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37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Liu, Sijia Department of Computer Science & Engineering Michigan State University MI United States MIT-IBM Watson AI Lab IBM Research United States

ISBN: (纸本)9781577358800

Deep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as lack of optimization generality and lack of optimization scalability. My research highlights will offer a holistic understanding of optimization foundations for robust ai, peer into their emerging challenges, and present recent solutions developed by my research group. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Alzheimer's disease detection using Multi Atlas Segmentation based on Temporal Group Sparse Regression

Alzheimer's disease detection using Multi Atlas Segmentation...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Velkumar, K. Chokkalingam, Bala Subramanian Solairaj, A. Vignesh, L.S. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Virudhunagar India Nadar Saraswathi College of Engineering and Technology Theni India Nadar Saraswathi College of Engineering and Technology Department of Computer Science and Engineering Theni India Nadar Saraswathi College of Engineering and Technology Deapartment of Ai&ds Theni India

ISBN: (纸本)9798350317060

Imaging genomics stands as a valuable approach for identifying potential genetic factors associated with Alzheimer's disease (AD) within genomic and imaging datasets. Most existing techniques in imaging genomics employ a linear model to investigate the connection between quantitative traits (QTs) derived from brain imaging and genetic data (single nucleotide polymorphisms). This method often fails to consider the connections between clusters of Quantitative Traits (QTs) and Single Nucleotide Polymorphisms (SNPs), as well as the complex interactions which involves longitudinal imaging quantitative traits and Single Nucleotide Polymorphisms. In order to tackle these constraints, we introduce a fresh approach named Temporal Group Sparsity Regression and Additive Model (TGSRAM). This pioneering technique strives to reveal associations between longitudinal imaging QTs and SNPs, with the objective of pinpointing potential biomarkers for Alzheimer's Disease (AD). Our approach seeks to offer solutions to these challenges. Furthermore, we introduce an alternative methodology termed multi-atlas-based morphometry in our study. This methodology assesses morphometric representations of the same image across numerous atlas spaces. By generating representations from various atlases, we harness supplementary insights to distinguish between different groups and mitigate the adverse impacts stemming from registration errors. In practice, each subject under investigation undergoes registration to multiple atlases, from which adaptive regional features are extracted. Subsequently, a correlation and relevance-based approach is employed to collectively select features from diverse atlases. This is followed by the ultimate classification step using the hybrid support vector machine (HSVM). © 2023 IEEE.

关键词： Genome

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Scalable high-repetition-rate sub-half-cycle terahertz pulses from spatially indirect interband transitions

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Light(science & Applications) 2022年第6期11卷 1304-1311页

作者： Christian Meineke Michael Prager Johannes Hayes Qiannan Wen Lukas Zheyi Kastner Dieter Schuh Kilian Fritsch Oleg Pronin Markus Stein Felix Schafer Sangam Chatterjee Mackillo Kira Rupert Huber Dominique Bougeard Department of Physics University of Regensburg93040 RegensburgGermany Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMl 48109USA Faculty of Electrical Engineering Helmut Schmidt University22043 HamburgGermany Institute of Experimental Physics I Justus Liebig University Giessen35392 GiessenGermany

Intense phase-locked terahertz(THz)pulses are the bedrock of THz lightwave electronics,where the carrier field creates a transient bias to control electrons on sub-cycle time *** applications such as THz scanning tunnelling microscopy or electronic devices operating at optical clock rates call for ultimately short,almost unipolar waveforms,at megahertz(MHz)repetition ***,we present a flexible and scalable scheme for the generation of strong phase-locked THz pulses based on shift currents in type-ll-aligned epitaxial semiconductor *** measured THz waveforms exhibit only 0.45 optical cycles at their centre frequency within the full width at half maximum of the intensity envelope,peak fields above 1.1 kVcm^(-1) and spectral components up to the mid-infrared,at a repetition rate of 4 *** only positive half-cycle of this waveform exceeds all negative half-cycles by almost four times,which is unexpected from shift currents *** detailed analysis reveals that local charging dynamics induces the pronounced positive THz-emission peak as electrons and holes approach charge neutrality after separation by the optical pump pulse,also enabling ultrabroadband *** unipolar emitters mark a milestone for flexibly scalable,next-generation high-repetition-rate sources of intense and strongly asymmetric electric field transients.

关键词： pump repetition cycle

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Implementation and Comparative Analysis of Various Energy-Efficient Clustering Schemes in AODV 7th

Implementation and Comparative Analysis of Various Energy-Ef...

