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A Systematic Map for Improving teaching and learning in Undergraduate Operating Systems Courses

IEEE ACCESS 2018年 6卷 60974-60992页

作者： Pamplona, Sonia Medinilla, Nelson Flores, Pamela Madrid Open Univ Dept Comp Sci Madrid 28400 Spain Univ Politecn Madrid Dept Informat Syst & Languages & Software Engn Campus Montegancedo E-28660 Madrid Spain Escuela Politec Nacl Fac Syst Engn Quito 170517 Ecuador

Operating Systems (OS) is an important area of knowledge included in virtually all undergraduate computing curricula and in some engineering curricula as well. teaching and learning an OS undergraduate course have always been a challenge. Several different approaches have been used for OS teaching and learning. Nevertheless, it is not easy for a teacher to choose one of them. No guidelines are available on how to choose one of them to match the specific objectives of each OS course. The objective of this paper is to analyze the approaches that have been used to improve OS teaching and learning by applying a systematic map. In particular, we consider the following dimensions: learning objectives, assessment, empirical study, methodology, and mode (face-to-face, online, or blended). The systematic map devised in this paper is focused on the time span from 1995 to 2017 and considered six of the major publications on the Computer Science Education. We considered three journals (the Journal of engineering Education, the IEEE TRANSACTIONS ON EDUCATION, and the international Journal of engineering Education) and three conferences (the ACM Technical Symposium on Computer Science Education-SIGCSE, the conference on computing Education Research-ITiCSE, and the international conference on computing Education Research-Koli). A total of 55 papers were included in the study after performing a search based on the inclusion/exclusion criteria. Nine approaches to improve OS teaching and learning were identified and analyzed. Furthermore, the implications for OS instructors and for research in this field are discussed.

关键词： Computer science education operating systems systematic map

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Analyzing Bank Customer Behavior: Segmentation and Prediction Using Big Data Analytics 1st

Analyzing Bank Customer Behavior: Segmentation and Predictio...

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1st international Research conference on computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Miriam, D. Doreen Hephzibah Robin, C. R. Rene Chennai India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031823824

Businesses use customer segmentation as a strategic tool to divide their heterogeneous client base into discrete groups according to demands, behaviors, or common traits. An overview of consumer segmentation techniques is given in this paper, with emphasis on the role that this technique plays in promoting focused marketing campaigns, raising customer satisfaction levels, and allocating resources as efficiently as possible. This research investigates the effectiveness of real-time banking transaction data for customer segmentation using a large dataset 1048567 transaction. We explore various unsupervised machine learning algorithms to uncover hidden patterns and group customers with similar financial behavior. The methods employed include K-means clustering with standard scaling for normalization, hierarchical clustering with an agglomerative approach for building a hierarchy of clusters, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for identifying clusters of arbitrary shapes, and Principal Component Analysis (PCA) for dimensionality reduction to focus on the most significant features within the data. By applying these diverse algorithms, we aim to achieve a comprehensive understanding of customer segmentation based on real-time transaction patterns. The analysis will reveal distinct customer groups based on factors such as frequency, type, and value of transactions, allowing banks to develop targeted strategies for improved customer satisfaction and retention. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dimensionality reduction

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Enhancing Robot Navigation in Crowded Spaces Through Systematic Strategy Selection

Enhancing Robot Navigation in Crowded Spaces Through Systema...

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Annual IEEE Systems conference

作者： Kleber Cabral Jean-Alexis Delamer Jefferson Silveira Sidney N. Givigi School of Computing Queen's University Kingston ON Canada Department of Computer Science St. Francis Xavier University NS Canada Dept. of Electrical and Computer Engineering Queen's University Kingston ON Canada

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

This paper presents a framework for the online selection of autonomous navigation strategies in real-time for robots operating in crowded and dynamic environments. Traditional path-planning algorithms emphasize the computation of collision-free trajectories but may not account for more complex safety considerations that arise specifically when navigating around humans or other dynamic entities. To address these challenges, we propose a framework that evaluates a set of navigation strategies trained with deep reinforcement learning and selects the optimal strategy based on various metrics including safety, motion efficiency, and adaptability to changing scenarios. On one hand, the navigation strategies are trained using a reward function that incorporates objective metrics such as obstacle clearance and distance to the goal. On the other hand, the online evaluation of the strategies leverages a variety of additional performance metrics such as personal space intrusion, smoothness of path, control stability, and overall efficiency of control commands. The framework dynamically switches strategies to optimize navigation performance while balancing safety with control and path optimality. Simulated results show that the online selection of strategies can accomplish safer and more efficient navigation. Experimental results in real-world demonstrate the benefits of our approach, showing improved adaptability and context-aware decision-making compared to single-strategy navigation.

关键词： Hands Systematics Navigation Heuristic algorithms Aerospace electronics Extraterrestrial measurements Stability analysis Real-time systems Safety Trajectory

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Enhancing Rhetorical Role Identification in Legal Documents Using Large Language Models and IN_place Data Augmentation 3rd

Enhancing Rhetorical Role Identification in Legal Documents...

