检索结果-内蒙古大学图书馆

International Workshop on Secure Internet of Things (SIoT)

作者： Morsinaldo Medeiros Mariana Azevedo Miguel Amaral Carlos M. D. Viegas Marianne Silva Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte - Natal Brazil INEGI Faculty of Enzineering University of Porto Porto Portugal

Driven by the Internet of Things (IoT) revolution, the automotive industry is transforming itself significantly. Ve-hicles, once merely transportation machines, have now evolved into complex networks of sensors and actuators. This evolution heralds an era of intelligent and connected vehicles that collect and analyze real-time data, benefiting various sectors. In response to this scenario, this paper introduces a framework for collecting and processing vehicular information, leveraging the capabilities of Edge Computing. The framework integrates the OBD-II Edge (Freematics ONE+) technology for efficient data acquisition, harnessing the power of Edge Computing to enable real-time data processing and immediate response at the network's edge. Additionally, Node-RED and Kafka technologies are utilized for efficient data handling and routing, while the Timescale database ensures optimized storage of time-series data. Finally, the Streamlit tool provides an intuitive data visualization interface, enhancing data analysis and decision-making processes. Evaluated through a real-case scenario study, this proposal has proven viable and effective, providing analyses that directly contribute to vehicular safety and preventive maintenance, while also enhancing logistical operations in the field.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Explainable Deep Learning Model for Prediction of Severity of Alzheimer’s Disease

An Explainable Deep Learning Model for Prediction of Severit...

引用

IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Godwin Ekuma Daniel B. Hier Tayo Obafemi-Ajayi Computer Science Dept. Missouri State University Springfield MO USA Electrical & Computer Eng. Dept Missouri University of Science & Technology Rolla MO USA Engineering Program Missouri State University Springfield MO USA

Deep Convolutional Neural Networks (CNNs) have become the go-to method for medical imaging classification on various imaging modalities for binary and multiclass problems. Deep CNNs extract spatial features from image data hierarchically, with deeper layers learning more relevant features for the classification application. Despite the high predictive accuracy, usability lags in practical applications due to the black-box model perception. Model explainability and interpretability are essential for successfully integrating artificial intelligence into healthcare practice. This work addresses the challenge of an explainable deep learning model for the prediction of the severity of Alzheimer’s disease (AD). AD diagnosis and prognosis heavily rely on neuroimaging information, particularly magnetic resonance imaging (MRI). We present a deep learning model framework that integrates a local data-driven interpretation method that explains the relationship between the predicted AD severity from the CNN and the input MR brain image. The deep explainer uses SHapley Additive exPlanation values to quantity the contribution of different brain regions utilized by the CNN to predict outcomes. We conduct a comparative analysis of three high-performing CNN models: DenseNet121, DenseNet169, and Inception-ResNet-v2. The framework shows high sensitivity and specificity in the test sample of subjects with varying levels of AD severity. We also correlated five key AD neurocognitive assessment outcome measures and the APOE genotype biomarker with model misclassifications to facilitate a better understanding of model performance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP

arXiv

引用

arXiv 2024年

作者： Kim, Eunji Shim, Kyuhong Chang, Simyung Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Korea Republic of Qualcomm AI Research Qualcomm Korea YH Korea Republic of Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

A text encoder within Vision-Language Models (VLMs) like CLIP plays a crucial role in translating textual input into an embedding space shared with images, thereby facilitating the interpretative analysis of vision tasks through natural language. Despite the varying significance of different textual elements within a sentence depending on the context, efforts to account for variation of importance in constructing text embeddings have been lacking. We propose a framework of Semantic Token Reweighting to build Interpretable text embeddings (SToRI), which incorporates controllability as well. SToRI refines the text encoding process in CLIP by differentially weighting semantic elements based on contextual importance, enabling finer control over emphasis responsive to data-driven insights and user preferences. The efficacy of SToRI is demonstrated through comprehensive experiments on few-shot image classification and image retrieval tailored to user preferences. © 2024, CC BY-NC-SA.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Phortex: Physically-Informed Operational Robotic Trajectories for Scientific Expeditions

IEEE Transactions on Field Robotics

引用

IEEE Transactions on Field Robotics 2024年 1卷 446-475页

作者： Victoria L. Preston Genevieve E. Flaspohler John W. Fisher Anna P. M. Michel Nicholas Roy Applied Ocean Physics and Engineering Massachusetts Institute of Technology and Woods Hole Oceanographic Institution Joint Program Cambridge MA USA Olin College of Engineering Needham MA USA Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Rhiza Research Oakland CA USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Woods Hole MA USA

