bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Vehicle Ego Body Extraction for Reducing False Detections in Automated Driving Applications
Authors: Hogan Ciarán , Sistu Ganesh 
Published: February 2023

AbstractFisheye cameras are extensively employed in autonomous vehicles due to their wider field of view, which produces a complete 360-degree image of the vehicle with a minimum number of sensors. The drawback of having a broader field of view is that it may include undesirable portions of the vehicle’s ego body in its perspective. Due to objects’ reflections on the car body, this may produce false positives in perception systems. Processing ego vehicle pixels also uses up unnecessary computing power. Unexpectedly, there is no literature on this relevant practical problem. To our knowledge, this is the first attempt to discuss the significance of autonomous ego body extraction for automobile applications that are crucial for safety. We also proposed a simple deep learning model for identifying the vehicle’s ego-body. This model would enable us to eliminate any pointless processing of the car’s bodywork, eliminate the potential for pedestrians or other objects to be mistakenly detected in the car’s ego-body reflection, and finally, check to see if the camera is mounted incorrectly. The proposed network is a U-Net model with a Res-Net50 encoder pre-trained on ImageNet and trained for binary semantic segmentation on vehicle ego-body data. Our training data is an internal Valeo dataset with 10K samples collected by three separate car lines across Europe. This proposed network could then be integrated into the vehicles existing perception system by extracting the ego-body contour data and supplying this to the other algorithms which then ignore the area outside the contour coordinates. The proposed network can run at set intervals to save computing power and to check if the camera is misaligned by comparing the new contour data to the previous data.
Graph-Based Diffusion Method for Top-N Recommendation
Authors: Zhou Yifei , Hayes Conor 
Published: February 2023

AbstractData that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data may be linked to item data. With such a model, the task of recommending new items to users or making new connections between items can be undertaken by algorithms designed to establish the relatedness between vertices in a graph. One such class of algorithm is based on the random walk, whereby a sequence of connected vertices are visited based on an underlying probability distribution and a determination of vertex relatedness established. A diffusion kernel encodes such a process. This paper demonstrates several diffusion kernel approaches on a graph composed of user-item and item-item relationships. The approach presented in this paper, RecWalk*, consists of a user-item bipartite combined with an item-item graph on which several diffusion kernels are applied and evaluated in terms of top-n recommendation. We conduct experiments on several datasets of the RecWalk* model using combinations of different item-item graph models and personalised diffusion kernels. We compare accuracy with some non-item recommender methods. We show that diffusion kernel approaches match or outperform state-of-the-art recommender approaches.
CouRGe: Counterfactual Reviews Generator for Sentiment Analysis
Authors: Carraro Diego , Brown Kenneth N. 
Published: February 2023

AbstractPast literature in Natural Language Processing (NLP) has demonstrated that counterfactual data points are useful, for example, for increasing model generalisation, enhancing model interpretability, and as a data augmentation approach. However, obtaining counterfactual examples often requires human annotation effort, which is an expensive and highly skilled process. For these reasons, solutions that resort to transformer-based language models have been recently proposed to generate counterfactuals automatically, but such solutions show limitations.In this paper, we present CouRGe, a language model that, given a movie review (i.e. a seed review) and its sentiment label, generates a counterfactual review that is close (similar) to the seed review but of the opposite sentiment. CouRGe is trained by supervised fine-tuning of GPT-2 on a task-specific dataset of paired movie reviews, and its generation is prompt-based. The model does not require any modification to the network’s architecture or the design of a specific new task for fine-tuning.Experiments show that CouRGe’s generation is effective at flipping the seed sentiment and produces counterfactuals reasonably close to the seed review. This proves once again the great flexibility of language models towards downstream tasks as hard as counterfactual reasoning and opens up the use of CouRGe’s generated counterfactuals for the applications mentioned above.
Meme Sentiment Analysis Enhanced with Multimodal Spatial Encoding and Face Embedding
Authors: Hazman Muzhaffar , McKeever Susan , Griffith Josephine 
Published: February 2023

