bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cognitive radio resource scheduling using an adaptive multiobjective evolutionary algorithm
Authors: Wang Hongbo , Wang Yizhe , Zeng Fanbing , Wang Jin 
Published: March 2024

AbstractWith the proliferation of IoT devices and the increasing popularity of location-oriented services in cyber-physical-social systems, the cognitive engines of these systems have taken on a multitude of parameters across various dimensions, making it impractical and time-consuming to search for the exact optimal solution. To address this challenge, the use of nature-inspired or evolutionary algorithms to find satisfactory solutions in a timely manner has gained significant attention, with reference point-based algorithms being one of the prominent approaches. However, when dealing with nonuniform, degenerate, and discrete Pareto fronts in the target space, using a considerable number of reference points may become ineffective, leading to a loss of diversity in exploration and exploitation during the problem-solving process. Consequently, the distribution of the solutions is adversely affected. To overcome this challenge, this paper presents a strategy to estimate the eigenvalues of the Pareto front in a timely manner. When encountering nonuniform, degenerate, and discrete Pareto fronts, a combination of radial space partitioning and angle selection mechanisms is employed to address these issues. Subsequently, an adaptive selection-based many-objective evolutionary algorithm (ASMaOEA) is proposed. Extensive comparisons with several competing methods on 31 representative benchmark problems demonstrate that ASMaOEA can provide a flexible configuration for decision engines in three typical scenarios involving cyber-physical-social systems. Furthermore, the analysis confirms that ASMaOEA can reduce the bit error rate and improve the system’s throughput, thereby offering substantial benefits to the overall performance of the system.
Theory of Mind and Social Informant Discrepancy in Autism
Authors: Collins Alister S. , Carroll Kevin J. , Gerber Alan H. , Keenan Elliot Gavin , Lerner Matthew D. 
Published: March 2024

AbstractWhen autistic youth are asked to assess their own social skills, they frequently rate themselves more favorably than their parents rate them. The magnitude of this informant discrepancy has been shown to relate to key clinical outcomes such as treatment response. It has been proposed that this discrepancy arises from difficulties with Theory of Mind. Participants were 167 youth 11 to 17 years old; 72% male, and their parents. Youth completed self-report measures of social skills and social cognitive tasks, while their parents completed questionnaires regarding social skills. A repeated-measures ANOVA indicated both non-autistic and autistic youth rated themselves more favorably than their parents rated them across all measures. Zero-order correlations revealed that raw differences between parent- and participant-report were negatively correlated with scores on parent-reported Theory of Mind measures. However, polynomial analysis did not indicate interaction effects between parent- and participant-report on any of the measures used. Polynomial regression revealed that increases in parent-reported social skill predicted larger increases in parent-report Theory of Mind at low levels of parent-reported social skill compared to high levels of parent-reported social skill. Participant-report social skills predicted performance on a behavioral Theory of Mind test in a curvilinear fashion, such that the relationship was positive at low levels of participant-reported social skills, but negative at high levels. This study replicates the finding that raw difference score analyses may result in illusory effects that are not supported when using more contemporary analysis methods, and that more complex and subtle relationships between social insight and perspective-taking exist within autistic youth.
Incorporating AI in foreign language education: An investigation into ChatGPT’s effect on foreign language learners
Authors: Karataş Fatih , Abedi Faramarz Yaşar , Ozek Gunyel Filiz , Karadeniz Derya , Kuzgun Yasemin 
Published: March 2024

AbstractChatGPT, an artificial intelligence application, has emerged as a promising educational tool with a wide range of applications, attracting the attention of researchers and educators. This qualitative case study, chosen for its ability to provide an in-depth exploration of the nuanced effects of AI on the foreign language learning process within its real-world educational context, aimed to utilize ChatGPT in foreign language education, addressing a gap in existing research by offering insights into the potential, benefits, and drawbacks of this innovative approach. The study involved 13 preparatory class students studying at the School of Foreign Languages at a university in Turkey. The students were introduced to ChatGPT through learning experiences over a span of four weeks by the researcher as a language teacher. The qualitative data collected from the interviews were analysed using thematic analysis. The findings suggest that ChatGPT positively affects students’ learning experiences, especially in writing, grammar, and vocabulary acquisition, and enhances motivation and engagement through its versatile and accessible nature in various learning activities. These insights contribute to understanding the utility and constraints of employing ChatGPT technology in foreign language instruction and can inform educators and researchers in developing effective teaching strategies and in designing curricula.
Digital competence of Vietnamese citizens: An application of digcomp framework and the role of individual factors
Authors: Nguyen Trung Quang , Ngoc Pham Thi Anh , Phuong Hoang Ai , Duy Dang Pham Thien , Hiep Pham Cong , McClelland Robert , Noroozi Omid 
Published: March 2024

