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arXiv 2023年

作者： Fu, Lei Morón, Paola Torrico Queralta, Jorge Peña Hästbacka, David Edelman, Harry Westerlund, Tomi Turku Intelligent and Embedded Robotic Systems University of Turku Finland Department of Engineering and Business Turku Univesity of Applied Sciences Finland Computing Sciences Faculty of Information Technology and Communication Sciences Tampere University Finland

Remote identification of Unmanned Aerial Vehicles (UAVs) is becoming increasingly important since more UAVs are being widely used for different needs in urban areas. For example, in the US and in the EU, identification and position broadcasting is already a requirement for the use of drones. However, the current solutions do not validate the position of the UAV but its identity, while trusting the given position. Therefore, a more advanced solution enabling the proof of location is needed to avoid spoofing. We propose the combination of a permissioned blockchain managed by public authorities together with UWB-based communication to approach this. Specifically, we leverage the identity management tools from Hyperledger Fabric, an open-source permissioned blockchain framework, and ultra-wideband (UWB) ranging, leading to situated communication (i.e., simultaneous communication and localization). This approach allows us to prove both the UAV identity and also the location it broadcasts through interaction with ground infrastructure in known locations. Our initial experiments show that the proposed approach is viable and UWB transceivers can be used for UAVs to validate both their identity and position with ground infrastructure deployed in known locations. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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Enhancing cloud application performance through artificial intelligence

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AIP Conference Proceedings 2025年第1期3280卷

作者： Mohamed Ashik Mohamed Mohammed Musallam Bakhit Al Mahri Devarajan Veerasamy Sunil Thomas Karickom Mohammed Sulaiman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Salalah OMAN

This research paper delves into the evolving convergence of Artificial Intelligence (AI) and cloud computing, with a specific emphasis on leveraging AI techniques to enhance the performance of cloud applications. As the demand for efficient and responsive cloud services continues to escalate, integrating AI into cloud environments has demonstrated promising potential for optimizing resource allocation, predicting trends, detecting faults, and enhancing overall user experiences. The paper offers a comprehensive analysis of diverse AI-driven methodologies, including machine learning, neural networks, and predictive analytics, which can be applied to bolster the performance of cloud applications. Furthermore, it addresses challenges, opportunities, and potential future trajectories within this dynamic domain.

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Using Contextual Text Mining Algorithm to Analysis Yearly Trend for Population Ageing and Declining Fertility with Government Science and technology Projects in Taiwan

Using Contextual Text Mining Algorithm to Analysis Yearly Tr...

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作者： C.Y.Chuang M.C.Huang Y.Y.Lin Y.H.Hsiao T.C.Wang Science & Technology Policy Research and Information Center Applied Research Laboratories S Center for High-performance Computing Applied Research Laboratories Center for High-performance Computing Applied Research Laboratories

The growth of the world population has been steadily slowing down, primarily owing to an almost worldwide decline of fertility and ageing of population. In Taiwan, the fertility rate is experiencing long-term downtrends, and consequently, ageing of their populations. In view of this, the purpose of this paper is to analyze the yearly trend for population ageing and declining fertility. We proposed the yearly trend analysis based on contextual text mining methods from government science and technology projects to extract the Chinese pattern and word about the population ageing and declining fertility. The government science and technology projects, which archives research projects sponsored by government, are retrieved from Government Research Bulletin(GRB) from 1981 to April 2021. Then, the two issue of policy "Major trends and the importance of issues" and "Policies and action plans" combine the population ageing and declining fertility with yearly trend are explored and discussed. The obtained results can be analyzed the yearly trend about fertility rate, elderly population, and youth workforce with population ageing and declining fertility on the first policy issue "Major trends and the importance of issues". And it can also be analyzed the yearly trend about formulates the policies and action plan on the second policy issue "Policies and action plans" in Taiwan. According to the results of the analysis, we observe that the fertility rate and youth workforce have been a progressive decrease every year. Within 10 years, it will be expected to transition into a super-aged society about the elderly population. In the future,the specify topic of population ageing and declining fertility with yearly trend will be analyzed on other policy issues by *** analysis with yearly trend is used by the government to evaluate how to allocate research funding to the specify topic and it can be as the reference for decision of government in Taiwan.

关键词： population ageing declining fertility contextual text mining TFIDF word segmentation Word2Vec sBERT

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Glioma/Glioblastoma Detection in Brain MRI using Pre-trained Deep-Learning Scheme 3

Glioma/Glioblastoma Detection in Brain MRI using Pre-trained...

