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arXiv 2023年

作者： Xu, Lily Rolf, Esther Beery, Sara Bennett, Joseph R. Berger-Wolf, Tanya Birch, Tanya Bondi-Kelly, Elizabeth Brashares, Justin Chapman, Melissa Corso, Anthony Davies, Andrew Garg, Nikhil Gaylard, Angela Heilmayr, Robert Kerner, Hannah Klemmer, Konstantin Kumar, Vipin MacKey, Lester Monteleoni, Claire Moorcroft, Paul Palmer, Jonathan Perrault, Andrew Thau, David Tambe, Milind School of Engineering and Applied Sciences Harvard University United States Harvard University United States United States Department of Computer Science University of Colorado Boulder United States Electrical Engineering and Computer Science Massachusetts Institute of Technology United States Department of Biology Carleton University Canada Department of Computer Science and Engineering The Ohio State University United States Department of Electrical and Computer Engineering The Ohio State University United States Department of Evolution Ecology and Organismal Biology The Ohio State University United States Translational Data Analytics Institute The Ohio State University United States Google United States Electrical Engineering and Computer Science University of Michigan United States Department of Environmental Science Policy and Management University of California Berkeley United States Department of Aeronautics and Astronautics Stanford University United States Department of Organismic and Evolutionary Biology Harvard University United States Operations Research and Information Engineering Cornell Tech United States African Parks South Africa Bren School of Environmental Science and Management University of California Santa Barbara United States School of Computing and Augmented Intelligence Arizona State University United States Microsoft Research United States Department of Computer Science and Engineering University of Minnesota United States Inria Paris France United States Wwf Global Science United States

In this white paper, we synthesize key points made during presentations and discussions from the AI-Assisted Decision Making for Conservation workshop, hosted by the Center for Research on Computation and Society at Harvard University on October 20-21, 2022. We identify key open research questions in resource allocation, planning, and interventions for biodiversity conservation, highlighting conservation challenges that not only require AI solutions, but also require novel methodological advances. In addition to providing a summary of the workshop talks and discussions, we hope this document serves as a call-to-action to orient the expansion of algorithmic decision-making approaches to prioritize real-world conservation challenges, through collaborative efforts of ecologists, conservation decision-makers, and AI researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Conservation

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Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks

Image Analysis of MRI-based Brain Tumor Classification and S...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： R. Saranya R Pushpakumar P Srinivasan N Aparna K. Karthika Amit Chauhan Department of Computer science with Data Analytics PSG College of Arts and Science Coimbatore India Department of Information Technology Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Sona College of Technology Salem Tamilnadu India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Verappanur Tamil Nadu India Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Life Sciences School of Sciences CHRIST (Deemed to be university) Bengaluru Karnataka India

Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method.

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Understanding the dynamics between vaping and cannabis legalization using Twitter opinions

arXiv

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arXiv 2021年

作者： Adhikari, Shishir Uppal, Akshay Mermelstein, Robin Berger-Wolf, Tanya Zheleva, Elena Computer Science University of Illinois at Chicago United States Psychology Institute for Health Research and Policy University of Illinois at Chicago United States Computer Science and Engineering Electrical and Computer EngineeringEvolution Ecology and Organismal Biology Translational Data Analytics Institute Ohio State University United States

Cannabis legalization has been welcomed by many U.S. states but its role in escalation from tobacco e-cigarette use to cannabis vaping is unclear. Meanwhile, cannabis vaping has been associated with new lung diseases and rising adolescent use. To understand the impact of cannabis legalization on escalation, we design an observational study to estimate the causal effect of recreational cannabis legalization on the development of pro-cannabis attitude for e-cigarette users. We collect and analyze Twitter data which contains opinions about cannabis and JUUL, a very popular e-cigarette brand. We use weakly supervised learning for personal tweet filtering and classification for stance detection. We discover that recreational cannabis legalization policy has an effect on increased development of pro-cannabis attitudes for users already in favor of e-cigarettes. © 2021, CC BY-SA.

关键词： Tobacco

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Awareness requirement and performance management for adaptive systems: a survey

arXiv

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arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

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The Future of data Protection: Empowering data Security with Identity-Centric Blockchain Technology

The Future of Data Protection: Empowering Data Security with...

