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Students’ performance on statistics course through online learning

AIP Conference Proceedings 2024年第1期2895卷

作者： Adina Najwa Kamarudin Siti Rohani Mohd Nor Nurain Ibrahim Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia 81310 Johor Bahru Johor Darul Ta'zim Malaysia Centre of Statistical and Decision Science Studies Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA 40450 Shah Alam Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia

Online learning has taken place since COVID-19 pandemic that force the immediate migration. Despite current challenges of online education, learning statistics has added extra difficulty to the students as this subject requires proper visualization for the unseen theoretical concept. Thus, many studies have been conducted on the appropriate online tools for learning statistics and have suggested flexibility in the assessment method. Researchers have more excitement in comparing the achievement of students between face-to-face and online learning, but still lack in investigating the performance during entire online learning period in pandemic. This study is focused on to investigate the achievement of statistics course among second year engineering students during one of the semesters when it is conducted fully online until the end of the semester. The objectives of this study are, to determine the overall achievement of engineering students towards statistics course, to find whether there is any significant difference of performance between genders, type of students and schools, and lastly to list common errors when answering probability questions. Results found concluded that all course learning outcome are successfully achieved although during online learning. There is no significant difference found between male and female students and local students have gained more marks as compared to international students in some assessments. During Test 1, when answering question relating to probability, there are five mistakes identified which are misinterpret of the problem, inaccurate in calculating the probability, unable to identify the distribution, lack of understanding of a distribution, and incomplete solution. The findings from this study may help lecturers in develop assessment questions to tackle the common errors and strategize an impactful online teaching.

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The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community

arXiv

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arXiv 2021年

作者： Gothard, Kelly Dewhurst, David Rushing Minot, Joshua R. Adams, Jane Lydia Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science Vermont Advanced Computing Core The University of Vermont BurlingtonVT05401 United States Charles River Analytics 625 Mount Auburn Street CambridgeMA02138 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05401 United States Department of Computer Science University of Vermont BurlingtonVT05401 United States

Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of 'incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language use on Reddit, a global online message board, we contextualize the incel community's online expressions of misogyny and real-world acts of violence perpetrated against women. After assembling around three million comments from incel-themed Reddit channels, we analyze the temporal dynamics of a data driven rank ordering of the glossary of phrases belonging to an emergent incel lexicon. Our study reveals the generation and normalization of an extensive coded misogynist vocabulary in service of the group's identity. Copyright © 2021, The Authors. All rights reserved.

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Creating expert knowledge by relying on language learners: A generic approach for mass-producing language resources by combining implicit crowdsourcing and language learning 12

Creating expert knowledge by relying on language learners: A...

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12th International Conference on Language Resources and Evaluation, LREC 2020

作者： Nicolas, Lionel Lyding, Verena Borg, Claudia Forascu, Corina Fort, Karën Zdravkova, Katerina Kosem, Iztok Cibej, Jaka Holdt, Špela Arhar Millour, Alice König, Alexander Rodosthenous, Christos Sangati, Federico Ul Hassan, Umair Katinskaia, Anisia Barreiro, Anabela Aparaschivei, Lavinia HaCohen-Kerner, Yaakov Institute for Applied Linguistics Eurac Research Bolzano Italy Faculty of Information and Communication Technology University of Malta Msida Malta Faculty of Computer Science Alexandru Ioan Cuza University of Iasi Romania Sorbonne Université STIH - EA 4509 France Ss. Cyril and Methodius University Faculty of Computer Science and Engineering Macedonia Faculty of Arts University of Ljubljana Slovenia CLARIN ERIC Netherlands Computational Cognition Lab Open University of Cyprus Cyprus Orientale University of Naples Italy Insight Centre for Data Analytics National University of Ireland Galway Ireland Department of Computer Science University of Helsinki Finland Human Languages Technologies Lab INESC-ID Lisbon Portugal Israel

