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11th Conference on Cloud Computing, Big Data and Emerging Topics, JCC-BD and ET 2023

作者： Faqir-Rhazoui, Youssef García, Carlos Department of Computer Architecture and Automatics Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9783031409417

Python’s popularity has grown rapidly over the past decade due to its high productivity factor. While Python is popular in scientific computing, its interpreted nature and limitations on parallelization result in slower performance compared to compiled languages. Despite this, Python is still a valuable tool for high-performance computing (HPC) due to its ease of programming and optimization capabilities. This paper discusses the performance gap between Python and compiled languages like SYCL when working with heterogeneous computing environments and the challenges in developing portable and high-performance native extensions. It highlights the development of the dpctl Python library, which provides a portable and architecture-agnostic API for writing extensions and supports asynchronous SYCL kernel execution. We developed four benchmarks across CPU, integrated GPU, and discrete Intel Arc GPU, finding that SYCL outperformed Python in all cases, but optimizations could be made to improve Python performance. This research also highlights issues encountered when porting Python code, limitations in using Numpy, and current Intel Arc performance issues. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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Design of a Robust IEEE Compliant Floating - Point Divide and Square Root Using Iterative Approximation 16

Design of a Robust IEEE Compliant Floating - Point Divide an...

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16th IEEE Latin American Symposium on Circuits and Systems, LASCAS 2025

作者： Sager, Carson Stine, James E. Oklahoma State University Vlsi Computer Architecture Research Group Electrical and Computer Engineering Department StillwaterOK74078 United States

ISBN: (纸本)9798331522124

In this paper, we discuss an IEEE 754 compliant normalized floating-point divide and square root unit that utilizes iterative approximation. We provide a robust architecture that allows multiple formats and all IEEE 754 rounding modes while still exhibiting high-performance. Moreover, we also adhere to the IEEE 754 2019 standard and demonstrate methods for rounding results to all five rounding modes using iterative approximation. Performance, Power, and Area estimates are determined from physical synthesis using ARM-based standard cells in a TSMC 28nm process. This paper also presents comparisons versus other implementations and demonstrates the efficient of the approach presented here. © 2025 IEEE.

关键词： Integrated circuit design

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Re-exploring the Kayseri Culture Route by Using Deep Learning for Cultural Heritage Image Classification Cultural Heritage Image Classification by Using Deep Learning: Kayseri Culture Route 24

Re-exploring the Kayseri Culture Route by Using Deep Learnin...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Kevseroglu, Ozlem Kurban, Rifat Department of Architecture Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Abdullah Gul University Kayseri38080 Turkey

ISBN: (纸本)9798400716928

The categorization of images captured during the documentation of architectural structures is a crucial aspect of preserving cultural heritage in digital form. Dealing with a large volume of images makes this categorization process laborious and time-consuming, often leading to errors. Introducing automatic techniques to aid in sorting would streamline this process, enhancing the efficiency of digital documentation. Proper classification of these images facilitates improved organization and more effective searches using specific terms, thereby aiding in the analysis and interpretation of the heritage asset. This study primarily focuses on applying deep learning techniques, specifically SqueezeNet convolutional neural networks (CNNs), for classifying images of architectural heritage. The effectiveness of training these networks from scratch versus fine-tuning pre-existing models is examined. In this study, we concentrate on identifying significant elements within images of buildings with architectural heritage significance of Kayseri Culture Route. Since no suitable datasets for network training were found, a new dataset was created. Transfer learning enables the use of pre-trained convolutional neural networks to specific image classification tasks. In the experiments, 99.8% of classification accuracy have been achieved by using SqueezeNet, suggesting that the implementation of the technique can substantially enhance the digital documentation of architectural heritage. © 2024 Owner/Author.

