bluuebunny/crossref_metadata_2025_split huggingface dataset:
Greek sign language recognition for an education platform
Authors: Papadimitriou Katerina , Potamianos Gerasimos , Sapountzaki Galini , Goulas Theodoros , Efthimiou Eleni , Fotinea Stavroula-Evita , Maragos Petros 
Published: July 2023

AbstractRecent advances in sign language (SL) technologies, along with demand for SL education, have led to increased interest in developing tools that enable automatic assessment of learners’ SL video productions, helping both students and their instructors. At the very least, such tools should perform automatic SL recognition (SLR) of non-studio quality videos in a signer-independent (SI) fashion, thus providing simple binary feedback on learners’ signing under realistic usage scenarios. Motivated by the above and the lack of any such tools for the Greek SL (GSL), we have been developing the “SL-ReDu” education platform for both receptive and productive GSL learning and student assessment. In this paper, we present our SLR module for GSL, developed for and integrated to the “SL-ReDu” system. The module incorporates state-of-the-art deep-learning based visual detection, feature extraction, and classification, operates in an SI mode on web-cam videos, and accommodates a small-size vocabulary of isolated signs and continuously fingerspelled letter sequences. We train the module on collected GSL data and demonstrate its superiority over a number of alternative SLR algorithms. We then conduct its objective evaluation within the “SL-ReDu” system and carry out a subjective evaluation of the overall platform, obtaining very satisfactory results in both.
A homogenized constrained mixture model of cardiac growth and remodeling: analyzing mechanobiological stability and reversal
Authors: Gebauer Amadeus M. , Pfaller Martin R. , Braeu Fabian A. , Cyron Christian J. , Wall Wolfgang A. 
Published: July 2023

AbstractCardiac growth and remodeling (G&R) patterns change ventricular size, shape, and function both globally and locally. Biomechanical, neurohormonal, and genetic stimuli drive these patterns through changes in myocyte dimension and fibrosis. We propose a novel microstructure-motivated model that predicts organ-scale G&R in the heart based on the homogenized constrained mixture theory. Previous models, based on the kinematic growth theory, reproduced consequences of G&R in bulk myocardial tissue by prescribing the direction and extent of growth but neglected underlying cellular mechanisms. In our model, the direction and extent of G&R emerge naturally from intra- and extracellular turnover processes in myocardial tissue constituents and their preferred homeostatic stretch state. We additionally propose a method to obtain a mechanobiologically equilibrated reference configuration. We test our model on an idealized 3D left ventricular geometry and demonstrate that our model aims to maintain tensional homeostasis in hypertension conditions. In a stability map, we identify regions of stable and unstable G&R from an identical parameter set with varying systolic pressures and growth factors. Furthermore, we show the extent of G&R reversal after returning the systolic pressure to baseline following stage 1 and 2 hypertension. A realistic model of organ-scale cardiac G&R has the potential to identify patients at risk of heart failure, enable personalized cardiac therapies, and facilitate the optimal design of medical devices.
An ontology-based approach to engineering ethicality requirements
Authors: Guizzardi Renata , Amaral Glenda , Guizzardi Giancarlo , Mylopoulos John 
Published: July 2023