ISBN: (纸本)9791095546344

We introduce in this paper a generic approach to combine implicit crowdsourcing and language learning in order to mass-produce language resources (LRs) for any language for which a crowd of language learners can be involved. We present the approach by explaining its core paradigm that consists in pairing specific types of LRs with specific exercises, by detailing both its strengths and challenges, and by discussing how much these challenges have been addressed at present. Accordingly, we also report on on-going proof-of-concept efforts aiming at developing the first prototypical implementation of the approach in order to correct and extend an LR called ConceptNet based on the input crowdsourced from language learners. We then present an international network called the European Network for Combining Language Learning with Crowdsourcing Techniques (enetCollect) that provides the context to accelerate the implementation of the generic approach. Finally, we exemplify how it can be used in several language learning scenarios to produce a multitude of NLP resources and how it can therefore alleviate the long-standing NLP issue of the lack of LRs. © European Language Resources Association (ELRA), licensed under CC-BY-NC

关键词： Crowdsourcing

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Seeing biodiversity: Perspectives in machine learning for wildlife conservation

arXiv

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arXiv 2021年

作者： Tuia, Devis Kellenberger, Benjamin Beery, Sara Costelloe, Blair R. Zuffi, Silvia Risse, Benjamin Mathis, Alexander Mathis, Mackenzie Weygandt van Langevelde, Frank Burghardt, Tilo Kays, Roland Klinck, Holger Wikelski, Martin Couzin, Iain D. van Horn, Grant Crofoot, Margaret C. Stewart, Charles V. Berger-Wolf, Tanya Switzerland United States Max Planck Institute of Animal Behavior Germany Centre for the Advanced Study of Collective Behaviour University of Konstanz Germany Department of Biology University of Konstanz Germany Institute for Applied Mathematics and Information Technologies IMATI-CNR Italy Computer Science Department University of Münster Germany Switzerland Environmental Sciences Group Wageningen University Netherlands Computer Science Department University of Bristol United Kingdom Department of Forestry and Environmental Resources North Carolina State University United States North Carolina Museum of Natural Sciences United States Cornell Lab of Ornithology Cornell University United States Department of Computer Science Rensselaer Polytechnic Institute United States Translational Data Analytics Institute Ohio State University United States

data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation. © 2021, CC BY-NC-SA.

关键词： Ecology

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Contrastive losses and solution caching for predict-and-optimize

arXiv

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arXiv 2020年

作者： Mulamba, Maxime Mandi, Jayanta Diligenti, Michelangelo Lombardi, Michele Bucarey, Victor Guns, Tias Data Analytics Laboratory Vrije Universiteit Brussel Belgium Department of Information Engineering and Mathematical Sciences University of Siena Italy Dipartimento di Informatica - Scienza e Ingegneria University of Bologna Italy Department of Computer Science KU Leuven Belgium

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimal systems

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Time series modelling and forecasting of mpox incidence and mortality in Nigeria

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BMC Infectious Diseases 2025年第1期25卷 794页

作者： Bakare, Emmanuel Afolabi Mogbojuri, Oluwaseun Akinlo Oniyelu, Dolapo Oluwaseun Abidemi, Afeez Daniel, Deborah Oluwatobi Olasupo, Idowu Isaac Osikoya, Samuel Abidemi Nwana, Aaron Onyebuchi Olorunfemi, Ronke Dorcas Olagbami, Samson Oluwafemi International Centre for Applied Mathematical Modelling and Data Analytics Federal University Oye-Ekiti Ekiti State Nigeria Department of Mathematics Federal University Oye-Ekiti Ekiti State Nigeria Department of Mathematical Sciences Adekunle Ajasin University Akungba-Akoko Nigeria Department of Computer Science Federal University Oye-Ekiti Ekiti State Nigeria Department of Mathematical Sciences Federal University of Technology Akure Nigeria Department of Animal and Environmental Biology Federal University Oye-Ekiti Ekiti State Nigeria