关键词： Image classification

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Location Detection of an Object Non Line of Sight with Audio Signal 8

Location Detection of an Object Non Line of Sight with Audio...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Olgun, Nevzat Calisan, Mucahit Afyon Kocatepe University Department of Software Engineering Afyonkarahisar Turkey Engineering and Architecture Faculty Department of Computer Engineering Bingöl Turkey

ISBN: (纸本)9798331531492

Locating objects Non-Line-of-Sight is an important challenge in many fields such as defense applications, autonomous vehicles, natural disasters, etc. With the advancement of signal processing techniques, there has been an increased interest in the detection of objects in hidden areas. In this respect, audio signals have a strong potential as a low-cost technology for object localization. This study presents an experimental application to detect the location of hidden objects Non-Line-of-Sight based on audio signals. The signals from the sound source strike the hidden object through a reflecting surface, such as a wall, and are received back from the reflecting surface as secondary signals. In this study, object detection can be performed with acoustic sound signals without physically intervening in an area beyond the line of sight and without locating any sensors in this area. The experimental results obtained from the study show that the position detection of an object located Non-Line-of-Sight can be done satisfactorily. © 2024 IEEE.

关键词： Audio signal processing

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Efficient thread-to-core mapping alternatives for application-level redundant multithreading

Efficient thread-to-core mapping alternatives for applicatio...

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作者： Arslan, Sanem Ünsal, Osman Computer Engineering Department Marmara University Istanbul Turkey Computer Science - Computer Architecture For Parallel Paradigms Barcelona Supercomputing Center Barcelona Spain

Redundant multithreading (RMT) is an effective thread-level replication method to improve the reliability requirements of applications. Although it significantly improves the robustness of applications, it comes with additional performance overhead since the redundant threads might share the same core resources. In our previous study [Efficient selective replication of critical code regions for SDC mitigation leveraging redundant multithreading. J Supercomput 2021;77:14130-14160], we presented an efficient software-level RMT approach, where we execute the most critical code regions with three threads to correct errors. In this study, we focus on further improving the performance of our software-level RMT method by presenting a set of different thread-to-core mapping alternatives. We provide different static mapping methods, which require preliminary information about the applications, such as execution time, instruction-per-cycle (IPC), or cache usage patterns, and a set of dynamic mapping methods, which map threads to cores dynamically at runtime without requiring any additional information. The dynamic mapping methods decide which threads are mapped to which cores at each scheduling point based on the IPC, cache miss rate, or cache access values of each thread as well as each core. Experimental results show that the dynamic mapping method, which maps threads to cores based on IPC values, outperforms all other static and dynamic methods. It also outperforms our baseline model, where the operating system handles the thread-to-core mappings by 8%, 7%, and 20% based on average speedup, harmonic speedup, and mean slowdown metrics. © 2023 John Wiley & Sons, Ltd.

关键词： Mapping

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Multimodal Input Classification for a Companion Pet Robot using Deep Learning 24

Multimodal Input Classification for a Companion Pet Robot us...

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8th International Conference on Advances in Artificial Intelligence, ICAAI 2024

作者： Alhouli, Sarah Yousef Almania, Nora Abdullah Sharma, Jitendra Kumar Sahoo, Deepak Ranjan Department of Computer Science Swansea University Swansea United Kingdom Shuwaikh Kuwait Department of Computer Science Shaqra University Shaqra Saudi Arabia Architecture & Town Planning Department Universite Libre de Bruxelles Bruxelles Belgium

ISBN: (纸本)9798400718014

Social robots have emerged recently as a technology that could provide social support and increase companionship, especially for older adults. Effective human-robot interaction requires social robots to interact empathically and naturally by using Artificial Emotional Intelligence (AEI), which allows them to recognise different multimodal inputs and engage with users emotionally. To this end, we developed AEI companion pet robot classification system that incorporated various multimodal inputs, including hand gestures, emotions from images, emotions from voice annotations, and speech detection. We utilised different deep learning approaches for each mode and adopted lightweight computationally efficient models. For multimodal input detection, we extended the MediaPipe framework with a mathematical approach for hand gestures, integrated an updated MobileNet transfer learning model for facial emotions, employed a Convolutional Neural Network (CNN) model for detecting emotions from voice, and used a lightweight Whisper model as an automatic speech recognition (ASR). Our findings indicated that the modified MobileNet lightweight model achieved an accuracy of 84.60% along with an efficient gesture detection system with an accuracy of 90% integrated with an effective CNN model of 86.89% accuracy. Our results imply that developing a multimodal AEI companion pet robot could promote future research in the development of emotionally intelligent companion robots. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Robust Proxy Sensor Model for Estimating Black Carbon Concentrations Using Low-Cost Sensors 1

Robust Proxy Sensor Model for Estimating Black Carbon Concen...

