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66th Annual Meeting of the International Society for the Systems sciences, ISSS 2022

作者： Coetzee, Wilma Goede, Roelien Unit for Data Science and Computing North-West University South Africa

For people who are illiterate, the opportunities for employment and improving their situation presents an overwhelming challenge. In the world of the 21st century people who are data illiterate, will increasingly find themselves in the same situation, irrespective of their field of employment. Companies and society need people who can analyse data and use the resulting information to direct decisions. Another aspect of the 21st century that society struggles with, is the increasing prevalence of mental illness due to work pressures, failing marriages, torn family structures, isolation and so forth. The mental health of students is a major cause for concern since one in three students show signs of depression and anxiety. Within this context there is a need to develop a plan to address the instructional design for data science students. They need to learn relevant content in a manner that will equip them to succeed in the workplace and society at large within a context where their wellbeing will be nurtured. The study employs the Systems Informed Positive Psychology perspective to direct the process of improving the design of data science modules. Critical Systems Thinking determines how different components are interlinked, and how they can best be changed to attain the overall goal by listening to the voices of all the affected parties. Directed content analysis of comments received from 284 students participating in an introductory statistics module at a South African university indicates that students intuitively mentioned several aspects of the Flourishing Classrooms Systems Model, thereby indicating that students would like a thriving classroom. The students' voice indicated that things like emotional bonding and supportiveness are important to them. Little classroom flexibility is experienced by the students, it may be due to lecturers who are lacking in leadership in the classroom and are unwilling to negotiate on matters with the students. Students indicated

关键词： Students

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

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6th International Conference on Advanced Aspects of Software Engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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Detection of Brain Tumor from MRI Images Using VGG-16 CNN Model with Transfer Learning 3

Detection of Brain Tumor from MRI Images Using VGG-16 CNN Mo...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Singh Rathore, Bhanu Pratap Dave, Rutul Dhanasekaran, K. Priyadarsini, K. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350322842

Brain tumors are a serious health concern caused by the abnormal growth of cells in or around the brain. These tumors can either be benign or malignant. Few of the causes for Brain tumors are genetic mutations, exposure to radiation, or environmental factors. Early detection of brain tumors is essential for effective treatment and improved patient outcomes. To address this, a novel approach has been proposed for brain tumor detection using transfer learning with the VGG-16 convolutional neural network (CNN) architecture. By leveraging the pre-trained VGG-16 model on the ImageNet dataset, which provides a broad range of data for learning and representing complex features of various objects, meaningful features can be extracted from MRI scans and differentiate between tumor and normal tissue accurately. This refined VGG-16 model can provide a more precise and reliable diagnosis, which is crucial for successful treatment. This research study demonstrates that the utilization of VGG-16 model for brain tumor diagnosis can provide an efficient and accurate method for identifying brain tumors. Moreover, this approach can be applied to develop automated systems to assist medical professionals in diagnosing and treating brain tumors more effectively. © 2023 IEEE.

关键词： Convolutional neural networks

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A Survey on Usage of Multimedia databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance 46

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Horvat, M. Jercic, P. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

ISBN: (纸本)9789532331042

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors' intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers' opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey. © 2023 MIPRO Croatian Society.

关键词： database systems

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Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise 38

Resfusion: Denoising Diffusion Probabilistic Models for Imag...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Zhenning Zheng, Haoshuai Xu, Chen Dong, Changsheng Pan, Bin Xie, Xueshuo He, Along Li, Tao Fu, Huazhu College of Computer Science Nankai University China School of Statistics and Data Science Nankai University China Haihe Lab of ITAI China Institute of High Performance Computing A*STAR Singapore

Recently, research on denoising diffusion models has expanded its application to the field of image restoration. Traditional diffusion-based image restoration methods utilize degraded images as conditional input to effectively guide the reverse generation process, without modifying the original denoising diffusion process. However, since the degraded images already include low-frequency information, starting from Gaussian white noise will result in increased sampling steps. We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images. The form of our inference process is consistent with the DDPM. We introduced a weighted residual noise, named resnoise, as the prediction target and explicitly provide the quantitative relationship between the residual term and the noise term in resnoise. By leveraging a smooth equivalence transformation, Resfusion determine the optimal acceleration step and maintains the integrity of existing noise schedules, unifying the training and inference processes. The experimental results demonstrate that Resfusion exhibits competitive performance on ISTD dataset, LOL dataset and Raindrop dataset with only five sampling steps. Furthermore, Resfusion can be easily applied to image generation and emerges with strong versatility. Our code and model are available at https://***/nkicsl/Resfusion. © 2024 Neural information processing systems foundation. All rights reserved.

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Exploring Machine Learning in IoT Smart Home Automation 8

Exploring Machine Learning in IoT Smart Home Automation

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8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023

作者： Waseem, Quadri Wan Din, Wan Isni Sofiah Bin Ab Rahman, Azamuddin Nisar, Kashif Faculty of Computing Pekan Malaysia AnalytiCray Kuala Lumpur55100 Malaysia Swinburne University of Technology Sydney Professional Computing Data Science ParramattaNSW2150 Australia

ISBN: (纸本)9798350310931

The Internet of Things (IoT) has evolved in these years. Various types of organizations, industries, research domains and almost all types of intelligent future applications are utilizing the advantages of IoT. These applications include smart homes, smart cities, smart infrastructure smart communities, smart healthcare, smart agriculture and many more. 'Smart Homes' has emerged as one the latest Internet of Things (IoT) applications known to automate household equipment's using remote or automated functioning from remote locations to improve the quality of life for its inhabitants. For a smart home system to function effectively, the machine learning (ML) implementation must go beyond basic remote control and simple automation. To fully realize its potential and provide homeowners with tremendous and unexpected benefits, more research and development in the fields of machine intelligence and smart home automation are required. In this research work, we aim to traverse ML in IoT smart home automation by classifying the home automation applications. We propose a taxonomy of machine learning (ML) for smart homes based on its application. This research also includes related surveys and literature reviews along with open challenges and issues as well as future directions in detail. © 2023 IEEE.