AbstractInternet memes are characterised by the interspersing of text amongst visual elements. State-of-the-art multimodal meme classifiers do not account for the relative positions of these elements across the two modalities, despite the latent meaning associated with where text and visual elements are placed. Against two meme sentiment classification datasets, we systematically show performance gains from incorporating the spatial position of visual objects, faces, and text clusters extracted from memes. In addition, we also present facial embedding as an impactful enhancement to image representation in a multimodal meme classifier. Finally, we show that incorporating this spatial information allows our fully automated approaches to outperform their corresponding baselines that rely on additional human validation of OCR-extracted text.
Automatic Linking of Podcast Segments to Topically Related Webpages
Authors: McKeon Carla , Rocha Claudio , Jones Gareth J. F. 
Published: February 2023

AbstractPodcasts are becoming an increasingly popular source of information. However, they often rely on the topical knowledge of the listener in order for them to be fully understood. We describe an investigation into methods to augment the contents of podcasts with related information from the Web. We seek to identify webpages related to segments within a podcast. NLP techniques are used to analyze audio podcast transcripts and link these to related content. We propose and examine 10 methods for automatically generating search queries from transcript segments, which are then used to search for related content on the web. The relevance of retrieved webpages to retrieved content is evaluated using crowdsourcing via Amazon Mechanical Turk. Extracting key phrases directly from the podcasts using YAKE was the most successful approach with more than 90% returned pages assessed as relevant, with precision at rank 1 and rank 3 above 0.9.
A Large Neighborhood Search Approach for the Data Centre Machine Reassignment Problem
Authors: Souza Filipe , Grimes Diarmuid , O’Sullivan Barry 
Published: February 2023

AbstractOne of the main challenges in data centre operations involves optimally reassigning running processes to servers in a dynamic setting such that operational performance is improved. In 2012, Google proposed the Machine Reassignment Problem in collaboration with the ROADEF/Euro challenge. A number of complex instances were generated for evaluating the submissions. This work focuses on new approaches to solve this problem.In particular, we propose a Large Neighbourhood Search approach with a novel, domain-specific heuristic for neighborhood selection. This heuristic uses the unbalanced resource usage on the machines to select the most promising processes in each iteration. Furthermore, we compare two search strategies to optimise the sub-problems. The first one is based on the concept of Limited Discrepancy Search, albeit tailored to large scale problems; and the second approach involves the standard combination of constraint programming with random restart strategies.An empirical evaluation on the widely studied instances from ROADEF 2012 demonstrates the effectiveness of our approach against the state-of-the-art, with new upper bounds found for three instances.
Computational Phenotyping of Decision-Making over Voice Interfaces
Authors: Zhang Lili , Mukherjee Ruben , Wadhai Piyush , Muehlhausen Willie , Ward Tomas 
Published: February 2023

AbstractResearch on human reinforcement learning and decision-making behaviour has traditionally used visual-based symbols and graphics in the experimental paradigms. Such research leads to improved understanding of human decision-making and has application in fundamental research in cognitive neuroscience. In clinical domains, the approach holds out the possibility for the development of computationally-derived biomarkers suitable for use in psychiatry. Scaling this experimental approach through pervasive computing can help create larger datasets which will be necessary for normative studies. This will require the expansion of these experimental approaches beyond conventional visual representations. People receive information and interact with their environments through various senses. In particular, our sense of hearing in conjunction with speech represents a ubiquitous modality for learning and for updating our knowledge of the world. Consequently, it represents an important path for the investigation of human decision-making which is now experimentally accessible via rapid advances in voice-enabled intelligent personal assistants (IPAs). Examples include Amazon’s Alexa technology and Google’s Voice Assistant. However, to date no studies have demonstrated the feasibility of delivering such experimental paradigms over such voice technologies. Consequently in this study, we compared the performance of the same group of participants on the traditional visual-based and for the first time, a conversational voice-based, two-armed bandit task. Reinforcement learning models were fitted to the data to represent the characteristics of the underlying cognitive mechanisms in the task. Both model-independent behavioural measures and model-derived parameters were compared. The results suggest that participants demonstrated higher shifting rates in the voice-based version of the task. The computational modelling analysis revealed that participants adopted similar learning rates under the two versions of the interfaces, but more decision noise was introduced in the voice-based task as reflected by the decreased value of the inverse temperature parameter. We suggest that the elevated shifting rate is derived from the increased noise in the voice interface instead of a change in the learning strategy of the participants. Higher intensity of the control adjustments (click touch versus speak) might be one of the sources of noise, thus it is important to think further regarding the design of the voice interface if we wish to apply voice-enabled IPAs to measure human decision-making in their daily environments in the future.
Entity Resolution for Multiple Sources with Extended Approach
Authors: Huu Phuc Pham , Nie Dongyun , Scriney Michael 
Published: February 2023

