bluuebunny/crossref_metadata_2025_split huggingface dataset:
Collaborative intrusion detection using weighted ensemble averaging deep neural network for coordinated attack detection in heterogeneous network
Authors: Wardana Aulia Arif , Kołaczek Grzegorz , Warzyński Arkadiusz , Sukarno Parman 
Published: July 2024

AbstractDetecting coordinated attacks in cybersecurity is challenging due to their sophisticated and distributed nature, making traditional Intrusion Detection Systems often ineffective, especially in heterogeneous networks with diverse devices and systems. This research introduces a novel Collaborative Intrusion Detection System (CIDS) using a Weighted Ensemble Averaging Deep Neural Network (WEA-DNN) designed to detect such attacks. The WEA-DNN combines deep learning techniques and ensemble methods to enhance detection capabilities by integrating multiple Deep Neural Network (DNN) models, each trained on different data subsets with varying architectures. Differential Evolution optimizes the model’s contributions by calculating optimal weights, allowing the system to collaboratively analyze network traffic data from diverse sources. Extensive experiments on real-world datasets like CICIDS2017, CSE-CICIDS2018, CICToNIoT, and CICBotIoT show that the CIDS framework achieves an average accuracy of 93.8%, precision of 78.6%, recall of 60.4%, and an F1-score of 62.4%, surpassing traditional ensemble models and matching the performance of local DNN models. This demonstrates the practical benefits of WEA-DNN in improving detection capabilities in real-world heterogeneous network environments, offering superior adaptability and robustness in handling complex attack patterns.
Improved quantitative parameter estimation for prostate T2 relaxometry using convolutional neural networks
Authors: Bolan Patrick J. , Saunders Sara L. , Kay Kendrick , Gross Mitchell , Akcakaya Mehmet , Metzger Gregory J. 
Published: July 2024

Abstract
Objective
Quantitative parameter mapping conventionally relies on curve fitting techniques to estimate parameters from magnetic resonance image series. This study compares conventional curve fitting techniques to methods using neural networks (NN) for measuring T2 in the prostate.

Materials and methods
Large physics-based synthetic datasets simulating T2 mapping acquisitions were generated for training NNs and for quantitative performance comparisons. Four combinations of different NN architectures and training corpora were implemented and compared with four different curve fitting strategies. All methods were compared quantitatively using synthetic data with known ground truth, and further compared on in vivo test data, with and without noise augmentation, to evaluate feasibility and noise robustness.

Results
In the evaluation on synthetic data, a convolutional neural network (CNN), trained in a supervised fashion using synthetic data generated from naturalistic images, showed the highest overall accuracy and precision amongst the methods. On in vivo data, this best performing method produced low-noise T2 maps and showed the least deterioration with increasing input noise levels.

Discussion
This study showed that a CNN, trained with synthetic data in a supervised manner, may provide superior T2 estimation performance compared to conventional curve fitting, especially in low signal-to-noise regions.
Genotype and Year Effects on Some Production Characteristics in Three Blueberry Cultivars (Vaccinium corymbosum L.)
Authors: Cosmulescu Sina , Merca Laies Maria Marina , Sărățeanu Veronica 
Published: July 2024

AbstractThe current trend of expanding blueberry plantations requires studies on the production capacity of blueberry cultivars in different cultivation areas. The paper’s aim was to follow the effect of genotype and environment on some production characteristics of three blueberry cultivars (‘Duke’, ‘Hannah’s Choice’ and ‘Elliott’) grown in the Banat region of Romania. The average fruit weight, production per bush and production per hectare were calculated. The analysis of obtained data highlighted a variability from one cultivar to another, and within the cultivar from one year to another. The simulated models have highlighted the highly significant influence of considered factors combined (cultivar*year) on production features. ‘Duke’ and ‘Elliott’ cultivars have the best results in the study area, that is, ‘Duke’ cultivar can provide blueberries for market early (from June) and ‘Elliott’ cultivar provides blueberries during the summer–autumn time interval. The outputs are very useful, bearing in mind the low amount of available information from the literature.
Rechtfertigen hohe Behandlungskosten die Euthanasie des Heimtieres oder das grundsätzlich strafbare Unterlassen einer veterinärmedizinisch gebotenen Behandlung? – Über den schmalen Grat zwischen Tierhalterinteressen und Strafbarkeit
Authors: Poplat Tim 
Published: July 2024