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3rd international conference on computing, Communication and learning, CoCoLe 2024

作者： Sheik, Reshma Namani, Chandrababu Prasanna, P. Nirmala, S. Jaya National Institute of Technology Tamil Nadu Trichy India TKM College of Engineering Kerala Kollam India MVSR Engineering College Telangana Hyderabad India

ISBN: (纸本)9783031790409

Understanding the rhetorical roles of sentences within legal documents is crucial for various downstream tasks, including semantic search, summarization, and case law analysis. However, the complex structure of legal case documents, coupled with the interplay of various themes, poses challenges even for human experts. In this paper, we perform data augmentation using open-sourced Large Language Models (LLMs) to automate the identification of rhetorical roles. Specifically, we explore data augmentation techniques to address class imbalance issues within datasets, introducing a novel augmentation technique called IN_Place Augmentation to mitigate linear dependency issues. Additionally, a comparative analysis of two neural network architectures, InLegalBERT and Hierarchical BiLSTM, combined with Conditional Random Fields (Hier-BiLSTM-CRF), integrating different types of sentence embeddings was conducted. We fine-tune the InLegalBERT model using our proprietary dataset and utilize the fine-tuned embeddings to train the Hier-BiLSTM-CRF model. Our evaluations demonstrate the efficacy of the fine-tuned InLegalBERT model across diverse legal contexts, showcasing significant improvements in the Land & Property domain with an increase in the weighted average F1 score from 0.729 to 0.884, and in the Criminal domain with an increase in the macro average F1 score from 0.631 to 0.817. In summary, our contributions include leveraging LLM- Mistral 7B for data augmentation, introducing IN_Place augmentation, utilizing domain-specific transformer InLegalBERT, and developing a hybrid model integrating InLegalBERT with Hier-BiLSTM-CRF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantics

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Enhancing Tomato Crop Health: Leveraging Modified InceptionResNetV2 for Disease Detection 4th

Enhancing Tomato Crop Health: Leveraging Modified InceptionR...

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4th international conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Zubair, Fida Saleh, Moutaz Computer Science and Engineering Department Qatar University Doha Qatar

ISBN: (纸本)9783031821523

The proposed research aims to address the critical need for robust disease detection in tomato crops in the Middle East, given the essential role of tomatoes in the region's food security. Recognizing the significance of plant disease detection becomes important for securing a stable supply of this essential crop. This research aims to employ transfer learning and A modified InceptionResNetV2 architecture for the purpose of disease detection. The study evaluates the efficiency of this model with two datasets: Plant Village and Noulam. The Proposed Model Surpasses Prior Works: Achieving 99.8% and 99.7% Accuracies on Respective Datasets for Tomato Disease Detection. The primary objective of this research is to enhance the early detection of tomato diseases, contributing to the efficiency and sustainability of tomato cultivation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fruits

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Secure Multi-party Computation for Privacy Preservation in Collaborative Networks

Secure Multi-party Computation for Privacy Preservation in C...

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Automation and Computation (AUTOCOM), international conference on

作者： Godwin Premi. M.S Awakash Mishra Ashmeet Kaur Jyoti Ranjan Sahoo Prateek Aggarwal Sanjiv Mathur Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Centre for Internet of Things Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India Adjunct Faculty School of Mangement PG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Organizational and individual resource sharing is gaining significant attention in collaborative networks, focusing on the functioning of groups over various tasks. However, sharing sensitive data, such as personal information and intellectual property, makes security and privacy a big problem. This is work done in secure multi-party computation (SMPC), which is a very promising solution because, in SMPC, multiple parties can compute a certain function on their input data without revealing them to each other. Secure Multiparty Computation (SMPC) SMPC is a cryptographic technique that allows multiple parties to compute a function over their input data sets while keeping those inputs private. The promise of a new computing environment enabling everyone to calculate in the dark, so no one sees or even knows the data being processed exists, is achieved through sophisticated cryptographic algorithms and protocols that split the computation among partners without ever revealing the underlying data. In this abstract, we provide an overview of the state-of-the-art regarding SMPC for privacy preservation in collaborative networks. This paper highlights the challenges and opportunity areas in adopting Secure Multi-Party Computation within a collaborative networking ecosystem, along with the advantages of this methodology. It has also been SMPC, a great offer of real-world use to share information in health networks, financial transactions, and between organizations. In conclusion, this abstract highlights the significance of privacy preservation for collaborative networks and demonstrates that SMPC can be a potential candidate for realizing such a mechanism.

关键词： Data privacy Privacy Federated learning Intellectual property Organizations Multi-party computation Outsourcing Resource management Medical diagnosis Protection

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EnigmaArt: Dual Image Encryption and Compression via Autoencoding and Pixel Conversion 4th

EnigmaArt: Dual Image Encryption and Compression via Autoe...

