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ieee Intelligent Vehicles symposium (IV)

作者： Rivera, Esteban do Nascimento, Ana Clara Serra Lienkamp, Markus Tech Univ Munich Sch Engn & Design Dept Mobil Syst Engn Inst Automot TechnolMunich Inst Robot & Machine Munich Germany

ISBN: (纸本)9798350348811;9798350348828

Today, deep learning detectors for autonomous driving are delivering impressive results on public datasets and in real-world applications. However, these detectors require large amounts of data, especially labeled data, to achieve the performance needed to ensure safe driving. The process of collecting and tagging data is expensive and cumbersome. Therefore, the recent focus of the industry has been on how to achieve similar performance while limiting the amount of labeled data required to train such models. Within the cross-modal active learning paradigm, we propose and analyze new strategies to exploit the inconsistencies between camera and LiDAR detectors to improve sampling efficiency and label only the samples that promise improvements for model training. For this, we leverage the 2D projection of the bounding boxes to equalize the output quality of camera and LiDAR detections. Finally, we achieve up to 0.6% AP improvement for camera and 2% improvement for LiDAR over random sampling on the KITTI dataset using a sampling strategy based on the number of detected objects.

关键词： active learning object detection autonomous driving data efficiency multimodality

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analysis of Information Security in a Corporate Environment-a Human Perspective 22

Analysis of Information Security in a Corporate Environment-...

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2024 ieee 22nd World symposium on Applied Machine Intelligence and Informatics, SAMI 2024

作者： Tick, Andrea Szabo-Harka, Nikolett Óbuda University Keleti Károly Faculty of Business and Management Budapest Hungary

ISBN: (纸本)9798350317206

Information security (InfoSec) as becoming one of the most important fields of information systems at corporate level accounts for a large proportion of data losses, attacks and breaches. One of the possible proactive and preventive measures is to train employees and raise their information security awareness. The aim of the research to reveal with the help of a quantitative analysis how companies can effectively interfere in the process and prepare their employees to be alert and aware in this specific field during regular business operations. The results show that large enterprises and small and medium-sized enterprises (SMEs) are differently prepared for employee training and to build their information security shield. © 2024 ieee.

关键词： Security of data

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Proceedings - 2023 ieee visualization in data Science, VDS 2023

Proceedings - 2023 IEEE Visualization in Data Science, VDS 2...

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2023 ieee visualization in data Science, VDS 2023

ISBN: (纸本)9798350330205

The proceedings contain 5 papers. The topics discussed include: Aardvark: comparative visualization of data analysis scripts;NeighViz: towards better understanding of neighborhood effects on social groups with spatial data;a declarative specification for authoring metrics dashboards;visual comparison of text sequences generated by large language models;and HPCClusterScape: increasing transparency and efficiency of shared high-performance computing clusters for large-scale AI models.

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CORRELATION OF CARBON MONOXIDE LEVELS BETWEEN GROUND MEASUREMENTS AND SENTINEL-5P SATELLITE data USING ARTIFICIAL INTELLIGENCE MODELS

CORRELATION OF CARBON MONOXIDE LEVELS BETWEEN GROUND MEASURE...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Jurczynski, Dawid Silesian Tech Univ Gliwice Poland

ISBN: (纸本)9798350360332;9798350360325

In recent years, we have observed a growing interest in using satellite technology to monitor air quality on Earth. This article presents an attempt to correlate carbon monoxide (CO) levels between ground-based data and data obtained from the Sentinel-5P satellite mission. Using modern artificial intelligence models, such as deep neural networks, a multidimensional data analysis was conducted to identify patterns and dependencies. This study represents an important step towards the integration of ground and satellite data for more accurate monitoring and forecasting of global air quality, as well as an attempt to provide information on the carbon monoxide levels on Earth in the absence of ground-based sensors. It highlights the potential for using advanced technology in environmental science to tackle global challenges. The article further delves into the methodology used for data processing and analysis, emphasizing the robustness and accuracy of AI techniques in handling large datasets. This research also opens avenues for future studies focusing on other harmful pollutants, offering a comprehensive view of the environmental health of our planet. This innovative approach is not only critical for environmental monitoring but also holds significant implications for public health, policy-making, and sustainable development.