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7th International Conference on Big Data and Cloud Computing Challenges, ICBCC 2022

作者： Dhanabal, S. William, P. Vengatesan, K. Harshini, R. Kumar, V. D. Ambeth Yuvaraj, S. Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Trichy Tholurpatti India Department of Information Technology Sanjivani College of Engineering SPPU Pune India Department of Computer Engineering Sanjivani College of Engineering Kopargaon India Department of AI&DS Panimalar Engineering College Chennai600123 India Department of Electronics & Communication SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Engineering Mizoram University Aizawl796004 India

ISBN: (纸本)9789819910502

The wireless sensor network allows for the surveillance of physical environments as well as the implementation of timely actions based on sensor data. The major objective of this sort of network is to extend the life of the network by utilizing energy-efficient connection technologies. Cooperation between sensor nodes and agents, on the other hand, must occur as quickly as feasible. This study offers a clustering methodology for building an automated framework for a WSN network's organization. We explore MANET security problems and suggest a few potential research areas. Many key and trust management systems have been devised to avoid external assaults from outsiders, and various secure MANET routing protocols have been proposed to prevent internal attacks from within the MANET system. A novel intrusion detection framework built particularly for MANET has been studied in terms of intrusion detection. Both preventive and detection approaches will be utilized to address the security problems in MANET. A hierarchical, salable energy optimization-based network model is used in the proposed clustering protocol. Because sensor node battery resources are limited and the physics detection cycle is brief, one of the most significant wireless sensor network techniques is to create energy-efficient network life routing protocols (WSN). This study presents a technique to heterogeneous clustering that is energy efficient. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Investigation of air Effluence Using IoT and Machine Learning 6th

Investigation of Air Effluence Using IoT and Machine Learn...

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6th International Conference on Emerging Technologies in Computing, iCETiC 2023

作者： Shakil, Shalah Uddin Perbhez Kashem, Mohammod Abul Islam, Md. Monirul Nayan, Nasim Mahmud Uddin, Jia Department of Computer Science and Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Birulia Savar Dhaka1216 Bangladesh Dhaka1212 Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031502149

air pollution poses a significant issue in numerous cities worldwide, impacting public health and the environment. We study three significant cities under the Dhaka division, including Kuril Bishow Road, Uttara, and Tongi. Traditional air quality monitoring methods often need more coverage and accuracy. Leveraging Internet of Things (IoT) technology as well as machine learning (ml) algorithms, this study deploys an IoT-based sensor network using Arduino boards and various devices, including MQ135, DHT22, PM2.5, MQ9, and dust sensors to gather real-time values on air pollutants. The gathered data, including sulfur dioxide, ozone, particulate matter 2.5, nitrogen dioxide, particulate matter 10, as well as carbon monoxide, provides a comprehensive view of city pollution levels. ml models such as linear regression, decision trees, K-Nearest Neighbors (KNN), Naive Bayes (NB), Gradient Boosting (GB), and Random Forest classifiers are applied to predict pollution levels using environmental parameters. The Random Forest classifier achieves an impressive prediction accuracy of 97.2 %. Evaluation metrics, including precision, recall, F1 score, Kappa score, mean square error (MSE), root mean square error(RMSE), along mean absolute error (MAE), are used to assess the performance of the models. This study demonstrates the potential of IoT technology along with ml algorithms in accurately predicting air pollution levels, aiding in environmental management and public health efforts in urban areas. © 2024, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Multi-agent Scheduling for Shared Manufacturing Systems 6

Multi-agent Scheduling for Shared Manufacturing Systems

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Duran, Ege Ozturk, Cemalettin O'Sullivan, Barry SFI Centre for Research Training in AI School of Computer Science IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments. © 2025 The Authors.

关键词： Mixed-integer linear programming

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Novel Audio Mosaic Using LPC-Coefficient and Excitation-Codeword Permutations

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IEEE Signal Processing Letters 2024年 31卷 1690-1694页

作者： Tsai, Morris C.-H. Wu, Hsiao-Chun Huang, Scott C.-H. Chang, Shih Yu National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan City32003 Taiwan San Jose State University Department of Applied Data Science San JoseCA95192 United States

In this letter, a novel light-weight audio mosaic scheme using permutations of linear predictive coding (LPC) coefficients as LPC has been widely adopted in many audio codecs (coder-encoders) nowadays. We establish the theoretical secrecy analysis with respect to the degree of the original LPC polynomial and the minimum-phase population of permuted LPC polynomials. The mosaic (destructuring) performance in terms of DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) is also evaluated through simulations. In comparison with the existing audio-mosaic method using waveform permutations, our proposed new audio-mosaic scheme using LPC-coefficient permutations can achieve a much better mosaic performance (a much higher DCT-KLD) subject to the same key size. © 1994-2012 IEEE.

