XAI solution towards trustworthy AI in healthcare.
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning

Offline reinforcement learning (RL) Algorithms are often designed with environments such as MuJoCo in mind, in which the planning horizon is extremely long and no noise exists. We compare model-free, model-based, as well as hybrid offline RL approaches on various industrial benchmark (IB) datasets to test the algorithms in settings closer to real world problems, including complex noise and partially observable states. We find that on the IB, hybrid approaches face severe difficulties and that simpler algorithms, such as rollout based algorithms or model-free algorithms with simpler regularizers perform best on the datasets.
Adaptive Control of Robotic Manipulators using Deep Neural Networks

In this paper, we present a lifelong deep learning-based control of robotic manipulators with nonstandard adaptive laws using singular value decomposition (SVD) based direct tracking error driven (DTED) approach. Moreover, we incorporate concurrent learning (CL) to relax persistency of excitation condition and elastic weight consolidation (EWC) for lifelong learning on different tasks in the adaptive laws. Simulation results confirm theoretical conclusions.
Quantum reinforcement learning method and application based on value function

The Multi-Arm bandit(MAB) problem is a classical problem in the field of reinforcement learning with only one state. The Grid problem is a multi-state problem for reinforcement learning. In this work, we focus on how to combine the classical value function method to quantum computation, and we propose three novel quantum reinforcement learning(QRL) algorithms for the MAB problem and one novel QRL algorithm, which is combined with the quantum random walk and Grover algorithm, for the Grid problem. From the experiments, the learning process is speed-up by combining the value function with quantum computation.
A Logic of “Black Box” Classifier Systems

Binary classifiers are traditionally studied by propositional logic ( $$\textsf{PL}$$ ). $$\textsf{PL}$$ can only represent them as white boxes, under the assumption that the underlying Boolean function is fully known. Binary classifiers used in practical applications and trained by machine learning are however opaque. They are usually described as black boxes. In this paper, we provide a product modal logic called PLC (Product modal Logic for binary input Classifier) in which the notion of “black box” is interpreted as the uncertainty over a set of classifiers. We give results about axiomatics and complexity of satisfiability checking for our logic. Moreover, we present a dynamic extension in which the process of acquiring new information about the actual classifier can be represented.
Sustainable Risk Identification Using Formal Ontologies

The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.
Rules, Reciprocity, and Emojis: An Exploratory Study on Flirtatious Texting with Romantic Partners

Romantic partners expect and follow implicit rules for flirtatious texting. This mixed-methods exploratory study reviewed survey data and content analyzed flirtatious texts from 200 participants to discover specific communication rules and the level of flirtatious texting similarity. Each participant uploaded five to seven screenshots of recent flirtatious texts for analysis. Coding included similarity in emoji use, message length, timing, and the number of texts sent. Results from the quantitative survey, open-ended questions, and coding of 1042 uploaded screenshots revealed that communicators desire and provide a high degree of similarity when flirting, including emoji use as well as the length, frequency, and timing of texts.
Enhanced Resolution of FY4 Remote Sensing Visible Spectrum Images Utilizing Super-Resolution and Transfer Learning Techniques

Remote sensing images acquired by the FY4 satellite are crucial for regional cloud monitoring and meteorological services.Inspired by the success of deep learning networks in image superresolution, we applied image super-resolution to FY4 visible spectrum (VIS) images.However, training a robust network directly for FY4 VIS image super-resolution remains challenging due to the limited provision of high resolution FY4 sample data.Here, we propose a super-resolution and transfer learning model, FY4-SR-Net.It is composed of pre-training and fine-tuning models.The pre-training model was developed using a deep residual network and a large number of FY4 A 4km and 1km resolution VIS images as the training data.The knowledge derived from 4 km to 1 km resolution images was incorporated into FY4 B 1 km to 0.25 km resolution VIS images.The FY4-SR-Net is finetuned by incorporating limited 1km and 0.25km resolution panchromatic (PAN) images, and then producing 1km superresolution VIS images of the FY4 satellite.Using the one-day FY4 test dataset for qualitative and quantitative evaluations, the FY4-SR-Net outperformed the classic bicubic interpolation approach with a 16.12% reduction in root mean square error (RMSE) and a 2.97% rise in peak signal-to-noise ratio (PSNR) averages.The structural similarity (SSIM) value average increased by 0.0026.This work provides a new precedent for improving the spatial resolution of FY4 series meteorological satellites, which has important scientific significance and application properties.
The Effect of Embodied Anthropomorphism of Personal Assistants on User Perceptions