关键词： carbon monoxide Sentinel-5P artificial intelligence machine learning air quality monitoring

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Design of High-Performance and Compact CAM for Supporting data-Intensive Applications

Design of High-Performance and Compact CAM for Supporting Da...

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ieee International symposium on Circuits and Systems (ISCAS)

作者： Liu, Liu Laguna, Ann Franchesca Niemier, Michael Hu, Xiaobo Sharon Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA De La Salle Univ Comp Technol Manila Philippines

ISBN: (数字)9798350330991

ISBN: (纸本)9798350330991;9798350331004

Content addressable memory (CAM) is a special-purpose search engine that can support parallel search directly in memory. CAMs are of increasing interest for machine learning and data analytics applications that require intensive search operations. However, conventional CMOS CAMs have large cell areas and high energy consumption, which limits applicability. Also, many data-intensive applications need more efficient data representation and approximate matching functions, which may not be efficiently realized by conventional ternary CAMs. As such, we introduce a more compact and high-performance CAM design based on non-volatile ferroelectic FET devices. Furthermore, we present a reconfigurable CAM design, MHCAM, to support approximate search for multi-dimensional data. We use DNA alignment as a proxy application to illustrate the design's application-level benefits.

关键词： Energy consumption Cross layer design data analysis Nonvolatile memory Microprocessors Field effect transistors DNA

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DeFraudify4ALL: Prototyping and Validation of a System for Fraud Detection with Big data and Cloud Technology 30

DeFraudify4ALL: Prototyping and Validation of a System for F...

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30th ieee International symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Popovici, Eduard-Cristian Stalidi, Cosmina Teodoras, Dan-Alexandru Suciu, George Cosma, Ioan POLITEHNICA University of Bucharest ETTI Faculty Telecommunication Department Bucharest Romania Beia Consult International R and D Department Bucharest Romania

ISBN: (纸本)9798331539511

This paper presents the prototyping and validation of a Financial data Management and analysis System designed to enhance fraud detection using advanced Big data and Cloud technologies. The system incorporates a Java-based worker for data retrieval and analysis from various sources, including external APIs. The processed data is efficiently stored in a data Lake utilizing Dremio, Apache Arrow, and Apache Parquet technologies. By leveraging cloud platforms such as AWS, the system ensures scalability, security, and flexibility. Additionally, the integration of sophisticated data visualization tools and AI algorithms, particularly Random Forest, enhances the system's ability to detect fraudulent activities. © 2024 ieee.

关键词： Cloud platforms

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Global Meets Local: Dual Activation Hashing Network for large-Scale Fine-Grained Image Retrieval

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2024年第11期36卷 6266-6279页

作者： Jiang, Xin Tang, Hao Li, Zechao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

In the Internet era, the exponential growth of fine-grained image databases poses a considerable challenge for efficient information retrieval. Hashing-based approaches gained traction for their computational and storage efficiency, yet fine-grained hashing retrieval presents unique challenges due to small inter-class and large intra-class variations inherent to fine-grained entities. Thus, traditional hashing algorithms falter in discerning these subtle, yet critical, visual differences and fail to generate compact yet semantically rich hash codes. To address this, we introduce a Dual Activation Hashing Network (DAHNet) designed to convert high-dimensional image data into optimized binary codes via an innovative feature activation paradigm. The architecture consists of dual branches specifically tailored for global and local semantic activation, thereby establishing direct correspondences between hash codes and distinguishable object parts through a hierarchical activation pipeline. Specifically, our spatial-oriented semantic activation module modulates dominant visual regions while amplifying the activations of subtle yet semantically rich areas in a controlled manner. Building on these activated visual representations, the proposed inter-region semantic enrichment module further enriches them by unearthing semantically complementary cues. Concurrently, DAHNet integrates a channel-oriented semantic activation module that exploits channel-specific correlations to distill contextual cues from spatially-activated visual features, thereby reinforcing robust learning to hash. To maintain the similarity of the original entities, we amalgamate final hash codes from both activation branches, capturing both local textural details and global structural information. Comprehensive evaluations on five fine-grained image retrieval benchmarks demonstrate DAHNet's superior performance over existing state-of-the-art hashing solutions, especially on 12-bit, improving performance by

关键词： Image retrieval Semantics visualization Codes Feature extraction Task analysis Streams Feature activation fine-grained hashing fine-grained image retrieval learning to hash

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Evaluating the Recommendations of LLMs to Teach a visualization Technique using Bloom's Taxonomy

Evaluating the Recommendations of LLMs to Teach a Visualizat...

