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Proceedings of the 28th European Conference on Pattern Languages of Programs

作者： Souhaila Serbout Amine El Malki Cesare Pautasso Uwe Zdun Software Institute Università della Svizzera italiana Switzerland Faculty of Computer Science Software Architecture Research Group and UniVie Doctoral School Computer Science DoCS University of Vienna Austria Faculty of Computer Science Software Architecture Research Group University of Vienna Austria

ISBN: (纸本)9798400700408

The API Rate Limit pattern controls the rate at which clients make API requests by counting the number of requests in a specified time interval and reacting against abusive clients, in order to protect the limited resources of the API from exhaustion and denial of service attacks. This practice helps service providers to prevent abuse and ensure fair resource allocation, maintain system stability, monitor and control service availability, protect against DDoS attacks In this research paper, we have identified patterns covering the API Rate Limit pattern adoption starting from its documentation to its implementation. Our objective is to elucidate the trade-offs associated with different identified patterns and offer guidance to developers in making informed decisions when choosing the most suitable Rate Limit method, scope, and granularity for their service. By providing a comprehensive overview of how to adopt the Rate Limit pattern, this paper aims to enhance the understanding of how APIs can be designed to facilitate high scalability, security, reliability, and service availability. Furthermore, we present each pattern along with known uses observed in real-world APIs and technologies.

关键词： Application Programming Interfaces Pattern Language Rate Limit

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Cox-ResNet: A Survival Analysis Model Based on Residual Neural Networks for Gene Expression Data 19

Cox-ResNet: A Survival Analysis Model Based on Residual Neur...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Yin, Qingyan Chen, Wangwang Wu, Ruiping Wei, Zhi School of Science Xi'an University of Architecture and Technology Xi'an China New Jersey Institute of Technology Department of Computer Science Newark United States

ISBN: (纸本)9781665472432

Survival analysis with genomics data provides a deep understanding of biological processes related to prognosis and disease progression at the molecular level. However, high-dimensional small sample genome data causes computational challenges in survival analysis. To address this problem of overfitting and poor interpretation of existing models, we applied the deep learning technology to genome data and proposed a survival analysis model based on an image-based residual neural network model, called Cox-ResNet. High-dimensional gene expression data was embedded into 2D images according to gene positions on chromosomes, and then a residual network model based on Cox proportional hazards was introduced to perform survival analysis. We demonstrated the performance of Cox-ResNet on five datasets of different cancer types from TCGA, comparing it with the cutting-edge survival analysis methods. The Cox-ResNet model not only shows better performance in prediction accuracy, but also biologically interpretable, by generating heat-maps and prognostic genes for high-risk groups with the guided Grad-Cam visualization method. By performing protein-protein interaction network analysis, we examined hub genes and their biological functions for the bladder cancer. These findings confirm that Cox-ResNet model provides a new solution for discovering the driver genes of poor cancer prognosis. © 2022 IEEE.

关键词： Deep learning

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Exploring the Use of WebAssembly in HPC

arXiv

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arXiv 2023年

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Chair of Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian Institute of Information Technology Kerala Kottayam India

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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Cost/Benefit Analysis of AIoT Image Sensing for Construction Safety Monitoring

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Journal of Engineering, Project, and Production Management 2024年第3期14卷 1-13页

作者： Wang, Rong-Jing Yu, Wen-Der Liao, Hsien-Chou Chang, Hsien-Kuan Lim, Zi-Yi Architecture and Building Research Institute Ministry of Interior New Taipei23143 Taiwan Department of Construction Engineering Chaoyang University of Technology Taichung413310 Taiwan Department of Computer Science and Information Technology Chaoyang University of Technology Taichung413310 Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung413310 Taiwan

Rapid advances in deep learning and computer vision enable traditional cloud-based decision-making through edge computing with the Artificial Intelligent Internet of Things (AIoT) image sensors (AIoT-IS), thus improving the timeliness and security of image recognition. This study is indented to investigate the potential costs and benefits of AIoT-IS applications. This study summarizes AIoT-IS application scenarios for construction safety monitoring and proposes a cost/benefit analysis method for AIoT-IS implementation projects. According to the case study results, AIoT-IS achieves significant benefits, with a Net Present Value Index (NPVI) of 19.17% and a Benefit/Cost Ratio (BCR) of 4.65 as applied to construction site safety monitoring. Interviews with domain experts also provided qualitative feedback, pointing to the directions for future research. The proposed method is applicable for the decision-making of AIoT-IS adoption and the feasibility assessment of other innovative construction technologies. Copyright © Journal of Engineering, Project, and Production Management (EPPM-Journal).