We investigate the impact of anthropomorphism on embodied AI through a study of personal assistants (PA). The effects of physical embodiment remain underexplored while the consumer market for PAs shows an increase in the diversity of physical appearances of these products. We designed three fictional personal assistants with varying levels of embodied anthropomorphism. We validated that our prototypes differed significantly in levels of anthropomorphism (N = 26). We developed a set of identical videos for each device, demonstrating realistic end-user interaction across six scenarios. Using a between-subject video survey study (N = 150), we evaluate the impact of different levels of embodied anthropomorphism on the perception of personal assistants. Our results show that while anthropomorphism did not significantly affect the perception of Overall Goodness, it affected perceptions of Perceived Intelligence, Likeability, and the device's Pragmatic Qualities. Finally, we discuss the implications of the identified relationships between anthropomorphism and user confidence in embodied AI systems.
Price co-movements in decentralized financial markets

In decentralized finance (Defi), market participants are allowed to have the right to manage their own funds as opposed to centralized finance (Cefi) with a central custodian, centralized exchanges (CEX). Most Defi projects provide their own service and simultaneously issue a unique token that can be traded in decentralized exchanges (DEX). However, the values of these tokens have rarely been studied. We confirm that the prices of tokens in the Defi market have a persistent tendency to move together. We also demonstrate that the correlation gradually increased from the Defi market's inception and the price co-movement increased in a bear market, and conversely, decreased in a bull market. Specifically, we find a notable difference in the level of price co-movement between CEX cryptocurrencies and DEX tokens.
Which Words Matter Most? Operationalizing Lexical Prevalence For Rank-Ordered Word Lists

Abstract The words in a language or language variety are often rank ordered in lists that are meant to reflect the relative importance of those words to language users and learners of a language. This rank ordering is done on the basis of the relative prevalence of words in a corpus. Lexical prevalence is often operationalized as measures of frequency, dispersion, or adjusted frequency. Yet, to date, there is no consensus on best practices for identifying and ranking prevalent words in a corpus, or for evaluating the degree to which a word’s importance is reflected through its prevalence. We begin this paper by introducing and describing a wide range of corpus-based measures for quantifying lexical prevalence. We then carry out two case studies on the Duolingo University Textbook Corpus to evaluate the methods for their ability to identify and appropriately rank words in terms of their importance. We conclude with recommendations for word list creators and researchers and practitioners interested in word importance.
Cooperation Between Automatic and Interactive Software Verifiers

Abstract The verification community develops two kinds of verification tools: automatic verifiers and interactive verifiers. There are many such verifiers available, and there is steady progress in research. However, cooperation between the two kinds of verifiers was not yet addressed in a modular way. Yet, it is imperative for the community to leverage all possibilities, because our society heavily depends on software systems that work correctly. This paper contributes tools and a modular design to address the open problem of insufficient support for cooperation between verification tools. We identify invariants as information that needs to be exchanged in cooperation, and we support translation between two ‘containers’ for invariants: program annotations and correctness witnesses. Using our new building blocks, invariants computed by automatic verifiers can be given to interactive verifiers as annotations in the program, and annotations from the user or interactive verifier can be given to automatic verifiers, in order to help the approaches mutually to solve the verification problem. The modular framework, and the design choice to work with readily-available components in off-the-shelf manner, opens up many opportunities to combine new tools from existing components. Our experiments on a large set of programs show that our constructions work, that is, we constructed tool combinations that can solve verification tasks that the verifiers could not solve before.
Serverless High-Performance Computing over Cloud

