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Proceedings of the IEEE Dallas Circuits and Systems Workshop (DCAS)

作者： Morgana M. A. Da Rosa Guilherme Paim Henrique B. Seidel Sé rgio Almeida Eduardo A. C. Da Costa Sergio Bampi Graduate Program on Electronic Engineering and Computing Universidade Cat&#x00F3 lica de Pelotas (UCPel) Brazil Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781665498852

ISBN: (纸本)9781665498869

Approximate transform kernels have emerged as an alternative design to reduce the hardware complexity of digital signal processors by exploiting the intrinsic error resilience of many embedded consumer electronics applications such as signal, image and video processing, and computer vision. The approximate level-4 Haar wavelet transform (AxHWT-4) is the state-of-the-art approximate transform for ECG (electrocardiogram) signal processing and reduces more than nine times the energy consumption compared to the exact transform. In this work, we propose an approximate level-3 Haar wavelet transform (AxHWT-3) for increasing the hardware design efficiency with related to the AxHWT-4 while obtaining higher R-peak detection accuracy in the electrocardiogram signal. Our AxHWT-3 approximation is performed in the Haar matrix kernel to eliminate its multiplications which in the hardware design results in savings of energy consumption and VLSI hardware area. Our AxHWT3 proposal achieves an R-peak detection accuracy of more than 99% and reduces the energy consumption by 15.98 times and the VLSI area by 12.64 times compared to the exact solution (i.e., the original level-3 Haar). In addition, the proposed AxHWT3 outperforms the state-of-the-art AxHWT-4 with a savings of about 32% in power dissipation and 19.5% in VLSI area.

关键词： Wavelet transforms Energy consumption Computer architecture Very large scale integration Electrocardiography Signal processing Hardware

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Authentic or artificial intelligence? Faculty’s perspectives on the ChatGPT’s impact on U.S. urban planning Ph.D. programs

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Frontiers of Urban and Rural Planning 2024年第1期2卷 1-8页

作者： Ye, Xinyue Zhai, Wei Du, Jiaxin Van Zandt, Shannon Ye, Yuning Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Urban and Regional Planning Program University of Texas at San Antonio San Antonio USA College of Computing Grand Valley State University Allendale USA

This commentary article explores the impact of ChatGPT on Urban Planning Ph.D. programs within universities in the United States. Our survey reveals a high awareness of Artificial Intelligence tools (e.g. ChatGPT) among faculty yet highlights a cautious approach to integrating ChatGPT into curricula and preliminary exam settings. While some educators recognize ChatGPT’s potential to enhance research efficiency and language proficiency, especially for international students, concerns about academic integrity and the over-reliance on AI are prevalent. The commentary underscores the need for a balanced approach, advocating for ChatGPT’s use as a supplementary tool while maintaining critical thinking in doctoral preliminary exams.

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Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms

arXiv

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arXiv 2024年

作者： Loss, Flavio P. da Cunha, Pedro H. Rocha, Matheus B. Zanoni, Madson Poltronieri de Lima, Leandro M. Nascimento, Isadora Tavares Rezende, Isabella Canuto, Tania R.P. de Paula Vieira, Luciana Rossoni, Renan Santos, Maria C.S. Frasson, Patricia Lyra Romão, Wanderson Filgueiras, Paulo R. Krohling, Renato A. Labcin - Nature Inspired Computing Lab Brazil PPGI - Graduate Program in Computer Science Department of Chemistry PPGQ - Graduate Program in Chemistry Department of Specialized Medicine Brazil ES Vitória29075-910 Brazil Secretary of Health of the Espírito Santo State Vitória Brazil ES Vitória29040-780 Brazil

Skin lesions are classified in benign or malignant. Among the malignant, melanoma is a very aggressive cancer and the major cause of deaths. So, early diagnosis of skin cancer is very desired. In the last few years, there is a growing interest in computer aided diagnostic (CAD) using most image and clinical data of the lesion. Although we have seen an increasing progress in CAD of skin lesions, these sources of information present limitations due to their inability to provide information of the molecular structure of the lesion. NIR spectroscopy may provide an alternative source of information to automated CAD of skin lesions. The most commonly used techniques and classification algorithms used in spectroscopy are Principal Component Analysis (PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and Support Vector Machines (SVM). Nonetheless, there is a growing interest in applying the modern techniques of machine and deep learning (MDL) to spectroscopy. One of the main limitations to apply MDL to spectroscopy is the lack of public datasets. Since there is no public dataset of NIR spectral data to skin lesions, as far as we know, an effort has been made and a new dataset named NIR-SC-UFES, has been collected, annotated and analyzed generating the gold-standard for classification of NIR spectral data to skin cancer. Next, the machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional neural network (1D-CNN) and standard algorithms as SVM and PLS-DA were investigated to classify cancer and non-cancer skin lesions. Experimental results indicate the best performance obtained by LightGBM with pre-processing using standard normal variate (SNV), feature extraction and data augmentation with Generative Adversarial Networks (GAN) providing values of 0.839 for balanced accuracy, 0.851 for recall, 0.852 for precision, and 0.850 for F-score. The obtained results indicate the first steps in CAD of skin lesions aiming the automated triage of patients with