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Communication & Computing (WCONF), World Conference on

作者： A. Heney Juliet Vinithra Banu T K. Suresh Kumar D Kirubakaran M Dinesh K. Thilagam Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of MBA Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.K Engineering College Chennai Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Velammal Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

In response to the formidable challenges surrounding data security, particularly in the face of evolving threats and vulnerabilities, this study introduces the Advanced Blockchain-Enhanced data Security Protocol (ABE-DSP) as a cutting-edge solution. Acknowledging the complexities of safeguarding sensitive information, ABE-DSP is a meticulously crafted and sophisticated framework that aims to fortify the security and integrity of data transactions through the integration of blockchain technology. This protocol addresses the pressing challenge of ensuring robust data security by placing a primary focus on enhancing overall system security and refining user authentication processes. Within the context of these challenges, the study explores the intricacies of ABE-DSP's design and implementation. Furthermore, the proposed method demonstrates its effectiveness by achieving a remarkable success rate, processing 97% of received packets with precision. This high percentage underscores the protocol's efficacy and reliability, positioning it as a robust and advanced tool for overcoming the contemporary challenges associated with data security. This study contributes valuable insights to the ongoing discourse on leveraging blockchain technology for identity-centric data security, offering a promising avenue for addressing and mitigating the challenges faced in the dynamic landscape of information protection.

关键词： Protocols Navigation data security Face recognition Refining Authentication Pressing Blockchains Reliability Protection

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Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution

arXiv

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arXiv 2022年

作者： Wang, Xiwen Zhou, Rui Ying, Jiaxi Palomar, Daniel P. The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Shenzhen Research Institute of Big Data Shenzhen China The Department of Mathematics Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Department of Electronic and Computer Engineering Department of Industrial Engineering and Decision Analytics Hong Kong University of Science and Technology Kowloon Hong Kong

Since Markowitz’s mean-variance framework, optimizing a portfolio that strikes a trade-off between maximizing profit and minimizing risk has been ubiquitous in the financial industry. Initially, profit and risk were measured by the first two moments of the portfolio’s return, a.k.a. the mean and variance, which are sufficient to characterize a Gaussian distribution. However, it is broadly believed that the first two moments are not enough to capture the characteristics of the returns’ behavior, which have been recognized to be asymmetric and heavy-tailed. Although portfolio designs involving the third and fourth moments, i.e., skewness and kurtosis, have been demonstrated to outperform the conventional mean-variance framework, they present non-trivial challenges. Specifically, in the classical framework, the memory and computational cost of computing the skewness and kurtosis grow sharply with the number of assets. To alleviate the difficulty in high-dimensional problems, we consider an alternative expression for high-order moments based on parametric representations via a generalized hyperbolic skew-t distribution. Then, we reformulate the high-order portfolio optimization problem as a fixed-point problem and propose a robust fixed-point acceleration algorithm that solves the problem in an efficient and scalable manner. Empirical experiments also attest to the efficiency of our proposed high-order portfolio optimization framework, which presents low complexity and significantly outperforms the state-of-the-art methods by 2 ∼ 4 orders of magnitude. Copyright © 2022, The Authors. All rights reserved.

关键词： Profitability

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Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

arXiv

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arXiv 2022年

作者： Masum, Mohammad Nazim, Mohammad Jobair Hossain Faruk, Md. Shahriar, Hossain Valero, Maria Abdullah Hafiz Khan, Md. Uddin, Gias Barzanjeh, Shabir Saglamyurek, Erhan Rahman, Akond Ahamed, Sheikh Iqbal Analytics and Data Science Institute Kennesaw State University Kennesaw United States Department of Computer Science Kennesaw State University MariettaGA United States Department of Software Engineering Kennesaw State University MariettaGA United States Department of Information Technology Kennesaw State University Marietta United States Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University Tennessee United States Department of Computer Science Marquette University Wisconsin United States

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises- quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks. © 2022, CC BY.

