检索结果-内蒙古大学图书馆

2024 systems and information engineering Design Symposium, SIEDS 2024

作者： Rogers, Adam Dibsie, Carter Kuzneski, Ethan Underwood, David Brozey, Robert Sullivan, Lauren Robinson, Donald Porter, Michael D. University of Virginia Systems and Information Engineering United States Titus Technology LLC United States University of Virginia Systems and Information Engineering Data Science United States

ISBN: (纸本)9798350385144

Breakthrough technologies have the potential to disrupt markets and society. Anticipating such disruptions is crucial for policymakers, investors, and businesses in being proactive with regard to regulatory policies and in allocating resources effectively. This project aims to develop an analytical approach to identify companies that will lead in developing breakthrough technologies. The analysis focuses on the semiconductor industry, which has seen rapid growth in recent decades, surging from 139 billion in revenue in 2001 to 573.5 billion in 2022. Our systematic approach to predicting technological disruption in the semiconductor industry involves leveraging a combination of quantitative company data, human-centric elements, and feature engineering. data was collected on 244 private semiconductor companies between 2012 and 2018, encompassing information about leadership profiles, research endeavors, media exposure, and financial performance. Two models were developed: A penalized regression model, and a boosted tree model, both aimed at forecasting the probability of a company achieving a valuation exceeding 500 million within five years of its first funding deal. Key variables such as the number of employees, year founded, total invested equity, number of active patents, and country of origin emerged as significant predictors of company success. This paper discusses the performance of our models and explores applying our findings to identify disruptive companies across industries. © 2024 IEEE.

关键词： Semiconductor device manufacture

来源：评论

学校读者我要写书评

暂无评论

DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： Expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Characterizing diseases using genetic and clinical variables:A data analytics approach

引用

Quantitative Biology 2024年第3期12卷 271-285页

作者： Madhuri Gollapalli Harsh Anand Satish Mahadevan Srinivasan Engineering Department Penn State Great ValleyMalvernPennsylvaniaUSA Department of Systems and Information Engineering School of Engineering and Applied ScienceUniversity of VirginiaCharlottesvilleVirginiaUSA

Predictive analytics is crucial in precision medicine for personalized patient *** aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease *** achieve this,experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical *** this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MLR)technique was applied for classifying the diseased tissue *** reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical *** results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically ***,it was evident that both clinical and genetic variables were important in predicting the diseased tissue *** top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of ***,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR *** classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical *** study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.

关键词： clustering k-means L1000 dataset analysis landmark genes multinomial logistic regression non-landmark genes principal component analysis tissue classification

来源：评论

学校读者我要写书评

暂无评论

SACNN-IDS: A self-attention convolutional neural network for intrusion detection in industrial internet of things

引用

CAAI Transactions on Intelligence Technology 2024年第6期9卷 1398-1411页

作者： Mimonah Al Qathrady Safi Ullah Mohammed S.Alshehri Jawad Ahmad Sultan Almakdi Samar M.Alqhtani Muazzam A.Khan Baraq Ghaleb Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Computer Science Quaid-i-Azam UniversityIslamabadPakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam UniversityIslamabadPakistan

Industrial Internet of Things(IIoT)is a pervasive network of interlinked smart devices that provide a variety of intelligent computing services in industrial *** IIoT nodes operate confidential data(such as medical,transportation,military,etc.)which are reachable targets for hostile intruders due to their openness and varied *** Detection systems(IDS)based on Machine Learning(ML)and Deep Learning(DL)techniques have got significant ***,existing ML and DL-based IDS still face a number of obstacles that must be *** instance,the existing DL approaches necessitate a substantial quantity of data for effective performance,which is not feasible to run on low-power and low-memory *** and fewer data potentially lead to low performance on existing *** paper proposes a self-attention convolutional neural network(SACNN)architecture for the detection of malicious activity in IIoT networks and an appropriate feature extraction method to extract the most significant *** proposed architecture has a self-attention layer to calculate the input attention and convolutional neural network(CNN)layers to process the assigned attention features for *** performance evaluation of the proposed SACNN architecture has been done with the Edge-IIoTset and X-IIoTID *** datasets encompassed the behaviours of contemporary IIoT communication protocols,the operations of state-of-the-art devices,various attack types,and diverse attack scenarios.

关键词： convolutional neural network deep learning industrial internet of things intrusion detection self-attention

来源：评论

学校读者我要写书评

暂无评论

Augmenting the Perceptual Experience of Being Faced Using Gaze Modeling during Online Video Viewing

Augmenting the Perceptual Experience of Being Faced Using Ga...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Nakajima, Yuto Hirokawa, Masakazu Hassan, Modar Suzuki, Kenji Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Japan Nec Corporation Data Science Laboratories Japan Institute of Systems and Information Engineering University of Tsukuba Japan

ISBN: (纸本)9781665410205

Live Performances have been conducted online in recent years due to the influence of the COVID-19 pandemic. This generated a novel problem of losing eye contact with the performers, which deteriorates the viewing experience in the online setting. This study proposes a feedback system based on gaze modeling to augment the perceptual experience of being faced by the performer during an online live performance viewing. This system aims to improve the experience of viewing online live performances by recreating a perceptual experience of interaction between the performer and the audience. The proposed system consists of a gaze recognition unit that judges whether the performer is looking at the camera based on gaze modeling in the video, and a stimulus presentation unit that presents the judgement result to the audience through vibration stimulus. This paper describes the gaze recognition model, experiments to determine the parameters to be used in the model, and the evaluation of live performance viewing experiments using the proposed system. © 2024 IEEE.

