{"title":"A Comparative Study of Preference Ordering Methods for Multi-Criteria Ranking","authors":"Yong Zheng, D. Wang","doi":"10.1109/SDS57534.2023.00023","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00023","url":null,"abstract":"Multi-criteria recommender systems are capable of enhancing recommendation quality by taking into account user preferences across multiple criteria. A promising approach that has recently emerged is multi-criteria ranking, which employs Pareto ranking to determine a ranking score based on the dominance relation of predicted multi-criteria ratings. While this technique can be integrated with existing MCRS models, the issue of dimensionality remains a challenge. To tackle similar problems, other preference ordering methods have been proposed in the field of multi-objective optimization. This study presents a comparative analysis of preference ordering methods for multicriteria ranking, along with insights obtained from experiments conducted on four real-world datasets.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"112S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116502458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Deep Discriminant Fractional-order Canonical Correlation Analysis For Information Fusion","authors":"Lei Gao, Ling Guan","doi":"10.1109/SDS57534.2023.00015","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00015","url":null,"abstract":"The advance of sensory and computing technology has attracted wide attention in the study of intelligent information fusion for multimedia computing and analysis. As a result, information fusion has been taking center stage in the intelligent multimedia and machine learning communities. In this paper, a deep discriminant fractional-order canonical correlation analysis (DDFCCA) method is proposed with application to information fusion. Benefiting from the integration of deep cascade neural networks (NNs) with discriminant power of the fractionalorder correlation matrix across multiple data/information sources, the proposed DDFCCA method demonstrates the ability to generate high quality data/information representation. To verify the effectiveness and generic nature of the proposed method, we conduct experiments on three database (MNIST database, RML audio emotional database, and Caltech101 database). Experimental results validate the superiority of the DDFCCA method over stateof-the-art for information fusion.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130092071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrei Claudiu Roibu, S. Adaszewski, Torsten Schindler, Stephen M. Smith, Ana I. L. Namburete, F. Lange
{"title":"Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes","authors":"Andrei Claudiu Roibu, S. Adaszewski, Torsten Schindler, Stephen M. Smith, Ana I. L. Namburete, F. Lange","doi":"10.1109/SDS57534.2023.00010","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00010","url":null,"abstract":"Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibilityweighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and nonimaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129890078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Iglesias, Rong-xiao Guo, Pedro Nucci, Claudio Miceli, M. Bolic
{"title":"Automated Extraction of IoT Critical Objects from IoT Storylines, Requirements and User Stories via NLP","authors":"C. Iglesias, Rong-xiao Guo, Pedro Nucci, Claudio Miceli, M. Bolic","doi":"10.1109/SDS57534.2023.00022","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00022","url":null,"abstract":"The first step to designing a resilient Internet of Things (IoT) application is to identify IoT critical objects (services, devices and resources) in the design phase. However, this step is a time-intensive task, because they are manually identified from storylines, requirements and user stories and have other challenges. In this work, we assessed the usefulness of Named Entity Recognition (NER) models to automatically identify IoT critical objects as a way to make a modelling process faster and less prone to errors. This was performed with the development of five NER models based on five different architectures (Spacy, BERT, Transformers, LSTM-CRF and ELMo) that were trained and tested with a large dataset with 7396 annotated sentences. Our results indicate that all NER models had satisfactory performance, but BERT had the best one and can be useful to support the time-intensive step of the early stages of the development of resilient IoT systems. Furthermore, these NER models have a high potential to be extended to a framework to automatically extract IoT critical objects from documents (storyline and requirements) and list all possible IoT threats and resilient countermeasures that can be used in the design of a resilient IoT application.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122359851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raphael Emberger, J. Boss, Daniel Baumann, Marko Seric, Shufan Huo, Lukas Tuggener, E. Keller, Thilo Stadelmann
{"title":"Video Object Detection for Privacy-Preserving Patient Monitoring in Intensive Care","authors":"Raphael Emberger, J. Boss, Daniel Baumann, Marko Seric, Shufan Huo, Lukas Tuggener, E. Keller, Thilo Stadelmann","doi":"10.1109/SDS57534.2023.00019","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00019","url":null,"abstract":"Patient monitoring in intensive care units, although assisted by biosensors, needs continuous supervision of staff. To reduce the burden on staff members, IT infrastructures are built to record monitoring data and develop clinical decision support systems. These systems, however, are vulnerable to artifacts (e.g. muscle movement due to ongoing treatment), which are often indistinguishable from real and potentially dangerous signals. Video recordings could facilitate the reliable classification of biosignals using object detection (OD) methods to find sources of unwanted artifacts. Due to privacy restrictions, only blurred videos can be stored, which severely impairs the possibility to detect clinically relevant events such as interventions or changes in patient status with standard OD methods. Hence, new kinds of approaches are necessary that exploit every kind of available information due to the reduced information content of blurred footage and that are at the same time easily implementable within the IT infrastructure of a normal hospital. In this paper, we propose a new method for exploiting information in the temporal succession of video frames. To be efficiently implementable using off-the-shelf object detectors that comply with given hardware constraints, we repurpose the image color channels to account for temporal consistency, leading to an improved detection rate of the object classes. Our method outperforms a standard YOLOv5 baseline model by +1.7% mAP@.5 while also training over ten times faster on our proprietary dataset. We conclude that this approach has shown effectiveness in the preliminary experiments and holds potential for more general video OD in the future.