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SSRN 2024年

作者： Balamurugan, A. Varshney, Sudeep Raghuveer, K. Kumar, Kishore M. Misra, Siddharth Prabhu, M. Ramkumar Department of Management Studies School of Commerce and Management Bharath Institute of Higher Education and Research Agarm-then Road Tamilndu Selaiyur Chennai79 India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Uttar Pradesh Greater Noida201301 India Department of Business Management V.R.Siddhartha Engineering College A.P Kanuru20007 India CMR Technical Campus Telangana Hyderabad501401 India Marketing Birla Global University Odisha Bhubaneswar India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

In the era of expanding digital entertainment of video games, understanding their impact on students and identifying associated factors is crucial for educational and developmental considerations. Video game use has an impact on social conduct, academic achievement, and general well-being in addition to being merely entertaining. The study's objective is to provide insights into the complex nature of video game engagement among the demographic by examining variables like social media usage, parental involvement, and device ownership. To determine the extent to students use video games and factors influence their gaming habits. The study utilized a Video Game Addiction Scale alongside a comprehensive study. Participants were extended to 200 students enrolled in 8th and 9th grades across multiple schools to participate in the survey. To investigate relationships and associations among variables, statistical studies are performed, such as inferential tests and descriptive statistics. The findings provide insight into potential variables that might influence both favourable and unfavourable features of video game participation. Participants can develop an environment that motivates students to play video games responsibly and in a balanced manner by acknowledging the complexity of the elements that influence gaming behaviour © 2024, The Authors. All rights reserved.

关键词： Students

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research on reversible functions having component functions with specified properties: An overview 12th

Research on reversible functions having component functions ...

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12th International Conference on Reversible Computation, RC 2020

作者： Kerntopf, Pawel Moraga, Claudio Podlaski, Krzysztof Stanković, Radomir Institute of Computer Science Warsaw University of Technology Warsaw Poland Faculty of Computer Science Technical University of Dortmund Dortmund Germany Faculty of Physics and Applied Informatics University of Lódź Lódź Poland Department of Computer Science Faculty of Electronic Engineering University of Niš Niš Serbia

ISBN: (纸本)9783030473600

In the traditional logic synthesis, different classifications of non-reversible Boolean functions have found many applications. Recently, some attempts to deal with classifications of reversible functions have been published. In this paper, an overview of our results towards constructing a new classification of reversible functions is presented. These results were obtained due to our discussions during two Short Term Scientific Missions (STSMs) as well as during our further research in the framework of COST Action IC1405 "Reversible Computation - Extending Horizons of Computing" and were published in five papers. © The Author(s) 2020.

关键词： Logic Synthesis

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Discriminative features matter: Multi-layer bilinear pooling for camera localization 30

Discriminative features matter: Multi-layer bilinear pooling...

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30th British Machine Vision Conference, BMVC 2019

作者： Wang, Xin Wang, Xiang Wang, Chen Bai, Xiao Wu, Jing Hancock, Edwin Robert School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Department of Computer Science University of York York United Kingdom

Deep learning based camera localization from a single image has been explored recently since these methods are computationally efficient. However, existing methods only provide general global representations, from which an accurate pose estimation can not be reliably derived. We claim that effective feature representations for accurate pose estimation shall be both "informative" (focusing on geometrically meaningful regions) and "discriminative" (accounting for different poses of similar images). Therefore, we propose a novel multi-layer factorized bilinear pooling module for feature aggregation. Specifically, informative features are selected via bilinear pooling, and discriminative features are highlighted via multi-layer fusion. We develop a new network for camera localization using the proposed feature pooling module. The effectiveness of our approach is demonstrated by experiments on an outdoor Cambridge Landmarks dataset and an indoor 7 Scenes dataset. The results show that focusing on discriminative features significantly improves the network performance of camera localization in most cases. Codes will be available soon. © 2019. The copyright of this document resides with its authors.

关键词： Deep learning

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COMPUTATION-EFFICIENT ERA: A COMPREHENSIVE SURVEY OF STATE SPACE MODELS IN MEDICAL IMAGE ANALYSIS

arXiv

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arXiv 2024年

作者： Heidari, Moein Kolahi, Sina Ghorbani Karimijafarbigloo, Sanaz Azad, Bobby Bozorgpour, Afshin Hatami, Soheila Azad, Reza Diba, Ali Bagci, Ulas Merhof, Dorit Hacihaliloglu, Ilker School of Biomedical Engineering University of British Columbia BC Canada Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Electrical Engineering and Computer Science Department South Dakota State University Brookings United States Faculty of Mechanical Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany Lunit Inc. Amsterdam Netherlands Department of Radiology Northwestern University Chicago United States Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Department of Radiology Department of Medicine University of British Columbia BC Canada

Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emergence of transformers has altered this paradigm due to their superior performance. Built upon these advances, Vision Transformers (ViTs) have conjoined Convolutional Neural Networks (CNNs) as two leading foundational models for learning visual representations. However, transformers are hindered by the O(N2) complexity of their attention mechanisms, while CNNs lack global receptive fields and dynamic weight allocation. State Space Models (SSMs), specifically the Mamba model with selection mechanisms and hardware-aware architecture, have garnered immense interest lately in sequential modeling and visual representation learning, challenging the dominance of transformers by providing infinite context lengths and offering substantial efficiency maintaining linear complexity in the input sequence. Capitalizing on the advances in computer vision, the field of medical imaging has heralded a new epoch with Mamba models. Intending to help researchers navigate the surge, this survey seeks to offer an encyclopedic review of Mamba models in medical imaging. Specifically, we start with a comprehensive theoretical review forming the basis of SSMs, including Mamba architecture and its alternatives for sequence modeling paradigms in this context. Next, we offer a structured classification of Mamba models in the medical field and introduce a diverse categorization scheme based on their application, imaging modalities, and targeted organs. Consequently, we explore various applications and their practical use cases in medicine, such as image segmentation, reconstruction, registration, classification, language processing, multi-modal understanding, and other medically related challenges. Finally, we summarize key challenges, discuss different future research directions of the SSMs in the medical domain, and prop

关键词： Medical applications

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Chemical Property-Guided Neural Networks for Naphtha Composition Prediction

Chemical Property-Guided Neural Networks for Naphtha Composi...

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IEEE International Conference on Industrial informatics (INDIN)

作者： Chonghyo Joo Jeongdong Kim Hyungtae Cho Jaewon Lee Sungho Suh Junghwan Kim Department of Chemical and Biomolecular Engineering Yonsei University Seoul Republic of Korea Green Materials & Processes R&D Group Korea Institute of Industrial Technology Ulsan Republic of Korea Department of Computer Science RPTU Kaiserslautern-Landau Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

The naphtha cracking process heavily relies on the composition of naphtha, which is a complex blend of different hydrocarbons. Predicting the naphtha composition accurately is crucial for efficiently controlling the cracking process and achieving maximum performance. Traditional methods, such as gas chromatography and true boiling curve, are not feasible due to the need for pilot-plant-scale experiments or cost constraints. In this paper, we propose a neural network framework that utilizes chemical property information to improve the performance of naphtha composition prediction. Our proposed framework comprises two parts: a Watson K factor estimation network and a naphtha composition prediction network. Both networks share a feature extraction network based on Convolutional Neural Network (CNN) architecture, while the output layers use Multi-Layer Perceptron (MLP) based networks to generate two different outputs - Watson K factor and naphtha composition. The naphtha composition is expressed in percentages, and its sum should be 100%. To enhance the naphtha composition prediction, we utilize a distillation simulator to obtain the distillation curve from the naphtha composition, which is dependent on its chemical properties. By designing a loss function between the estimated and simulated Watson K factors, we improve the performance of both Watson K estimation and naphtha composition prediction. The experimental results show that our proposed framework can predict the naphtha composition accurately while reflecting real naphtha chemical properties.

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Resilient intrusion detection system for cloud containers

Resilient intrusion detection system for cloud containers

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作者： Abed, Amr S. Azab, Mohamed Clancy, Charles Kashkoush, Mona S. Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Informatics Research Institute City of Scientific Research and Technological Applications Alexandria Egypt Hume Center for National Security and Technology Virginia Tech ArlingtonVA United States

The lightweight virtualisation and isolated execution offered by Linux containers qualify it to be the dominant virtualisation platform for cloud-based applications. The fact that Linux containers run on the same host while sharing the same kernel opens the door for new attacks. However, limited research has been conducted in the area of securing cloud containers. This paper presents a resilient intrusion detection and resolution system for cloud-based containers. The system relies on two main pillars, a real-time smart behaviour monitoring mechanism to detect maliciously behaving containers, and a moving-target defence approach that applies runtime container migration to quarantine such containers and to minimise attack dispersion. To avoid zero-day targeted attacks, the system also induces random live migrations between running containers to obfuscate its execution behaviour. Such obfuscation makes it harder for attackers to execute their targeted attacks. The system was tested by a big-data application using a container-based Apache Hadoop cluster to demonstrate the system's ability to automatically deploy, monitor, detect, and respond to maliciously behaving applications by live migration or by rolling back the container to a safe state. Results showed that the proposed system efficiently ensure safe and secure container operation. © 2020 Inderscience Enterprises Ltd.

关键词： Containers

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Overheating-Avoidance Remapping Scheme for Reliability Enhancement of 3D PCM Storage Systems 20

Overheating-Avoidance Remapping Scheme for Reliability Enhan...

