Underwater Wireless sensor network (UWSN) has become a main topic in the research of underwater communication with more research challenges. One of the main issues in the UWSN communication process is Quality of Service (QoS). Therefore, for enhancing the QoS in the UWSN a novel Clustering Hello routing based Honey Badger GoogleNet (CHbHBG) model is proposed. Primarily, the required sensor hubs are placed in the underwater communication environment. Further, the energy usage of each node is monitored and energy-efficient cluster head is selected by the proposed mechanism. Moreover, the data rate resources were predicted and allocated at the channel using the fitness process of the model. The optimal allocation process improves the QoS in the network. To prove the efficacy of the system, the metrics including throughput, network lifetime, latency, energy consumption, PDR, transmission loss, and path creation time are validated and compared with the recent models. The developed model attained the higher network performance as 99.72% PDR, 949.2kbps throughput, 4004.31s network lifetime, and 230.84J energy consumption.

We present 'Resound', a Research through Design inquiry into alternative techno-spiritual practices of a UK Buddhist community, informed by a first-person and participatory approach with the members. In this pictorial we portray a moment of reflection as we consolidate our design work towards deployment with the community. We introduce the Resound Sphere, a materialisation of our learning and speculations to date, designed as a research product to empirically explore alternatives for how tangible interaction could mediate religious/spiritual practices. We contribute with the framing of a design space, the presentation of our design approach and artefact response to this design space.

For generative AI this purpose is quite clear: It’s about putting pressure on human labour. If you can generate somewhat passable prose or images or software code using an “AI” you can either try to run your business with fewer people (who tend to want to be paid) or (more realistically) you can push people’s wages down by always pointing at “the AI” when someone wants a raise or does not want to do unpaid overtime. The actual problem generative AI is trying to solve is having to pay people for their work.

In an increasingly diverse and digitalized society, educators require competencies in media education and diversity-sensitive teaching. In this design-based research study, we further developed an action-oriented pedagogical concept to foster Teacher Educator Technology Competencies (TETCs) focusing on media education, diverse learners’ needs, and social augmented reality (AR) technologies. Accordingly, a professional development workshop was implemented at a German university. In a qualitative empirical case study involving a convenience sample of four teacher educators, data were collected from participants’ lesson designs before and after the workshop, as well as from a focus group discussion. Findings indicate that participants reflected on various legal and ethical aspects, as well as on social responsibility when considering the integration of social AR technologies in education. Additionally, participants demonstrated increased awareness of diverse students’ needs in this context. Based on these findings, implications for future pedagogical practices and research in higher education are discussed.

Wenngleich die Beteiligung chinesischer Unternehmen an ausländischen Unternehmen in den letzten Jahren deutlich abgenommen hat, bleiben die chinesischen Investitionsaktivitäten in Deutschland auf einem stabil hohen Niveau. Der Einstieg chinesischer Investoren hat tiefgreifende Veränderungen in der Unternehmenskultur deutscher Standorte zur Folge. Mit den Übernahmen entsenden die meisten chinesischen Gesellschafter chinesische Manager*innen in die Geschäftsführung der Tochtergesellschaften. Diese personellen Veränderungen in der lokalen Führungsebene haben direkte Auswirkungen auf die Betriebsabläufe und die betriebliche Mitbestimmung. Zwar gibt es keine Hinweise darauf, dass chinesische Gesellschafter und das Management die Arbeit der Mitbestimmungsakteure proaktiv verhindern, allerdings suchen sie auch nicht die Zusammenarbeit mit den Arbeitnehmervertretungen. Diese Abwesenheit in der Sozialpartnerschaft wird besonders problematisch, wenn Krisensituationen eine gemeinsame Lösungsfindung erfordern. Von besonderer Bedeutung sind insbesondere vier Faktoren, die sich negativ auf die betriebliche Mitbestimmung auswirken können: eingeschränkter Informationszugang, sprachliche Barrieren, paternalistische Unternehmensführung und der Einfluss von Parteirichtlinien.