ZusammenfassungErkrankt das Heimtier, fragt der Tierarzt typischerweise vor einer veterinärmedizinisch gebotenen
Behandlung, ob sich der Tierhalter die anfallenden Kosten leisten kann. Das suggeriert einen viel weiteren
Handlungs- und Entscheidungsspielraum des Tierhalters, als ihn §17 TierSchG gewährt. Angesichts
der bisher allenfalls rudimentären Befassung des Schrifttums mit diesem Thema, fehlt es an hinreichender
Klarheit, ob und inwiefern hohe Behandlungskosten für die Euthanasie oder das strafbare Unterlassen
einer solchen Behandlung als Ausschluss- oder Rechtfertigungsgrund angeführt werden können. Ein
Blick auf §266a StGB zeigt indes, dass die Zahlungsunfähigkeit der Rechtsordnung als Ausschlussmöglichkeit
keinesfalls fremd ist. Zugleich verdeutlicht er, dass nur in seltenen Ausnahmefällen die Kostenhöhe
den tierlichen Interessen erfolgreich entgegengehalten werden kann.
An Effective Teaching Pedagogy Involving an Online Learning Platform under a Multi-Campus Teaching Model
Authors: Poon Pak-Lok , Tang Sau-Fun 
Published: July 2024

AbstractNowadays, many universities around the globe have offered distance learning to their students in addition to the traditional face-to-face learning mode. Furthermore, a number of universities have multiple campuses, thereby requiring these universities to adopt a multi-campus learning model. This setting creates numerous teaching and learning challenges. This paper aims to: (a) explore what major teaching and learning challenges are faced by Central Queensland University (CQU) due to its multiple campuses and a diverse student cohort involving both on-campus and off-campus students, and (b) discuss our teaching pedagogy (including the use of an online learning platform) for an information systems analysis and design (ISAD) unit at CQU with a view to alleviating these teaching/learning challenges. We analyzed qualitatively and quantitatively the effectiveness of our teaching pedagogy in addressing the teaching/learning challenges. We performed a two-sample t-test involving a total of 216 ISAD students and found that, by adopting our teaching pedagogy, there was no significant difference in academic performance between urban (mostly on-campus) and regional (mostly off-campus) students. Also, our teaching pedagogy reduced the dropout percentages of both urban and regional students, particularly the regional students.
Extrapolation is not the same as interpolation
Authors: Wang Yuxuan , King Ross D. 
Published: July 2024

AbstractWe propose a new machine learning formulation designed specifically for extrapolation. The textbook way to apply machine learning to drug design is to learn a univariate function that when a drug (structure) is input, the function outputs a real number (the activity): f(drug) $$\rightarrow$$
→
activity. However, experience in real-world drug design suggests that this formulation of the drug design problem is not quite correct. Specifically, what one is really interested in is extrapolation: predicting the activity of new drugs with higher activity than any existing ones. Our new formulation for extrapolation is based on learning a bivariate function that predicts the difference in activities of two drugs F(drug1, drug2) $$\rightarrow$$
→
difference in activity, followed by the use of ranking algorithms. This formulation is general and agnostic, suitable for finding samples with target values beyond the target value range of the training set. We applied the formulation to work with support vector machines , random forests , and Gradient Boosting Machines . We compared the formulation with standard regression on thousands of drug design datasets, gene expression datasets and material property datasets. The test set extrapolation metric was the identification of examples with greater values than the training set, and top-performing examples (within the top 10% of the whole dataset). On this metric our pairwise formulation vastly outperformed standard regression. Its proposed variations also showed a consistent outperformance. Its application in the stock selection problem further confirmed the advantage of this pairwise formulation.
Noise propagation in linear stationary iterations
Authors: Elfving Tommy 
Published: July 2024

