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A Lightweight Edge-CNN-Transformer model for Detecting Coordinated Cyber and Digital Twin Attacks in Cooperative Smart Farming

A Lightweight Edge-CNN-Transformer Model for Detecting Coord...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Praharaj, Lopamudra Gupta, Deepti Gupta, maanak Tennessee Tech University Dept. of Computer Science CookevilleTN38505 United States Texas A&m University - Central Texas Dept. of Computer Information Systems TX United States

ISBN: (纸本)9798350362480

The agriculture sector is increasingly adopting innovative technologies to meet the growing food demands of the global population. To optimize resource utilization and minimize crop losses, farmers are joining cooperatives to share their data and resources among member farms. However, while farmers benefit from this data sharing and interconnection, it exposes them to cybersecurity threats and privacy concerns. A cyberattack on one farm can have widespread consequences, affecting the targeted farm as well as all member farms within a cooperative. For instance, a de-authentication attack prevents sensors from connecting to the network, obstructing the farming application from receiving real-time data. This disruption obstructs decision-making for all member farms that rely on this data from an attacked farm. Further, farmers have adopted digital twin (DT) technology that facilitates a virtual farm replica that encompasses vital aspects of farming, such as crop cultivation, soil composition and weather conditions. However, it's critical to acknowledge that attackers can target these digital twins (DTs), potentially disrupting real physical farm *** this research, we address existing gaps by proposing a novel and secure architecture for Cooperative Smart Farming (CSF). First, we highlight the role of edge-based DTs in enhancing the efficiency and resilience of agricultural operations. To validate this, we develop a test environment for CSF, implementing various cyberattacks on both the DTs and their physical counterparts using different attack vectors. We collect two smart farming network datasets to identify potential threats. After identifying these threats, we focus on preventing the transmission of malicious data from compromised farms to the central cloud server. To achieve this, we propose a CNN-Transformer-based network anomaly detection model, specifically designed for deployment at the edge. As a proof of concept, we implement this model and evaluate

关键词： Amazon Web Services (AWS) Attacks Azure Digital Twin CNN Cooperative Smart Farming (CSF) Digital Twin (DT) mL algorithms Security

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mentorship and Legitimate Peripheral Participation in the Research Laboratory 54

Mentorship and Legitimate Peripheral Participation in the Re...

引用

54th IEEE Frontiers in Education Conference, FIE 2024

作者： Hordemann, Glen Quek, Francis Texas A&m University Dept. of Electrical Eng. and Computer Science College StationTX United States

ISBN: (纸本)9798350351507

This research-to-practice paper investigates how undergraduates organically transition into researchers through the lens of Legitimate Peripheral Participation, Legitimate Peripheral Participation is a contextual learning phenomenon through which new members become core members of a community. These peripheral members begin with the execution of peripheral low-risk and simple activities, gradually accruing knowledge and experience as they take on more complex and essential activities. They gain legitimacy and access to more complex involvement through interaction with, and mentorship by, senior members of the community. Our research examines how seven undergraduate students, who are involved with a research lab, transition from peripheral roles to core membership through observation and peripheral engagement, skill development, community integration, increasing responsibility, and collaborative projects. These undergraduates interact with and are mentored by both graduate students and faculty members in an organic fashion, with no assigned mentorship roles existing between the undergraduate students and graduate students or faculty. The focus of our research is not on how one recruits undergraduates to work in a research lab, but on how the identity of an undergraduate as a researcher, both in their own mind and in those of their coworkers, is nurtured and developed. Through the process of surveys and interviews we extract the core elements of the undergraduates' experience and explore factors that might contribute to or hinder their transition into being more active researchers. The degree of involvement and identification as a researcher varied significantly between the undergraduate students. The key elements we discover at social engagement, physical presence, and near-peer interaction. © 2024 IEEE.

关键词： Students

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Fast Distributed memory Parallel Algorithms for Finding Connected Components in Large Graphs

Fast Distributed Memory Parallel Algorithms for Finding Conn...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Khan, maleq Boddu, Sharon Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science United States

ISBN: (纸本)9798350362480

Finding the connected components in a graph is a fundamental problem in graph theory and network science. A connected component in a graph is a maximal set of vertices such that there is a path between any two vertices in the component. In a serial setting, connected components can be efficiently computed by running a breadth-first search (BFS). However, BFS-based algorithms do not lead to good parallelization. At the early stage, parallel algorithms for the connected component problem were developed for the PRAm model and shared-memory systems. With the availability of big data and big graphs and the development of a new area called network science, there is a renewed interest in developing parallel algorithms for this problem. In recent years, a number of distributed-memory parallel algorithms have been developed. These algorithms are based on mainly Shiloach and Vishkin's PRAm algorithm, called the SV algorithm, or some variants of SV. In this paper, we present a PRAm algorithm, called SELP, which is elegant and significantly simpler than the SV algorithm. Based on SELP, we develop a distributed-memory parallel algorithm, called DSELP. In addition, we incorporate some novel techniques to minimize communications among the processors. Experimental results show that our algorithms outperform the recent state-of-the-art distributed-memory algorithms significantly on a wide variety of graphs, and scale very well to a large number of processors. © 2024 IEEE.