This paper offers a critique of war from an existentialist-phenomenological perspective. Drawing on Martin Heidegger’s theory of ontology and Maurice Merleau-Ponty’s theory of perception, it develops a framework which integrates war and the body – and thus ontology and embodiment – in Critical War Studies. Two arguments are advanced: first, that war is in so far as we embody it (implying that the way in which we embody it determines the way in which it is); second, that the embodiment of war is essentially an agential activity. Thereby, this paper provides impetus for an ontological and moral re-avowal of war in critical academic discourse (for understanding war not primarily as a tragic fate but as our shameful doing). This, in turn, facilitates new perspective for interpretation and critique – to the extent, for example, that understanding the logic of war’s agential embodiment discloses what would constitute, and be necessary for, its disembodiment. Moreover, the paper points to clear possibilities for future research – for clarifying, for instance, the ontological upheaval latent in the prospect of future war.

We present SceneFactory, a workflow-centric and unified framework for incremental scene modeling, that conveniently supports a wide range of applications, such as (unposed and/or uncalibrated) multi-view depth estimation, LiDAR completion, (dense) RGB-D/RGB-L/Mono/Depth-only reconstruction and SLAM. The workflow-centric design uses multiple blocks as the basis for constructing different production lines. The supported applications, i.e., productions avoid redundancy in their designs. Thus, the focus is placed on each block itself for independent expansion. To support all input combinations, our implementation consists of four building blocks that form SceneFactory: (1) tracking, (2) flexion, (3) depth estimation, and (4) scene reconstruction. The tracking block is based on Mono SLAM and is extended to support RGB-D and RGB-LiDAR (RGB-L) inputs. Flexion is used to convert the depth image (untrackable) into a trackable image. For general-purpose depth estimation, we propose an unposed & uncalibrated multi-view depth estimation model (U2 -MVD) to estimate dense geometry. U2-MVD exploits dense bundle adjustment to solve for poses, intrinsics, and inverse depth. A semantic-aware ScaleCov step is then introduced to complete the multi-view depth. Relying on U2-MVD, SceneFactory both supports user-friendly 3D creation (with just images) and bridges the applications of Dense RGB-D and Dense Mono. For high-quality surface and color reconstruction, we propose Dual-purpose Multi-resolutional Neural Points (DM-NPs) for the first surface accessible Surface Color Field design, where we introduce Improved Point Rasterization (IPR) for point cloud based surface query. We implement and experiment with SceneFactory to demonstrate its broad applicability and high flexibility. Its quality also competes or exceeds the tightly-coupled state of the art approaches in all tasks. We contribute the code to the community

Centuries of beekeeping restricted the honeybees’ role to a producer of raw material, such as honey or wax, which is then harvested and processed, resulting in all artistic value being added by humans. In this project, we regard honeybees (Apis mellifera) as co-creators and explore the joint creations of our two species. During our first season, we scoped the co-creative space over the course of four months. We present image material of sculptures that exceed bees’ natural building behavior and contribute preliminary insights on artifacts originating from human-bee co-creation. We reflect on how human-introduced wax shapes made the bees deviate from their regular comb forms and discuss future paths of multi-species co-creation, temporality, material as well as ethical aspects. Our preliminary insights raise questions to be developed in discussions with the TEI community and answered in future work during the upcoming bee seasons.

With rapid advances in Artificial Intelligence (AI) impacting human health and wellbeing, scholarly discourse should focus equally on the prospective opportunities and harms of Human-AI Interaction (HAI) in Computer-Supported Collaborative Work and Social Computing (CSCW). This panel invites critical interdisciplinary discussion around the (un)designing of AI by asking: how, when, where, and why should AI (not) be involved in sociotechnical systems for mental and spiritual health and wellbeing? Motivated by functional, technical, and ethical concerns, the panel aims to ensure that: (1) progress in HAI for mental and spiritual health is informed by expertise from the respective clinical disciplines; (2) ethical and responsible design principles lie at the core of research motivations and methodologies; and (3) AI hype can be tempered by caution given its impacts on marginalized and stigmatized groups. A panel of respected experts in mental health, spiritual care, and AI will discuss CSCW topics regarding HAI in contexts of clinical practice (e.g., electronic health records, patient portals, decision-making and referral systems, technology-supported interactions during bedside care or clinical appointments) as well as social contexts beyond the clinic (e.g., social apps, online health communities and social media, and computer-mediated communication in spiritual/religious groups).

