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FedMUP: Federated learning driven malicious user prediction model for secure data distribution in cloud environments

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Applied Soft Computing 2024年 157卷

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Kumar, Jatinder Singh, Ashutosh Kumar Department of Computer Science & Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Computer Science & Engineering University of Aizu Aizuwakamatsu Fukushima Japan Department of Computer Applications SRM Institute of Science and Technology NCR Campus-Ghaziabad201204 India Department of Computer Applications National Institute of Technology Kurukshetra136119 India Department of Computer Science & Engineering Indian Institute of Information Technology Bhopal462003 India Department of Computer Science The University of Economics and Human Sciences Warsaw01043 Poland

Cloud computing is flourishing at a rapid pace. Significant consequences related to data security appear as a malicious user may get unauthorized access to sensitive data which may be misused, further. This raises an alarm-ringing situation to tackle the crucial issue related to data security and proactive malicious user prediction. This article proposes a Federated learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments (FedMUP). This approach firstly analyzes user behavior to acquire multiple security risk parameters. Afterward, it employs the federated learning-driven malicious user prediction approach to reveal doubtful users, proactively. FedMUP trains the local model on their local dataset and transfers computed values rather than actual raw data to obtain an updated global model based on averaging various local versions. This updated model is shared repeatedly at regular intervals with the user for retraining to acquire a better, and more efficient model capable of predicting malicious users more precisely. Extensive experimental work and comparison of the proposed model with state-of-the-art approaches demonstrate the efficiency of the proposed work. Significant improvement is observed in the key performance indicators such as malicious user prediction accuracy, precision, recall, and f1-score up to 14.32%, 17.88%, 14.32%, and 18.35%, respectively. © 2024 Elsevier B.V.

关键词： Forecasting

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Designing Towards an Application to Find a Nurse 23rd

Designing Towards an Application to Find a Nurse

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23rd International Conference on human-computer interaction, HCII 2021

作者： Fournier, Hélène Katsuragawa, Keiko Singh, Karanpreet Bin Hannan, Nabil Kondratova, Irina Human-Computer Interaction Digital Technologies Research Centre National Research Council Canada MonctonNB Canada Human-Computer Interaction Digital Technologies Research Centre National Research Council Canada WaterlooON Canada Human-Computer Interaction Digital Technologies Research Centre National Research Council Canada FrederictonNB Canada Computer Science University of Waterloo WaterlooON Canada Computer Science University of New Brunswick FrederictonNB Canada

ISBN: (纸本)9783030786342

In this fast-paced technological era, the demand and the usage of applications which make our life easier have increased dramatically. This need has grown especially for the aging population who require help more than ever. Older adults (65+) need an effective way to communicate with a healthcare professional in case they require any advice about their health or schedule an in-person checkup. The current COVID-19 pandemic has accelerated the push for remote check-ins and virtual care especially for older adults who are medically or socially vulnerable. Remote health services are being promoted as a means of preserving the patient-health care provider relationship at times when in-person visits are not practical or feasible, especially during COVID-19 and in the future. This paper describes an application to support vulnerable older adults in their home, with more responsive home health care services which can potentially reduce the number of unnecessary trips to the hospital for non-emergency situations. The process of designing the application will be described, including workflow, storyboards, and key steps in the validation process. The paper will also discuss future research directions. © 2021, Her Majesty the Queen in Right of Canada.

关键词： Design

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Shaping and Being Shaped by Drones: Programming in Perception–Action Loops

Shaping and Being Shaped by Drones: Programming in Perceptio...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Sondoqah, Mousa Abdesslem, Fehmi Ben Popova, Kristina McGregor, Moira La Delfa, Joseph Garrett, Rachael Lampinen, Airi Mottola, Luca Höök, Kristina Politecnico di Milano Milan Italy Department of Computer Science RISE Research Institutes of Sweden Stockholm Sweden Media Technology and Interaction Design KTH Royal Institute of Technology Stockholm Sweden Department of Computer and Systems Sciences Stockholm University Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Politecnico di Milano RISE Research Institutes of Sweden Milan Italy