Mobile robots are increasingly used to collect valuable in situ samples during scientific expeditions. However, many phenomena of scientific interest—deep-sea hydrothermal plumes, algal blooms, warm-core eddies, and lava flows—are spatiotemporal distributions that evolve on spatial and temporal scales that complicate sample collection. Here, we consider the problem of charting the space-time dynamics of deep-sea hydrothermal plumes with the state-of-the-art autonomous underwater vehicle (AUV) Sentry. In the hydrothermal plume charting problem, the plume state is driven by complicated and unobserved dynamics in the deep sea. To effectively sample the moving plume, an autonomy system must infer plume dynamics from sparse, point observations, while respecting operational constraints of AUV Sentry that restrict the set of possible trajectories to nonadaptive, uniform-coverage patterns. We frame the plume charting problem as a sequential decision-making problem, and formulate a mission planner Phortex: physically-informed operational robotic trajectories for expeditions that strategically designs full mission trajectories for Sentry, where each mission plan is informed by the observations of the last. Phortex is composed of a trajectory optimizer that maximizes expected samples collected within a moving plume, and Phumes: physically-informed uncertainty models for environment spatiotemporality, a modeling framework that leverages an embedded simulator of idealized plume physics as an inductive bias to enable dynamics learning from extreme partial observations and few Sentry deployments. In both simulation and in field trials at a hydrothermal site in the Gulf of California, we demonstrate that Sentry using Phortex learns to track a moving hydrothermal plume and gather samples that significantly improve upon baseline spatial and temporal diversity for use in downstream science tasks.

关键词： Trajectory Robots Robot sensing systems Vehicle dynamics Spatiotemporal phenomena Predictive models Decision making Uncertainty Data models

来源：评论

学校读者我要写书评

暂无评论

A Detailed Analysis of Qualitative and Quantitative Factors in Realization of 6G Communication

A Detailed Analysis of Qualitative and Quantitative Factors ...

引用

International Conference on Internet of Things: Systems, Management and Security

作者： Mohammad Alja’Afreh Ali Karime Sahel Alouneh Abdulmotaleb El Saddik Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE School of Electrical Engineering and Computer Science University of Ottawa Canada Department of Electrical and Computer Engineering The Royal Military College of Canada Canada Cybersecurity Program College of Engineering AL AIN University UAE Computer Engineering Department German Jordanian University

Undoubtedly, the world has so far faced a pandemic which is reshaping daily lives and business activities. Even at current endemic stages, special focus on maintaining physical distancing norms for curbing the expeditious spread of the disease, many institutions, individuals and industries rely on communications networks or telecoms for ensuring service consistency to avoid complete termination of their business operations and other activities. This has put enormous pressure on mobile networks and communication systems thereby making the technology experts to think more about introducing rapid speed, vast coverage and high connectivity networks. The extensive application of fresh communication networks and enabling technologies have impelled the advent of 6G communication networks. As 6G is still in its inception phase, its complete realization requires a proper and high understanding of diverse quantitative and qualitative factors supporting its deployment. From this standpoint, this survey article intends to deeply explore 6G networks, their significance and prerequisites. This paper provides a succinct theoretical background of 6G technology, and reviews the diverse enabling technologies and existing works undertaken on core technologies. It explores the prevailing gaps in research for providing readers to gain information regarding challenges in perfect 6G network realization and implementation thus paving the road for a successful 6G vision.

关键词： 6G mobile communication Systematics Pandemics Roads Telecommunications Communication networks Security

来源：评论

学校读者我要写书评

暂无评论

INTERPRETABLE LANGUAGE MODELING VIA INDUCTION-HEAD NGRAM MODELS

arXiv

引用

arXiv 2024年

作者： Kim, Eunji Mantena, Sriya Yang, Weiwei Singh, Chandan Yoon, Sungroh Gao, Jianfeng Microsoft Research United States Department of Electrical and Computer Engineering Seoul National University Korea Republic of Stanford University United States Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

Recent large language models (LLMs) have excelled across a wide range of tasks, but their use in high-stakes and compute-limited settings has intensified the demand for interpretability and efficiency. We address this need by proposing Induction-head ngram models (Induction-Gram), a method that builds an efficient, interpretable LM by bolstering modern ngram models with a hand-engineered "induction head". This induction head uses a custom neural similarity metric to efficiently search the model’s input context for potential next-word completions. This process enables Induction-Gram to provide ngram-level grounding for each generated token. Moreover, experiments show that this simple method significantly improves next-word prediction over baseline interpretable models (up to 26%p) and can be used to speed up LLM inference for large models through speculative decoding. We further study Induction-Gram in a natural-language neuroscience setting, where the goal is to predict the next fMRI response in a sequence. It again provides a significant improvement over interpretable models (20% relative increase in the correlation of predicted fMRI responses), potentially enabling deeper scientific investigation of language selectivity in the brain. The code is available at https://***/ejkim47/induction-gram. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

引用

Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP), International Conference on

作者： Rissa Rahmania Yaya Heryadi Lukas Lukas Wayan Suparta Ilvico Sonata Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Group (CEG) Faculty of Engineering Universitas Katolik Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products.