Artificial intelligence systems, particularly those based on large language models, are increasingly prevalent in personal and professional settings, making the skill of prompt engineering—formulating effective AI inputs—vital. This paper explores the effects and how to enhance students' prompt engineering skills based on a multi-study design. Our first study confirmed the baseline hypothesis that prompt engineering can predict AI output quality, framing it as a critical skill. We then investigated whether instructional designs (worked examples & instructions), could develop prompting skills. Using worked examples, the second study tested the effectiveness of instructional materials on enhancing prompting skills. The experiment involved 245 students who demonstrated that a brief exposure to a worked example-based prompting guide significantly improved their ability to deploy targeted prompting strategies. These findings suggest that integrating worked examples into curricula could effectively equip students with essential prompting skills, offering both theoretical and practical implications for AI education.

Increasing evidence suggests an essential function for autophagy in unconventional protein secretion (UPS). However, despite its relevance for the secretion of aggregate-prone proteins, the mechanisms of secretory autophagy in neurons have remained elusive. Here we show that the lower motoneuron disease-associated guanine exchange factor Plekhg5 drives the UPS of Sod1. Mechanistically, Sod1 is sequestered into autophagosomal carriers, which subsequently fuse with secretory lysosomal-related organelles (LROs). Exocytosis of LROs to release Sod1 into the extracellular milieu requires the activation of the small GTPase Rab26 by Plekhg5. Deletion of Plekhg5 in mice leads to the accumulation of Sod1 in LROs at swollen presynaptic sites. A reduced secretion of toxic ALS-linked SOD1G93A following deletion of Plekhg5 in SOD1G93A mice accelerated disease onset while prolonging survival due to an attenuated microglia activation. Using human iPSC-derived motoneurons we show that reduced levels of PLEKHG5 cause an impaired secretion of ALS-linked SOD1. Our findings highlight an unexpected pathophysiological mechanism that converges two motoneuron disease-associated proteins into a common pathway.

Whilst there is increasing work investigating the role of digital documentation, interpretation, and augmentation of cultural heritage, such interventions have largely focused on visual and sometimes auditory modalities, neglecting the full spectrum of human senses. With this workshop we seek to bring together an interdisciplinary group of researchers, designers, practitioners and community members to explore creative approaches for documenting and experiencing cultural heritage’s rich sensory dimensions extending beyond visual-based approaches to encompass sound, smell, taste, and touch. The workshop directly aligns with the conference’s exploration of "Why Design?" by utilising design as a powerful, empathetic, and participatory tool for safeguarding cultural heritage. Our goal is to extend our understanding of concepts, methods and technologies for capturing and experiencing sensory heritage, advocating for a holistic approach that celebrates and communicates the profound sensory diversity of human cultures, inspiring a shift in how we document, interpret and share cultural heritage.

The COVID-19 pandemic has intensified our dependence on screen-based devices, re-shaping how we connect with one another. Motivated by a yearning for alternative and post-pandemic religious and spiritual (R/S) practice, we pursue a Research-through-Design (RtD) project, Resound, exploring mediated technospiritual connections with a UK-based Buddhist community. This pictorial depicts the complexity of (i) designing with spiritual-corporeal selves engaged in community-centred and ritualistic practices; (ii) and tangible artefacts and sonic environments being made, configured and practiced with. We reflect on this ongoing material engagement as making tangible alternatives through techno-spiritual practice.

