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Temporal Attention Gate Network With Temporal Decomposition for Improved Prediction Accuracy of Univariate Time-Series data

Temporal Attention Gate Network With Temporal Decomposition ...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Sunghyun Sim Dohee Kim Seok Chan Jeong Industrial Management & Big Data Engineering Major Div. of Industrial Convergence System Engineering Dong-Eui University Busan South Korea Dept. of Industrial Engineering Industrial Data Science & Engineering Major Pusan National University Busan South Korea Dept. of e-Business College of Commerce and Economics & AI Grand ICT Research Center Dong-Eui University

Time-series forecasting has widely been addressed in data science and various domains, but many limitations persist in terms of prediction accuracy. We propose a network architecture called temporal attention gate network (TAGNet) to improve the prediction accuracy of time-series prediction. TAGNet integrates new concepts of temporal filter and temporal attention gate. First, the temporal filter learns information embedded in time-series data by decomposing the input data through variational mode decomposition. Second, the temporal attention gate learns the relationship between the decomposed time-series signals and hidden states to learn their relationships. To verify the performance of the proposed TAGNet, a comparative experiment was conducted on three univariate time-series datasets. The results show that the prediction performance improves by 15% on average for short-, medium-, and long-term predictions compared with various deep learning methods.

关键词： Deep learning Limiting Microprocessors Decision making Logic gates Network architecture data science

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Design of photoacoustic modular helmet for neonatatal neurocritical care

Design of photoacoustic modular helmet for neonatatal neuroc...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Ananya Tandri Keshuai Xu May W. Chen Emad Boctor Jeeun Kang Dept. Biomedical Engineering Johns Hopkins University Baltimore MD USA Dept. Computer Science Johns Hopkins University Baltimore MD USA Dept. Pediatrics Johns Hopkins University Baltimore MD USA Lab. for Computational Sensing & Robotics Johns Hopkins University Baltimore MD USA Dept. Anesthesiology and Critical Care Medicine Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Perinatal arterial ischemic stroke (PAIS) is a form of focal vascular neurological injury that occurs during or shortly after birth. We recently proposed a modular PA helmet (PAH) system, in which multiple modules constitutes a flexible helmet to accommodate any neonatal head shape and size to provide volumetric monitoring of neonatal brain oxygen saturation. Here, optical tracking of the PAH configuration enables spatial image reconstruction. Given the large freedom in PAH configuration, it is critical to understand the implications of various design parameters in imaging quality. In this paper, we present our simulation-aided design approach using acoustic and optical simulation tools. Our design criteria are to optimize 1) signal contrast and 2) spatial resolution with different number of modules and their mean distances, and is further challenged with modular position error inevitable in modular position recognition. Results of the simulation established an acceptable error threshold for optical tracking and informed module prototype configuration and shape. The prototype based on the optimal design specifications is also demonstrated.

关键词： Pediatrics Head Shape Prototypes Optical saturation Optical variables control Optical imaging Adaptive optics Acoustics Safety

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Enhancing Intrusion Detection Systems for Secure E-Commerce Communication Networks

Enhancing Intrusion Detection Systems for Secure E-Commerce ...

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Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), International Conference on th

