bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomics in neglected and underutilized fruit crops: A chromosome‐scale genome sequence of cherimoya (Annona cherimola)
Authors: Talavera Alicia , Fernandez‐Pozo Noe , Matas Antonio J. , Hormaza Jose I. , Bombarely Aureliano 
Published: February 2023

Societal Impact StatementCherimoya has been an important food source since Pre‐Columbian times in the Americas. Although it is currently considered an underutilized fruit crop, it is still important at the local level in several regions of Central and South America, and has a clear niche for expansion in regions with subtropical climates. However, the availability of genomic resources to facilitate breeding programs and improve the understanding of Annonaceae genetic diversity is limited. In this work, we provide an important resource to fill this knowledge gap, with the hope that it will ultimately increase this crop's resilience under different climate change scenarios, which will increase food security in regions with subtropical climates.Summary

Cherimoya (Annona cherimola Mill.) is a perennial fruit tree crop native to the Neotropics valued since pre‐Columbian times by different native American civilizations. It belongs to the Annonaceae, the largest family of the Magnoliid clade, sister to the eudicot and monocot clades of angiosperms. Despite its excellent organoleptic and nutritive qualities, this crop remains underutilized, although it has a clear niche for expansion in regions with subtropical climates. To date, no previous significant genomic information is available for this species, which would be a key tool to optimize breeding programs and advance in the study and conservation of its extant genetic diversity.
A combination of different sequencing technologies (Illumina, Pacific Biosciences) has been implemented in order to assemble a chromosome‐level reference genome of A. cherimola.
The final reference genome resulted in an assembly of 1.13 Gb and N50 of 170.86 Mb, anchored into seven pseudomolecules and with a completeness of 95.6%. A total of 41,413 protein‐coding genes were identified, many of which were related to secondary metabolism, defense mechanisms, stress response, and development.
The results of this study provide novel significant genomic resources not only for cherimoya and other species of the Annonaceae but also for understanding the evolution of the earlier divergent angiosperms.
A prototype autonomous robot for underwater crime scene investigation and emergency response
Authors: Rymansaib Zuhayr , Thomas Benjamin , Treloar Alfred Anthony , Metcalfe Benjamin , Wilson Peter , Hunter Alan 
Published: February 2023

AbstractUnderwater crime scene investigation and emergency response are tasks typically carried out by divers constituting part of a specialist team. Operating in such dynamic environments, often with poor visibility and risk of concealed hazards, can be time consuming and dangerous. Autonomous uncrewed vessels with underwater acoustic imaging sensors have been used for similar purposes in other fields (e.g., hydrography, naval mine countermeasures [MCMS], etc.) but have not been adopted in this specific application domain. The Police Robot for Inspection and Mapping of underwater Evidence (PRIME) is an autonomous uncrewed surface vessel that is being developed for this purpose. It is a novel application of existing robotic technology that is intended to be used within an end‐to‐end police and emergency underwater search process. It aims to enhance the effectiveness, efficiency, and safety of divers by autonomously locating and highlighting target objects or regions of interest, as well as benign regions, thereby reducing their time spent underwater. Side‐scan imaging sonars are used to sense the underwater environment using techniques leveraged from the similar application domain of naval MCMs. The system autonomously generates actionable intelligence in the form of simplified coverage and anomaly maps for easy interpretation by the dive team. These are communicated to shore in real‐time and georeferenced on satellite maps. This paper details the PRIME system prototype and presents results from initial field experimentation. The prototype has been operated in various urban, shallow‐water environments. The experimental results shown here were collected in Bristol Harbour (the UK) with a water depth of approximately 5 m. In the experiment, a clothed mannequin resembling a human body was deployed on the muddy floor. Autonomous searches were executed and the body was detected successfully as an anomaly against the background, illustrating the feasibility and viability of the system as an autonomous robotic aid for locating missing persons in a representative, unstructured, and dynamic real‐world environment.
Interdisciplinarity, Transdisciplinarity, and Health Humanities: Eye Tracking, Ableism, Disability, and Art Creation
Authors: Riegel Christian , Robinson Katherine M. 
Published: February 2023

AbstractThis chapter examines a transdisciplinary research project that develops eye tracking hardware and software for the purpose of art creation. Interdisciplinarity and transdisciplinarity are defined in relation to the development of the health humanities as a field that inherently draws from multiple disciplines. Transdisciplinary research is seen to transcend disciplinary boundaries and to integrate community collaboration as a mode that is geared to addressing social challenges. Eye tracking art creation relies solely on eye movements to create art on digital screens and thus has implications for individuals with limited mobility. Disability is defined in relation to ableism, which is the discriminatory practice of enforcing a corporeal norm. We discuss how technology development that has implications for individuals with disabilities, such as ours, must resist ableist tendencies to attempt to solve disability as a problem that requires a cure. Thus, we frame our research project that has as its goal the development of tools that provide the enjoyment of art creation above all.
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Authors: Elkelany Amany , Ross Robert , Mckeever Susan 
Published: February 2023