AbstractAssessment of digital competence for citizens could support the Education Action plan at a national level, a regional level, or even at a global level to enhance the digital skills and competence gap for work and life for digital transformation. This study applies the DigComp framework through a self-administered online survey to 723 citizens in Vietnam, using a sequential exploratory mixed methods research design. Data were analysed using factor analysis, standard descriptive statistics, and simple regression methods, followed by qualitative content analysis from interviews with ten respondents and a focus group discussion with eight stakeholders on digital competence. The results confirmed the validity of the DigComp framework for measuring the digital competence of Vietnamese citizens. Overall, the proficiency level of the 723 surveyed Vietnamese citizens falls between basic and intermediate levels, being more advanced in areas in information and data literacy and communication and collaboration, compared to digital content creation, safety, and problem solving. The results further showed that male citizens with a higher education level, working in the public sector, or living in province with high provincial digital transformation index rankings perceived themselves to be more digital competent than females with a lower educational level, working in the private sector, or living in the province with low provincial digital transformation index rankings. To improve citizens’ digital competence, training should be designed that considers not only citizens’ digital competence profiles but also their individual characteristics.
Implementation of an early alert system in quizzes of a high complexity subject in higher education: Improvement of student performance and teacher perception
Authors: Cabezas Felipe , Burgos Luis , Darrigol Josefina , Zúñiga Mario 
Published: March 2024

AbstractEarly Alert Systems (EAS) play a fundamental role in education by supporting the learning process and evaluating student performance. In this study, an innovative EAS designed to specifically identify weaknesses in Cognitive Skills (CS) in first-year higher education students is presented, focusing on a challenging course at a South American university. Through big data technology, approval rates linked to various cognitive skills were evaluated using quizzes. Subsequently, teachers, after analysis and dialogue, applied adapted strategies to strengthen these skills in their classes, which were evaluated in subsequent exams. The sample under study included 1,691 students from various health majors enrolled in a complex subject. The experimental group (994 students) that participated in the EAS was compared with a control group (697 students). Evaluations validated by the academic team were used, applying three quizzes throughout the 2022-20 academic period, and the results were analysed with the Power BI computer tool, generating online reports that highlighted CS by section. The EAS, based on teacher surveys, is innovative and it also positively impacts student achievement, evidenced in overall approval rates, by teacher and section. In addition to promoting collaboration among professors, it also improves the teaching quality and suggests a positive impact on the learning of complex subjects in Higher Education Institutions (HEIs). This comprehensive approach to early monitoring of student performance shows promise for the ongoing improvement of educational quality.
Exploring Portuguese preschool educators’ attitudes and practices on information and communication technology (ICT)
Authors: Akyar Begum Canaslan , Monteiro Angélica , Fernandes Preciosa 
Published: March 2024

AbstractThis study aims to explore preschool educators’ attitudes towards to information and communication technology (ICT), its factors and implementation of ICT tools in preschool education by the educators. To this end, a mixed-method research approach was applied, comprising qualitative and quantitative data, collected using survey and field observation. The data, gathered from 58 preschool educators from the north of Portugal, were analysed by SPSS and the qualitative data gathered by five classroom observations analysed by thematic analysis method. Findings indicated that preschool educators have positive attitudes towards ICT integration in education regardless of age, years of teaching experience and attending professional course predictors. Additionally, educators use ICT tools as materials to enhance classroom activities, engage children, and conduct research with them. The results of this study reveal that preschool educators hold positive attitudes toward ICT and actively implement activities using ICT. However, educators are still the main users of ICT in the classroom. It was also observed that as these five classrooms lack the technological infrastructure, the educators usually rely on their personal resources. The lack of ICT resources could affect the independent use of ICT by the children. According to the findings, the study recommends that there are some schools in Portugal that lack ICT infrastructure. In order to overcome this situation, school leaders and policymakers should invest in ICT infrastructure in preschool institutions in Portugal that face a lack of ICT tools.
Enacting algorithms: Evolution of the algorythmics storytelling
Authors: Kátai Zoltán , Osztián Pálma-Rozália , Iclanzan David 
Published: March 2024