关键词： Support vector machines

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Wordification: A New Way of Teaching English Spelling Patterns

Wordification: A New Way of Teaching English Spelling Patter...

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Computer Science, Computer Engineering, & Applied computing (CSCE), Congress in

作者： Lexington Whalen Dalton Craven Shashank Comandur Nathan Bickel Homayoun Valafar Stanley Dubinsky College of Computing and Engineering University of South Carolina Columbia SC United States of America Linguistics Program College of Arts and Sciences University of South Carolina Columbia SC United States of America

Literacy, or the ability to read and write, is a crucial indicator of success in life and greater society. It is estimated that 85% of people in juvenile delinquent systems cannot adequately read or write, that more than half of those with substance abuse issues have complications in reading or writing and that two-thirds of those who do not complete high school lack proper literacy skills [1]. Furthermore, young children who do not possess reading skills matching grade level by the fourth grade are approximately 80% likely to not catch up at all [2]. Many may believe that in a developed country such as the United States, literacy fails to be an issue; however, this is a dangerous misunderstanding. Globally an estimated 1.19 trillion dollars are lost every year due to issues in literacy; in the USA, the loss is an estimated 300 billion [3]. To put it in more shocking terms, one in five American adults still fail to comprehend basic sentences [4]. Making matters worse, the only tools available now to correct a lack of reading and writing ability are found in expensive tutoring or other programs that oftentimes fail to be able to reach the required audience. In this paper, our team puts forward a new way of teaching English spelling and word recognitions to grade school students in the United States: Wordification. Wordification is a web application designed to teach English literacy using principles of linguistics applied to the orthographic and phonological properties of words in a manner not fully utilized previously in any computer-based teaching application.

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Evaluation of Imprecise Subtractors into Test Zone Search for VVC Encoding

Evaluation of Imprecise Subtractors into Test Zone Search fo...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Rafael Ferreira Luciano Agostini Cláudio M. Diniz Bruno Zatt ViTech Group Graduate Program in Computing Federal University of Pelotas (UFPel) Pelotas Brazil PGMICRO Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

The error-tolerant behavior of some video encoding algorithms has motivated researchers to exploit approximate computing in the seek for energy-efficient coding systems. If approximate adders have been widely evaluated, approximate subtractors did not receive much attention. Subtractors, however, are extensively used during the encoding process and optimizing them represents an opportunity for further improving energy efficiency. This study explores the adoption of approximate subtractors in Versatile Video Coding (VVC) encoders, with a specific focus on the distortion metric used in the Test Zone Search (TZS) algorithm for inter-picture prediction. The detailed analysis reveals the impact of imprecise subtractors on each stage of TZS, presenting unprecedented data about the number of calls and energy consumption per stage. The results show that the use of approximate subtractors leads to changes in the number of calls for each TZS stage, induces a minimal impact in terms of coding efficiency, and enables nearly 6% reduction in energy consumption. Therefore, this work provides substantial contributions to the understanding and to the optimization of video coding processes, energy-constrained scenarios.

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Expanding PyAFAR: A Novel Privacy-Preserving Infant AU Detector

Expanding PyAFAR: A Novel Privacy-Preserving Infant AU Detec...

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International Conference on Automatic Face and Gesture Recognition

作者： Itir Onal Ertugrul Saurabh Hinduja Maneesh Bilalpur Daniel S. Messinger Jeffrey F. Cohn Department of Information and Computing Sciences Utrecht University Utrecht The Netherlands Department of Psychology University of Pittsburgh Pittsburgh PA USA Intelligent Systems Program University of Pittsburgh Pittsburgh USA Department of Psychology University of Miami Miami FL USA

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

We enhance PyAFAR 1 1 Code will be available on: https:\\***/PyAFARI, an open source, Python-based library for facial action unit detection by introducing a privacy-protected infant AU detector. To prevent reconstruction of the training images, we train the infant AU detector by extracting histogram of gradients (HoG) features and using an efficient Light Gradient Boosting Machine (LightGBM) classifier. Models are trained with two large, well-annotated databases. The performance of our approach is comparable to previously developed deep models that have not been released due to privacy concerns. Our models are available for use and further fine-tuning, contributing to the advancement of facial action unit detection.