AbstractRecently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments.
Challenges Associated with the Adoption of Artificial Intelligence in Medical Device Software
Authors: Cepeda Zapata Karla Aniela , Ward Tomás , Loughran Róisín , McCaffery Fergal 
Published: February 2023

AbstractThe utilization of Artificial Intelligence (AI) has changed and enhanced several industries across the world, such as education, research, manufacturing and healthcare. The potential of AI to create new and enhanced applications that can benefit patients and physicians has created interest and enthusiasm, especially in a Medical Device Software (MDS) context. Although, the adoption of AI in MDS has also brought concerns for regulatory agencies and policymakers. The complexity of AI has challenged the standard requirements set by regulatory agencies, especially in the context of the differences between traditional MDS and AI. Additionally, the unique capacity of AI to continuous learning for optimal performance in real-world settings may also bring potential harm and risk to patients and physicians. The challenges discussed in this paper are in relation to: (1) Software Development Life Cycle (SDLC) frameworks; (2) learning processes and adaptability of AI algorithms; (3) explainability and traceability; and (4) conflictive terminology. At the end of this paper, conclusions and future work are presented to contribute to the safety and methodical implementation of AI in health care settings.
An Intelligent Empowering Agent (IEA) to Provide Easily Understood and Trusted Health Information Appropriate to the User Needs
Authors: Alfano Marco , Kellett John , Lenzitti Biagio , Helfert Markus 
Published: February 2023

AbstractMost members of the public, including patients, usually obtain health information from Web searches using generic search engines, which is often overwhelming, too generic, and of poor quality. Although patients may be better informed, they are often none the wiser and not empowered to communicate with medical professionals so that their care is compatible with their needs, values, and best interests. Intelligent Empowering Agents (IEA) use AI to filter medical information and assist the user in the understanding of health information about specific complaints or health in general. We have designed and developed a prototype of an IEA that dialogues with the user in simple language, collects health information from the Web, and provides tailored, easily understood, and trusted information. It empowers users to create their own comprehensive and objective opinion on health matters that concern them. This paper describes the IEA main characteristics and presents the results of subjective and objective tests carried out to assess the effectiveness of the IEA.
Comparison and Analysis of 3 Key AI Documents: EU’s Proposed AI Act, Assessment List for Trustworthy AI (ALTAI), and ISO/IEC 42001 AI Management System
Authors: Golpayegani Delaram , Pandit Harshvardhan J. , Lewis Dave 
Published: February 2023

AbstractConforming to multiple and sometimes conflicting guidelines, standards, and legislations regarding development, deployment, and governance of AI is a serious challenge for organisations. While the AI standards and regulations are both in early stages of development, it is prudent to avoid a highly-fragmented landscape and market confusion by finding out the gaps and resolving the potential conflicts. This paper provides an initial comparison of ISO/IEC 42001 AI management system standard with the EU trustworthy AI assessment list (ALTAI) and the proposed AI Act using an upper-level ontology for semantic interoperability between trustworthy AI documents with a focus on activities. The comparison is provided as an RDF resource graph to enable further enhancement and reuse in an extensible and interoperable manner.
AI and ML in School Level Computing Education: Who, What and Where?
Authors: Mahon Joyce , Becker Brett A. , Namee Brian Mac 
Published: February 2023

AbstractThis paper presents the results of a systematic review of the literature relating to artificial intelligence (AI) and machine learning (ML) education at school level. We conducted a search of the ACM Full-text Collection and 33 papers from the 197 search results were selected for analysis. In this context, we considered the research questions: 1) Who has been the focus of the research?, 2) What course content appears in the research?, and 3) Where has the research taken place? We find that there has been a recent marked increase in research on AI/ML for school level education, although most of this has been based in the United States. The majority of this research focuses on students, with very little specifically addressing teachers, experts, parents, or the wider school community. There is also a lack of attention paid to research focused on women or those from historically underrepresented groups and equity of access to AI/ML courses for school-level students. Finally, the content covered in the courses described in this research varies widely, possibly because there is so little alignment to computer science (CS) frameworks or curricula.
Intelligent Image Compression Using Traffic Scene Analysis
Authors: Bowden David , Grimes Diarmuid 
Published: February 2023