AbstractWe consider a stationary iteration for solving a linear system of arbitrary order. The method includes, e.g. Kaczmarz iteration, the Landweber iteration and the SOR (Gauss-Seidel) iteration. A study of the behavior of the iterates, both theoretically and experimentally, is performed. In particular we compare the behavior with and without noise in the data. The results give insight into the interplay between noise free and noisy iterates. For comparision we also included a Krylov type method CGLS in the experiments. As expected CGLS works well for noise free data but also tends to amplify the noise faster than the other methods, thus making it more critical when to stop the iterations.
Selection of outcome measurement instruments for a core outcome set for trials aimed at improving appropriate polypharmacy in older people in primary care: a Delphi consensus study
Authors: Alqahtani Mubarak N. , Barry Heather E. , Hughes Carmel M. 
Published: July 2024

Abstract
Background
Despite developing a polypharmacy core outcome set (COS) in primary care, it is not clear how these outcomes should be measured.

Aim
To select outcome measurement instruments (OMIs) for a COS targeting appropriate polypharmacy in older patients in primary care.

Method
Following the Consensus-based Standards for the selection of health Measurement Instruments (COSMIN) guideline, OMIs were identified from a Cochrane review focusing on appropriate polypharmacy. The quality of OMIs was assessed using a published checklist. Subsequently, two rounds of Delphi questionnaires were conducted via the SoGoSurvey® platform, engaging stakeholders (researchers, clinicians and journal editors specialising in geriatric primary care) to achieve consensus on OMIs using a scale encompassing “agree”, “disagree”, or “unsure”. Consensus was achieved if 70% or more participants chose “agree” and 15% or fewer chose “disagree.”

Results
The quality of 20 OMIs identified from the Cochrane review was evaluated. Seven OMIs were selected based on meeting the COSMIN guideline’s minimum requirements. Out of 188 potential participants, 57 (30.3%) consented to participate. Rounds 1 and 2 of Delphi exercises were completed by 50 respondents, achieving agreement on three OMIs: ‘number of serious adverse drug reactions (ADRs)’ (98%), ‘number of deaths’ (76%), and ‘number of patients who fell’ (70%) for measuring ‘serious ADRs,’ ‘mortality,’ and ‘falls,’ respectively. No agreement was reached for ‘medication appropriateness,’ ‘medication side-effects,’ ‘quality of life,’ and ‘medication regimen complexity.’

Conclusion
OMIs were selected for a limited number of outcomes in the polypharmacy COS. Future research should identify suitable OMIs for the remaining four outcomes.
A comprehensive survey of federated transfer learning: challenges, methods and applications
Authors: Guo Wei , Zhuang Fuzhen , Zhang Xiao , Tong Yiqi , Dong Jin 
Published: July 2024

AbstractFederated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.
Bewertung von Instrumenten zur Diagnosefindung des Restless-Legs-Syndroms im Kindes- und Jugendalter
Authors: Mollin Julian , Klug Lisa , Joost Theresa , Beyer Uta , Mayer Frank , Erler Thomas 
Published: July 2024

Zusammenfassung
Hintergrund
Das Restless-Legs-Syndrom (RLS) ist eine schlafbezogene Bewegungsstörung mit Beinbewegungen, die durch Missempfindungen verursacht werden. Die Symptome treten vorwiegend abends oder nachts auf und bessern sich durch Bewegung. Im Kindes- und Jugendalter wird das RLS meist spät diagnostiziert, weshalb es häufig zu einer verzögerten Behandlung und einem erschwerten Zugang zu spezialisierter Versorgung kommt. Die Diagnose wird mit Hilfe der Internationalen Restless-Legs-Syndrom-Study-Group(IRLSSG)-Kriterien, unter Berücksichtigung einer alters- und entwicklungsabhängigen Symptombeschreibung, gestellt.