关键词： Training Gold Privacy Histograms Face recognition Detectors Gesture recognition

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2D Basement Relief Inversion using Sparse Regularization.

arXiv

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arXiv 2024年

作者： Barboza, Francisco Márcio da Cunha Romão Silva, E. Arthur Anthony de Carvalho, Bruno Motta Department of Computing and Technology Federal University of Rio Grande do Norte Rio Grande do Norte Caicó Brazil Graduate Program on Systems and Computing Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Rio Grande do Norte Natal Brazil

Basement relief gravimetry is a key application in geophysics, particularly important for oil exploration and mineral prospecting. It involves solving an inverse geophysical problem, where the parameters of a geological model are inferred from observed data. In this context, the geological model consists of the depths of constant-density prisms representing the basement relief, and the data correspond to the gravitational anomalies caused by these prisms. Inverse geophysical problems are typically ill-posed, as defined by Hadamard, meaning that small perturbations in the data can result in large variations in the solutions. To address this instability, regularization techniques, such as those proposed by Tikhonov, are employed to stabilize the solutions. This study presents a comparative analysis of various regularization techniques applied to the gravimetric inversion problem, including Smoothness Constraints, Total Variation, the Discrete Cosine Transform (DCT), and the Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization methods are commonly used in inverse geophysical problems because of their ability to find optimal parameters that minimize the objective function—in this case, the depths of the prisms that best explain the observed gravitational anomalies. The Genetic Algorithm (GA) was selected as the optimization technique. GA is based on Darwinian evolutionary theory, specifically the principle of natural selection, where the fittest individuals in a population are selected to pass on their traits. In optimization, this translates to selecting solutions that most effectively minimize the objective function. The results, evaluated using fit metrics and cumulative error analysis, demonstrate the effectiveness of all the regularization techniques and the Genetic Algorithm. Among the methods tested, the Smoothness constraint was briefly the most effective for the first and second synthetic models. For the third model, which was based on re

关键词： Discrete wavelet transforms

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Three-Dimensional Wound Reconstruction using Point Descriptors: A Comparative Study

Three-Dimensional Wound Reconstruction using Point Descripto...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： José R. A. Souto Fellipe M. C. Barbosa Bruno M. Carvalho Graduate Program in Systems and Computing Federal University of Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Brazil

Precise measurements of chronic wound areas are very important for measuring the efficacy of different treatments, since it provides a clear picture of the chosen treatment evolution. The most used techniques still in use for measuring the wound areas involve manual measuring, paper scales and/or acetate tracing, with each health professional possibly measuring the wounds in slightly different ways, specially when dealing with the wound's depth. Thus, it is common to observe inconsistent wound area estimations by different health workers on the same wounds. Moreover, the measuring process requires direct contact with the wound, increasing the contamination and infection risks, besides possibly causing discomfort for the patient. An alternative that has already been proved successful is the use of Structure from Motion (SfM) to recover three-dimensional representations of a wound's surface and estimate that area. In this work, we analyze the effectiveness of several widely used point descriptors (BRIEF, DRINK, FREAK, ORB, SIFT and SURF) in the SfM wound reconstruction task when applied to real images of realistic synthetic wounds made of latex placed on different areas of the body. The results showed that SIFT, SURF and DRINK produced better area estimates. Since the execution time is a factor, we selected DRINK as the standard descriptor for our system, since it is approximately 9.3 and 4.9 times faster than SIFT and SURF, respectively.

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Acceptance Analysis of IWB Systems in Education using UTAUT2 Model: The Moderating Role of Perceived Technological Innovations

Acceptance Analysis of IWB Systems in Education using UTAUT2...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Noelyn M. De Jesus Eltimar T. Castro Cory Antonio Buyan College of Informatics & Computing Sciences Batangas State University Batangas Phlippines Graduate Program Department Technological Institute of the Philippines Manila Philippines Business Technology Ballard Power Systems Burnaby Canada

Interactive Whiteboard (IWB) Systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members’ intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members’ behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members’ usage behavior when using IWB systems in teaching.

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