A decade after Buie and Blythe’s review "Spirituality: There’s an App for That! (But Not a Lot of Research)", this sequel assesses the evolving landscape of Human-Computer Interaction (HCI) research on religion and spirituality. While the enduring importance of religion and spirituality for humanity and its influence on technology use remains, the last decade has seen transformative shifts catalysed by technological advances and the global impact of the COVID-19 pandemic. This paper explores whether and how HCI research on religion and spirituality has also changed. Providing a snapshot of the current research, we document and reflect on changes in the lines of research with a shift towards community, an increased consideration of religion and spirituality in related areas such as health, education, and society, and the broadening of challenges for HCI research on religion and spirituality.

Tangible artifacts and embodied experiences are central to religious and spiritual (R/S) practices, and many HCI researchers and interaction designers highlight the importance of materiality and physicality in design. In this review paper, we bring these perspectives together and examine 44 examples of R/S tangible interactive artifacts (TIAs) from academia, art, industry, and R/S communities to understand their specifics and guide future HCI research and design. We analyze these artifacts and map out a design space for R/S TIAs by matching identified characteristics of R/S TIAs with a framework from the study of material religion. The descriptive and generative R/S TIA Design Space covers insights into bodies, things, places, practices, and backgrounds. This paper offers a novel contribution to HCI research on the value and importance of tangibility and embodiment in technology-mediated practices in R/S contexts and serves as a source for future R/S TIA creation and research.

This paper intends to address the challenge of personalized recipe recommendation in the realm of diverse culinary preferences. The problem domain involves recipe recommendations, utilizing techniques such as association analysis and classification. Association analysis explores the relationships and connections between different ingredients to enhance the user experience. Meanwhile, the classification aspect involves categorizing recipes based on user-defined ingredients and preferences. A unique aspect of the paper is the consideration of recipes and ingredients belonging to multiple classes, recognizing the complexity of culinary combinations. This necessitates a sophisticated approach to classification and recommendation, ensuring the system accommodates the nature of recipe categorization. The paper seeks not only to recommend recipes but also to explore the process involved in achieving accurate and personalized recommendations.

Religion and spirituality (R/S) are an important part of many people’s lives, and while HCI is increasingly engaged in research and design in specific R/S contexts, there are many cases where researchers or designers encounter R/S outside of such contexts. In this workshop, we seek to bring together HCI researchers and designers across all research areas and with varying levels of experience with R/S to discuss encounters with R/S in their work, either intentional or not, and to develop principles and strategies to guide HCI research and design that intersects with R/S. Through this workshop, we also seek to further establish a network of scholars who can provide each other support in navigating R/S-related challenges and opportunities in their work and can participate in publication-oriented collaborations that consider the intersection of R/S and HCI.

The COVID-19 pandemic accelerated the development of alternative formats for religious rituals, such as Protestant online worship services. However, current design approaches focus on problem-solving, and the resulting online solutions merely imitate the offline status quo. To overcome these limitations, we suggest adopting a provotype approach that allows for a more holistic, open-ended dialogue with those affected. We iteratively developed a first provotype in response to tensions found in observation-based field research, aiming to test whether and how it can trigger productive impulses for exploring future technology-mediated worship services based on existing experiences and perspectives. The resulting God-I-Box exaggerates individuality and allows congregants to act almost like liturgists. An analysis of congregants’ and pastors’ (online) first encounters with the God-I-Box revealed three reaction modes: spontaneous emotions, reflective coping, and exploratory imagination. We conclude with reflections and recommendations for provocative research and design in this context and beyond.

Local civic participation is essential to democracy. Yet, citizens need to be informed about local matters to get involved. Becoming and staying informed about developments in one’s neighbourhood is difficult as local knowledge is scattered among online and offline sources, hard to find and understand. Using participatory Contextual Design we curated a novel form of participation with the interactive artifact "hubbel", which combines the digital and the analogue through a hybrid letterbox to crowd-source local knowledge. During our two-month in-the-wild evaluation, the hubbel was heavily used (260 postcards, 2067 visits). It stimulated democratic discourse and (offline) political participation by rendering tacit local issues visible. We propose ways to improve the hubbel’s design so that citizens can more easily share their expertise with others and to avoid harmful consequences like pseudo-participation. Finally, the hubbel demonstrates why analogue components are indispensable for civic participation tools. Supplements and open-source code: OSF-Link.