关键词： Cost benefit analysis

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ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for Rapid Intraoperative Histology 7

ThickV-Stain: Unprocessed Thick Tissues Virtual Staining for...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Shi, Lulin Hou, Xingzhong Wong, Ivy H.M. Chan, Simon C.K. Chen, Zhenghui Lo, Claudia T.K. Wong, Terence T.W. Translational and Advanced Bioimaging Laboratory Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

Virtual staining has shown great promise in realizing a rapid and low-cost clinical alternative for pathological examinations, eliminating the need for chemical reagents and laborious staining procedures. However, most of the previous studies mainly focus on thin slice samples, which still require tissue sectioning and are unsuitable for intraoperative use. In this paper, we propose a multi-scale model to virtually stain label-free and slide-free biological tissues, allowing hematoxylin- and eosin- (H&E) staining generation in less than a minute for an image with 100 million pixels. We name this ThickV-Stain model, specifically developed to virtually stain intricated and unprocessed thick tissues. We harness the ability of a multi-scale network to encourage the model to capture multiple-level micromorphological characteristics from low-resolution images. Experimental results highlight the advantages of our method for virtual staining on unprocessed thick samples. We also show the effectiveness of ThickV-Stain on thin sections, showing generalizability to other clinical workflows. The proposed method enables us to obtain virtually stained images from unstained samples within minutes and can be seamlessly integrated with downstream pathological analysis tasks, providing an efficient alternative scheme for intraoperative assessment as well as general pathological examination. © 2024 CC-BY 4.0, L. Shi, X. Hou, I.H.M. Wong, S.C.K. Chan, Z. Chen, C.T.K. Lo & T.T.W. Wong.

关键词： Tissue

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A New Approach Towards Identification of Individuals Belonging to Different Subpopulations Using Machine Learning Models and CLC Data 24

A New Approach Towards Identification of Individuals Belongi...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Charytanowicz, Malgorzata Perzanowski, Kajetan Januszczak, Maciej Woloszyn-Galeza, Aleksandra Sobczuk, Maria Kulczycki, Piotr Lublin University of Technology Department of Computer Science Lublin Poland Systems Research Institute Polish Academy of Sciences Warsaw Poland John Paul II Catholic University of Lublin Institute of Biological Sciences Lublin Poland Polish Academy of Sciences Museum and Institute of Zoology Ustrzyki Dolne Poland Warsaw University of Life Sciences Chair of Animal Genetics and Conservation Warsaw Poland AGH University of Krakow Faculty of Physics and Applied Computer Science Krakow Poland

ISBN: (纸本)9798331530631

Spatial distributions of wisents (European bison) Bison bonasus (Linnaeus 1758), were studied in the Bieszczady Mountains (south-eastern Poland) on the basis of telemetric data obtained from two subpopulations: the western - inhabiting the forest districts of Baligród, Komańcza, Cisna and Lesko, and the eastern - dwelling within the districts of Lutowiska, Stuposiany, and in Bieszczadzki National park. Data was collected between 2002-2021. In our study, we propose a novel approach for classifying wisent subpopulations that utilizes machine learning methods and CLC data. For this purpose, the performances of eight algorithms: Naive Bayes, Logistic Regression, Support Vector Machine, Multilayer Perceptron, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbors and Decision Tree were investigated. The algorithms were compared according to the following indicators: accuracy, Cohen's-Kappa, precision, recall and F1 score. Their assessment was enhanced through the application of statistical inference (the Friedman test with post-hoc analysis) and SHAP values. The lowest results were achieved by Naive Bayes and Logistic Regression methods (accuracy of 73.69% and 74.43%, respectively), whilst significantly higher results were achieved by eXtreme Gradient Boosting, k-Nearest Neighbors and Decision Tree classifiers, with the classification accuracy exceeding 90% (91.81%, 92.8%, 93.47%, respectively). Based on the results, we conclude that CLC data represents a valuable source of information regarding the affinity of wisents towards different habitat conditions. © 2024 IEEE.

关键词： Adversarial machine learning

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Quality diversity through human feedback: towards open-ended diversity-driven optimization 24

Quality diversity through human feedback: towards open-ended...