Ziel der Arbeit
Bewertung von Instrumenten zur Diagnostik des RLS im Kindes- und Jugendalter.

Methodik
In einer dreiphasigen Studie wurden zur Diagnostik des RLS ein Fragebogen, ein halbstandardisiertes Interview und eine Polysomnographie (PSG) durchgeführt.

Ergebnisse
Für 1076 Proband/innen wurde anhand des Fragebogens ein individueller RLS-Index-Score (−1,9 ± 9,5 Punkte) zur Vorhersage eines möglichen RLS berechnet. Bei 188 auffälligen Proband/innen (RLS-Index-Score 8,0 ± 7,0 Punkte) erfolgte ein halbstandardisiertes Interview zur detaillierten Anamneseerhebung. Die Diagnose eines „wahrscheinlichen“ oder „unklaren“ RLS erhielten 60 Probanden. Bei 15 Proband/innen (RLS-Index-Score 13,3 ± 2,7 Punkte) erfolgte eine zweinächtige PSG. Fünf Proband/innen zeigten einen auffälligen PLMS-Index (periodische Beinbewegungen im Schlaf). Aus den Studiendaten, klinischer Erfahrung und der Literatur wurde ein Algorithmus erarbeitet.

Diskussion
Zur Diagnosestellung des RLS im Kindes- und Jugendalter bedarf es einer strukturierten Diagnostik. Der vorgestellte Algorithmus gibt ein aufbauendes Vorgehen vor, welches eine einheitliche Diagnosestellung und Therapieentscheidung ermöglicht.
What Western Philosophers of Technology Might Learn from Li Bocong’s Philosophy of Engineering
Authors: Wang Nan , Mitcham Carl 
Published: July 2024

AbstractThis essay aims to rectify a failure on the part of Western philosophers of technology to attend to the creative philosophical work of Li Bocong at the University of Chinese Academy of Sciences in Beijing. After a brief account of Li Bocong’s personal contacts with the West and some remarks on his relationship to Marxism, we take up three aspects of his philosophy that can contribute to enlarging Western philosophical thinking about engineering and technology: (1) Li’s analysis of engineering as more than design, (2) his argument for the relevance of the sociology of engineering, and (3) his conceptualization of engineering ethics as more than professional ethics.
Impact and Assessment of Research Integrity Teaching: A Systematic Literature Review
Authors: Crean Daniel , Gordijn Bert , Kearns Alan J. 
Published: July 2024

AbstractPresented here is a systematic literature review of what the academic literature asserts about: (1) the stages of the ethical decision-making process (i.e. awareness, reasoning, motivation, and action) that are claimed to be improved or not improved by RI teaching and whether these claims are supported by evidence; (2) the measurements used to determine the effectiveness of RI teaching; and (3) the stage/s of the ethical decision-making process that are difficult to assess. Regarding (1), awareness was the stage most claimed to be amenable to improvement following RI teaching, and with motivation being the stage that is rarely addressed in the academic literature. While few, some sources claimed RI teaching cannot improve specific stages. With behaviour (action) being the stage referenced most, albeit in only 9% of the total sources, for not being amenable to improvement following RI teaching. Finally, most claims were supported by empirical evidence. Regarding (2), measures most frequently used are custom in-house surveys and some validated measures. Additionally, there is much debate in the literature regarding the adequacy of current assessment measures in RI teaching, and even their absence. Such debate warrants caution when we are considering the empirical evidence supplied to support that RI teaching does or does not improve a specific stage of the decision-making process. Regarding (3), only behaviour was discussed as being difficult to assess, if not impossible. In our discussion section we contextualise these results, and following this we derive some recommendations for relevant stakeholders in RI teaching.
An algebraic approach to the Fundamental Theorem of Algebra
Authors: Mandal Priyabrata , Sheikh Sajad A. 
Published: July 2024

AbstractIn this paper, we investigate the algebraic counterpart of the Fundamental Theorem of Algebra. We explore the concept of real-closed fields and quadratic forms. We show, by means of Galois theory, that $$\mathcal {F}(\sqrt{-1})$$