AbstractEntity Resolution is a technique to find similar records that may refer to the same entity from one or many resources. It is mainly used in data integration or data cleaning with the existence of Big Data. It not only helps organisations have clean data, but it also provides a unified view of their data for later analysis. However, there is no one solution fitting all duplication issues. Because of the fact that the data itself is heterogeneous and varied. This paper focuses on finding the answers to the usefulness of a combination of different matching approaches, token blocking versus standard blocking and how other domain runs by examining how well they perform in different scenarios. To achieve these answers, this paper outline details and setups for these experiments to execute. A detailed evaluation demonstrates the effectiveness of the approaches with multiple datasets.
A Machine Learning Approach to Industry Classification in Financial Markets
Authors: Dolphin Rian , Smyth Barry , Dong Ruihai 
Published: February 2023

AbstractIndustry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification.
Methodologisches Rahmenmodell: Grounded-Theory-Methodologie
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungAufstellungen erzeugen unendlich viele Informationen. Aus dieser Fülle an Daten jene herauszufiltern, die tatsächlich Neues enthalten, stellt eine methodische Herausforderung dar. In der Grounded-Theory-Methodologie (GTM) habe ich ein geeignetes Verfahren gefunden, das diesem Prozess Präzision und Regelgeleitetheit verleiht. Auch mit ihrem explorativen Fokus passt die GTM ideal zu meinem Forschungsvorhaben.
Aufstellung 2: Das innere Wesen des Marketing
Authors: Rieck Louise Ina Hannah 
Published: February 2023

ZusammenfassungMein initiales Erkenntnisinteresse bei dieser Aufstellung gilt dem Erkunden des inneren Wesens des Marketing: Liegt die einzig mögliche Seinsform von Marketing (wie es der Titel eines Leitmediums der Branche suggerieren mag) im „Werben & Verkaufen“? Zeigt sich in dieser Rolle bereits sein Wesenskern, seine wesentliche Bestimmung? Oder gibt es Spielräume darüber hinaus, und könnte Marketing auch noch ganz anders (und transformativ) nützlich sein?
Methode
Authors: Villioth Lisa 
Published: February 2023

ZusammenfassungIm nun folgenden Kapitel wird die methodische Herangehensweise dieser Arbeit erläutert. Dazu werden zuerst der Mixed-Method-Ansatz vorgestellt und die einzelnen Schritte des Feldzugangs sowie der Materialerhebung beschrieben. In Anlehnung an die Grounded Theory Methode (GTM) wurde hierbei mit einem explorativen und qualitativen Ansatz gearbeitet. Anschließend werden die Faktoren für die Sample-Zusammensetzung erläutert und die Interview-Partner*innen jeweils mit einem Kurzprofil vorgestellt. Danach folgen eine Darstellung der Kategorienbildung nach der inhaltlich strukturierenden qualitativen Inhaltsanalyse und eine Ausführung zur Typenbildung. Ziel dieses Kapitels ist es, das methodische Vorgehen der Arbeit nachvollziehbar zu machen.
Praktiken: Die Protest-Aktivist*innen der Umweltschutz-Bewegung auf der Straße, im Web 1.0 und im Web 2.0
Authors: Villioth Lisa 
Published: February 2023