关键词： Consensus algorithm

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Spatial Transformer Network YOLO model for Agricultural Object Detection 23

Spatial Transformer Network YOLO Model for Agricultural Obje...

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23rd IEEE International Conference on machine Learning and Applications, ICmLA 2024

作者： Zambre, Yash Vivek Rajkitkul, Ekdev mohan, Akshatha Peeples, Joshua Texas A&m University Department of Electrical and Computer Engineering College StationTX United States Texas A&m University Department of Computer Science and Engineering College StationTX United States

ISBN: (纸本)9798350374889

Object detection plays a crucial role in the field of computer vision by autonomously locating and identifying objects of interest. The You Only Look Once (YOLO) model is an effective single-shot detector. However, YOLO faces challenges in cluttered or partially occluded scenes and can struggle with small, low-contrast objects. We propose a new method that integrates spatial transformer networks (STNs) into YOLO to improve performance. The proposed STN-YOLO aims to enhance the model's effectiveness by focusing on important areas of the image and improving the spatial invariance of the model before the detection process. Our proposed method improved object detection performance both qualitatively and quantitatively. We explore the impact of different localization networks within the STN module as well as the robustness of the model across different spatial transformations. We apply the STN- YOLO on benchmark datasets for Agricultural object detection as well as a new dataset from a state-of-the-art plant phenotyping greenhouse facility. Our code and dataset are publicly available11https://***/Advanced-Vision-and-Learning-Lab/STN-YOLO. © 2024 IEEE.

关键词： Object detection

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A Knowledge-Driven Vowel-Based Approach of Depression Classification from Speech Using Data Augmentation 48

A Knowledge-Driven Vowel-Based Approach of Depression Classi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Feng, Kexin Chaspari, Theodora Texas A&m University Computer Science and Engineering United States

ISBN: (纸本)9781728163277

We propose a novel explainable machine learning (mL) model that identifies depression from speech, by modeling the temporal dependencies across utterances and utilizing the spectrotemporal information at the vowel level. Our method first models the variable-length utterances at the local-level into a fixed-size vowel-based embedding using a convolutional neural network with a spatial pyramid pooling layer (vowel CNN). Following that, the depression is classified at the global-level from a group of vowel CNN embeddings that serve as the input to another 1D CNN (depression CNN). Different data augmentation methods are designed for both the training of vowel CNN and depression CNN. We investigate the performance of the proposed system at various temporal granularities when modeling short, medium, and long analysis windows, corresponding to 10, 21, and 42 utterances, respectively. The proposed method reaches comparable performance with previous state-of-the-art approaches and depicts explainable properties with respect to the depression outcome. The findings from this work may benefit clinicians by providing additional intuitions during joint human-mL decision-making tasks. © 2023 IEEE.

关键词： convolutional neural network data augmentation knowledge-driven mental health speech vowel

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Current Sensorless Deadbeat Predictive Control for Permanent-magnet Synchronous motor Drives 7

Current Sensorless Deadbeat Predictive Control for Permanent...

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7th IEEE Student Conference on Electric machines and Systems, SCEmS 2024

作者： Hu, Zhemin Yang, ming Xu, Dianguo Texas A&m University Dept. of Electrical & Computer Engineering College Station United States Harbin Institute of Technology Dept. of Electrical Engineering Harbin China

ISBN: (纸本)9798350366945

This paper proposes a current sensorless permanent magnet synchronous motor (PmSm) drive scheme based on deadbeat predictive control in which a space vector pulse modulator is introduced for feedback currents to track reference currents accurately. Current sensors are omitted by replacing actual currents with predicted currents, thus only a position sensor is needed and a low-cost servo system is achieved. PI control is adopted for the speed loop. Simulation comparisons between control with current sensors and current sensorless control under external disturbances or parameter inconsistency are conducted to indicate that the performance of current sensorless control is almost the same as that of control with current sensors when the prediction model is accurate. © 2024 IEEE.