ISBN: (纸本)9798400705830

In a long-term commitment to designing for the aesthetics of human–drone interactions, we have been troubled by the lack of tools for shaping and interactively feeling drone behaviours. By observing participants in a three-day drone challenge, we isolated components of drones that, if made transparent, could have helped participants better explore their aesthetic potential. Through a bricolage approach to analysing interviews, feld notes, video recordings, and inspection of each team’s code, we describe how teams 1) shifted their eforts from aiming for seamless human–drone interaction, to seeing drones as fragile, wilful, and prone to crashes;2) engaged with intimate, bodily interactions to more precisely probe, understand and defne their drone’s capabilities;3) adopted diferent workaround strategies, emphasising either training the drone or the pilot. We contribute an empirical account of constraints in shaping the potential aesthetics of drone behaviour, and discuss how programming environments could better support somaesthetic perception–action loops for design and programming purposes. © 2024 Copyright held by the owner/author(s).

关键词： Drones

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AI Literacy: Finding Common Threads between Education, Design, Policy, and Explainability

AI Literacy: Finding Common Threads between Education, Desig...

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study 2

Harnessing Deep Learning for Effective Extractive Text Summa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Alsekait, Deema Mohammed Almanaseer, Waref Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Baligh, Salma Alaa, Hana Hegazy, Jasmine Magdy, Julia Eldmrat, Alaa Tariq Abdelminaam, Diaa Salama Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Saudi Arabia Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Midocean University Faculty of Human Sciences United Arab Emirates Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Egypt MEU Research Unit Middle East University Faculty of Computers Amman11831 Jordan

ISBN: (纸本)9798350362633

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context. © 2024 IEEE.

关键词： Contrastive Learning

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Learning Medical Subject Headings in PubMed Articles to Enhance Deep Predictions 11

Learning Medical Subject Headings in PubMed Articles to Enha...

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11th IEEE International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024

作者： Dashdorj, Zolzaya Jargalsaikhan, Zoljargal Grigorev, Stanislav Trufanov, Andrey Kang, Tae-Koo Altangerel, Erdenebaatar Mongolian University of Science and Technology Computer Science Ulaanbaatar Mongolia School of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk Russia Sangmyung University Department of Human Intelligence and Robot Engineering Seoul Korea Republic of

ISBN: (纸本)9798350350142

MeSH is a vocabulary of terms used in the life sciences and biomedical fields. A large amount of Medical Subject Headings (MeSH) occur in PubMed articles with different weights. In this research, we analyze a disease-symptom network exploiting MeSH metadata co-occurring in 7 million PubMed articles and configure a deep model in the context of biomedical and health care data. Optimized and efficient data networks are important to compile machine learning models. We optimize the deep model by reducing important features. Our result highlights that the degree of the severity of symptoms associated with a disease improves the accuracy of disease prediction models. The network was well optimized by reducing the number of symptoms. This finding may have important implications in healthcare: healthcare chatbots, clinical significance, practical implications. © 2024 IEEE.

关键词： Data Analytics

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Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers 48

Lattice-Free Sequence Discriminative Training for Phoneme-Ba...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Zijian Zhou, Wei Schluter, Ralf Ney, Hermann Rwth Aachen University Human Language Technology and Pattern Recognition Computer Science Department Aachen52074 Germany AppTek GmbH Aachen52062 Germany

ISBN: (纸本)9781728163277

Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training objectives, namely lattice-free maximum mutual information, lattice-free segment-level minimum Bayes risk, and lattice-free minimum Bayes risk, which are used for the final posterior output of the phoneme-based neural transducer with a limited context dependency. Compared to criteria using N-best lists, lattice-free methods eliminate the decoding step for hypotheses generation during training, which leads to more efficient training. Experimental results show that lattice-free methods gain up to 6.5% relative improvement in word error rate compared to a sequence-level cross-entropy trained model. Compared to the N-best-list based minimum Bayes risk objectives, lattice-free methods gain 40% - 70% relative training time speedup with a small degradation in performance. © 2023 IEEE.