Relation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English.In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems. First, we present SRED\($^\textrmFM$\), an automatically annotated dataset covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances. Second, we propose RED\($^\textrmFM$\), a smaller, human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems. To demonstrate the utility of these novel datasets, we experiment with the first end-to-end multilingual RE model, mREBEL, that extracts triplets, including entity types, in multiple languages. We release our resources and model checkpoints at [\urlhttps://www.github.com/babelscape/rebel](\urlhttps://www.github.com/babelscape/rebel).

Time-efficient solutions for querying RDF knowledge graphs depend on indexing structures with low response times to answer SPARQL queries rapidly. Hypertries---an indexing structure we recently developed for tensor-based triple stores---have achieved significant runtime improvements over several mainstream storage solutions for RDF knowledge graphs. However, the space footprint of this novel data structure is still often larger than that of many mainstream solutions. In this work, we detail means to reduce the memory footprint of hypertries and thereby further speed up query processing in hypertrie-based RDF storage solutions. Our approach relies on three strategies: (1) the elimination of duplicate nodes via hashing, (2) the compression of non-branching paths, and (3) the storage of single-entry leaf nodes in their parent nodes. We evaluate these strategies by comparing them with baseline hypertries as well as popular triple stores such as Virtuoso, Fuseki, GraphDB, Blazegraph and gStore. We rely on four datasets/benchmark generators in our evaluation: SWDF, DBpedia, WatDiv, and WikiData. Our results suggest that our modifications significantly reduce the memory footprint of hypertries by up to 70% while leading to a relative improvement of up to 39% with respect to average Queries per Second and up to 740% with respect to Query Mixes per Hour.

Entity typing in knowledge graphs (KGs) aims to infer missing types of entities and might be considered one of the most significant tasks of knowledge graph construction since type information is highly relevant for querying, quality assurance, and KG applications. While supervised learning approaches for entity typing have been proposed, they require large amounts of (manually) labeled data, which can be expensive to obtain. In this paper, we propose a novel approach for KG entity typing that leverages semi-supervised learning from massive unlabeled data. Our approach follows a teacher-student paradigm that allows combining a small amount of labeled data with a large amount of unlabeled data to boost performance. We conduct several experiments on two benchmarking datasets (FB15k-ET and YAGO43k-ET). Our results demonstrate the effectiveness of our approach in improving entity typing in KGs. Given type information for only 1% of entities, our approach ASSET predicts missing types with a F1-score of 0.47 and 0.64 on the datasets FB15k-ET and YAGO43k-ET, respectively, outperforming supervised baselines.

Social media plays a significant role in disaster management by providing valuable data about affected people, donations and help requests. Recent studies highlight the need to filter information on social media into fine-grained content labels. However, identifying useful information from massive amounts of social media posts during a crisis is a challenging task. In this paper, we propose I-AID, a multimodel approach to automatically categorize tweets into multi-label information types and filter critical information from the enormous volume of social media data. I-AID incorporates three main components: i) a BERT- based encoder to capture the semantics of a tweet and represent as a low-dimensional vector, ii) a graph attention network (GAT) to apprehend correlations between tweets' words/entities and the corresponding information types, and iii) a Relation Network as a learnable distance metric to compute the similarity between tweets and their corresponding information types in a supervised way. We conducted several experiments on two real publicly-available datasets. Our results indicate that I-AID outperforms state-of- the-art approaches in terms of weighted average F1 score by +6% and +4% on the TREC-IS dataset and COVID-19 Tweets, respectively.