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1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities, EnvSys 2023

作者： Paredes-Ahumada, Juan Ferrer-Cid, Pau Barcelo-Ordinas, Jose M. Garcia-Vidal, Jorge Reche, Cristina Viana, Mar Computer Architecture Department Universitat Politecnica de Catalunya Barcelona Spain Barcelona Spain

ISBN: (纸本)9798400702143

Air quality monitoring sensor networks focusing on air pollution measure pollutants that are regulated by the authorities, such as CO, NO2, NO, SO2, O3, and particulate matter (PM10, PM2.5). However, there are other pollutants, such as black carbon (BC), which are not regulated, have a major impact on health, and are rarely measured. One solution is to use proxies, which consist of creating a mathematical model that infers the measurement of the pollutant from indirect measurements of other pollutants. In this paper, we propose a robust machine learning proxy (RMLP) framework for estimating BC based on nonlinear machine learning methods, calibrating the low-cost sensors (LCSs), and adding robustness against noise and data missing in the LCS. We show the impact of LCS data aggregation, denoising and missing imputation on BC estimation, and how the concentrations estimated by the BC proxy approximate the values obtained by a reference instrument with an accurate BC sensor. © 2023 ACM.

关键词： Sensor networks

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WIP: Exploring the Role of Sentiment in Tutor-Student Interaction. The Case Study of CS and architecture Formative Studio Critiques 54

WIP: Exploring the Role of Sentiment in Tutor-Student Intera...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Sopher, Hadas Hajaj, Chen Gero, John S. Ariel University School of Architecture Ariel Israel Ariel University Department of Industrial Engineering and Management Ariel Israel University of North Carolina at Charlotte Department of Computer Science and School of Architecture Charlotte United States

ISBN: (纸本)9798350351507

This work-in-progress research presents a novel approach to assessing tutor-student interactions in design studio critiques, thereby advancing studio assessment methodologies. Following constructivist theories, students learn by practicing professional design behaviors. They introduce new concepts and progress according to the tutor's feedback and demonstration. Despite their principal role, critique interactions are often tutor-centric and ambiguous, resulting in negative sentiments that may hinder participation. Current methods measure solely learners' performance of design practices while neglecting the cognitive actions involved, such as the learner's sentiment. This gap limits assessing the effectiveness of the interaction. In this study, we measure the effect of sentiment on introducing new design concepts, identified as first occurrences (FOs). We demonstrate the approach in two natural case studies comprising six critiques of three CS students and three architecture students. NLP algorithms are developed to quantify the distribution of FOs generated by students and tutors within positive and negative sentiment environments. Changes in the sentiment throughout critiques measure the temporal role of sentiment in generating FOs. Observations exhibit differences in sentiment between CS and architecture students. In both courses, more learner-centric FOs occurred in a positive sentiment environment. Using explicit assessment methods opens new possibilities for establishing real-time feedback systems, leading studio-based education to its next step in reshaping constructivist pedagogy. © 2024 IEEE.

关键词： Real time systems

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Developing Robot Prototypes to Explore Robot-Facilitated Family Routines 20

Developing Robot Prototypes to Explore Robot-Facilitated Fam...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： He, Xinning Xu, Michael F. Cagiltay, Bengisu Mutlu, Bilge School of Architecture Tsinghua University Beijing China University of Wisconsin-Madison Department of Computer Sciences MadisonWI United States

ISBN: (纸本)9798350378931

In this late-breaking report, we present our design process motivated to build tangible, cost-effective, child- and family-friendly social robot prototypes aimed to (1) support the practical needs of families, as well as (2) serve as a feasible research platform to mitigate technical and logistical challenges faced within conducting inhome HRI studies. We apply a research through design approach, combined with participatory design methods, to produce functional prototypes. In future work, we are preparing to continue improving prototype design by conducting (1) iterative co-design sessions with artists and product designers focusing on the hardware design, (2) iterative co-design sessions with families focusing on the interaction design, and (3) long-term technology probe studies in homes to evaluate the effectiveness of the technological platform. We aim to solicit feedback from the HRI community on our design process and prototype research platform. © 2025 IEEE.

关键词： Social robots

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