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2022 CHI Conference on Human Factors in Computing systems, CHI EA 2022

作者： Pine, Kathleen Bossen, Claus Holten Møller, Naja Miceli, Milagros Lu, Alex Jiahong Chen, Yunan Horgan, Leah Su, Zhaoyuan Neff, Gina Mazmanian, Melissa College of Health Solutions Arizona State University United States Digital Design and Information Studies Aarhus University Denmark Department of Computer Science University of Copenhagen Denmark Weizenbaum Institute for the Networked Society Technische Universität Berlin Germany School of Information University of Michigan United States University of California Irvine Department of Informatics United States Minderoo Centre for Technology and Democracy University of Cambridge United Kingdom

ISBN: (纸本)9781450391566

In the wake of the hype around big data, artificial intelligence, and "data-drivenness,"much attention has been paid to developing novel tools to capitalize upon the deluge of data being recorded and gathered automatically through IT systems. While much of this literature tends to overlook the data itself - sometimes even characterizing it as "data exhaust"that is readily available to be fed into algorithms, which will unlock the insights held within it - a growing body of literature has recently been directed at the (often intensive and skillful) work that goes into creating, collecting, managing, curating, analyzing, interpreting, and communicating data. These investigations detail the practices and processes involved in making data useful and meaningful so that aims of becoming data-driven' or data-informed' can become real. Further, In some cases, increased demands for data work have led to the formation of new occupations, whereas at other times data work has been added to the task portfolios of existing occupations and professions, occasionally affecting their core identity. Thus, the evolving forms of data work are requiring individual and organizational resources, new and re-tooled practices and tools, development of new competences and skills, and creation of new functions and roles. While differences exist across the global North and the global South experience of data work, such factors of data production remain paramount even as they exist largely for the benefit of the data-driven system [21, 32]. This one-day workshop will investigate existing and emerging tasks of data work. Further, participants will seek to understand data work as it impacts: individual data workers;occupations tasked with data work (existing and emerging);organizations (e.g. changing their skill-mix and infrastructuring to support data work);and teaching institutions (grappling with incorporation of data work into educational programs). Participants are required to submit a positio

关键词： Employment

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Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

arXiv

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arXiv 2024年

作者： Yang, Bo Zhang, Xueyao Yu, Zhiwen Cao, Xuelin Huang, Chongwen Alexandropoulos, George C. Zhang, Yan Debbah, Mérouane Yuen, Chau School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China School of Cyber Engineering Xidian University Shaanxi Xi’an710071 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Department of Informatics University of Oslo Oslo0316 Norway Center for 6G Technology The Khalifa University of Science and Technology Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore Singapore

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Analysis of optimized response time in a new disaster management model by applying metaheuristic and exact methods

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Smart and Resilient Transportation 2022年第1期4卷 22-42页

作者： Armin Mahmoodi Milad Jasemi Zergani Leila Hashemi Richard Millar Department of Industrial Engineering Islamic Azad University Masjed Soleyman Iran Department of Computer Informatics Stephens College of Business University of Montevallo Montevallo Alabama USA Department of Industrial Management Islamic Azad University Tehran Iran Department of Engineering Management and Systems Engineering George Washington University Washington DC District of Columbia USA

Purpose The purpose of this paper is to maximize the total demand covered by the established additive manufacturing and distribution centers and maximize the total literal weight assigned to the drones. Design/methodology/approach Disaster management or humanitarian supply chains (HSCs) differ from commercial supply chains in the fact that the aim of HSCs is to minimize the response time to a disaster as compared to the profit maximization goal of commercial supply chains. In this paper, the authors develop a relief chain structure that accommodates emerging technologies in humanitarian logistics into the two phases of disaster management – the preparedness stage and the response stage. Findings Solving the model by the genetic and the cuckoo optimization algorithm (COA) and comparing the results with the ones obtained by The General Algebraic Modeling System (GAMS) clear that genetic algorithm overcomes other options as it has led to objective functions that are 1.6% and 24.1% better comparing to GAMS and COA, respectively. Originality/value Finally, the presented model has been solved with three methods including one exact method and two metaheuristic methods. Results of implementation show that Non-dominated sorting genetic algorithm II (NSGA-II) has better performance in finding the optimal solutions.