AbstractIn a world where Artificial Intelligence (AI) is pervasive, humans may feel threatened or at risk by giving up control to machines. In this context, ethicality becomes a major concern to prevent AI systems from being biased, making mistakes, or going rogue. Requirements Engineering (RE) is the research area that can exert a great impact in the development of ethical systems by design. However, proposing concepts, tools and techniques that support the incorporation of ethicality into the software development processes as explicit requirements remains a great challenge in the RE field. In this paper, we rely on Ontology-based Requirements Engineering (ObRE) as a method to elicit and analyze ethicality requirements (‘Ethicality requirements’ is adopted as a name for the class of requirements studied in this paper by analogy to other quality requirements studied in software engineering, such as usability, reliability, and portability, etc. The use of this term (as opposed to ‘ethical requirements’) highlights that they represent requirements for ethical systems, analogous to how ‘trustworthiness requirements’ represent requirements for trustworthy systems. To put simply: the predicates ‘ethical’ or ‘trustworthy’ are not meant to be predicated over the requirements themselves). ObRE applies ontological analysis to ontologically unpack terms and notions that are referred to in requirements elicitation. Moreover, this method instantiates the adopted ontology and uses it to guide the requirements analysis activity. In a previous paper, we presented a solution concerning two ethical principles, namely Beneficence and Non-maleficence. The present paper extends the previous work by targeting two other important ethicality principles, those of Explicability and Autonomy. For each of these new principles, we do ontological unpacking of the relevant concepts, and we present requirements elicitation and analysis guidelines, as well as examples in the context of a driverless car case. Furthermore, we validate our approach by analysing the requirements elicitation made for the driverless car case in contrast with a similar case, and by assessing our method’s coverage w.r.t European Union guidelines for Trustworthy AI.
Low cross-linked terpenes-based porous polymers with reduced content of divinylbenzene: synthesis, physicochemical properties and sorption abilities
Authors: Sobiesiak M. , Banaszek P. 
Published: July 2023

AbstractThe low cross-linked porous polymers were prepared using terpene compounds (citral, limonene and pinene) and divinylbenzene with a suspension method. The prepared materials were characterized by: ATR-FTIR, low temperature nitrogen adsorption–desorption, TGA, swelling ratio and solid phase extraction (SPE) experiments. ATR-FTIR verified the chemical structures of the polymers. All the materials had developed internal structure with SBET in the range of 45–190 m2/g and high thermal stability. Sorption properties of the terpene-based polymers were tested for phenolic compounds (phenol, 2-chlorophenol, 2,4-dichlorophenol and 2,4,6-trichlorophenol) and popular non-steroidal anti-inflammatory drugs (aspirin, paracetamol and ibuprofen) and an antibiotic (ampicillin) with a dynamic solid phase extraction (SPE) method. The recoveries of 2,4-dichlorophenol and 2,4,6-trichlorophenol remained at a level 80–100% for all new materials even for 10–13 concentration cycles. Very high recoveries (70–100%) were also obtained for ibuprofen and aspirin from the citral- and limonene-based polymers using one-component solutions. However, when using ternary component solutions, the maximum recoveries of ibuprofen reached 70%. Paracetamol recoveries did not exceed 20%, while these for ampicillin ranged from 40 to 80%. The performed studies have proven that the process is affected by both chemical nature of adsorbents and adsorbates. Especially in the case of multicomponent solutions, the acid—base balance of solutes in the solution and on the polymer—solution interface should be taken into account as an important factor determining obtained recoveries.
High-frame rate homography and visual odometry by tracking binary features from the focal plane
Authors: Murai Riku , Saeedi Sajad , Kelly Paul H. J. 
Published: July 2023

AbstractRobotics faces a long-standing obstacle in which the speed of the vision system’s scene understanding is insufficient, impeding the robot’s ability to perform agile tasks. Consequently, robots must often rely on interpolation and extrapolation of the vision data to accomplish tasks in a timely and effective manner. One of the primary reasons for these delays is the analog-to-digital conversion that occurs on a per-pixel basis across the image sensor, along with the transfer of pixel-intensity information to the host device. This results in significant delays and power consumption in modern visual processing pipelines. The SCAMP-5—a general-purpose Focal-plane Sensor-processor array (FPSP)—used in this research performs computations in the analog domain prior to analog-to-digital conversion. By extracting features from the image on the focal plane, the amount of data that needs to be digitised and transferred is reduced. This allows for a high frame rate and low energy consumption for the SCAMP-5. The focus of our work is on localising the camera within the scene, which is crucial for scene understanding and for any downstream robotics tasks. We present a localisation system that utilise the FPSP in two parts. First, a 6-DoF odometry system is introduced, which efficiently estimates its position against a known marker at over 400 FPS. Second, our work is extended to implement BIT-VO—6-DoF visual odometry system which operates under an unknown natural environment at 300 FPS.
Dietary and habitat specialization, eye size, clutch size, and aerial lifestyle predict avian fragmentation sensitivity in an Andean biodiversity hotpot
Authors: Jones Harrison H. , Bedoya-Durán María Juliana , Colorado Z. Gabriel J. , Londoño Gustavo , Robinson Scott K. 
Published: July 2023

