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arXiv 2025年

作者： Hassan, Bryar A. Qader, Shko M. Hassan, Alla A. Lu, Joan Ahmed, Aram M. Majidpour, Jafar Rashid, Tarik A. Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal KRI Sulaimani46023 Iraq Information Technology Department Kalar Technical Institute Garmian Polytechnic University Sulaimani Iraq Department of Database Technology Computer Science Institute Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science School of Computing and Engineering University of Huddersfield Queensgate HuddersfieldHD1 3DH United Kingdom Department of Computer Science University of Raparin Rania Iraq

Automating the extraction of concept hierarchies from free text is advantageous because manual generation is frequently labor- and resource-intensive. Free result, the whole procedure for concept hierarchy learning from free text entails several phases, including sentence-level text processing, sentence splitting, and tokenization. Lemmatization is after formal context analysis (FCA) to derive the pairings. Nevertheless, there could be a few uninteresting and incorrect pairings in the formal context. It may take a while to generate formal context;thus, size reduction formal context is necessary to weed out irrelevant and incorrect pairings to extract the concept lattice and hierarchies more quickly. This study aims to propose a framework for reducing formal context in extracting concept hierarchies from free text to reduce the ambiguity of the formal context. We achieve this by reducing the size of the formal context using a hybrid of a WordNet-based method and a frequency-based technique. Using 385 samples from the Wikipedia corpus and the suggested framework, tests are carried out to examine the reduced size of formal context, leading to concept lattice and concept hierarchy. With the help of concept lattice-invariants, the generated formal context lattice is compared to the normal one. In contrast to basic ones, the homomorphic between the resultant lattices retains up to 98% of the quality of the generating concept hierarchies, and the reduced concept lattice receives the structural connection of the standard one. Additionally, the new framework is compared to five baseline techniques to calculate the running time on random datasets with various densities. The findings demonstrate that, in various fill ratios, hybrid approaches of the proposed method outperform other indicated competing strategies in concept lattice performance. © 2025, CC BY.

关键词： Text mining

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Optimal Load Distribution for the Detection of VM-Based DDoS Attacks in the Cloud

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IEEE Transactions on Services Computing 2020年第1期13卷 114-129页

作者： Wahab, Omar Abdel Bentahar, Jamal Otrok, Hadi Mourad, Azzam Concordia Institute for Information Systems Engineering Concordia University MontréalQCH4B 1R6 Canada Department of ECE Khalifa University of Science Technology and Research Al Zafranah Abu Dhabi127788 United Arab Emirates Department of Mathematics and Computer Science Lebanese American University Beirut1102 2801 Lebanon

Distributed Denial of Service (DDoS) constitutes a major threat against cloud systems owing to the large financial losses it incurs. This motivated the security research community to investigate numerous detection techniques to limit such attack's effects. Yet, the existing solutions are still not mature enough to satisfy a cloud-dedicated detection system's requirements since they overlook the attacker's wily strategies that exploit the cloud's elastic and multi-tenant properties, and ignore the cloud system's resources constraints. Motivated by this fact, we propose a two-fold solution that allows, first, the hypervisor to establish credible trust relationships toward guest Virtual Machines (VMs) by considering objective and subjective trust sources and employing Bayesian inference to aggregate them. On top of the trust model, we design a trust-based maximin game between DDoS attackers trying to minimize the cloud system's detection and hypervisor trying to maximize this minimization under limited budget of resources. The game solution guides the hypervisor to determine the optimal detection load distribution among VMs in real-time that maximizes DDoS attacks' detection. Experimental results reveal that our solution maximizes attacks' detection, decreases false positives and negatives, and minimizes CPU, memory and bandwidth consumption during DDoS attacks compared to the existing detection load distribution techniques. © 2008-2012 IEEE.

关键词： Cloud computing

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Ultrafast single-channel machine vision based on neuro-inspired photonic computing

arXiv

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arXiv 2023年

作者： Yamaguchi, Tomoya Arai, Kohei Niiyama, Tomoaki Uchida, Atsushi Sunada, Satoshi Graduate School of Natural Science and Technology Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Faculty of Mechanical Engineering Institute of Science and Engineering Kanazawa University Kakuma-machi Ishikawa Kanazawa920-1192 Japan Department of Information and Computer Sciences Saitama University 255 Shimo-Okubo Sakura-ku Saitama City Saitama338-8570 Japan PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan

High-speed machine vision is increasing its importance in both scientific and technological applications. Neuro-inspired photonic computing is a promising approach to speed-up machine vision processing with ultralow latency. However, the processing rate is fundamentally limited by the low frame rate of image sensors, typically operating at tens of hertz. Here, we propose an image-sensor-free machine vision framework, which optically processes real-world visual information with only a single input channel, based on a random temporal encoding technique. This approach allows for compressive acquisitions of visual information with a single channel at gigahertz rates, outperforming conventional approaches, and enables its direct photonic processing using a photonic reservoir computer in a time domain. We experimentally demonstrate that the proposed approach is capable of high-speed image recognition and anomaly detection, and furthermore, it can be used for high-speed imaging. The proposed approach is multipurpose and can be extended for a wide range of applications, including tracking, controlling, and capturing sub-nanosecond phenomena. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

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An interface machine for cloud technology and multicore processing of intelligent development communication network

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AIP Conference Proceedings 2022年第1期2519卷

作者： S. Sivaprakash Nithya Sampath Manoj Kumar Vemula G. Nagarajan 1Department of Information Technology CMR Engineering College Hyderabad Telangana India 2School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India 3Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India 4Department of Information Technology K. S. R. College of Engineering Tiruchengode Tamil Nadu India

Postmodern systems engineering technology, with the newest technology, massively stimulates the growth of intelligent productivity. In an intelligent development system, more and more computers are linked to the Web to receive a huge amount of data in any step—the product’s lifecycle. The surface intelligent output model allows big data to study and encourages large-scale industrial coordination with a new set of technologies and services. But, But Disposition in high speed and low latency real-time implementations is reduced by multiple factors, including network capacity; overflow capacity, and the number of sensor nodes. Fog and edge computing have increased the cloud’s processing, internetworking capability to the edge to address these problems. In this article, a hierarchical relevant provision for additive factories is presented based on cloud processing, fog computing, and computing capabilities. The technology is expected for the modern two-story level, opening up a vibrant view of future industrial processes.

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Sustainable Innovation Post-Pandemic: Perspectives from the US, Canada, Norway, and Thailand

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IEEE engineering Management Review 2025年

作者： Intahchomphoo, Channarong Gundersen, Odd Erik Peyton, Liam Wechayachai, Omkaew University of Ottawa School of Engineering Design and Teaching Innovation Faculty of Engineering ONK1N 6N5 Canada Norwegian University of Science and Technology Department of Computer Science Faculty of Information Technology and Electrical Engineering TrondheimNO7491 Norway Bangkok Thailand

This paper explores perspectives for improved sustainable innovation for the post COVID-19 pandemic era. We conducted online interviews between October and December 2020 with 16 individuals working in social entrepreneurship, tech startups, established companies, and non-profit business support organizations across the US, Canada, Norway, and Thailand. Our findings reveal a shared commitment to four themes related to sustainable innovation post-pandemic: (1) thinking of local problems as global problems, in the same way that innovation technologies and businesses can cause global disruptions;(2) including historically underserved populations in innovation business activities, as it is also important to truly understand how technologies and innovation businesses impact the lives of people experiencing low-socioeconomic status;(3) helping people in remote communities, low- and middle-income countries, and wartorn areas is essential – offering hands-on basic computing training could be an option for helping people gain employment in the digital economy;and (4) having governments lead postpandemic sustainable innovation projects and then invite businesses to participate. In addition, this paper proposes a theoretical framework for fostering innovation in resourceconstrained environments following large-scale disruptions. © 1973-2011 IEEE.

关键词： Nonprofit organization

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Healthcare Android application for elderly and disabled people

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AIP Conference Proceedings 2023年第1期2523卷

作者： M. Kumarasamy Jaiprakash Narain Dwivedi Balachandra Pattanaik 1Department of Computer Science College of Engineering and Technology Wollega University P.O. Box. 395 Nekemte Ethiopia 2Department-ECE Chandigarh University Mohali Punjab India 3Department of Electrical and Computer Engineering Wollega University Nekemte Ethiopia Africa

Machine learning and big data are influencing almost every area of modern life, including entertainment, commerce, and healthcare. Netflix knows which films and series people love to watch, Amazon knows which things people want to buy when and where, and Google knows which symptoms and illnesses they search for. All of this data may be utilized for highly thorough personal profiling, which may be useful for behavioral understanding and marketing but also has the ability to anticipate healthcare trends. There is a lot of hope that the use of artificial intelligence (AI) could result in significant advances in all aspects of healthcare, from diagnosis to therapy. It is widely assumed that AI technologies would support and enhance human labor rather than completely replacing the employment of physicians and other healthcare professionals. AI is ready to assist healthcare workers with a wide range of activities, from administrative workflow to clinical documentation and patient outreach, as well as specialized assistance in image analysis, medical device automation, and patient monitoring. In this chapter, we will look at some of the key applications of AI in healthcare, including those that are directly related to healthcare as well as those that are part of the healthcare value chain, such as drug research and ambient assisted living.