The World Health Organization (WHO) declared mpox a Public Health Emergency of International Concern (PHEIC) twice, in response to the global outbreak, first in May 2022 and again in August 2024, after a span of 2 years and 3 months. African countries continue to be a hotspot for the ongoing mpox outbreaks and Nigeria has contributed substantially in exporting the virus to other countries, highlighting the need for an in-depth analysis of outbreak patterns and forecasting to inform public health policy. This study used the Auto-Regressive Integrated Moving Average (ARIMA) model to perform a 14-month forecast of mpox cases and mortality in Nigeria using mpox monthly routine data. The data were split into two portions;70% for training, used to estimate the parameters of the forecasting model and 30% for testing, used to evaluate the model’s accuracy. Wavelet analysis was used to decompose the time series into its various frequency components, enabling a multi-resolution analysis of the data. The ARIMA model forecasted an average of 13 mpox cases per month and zero mortality over a 14-month period. The wavelet power spectrum revealed a strong annual cycle between June 2022 and June 2023. In order to sustain the forecasted downward trend in mpox cases in the coming months, it is essential that the National Mpox Technical Working Group (TWG) of Nigeria Centre for Disease Control and Prevention (NCDC) continue to coordinate scale up of vaccine coverage and improve surveillance especially in high risk area. The findings will ultimately improve focused interventions and knowledge of mpox outbreak patterns by guiding public health policy, allocating resources optimally, and preparing health systems for potential outbreaks. © The Author(s) 2025.

关键词： ARIMA Forecasting Mpox Time series analysis Wavelet analysis

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Enhancing the Prediction of Breast Cancer Progression Through Multi-modal data Transformation

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Abdullakutty, Faseela Akbari, Younes Al-Maadeed, Somaya Bouridane, Ahmed Rifat Hamoudi, Rifat Department of Computer Science and Engineering Qatar University Doha Qatar Center for Data Analytics and Cybernetics University of Sharjah Sharjah United Arab Emirates Research Institute for Medical and Health Sciences University of Sharjah Sharjah United Arab Emirates Clinical Sciences Department College of Medicine University of Sharjah Sharjah United Arab Emirates BIMAI-Lab Biomedically Informed Artificial Intelligence Laboratory University of Sharjah Sharjah United Arab Emirates

The ability to predict breast cancer metastases is essential for making effective clinical decisions and managing patients. Traditional models predominantly rely on structured clinical data, which often lacks essential contextual details, limiting their predictive accuracy. In order to address this limitation, a multi-modal approach is introduced in which structured data is transformed into unstructured text, while contextual richness is preserved. Using this text, synthetic images are generated across three key diagnostic modalities, histopathology, mammography, and ultrasound, to enhance predictive capabilities. Based on converted text data, a pre-trained diffusion model was used to generate synthetic medical images in histopathology, mammography, and ultrasound modalities. The impact of a variety of text description variants on image quality and metastasis prediction was assessed. Comprehensive tumor descriptions or a combination of histological type and differentiation status were the most effective generation strategies. A comparison was conducted between three prediction approaches: a unimodal approach, an early fusion approach based on concatenation, and the Multi Co-Guided Attention (MCGA) approach. Through mutual attention, MCGA enhances feature alignment by addressing inter- and intra-modal heterogeneity and capturing complex relationships. Unimodal and multi-modal methods were evaluated with the application of SMOTE to mitigate the impact of data imbalance. Multi-modal fusion significantly outperforms unimodal methods, especially when class imbalances are mitigated by using SMOTE. When SMOTE was applied, Ultrasound+BERT achieved the highest level of accuracy (0.90), followed by Histopathology+BERT (0.88), and Mammogram+BERT (0.88). As compared to early fusion, MCGA demonstrated better class balance and improved minority class detection. Incorporating unstructured text with synthetic imaging modalities improves the accuracy of metastasis prediction by pres