AbstractThe quantity of images generated at the edge of the Cloud is growing year-on-year, which puts an increasing strain on existing telecommunications infrastructure. There is also an associated increased cost for transmission bandwidth and storage of video images in the Cloud. In our modern society we tend to accumulate data, and are reluctant to throw it away, without asking “what is the value of this data?” and “do we need it?”. One of the major sources of video streams are the increasing number of traffic cameras, used to maintain the efficient flow of vehicles on our roads. In this work we focus on images taken from road traffic cameras, and show how their transmission bandwidth and storage requirements can be reduced. By analysing video feeds on a simulated edge device, we have shown that it is possible to extract objects of interest from the image, and discard or dramatically reduce irrelevant information in the content. Our technique also generates associated metadata, in the form of JSON-LD, which annotates the original image and maintains its semantic fidelity and provenance after compression. Our technique is compatible with conventional compression techniques, and thus the potential bandwidth savings would be incremental. We present the potential savings that can be made in the transmission and storage of unstructured data, as well as some of the challenges still to be overcome.
Cryptocurrency Volatility Index: An Efficient Way to Predict the Future CVI
Authors: Nguyen An Pham Ngoc , Crane Martin , Bezbradica Marija 
Published: February 2023

AbstractThe Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods like Simple Moving Average, Random Forest and Support Vector Regression. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations.
Unimodal and Multimodal Representation Training for Relation Extraction
Authors: Cooney Ciaran , Heyburn Rachel , Madigan Liam , O’Cuinn Mairead , Thompson Chloe , Cavadas Joana 
Published: February 2023

AbstractMultimodal integration of text, layout and visual information has achieved SOTA results in visually rich document understanding (VrDU) tasks, including relation extraction (RE). However, despite its importance, evaluation of the relative predictive capacity of these modalities is less prevalent. Here, we demonstrate the value of shared representations for RE tasks by conducting experiments in which each data type is iteratively excluded during training. In addition, text and layout data are evaluated in isolation. While a bimodal text and layout approach performs best (F1 = 0.684), we show that text is the most important single predictor of entity relations. Additionally, layout geometry is highly predictive and may even be a feasible unimodal approach. Despite being less effective, we highlight circumstances where visual information can bolster performance. In total, our results demonstrate the efficacy of training joint representations for RE.
Run-Time Norms Synthesis in Dynamic Environments with Changing Objectives
Authors: Riad Maha , Ghanadbashi Saeedeh , Golpayegani Fatemeh 
Published: February 2023

AbstractNormative Multi-Agent Systems (NorMAS) can model real-world applications as multi-agent systems and facilitate the coordination of the social behaviour of various entities (agents) interacting in an environment using norms. Aligning such norms with the objectives of the agents is crucially important to ensure that applying the norms would not affect the achievement of their objectives. However, when the environment is dynamic, agents can face unseen situations and might need to change their objectives accordingly. Therefore, it becomes more challenging to understand the change, synthesise norms, and align them with such dynamic objectives. This paper introduces a Dynamic Objectives and Norms Synthesizer and Reasoner (DONSR) model to align objectives and norms using a utility-based approach. An ontology-based schema, forward reasoning, and backward reasoning are used to identify the change in the environment and synthesise new objectives. Case-based reasoning enables the dynamic changing and reasoning of previously created objectives and synthesising norms. DONSR is evaluated using multiple simulated traffic scenarios, including different unseen situations (emergency events).Results show that norms can be synthesised and maintained efficiently while the objectives are being created and changed. Further, DONSR showed its efficacy in handling unseen situations, creating new objectives, and aligning them with the created norms.
Modellskizze eines anderen, zukunftsorientierten Marketing
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungZum vorläufigen Ende meiner Erkundungsreise ist ein Modell eines zukunftsorientierten Marketing entstanden, wie ich es mir zu Beginn meines Forschungsweges nicht hätte ausmalen können: Die Zukunftsfähigkeit der Profession liegt nicht in einer Fortsetzung des herkömmlichen Mindset, wie es in Komposita wie „Nachhaltigkeitsmarketing“ respektive „Green Marketing“ semantisch zum Ausdruck kommt. Notwendig scheint vielmehr, Marketing radikal anders zu denken.
Theoretische Bezugsrahmen
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungTheoretische Bezugsrahmen sorgen in der Forschung mit Aufstellungen für die notwendige theoretische Sensibilität. Die von mir herangezogenen Theorien beschäftigen sich mit der Bewältigung von Komplexität: Niklas Luhmanns Blick ist auf die System-Umwelt-Grenze ausgerichtet. Die jüngeren Gesellschafts- und Systemtheorien gehen von einer systemisch-integralen bzw. holonischen Denkweise aus.
Zusammenfassung: Methodologische Grundlagen
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungDas Forschungsdesign dieser Arbeit gründet in genauem Beobachten und geordnetem Denken. Es lädt dazu ein, Irritationen zuzulassen, die bisherige Sicht der Dinge (zumindest für den Moment) loszulassen und auf diesem Wege – ganz spielerisch und leicht – neue Perspektiven und Möglichkeiten zu entdecken.
Zusammenfassung: Systemwissen
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungIm analytischen, ersten Teil meiner Arbeit habe ich eine teils völlig neue Sicht auf meinen Forschungsgegenstand gewonnen. Vor diesem Hintergrund konkretisiere und – wo nötig – revidiere ich meine initialen forschungsleitenden Fragen und bestimme für den weiteren Untersuchungsverlauf Schwerpunkte, die mir besonders geeignet erscheinen, neues konzeptrelevantes Material hervorzubringen.
Digitale Lernumgebungen zur Vor- und Nachbereitung realer Experimentiereinheiten
Authors: Neff Sascha , Engl Alexander , Risch Björn 
Published: February 2023