关键词： Video analysis

来源：评论

学校读者我要写书评

暂无评论

A Novel High-Efficiency Transaction Verification Scheme for Blockchain systems

引用

Computer Modeling in engineering & sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

来源：评论

学校读者我要写书评

暂无评论

systems Analysis of Bias and Risk in AI-Enabled Medical Diagnosis

Systems Analysis of Bias and Risk in AI-Enabled Medical Diag...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Moghadasi, Negin Piran, Misagh Baek, Stephen Valdez, Rupa S. Porter, Michael D. Johnson, DeAndre Lambert, James H. University of Virginia Johnson & Johnson Medtech Dept. of Systems and Information Engineering United States Nuclear Medicine and Molecular Imaging Heart and Diabetes Center North-Rhine Westphalia Ruhr University of Bochum Dept. of Radiology Germany School of Data Science University of Virginia United States Engineering School of Data Science University of Virginia Dept. of Systems and Information United States Engineering University of Virginia Dept. of Systems and Information United States

ISBN: (纸本)9781665430654

AI technologies have made significant advancements across various sectors, especially healthcare. Although AI algorithms in healthcare showcase remarkable predictive capabilities, apprehensions have emerged owing to errors, biases, and a lack of transparency. These concerns have led to a decline in trust among clinicians and patients, while also posing the risk of further accentuating pre-existing biases against marginalized groups and exacerbating inequities. This paper presents a scenario-based preferences risk register11Denotes a methodically arranged document or database detailing potential risks linked to particular scenarios or situations. framework for identifying and accounting AI algorithm biases in diagnosing diseases. The framework is demonstrated with a realistic case study on cardiac sarcoidosis. The framework identifies success criteria, initiatives, emergent conditions and the most and least disruptive scenarios. The success criteria align with the National Institute of Standards and Technology AI Risk Management Framework (NIST AI RMF) trustworthy AI characteristics, and the scenarios are based on various statistical/computational bias that causes algorithmic bias. The framework provides valuable guidance for leveraging AI in healthcare, enhancing objective designs, and mitigating risks by adopting a figure of merit to score the initiatives and measuring the disruptive order. By prioritizing transparency, trustworthy AI, and identifying the most and least disruptive scenarios/biases, the framework promotes responsible and effective use of AI technologies in healthcare. © 2023 IEEE.

关键词： Algorithm ethics cardiac sarcoidosis computational intelligence disease progression legal implications risk management scenario-based preferences social impacts

来源：评论

学校读者我要写书评

暂无评论

CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions

引用

Proceedings of the ACM on Human-Computer Interaction 2024年第CSCW2期8卷 1-31页

作者： Wang, Zhiyuan Hassan, Nusayer Lebaron, virginia Flickinger, Tabor E. Ling, David Edwards, James Wu, Congyu Boukhechba, Mehdi Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Nursing University of Virginia CharlottesvilleVA United States School of Medicine University of Virginia CharlottesvilleVA United States Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY United States Johnson & Johnson Innovative Medicine TitusvilleNJ United States

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice" communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Hospital data processing

来源：评论

学校读者我要写书评

暂无评论

Geospatial Analysis of Extreme Temperature Impacts in Agricultural systems Using Machine Learning 10

Geospatial Analysis of Extreme Temperature Impacts in Agricu...

引用

10th IEEE International Symposium on systems engineering, ISSE 2024

作者： Wheeler, Rayshaun L. Miller, John S. Smith, Gia N. Lambert, James H. Pennetti, Cody A. Louis, Garrick E. Johnson, Deandre A. University of Virginia Department of Civil & Environmental Engineering CharlottesvilleVA United States University of Virginia School of Data Science CharlottesvilleVA United States University of Virginia Department of Systems & Information Engineering CharlottesvilleVA United States University of Virginia Department of Engineering & Society CharlottesvilleVA United States

ISBN: (纸本)9798350353723

Recent higher temperatures, observed compared to previous years, have led to more frequent and intense heat waves, adversely affecting agriculture and human health. This paper explores using Inverse Distance Weighting (IDW) and Kriging to develop GIS layers for a heat-based risk index. Recent increases in global mean temperatures have led to more frequent and intense heat waves, impacting food supply, vegetation, and human health. Optimal plant growth temperatures in temperate climates range from 20°C to 30°C, with nighttime temperatures also being critical. Exceeding these temperature ranges can reduce plant immunity, cause disease, and result in poor crop yields. For example, mango cultivars need a "safe"temperature range of 10°C to 12°C to avoid damage. The intensification of higher temperatures established July 2023 record the hottest days on Earth, highlighting the need for effective monitoring and management tools. IDW, a deterministic method, offers a straightforward and efficient approach by assuming closer points have more influence. Kriging, a probabilistic geostatistical technique, accounts for spatial autocorrelation and prediction uncertainty, making it suitable for complex datasets. The study compares these methods' performance in creating continuous surfaces representing heat-based risk. IDW is simple and efficient but less accurate in sparsely populated areas, while Kriging is more accurate and detailed but requires more computational resources and expertise. An automated workflow for data preprocessing, model training, and cross-validation ensures consistent results. The study recommends adopting IDW for initial assessments and transitioning to Kriging as expertise grows. Enhanced data collection on temperature and population density is also advised to improve GIS layer accuracy, aiding better decision-making and resource allocation to mitigate extreme heat effects on agriculture and public health. © 2024 IEEE.

关键词： Anomaly detection Climate change Complex systems Risk management System identification

来源：评论

学校读者我要写书评

暂无评论

ViGT: proposal-free video grounding with a learnable token in the transformer

引用

science China(information sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：