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2025 ieee International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Akilesh, S. Sekar, Rajeev Om Kumar, C.U. Prakalya, D. Suguna, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

This paper presents a multi-agent hierarchical workflow tailored for automating data analysis, code generation, and visualization, focusing specifically on user-provided CSV datasets. The workflow integrates AlphaCodium with LangChain, LangGraph, and GPT, enabling autonomous code generation, unit test development, and debugging. The system operates by analyzing the uploaded dataset, assigning agents that work sequentially: a programmer agent generates code using the Skeleton-Of-Code approach, a unit test agent verifies the code, and an executor agent runs the code. A debugging agent is also included to identify and resolve any issues. The AI agents involved are capable of dynamically accessing online resources, including documentation and references to enhance their decision-making processes. This work attempts to exemplify the application of AI in automating not only code execution but also the planning and validation stages by writing unit tests in the software development process to reflect the increasing role of AI in advancing automation for rapid code generation within the software industry. © 2025 ieee.

关键词： Software design

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FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs 32

FINESSD: Near-Storage Feature Selection with Mutual Informat...

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32nd ieee Annual International symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Kyparissas, Nikolaos Brown, Gavin Luján, Mikel The University of Manchester Department of Computer Science United Kingdom

ISBN: (纸本)9798350372434

Feature selection is the data analysis process that selects a smaller and curated subset of the original dataset by filtering out data (features) which are irrelevant or redundant. The most important features can be ranked and selected based on statistical measures, such as mutual information. Feature selection not only reduces the size of dataset as well as the execution time for training Machine Learning (ML) models, but it can also improve the accuracy of the inference. This paper analyses mutual-information-based feature selection for resource-constrained FPGAs and proposes FINESSD, a novel approach that can be deployed for near-storage acceleration. This paper highlights that the Mutual Information Maximization (MIM) algorithm does not require multiple passes over the data while being a good trade-off between accuracy and FPGA resources, when approximated appropriately. The new FPGA accelerator for MIM generated by FINESSD can fully utilize the NVMe bandwidth of a modern SSD and perform feature selection without requiring full dataset transfers onto the main processor. The evaluation using a Samsung SmartSSD over small, large and out-of-core datasets shows that, compared to the mainstream multiprocessing Python ML libraries and an optimized C library, FINESSD yields up to 35 × and 19 ×speedup respectively while being more than 70 × more energy efficient for large, out-of-core datasets. © 2024 ieee.

关键词： large datasets

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Comparative Causal analysis of Network Log data in Two large ISPs

Comparative Causal Analysis of Network Log Data in Two Large...

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ieee/IFIP Network Operations and Management symposium

作者： Kobayashi, Satoru Shima, Keiichi Cho, Kenjiro Akashi, Osamu Fukuda, Kensuke NII Tokyo Japan IIJ Tokyo Japan NII Sokendai Tokyo Japan

ISBN: (纸本)9781665406017

Towards a collaborative analysis of log data obtained from multiple networks, we first need to clarify what kind of information is available as transferable knowledge between different networks. However, we cannot directly compare network log data from different sources because the data largely depends on the network architecture and equipment. In this paper, we focus on relational information among network log events that follow standardized network protocols regardless of network environment. We propose a comparative analysis approach relying on causality between log time-series. In this approach, we classify log messages into anonymized log time-series with log templates, reduce the number of log time-series to decrease processing time, and apply causal discovery with the PC algorithm. To decrease the processing time of causal analysis, we propose a new preprocessing method that reduces the number of log time-series without any domain knowledge (i.e., available in any ISPs). We compare log data obtained from two nationwide ISPs to demonstrate the effectiveness of the causal approach in comparative analysis.

关键词： Network management Log analysis Causal discovery Comparative analysis

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Benchmarking Joint Face Spoofing and Forgery Detection With Visual and Physiological Cues

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ieee TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第5期21卷 4327-4342页

作者： Yu, Zitong Cai, Rizhao Li, Zhi Yang, Wenhan Shi, Jingang Kot, Alex C. Great Bay Univ Sch Comp & Informat Technol Dongguan 523000 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Peng Cheng Lab Shenzhen 518066 Peoples R China Nanyang Technol Univ ROSE Lab Singapore 639798 Singapore Xi An Jiao Tong Univ Xian 710049 Peoples R China