关键词： Humanitarian logistics Drones delivery Operation research in disaster relief Additive manufacturing Cuckoo optimization algorithm Genetic algorithm

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WindowSHAP: An Efficient Framework for Explaining Time-series Classifiers based on Shapley Values

arXiv

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arXiv 2022年

作者： Nayebi, Amin Tipirneni, Sindhu Reddy, Chandan K. Foreman, Brandon Subbian, Vignesh Department of Systems and Industrial Engineering University of Arizona AZ United States Department of Computer Science Virginia Tech VA United States College of Medicine University of Cincinnati OH United States

Unpacking and comprehending how black-box machine learning algorithms (such as deep learning models) make decisions has been a persistent challenge for researchers and end-users. Explaining time-series predictive models is useful for clinical applications with high stakes to understand the behavior of prediction models, e.g., to determine how different variables and time points influence the clinical outcome. However, existing approaches to explain such models are frequently unique to architectures and data where the features do not have a time-varying component. In this paper, we introduce WindowSHAP, a model-agnostic framework for explaining time-series classifiers using Shapley values. We intend for WindowSHAP to mitigate the computational complexity of calculating Shapley values for long time-series data as well as improve the quality of explanations. WindowSHAP is based on partitioning a sequence into time windows. Under this framework, we present three distinct algorithms of Stationary, Sliding and Dynamic WindowSHAP, each evaluated against baseline approaches, KernelSHAP and TimeSHAP, using perturbation and sequence analyses metrics. We applied our framework to clinical time-series data from both a specialized clinical domain (Traumatic Brain Injury - TBI) as well as a broad clinical domain (critical care medicine). The experimental results demonstrate that, based on the two quantitative metrics, our framework is superior at explaining clinical time-series classifiers, while also reducing the complexity of computations. We show that for time-series data with 120 time steps (hours), merging 10 adjacent time points can reduce the CPU time of WindowSHAP by 80% compared to KernelSHAP. We also show that our Dynamic WindowSHAP algorithm focuses more on the most important time steps and provides more understandable explanations. As a result, WindowSHAP not only accelerates the calculation of Shapley values for time-series data, but also delivers more understandable

关键词： Time series

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A Data-driven Method for BioBrick Quality Assessment 2

A Data-driven Method for BioBrick Quality Assessment

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2nd IEEE International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2021

作者： An, Ning Liu, Shuo Ding, Huitong Wang, Haipeng Zhang, Lingling Alterovitz, Gil School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China College of Nursing and Health Sciences University of Massachusetts Boston Boston United States Department of Medicine Harvard Medical School Boston United States

ISBN: (纸本)9780738131221

BioBrick standard biological part (shortened as BioBrick), a widely used gene segment, faces severe quality challenges due to the crowdsourcing data collection process. The complex and expensive quality control at the sequencing level, however, has not been able to check a large number of samples effectively. To tackle this problem, this paper proposes a data-driven method for the quantitative assessment of BioBrick's quality. This method only needs the data itself without the requirement for quality labels in advance, and it generates a score for each BioBrick that explains its accuracy under sequence recognition. First, a group of one-vs-rest classifiers is constructed and compared with multiple learning strategies for sequence analysis. The accuracy score is then calculated by leveraging the divergence of these classifiers. Experimental results demonstrate that the proposed method achieves good performance on BioBricks classification and quality assessment. Furthermore, the range of the most informative subregion of BioBricks narrows down to 1-150 base positions, of which 5 base positions can achieve more than 72.9% accuracy for BioBrick classification. By formulating data-driven quality calculator, this paper highlights the application of machine learning to facilitate BioBrick quality assessment in a more generalized fashion. © 2021 IEEE.

关键词： Machine learning

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

引用

International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Advanced Deep Learning Human Herpes Virus 6 (HHV-6) Molecular Detection in Understanding Human Infertility

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Computational Intelligence and Neuroscience 2022年 2022卷 1422963页

作者： Alazzam, Malik Bader Al-Radaideh, Ahmad Tawfig Binsaif, Nasser Alghamdi, Ahmed S. Rahman, Md Adnan Faculty of Computer Science and Informatics Amman Arab University Amman Jordan Department of MIS College of Business Jadara University Irbid Jordan Department of E-Commerce College of Administrative and Financial Sciences Saudi Electronic University Riyadh Saudi Arabia Department of Computer Engineering Collage of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Dhaka Bangladesh

To see if HHV-6 may be a cause of infertility, researchers looked at 18 men and 10 women who had unexplained critical fertility and had at least one prior pregnancy. HHV-6 DNA was discovered in both infertile and fertile peripheral blood mononuclear cells (PBMC) (12 and 14%, respectively);endometrial epithelial cells from 4/10 (40%) infertile women were positive for HHV-6 DNA;this viral DNA was not found in the endometrium of fertile women. When endometrial epithelial cells were cultivated, they produced viral early and late proteins, suggesting the existence of an infectious virus. Endometrial HHV-6 infection creates an aberrant NK cell and cytokine profile, resulting in a uterine domain that is not favorable to conception, according to the findings. To corroborate the findings, studies of extra fertile and barren women should be done. Semen samples were taken from 18 guys who visited the Government General Hospital Guntur's infertility department because they were having reproductive issues with their partners. Herpes virus DNA has been discovered in the sperm of symptomatic fertile and infertile male patients on rare instances. Furthermore, researchers must investigate the role of viral diseases in male infertility. © 2022 Malik Bader Alazzam et al.