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Proceedings of the 41st International Conference on Machine Learning

作者： Li Ding Jenny Zhang Jeff Clune Lee Spector Joel Lehman Manning College of Information & Computer Sciences University of Massachusetts Amherst Department of Computer Science University of British Columbia and Vector Institute Department of Computer Science University of British Columbia and Vector Institute and Canada CIFAR AI Chair Department of Computer Science Amherst College and Manning College of Information & Computer Sciences University of Massachusetts Amherst Stochastic Labs

Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when RLHF is commonly used to optimize for average human preferences, especially in generative tasks that demand diverse model responses. Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics. This paper introduces Quality Diversity through Human Feedback (QDHF), a novel approach that progressively infers diversity metrics from human judgments of similarity among solutions, thereby enhancing the applicability and effectiveness of QD algorithms in complex and open-ended domains. Empirical studies show that QDHF significantly outperforms state-of-the-art methods in automatic diversity discovery and matches the efficacy of QD with manually crafted diversity metrics on standard benchmarks in robotics and reinforcement learning. Notably, in open-ended generative tasks, QDHF substantially enhances the diversity of text-to-image generation from a diffusion model and is more favorably received in user studies. We conclude by analyzing QDHF's scalability, robustness, and quality of derived diversity metrics, emphasizing its strength in openended optimization tasks. Code and tutorials are available at https://***/qdhf.

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Optimal Access Point Centric Clustering for Cell-Free Massive MIMO Using Gaussian Mixture Model Clustering

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 675-687页

作者： Biswas, Pialy Mallik, Ranjan K. Letaief, Khaled B. Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden01062 Germany Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi Hauz Khas110016 India The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering New Territories Hong Kong

This paper proposes a Gaussian mixture model (GMM) based access point (AP) clustering technique in cell-free massive MIMO (CFMM) communication systems. The APs are first clustered on the basis of large-scale fading coefficients, and the users are assigned to each cluster depending on the channel gain. As the number of clusters increases, there is a degradation in the overall data rate of the system, causing a trade-off between the cluster number and average rate per user. To address this problem, we present an optimization problem that optimizes both the upper bound on the average downlink rate per user and the number of clusters. The optimal number of clusters is intuitively determined by solving the optimization problem, and then grouping the APs and users. As a result, the computation expense is much lower than the current techniques, since the existing methods require evaluations of the network performance in multiple iterations to find the optimal number of clusters. In addition, we analyze the performance of both balanced and unbalanced clustering. Numerical results will indicate that the unbalanced clustering yields a superior rate per user while maintaining a lower level of complexity compared to the balanced one. Furthermore, we investigate the statistical analysis of the spectral efficiency (SE) per user in the clustered CFMM. The findings reveal that the SE per user can be approximated by the logistic distribution. © 2023 CCBY.

关键词： Fading channels

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Boosting Solar Energy Production of a Building using New Architectural Design

Boosting Solar Energy Production of a Building using New Arc...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Khalvati, Ladan Sheykhshabani, Zahra Ebrahimi Balal, Afshin Arbabzadeh, Negin Texas Tech University Department of Design Lubbock United States Pars Institute of Art and Architecture Architecture Department Tehran Iran Texas Tech University Department of Electrical and Computer Engineering Lubbock United States College of Architecture Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798350323917

Fossil fuels and air pollution are significant problems nowadays, causing many countries to increase their investments in renewable energy development. As the world's energy demand grows, governments are looking into and developing alternative energy sources like solar energy. Solar energy is one of the most promising solutions to address the above problem. In addition, the development of sustainable design in architecture has become a crucial factor in the construction industry as the world faces the challenges of climate change and diminishing fossil fuel reserves. Architects are now seeking innovative ways to incorporate sustainable and renewable energy sources into their designs. Solar energy, as a sustainable and clean energy source, has gained significant attention in recent years. It offers an opportunity to reduce the dependence on non-renewable energy sources and lower carbon emissions. Additionally, the use of solar technology in architectural designs can enhance the aesthetic appeal of buildings while providing energy efficiency. Building-integrated photovoltaic systems have emerged as a popular solution that allows for the integration of photovoltaic cells into the structure of buildings. In this paper, a new architectural design for a building in Tehran is proposed, which leads to the increase of generated solar energy. Needless to say, the designs must be beautiful to look at, functional, and good for the environment. Grasshopper and rhino are utilized to demonstrate the design. In addition, System Advisor Model (SAM) is implemented to study the economic analysis of the solar photovoltaic system planted on the building. The simulation result indicates that with the new design, the power production is increased from 21437 kWh to 69484 kWh. In addition, by using the proposed system in comparison with fossil fuel power plants, a huge amount of air pollution is will be removed from the air. © 2023 IEEE.

关键词： Solar energy

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Real-Time Capability of DLR’S Beamforming Synthetic Aperture Radar Processing architecture

Real-Time Capability of DLR’S Beamforming Synthetic Apertur...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Maron Schlemon Martin Schulz Rolf Scheiber Marc Jäger Joel Amao Oliva German Aerospace Center (DLR) Microwaves and Radar Institute Germany Chair of Computer Architecture and Parallel Systems Technical University of Munich Germany

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft’s limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

关键词： Three-dimensional displays Array signal processing Instruments computer architecture Radar imaging Real-time systems Software

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