关键词： Cryptography

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Demystifying the Applications of Internet of Medical Things (IoMT) for smart health care system

Demystifying the Applications of Internet of Medical Things ...

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Data, Information and Computing science (CDICS), International Conference on

作者： Ruqqaiya Begum Polisetty Swetha Koti Tejasvi M. A. Jabbar Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350382778

ISBN: (纸本)9798350382785

As Internet of Things (IoT) devices become more commonplace and are used in more aspects of daily life, a bright future for people, data, and processes is emerging. Because of its potential to enhance patient care as well as its capacity to deliver more trustworthy clinical data, boost productivity, and save costs, In recent years, the Internet of Medical Things (IoMT) has a strategic goal for future e-healthcare. It is understandable why so many healthcare organization’s nowadays want to take advantage of what the IoMT has to offer. Its infrastructure includes interconnected medical equipment, software, and care systems and services. For everyone to understand how IoMT is enhancing the health care system with the aid of other technologies, we have described the implementation of IoMT in detail in this paper. Along with this, we have also discussed the different security concerns and difficulties the healthcare system is experiencing due to the IoMT’s information-gathering shortcomings.

关键词： Productivity Biomedical equipment Costs Smart healthcare Internet of Medical Things Software Security

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A Novel Image Caption Generation Based on CNN and RNN

A Novel Image Caption Generation Based on CNN and RNN

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Hemlata Parmar Manish Rai Utsav Krishan Murari Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India Department of Journalism and Mass Communication Sharda University Greater Noida Greater Noida India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The field of image comprehension has experienced notable advancements as a result of the emergence of deep learning techniques and advancements in computer vision technology. The effort of generating coherent and detailed textual descriptions for images, known as image labeling, is a significant challenge that requires the integration of natural language processing and image processing techniques. This abstract provides an in-depth analysis of approaches employed for generating photo labels, with a particular focus on current advancements within the sector. The generation of picture labels involves the use of two main components in the pipeline, namely syntax interpreter and a photographic converter. The picture encoder uses (CNNs) to process images. For extracting visual The field of image comprehension has experienced notable advancements as a result of the emergence of deep learning techniques and advancements in computer vision technology. The effort of generating coherent and detailed textual descriptions for images, known as image labeling, is a significant challenge that requires the integration of natural language processing and image processing techniques. For extracting visual details from input pictures. To generate labels on a individual basis, a language decoder employs (RNNs) like(LSTM) or (GRUs) to analyse the context representation of the image. The results obtained from this study underscore the advantages of employing (GRU) in comparison to (LSTM) for the purpose of generating labels and constructing sentences. In addition to this, recent advancements in transformer-based design, such as the transformer model, have shown promising statistics concerning labelling of pictures. The models have demonstrated the ability to effectively imitate the connections between visual and textual modalities, and they achieve this by leveraging the self-attention mechanism found in transformers, which allows for capturing long-range interdependencies. The production

关键词： Deep learning Visualization computer vision Reinforcement learning Syntactics Transformers Feature extraction Natural language processing Labeling Proposals

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Comprehensive Approaches to Securing Multi-Cloud Architectures: Best Practices and Emerging Solutions 7

Comprehensive Approaches to Securing Multi-Cloud Architectur...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Nair, Raveena R Sreevidya, D. Mohan, Chinnem Rama Banerjee, Joydeep Chouhan, Kuldeep Dharamvir Saveetha School of Law Chennai India and Ai & Ds Hyderabad India Narayana Engineering College Nellore Department of Computer Science and Engineering Andhra Pradesh India Nshm Knowledge Campus Durgapur West Bengal Kolkata India Sharda School of Engineering and Technology Department of Computer Science Applications Uttar Pradesh Greater Noida India The Oxford College of Engineering Department of Mca Bommanahalli Karnataka Bengaluru India

ISBN: (纸本)9798350350067

With an emphasis on multi-cloud networks, this research paper offers a synopsis of cloud computing techniques. It covers the fundamentals of cloud programming, the difficulties that cloud computing faces, and the ways that multi-cloud systems, such as cloud hybridization, federation clouds, cross-clouds, and multi-clouds, might help solve these problems. The opportunity of multi-cloud structures in combination with modern technologies such as Big Data and also Machine Learning (ml) is explored as well in the study, leading to some fascinating breakthroughs. To assist researchers, grasp the difficulties and generate enthusiasm within the community for the upcoming pairing of multi-cloud services with additional cutting-edge technologies, the aim is to present a contemporary perspective on multi-cloud technology and solve outstanding concerns. © 2024 IEEE.

关键词： Cloud platforms

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