作者： Nagagopiraju Vullam D Roja NagaMalleswara Rao Sai Srinivas Vellela Lakshma Reddy Vuyyuru K Kiran Kumar Dept. of CSE Chalapathi Institute of Engineering and Technology Guntur AP India Dept. of CSE-Data Science Chalapathi Institute of Technology Guntur AP India Dept. of CSE Chalapathi Institute of Technology Guntur AP India Dept. of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Rapid advancements in information and communication technologies have permeated various sectors, leading to an exponential growth in Internet users. However, this surge in connectivity has also given rise to a corresponding increase in cyber-attacks. E-commerce applications, which have become integral to our daily lives, encompass online banking, marketing, trading, and numerous other online businesses. To safeguard these critical systems and networks, Network Intrusion Detection Systems (NIDS) play a pivotal role by thwarting unauthorized access and countering various cyber threats. Presently, the prevailing NIDS systems predominantly rely on the Backward Oracle Matching (BOM) algorithm. While effective in reducing false alarms, this algorithm has been associated with a high packet drop ratio. In light of these limitations, this paper examines the existing NIDS systems and the various pattern-matching techniques they employ, shedding light on their weaknesses and constraints. To overcome these shortcomings, the paper introduces an enhanced iteration of the BOM algorithm. This enhanced version leverages multiple pattern-matching methods, enhancing the overall performance of the NIDS system and subsequently fortifying network security. To validate the proposed solution, comprehensive simulations were conducted using established datasets such as Snort and NSL-KDD. The experimental results unequivocally demonstrate the superior performance of the proposed solution compared to existing approaches. Specifically, it achieved a remarkable 5.17% improvement in detection rate while concurrently reducing the false alarm rate by 0.22% compared to the existing solution. IN summary, this paper addresses critical issues inherent in current NIDS systems, providing a robust solution through an enhanced BOM algorithm that incorporates multiple pattern- Matching methods. The experimental findings underscore the effectiveness of this approach in bolstering network security, offering p

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Variations in Ground Motion Amplification in the Los Angeles Basin due to the 2019 M7.1 Ridgecrest Earthquake: Implications for the Long-Period Response of Infrastructure

Variations in Ground Motion Amplification in the Los Angeles...

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Lifelines 2022 Conference: 1971 San Fernando Earthquake and Lifeline Infrastructure

作者： Kohler, Monica D. Filippitzis, Filippos Graves, Robert Massari, Anthony Heaton, Thomas Clayton, Robert Bunn, Julian Guy, Richard Mani Chandy, K. Division of Engineering and Applied Science Dept. of Mechanical and Civil Engineering California Institute of Technology PasadenaCA United States US Geological Survey PasadenaCA United States Dept. of Engineering Civil Environmental Geodetic Engineering Ohio State Univ ColumbusOH United States Division of Geological and Planetary Sciences Seismological Laboratory California Institute of Technology PasadenaCA United States Division of Physics Math and Astronomy Center for Data-Driven Discovery California Institute of Technology PasadenaCA United States Division of Engineering and Applied Science Dept. of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States

ISBN: (数字)9780784484449

ISBN: (纸本)9780784484449

Coherent patterns and large variations in ground shaking amplification were observed in the Los Angeles basin during the 2019 M7.1 Ridgecrest earthquake. In particular, 3 s to 6 s responses showed variations due to shallow basin geological structure that have implications for the response to large earthquakes of mid-rises, high-rises, long-span bridges, and fuel storage tanks, even if epicentral distances are several hundred kilometers. The Ridgecrest strong-motion data were recorded by seismic stations from the spatially dense Community Seismic Network, the Southern California Seismic Network, and the California Strong Motion Instrumentation Program. The mainshock observations are compared at the same locations with ground motion simulations to examine the regions that experienced the largest shaking, and to investigate the geological sources of large-amplitude shaking. The simulations were computed for the two most commonly used regional community seismic velocity models, CVM-S4.26.M01 ('CVM-S') and CVM-H 15.1.0 ('CVM-H'). Both observations and simulations are used in dynamic analysis with a finite-element model of an existing high-rise with 6-s fundamental horizontal periods, located in downtown Los Angeles. The geographical variation in maximum story drift, story-level shear force, and story-level moment values suggest that the excitation of a hypothetical high-rise located in an area characterized by the largest 6-s PSA values could be significantly larger than in a downtown Los Angeles location. Ground motion simulations using the CVM-H velocity model more closely predict the long-period site amplifications in greater Los Angeles, particularly in the south-central San Fernando Valley, than simulations using CVM-S. © 2022 Lifelines 2022: 1971 San Fernando Earthquake and Lifeline Infrastructure - Selected Papers from the Lifelines 2022 Conference. All rights reserved.