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122190680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Recommendation System for Journals based on ELMo and Deep Learning","authors":"Mahmoud Hemila, Heiko Rölke","doi":"10.1109/SDS57534.2023.00021","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00021","url":null,"abstract":"Choosing the right journal to publish research studies is critical for researchers. Despite its importance, the task of determining the suitable and high-ranking journal for publishing can be challenging due to several factors such as the growing number of the available journals and the fact that every journal has its specific area of expertise. In this paper we investigate content-based journal recommendation systems that rely on using NLP to analyze features of existing journals and use those to pre-select a particular number of suitable journals for a new paper. Our experiments are based on the ELMo feature engineering mechanism and use different deep learning neural network architectures (CNN, RNN). We used datasets from the disciplines physics, chemistry and biology, with each containing data of more than 750000 publications. The data source consists of the abstracts of the papers. The experiments show promising results with the accuracy of our models outperforming existing models. Specffically, our RNN model can achieve 83% accuracy when using data from physics by considering top-20 journals.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134065379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deepti Saravanan, Jahnavi Swetha Pothineni, Anasse Bari
{"title":"Applying Predictive Analytics to Climate Change: Predicting Temperature Rise Using Human Behavior Alternative Data","authors":"Deepti Saravanan, Jahnavi Swetha Pothineni, Anasse Bari","doi":"10.1109/SDS57534.2023.00020","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00020","url":null,"abstract":"Climate change is a threat to humans and nature. Rise in temperature differs from one region to another around the world. In the general case, warming is relatively higher in land areas than in seas and oceans. Temperature rise is leading to sea level rise, ice melt, ocean precipitation, ocean currents, as well as, major risks to life on land and water. One of the most significant contributors to this threat is greenhouse gas emissions. Understanding the main human factors that cause greenhouse gas emissions is necessary to make data-driven decisions for future actions to combat climate change. In this study, we investigate human activities that contribute to increases in temperature by analyzing new alternative data sources that include internet usage, gas CO2, oil CO2, consumption CO2, air travel, meat consumption, population, car sales, GDP, and housing data, among others. Experimental results indicate that CO2-re1ated features and fertilizer consumption showed a relationship with land temperature. While internet usage patterns did not show any significant correlation, the air travel data showed a reverse effect. The analytics approach and algorithms presented in this paper have the potential to serve as a supplement tool to track and detect emerging predictive features of temperature rise.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133665675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Curdin Marxer, Heiko Rölke, Alex Alfieri, M. Halatsch
{"title":"Case Study: Natural Language Processing (NLP) with Open Data for Drug Repositioning in Glioblastoma Therapy","authors":"Curdin Marxer, Heiko Rölke, Alex Alfieri, M. Halatsch","doi":"10.1109/SDS57534.2023.00009","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00009","url":null,"abstract":"The development and discovery of new drugs is a long, costly and risky endeavour with low rate of success. It is a well-known practice to identify new uses for already existing drugs or known active compounds, referred to as “drug repositioning’’ or “drug repurposing This case study uses NLP techniques and open data sources to explore their potential in identifying drugs for glioblastoma therapy beyond the obvious choices documented in established databases like DrugBank.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"10 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114103855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paul-Philipp Luley, Jan Deriu, Peng Yan, Gerrit A. Schatte, Thilo Stadelmann
{"title":"From Concept to Implementation: The Data-Centric Development Process for AI in Industry","authors":"Paul-Philipp Luley, Jan Deriu, Peng Yan, Gerrit A. Schatte, Thilo Stadelmann","doi":"10.1109/SDS57534.2023.00017","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00017","url":null,"abstract":"We examine the paradigm of data-centric artificial intelligence (DCAI) as a solution to the obstacles that small and medium-sized enterprises (SMEs) face in adopting AI. While the prevalent model-centric approach emphasizes collecting large amounts of data, SMEs often suffer from small datasets, data drift, and sparse ML knowledge, which hinders them from implementing AI. DCAI, on the other hand, emphasizes to systematically engineer the data used to build an AI system. Our contribution is to provide a concrete, transferable implementation of a DCAI development process geared towards industrial application, specffically in machining and manufacturing, and demonstrate how it enhances data quality by fostering collaboration between domain experts and ML engineers. This added value can place AI at the disposal of more SMEs. We provide the necessary background for practitioners to follow the rationale behind DCAI and successfully deploy the provided process template.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129191601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fine-Tuning Languages: Epistemological Foundations for Ethical AI in Journalism","authors":"Laurence Dierickx, C. Lindén","doi":"10.1109/SDS57534.2023.00013","DOIUrl":"https://doi.org/10.1109/SDS57534.2023.00013","url":null,"abstract":"The development of AI-based solutions in newsrooms aims to automate specific tasks or help journalists understand complex data to improve reporting and news dissemination. The professionals involved in these editorial processes are neither trained in journalism nor consider themselves journalists. They also have different views on what technology and journalism are or should be. At the same time, there is a recognized need for blending AI systems with journalistic values. From an epistemological perspective, the challenge is to apprehend the shifts in meanings and approaches to the critical concepts of accuracy, objectivity, and transparency.","PeriodicalId":150544,"journal":{"name":"2023 10th IEEE Swiss Conference on Data Science (SDS)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}