Face anti-spoofing (FAS) and face forgery detection play vital roles in securing face biometric systems from presentation attacks (PAs) and vicious digital manipulation (e.g., deepfakes). Despite satisfactory performance upon large-scale data and powerful deep models, recent advances in face spoofing and forgery detection approaches usually focus on 1) unimodal visual appearance or physiological (i.e., remote photoplethysmography (rPPG)) cues;and 2) separated feature representation for FAS or face forgery detection. On one side, unimodal appearance and rPPG features are respectively vulnerable to high-fidelity face 3D mask and video replay attacks, inspiring us to design reliable multi-modal fusion mechanisms for generalized FAS. On the other side, there are rich common features across FAS and face forgery detection tasks (e.g., periodic rPPG rhythms and vanilla appearance for bonafides), providing solid evidence to design a joint FAS and face forgery detection system in a multi-task learning fashion. In this paper, we establish the first joint face spoofing and forgery detection benchmark using both visual appearance and physiological rPPG cues. To enhance the rPPG periodicity discrimination, we design a two-branch physiological network using both facial spatio-temporal rPPG signal map and its continuous wavelet transformed counterpart as inputs. To mitigate the modality bias and improve the fusion efficacy, we conduct a weighted batch and layer normalization for both appearance and rPPG features before multi-modal fusion. We also investigate prevalent deep models, feature fusion strategies and multi-task learning configurations for joint face spoofing and forgery detection. We find that the generalization capacities of both unimodal (appearance or rPPG) and multi-modal (appearance+rPPG) models can be obviously improved via joint training on these two tasks. We hope this new benchmark will facilitate the future research of both FAS and deepfake detection communitie

关键词： Faces Forgery Task analysis visualization Physiology Face recognition Deepfakes Face anti-spoofing face forgery detection deepfake rPPG fusion joint training multi-task learning

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AN EARTH SYSTEM DIGITAL TWIN FOR FLOOD PREDICTION AND analysis

AN EARTH SYSTEM DIGITAL TWIN FOR FLOOD PREDICTION AND ANALYS...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Huang, Thomas David, Cedric Oadia, Catalina Roberts, Joe T. Kumar, Sujay V. Stackhouse, Paul Borges, David Baillarin, Simon Blanchet, Gwendoline Kettig, Peter CALTECH NASA Jet Prop Lab Pasadena CA 91125 USA NASA Goddard Space Flight Ctr Greenbelt MD USA NASA Langley Res Ctr Hampton VA 23665 USA Ctr Natl Etud Spatiales Toulouse France

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observation data, and connecting them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA's Advanced Information Systems Technology (AIST)'s Integrated Digital Earth analysis System (IDEAS) project is to establish an extensible architectural solution to develop digital twins of our physical environment for Earth Science. IDEAS delivers a formal system architecture with mechanisms for the outputs of one model to feed into others;for driving models with observation data;and for harmonizing observation data and model outputs for analysis. To validate and demonstrate the IDEAS architecture, this project collaborates with the Space Climate Observatory (SCO)'s FloodDAM project and the Centre National d'Etudes Spatiales (CNES) to focus on floods detection, prediction and their impacts.

关键词： digital twin flood hydrology natural disaster remote sensing earth observation

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A BIBLIOMETRIC REVIEW AND analysis OF MODIS LAND SURFACE TEMPERATURE data APPLICATION IN AFRICA.

A BIBLIOMETRIC REVIEW AND ANALYSIS OF MODIS LAND SURFACE TEM...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Jombo, S. Adelabu, S. A. Univ Free State Dept Geog Bloemfontein Campus Bloemfontein South Africa

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

The Moderate Resolution Imaging Spectroradiometer (MODIS) has become one of the widely used sensors to acquire continuous land surface temperature (LST) over large areas. However, despite the progress in the use of MODIS LST data globally, Africa still lags in the number of publications that utilized the data in research. The data extraction and analysis were done using bibliometric analysis in R statistical software. A total of 59 articles that used MODIS LST data in Africa were generated. The results indicated a steady rise in publication since 2006. China was the most contributing country to publications and Remote Sensing was the most active journal. MODIS was the most common word used in the generated publications. This study is vital in showing improvements in the application of MODIS LST data in Africa which provides information and encourages researchers to use new and unique models that can assist in global warming mitigation.

关键词： MODIS LST Africa Bibliometric Scopus Web of Science

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Effective Throughput analysis of Different Task Execution Strategies for Mid-Air Fitts' Tasks in Virtual Reality

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第11期28卷 3939-3947页

作者： Batmaz, Anil Ufuk Stuerzlinger, Wolfgang Concordia Univ Comp Sci Dept Montreal PQ Canada Simon Fraser Univ Sch Interact Arts & Amp Technol Burnaby BC Canada

Fitts' law and throughput based on effective measures are two mathematical models frequently used to analyze human motor performance in a standardized pointing task, e.g., to compare the performance of input and output devices. Even though pointing has been deeply studied in 2D, it is not well understood how different task execution strategies affect throughput in pointing in 3D virtual environments. In this work, we examine the effective throughput measure, claimed to be invariant to task execution strategies, in Virtual Reality (VR) systems with three such strategies, "as fast, as precise, and as fast and as precise as possible" for ray casting and virtual hand interaction, by re-analyzing data from a 3D pointing IS0 9241-411 study. Results show that effective throughput is not invariant for different task execution strategies in VR, which also matches a more recent 2D result. Normalized speed vs. accuracy curves also did not fit the data. We thus suggest that practitioners, developers, and researchers who use MacKenzie's effective throughput formulation should consider our findings when analyzing 3D user pointing performance in VR systems.