AbstractVisual storytelling, particularly through dance choreographies as showcased in previous AlgoRythmics performances, has been effective in communicating relatively straightforward algorithms in an engaging and memorable way. Nevertheless, when addressing complex algorithmic concepts, an approach with greater expressiveness and flexibility becomes necessary. Consequently, this study introduces stage performances as an innovative solution, using cinematic representation to successfully convey and communicate these intricate concepts and processes. To evaluate the effectiveness of this approach, a short film was designed, produced, and showcased to a second-semester CS2 university course audience studying programming techniques. Following an opening scene that establishes the context, the subsequent three acts vividly depict ad hoc, greedy, and dynamic programming solutions in response to the posed programming challenge. After the screening, a questionnaire was administered, built on four key constructs of the Technology Acceptance Model, as well as other potential facilitating factors. The study reveals 100% positive perceptions of educational benefits, with the vast majority of students expressing agreement regarding the utility, enjoyment, engagement, creativity, filmic quality, and cognitive benefits of short films. Additionally, a remarkable 96% reported the intent to utilize this approach. Our subsequent Structural Equation Modeling analysis discovered that students whose learning styles were in sync with this approach demonstrated a robust correlation between their perception of the method’s value, their enjoyment of the process, and their overall attitude towards this pedagogical method. This study confirms the potential of visual storytelling through short films as an effective tool for delivering programming education. The findings provide valuable insights for computer science educators seeking to engage learners and convey complex information in an attractive and effective way.
Comparison of dental topography of marmosets and tamarins (Callitrichidae) to other platyrrhine primates using a novel freeware pipeline
Authors: de Vries Dorien , Janiak Mareike C. , Batista Romina , Boubli Jean P. , Goodhead Ian B. , Ridgway Emma , Boyer Doug M. , Clair Elizabeth St. , Beck Robin M. D. 
Published: March 2024

AbstractDental topographic metrics (DTMs), which quantify different aspects of the shape of teeth, are powerful tools for studying dietary adaptation and evolution in mammals. Current DTM protocols usually rely on proprietary software, which may be unavailable to researchers for reasons of cost. We address this issue in the context of a DTM analysis of the primate clade Platyrrhini (“New World monkeys”) by: 1) presenting a large comparative sample of scanned second lower molars (m2s) of callitrichids (marmosets and tamarins), previously underrepresented in publicly available datasets; and 2) giving full details of an entirely freeware pipeline for DTM analysis and its validation. We also present an updated dietary classification scheme for extant platyrrhines, based on cluster analysis of dietary data extracted from 98 primary studies. Our freeware pipeline performs equally well in dietary classification accuracy of an existing sample of platyrrhine m2s (excluding callitrichids) as a published protocol that uses proprietary software when multiple DTMs are combined. Individual DTMs, however, sometimes showed very different results in classification accuracies between protocols, most likely due to differences in smoothing functions. The addition of callitrichids resulted in high classification accuracy in predicting diet with combined DTMs, although accuracy was considerably higher when molar size was included (90%) than excluded (73%). We conclude that our new freeware DTM pipeline is capable of accurately predicting diet in platyrrhines based on tooth shape and size, and so is suitable for inferring probable diet of taxa for which direct dietary information is unavailable, such as fossil species.
NEK2 affects the ferroptosis sensitivity of gastric cancer cells by regulating the expression of HMOX1 through Keap1/Nrf2
Authors: Wu Jianyong , Luo Desheng , Tou Laizhen , Xu Hongtao , Jiang Chuan , Wu Dan , Que Haifeng , Zheng Jingjing 
Published: March 2024