The sheer size of modern knowledge graphs has led to increased attention being paid to the entity summarization task. Given a knowledge graph T and an entity e found therein, solutions to entity summarization select a subset of the triples from T which summarize e's concise bound description. Presently, the best performing approaches rely on sequence-to-sequence models to generate entity summaries and use little to none of the structure information of T during the summarization process. We hypothesize that this structure information can be exploited to compute better summaries. To verify our hypothesis, we propose GATES, a new entity summarization approach that combines topological information and knowledge graph embeddings to encode triples. The topological information is encoded by means of a Graph Attention Network. Furthermore, ensemble learning is applied to boost the performance of triple scoring. We evaluate GATES on the DBpedia and LMDB datasets from ESBM (version 1.2), as well as on the FACES datasets. Our results show that GATES outperforms the state-of-the-art approaches on 4 of 6 configuration settings and reaches up to 0.574 F-measure. Pertaining to resulted summaries quality, GATES still underperforms the state of the arts as it obtains the highest score only on 1 of 6 configuration settings at 0.697 NDCG score. An open-source implementation of our approach and of the code necessary to rerun our experiments are available at https://github.com/dice-group/GATES.

Besides the challenge that a human can ask one question in many different ways, a key aspect in Question Answering approaches over Knowledge Graphs (KGQA) is to deal with the vast amount of information present in the knowledge graphs. Modern real-world knowledge graphs contain nearly millions of entities and relationships. Additionally, they are enriched with new facts every day. However, not all facts are relevant for answering particular questions, thus fostering several challenges to KGQA systems, which require interpretable and query-able data. One solution to filtering the extra data in knowledge graphs is to rely on graph summarization techniques. Graph-based summarization approaches aim to resize knowledge graphs to be more concise and precise by storing only relevant information. In this paper, we propose a framework named LAUREN that applies different summarization techniques on knowledge graphs to be used in KGQA systems. Our experiments show that LAUREN summarizes large knowledge graphs such as DBpedia by 2 million entities and its summarization still achieves the same performance on both question answering and linking tasks compared to the complete DBpedia.

Despite many advances, clinical decision support tools (DSTs) often suffer from implementation and acceptance problems in the actual clinical context. We suggest that considering psychological needs-based and embodied user experience theories in the design of DSTs could help to overcome these problems. To examine this idea, we iteratively developed a DST called Cassandra supporting anesthetic teams in crisis management, specifically focusing on psychological needs and fluent interaction with the social and physical environment. We preliminarily evaluated Cassandra in a medical simulation, requiring anesthetic teams to handle a crisis. Although not all features of Cassandra had the intended effect, the results indicated that interacting with Cassandra supported the fulfillment of the identified needs for autonomy, competence, and relatedness and was seamlessly integrated into existing diagnostic processes. Considering user experience theories for the design of DSTs seems a promising way to overcome implementation and acceptance problems and eventually improve patient safety.

Today's busy lifestyle can make us feel overwhelmed. Mindfulness meditation supports our well-being by slowing things down and drawing our attention to the present moment. Accordingly, a wealth of guided mindfulness meditation apps have emerged in the past years. Despite the many apps and an increasing interest within the HCI literature, there is little consensus of how to evaluate these apps and how to derive opportunities for re-design from the results. Our new MindPeaks method utilizes electroencephalography (EEG) to link design elements to meditative states and the user`s self-regulation of attention. This information can help designers to understand and optimize the efficacy of, for example, applications supporting mindfulness meditation.

Participatory Design (PD) has long been described as a way to democratize technology development by involving those affected by the outcomes. Besides a good fit of technology as the outcome, PD allows participants to ‘have a say’, and supports ‘mutual learning’ and ‘co-realization’. A diverse range of PD methods has been developed, but there is a shortage of empirical studies showing whether (and why) these methods help pursue PD ideals. We therefore report on a case study of a short-term evaluation approach that may exemplary close this gap, and that allowed us to gain more knowledge on how our method affected participants. Participants perceived moderate to high feelings of ‘having a say’, ‘mutual learning’, and ‘co-realization’. In future research, mutual learning may exemplarily be improved by introducing a changing peer-to-peer procedure to the method. The evaluation may further be advanced by taking expectations into account.