关键词： Fitts' task Virtual Reality Effective Throughput Speed-Accuracy Trade-off

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Leveraging Urban Big data for Informed Business Location Decisions: A Case Study of Starbucks in Tianhe District, Guangzhou City

Leveraging Urban Big Data for Informed Business Location Dec...

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2023 ieee International Conference on Industrial Engineering and Engineering Management, IEEM 2023

作者： Xiang, Yan Chang, Danni Feng, Xuan School of Design Shanghai Jiao Tong University Shanghai China College of Engineering Seoul National University Seoul Korea Republic of

ISBN: (纸本)9798350323153

With the development of the information age, cities provide a large amount of data that can be analyzed and utilized to facilitate the decision-making process. Urban big data and analytics are particularly valuable in the analysis of business location decisions, providing insight and supporting informed choices. By examining data relating to commercial locations, it becomes possible to analyze various spatial characteristics and derive the feasibility of different locations. This analytical approach contributes to effective decision-making and the formulation of robust location strategies. To illustrate this, the study focuses on Starbucks cafes in the Tianhe District of Guangzhou City, China. Utilizing data visualization maps, the spatial distribution characteristics and influencing factors of Starbucks locations are analyzed. By examining the geographical coordinates of Starbucks, main distribution characteristics are identified. Through this analysis, it explores the factors influencing the spatial layout of commercial store locations, using Starbucks as a case study. The findings offer valuable insights into the management of industrial layout and the location strategies of commercial businesses in urban environments, opening avenues for further research and development in this field. © 2023 ieee.

关键词： data analysis location strategies spatial distribution Urban big data

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Topological Simplifcation of Jacobi Sets for Piecewise-Linear Bivariate 2D Scalar Fields with Adjustment of the Underlying data

Topological Simplifcation of Jacobi Sets for Piecewise-Linea...

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2024 ieee Topological data analysis and visualization, TopoInVis 2024

作者： Raith, Felix Scheuermann, Gerik Heine, Christian Leipzig University Germany Leipzig University ScaDS.AI Germany

ISBN: (纸本)9798331528447

Jacobi sets are an important tool to study the relationship between functions. Defined as the set of all points where the function's gradients are linearly dependent, Jacobi sets extend the notion of critical point to multifields. In practice, Jacobi sets for piecewise-linear approximations of smooth functions can become very complex and large due to noise and numerical errors. Existing methods that simplify Jacobi sets exist, but either do not address how the functions' values have to change in order to have simpler Jacobi sets or remain purely theoretical. In this paper, we present a method that modifies 2D bivariate scalar fields such that Jacobi set components that are due to noise are removed, while preserving the essential structures of the fields. The method uses the Jacobi set to decompose the domain, stores the and weighs the resulting regions in a neighborhood graph, which is then used to determine which regions to join by collapsing the image of the region's cells. We investigate the influence of different tie-breaks when building the neighborhood graphs and the treatment of collapsed cells. We apply our algorithm to a range of datasets, both analytical and real-world and compare its performance to simple data smoothing. © 2024 ieee.

关键词： Piecewise linear techniques

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Enabling data Reduction for Flash-X Simulations

Enabling Data Reduction for Flash-X Simulations

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and analysis, SC Workshops 2024

作者： Jain, Rajeev Tang, Houjun Dhruv, Akash Byna, Suren Argonne National Laboratory United States Lawrence Berkeley National Laboratory United States The Ohio State University United States

ISBN: (纸本)9798350355543

High-fidelity physics simulation codes, such as Flash-X, generate large amounts of simulation data. Much of the data written to files is sparse and can be compressed without significantly impacting the accuracy of the simulation or the quality of the visualizations. Reduced file sizes can significantly save storage space and bandwidth, and offer improved performance of visualization tools. Reduction in file size also allows the simulation to output more data for higher resolution/fidelity analysis. We introduced SZ3 and ZFP compression technologies into Flash-X as an effective data reduction strategy. We conducted experiments on the Frontier exascale supercomputer, evaluating both lossless and lossy compression techniques and quantifying their effects. We examined the impact of accuracy variations and chunk size variations for different Flash-X problems. Our study provides valuable insights and guidelines for simulation developers, helping them understand the best ways to adopt compression tailored to their specific problems. © 2024 ieee.

关键词： compression data reduction Flash-X simulation

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