ZusammenfassungDas nun folgende Kapitel befasst sich explizit mit den einzelnen Protestpraktiken, dem Handlungsrepertoire und der Intensität des Engagements der Interview-Partner*innen. Dazu wird zuerst der Praktiken-Begriff definiert, anschließend auf das Verständnis von Medien als Praktiken eingegangen und dann die Rolle von Medienpraktiken in Sozialen Bewegungen thematisiert. Danach folgt eine Beschreibung der Intensität des Engagements der Interview-Partner*innen. Anschließend werden die einzelnen Praktiken des Handlungsrepertoires der Aktivist*innen dargestellt, wobei eine Unterscheidung in (vorwiegend) Offline- und (vorwiegend) Online-Praktiken vorgenommen wird. Den Abschluss bildet eine Analyse des konkreten Einflusses von Campact und dem BUND auf das Handlungsrepertoire und die Intensität des Engagements der Interview-Partner*innen.
Wandel von Bürgerschaft? Ein Fazit zur Online- und Offline-Protestpartizipation
Authors: Villioth Lisa 
Published: February 2023

ZusammenfassungZiel dieser Arbeit war es, Folgen der Digitalisierung für die politische Protestpartizipation am Beispiel der Umweltschutz-Bewegung zu diskutieren und individuelle Voraussetzungen und Motive von Bürger*innen für eine Partizipation herauszuarbeiten. Dabei lag der Fokus insb. auf Ressourcen, Emotionen und Affekten, den konkreten Online- und Offline-Praktiken und den persönlichen Einstellungen der Individuen zu Straßenprotest, Online-Aktivismus und der Kritik des Clicktivism, sowie zum Verständnis von Bürgerschaft. Durch die Zusammensetzung des Samples mit älteren und jüngeren Interview-Partner*innen konnte untersucht werden, wie sich einerseits die konkreten Protestpraktiken einzelner Bürger*innen über die Jahrzehnte verändert haben, die früher nur Offline-Möglichkeiten zur Verfügung hatten und nun sowohl online als auch offline agieren können, und andererseits Digital Natives auf die Möglichkeit reagieren, sich sowohl im Netz als auch auf der Straße bzw. in Organisationen einbringen zu können. Die Erkenntnisse aus der Analyse des Interviewmaterials decken sich mit vielen in der bisherigen Literatur beschriebenen Forschungsergebnissen. Die vorliegende Arbeit kann jedoch auch relevante und notwendige Ergänzungen zum Thema beitragen und schlägt Überarbeitungen früherer Konzepte mit Blick auf die Auswirkungen der Digitalisierung vor. In der bisherigen Forschung nicht ausreichend berücksichtigt wurde z. B. das Thema Frustrationstoleranz.
Untersuchung der Lernwirksamkeit Tablet-PC-gestützter Videoanalyse im Mechanikunterricht der Sekundarstufe 2
Authors: Becker Sebastian , Gößling Alexander , Kuhn Jochen 
Published: February 2023

ZusammenfassungDigitale Medien, wie Smartphones und Tablet-PCs, sind im Alltag weitverbreitet. Die Mobilität dieser Geräte ermöglicht die Durchführung physikalischer Untersuchungen in einer für die Schülerinnen und Schüler alltäglichen, natürlichen Umgebung. Bei der Tablet-PC-gestützten Videoanalyse können natürliche Bewegungsabläufe, wie z. B. das Laufen oder Springen, mithilfe der im Tablet eingebauten Kamera aufgenommen und danach mithilfe geeigneter Apps unter physikalischen Gesichtspunkten analysiert werden. Um den Einsatz solcher digitaler Medien nachhaltig zu gestalten, bedarf es grundlegender, empirisch abgesicherter Aussagen zur Lernwirksamkeit dieser Medien. Aus verschiedenen multimedialen Lerntheorien kann z. B. für die Tablet-PC-gestützte Videoanalyse u. a. eine erhöhte Lernwirksamkeit im Vergleich zu traditionellem Unterricht vermutet werden. Diese Hypothese wurde in mehreren Studien untersucht. Ausgewählte Ergebnisse dieser Studien zu den interventionsinduzierten Emotionen, zur kognitiven Belastung und zum physikalischen Konzeptverständnis werden vorgestellt.
Eine digitale Spielumgebung zum Lehren und Lernen von Problemlösefähigkeit und Critical Thinking in den Naturwissenschaften
Authors: Dictus Christian , Tiemann Rüdiger 
Published: February 2023

