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ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter

SN Computer science 2023年第6期4卷 821页

作者： Pakrashi, Arjun Mac Namee, Brian School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman filter-based Heuristic ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets. Two variants are introduced based on the underlying component classifier algorithm, ML-KFHE-HOMER, and ML-KFHE-CC which uses HOMER and classifier chain (CC) as the underlying multi-label algorithms, respectively. ML-KFHE-HOMER and ML-KFHE-CC sequentially train multiple HOMER and CC multi-label classifiers and aggregate their outputs using the sensor fusion properties of the Kalman filter. Extensive experiments and detailed analysis were performed on thirteen multi-label datasets and eight other algorithms, which included state-of-the-art ensemble methods. The results show, for both versions, the ML-KFHE framework improves the predictive performance significantly with respect to bagged combinations of HOMER (named E-HOMER), also introduced in this paper, and bagged combination of CC, ensemble classifier chains (ECC), thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHE-HOMER variant was found to perform consistently and significantly better than the compared multi-label methods including existing approaches based on ensembles. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classifier fusion Ensembles Kalman filter Multi-label classification

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FAIRifying STEM data Ecosystem to Enhance data Reuse 53

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Namgay, Phub Wangdi, Pema Thinley, Sangay Namgyel, Thinley Uppsala University Department of Informatics and Media Uppsala Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9798350336429

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR data Principles in other areas of data-informed work and life. © 2023 IEEE.

关键词： Bhutan data Reuse FAIR data Principles FAIRifying data STEM data Ecosystem

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CouRGe: Counterfactual Reviews Generator for Sentiment Analysis 30th

CouRGe: Counterfactual Reviews Generator for Sentiment Anal...

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30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Carraro, Diego Brown, Kenneth N. Insight Centre for Data Analytics School of Computer Science and IT University College Cork Cork Ireland

ISBN: (纸本)9783031264375

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

关键词： Sentiment analysis

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Functional Connectivity Disruptions in Alzheimer’s Disease: A Maximum Flow Perspective 12th

Functional Connectivity Disruptions in Alzheimer’s Disease...

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12th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2023

作者： Stubby, Emma T. Razavi, Seyed Majid Khanmohammadi, Sina School of Computer Science University of Oklahoma Norman73019 United States Data Science and Analytics Institute University of Oklahoma Norman73019 United States

ISBN: (纸本)9783031827679

Alzheimer’s disease is a neurological disorder characterized by functional and structural atrophy, leading to symptoms like memory loss and cognitive decline. This study seeks to analyze the disruptions of functional connectivity pathways within the brain caused by Alzheimer’s disease from the maximum flow perspective. More specifically, we computed the maximum flow pathways within the functional brain networks, and compared it between healthy controls and Alzheimer’s patients. Our results suggest that the Alzheimer’s patients utilize pathways related to the default mode network (DMN) more frequently and display significant alterations in the usage of paths connected to the striate cortex (SC). The increased usage of DMN pathways might point to a compensation mechanism that facilitates interregional communications in Alzheimer’s patients. Understanding the nature of such a compensation mechanism could help develop new treatment options for Alzheimer’s patients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Cooperation and Contract Design in Project Management with Outsourcing

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Journal of Systems science and Systems Engineering 2023年第1期32卷 34-70页

作者： Xiaoqiang Cai Nicholas G.Hall Siyi Wang Feng Zhang School of Data Science The Chinese University of Hong KongShenzhen518172China Shenzhen Research Institute of Big Data Shenzhen518172China Department of Operations&Business Analytics Fisher College of BusinessOhio State UniversityColumbusOhioUSA School of Science and Engineering The Chinese University of Hong KongShenzhen518172China Suning Bank Nanjing210019China

We study a project management problem where the prime contractor needs to outsource tasks to subcontractors with the required *** execution of the project requires proper coordination among the subcontractors,as well as contract design by the prime contractor to incentivize the *** modeling the subcontractors’coordination problem as a cooperative game,we develop a profit sharing scheme to facilitate the subcontractors’*** consider two contract designs for the prime contractor:a uniform contract across all subcontractors,and a nonuniform one that customizes incentives for each *** propose efficient algorithms to solve the implicit optimization problems for optimal contract *** experiments show that the pooling effect of subcontractors’cooperation mitigates the negative impact of poor estimates about the crashing cost and resource *** observe three unexpected results through the randomized computation experiments:(i)the subcontractors’profits may decrease if they provide false information;(ii)it is safer for the prime contractor to overestimate subcontractors’crashing costs than underestimate them;and(iii)uniform contracts deliver more project profit for the subcontractors in the coalitions.

关键词： Project management with outsourcing subcontracting cooperative game contract design

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Enhancing Pavement Condition Assessment: A Comprehensive Review of Affordable Sensing Technologies for Cycle Tracks 11

Enhancing Pavement Condition Assessment: A Comprehensive Rev...