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2020 research in Adaptive and Convergent Systems, RACS 2020

作者： Lin, Yu-Chen Wang, Tse-Yuan Tsao, Che-Wei Chang, Yuan-Hao Chen, Jian-Jia Liu, Xue Kuo, Tei-Wei Institute of Information Science Academia Sinica Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Informatics TU Dortmund University Germany School of Computer Science McGill University Canada Department of Computer Science and Information Engineering National Taiwan University Taiwan College of Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9781450380256

With the trend of 3D architecture and higher access rate, Phase Change Memory (PCM) storage devices face the overheating issue. This work is motivated by the observation that PCM devices might change states of memory cells with high temperature, and it will hurt the reliability of 3D PCM storage systems. Hence, we propose an Overheating-Avoidance Remapping Scheme (OARS) that controls the temperature of PCM layers and achieves wear-leveling of PCM cells inside PCM devices. Besides, we also take remapping overhead into consideration. The experiments were conducted based on the representative realistic workloads, and the results demonstrate the efficacy of the proposed scheme. © 2020 ACM.

关键词： Phase change memory

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A scalable quantum gate-based implementation for causal hypothesis testing

arXiv

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arXiv 2022年

作者： Kundu, Akash Acharya, Tamal Sarkar, Aritra Institute of Theoretical and Applied Informatics Polish Academy of Sciences Joint Doctoral School Silesian University of Technology Gliwice Poland Quantum Intelligence Research Team Department of Quantum & Computer Engineering Delft University of Technology Delft2628 CD Netherlands Quantum Machine Learning Group Quantum Computing Division QuTech Delft2628 CJ Netherlands

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence. © 2022, CC BY.

关键词： Inference engines

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Optimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization

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informatics in Medicine Unlocked 2024年 44卷

作者： Bhuvaneswari Ramakrishnan, Akshay Sridevi, M. Vasudevan, Shriram K. Manikandan, R. Gandomi, Amir H. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirapalli India Asia Pacific and Japan Intel India Pvt. Ltd Bengaluru India Faculty of Engineering & Information Technology University of Technology Sydney Sydney NSW Australia University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary

A brain tumour is a malignant condition that spreads extremely quickly and requires rapid detection. In recent years, it has become apparent that deep learning is a promising technique for classifying brain tumours. To identify these tumours, this work proposes a hybrid CNN architecture, implementing InceptionV3, ResNet-50, VGG16, and DenseNet. To evaluate this approach, a total of 3929 images was obtained from Kaggle, including 2556 non-tumorigenic and 1373 tumorigenic specimens. Initially, the method extracts features from MRI pictures, then segments the tumour using the mask images. Subsequently, the segmented tumour image is fused with the original image, and finally the fused image is classified with the assistance of four distinct CNN models, specifically InceptionV3, ResNet, DenseNet, and VGG16. To enhance the performance of the hybrid architecture, these models were further optimized with oneAPI, which allows a comparative analysis of each individual model for classification. To evaluate the effectiveness of medical image classification models, a variety of evaluation criteria have been utilized extensively. We highlight the use of mean Intersection over Union (mIoU) as a more acceptable and intuitive statistic for evaluating the balance between true positives, false positives, and false negatives. This is in response to recent breakthroughs that have been made. © 2023 The Authors

关键词： Brain tumour Deep learning oneAPI Optimization Tumorigenic

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Quantum Software engineering: Roadmap and Challenges Ahead

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ACM Transactions on Software engineering and Methodology 2025年第5期34卷

作者： Murillo, Juan Manuel Garcia-Alonso, Jose Moguel, Enrique Barzen, Johanna Leymann, Frank Ali, Shaukat Yue, Tao Arcaini, Paolo Pérez-Castillo, Ricardo de Guzmán, Ignacio García-Rodríguez Piattini, Mario Ruiz-Cortés, Antonio Brogi, Antonio Zhao, Jianjun Miranskyy, Andriy Wimmer, Manuel University of Extremadura Cáceres Spain University of Stuttgart Stuttgart Germany Simula Research Laboratory Oslo Norway and Computer Science Oslo Metropolitan University Oslo Norway Computer Science and Engineering Beihang University Beijing China National Institute of Informatics Chiyoda-ku Japan Faculty of Social Sciences and Information Technologies University of Castilla-La Mancha Talavera de la Reina Spain Institute of Technologies and Information Systems University of Castilla-La Mancha Ciudad Real Spain University of Castilla-La Mancha Ciudad Real Spain Computer Languages and Systems University of Seville Seville Spain Department of Computer Science University of Pisa Pisa Italy Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Toronto Metropolitan University Toronto ON Canada Johannes Kepler University Linz Linz Austria

As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective—key qualities of any industry-grade software—mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software engineering (QSE) as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyze in depth the current state of QSE research. From this analysis, the key areas of QSE are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing QSE. © 2025 Copyright held by the owner/author(s).

关键词： open challenges QSE Quantum Computing Quantum Software engineering

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