关键词： Digital storage

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Probabilistic decomposed linear dynamical systems for robust discovery of latent neural dynamics 24

Probabilistic decomposed linear dynamical systems for robust...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yenho Chen Noga Mudrik Kyle A. Johnsen Sankaraleengam Alagapan Adam S. Charles Christopher J. Rozell Machine Learning Center Georgia Institute of Technology and Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University Coulter Dept. of Biomedical Engineering Emory University and Georgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Machine Learning Center Georgia Institute of Technology and School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (纸本)9798331314385

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. Code is available at: https://***/siplab-gt/probabilistic-decomposed-linear-dynamical-systems

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An Enhancing Network Security: A Stacked Ensemble Intrusion Detection System for Effective Threat Mitigation

An Enhancing Network Security: A Stacked Ensemble Intrusion ...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

Modern cyber security relies heavily on intrusion detection in network traffic to quickly identify and mitigate security threats. Conventional intrusion detection systems frequently depend on lone, stand-alone models that could find it difficult to change with the ways that network attacks are evolving. In order to overcome this difficulty, this research study provides an ensemble-based strategy that improves threat detection effectiveness and accuracy by utilizing the strength of several intrusion detection models. Increasing security measures against data breaches and network invasions is essential due to the ever-growing usage of the Internet and networks. As intrusions are frequently hidden within of valid network packets, firewalls have a difficult time identifying and stopping them. Furthermore, the majority of network monitoring systems and algorithms find it increasingly difficult to handle the sheer volume of network traffic. Various intrusion detection strategies have been proposed in response to these issues, with machine learning techniques emerging as a possible route for handling these situations. This paper introduces an Intrusion Detection System (IDS) that leverages stacking ensemble learning. The core ensemble comprises three fundamental machine learning models: k-nearest-neighbours, Decision Tree, and Random Forest. To enhance classification performance, the proposed system combines a total of seven machine learning algorithms with preprocessing methods. Stacking ensembles improve system performance greatly by combining the results of these basic models with a meta-model represented by the Logistic Regression algorithm. The UNSW-NB15 dataset is used to assess the IDS’s efficacy. With an astounding 96.16% accuracy rate in the training phase and an even greater 97.95% accuracy rate in the testing phase, the suggested IDS performs remarkably well. Additionally, the precision scores are remarkable, with training scores of 97.78% and testing scores of

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Ensuring Trust in Genomics Research

Ensuring Trust in Genomics Research

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Erman Ayday Jaideep Vaidya Xiaoqian Jiang Amalio Telenti Department of Computer and Data Sciences Case Western Reserve University Cleveland OH Information Systems Department Management Science Rutgers University Newark NJ Department of Data Science and Artificial Intelligence University of Texas - Health Houston TX Dept. of Integrative Structural and Computational Biology Scripps Institute La Jolla CA

Reproducibility, transparency, representation, and privacy underpin the trust on genomics research in general and genome-wide association studies (GWAS) in particular. Concerns about these issues can be mitigated by technologies that address privacy protection, quality control, and verifiability of GWAS. However, many of the existing technological solutions have been developed in isolation and may address one aspect of reproducibility, transparency, representation, and privacy of GWAS while unknowingly impacting other aspects. As a consequence, the current patchwork of technological tools only partially and in an overlapping manner address issues with GWAS, sometimes even creating more problems. This paper addresses the progress in a field that creates technological solutions that augment the acceptance and security of population genetic analyses. The text identifies areas that are falling behind in technical implementation or where there is insufficient research. We make the case that a full understanding of the different GWAS settings, technological tools and new research directions can holistically address the requirements for the acceptance of GWAS.