关键词： Supply chains

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Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential

arXiv

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arXiv 2025年

作者： Gao, Chenyu Xu, Kaiwen Kim, Michael E. Zuo, Lianrui Li, Zhiyuan Archer, Derek B. Hohman, Timothy J. Moore, Ann Zenobia Ferrucci, Luigi Beason-Held, Lori L. Resnick, Susan M. Davatzikos, Christos Prince, Jerry L. Landman, Bennett A. Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Computer Science Vanderbilt University NashvilleTN United States Vanderbilt Memory and Alzheimer’s Center Vanderbilt University Medical Center NashvilleTN United States Department of Neurology Vanderbilt University Medical Center NashvilleTN United States Translational Gerontology Branch National Institute on Aging National Institutes of Health BaltimoreMD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD United States AI2D Center for AI and Data Science University of Pennsylvania Philadelphia United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States

Defacing is often applied to head magnetic resonance image (MRI) datasets prior to public release to address privacy concerns. The alteration of facial and nearby voxels has provoked discussions about the true capability of these techniques to ensure privacy as well as their impact on downstream tasks. With advancements in deep generative models, the extent to which defacing can protect privacy is uncertain. Additionally, while the altered voxels are known to contain valuable anatomical information, their potential to support research beyond the anatomical regions directly affected by defacing remains uncertain. To evaluate these considerations, we develop a refacing pipeline that recovers faces in defaced head MRIs using cascaded diffusion probabilistic models (DPMs). The DPMs are trained on images from 180 subjects and tested on images from 484 unseen subjects, 469 of whom are from a different dataset. To assess whether the altered voxels in defacing contain universally useful information, we also predict computed tomography (CT)-derived skeletal muscle radiodensity from facial voxels in both defaced and original MRIs. The results show that DPMs can generate high-fidelity faces that resemble the original faces from defaced images, with surface distances to the original faces significantly smaller than those of a population average face (p -4). For shin muscle, the correlation is statistically significant (p 0.05) when any defacing method is applied, suggesting that defacing might not only fail to protect privacy but also eliminate valuable information. We advocate two solutions for data sharing that comply with privacy: 1) share skull-stripped images along with measurements of facial and cranial features extracted before skull-stripping for public access, while acknowledging that this approach inherently compromises many research potentials;or 2) share the unaltered images with privacy enforced through policy restrictions. © 2025, CC BY-NC-ND.

关键词： Magnetic resonance imaging

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Energetic Content based Image Retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique

Energetic Content based Image Retrieval Scheme using Improve...

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International Conference on Smart Structures and Systems (ICSSS)

作者： S. Karkuzhali P. Malathi A Thenmozhi Velu Aiyyasamy Keerthika A G.S. Uthayakumar Department of General Engineering R.M.K. Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Department of Electronics and Communication Engineering Kings Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Meenakshi Sundararajan Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of ECE St. Joseph’s Institute of Technology Chennai Tamil Nadu India

One popular approach to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIR. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer-accessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and permanent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIR system. We introduce LAIRS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For a transparent evaluation of the proposed scheme’s efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth observational experiments for various CBIR tasks employing an image database have shown encouraging results, teaching us important lessons for making CBIR system more efficient. With the use of CBIR structures, a different collection can find similar pictures to the query image. There is little doubt that Google’s search per photo tool is perhaps the most widely used CBIR approach.

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Vortex Feature Positioning: Bridging Tabular IIoT data and Image-Based Deep Learning

arXiv

引用

arXiv 2023年

作者： Park, Jong-Ik Seong, Sihoon Lee, JunKyu Hong, Cheol-Ho Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Ave PittsburghPA15213 United States Department of Intelligent Semiconductor Engineering Chung-Ang University 84 Heukseok-ro Dongjak District Seoul06974 Korea Republic of Institute for Analytics and Data Science University of Essex Wivenhoe Park ColchesterCO4 3SQ United Kingdom

Tabular data from IIoT devices are typically analyzed using decision tree-based machine learning techniques, which struggle with high-dimensional and numeric data. To overcome these limitations, techniques converting tabular data into images have been developed, leveraging the strengths of image-based deep learning approaches such as Convolutional Neural Networks. These methods cluster similar features into distinct image areas with fixed sizes, regardless of the number of features, resembling actual photographs. However, this increases the possibility of overfitting, as similar features, when selected carefully in a tabular format, are often discarded to prevent this issue. Additionally, fixed image sizes can lead to wasted pixels with fewer features, resulting in computational inefficiency. We introduce Vortex Feature Positioning (VFP) to address these issues. VFP arranges features based on their correlation, spacing similar ones in a vortex pattern from the image center, with the image size determined by the attribute count. VFP outperforms traditional machine learning methods and existing conversion techniques in tests across seven datasets with varying real-valued attributes. © 2023, CC BY-NC-ND.

关键词： Vortex flow

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