Abstract HPC clouds may provide fast access to fully configurable and dynamically scalable virtualized HPC clusters to address the complex and challenging computation and storage-intensive requirements. The complex environmental, software, and hardware requirements and dependencies on such systems make it challenging to carry out our large-scale simulations, prediction systems, and other data and compute-intensive workloads over the cloud. The article aims to present an architecture that enables HPC workloads to be serverless over the cloud (Shoc), one of the most critical cloud capabilities for HPC workloads. On one hand, Shoc utilizes the abstraction power of container technologies like Singularity and Docker, combined with the scheduling and resource management capabilities of Kubernetes. On the other hand, Shoc allows running any CPU-intensive and data-intensive workloads in the cloud without needing to manage HPC infrastructure, complex software, and hardware environment deployments.
ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning

Despite notable results in various fields over the recent years, deep reinforcement learning (DRL) algorithms lack transparency, affecting user trust and hindering deployment to high-risk tasks. Causal confusion refers to a phenomenon where an agent learns spurious correlations between features which might not hold across the entire state space, preventing safe deployment to real tasks where such correlations might be broken. In this work, we examine whether an agent relies on spurious correlations in critical states, and propose an alternative subset of features on which it should base its decisions instead, to make it less susceptible to causal confusion. Our goal is to increase transparency of DRL agents by exposing the influence of learned spurious correlations on its decisions, and offering advice to developers about feature selection in different parts of state space, to avoid causal confusion. We propose ReCCoVER, an algorithm which detects causal confusion in an agent's reasoning before deployment, by executing its policy in alternative environments where certain correlations between features do not hold. We demonstrate our approach in the taxi and grid world environments, where ReCCoVER detects states in which an agent relies on spurious correlations and offers a set of features that should be considered instead.
On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-symbolic Predictors

A long-standing ambition in artificial intelligence is to integrate predictors’ inductive features (i.e., learning from examples) with deductive capabilities (i.e., drawing inferences from symbolic knowledge). Many methods in the literature support injection of symbolic knowledge into predictors, generally following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) predictors. However, to the best of our knowledge, running implementations of these algorithms are currently either proof of concepts or unavailable in most cases. Moreover, a unified, coherent software framework supporting them as well as their interchange, comparison, and exploitation in arbitrary ML workflows is currently missing. Accordingly, in this paper we present the design of PSyKI, a platform providing general-purpose support to symbolic knowledge injection into predictors via different algorithms. In particular, we discuss the overall architecture, and the many components/functionalities of PSyKI, invidually—providing examples as well. We finally demonstrate the versatility of our approach by exemplifying two custom injection algorithms in a toy scenario: Poker Hands classification.
Orality, multimodality and creativity in digital writing: Chinese users’ experiences and practices with bullet comments on <i>Bilibili</i>

Responding to recent calls in sociolinguistics and social semiotics to study the material, technological and embodied features of writing, this article examines Chinese users’ experiences and practices with the emergent technology of bullet comments on Bilibili, a major video-sharing platform and breeding ground for online subcultures. As one of the first studies based on in-depth interviews with its long term users, this article demonstrates that the unique design of bullet comments, by inserting writing into a moving screen, creates multimedia, multimodal semiotic affordances, reinforces “participatory spectacles,” and facilitates powerful multisensory, bonding experiences similar to the (secondary) orality culture. Users’ diverse range of adaptive and creative practices, which resemiotize available linguistic, visual and cultural resources to create new meaning-making possibilities, are analysed. Considering the recent “desubculturization” of Bilibili and its increased interactions with mainstream culture and the state media, this timely study is well positioned to capture users’ observations about this transition, and assess potential impacts on their experiences and writing practices. It is pointed out that the new synergy of youth nationalism and the technology of bullet comments reflects the complex interplay between writing as a technologized social-semiotic practice, and the wider social, cultural and political conditions.