AbstractThe fragmentation of tropical forests remains a major driver of avian biodiversity loss, particularly for insectivores, yet the mechanisms underlying area sensitivity remain poorly understood. Studies in lowland systems suggest that loss of food resources, changes to light microenvironments, increased nest predation, and dispersal limitation are possible mechanisms, but these are untested for montane tropical bird communities. In this study, we related avian functional traits to area sensitivity (quantified using beta estimates from a multi-species occupancy model) to test the above four hypotheses for a cloud forest bird community (both resident species and just resident insectivores) in the Colombian Western Andes. We found that species with more specialized diets and those that use the canopy and subcanopy (loss of food hypothesis), larger relative eye sizes (light microhabitat hypothesis), and larger clutch sizes (nest predation hypothesis) were significantly more area sensitive. By contrast, there was no support for the dispersal limitation hypothesis; instead, we found that insectivores with more pointed wing shapes, and more aerial lifestyles, were significantly more fragmentation sensitive. These results suggest that reduced vegetation structure, loss of late-successional plant species, and loss of epiphytic plants may reduce food availability in fragments. Similarly, the ability to tolerate higher light intensity near fragment edges, or when traversing matrix habitat, may be important for persistence in fragments and suggests that habitat configuration may be of special importance in fragmented Andean landscapes. Overall, a lack of information on foraging, movement, and breeding ecology complicates avian conservation in the Andes.
Animal sentience and the Capabilities Approach to justice
Authors: read eva , Birch Jonathan 
Published: July 2023

AbstractMartha Nussbaum’s Justice for Animals calls upon humanity to secure for all sentient beings the central capabilities they need to flourish. This essay review critically examines the ethical and scientific foundations of Nussbaum’s position. On the ethical side, we explore the tension between a robust defence of animal rights and political liberalism, which requires tolerance of a range of reasonable views. On the scientific side, we reflect on how our uncertainty regarding the distribution of sentience in the natural world leaves us uncertain about how many claimants of justice there are and about the relative strength of their claims. We bring out some further problems with Nussbaum’s views on animal killing and pest control. We also reflect on how animal welfare science might be done differently if guided by Nussbaum’s framework.
On mean elements in artificial-satellite theory
Authors: Lara Martin 
Published: July 2023

AbstractThe merits of a perturbation theory based on a mean-to-osculating transformation that is purely periodic in the fast angle are investigated. The exact separation of the perturbed Keplerian dynamics into purely short-period effects and long-period mean frequencies is achieved by a non-canonical transformation, which, therefore, cannot be obtained by Hamiltonian methods. For this case, the evolution of the mean elements strictly adheres to the average behavior of the osculating orbit. However, due to the unavoidable truncation of perturbation solutions, the fact that this kind of theory confines in the mean variations the long-period terms of the semimajor axis, how tiny they may be, can have adverse effects in the accuracy of long-term semi-analytic propagations based on it.
The limitations of irony detection in Dutch social media
Authors: Maladry Aaron , Lefever Els , Van Hee Cynthia , Hoste Véronique 
Published: July 2023