ZusammenfassungDie Teilhabe an der zunehmend digitalisierten Welt erfordert den Erwerb und die Vermittlung neuer Fähigkeiten. Zu diesen häufig als Key Competencies for Lifelong Learning oder 21st Century Skills bezeichneten Fähigkeiten, gehören unter anderem auch Problemlösen und Critical Thinking. Die Vermittlung dieser Fähigkeiten stellt aufgrund der Komplexität aktueller Probleme nicht nur inhaltlich eine große Herausforderung dar, sondern auch in Bezug auf die Motivation der Lernenden. Um dieser Problematik zu begegnen, haben wir die digitale Spielumgebung „MINT-Town“ entwickelt, welche durch die gezielte Einbettung von Gamification-Elementen motivierend wirkt und zur Vermittlung von Problemlösen und Critical Thinking genutzt werden kann. In diesem Kapitel stellen wir die Umgebung mit ihren bislang zwei Szenarien mit dem Schwerpunkt Chemie und einem einführenden Tutorial vor. Die mit RPG-Maker MV gestalteten Lernumgebungen sind im Retrodesign gehalten und orientieren sich an frühen Adventure-Games mit Dialogformaten. Sie sind bislang in Vorstudien durch erste Expertenratings evaluiert und weiterentwickelt worden; die Hauptstudie ist zum jetzigen Zeitpunkt für das Frühjahr 2023 vorgesehen.
Hall Droplet Sheets in Holographic QCD
Authors: Bigazzi Francesco , Cotrone Aldo L. , Olzi Andrea 
Published: February 2023

AbstractIn single-flavor QCD, the low energy description of baryons as Skyrmions is not available. In this case, it has been proposed by Komargodski that baryons can be viewed as kinds of charged quantum Hall droplets, or “sheets”. In this paper we propose a string theory description of the sheets in single-flavor holographic QCD, focusing on the Witten-Sakai-Sugimoto model. The sheets have a “hard” gluonic core, described by D6-branes, and a “soft” mesonic shell, dual to non-trivial D8-brane gauge field configurations. We first provide the description of an infinitely extended sheet with massless or moderately massive quarks. Then, we construct a semi-infinite sheet ending on a one-dimensional boundary, a “vortex string”. The holographic description allows for the precise calculation of sheet observables. In particular, we compute the tension and thickness of the sheet and the vortex string, and provide their four dimensional effective actions.
Radiative phase space extensions at all orders in r for self-dual Yang-Mills and gravity
Authors: Nagy Silvia , Peraza Javier 
Published: February 2023

AbstractWorking in the self-dual sector for Yang-Mills and gravity, we show how to construct an extended phase space at null infinity, to all orders in the radial expansion. This formalises the symmetry origin of the infrared behaviour of these theories to all subleading orders. As a corollary, we also derive a double copy mapping from a subset of YM gauge transformations to a subset of diffeomorphisms to all orders in the transformation parameters, which to our knowledge has not been presented before in the literature.
Convolution Integral Operators in Variable Bounded Variation Spaces
Authors: Angeloni Laura , Merentes Nelson J. , Valera-López Maira A. 
Published: February 2023

AbstractWorking in the frame of variable bounded variation spaces in the sense of Wiener, introduced by Castillo, Merentes, and Rafeiro, we prove convergence in variable variation by means of the classical convolution integral operators. In the proposed approach, a crucial step is the convergence of the variable modulus of smoothness for absolutely continuous functions. Several preliminary properties of the variable$$p(\cdot )$$p(·)-variation are also presented.