F
(

-
1

)

is algebraically closed if $$\mathcal {F}$$
F
is real-closed. Lastly, we explain the algebraic closure of $$\mathbb {R}(\sqrt{-1})=\mathbb {C}$$

R
(

-
1

)
=
C

by demonstrating the real-closeness of $$\mathbb {R}$$
R
.
Recovery of the Seagrass Zostera noltei and Commercial Bivalve Populations After Shellfish Harvesting
Authors: Román Marta , Vázquez Elsa , Simón Andrés , Román Salvador , Olabarria Celia 
Published: July 2024

AbstractConservation efforts have raised awareness about the impact of small-scale fisheries on the distribution of seagrass plants. The patterns of recovery of the seagrass Zostera noltei and of the commercial bivalves Cerastoderma edule, Ruditapes decussatus and Ruditapes philippinarum after shellfish harvesting were studied in a field experiment in a shellfish bed in NW Spain. Sample plots were subjected to a single disturbance in two types of shellfish harvesting treatments in three zones characterized by different harvesting frequency and seagrass density. The photosynthetic efficiency (Fv/Fm), shoot density, leaf length and carbohydrate content of Z. noltei were monitored every three months during one year, and the C and N content of leaves and biomass of plants were measured one year after the disturbance. The abundance of adults, juveniles and recruits and the condition index of adult bivalves were quantified after the experimental harvesting. Shoot density and biomass of Z. noltei remained low during the ten months after the disturbance but recovered to control values after one year. Carbohydrate contents of apical rhizomes were lower in disturbed (treated) plots, whereas no effect was observed on Fv/Fm. Denser and more complex seagrass patches recovered faster. The abundance of adult bivalves below commercial size was lower in the disturbed plots, while the abundance of adults of commercial size, juveniles and recruits did not vary, indicating that abundance and condition were not hampered by harvesting pressure. The findings also suggest that Z. noltei meadows can recover within one year of the impact of shellfish harvesting if the harvesting areas are rotated and dense patches are preserved.
Condensed-gradient boosting
Authors: Emami Seyedsaman , Martínez-Muñoz Gonzalo 
Published: July 2024

Abstract
This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration has to be trained. In this work, we propose the use of multi-output regressors as base models to handle the multi-class problem as a single task. In addition, the proposed modification allows the model to learn multi-output regression problems. An extensive comparison with other multi-output based Gradient Boosting methods is carried out in terms of generalization and computational efficiency. The proposed method showed the best trade-off between generalization ability and training and prediction speeds. Furthermore, an analysis of space and time complexity was undertaken.
Fostering Computation Competence with Non-Counting Strategies and Conceptual Subitizing in Grade 1: An Intervention Study in Inclusive Classrooms
Authors: Leuenberger Delia , Diener Marion , Wehren-Müller Maria , Hofmann-Villiger Andreas , Vogt Franziska , Moser Opitz Elisabeth 
Published: July 2024

Abstract
Computation competence (CC) with non-counting (NC) strategies is an important aim of Grade 1 mathematics education, but studies have shown that many students struggle to acquire these skills and continue to rely on counting strategies in later years. Therefore, even though counting is an important first strategy for young children, it is vital to support the development of CC with NC strategies from the earliest stages of mathematics education. This study examined the success of an intervention to promote the use of CC and NC strategies and conceptual subitizing (CS). The study was conducted with a sample of N = 1017 Grade 1 students, and investigated whether the achievement gain was affected by the intervention setting: cooperative learning, individual support for low achievers, a combination of the two settings, and a control group. Students in the combined setting had greater achievement gains in CC with NC strategy use and CS than the control group and outperformed those in the other intervention groups in CS. The combined setting also benefitted low achievers in mathematics for CS, but not for CC and strategy use. A combination of cooperative learning and individual support seems to be the best option for improving the CS and CC and NC strategy use of first graders, especially low achievers in mathematics.