AbstractIn this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic, syntactic, sentiment features, as well as two new data-driven features to model common sense. Based on patterns in the syntactic structure of tweets, we aim to model the presence of contrasting sentiments, a phenomenon that is known to be indicative of verbal irony and sarcasm. Feature selection, as well as voting ensemble techniques were implemented to enhance the classification performance. The final systems reach F1-scores up to 0.79, which are promising results for a task as difficult as irony detection. Besides a quantitative analysis, this paper also describes a thorough qualitative analysis of the system output. Although lexical cues appear to be very important to express irony, our analysis also revealed the need for more advanced modeling of common-sense knowledge to detect more subtle examples of irony.
MarIA and BETO are sexist: evaluating gender bias in large language models for Spanish
Authors: Garrido-Muñoz Ismael , Martínez-Santiago Fernando , Montejo-Ráez Arturo 
Published: July 2023

AbstractThe study of bias in language models is a growing area of work, however, both research and resources are focused on English. In this paper, we make a first approach focusing on gender bias in some freely available Spanish language models trained using popular deep neural networks, like BERT or RoBERTa. Some of these models are known for achieving state-of-the-art results on downstream tasks. These promising results have promoted such models’ integration in many real-world applications and production environments, which could be detrimental to people affected for those systems. This work proposes an evaluation framework to identify gender bias in masked language models, with explainability in mind to ease the interpretation of the evaluation results. We have evaluated 20 different models for Spanish, including some of the most popular pretrained ones in the research community. Our findings state that varying levels of gender bias are present across these models.This approach compares the adjectives proposed by the model for a set of templates. We classify the given adjectives into understandable categories and compute two new metrics from model predictions, one based on the internal state (probability) and the other one on the external state (rank). Those metrics are used to reveal biased models according to the given categories and quantify the degree of bias of the models under study.
Fine-tuning language models to recognize semantic relations
Authors: Roussinov Dmitri , Sharoff Serge , Puchnina Nadezhda 
Published: July 2023

AbstractTransformer-based pre-trained Language Models (PLMs) have emerged as the foundations for the current state-of-the-art algorithms in most natural language processing tasks, in particular when applied to context rich data such as sentences or paragraphs. However, their impact on the tasks defined in terms of abstract individual word properties, not necessary tied to their specific use in a particular sentence, has been inadequately explored, which is a notable research gap. Addressing this gap is crucial for advancing our understanding of natural language processing. To fill this void, we concentrate on classification of semantic relations: given a pair of concepts (words or word sequences) the aim is to identify the semantic label to describe their relationship. E.g. in the case of the pair green/colour, “is a” is a suitable relation while “part of”, “property of”, and “opposite of” are not suitable. This classification is independent of a particular sentence in which these concepts might have been used. We are first to incorporate a language model into both existing approaches to this task, namely path-based and distribution-based methods. Our transformer-based approaches exhibit significant improvements over the state-of-the-art and come remarkably close to achieving human-level performance on rigorous benchmarks. We are also first to provide evidence that the standard datasets over-state the performance due to the effect of “lexical memorisation.” We reduce this effect by applying lexical separation. On the new benchmark datasets, the algorithmic performance remains significantly below human-level, highlighting that the task of semantic relation classification is still unresolved, particularly for language models of the sizes commonly used at the time of our study. We also identify additional challenges that PLM-based approaches face and conduct extensive ablation studies and other experiments to investigate the sensitivity of our findings to specific modelling and implementation choices. Furthermore, we examine the specific relations that pose greater challenges and discuss the trade-offs between accuracy and processing time.
Robustness and efficiency of iteration schemes for variably saturated flow across the range of soils, initial and boundary conditions found in practice
Authors: Maier Denis , Montenegro Héctor , Odenwald Bernhard 
Published: July 2023