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A Skin Type Classification Method Using Mobile Device-Based Deep Learning Model

A Skin Type Classification Method Using Mobile Device-Based ...

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International Conference on Applied System Innovation (ICASI)

作者： Hung-Tse Chan Yan-Wei Liao Sin-Ye Jhong Shih-Che Chien Kai-Lung Hua Yung-Yao Chen Dept. of Electronic and Computer Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC Department of Engineering Science National Cheng Kung University No.1 University Road Tainan City Taiwan ROC National Chung-Shan Institute of Science and Technology No.481 6th Neighborhood Sec. Jia’an Zhongzheng Rd. Longtan Dist. Taoyuan City Taiwan ROC Dept. of Computer Science and Information Engineering National Taiwan Uni. of Science and Technology No.43 Keelung Rd. Sec.4 Da’an Dist. Taipei City Taiwan ROC

Skin care products should be tailored to suit different skin types. However, skin testing can be expensive and time-consuming, particularly for students or office workers who may need access to specialized equipment. In the present study, we developed a skin type detection system by using computer-vision and deep-learning techniques that can be easily accessed through a mobile phone application. Our system integrates with the TensorFlow Lite framework on the Android platform and therefore supports various hardware accelerations and easy model validation. TensorFlow Lite, an open-source library developed by Google, is a lightweight, cross-platform, machine-learning framework for mobile and Internet of Things devices. It also supports various hardware accelerations. Our experimental results reveal that the proposed method has an accuracy of 96% and is easy to use on mobile devices. This system provides a convenient and cost-effective means of identifying the skin type and selecting appropriate skin care products.

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Contract-based Cooperative Computation and Communication Resources Sharing in Mobile Edge Computing

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Journal of Grid Computing 2023年第1期21卷 14-14页

作者： Zheng, Yifeng Zou, Lushan Zhang, Wenjie Yang, Jingmin Yang, Liwei Lin, Ziqiong School of Computer Sciences Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University No.36 Xianqian Street Fujian Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology No.1 Section 3 Zhongxiao East Road Taibei610101 Taiwan College of Information and Electrical Engineering China Agricultural University No.17 Qinghua East Road Haidian District Beijing100000 China

In this paper, we consider a Mobile Edge Computing (MEC) system with a source transmitter (ST) and multiple relay nodes (RNs). ST has sufficient computing resources, but lacks communication ability, while the situation of RN is the opposite. To tackle this resource imbalance problem, we propose a cooperative computation and communication resources sharing strategy using contract theory under complete and incomplete information scenarios. The ST offers a contract combining computation time and relay power, and the RNs forward the computational results for ST in exchange for the usage of computation capacity. For complete information scenario, we derive the optimal contract to maximize the ST’s utility and we show the existence and uniqueness of optimal contract. We further explore the properties of a feasible contract for incomplete information scenario where the channel gain and relay cost are the private information of RNs and not known by ST. We analyze the necessary and sufficient conditions for a contract to be feasible and propose a sequence optimization algorithm to obtain the optimal contract. Finally, we show the feasibility and optimality of the contract through simulation results. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

关键词： Mobile edge computing

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ANDiE the Autonomous Neutron Diffraction Explorer

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Neutron News 2023年第2期34卷 6-7页

作者： Austin McDannald Matthias Frontzek Andrei T. Savici Mathieu Doucet Efrain E. Rodriguez Kate Meuse Jessica Opsahl-Ong Daniel Samarov Ichiro Takeuchi William Ratcliff A. Gilad Kusne 1 Materials Measurement Laboratory NIST Gaithersburg Maryland USA 2 Neutron Sciences Directorate ORNL Oak Ridge Tennessee USA 3 Department of Chemistry and Biochemistry University of Maryland College Park Maryland USA4 Maryland Quantum Materials Center College Park Maryland USA 5 Department of Computer Science Cornell University Ithaca New York USA 6 Department of Computer Science Rice University Houston Texas USA 7 Information Technology Laboratory NIST Gaithersburg Maryland USA 4 Maryland Quantum Materials Center College Park Maryland USA8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA 8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA9 NIST Center for Neutron Research NIST Gaithersburg Maryland USA 1 Materials Measurement Laboratory NIST Gaithersburg Maryland USA8 Department of Materials Science and Engineering University of Maryland College Park Maryland USA

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