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IS and T International symposium on Electronic Imaging 2024: visualization and data analysis, VDA 2024

作者： Joshi, Alark Srinivas, Chandana Firat, Elif E. Laramee, Robert S. University of San Francisco United States Cukurova University Turkey University of Nottingham United Kingdom

large Language Models (LLMs) have demonstrated a huge impact on education and literacy in recent years. We evaluated the recommendations provided by two popular LLMs (OpenAI's ChatGPT and Google's Bard) to educate novices on the topic of Parallel Coordinate Plots (PCPs) using Bloom's taxonomy. We present the results of a human-expert evaluation of the recommendations provided by both the LLMs with experts from the visualization literacy field. Based on the analysis of the expert evaluation, we found that while both the LLMs provided some relevant and practical recommendations, some of the recommendations were either too difficult for novices or were in the wrong cognitive process (according to Bloom's taxonomy). In some cases, the hallucinations led to recommendations that were completely inapplicable to Parallel Coordinate Plots literacy. © 2024, Society for Imaging Science and Technology.

关键词： visualization

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Toward the Use of Immersive Technologies for Interactive visualization

Toward the Use of Immersive Technologies for Interactive Vis...

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IS and T International symposium on Electronic Imaging 2024: visualization and data analysis, VDA 2024

作者： Wischgoll, Thomas Wright State University DaytonOH United States

Virtual and augmented reality technologies have significantly advanced and come down in price during the last few years. These technologies can provide a great tool for highly interactive visualization approaches of a variety of data types. In addition, setting up and managing a virtual and augmented reality laboratory can be quite involved, particularly with large-screen display systems. Thus, this keynote presentation will outline some of the key elements to make this more manageable by discussing the frameworks and components needed to integrate the hardware and software into a more manageable package. Examples for visualizations and their applications using this environment will be discussed from a variety of disciplines to illustrate the versatility of the virtual and augmented reality environment available in the laboratories that are available to faculty and students to perform their research. © 2024, Society for Imaging Science and Technology.

关键词： visualization

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MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic analysis with In-Storage Processing 51

MegIS: High-Performance, Energy-Efficient, and Low-Cost Meta...

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ACM/ieee 51st Annual International symposium on Computer Architecture (ISCA)

作者： Ghiasi, Nika Mansouri Sadrosadati, Mohammad Mustafa, Harun Gollwitzer, Arvid Firtina, Can Eudine, Julien Mao, Haiyu Lindegger, Joel Cavlak, Meryem Banu Alser, Mohammed Park, Jisung Mutlu, Onur Swiss Fed Inst Technol Zurich Switzerland POSTECH Pohang South Korea

ISBN: (纸本)9798350326598;9798350326581

Metagenomics, the study of the genome sequences of diverse organisms in a common environment, has led to significant advances in many fields. Since the species present in a metagenomic sample are not known in advance, metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases containing information on different species' genomes. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system to the rest of the system. In-storage processing can be a fundamental solution for reducing this overhead. However, designing an in-storage processing system for metagenomics is challenging because existing approaches to metagenomic analysis cannot be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MegIS, the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. MegIS is enabled by our lightweight design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) data mapping, and 5) lightweight in-storage accelerators. MegIS's design is flexible, capable of supporting different types of metagenomic input datasets, and can be integrated into various metagenomic analysis pipelines. Our evaluation shows that MegIS outperforms the state-of-the-art performance-and accuracy-optimized software metagenomic tools by 2.7x-37.2x and 6.9x-100.2x, respectively, while matching the accuracy of

关键词： Metagenomic analysis Near-data Processing Storage

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