ZusammenfassungIm Projekt Open MINT Labs werden virtuelle Labore für die naturwissenschaftlichen Fächer an den weiterführenden Schulen entwickelt und getestet. Ziel der Konzeption ist es, eine digitale Vor- und Nachbereitung realer Experimentiereinheiten anbieten zu können. Dies ermöglicht den Teilnehmenden unter anderem Einblicke in chemische Prozesse, welche in der realen experimentellen Durchführung nicht sichtbar gemacht werden können. Beispielhaft sei hier die Verdeutlichung submikroskopischer Vorgänge auf Teilchenebene genannt. Die projektbegleitende empirische Studie fokussiert auf die Nutzung und Wirksamkeit der virtuellen Labore sowie auf deren Transfer in den schulischen Unterricht. Im Rahmen des vorliegenden Beitrags wird zum einen die Konzeption des virtuellen Labors zum Sauerstoffgehalt eines Gewässers vorgestellt und zum anderen eine Teilstudie präsentiert, in der die Nutzungsmuster der virtuellen Labore („digitale Lernpfade“) anhand der Logfiles der Schülerinnen und Schüler analysiert werden.
Unterstützung von Experimenten zu Linsensystemen mit Simulationen, Augmented und Virtual Reality: Ein Praxisbericht
Authors: Mukhametov Sergey , Wörner Salome , Hoyer Christoph , Becker Sebastian , Kuhn Jochen 
Published: February 2023

ZusammenfassungBeim Lernen mit Experimenten spielt insbesondere die Verbindung der experimentellen Realität mit Modellvorstellungen und theoriebasierter Hypothesenbildung eine wichtige Rolle. Im Kontext der geometrischen Optik werden zur Vorhersage und Erklärung von optischen Abbildungen in Linsensystemen die sogenannten Konstruktionsstrahlen genutzt. Traditionell erfolgt die Darstellung der Konstruktionsstrahlen als zweidimensionale schematische Repräsentation jedoch getrennt vom Experiment. Eine Integration oder Ergänzung multimediabasierter Visualisierungen in das Experiment kann es Lernenden bei angemessener Unterstützung erleichtern, eine Verbindung der theoretischen Strahlengänge mit dem realen experimentellen Aufbau herzustellen. Dieser Praxisbericht beschreibt und vergleicht die Erweiterung realer Experimente durch verschiedene Arten virtueller Visualisierungen, sogenannte multiple externale Repräsentationen. Die Gestaltung der Repräsentationen sowie die Beurteilung ihrer Funktionen und wechselseitigen Beeinflussungen erfolgen dabei nach dem DeFT-Framework (Design, Function, Tasks). Zusätzlich berücksichtigt die Einbettung der Visualisierungen die Prinzipien der räumlichen und zeitlichen Kontiguität zur Reduktion lernirrelevanter kognitiver Belastung basierend auf Designprinzipien multimedialen Lernens. Ausgehend von Simulationen über Augmented-Reality-Experimente bis hin zu Virtual-Reality-Experimenten wird das naturwissenschaftliche Phänomen optischer Abbildungen durch Sammellinsen entlang des virtuellen Kontinuums exemplarisch an drei multimedialbasierten Erweiterungen des realen Experiments beschrieben. Die Möglichkeiten zur Einbindung von multiplen externen Repräsentationen werden abschließend vergleichend beschrieben und Vorteile der einzelnen Umsetzungen werden genannt.