Rituals are ubiquitous but not commonplace, help people to make sense of their life, and cultivate personal or social meaning. Although secularization and digitalization impact the occurrence of formal rituals, the need for marking life's transitions remains unchanged. New rituals emerge, such as marking relationship status by hanging love locks on bridges. Tangible technologies hold great potential for augmenting, changing, or enhancing ritual practices which often involve enactments and symbolic props. In this paper, we analyze individual stories of hanging love locks and derive six pointers for designing technology-mediated relationship transition rituals. We applied the pointers in the design of El Corazón, a tangible artifact for relationship transition rituals. The results of an evaluation with 20 sweethearts show that relationship rituals can be designed deliberately, that tangibles can shape ritual experiences and that technology-mediated rituals can provide people with new means of coping with relationship uncertainty.

The number and size of RDF knowledge graphs grows continuously. Efficient storage solutions for these graphs are indispensable for their use in real applications. We present such a storage solution dubbed Tentris. Our solution represents RDF knowledge graphs as sparse order-3 tensors using a novel data structure, which we dub hypertrie. It then uses tensor algebra to carry out SPARQL queries by mapping SPARQL operations to Einstein summation. By being able to compute Einstein summations efficiently, Tentris outperforms the commercial and open-source RDF storage solutions evaluated in our experiments by at least 1.8 times with respect to the average number of queries it can serve per second on three datasets of up to 1 billion triples. Our code, evaluation setup, results, supplementary material and the datasets are provided at https://tentris.dice-research.org/iswc2020.

Multimodal Interfaces (MMIs) have been considered to provide promising interaction paradigms for Virtual Reality (VR) for some time. However, they are still far less common than unimodal interfaces (UMIs). This paper presents a summative user study comparing an MMI to a typical UMI for a design task in VR. We developed an application targeting creative 3D object manipulations, i.e., creating 3D objects and modifying typical object properties such as color or size. The associated open user task is based on the Torrence Tests of Creative Thinking. We compared a synergistic multimodal interface using speech-accompanied pointing/grabbing gestures with a more typical unimodal interface using a hierarchical radial menu to trigger actions on selected objects. Independent judges rated the creativity of the resulting products using the Consensual Assessment Technique. Additionally, we measured the creativity-promoting factors flow, usability, and presence. Our results show that the MMI performs on par with the UMI in all measurements despite its limited flexibility and reliability. These promising results demonstrate the technological maturity of MMIs and their potential to extend traditional interaction techniques in VR efficiently.

Virtual reality (VR) has always been considered a promising medium to support designers with alternative work environments. Still, graphical user interfaces are prone to induce attention shifts between the user interface and the manipulated target objects which hampers the creative process. This work proposes a speech-and-gesture-based interaction paradigm for creative tasks in VR. We developed a multimodal toolbox (MTB) for VR-based design applications and compared it to a typical unimodal menu-based toolbox (UTB). The comparison uses a design-oriented use-case and mea-sures flow, usability, and presence as relevant characteristicsfor a VR-based design process. The multimodal approach (1) led to a lower perceived task duration and a higher reported feeling of flow. It (2) provided a higher intuitive use and a lower mental workload while not being slower than an UTB. Finally, it (3) generated a higher feeling of presence. Overall, our results confirm significant advantages of the proposed multimodal interaction paradigm and the developed MTB for important characteristics of design processes in VR.

In hospitals, clinicians often need to monitor several patients while performing other tasks. However, visual displays that show patients' vital signs are in fixed locations and auditory alarms intended to alert clinicians may be missed. Information such as spearcons (time-compressed speech earcons) that ‘travels’ with the clinician and is delivered by earpiece and/or head-worn displays (HWDs), might overcome these problems. In this study, non-clinicians monitored five simulated patients in three 10-min scenarios while performing a demanding tracking task. Monitoring accuracy was better for participants using spearcons and a HWD (88.7%) or a HWD alone (86.2%) than for participants using spearcons alone (74.1%). Participants using the spearcons and HWD (37.7%) performed the tracking task no differently from participants using spearcons alone (37.1%) but participants using the HWD alone performed worse overall (33.1%). The combination of both displays may be a suitable solution for monitoring multiple patients.