关键词： Modal analysis

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Chemically inspired Erdős-Rényi oriented hypergraphs

arXiv

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arXiv 2023年

作者： Garcia-Chung, Angel Bermúdez-Montaña, Marisol Stadler, Peter F. Jost, Jürgen Restrepo, Guillermo Tecnológico de Monterrey Escuela de Ingeniería y Ciencias Estado de México52926 Mexico Max Planck Institute for Mathematics in the Sciences Leipzig Germany Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany Bioinformatics Group Department of Computer Science Universität Leipzig Leipzig Germany Institute for Theoretical Chemistry University of Vienna Vienna Austria The Santa Fe Institute Santa FeNM United States Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Center for Scalable Data Analytics and Artificial Intelligence Universität Leipzig Leipzig Germany School of Applied Sciences and Engineering EAFIT University Medellín Colombia

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erdős-Rényi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry. © 2023, CC BY-SA.

关键词： Graph theory

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A ROBUST REPUTATION-BASED GROUP RANKING SYSTEM AND ITS RESISTANCE TO BRIBERY

arXiv

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arXiv 2020年

作者： Saúde, João Ramos, Guilherme Boratto, Ludovico Caleiro, Carlos Department of Electrical and Computer Engineering Carnegie-Melon University Pittsburgh United States Department of Electrical and Computer Engineering Faculty of Engineering University of Porto Porto Portugal Data Science and Big Data Analytics EURECAT Barcelona Spain SQIG - Instituto de Telecomunicações Dept. of Mathematics Instituto Superior Técnico University of Lisbon Lisbon Portugal

The spread of online reviews and opinions and its growing influence on people’s behavior and decisions, boosted the interest to extract meaningful information from this data deluge. Hence, crowdsourced ratings of products and services gained a critical role in business and governments. Current state-of-the-art solutions rank the items with an average of the ratings expressed for an item, with a consequent lack of personalization for the users, and the exposure to attacks and spamming/spurious users. Using these ratings to group users with similar preferences might be useful to present users with items that reflect their preferences and overcome those vulnerabilities. In this paper, we propose a new reputation-based ranking system, utilizing multipartite rating subnetworks, which clusters users by their similarities using three measures, two of them based on Kolmogorov complexity. We also study its resistance to bribery and how to design optimal bribing strategies. Our system is novel in that it reflects the diversity of preferences by (possibly) assigning distinct rankings to the same item, for different groups of users. We prove the convergence and efficiency of the system. By testing it on synthetic and real data, we see that it copes better with spamming/spurious users, being more robust to attacks than state-of-the-art approaches. Also, by clustering users, the effect of bribery in the proposed multipartite ranking system is dimmed, comparing to the bipartite case. Copyright © 2020, The Authors. All rights reserved.

关键词： data mining

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DCSO: Dynamic Combination of Detector Scores for Outlier Ensembles

arXiv

引用

arXiv 2019年

作者： Zhao, Yue Hryniewicki, Maciej K. Department of Computer Science University of Toronto Toronto Canada Data Assurance & Analytics PricewaterhouseCoopers Toronto Canada

Selecting and combining the outlier scores of different base detectors used within outlier ensembles can be quite challenging in the absence of ground truth. In this paper, an unsupervised outlier detector combination framework called DCSO is proposed, demonstrated and assessed for the dynamic selection of most competent base detectors, with an emphasis on data locality. The proposed DCSO framework first defines the local region of a test instance by its k nearest neighbors and then identifies the top-performing base detectors within the local region. Experimental results on ten benchmark datasets demonstrate that DCSO provides consistent performance improvement over existing static combination approaches in mining outlying objects. To facilitate interpretability and reliability of the proposed method, DCSO is analyzed using both theoretical frameworks and visualization techniques, and presented alongside empirical parameter setting instructions that can be used to improve the overall performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Anomaly detection

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