AbstractWater flow in partially saturated porous media is of importance in many disciplines such as hydrology, agriculture, environmental management or geotechnical engineering. A robust and accurate solution methodology applicable across the range of soils, initial and boundary conditions found in practice is difficult to identify. Three mass conservative iteration schemes for the approximation of the flow equation in partially saturated porous media are evaluated considering large contrasts in soil texture, initial moisture and boundary conditions typically found in real world application. The three schemes reviewed are the well established modified Picard algorithm (Celia-Scheme), and two promising approaches the L-Scheme and the Casulli-Scheme. A systematic variation of soil texture and initial moisture in an intense rainfall infiltration scenario is set up in order to consider the numerical challenges associated with high non-linearity. The present analysis provides insights into robustness and efficiency in solving variably saturated flow across the range of soils, initial and boundary conditions found in practice. Casulli’s scheme, in particular, provides an acceptable compromise between robustness and execution duration even under the severest conditions investigated, while the other schemes sacrifice one for the sake of the other. The authors believe that advances in the development of numerical schemes for partly saturated flow require the kind of methodology presented here in combination with relevant benchmark problems in order to enable a fair evaluation and comparison of numerical techniques across the related disciplines.
Analysis of genetic diversity and population structure in Cambodian melon landraces using molecular markers
Authors: Naznin Pervin Mst , Imoh Odirichi Nnennaya , Tanaka Katsunori , Sreynech Ouch , Shigita Gentaro , Sophea Yon , Sophany Sakhan , Makara Ouk , Tomooka Norihiko , Monden Yuki , Nishida Hidetaka , Kato Kenji 
Published: July 2023

AbstractGenetic diversity of Cambodian melons was evaluated by the analysis of 12 random amplified polymorphic DNA (RAPD) and 7 simple sequence repeat (SSR) markers using 62 accessions of melon landraces and compared with 231 accessions from other areas for genetic characterization of Cambodian melons. Among 62 accessions, 56 accessions were morphologically classified as small-seed type with seed lengths shorter than 9 mm, as in the horticultural groups Conomon and Makuwa. Gene diversity of Cambodian melons was 0.228, which was equivalent to those of the groups Conomon and Makuwa and smaller than those of Vietnamese and Central Asian landraces. A phylogenetic tree constructed from a genetic distance matrix classified 293 accessions into three major clusters. Small-seed type accessions from East and Southeast Asia formed clusters I and II, which were distantly related with cluster III consisting of large-seed type melon from other areas. All Cambodian melons belonged to cluster I (except three accessions) along with those from Thailand, Myanmar, Yunnan (China), and Vietnam (“Dua thom” in the northwest), thus indicating genetic similarity in these areas. In addition, the Cambodian melons were not differentiated among geographical populations. Conomon and Makuwa were classified into cluster II, together with melon groups from the plains of Vietnam. The presence of two groups of melons in Southeast Asia was also indicated by population structure and principal coordinate analysis. These results indicated a close genetic relationship between Cambodia and the neighboring countries, thus suggesting that Cambodian melons are not directly related to the establishment of Conomon and Makuwa.
The Construct Validity of the Childhood Joint Attention Rating Scale (C-JARS) in School-Aged Autistic Children
Authors: Birkeneder Sandy L. , Bullen Jennifer , McIntyre Nancy , Zajic Matthew C. , Lerro Lindsay , Solomon Marjorie , Sparapani Nicole , Mundy Peter 
Published: July 2023

AbstractPreliminary evidence from the Childhood Joint Attention Rating Scale (C-JARS; Mundy et al., 2017) suggests symptoms related to diminished joint attention and the spontaneous sharing of experience with others can be assessed with a parent-report measure in children and adolescents with autism. This study was designed to expand on the previous study by examining the validity of both a Social Symptom (SS) and a Prosocial (PS) scale of the C-JARS in a study of school-aged autistic children (n  = 89) with and without co-occurring intellectual disability (ID), as well as an age matched neurotypical sample (n  = 62). Results indicated that both C-JARS scales were sensitive and specific with respect to identifying the diagnostic status of the children. In addition, the PS scale was sensitive to differences in cognitive abilities (IQ) and sex differences in the autism group. These results are consistent with the hypothesis that joint attention and spontaneous sharing of experience symptoms are not only characteristic of preschool children with autism but may also constitute a developmentally continuous dimension of the social phenotype of autism that can be measured in school-aged children.