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A Study on AI Applications for Orthodontics and Malocclusion Detection Approaches

A Study on AI Applications for Orthodontics and Malocclusion...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Ankitha A Nayak Venugopala P S Ashwini B Padmashree G Department of Computer Science and Engineering Nitte NITTE (Deemed to be University) NMAM Institute of Technology Udupi India Dept. of Artificial Intelligence and Data Science NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Orthodontics is a dental specialty focused on treating, diagnosing, and preventing malocclusion and other dental irregularities. In a complex human digestive system, the anatomy of teeth has a significant role. Moreover, maintaining a healthy dental anatomy is essential to ensure a vibrant lifestyle. Orthodontics plays a vital role in the health of teeth structure by providing solutions for crooked teeth, tooth decay, periodontal disease, TMJ syndrome, neck shoulder pain, headaches, and pleasing facial appearance. This paper showcases a comprehensive review of the background and evolution of the orthodontics area through the years, emphasizing more methodologies for fixing malocclusion. The field of orthodontics needs significant manual intervention throughout the entire process, starting from the initial detection of the problem to the final stage of rectifying the fault. This paper presents a fair comparative visualization of manual work cut down with technical advancements in orthodontics and malocclusion detection techniques, spanning from the Bio-mechanical to Artificial Intelligent era. For a better descriptive analysis, we have analyzed the various existing works in orthodontics and malocclusion detection connecting to a set of well-defined research questions. The study of available algorithms, technologies data sets, and gaps for orthodontics and malocclusion detection technique is outlined concerning observed results in the survey.

关键词： Surveys Reviews Pain Teeth Manuals Very large scale integration Dentistry Artificial intelligence Robots Periodic structures

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Kannada Word Detection in Heterogeneous Scene Images

Kannada Word Detection in Heterogeneous Scene Images

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： N Shikha R Pranav Nidhi R Singh V Umadevi Muzammil Hussain Dept. of Computer Science B.M.S. College of Engineering Bengaluru India Dept. of Electrical and Electronics B.M.S. College of Engineering Bengaluru India Dept. of Medical Electronics B.M.S. College of Engineering Bengaluru India Tika Data Services Pvt. Ltd. Bengaluru India

Text extraction from scene images has started gaining a lot of traction in recent years in the computer vision field as its applications is manifold. One of its sub-categories is scene text detection. Factors like complex backgrounds, curvature, orientation, image quality, and various font styles and sizes makes it a difficult task. Moreover, building a single language scene text detector in a multilingual setting, especially Indian scripts, is more challenging in contrast to a general text detector. In this Work We attempt the task of Kannada text localization as Well as Word detection in natural scenes, Which is underexplored in comparison to other Well-known Indian languages. To achieve this, We fine tune a You Only Look Once (YOLOv4) based object detection model on our Kannada scene text images dataset collected. Experiments on local boards and sign images captured on smartphone, showed a mean average precision of 93.5% and per image average detection speed of 30 milli-seconds; pointing towards real time use and integration With mobile applications.

关键词： Manifolds Location awareness Image quality Computer vision Detectors Object detection Signal processing

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Large-Area Ag/Cu Pastes Sintering on Cu Base Plates for Power Module Packaging

Large-Area Ag/Cu Pastes Sintering on Cu Base Plates for Powe...

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International Conference on Electronics Packaging (ICEP)

作者： Chin-Hao Tsai Felix Steiner Dai Ishikawa C. Robert Kao Thomas Blank Institute for Data Processing and Engineering Karlsruhe Institute of Technology Germany Department of Materials Science and Engineering National Taiwan University Inorganic Materials Research Dept. Resonac Corporation Ltd. Japan

In this research, Ag, Cu, mixed Ag-In, and mixed Ag-SAC sinter pastes are used for large-area bonding in second-level power module packaging. Due to the large sintering area, different patterns with/without drying channels are tested in the pressure/pressureless sinter process. Besides, various bonding conditions, such as temperature and pressure, are investigated. To analyze large-area sintering effects, scanning acoustic microscopy is used to observe the interface of joints. Finally, the reliability of the large area sintered joints is assessed by thermal shock and bending tests. To conclude, this study explores the feasibility of multiple sintering pastes for large-area sintering. The results reveal that these pastes exhibit reliable properties under proper bonding conditions.

关键词： Printing Microscopy Sintering Thermal shock Multichip modules Bending Reliability

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