Spearcons—time-compressed speech phrases—may be an effective way of communicating vital signs to clinicians without disturbing patients and their families. Four experiments tested the effectiveness of spearcons for conveying oxygen saturation (SpO2) and heart rate (HR) of one or more patients. Experiment 1 demonstrated that spearcons were more effective than earcons (abstract auditory motifs) at conveying clinical ranges. Experiment 2 demonstrated that casual listeners could not learn to decipher the spearcons whereas listeners told the exact vocabulary could. Experiment 3 demonstrated that participants could interpret sequences of sounds representing multiple patients better with spearcons than with pitch-based earcons, especially when tones replaced the spearcons for normal patients. Experiment 4 compared multiple-patient monitoring of two vital signs with either spearcons, a visual display showing SpO2 and HR in the same temporal sequence as the spearcons, or a visual display showing multiple patient levels simultaneously. All displays conveyed which patients were abnormal with high accuracy. Visual displays better conveyed the vital sign levels for each patient, but cannot be used eyes-free. All displays showed accuracy decrements with working memory load. Spearcons may be viable for single and multiple patient monitoring. Further research should test spearcons with more vital signs, during multitasking, and longitudinally.

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs.

In this paper, the authors describe an approach for sharing sensitive medical data with the consent of the data owner. The framework builds on the advantages of the Semantic Web technologies and makes it secure and robust for sharing sensitive information in a controlled environment. The framework uses a combination of Role-Based and Rule-Based Access Policies to provide security to a medical data repository as per the FAIR guidelines. A lightweight ontologywas developed, to collect consent from the users indicating which part of their data they want to share with another user having a particular role. Here, the authors have considered the scenario of sharing the medical data by the owner of data, say the patient, with relevant persons such as physicians, researchers, pharmacist, etc. To prove this concept, the authors developed a prototype and validated using the Sesame OpenRDF Workbench with 202,908 triples and a consent graph stating consents per patient.

Probabilistic programming languages (PPLs) unify techniques for the formal description of computation and for the representation and use of uncertain knowledge. PPLs have seen recent interest from the artificial intelligence, programming languages, cognitive science, and natural languages communities. This book explains how to implement PPLs by lightweight embedding into a host language. We illustrate this by designing and implementing WebPPL, a small PPL embedded in Javascript. We show how to implement several algorithms for universal probabilistic inference, including priority-based enumeration with caching, particle filtering, and Markov chain Monte Carlo. We use program transformations to expose the information required by these algorithms, including continuations and stack addresses. We illustrate these ideas with examples drawn from semantic parsing, natural language pragmatics, and procedural graphics.

In this paper we describe the Semantic Quran dataset, a multilingual RDF representation of translations of the Quran. The dataset was created by integrating data from two different semi-structured sources and aligned to an ontology designed to represent multilingual data from sources with a hierarchical structure. The resulting RDF data encompasses 43 different languages which belong to the most under-represented languages in the Linked Data Cloud, including Arabic, Amharic and Amazigh. We designed the dataset to be easily usable in natural-language processing applications with the goal of facilitating the development of knowledge extraction tools for these languages. In particular, the Semantic Quran is compatible with the Natural-Language Interchange Format and contains explicit morpho-syntactic information on the utilized terms. We present the ontology devised for structuring the data. We also provide the transformation rules implemented in our extraction framework. Finally, we detail the link creation process as well as possible usage scenarios for the Semantic Quran dataset.

The improvement of public health is one of the main indicators for societal progress. Statistical data for monitoring public health is highly relevant for a number of sectors, such as research (e.g. in the life sciences or economy), policy making, health care, pharmaceutical industry, insurances etc. Such data is meanwhile available even on a global scale, e.g. in the Global Health Observatory (GHO) of the United Nations's World Health Organization (WHO). GHO comprises more than 50 different datasets, it covers all 198 WHO member countries and is updated as more recent or revised data becomes available or when there are changes to the methodology being used. However, this data is only accessible via complex spreadsheets and, therefore, queries over the 50 different datasets as well as combinations with other datasets are very tedious and require a significant amount of manual work. By making the data available as RDF, we lower the barrier for data re-use and integration. In this article, we describe the conversion and publication process as well as use cases, which can be implemented using the GHO data.