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3rd International Conference on Intelligent computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sujitha, R. Angel Balaji, Gangadharam Mohammed, Mazin Abed Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Vytautas Magnus University Department of Applied Informatics Kaunas44404 Lithuania St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Omr Chennai600119 India M/S Tata Consultancy Services Limited Tcsl Siruseri Sez Unit Sipcot I.T. Park TN Chennai603103 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Convolutional Neural Network (CNN) supported medicinal image examination is extensively accepted due to its reputation and improved accuracy. The investigational outcome obtained with DLS along with a chosen classifier helps to achieve better detection results than the traditional and machine-learning methods. The proposed research examines the performance of pre-trained VGG16 and VGG19 schemes in detecting the brain tumour (Glioma/Glioblastoma) grade using different pooling methods. The classification is performed using SoftMax with five-fold cross-validation, and the products are compared and presented. The brain tumour images considered in this study are collected from The Cancer Imaging Archive (TCIA) dataset. This work considered 2000 images (1000 Glioma and 1000 Glioblastoma) of axial-plane with dimension of 224×224×3 pixels for the assessment, and the attained results are compared. The experimental outcome achieved with Python® confirms that the VGG16 with average-pooling provides a better classification accuracy (>99%) with Decision Tree (DT) compared with other methods considered. © 2022 IEEE.

关键词： Python

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Role of Artificial Intelligence in Digital Transformation for Enhancing Cybersecurity

Role of Artificial Intelligence in Digital Transformation fo...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Mohammad Riyaz Belgaum Ashraf Ali Mohammed Uzair Mohammed Ibrahim Enterprise Zainab Alansari Safeeullah Soomro Faculty of Computer Studies Arab Open University Bahrain A’ali Kingdom of Bahrain Computer Science and Engineerng Dr. K. V. Subba Reddy Institute of Technology Kurnool India Resource Planning Ebrahim Khaleel Kanoo Manama Kingdom of Bahrain Department of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman College of Engineering and Computing George Mason university Virginia United States University of Fairfax Salem VA

ISBN: (数字)9798331537012

ISBN: (纸本)9798331537029

Digital technology continues to evolve rapidly, driving innovation and societal change in nearly every field. Digital technology refers to using electronic devices, systems, and software to create, store, process, and transmit data in digital form. Currently, artificial intelligence (AI) is not just a tool but is a collaborator, where the integration in all the fields drives innovation much faster. As organizations increasingly adopt digital technologies, they face growing cybersecurity challenges. This paper explores enhancing cybersecurity with the integration of AI within the context of digital transformation. AI offers promising solutions in mitigating the cyber threats more effectively. This research examines key AI applications in cybersecurity, including threat detection, phishing, vulnerability and patch management, anomaly identification, automated incident response, network security, malware analysis and fraud detection. It also discusses the potential limitations and ethical considerations of AI-driven cybersecurity. This paper highlights the importance of integrating AI technologies with human expertise to create robust cybersecurity frameworks in the era of digital transformation and presents the challenges and future directions to researchers in the field of integration of AI in cybersecurity.

关键词： Technological innovation Digital transformation Prevention and mitigation Phishing Organizations Machine learning Threat assessment Computer security Artificial intelligence Predictive analytics

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Fault-tolerant quantum architectures based on erasure qubits

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Physical Review Research 2025年第1期7卷 013249-013249页

作者： Shouzhen Gu Alex Retzker Aleksander Kubica California Institute of Technology Pasadena California 91125 USA AWS Center for Quantum Computing Pasadena California 91125 USA Yale Quantum Institute & Department of Applied Physics Yale University New Haven CT 06520 USA Racah Institute of Physics The Hebrew University of Jerusalem Jerusalem 91904 Givat Ram Israel

The overhead of quantum error correction (QEC) poses a major bottleneck for realizing fault-tolerant computation. To reduce this overhead, we exploit the idea of erasure qubits, relying on an efficient conversion of the dominant noise into erasures at known locations. We start by introducing a formalism for QEC schemes with erasure qubits and express the corresponding decoding problem as a matching problem. Then, we propose and optimize QEC schemes based on erasure qubits and the recently introduced Floquet codes. Our schemes are well suited for superconducting circuits, being compatible with planar layouts. We numerically estimate the memory thresholds for the circuit noise model that includes spreading (via entangling operations) and imperfect detection of erasures. Our results demonstrate that, despite being slightly more complex, QEC schemes based on erasure qubits can significantly outperform standard approaches.