AbstractNEK2 is a serine/threonine protein kinase that is involved in regulating the progression of various tumors. Our previous studies have found that NEK2 is highly expressed in gastric cancer and suggests that patients have a worse prognosis. However, its role and mechanism in gastric cancer are only poorly studied. In this study, we established a model of ferroptosis induced by RSL3 or Erastin in AGS cells in vitro, and konckdown NEK2, HOMX1, Nrf2 by siRNA. The assay kit was used to analyzed cell viability, MDA levels, GSH and GSSG content, and FeRhoNox™-1 fluorescent probe, BODIPY™ 581/591 C11 lipid oxidation probe, CM-H2DCFDA fluorescent probe were used to detected intracellular Fe2+, lipid peroxidation, and ROS levels, respectively. Calcein-AM/PI staining was used to detect the ratio of live and dead cells, qRT-PCR and Western blot were used to identify the mRNA and protein levels of genes in cells, immunofluorescence staining was used to analyze the localization of Nrf2 in cells, RNA-seq was used to analyze changes in mRNA expression profile, and combined with the FerrDb database, ferroptosis-related molecules were screened to elucidate the impact of NEK2 on the sensitivity of gastric cancer cells to ferroptosis. We found that inhibition of NEK2 could enhance the sensitivity of gastric cancer cells to RSL3 and Erastin-induced ferroptosis, which was reflected in the combination of inhibition of NEK2 and ferroptosis induction compared with ferroptosis induction alone: cell viability and GSH level were further decreased, while the proportion of dead cells, Fe2+ level, ROS level, lipid oxidation level, MDA level, GSSG level and GSSG/GSH ratio were further increased. Mechanism studies have found that inhibiting NEK2 could promote the expression of HMOX1, a gene related to ferroptosis, and enhance the sensitivity of gastric cancer cells to ferroptosis by increasing HMOX1. Further mechanism studies have found that inhibiting NEK2 could promote the ubiquitination and proteasome degradation of Keap1, increase the level of Nrf2 in the nucleus, and thus promote the expression of HMOX1. This study confirmed that NEK2 can regulate HMOX1 expression through Keap1/Nrf2 signal, and then affect the sensitivity of gastric cancer cells to ferroptosis, enriching the role and mechanism of NEK2 in gastric cancer.
Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals
Authors: Chakraborty Sunen , Chatterjee Kingshuk , Dey Paramita 
Published: March 2024

AbstractImages once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU Image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an image deep learning models are an effective tool than any other methods. Models used in deep learning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deep learning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deep learning models along with some well-known traditional approaches can provide better results for detecting tampered images.
CNN-based Methods for Offline Arabic Handwriting Recognition: A Review
Authors: El Khayati Mohsine , Kich Ismail , Taouil Youssef 
Published: March 2024

AbstractArabic Handwriting Recognition (AHR) is a complex task involving the transformation of handwritten Arabic text from image format into machine-readable data, holding immense potential across various applications. Despite its significance, AHR encounters formidable challenges due to the intricate nature of Arabic script and the diverse array of handwriting styles. In recent years, Convolutional Neural Networks (CNNs) have emerged as a pivotal and promising solution to address these challenges, demonstrating remarkable performance and offering distinct advantages. However, the dominance of CNNs in AHR lacks a dedicated comprehensive review in the existing literature. This review article aims to bridge the existing gap by providing a comprehensive analysis of CNN-based methods in AHR. It covers both segmentation and recognition tasks, delving into advancements in network architectures, databases, training strategies, and employed methods. The article offers an in-depth comparison of these methods, considering their respective strengths and limitations. The findings of this review not only contribute to the current understanding of CNN applications in AHR but also pave the way for future research directions and improved practices, thereby enriching and advancing this critical domain. The review also aims to uncover genuine challenges in the domain, providing valuable insights for researchers and practitioners.
Tracking deformation velocity via PSI and SBAS as a sign of landslide failure: an open-pit mine-induced landslide in Himmetoğlu (Bolu, NW Turkey)
Authors: Eker Remzi , Aydın Abdurrahim , Görüm Tolga 
Published: March 2024

AbstractA destructive landslide occurred in Himmetoğlu village in Göynük District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between − 12 and 39 mm year−1 and between − 24 and 6 mm year−1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between − 16 and 31 mm year−1 and between − 18 and 20 mm year−1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.