关键词： Surveys computer vision Accuracy Image recognition Transformers Feature extraction Data augmentation Artificial intelligence Image classification

来源：评论

学校读者我要写书评

暂无评论

The Effect of Major Depressive Disorder on Producing Intelligible Speech

SSRN

引用

SSRN 2024年

作者： Seneviratne, Nadee Li, Leslie Espy-Wilson, Carol Department of Electrical and Computer Engineering University of Maryland College ParkMD20740 United States Program in Neuroscience and Cognitive Science Department of Linguistics University of Maryland College ParkMD20740 United States

Understanding the characteristics of depressed speech can provide insights on aspects to consider when developing biomarkers for automatic depression detection systems. We present the results of a speech intelligibility study that was conducted to assess the intelligibility of the speech of a female and a male subject when they were severely depressed and when they were in remission. Our analysis shows that, in general, the depressed speech is at least as intelligible as in-remission speech. This is further supported by the underlying "simpler" articulatory coordination of depressed speech as derived from the Articulatory Coordination Features. Acoustically, the simpler articulatory coordination means that there is less variability due to coarticulation in the speech signal. © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Thermal energy dependent transient permittivity of epsilon-near-zero material

引用

Science China(Physics,Mechanics & Astronomy) 2022年第8期65卷 96-101页

作者： Heng Wang Lixun Sun Kang Du Wending Zhang SooJin Chua Guixin Li Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information TechnologySchool of Physical Science and TechnologyNorthwestern Polytechnical UniversityXi’an710129China Department of Materials Science and Engineering Southern University of Science and TechnologyShenzhen518055China Shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen518055China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore LEES Program Singapore-MIT Alliance for Research&Technology(SMART)Singapore138602Singapore

Transparent conductive oxides exhibit attractive optical nonlinearity with ultrafast response and giant refractive index change near the epsilon-near-zero(ENZ) wavelength, originating from the intraband dynamics of conduction electrons. The optical nonlinearity of ENZ materials has been explained by using the overall-effective-mass and the overall-scattering-time of electrons in the extended Drude model. However, their response to optical excitation is yet the last building block to complete the theory. In this paper, the concept of thermal energy is theoretically proposed to account for the total energy of conduction electrons exceeding their thermal equilibrium value. The time-varying thermal energy is adopted to describe the transient optical response of indium-tin-oxide(ITO), a typical ENZ material. A spectrally-resolved femtosecond pump-probe experiment was conducted to verify our theory. By correlating the thermal energy with the pumping density, both the giant change and the transient response of the permittivity of ITO can be predicted. The results in this work provide a new methodology to describe the transient permittivities of ENZ materials, which will benefit the design of ENZ-based nonlinear photonic devices.

关键词： epsilon-near-zero optical nonlinearity transient response thermal energy hot electron indium tin oxide

来源：评论

学校读者我要写书评

暂无评论

MathReader: Text-to-Speech for Mathematical Documents

arXiv

引用

arXiv 2025年

作者： Hyeon, Sieun Jung, Kyudan Kim, Nam-Joon Ryu, Hyun Gon Do, Jaeyoung Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Mathematics Chung-Ang University Korea Republic of NVIDIA United States Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of

TTS (Text-to-Speech) document reader from Microsoft, Adobe, Apple, and OpenAI have been serviced worldwide. They provide relatively good TTS results for general plain text, but sometimes skip contents or provide unsatisfactory results for mathematical expressions. This is because most modern academic papers are written in LaTeX, and when LaTeX formulas are compiled, they are rendered as distinctive text forms within the document. However, traditional TTS document readers output only the text as it is recognized, without considering the mathematical meaning of the formulas. To address this issue, we propose MathReader, which effectively integrates OCR, a fine-tuned T5 model, and TTS. MathReader demonstrated a lower Word Error Rate (WER) than existing TTS document readers, such as Microsoft Edge and Adobe Acrobat, when processing documents containing mathematical formulas. MathReader reduced the WER from 0.510 to 0.281 compared to Microsoft Edge, and from 0.617 to 0.281 compared to Adobe Acrobat. This will significantly contribute to alleviating the inconvenience faced by users who want to listen to documents, especially those who are visually impaired. The code is available at https://***/hyeonsieun/MathReader. Copyright © 2025, The Authors. All rights reserved.

关键词：

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：