关键词： Viruses

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Effect of Spectrogram Preprocessing and Enhancement on Speaker Recognition Performance

Effect of Spectrogram Preprocessing and Enhancement on Speak...

引用

science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Adekunle A. Akinrinmade Emmanuel Adetiba Joke A. Badejo Segun I. Popoola Department of Electrical and Information Engineering (Computer Engineering) College of Engineering Covenant University Ota Ogun Nigeria HRA Institute for Systems Science Durban University South Africa Department of Electrical and Information Engineering College of Engineering Covenant University Ota Ogun Nigeria Department of Engineering Manchester Metropolitan University Manchester United Kingdom

Preprocessing in biometrics is the process of fine-tuning input data or features extracted from data by applying varying techniques to improve recognition performance. In most speaker recognition literature, researchers are silent about how the decision for such parameters used was chosen. This work systematically arrived at such decisions by carrying out several experiments on preprocessing and enhancement of spectrograms to see the effect on speaker recognition performance using a Convolutional Neural Network. First, different preprocessing experiments were carried out, one preprocessing parameter, in turn, was varied while the others were kept constant and eventually, the best parameters for all the preprocessing methods were combined to produce a spectrogram that yielded the best accuracy. The second part consists of enhancement experiments, where a series of image improvement techniques were applied to the spectrograms to further improve the initial accuracy. With the right parametric combination of the preprocessing techniques, speaker recognition improved by 25%. Spectrogram enhancements improved performance by a further 1%. Experimental results revealed that the dimensionality of a spectrogram can be significantly reduced with a very negligible drop in the overall performance which can enhance storage and computational requirements.

关键词： Training Deep learning Systematics Quantization (signal) Data preprocessing Feature extraction Speaker recognition

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Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Generation

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Wang, Chenyu Yan, Shuo Chen, Yixuan Wang, Xianwei Wang, Yujiang Dong, Mingzhi Yang, Xiaochen Li, Dongsheng Zhu, Rui Clifton, David A. Dick, Robert P. Lv, Qin Yang, Fan Lu, Tun Gu, Ning Shang, Li Fudan University School of Computer Science Shanghai China Fudan University Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China Oxford Suzhou Centre for Advanced Research Suzhou China University of Bath Bath United Kingdom University of Glasgow School of Mathematics and Statistics United Kingdom Microsoft Research Asia Shanghai China University of London Bayes Business School City United Kingdom University of Oxford Department of Engineering Science Oxford United Kingdom University of Michigan Department of Electrical Engineering and Computer Science College of Engineering Ann ArborMI United States University of Colorado Boulder BoulderCO United States

Denoising-based diffusion models have attained impressive image synthesis;however, their applications on videos can lead to unaffordable computational costs due to the per-frame denoising operations. In pursuit of efficient video generation, we present a Diffusion Reuse MOtion (Dr. Mo) network to accelerate the video-based denoising process. Our crucial observation is that the latent representations in early denoising steps between adjacent video frames exhibit high consistencies with motion clues. Inspired by the discovery, we propose to accelerate the video denoising process by incorporating lightweight, learnable motion features. Specifically, Dr. Mo will only compute all denoising steps for base frames. For a non-based frame, Dr. Mo will propagate the pre-computed based latents of a particular step with interframe motions to obtain a fast estimation of its coarse-grained latent representation, from which the denoising will continue to obtain more sensitive and fine-grained representations. On top of this, Dr. Mo employs a meta-network named Denoising Step Selector (DSS) to dynamically determine the step to perform motion-based propagations for each frame, ensuring the correct transformation of multi-granularity visual features. Extensive evaluations on video generation and editing tasks indicate that Dr. Mo delivers widely applicable acceleration for diffusion-based video generations while effectively retaining the visual quality and style. Video generation and visualization results can be found at https://***. © 1991-2012 IEEE.

关键词： HTTP

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An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

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Computational Intelligence and Neuroscience 2022年第0期2022卷 2933015页

作者： Alzubi, Ahmad Ali Tiwari, Shailendra Walia, Kuldeep Alanazi, Jazem Mutared Alzobi, Firas Ibrahim Verma, Rohit Computer Science Department Community College King Saud University Riyadh Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India Sri Guru Granth Sahib World University Fatehgarh Sahib Punjab India Department of Information Systems and Networks Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing National College of Ireland Dublin Ireland

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve. © 2022 Ahmad Ali AlZubi et al.

关键词： Diseases

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