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11th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2025

作者： Garcia, Jeziel Antonio Ayala Shah, Syed Baig, Muhammad Hassam Qureshi, Waqar Shahid Ullah, Ihsan School of Computer Science University of Galway Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Galway Ireland

ISBN: (纸本)9789897587450

This paper explores the potential of low-cost sensing technologies for assessing the condition of cycling track pavement. As cycling gains popularity, the demand for efficient pavement maintenance solutions increases. Machine survey-based pavement condition assessment often relies on expensive, specialized equipment, which is not always suitable for cycle tracks due to limited budgets and accessibility, hence the need for low-cost solutions. The integration of low-cost sensing technologies and data collection, such as inertial measurement units (IMUs), low-cost imaging sensors, and crowdsourced data collection, presents a promising alternative for cycle track pavement surface assessment. This paper highlights the advantages of employing out-of-the-shelf devices such as smartphones with GPS, cameras, and IMU, or action cameras with GPS and IMU to collect images, their location, and vehicle vibration in a specific orientation. This data is then used to estimate pavement roughness, detect surface distress, and compute pavement surface condition. Literature reviews reveal a significant gap in the utilization of these technologies for cycle tracks, suggesting a promising area for further research and application. Furthermore, it proposes a software framework for data collection and visualization to combine these technologies to enhance the efficiency and reliability of pavement assessment for cycling infrastructure at a lower cost than machine-based pavement assessment and faster and more quantitative than manual surveys. This paper emphasizes the need for more practical and scalable solutions that support the maintenance of sustainable transportation systems. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Budget control

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Knowledge Leaks in data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of Business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

data-driven business models imply the inter-organisational exchange of data or similar value objects. data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： Business model innovation data analytics data-driven business models Knowledge risks Risk management Value objects

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data Mining for Smart Cities: Traffic Congestion Prediction 14

Data Mining for Smart Cities: Traffic Congestion Prediction

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Mystakidis, Aristeidis Geromichalou, Olga Tjortjis, Christos School of Science & Technology International Hellenic University The Data Mining & Analytics Research Group Thessaloniki Greece

ISBN: (纸本)9798350318067

In this work, we utilized univariable and multivari-able regression models, including Linear Regression (LR), Ran-dom Forest (RF), Multi-Layer Perceptron (MLP), and Gradient Boosting (GB), to predict traffic flow at six intersections. We incorporate temporal features and seasonality time intervals be-tween the observations of the time series to achieve better results. GB outperformed the other models with regards to the coefficient of determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), as well as Explained Variance Score (EVS). One extra univariable approach was utilized, combining data from other highly correlated intersections. This strategy led to better models with regard to several metrics. All in all, the multivariable and highly correlated approach produced better results compared to univariable regression. © 2023 IEEE.

关键词： Smart city

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Sentiment Analysis on English and Greek Twitter data Regarding Vaccinations 14

Sentiment Analysis on English and Greek Twitter Data Regardi...

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14th International Conference on Information, Intelligence, Systems and Applications, IISA 2023

作者： Dontaki, Chrysoula Koukaras, Paraskevas Tjortjis, Christos School of Science & Technology International Hellenic University The Data Mining and Analytics Research Group Thessaloniki Greece

ISBN: (纸本)9798350318067

COVID-19 is one of the most discussed diseases worldwide. To control its spread, several vaccines have been developed and approved. However, low acceptance and worries about the efficacy of vaccines exist due to insufficient information, and social media influence. This study aims to classify people who are in favor or against vaccination, as well as people's preferences for three main types of available vaccines, Pfizer, Moderna and AstraZeneca. Twitter data were retrieved over seven months. This work proposes the adoption of an on-the-fly approach involving a sentiment analyzer that includes Machine Learning (ML). Moreover, this paper compares sentiment analysis techniques by applying the following Supervised ML algorithms: Logistic Regression (LR), Decision Trees (DT), Random Forest (RF), XGBoost Classifier (XGB) and Support Vector Machines (SVM). The dataset was divided into two periods in 2022: summer, and autumn. The main contribution is the achievement of high accuracy and F1-Score, when combining these models. The best results were obtained when tweets were analyzed using TextBlob and DT. Regarding the English tweets, the top accuracy was 97.99%, while the top F1-Score was 97.98% in the summer period. With Greek tweets, the performance was better in the autumn period reaching 99.92% accuracy and F1-Score. © 2023 IEEE.

关键词： COVID-19

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Detectability of hierarchical communities in networks

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Physical Review E 2024年第3期110卷 034306页

作者： Leto Peel Michael T. Schaub Department of Data Analytics and Digitalisation School of Business and Economics Department of Computer Science

We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analyzed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist additional phases in which the presence of multiple consistent partitions can either help or hinder detection. Accordingly, the detectability limit for nonhierarchical partitions typically provides insufficient information about the detectability of the complete hierarchical structure, as we highlight with several constructive examples.

关键词： Community structure Complex systems Network structure Patterns in complex systems Block models data analysis

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