关键词： neural transducer sequence discriminative training Speech recognition

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"This really lets us see the entire world:" Designing a conversational telepresence robot for homebound older adults

arXiv

引用

arXiv 2024年

作者： Hu, Yaxin Stegner, Laura Kotturi, Yasmine Zhang, Caroline Peng, Yi-Hao Huq, Faria Zhao, Yuhang Bigham, Jeffrey P. Mutlu, Bilge Department of Computer Sciences University of Wisconsin Madison United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Mathematics University of Wisconsin Madison United States

In this paper, we explore the design and use of conversational telepresence robots to help homebound older adults interact with the external world. An initial needfinding study (N=8) using video vignettes revealed older adults' experiential needs for robot-mediated remote experiences such as exploration, reminiscence and social *** then designed a prototype system to support these goals and conducted a technology probe study (N=11) to garner a deeper understanding of user preferences for remote experiences. The study revealed user interactive patterns in each desired experience, highlighting the need of robot guidance, social engagements with the robot and the remote bystanders. Our work identifies a novel design space where conversational telepresence robots can be used to foster meaningful interactions in the remote physical environment. We offer design insights into the robot's proactive role in providing guidance and using dialogue to create personalized, contextualized and meaningful *** Codes 68-06 © 2024, CC BY-NC-ND.

关键词： Robots

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Measuring Social Solidarity During Crisis:The Role of Design Choices

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Journal of Social Computing 2022年第2期3卷 139-157页

作者： Steffen Eger Dan Liu Daniela Grunow the Center for Cognitive Interaction Technology(CITEC) Bielefeld UniversityBielefeld 33615Germany the Computer Science Department Technical University DarmstadtDarmstadt 64289Germany the Faculty of Social Sciences Goethe University FrankfurtFrankfurt 60629Germany

Building on our previous work,we assess how social solidarity towards migrants and refugees has changed before and after the onset of the COVID-19 pandemic,by collecting and analyzing a large,novel,and longitudinal dataset of migration-related *** this end,we first annotate above 2000 tweets for(anti-)solidarity expressions towards immigrants,utilizing two annotation approaches(experts ***).On these annotations,we train a BERT model with multiple data augmentation strategies,which performs close to the human upper *** use this high-quality model to automatically label over 240000 tweets between September 2019 and June *** then assess the automatically labeled data for how statements related to migrant(anti-)solidarity developed over time,before and during the COVID-19 *** findings show that migrant solidarity became increasingly salient and contested during the early stages of the pandemic but declined in importance since late 2020,with tweet numbers falling slightly below pre-pandemic levels in summer *** the same period,the share of anti-solidarity tweets increased in a sub-sample of COVID-19-related *** findings highlight the importance of long-term observation,pre-and post-crisis comparison,and sampling in research interested in crisis related *** one of our main contributions,we outline potential pitfalls of an analysis of social solidarity trends:for example,the ratio of solidarity and anti-solidarity statements depends on the sampling design,i.e.,tweet language,Twitter-user accounts’national identification(country known or unknown)and selection of relevant *** our sample,the share of anti-solidarity tweets is higher in native(German)language tweets and among“anonymous”Twitter users writing in German compared to English-language tweets of users located in Germany.

关键词： social solidarity crises COVID-19 natural language processing

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End-To-End Training of a Neural HMM with Label and Transition Probabilities

End-To-End Training of a Neural HMM with Label and Transitio...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Mann, Daniel Raissi, Tina Michel, Wilfried Schluter, Ralf Ney, Hermann AppTek GmbH Aachen52062 Germany Rwth Aachen University Machine Learning and Human Language Technology Computer Science Department Aachen52074 Germany

ISBN: (纸本)9798350306897

We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modeled and learned explicitly. Most contemporary sequence-to-sequence models allow for from-scratch training by summing over all possible label segmentations in a given topology. In our approach there are explicit, learnable probabilities for transitions between segments as opposed to a blank label that implicitly encodes duration *** implement a GPU-based forward-backward algorithm that enables the simultaneous training of label and transition *** investigate recognition results and additionally Viterbi alignments of our models. We find that while the transition model training does not improve recognition performance, it has a positive impact on the alignment quality. The generated alignments are shown to be viable targets in state-of-the-art Viterbi trainings. © 2023 IEEE.

关键词： Hidden Markov models

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