关键词： Automation

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Crop Intelligence: Optimizing Agriculture through Machine Learning-guided Recommendations 4

Crop Intelligence: Optimizing Agriculture through Machine Le...

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4th International Conference on Multimedia Processing, Communication and Information Technology, MPCIT 2024

作者： Ankitha, K. Patil, Shruti Dept. of Artificial Intelligence and Data Science Nitte Karkala India SJEC Dept. of Intelligent Computing and Business System Vamanjur Mangalore India

ISBN: (纸本)9798350375466

Crop Intelligence, powered by Machine Learning (ML) and data analytics, is a transformative paradigm poised to revolutionize modern agriculture. This approach involves harnessing the capabilities of Machine Learning (ML) to provide tailored recommendations and insights to farmers, enabling data-driven decision-making for optimizing crop yield, resource utilization, and sustainability. This paper explores the potential and advancements in crop intelligence, focusing on how ML-guided recommendations can significantly enhance agricultural practices. Key challenges such as data quality, local adaptability, and user acceptance are identified and discussed, providing a holistic view of the landscape. Additionally, the paper highlights future directions, including personalized recommendations, integration of IoT and remote sensing, and collaborative platforms, as crucial steps toward realizing the full potential of crop intelligence. Ultimately, crop intelligence stands as a promising avenue to bolster global food security, sustainable farming, and the livelihoods of farmers through the fusion of agricultural knowledge with cutting-edge technology. © 2024 IEEE.

关键词： Artificial Intelligence Crop prediction KNN Machine Learning

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Chung’s functional law of the iterated logarithm for the Brownian sheet

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Frontiers of Mathematics in China 2022年第6期17卷 1015-1024页

作者： Yonghong LIU Ting ZHANG Yiheng TANG Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation School of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilin541004China

In this paper,we investigate functional limit problem for path of a Brownian sheet,Chung’s functional law of the iterated logarithm for a Brownian sheet is *** main tool in the proof is large deviation and small devi... 详细信息

关键词： Brownian sheet Chung’s functional law of the iterated logarithm

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RoCEL: Advancing Table Entity Linking through Distinctive Row and Column Contexts

RoCEL: Advancing Table Entity Linking through Distinctive Ro...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wang, Yuanzheng Fan, Yixing Guo, Jiafeng Zhang, Ruqing Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798891761643

Table entity linking (TEL) aims to map entity mentions in the table to their corresponding entities in a knowledge base (KB). The core of this task is to leverage structured contexts, specifically row and column contexts, to enhance the semantics of mentions in entity disambiguation. Most entity linking (EL) methods primarily focus on understanding sequential text contexts, making it difficult to adapt to the row and column structure of tables. Additionally, existing methods for TEL indiscriminately mix row and column contexts together, overlooking their semantic differences. In this paper, we explicitly distinguish the modeling of row and column contexts, and propose a method called RoCEL to capture their distinct semantics. Specifically, for row contexts in tables, we take the attention mechanism to learn the implicit relational dependencies between each cell and the mention. For column contexts in tables, we employ a set-wise encoder to learn the categorical information about the group of mentions. At last, we merge both contexts to obtain the final mention embedding for link prediction. Experiments on four benchmarks show that our approach outperforms the state-of-the-art (SOTA) baseline by about 1.5% on the in-domain dataset, and by 3.7% on average across three out-of-domain datasets. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Robotic Cognitive Behavioural Therapy: rCBT 16th

Robotic Cognitive Behavioural Therapy: rCBT

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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Samani, Hooman Zaraki, Abolfazl Davies, Daisy-Rose Fernando, Owen Noel Newton Creative Computing Institute University of the Arts London London United Kingdom and Robotics Research Group University of Hertfordshire Hatfield United Kingdom School of Engineering Computing and Mathematics University of Plymouth Plymouth United Kingdom College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9789819789627

This paper introduces Robotic Cognitive Behavioural Therapy (rCBT) as a novel approach to overcoming the limitations of traditional Cognitive Behavioural Therapy (CBT) in treating emotional and psychological issues. After a brief overview of CBT and its inherent challenges, this paper explores the innovative application of physical robots in therapy, building on the success of technology-based CBT methods that have effectively treated various mental health conditions. The proposed rCBT solution is a compact, portable, and cost-effective 3D-printed robot designed to aid individuals with anxiety or depression. This robot includes interactive features such as a diary, health questionnaires, daily mood monitoring, to-do lists, mindfulness exercises, and a crisis helpline, enabling users to track their emotional states and activities. These functions support healthcare professionals and users in identifying emotional patterns and triggers. The robot can monitor physiological signals like heart rate. This research aims to enhance the accessibility and effectiveness of CBT, suggesting that rCBT could significantly broaden the therapy’s reach and improve mental health outcomes for a larger population. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Social robots

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