关键词： Qubits

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Phase-field-based lattice Boltzmann method for the transport of insoluble surfactant in two-phase flows

arXiv

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arXiv 2024年

作者： Zhan, Chengjie Liang, Hong Chai, Zhenhua Shi, Baochang School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan430074 China Department of Physics Hangzhou Dianzi University Hangzhou310018 China

In this work, we present a general second-order phase-field model for the transport of insoluble surfactant in incompressible two-phase flows. In this model, the second-order local Allen-Cahn equation is applied for interface capturing, a general form of the simple scalar transport equation [S. S. Jain, J. Comput. Phys. 515, 113277 (2024)] is adopted for interface-confined surfactant, and the consistent and conservative Navier-Stokes equations with the Marangoni force is used for fluid flows. To solve this model, we further developed a mesoscopic lattice Boltzmann (LB) method, in which the LB model for surfactant transport equation is proposed under the general LB framework for the convection-diffusion type equation, and it can correctly recover the governing equation for surfactant transport. The accuracy of the present LB method is tested by several benchmark problems, and the numerical results show it has a good performance for the transport of the insoluble surfactant in two-phase flows. © 2024, CC BY-NC-ND.

关键词： Navier Stokes equations

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SYNOPSET: MULTISCALE VISUAL ABSTRACTION SET FOR EXPLANATORY ANALYSIS OF DNA NANOtechnology SIMULATIONS

arXiv

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arXiv 2022年

作者： Luo, Deng Kouyoumdjian, Alexandre Strnad, Ondřej Miao, Haichao Barišić, Ivan Viola, Ivan Saudi Arabia Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Center for Health and Bioresources Austrian Institute of Technology Austria

We propose a new abstraction set (SynopSet) that has a continuum of visual representations for the explanatory analysis of molecular dynamics simulations (MDS) in the DNA nanotechnology domain. By re-purposing the commonly used progress bar and designing novel visuals, as well as transforming the data from the domain format to a format that better fits the newly designed visuals, we compose this new set of representations. This set is also designed to be capable of showing all spatial and temporal details, and all structural complexity, or abstracting these to various degrees, enabling both the slow playback of the simulation for detailed examinations or very fast playback for an overview that helps to efficiently identify events of interest, as well as several intermediate levels between these two extremes. For any pair of successive representations, we demonstrate smooth, continuous transitions, enabling users to keep track of relevant information from one representation to the next. By providing multiple representations suited to different temporal resolutions and connected by smooth transitions, we enable time-efficient simulation analysis, giving users the opportunity to examine and present important phases in great detail, or leverage abstract representations to go over uneventful phases much faster. Domain experts can thus gain actionable insight about their simulations and communicate it in a much shorter time. Further, the novel representations are more intuitive and also enable researchers unfamiliar with MDS analysis graphs to better understand the simulation results. We assessed the effectiveness of SynopSet on 12 DNA nanostructure simulations together with a domain expert. We have also shown that our set of representations can be systematically located in a visualization space, dubbed SynopSpace, composed of three axes: granularity, visual idiom, and information layout type. Proposed at the end are general guidelines on the use of SynopSpace to generate

关键词： Molecular dynamics

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Star-Shaped Space of Solutions of the Spherical Negative Perceptron

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Physical Review Letters 2023年第22期131卷 227301-227301页

作者： Brandon Livio Annesi Clarissa Lauditi Carlo Lucibello Enrico M. Malatesta Gabriele Perugini Fabrizio Pittorino Luca Saglietti Department of Computing Sciences Bocconi University 20136 Milano Italy Department of Applied Science and Technology Politecnico di Torino 10129 Torino Italy Bocconi Institute for Data Science and Analytics 20136 Milano Italy Department of Electronics Information and Bioengineering Politecnico di Milano 20125 Milano Italy

Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed. Here, we consider the spherical negative perceptron, a prototypical nonconvex neural network model framed as a continuous constraint satisfaction problem. We introduce a general analytical method for computing energy barriers in the simplex with vertex configurations sampled from the equilibrium. We find that in the overparametrized regime the solution manifold displays simple connectivity properties. There exists a large geodesically convex component that is attractive for a wide range of optimization dynamics. Inside this region we identify a subset of atypical high-margin solutions that are geodesically connected with most other solutions, giving rise to a star-shaped geometry. We analytically characterize the organization of the connected space of solutions and show numerical evidence of a transition, at larger constraint densities, where the aforementioned simple geodesic connectivity breaks down.

关键词： Optimization problems Artificial neural networks Replica methods

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