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4th international conference on Advanced Network Technologies and Intelligent computing, ANTIC 2024

作者： Tiwari, Naveen Kumar Rajput, Shyam Singh Yadav, Aditya National Institute of Technology Patna Bihar Patna India Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9783031837890

Securing sensitive visual data is essential in the digital age. Our research presents a sophisticated model of image encryption that combines RNA pixel conversion with an autoencoder. A convolutional neural network (CNN) is used for efficient feature extraction. Convolutional encoding and RNA pixel conversion, which map pixels to RNA sequences (Adenine, Cytosine, Guanine, Uracil) enabling stronger encryption, are the model’s primary innovations. By guaranteeing that every encryption instance is distinct, this dynamic technique strengthens security. Our approach ensures security and picture integrity by demonstrating robust encryption capabilities with minimum reconstruction loss (0.0617), as tested on the MNIST dataset. Furthermore, the encryption procedure is optimized by a fractional order optimizer, FGL_Adam, which incorporates weight decay, learning rate modifications, and a security improvement factor. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Study of Lung Nodules Using CT Scan Image 1st

Study of Lung Nodules Using CT Scan Image

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1st international conference on Intelligent Systems in computing and Communications, ISCComm 2023

作者： Suganya, P. V. Raja Devi, T. Joshita, V. Velammal Engineering College Chennai India

ISBN: (纸本)9783031756078

Respiratory illness is the primary factor behind fatality associated with cancer globally, and timely identification is imperative for preserving lives. However, analyzing CT scans can be a burdensome task for radiologists and observer fatigue can lead to reduced performance. Timely identification of nodules is critical for effective treatment and improved survival rates. As a result of advancements in medical imaging technology, CT scans have become the primary approach for identifying lung nodules during the diagnostic phase. The proposed system utilizes an advanced convolutional neural network (CNN) architecture, trained using a vast dataset of annotated lung CT images, to accurately detect and classify lung nodules. The system incorporates various image pre-processing techniques to increase CT scan image quality and improve CNN performance. The suggested system attains notable precision and responsiveness in detecting lung nodules, potentially contributing to the timely identification and management of lung cancer. This report explores a project centered around the detection of lung nodules through the utilization of deep learning techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Transformer-GNN Based Graph Representation learning for Brain Functional Connectivity Network Analysis 25

Transformer-GNN Based Graph Representation Learning for Brai...

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Proceedings of the 2025 5th international conference on Bioinformatics and Intelligent computing

作者： Zhihua Huang Li Xiao Institute of Advanced Technology University of Science and Technology of China Hefei Anhui China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9798400712203

Recently, functional magnetic resonance imaging (fMRI) has been extensively utilized to explore brain functional connectivity networks (FCNs) and leverage brain networks for predicting behavioral information. Graph Neural Networks (GNNs) have garnered significant attention in FCN analysis, due to their established power for analyzing graph-structured data. However, there are two major problems impeding the research when utilizing GNNs: (1) the limited receptive field of GNNs which only aggregate relevant information from local neighbor nodes; (2) the over-smoothing problem that arises when employing deep GNNs. To address these issues, we propose an innovative model that integrate Transformer into GNNs to enhance the ability of GNNs to aggregate relevant but relatively distant nodes. In addition, GNNs can assist Transformer to capture local neighbor node information by leveraging the graph structure information. Therefore, in this new model combining Transformer and GNNs based on the characteristics of fMRI data, we have specially designed three layers: Transformer Layer, GNN Layer and Graph Readout Layer. Finally, the experimental results on the Human Connectome Project data demonstrated the superiority of our proposed model.

关键词： Brain Networks

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Identifying and Displaying EdTech Implementation Context Profiles 26th

Identifying and Displaying EdTech Implementation Context Pro...

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26th international conference on Artificial Intelligence and Applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Wright, Brian Tomasik, Christine Rivera, Alexandra Bush, Caroline Danek, Brennan School of Data Science University of Virginia Charlottesville VA United States EdTech Evidence Exchange Washington DC United States

ISBN: (纸本)9783031866227

Education technology (edtech) is increasingly prevalent in classrooms, yet 85% of the technologies currently implemented are a bad fit for the school, or are poorly implemented. This is especially problematic for poorly funded schools typically seen in minority communities. To address this limitation, the EdTech Evidence Exchange has collected survey responses to characterize the contexts in which technologies are being implemented. Variable selection via penalized regression and unsupervised methods extracts a subset of the factors that are the most informative in characterizing schools’ contexts. This subset is then used to fit a Gaussian Mixture Model to create soft clusters of schools with similar contexts. A new feature, “Schools Like Mine,” combines soft classification and Euclidean distance to identify and rank schools by similarity. This research will hopefully reduce the likelihood of failed software investments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Education Technology Gaussian Mixture Modeling Machine learning Survey Responses Variable Selection

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