关键词： Sensorless control

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Adversarial machine Learning Attack on machine Learning-based Controller for Solar Inverters

Adversarial Machine Learning Attack on Machine Learning-base...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： massa, Joaquin Kim, Kyoung-Tak Kim, Taesic Ahn, BoHyun Venayagamoorthy, Kumar Zeng, Jianwu Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States Soongsil University Dept. of Electrical Engineering Seoul Korea Republic of University of Missour-Columbia Dept. of Electrical Engineering and Computer Science Columbia United States Clemson University Dept. of Electrical and Computer Engineering Clemson United States Minnesota State University Mankato Dept. of Electrical and Computer Engineering and Technology Mankato United States

ISBN: (纸本)9798350376067

Adversarial machine learning (mL) attacks are stealthy attacks designed to mislead the mL model results. This paper explores adversarial mL attacks that generate adversarial noisy input data in an mL-based controller in a solar inverter. Three types of mL models, long short-term memory (LSTm), gated recurrent unit (GRU)), and bidirectional-LSTm (Bi-LSTm), are designed to replace proportional-integral (PI) controller-based vector control for a solar inverter and two white-box adversarial mL attacks (Basic Iterative method (BIm) attack and Fast Sign-Gradient method (FGSm)) are applied to the mL controllers. It is observed that the adversary mL attacks designed in stealthy way do not affect the PI controller, while significantly degrading performance of the mL-based controllers. moreover, the BIm attack is more effective than FGSm and Bi-LSTm-based controller is relatively robust to the attacks compared to peer. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review on the Applications of Blockchain-Based Smart Contracts in the Smart Grid 4

A Review on the Applications of Blockchain-Based Smart Contr...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Saleh, mohammad Alshaikh Khatri, Sunil P. Ghrayeb, Ali Refaat, Shady S. Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar University of Hertfordshire School of Physics Engineering and Computer Science United Kingdom

ISBN: (纸本)9798350306262

Investigations in industrial and computational research emphasize the progress of blockchain-enabled smart contracts due to their resilient features, which include decen-tralised transaction storage, autonomous contract regulation enforcement, and distributed trust creation. Blockchain-powered smart contracts can transform operational frameworks in various sectors aiming for superior service delivery. The advantages this technology brings involve a multitude of applications that span industries as cryptocurrencies, logistics systems, agricultural practices, real estate operations, and energy trading among others. Notably though one crucial emerging application is within smart grids (SG). The decentralization aspect of blockchain is one of the most significant leaps in computing for smart grids. The application of blockchain aligns with the vision of having a decentralized electric grid by distributing renewable energy sources. Therefore, numerous efforts are being conducted to examine the prospects for the relevance of smart contracts and blockchain to the smart grid system. To this end, this paper aims to review the essential elements of blockchain-based smart contracts to emphasize their current capabilities for enhancing the transition to a decentralized smart grid with proposals on future research directions. © 2024 IEEE.

关键词： Smart contract

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Grid Connected microinverter Based on Single Leg multimode Converter 4

Grid Connected Microinverter Based on Single Leg Multimode C...

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4th International Conference on Smart Grid and Renewable Energy, SGRE 2024

作者： Bayindir, Abdullah Berkay Sharida, Ali Bayhan, Sertac Abu-Rub, Haitham Texas A&M University Dept. of Electrical & Computer Engineering College StationTX77840 United States Texas A&M University at Qatar Dept. of Electrical & Computer Engineering Doha23874 Qatar Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798350306262

This paper proposes a novel topology for grid-connected microinverter system. The topology consists of a single-leg multi-mode converter (SLmmC) and a single-phase inverter which form the proposed microinverter. The SLmmC is responsible for regulating the intermediate DC link voltage, charging an internal battery, and managing the power sharing between the source and the battery. moreover, the single-phase inverter is connected to the intermediate DC link voltage from one side and performs supporting functions on the grid's side. The proposed topology provides multiple advantages over the existing solutions, including reduced number of components, reduced size, and higher efficiency as the number of required elements reduced. The proposed system is implemented using mATLAB/Simulink, and various investigations are carried out to demonstrate the correctness of the proposed solution. © 2024 IEEE.

关键词： Topology

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EmONA: Event-level moral Opinions in News Articles

EMONA: Event-level Moral Opinions in News Articles

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Lei, Yuanyuan miah, messal monem Qamar, Ayesha Reddy, Sai Ramana Tong, Jonathan Xu, Haotian Huang, Ruihong Department of Computer Science and Engineering Texas A&M University College StationTX United States

ISBN: (纸本)9798891761148

most previous research on moral frames has focused on social media short texts, little work has explored moral sentiment within news articles. In news articles, authors often express their opinions or political stance through moral judgment towards events, specifically whether the event is right or wrong according to social moral rules. This paper initiates a new task to understand moral opinions towards events in news articles. We have created a new dataset, EmONA1, and annotated event-level moral opinions in news articles. This dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels. Extracting event morality is a challenging task, as moral judgment towards events can be very implicit. Baseline models were built for event moral identification and classification. In addition, we also conduct extrinsic evaluations to integrate event-level moral opinions into three downstream tasks. The statistical analysis and experiments show that moral opinions of events can serve as informative features for identifying ideological bias or subjective events. ©2024 Association for Computational Linguistics.

关键词： Classification (of information)

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