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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Maram Alsharif Danda B. Rawat Department of Electrical Engineering & Computer Science Data Science and Cybersecurity Center (DSC2) Howard University Washington D.C. USA

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Machine learning based Intrusion detection (ML-IDS) has been long enforced for the protection of the IoT against malicious attacks. Researchers focused on improving ensemble intrusion detection methods in order to boost the efficiency of attack detection on resource limited IoT edge devices. However they confined their evaluation platforms to cloud or standalone computing facilities rather than the targeted physical devices. In this paper, we complement our work with feasibility study of managing ensemble-based ML-IDS combiner models on a resourceful physical edge device, and demonstrate the implied limitations of deploying such models in the IoT without cloud support. We also elaborate on the potential of cloud support to support distributed ensemble techniques, resulting in super container models that harness limited resources of IoT edge devices.

关键词： Support vector machines Cloud computing Computational modeling Image edge detection Intrusion detection Timing Ensemble learning Security Internet of Things Protection

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Improved classical shadows from local symmetries in the Schur basis

arXiv

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arXiv 2024年

作者： Grier, Daniel Liu, Sihan Mahajan, Gaurav Department of Computer Science and Engineering Department of Mathematics UC San Diego United States Department of Computer Science and Engineering UCSD California CA92092 United States Institute for Foundations of Data Science Yale University Connecticut CT06511 United States

We study the sample complexity of the classical shadows task: what is the fewest number of copies of an unknown state you need to measure to predict expected values with respect to some class of observables? Large joint measurements are likely required in order to minimize sample complexity, but previous joint measurement protocols only work when the unknown state is pure. We present the first joint measurement protocol for classical shadows whose sample complexity scales with the rank of the unknown state. In particular we prove O(√rB/ǫ2) samples suffice, where r is the rank of the state, B is a bound on the squared Frobenius norm of the observables, and ϵ is the target accuracy. In the low-rank regime, this is a nearly quadratic advantage over traditional approaches that use single-copy measurements. We present several intermediate results that may be of independent interest: a solution to a new formulation of classical shadows that captures functions of non-identical input states;a generalization of a "nice" Schur basis used for optimal qubit purification and quantum majority vote;and a measurement strategy that allows us to use local symmetries in the Schur basis to avoid intractable Weingarten calculations in the analysis. © 2024, CC BY.

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A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional data

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computers, Materials & Continua 2022年第3期70卷 5363-5381页

作者： Amgad Muneer Shakirah Mohd Taib Suliman Mohamed Fati Abdullateef O.Balogun Izzatdin Abdul Aziz Department of Computer and Information Sciences Universiti Teknologi PETRONASSeri Iskandar32160Malaysia Centre for Research in Data Science(CERDAS) Universiti Teknologi PETRONASSeri Iskandar32610PerakMalaysia Information Systems Department Prince Sultan UniversityRiyadh11586Saudi Arabia

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world *** issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big *** a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and ***,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and *** proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 *** the experiment,two issues have been investigated and solved to enhance the *** first is the dataset class imbalance,which solved using SMOTE *** second issue is the poor performance,which can be solved using one of the optimization *** optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and ***,Adamax optimization algorithm showed the best results when employed to train *** experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

关键词： Anomaly detection outlier detection unsupervised learning autoencoder deep learning hybrid model

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Property selling system with support for validation and verification process

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Procedia computer science 2023年 216卷 186-193页

作者： Karto Iskandar Christianto Maria Grace Herlina Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta 11480 Indonesia

Due to the COVID-19 pandemic, today's property trade transactions through the website have become common. This phenomenon requires the availability of a trustable property commerce website. This research aims to produce a website trusted for sellers and buyers of property in conducting trade. So, we need to know what information is required by the property validation process so that sellers and buyers feel safe committing the transactions. This website will help increase public trust because it is supported by a verification and validation process by third parties regarding property information. The research method begins with distributing questionnaires to discover the needs of property sellers and buyers, then developing a website using the waterfall methodology of the Laravel application framework. The result of this research is to establish a set of information needed for the property validation process that needs to be verified before conducting transactions. Finally, this web application can increase trust by 59.5%, whereas 37.6% gave the maximum value and 8.7% of respondents gave only moderate weight after being tested on 173 respondents.

关键词： Selling-buying property Property information Validation system Verification system

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Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks 2

Facial Emotion Detection Using Haar-Cascade Classifier and C...

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2020 2nd International Conference on science and Technology, ICoST 2020

作者： Riyantoko, P.A. Sugiarto Hindrayani, K.M. Data Science Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia Informatics Department Faculty of Computer Science UPN "veteran" Jawa Timur Indonesia

computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it's possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection. © 2021 Published under licence by IOP Publishing Ltd.

关键词： Emotion Recognition

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks

Early Detection and Classification of Tomato Leaf Blight Dis...

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data science and Network Security (ICDSNS), International Conference on

作者： Anangsha Halder Subhrendu Guha Neogi Shibdas Dutta Koustov Khamaru Arghya Bhattacharya Shovan Roy Department of Computer Science and Engineering The Neotia University Sarisha India Department of Information Technology DCG Data-Core System India Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability.

关键词： Plant diseases Image resolution Image color analysis Plants (biology) Image edge detection Machine learning Sensitivity and specificity Network security Sustainable development Monitoring

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SELF-Clustering Graph Transformer Approach to Model Resting State Functional Brain Activity

SELF-Clustering Graph Transformer Approach to Model Resting ...

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IEEE International Symposium on Biomedical Imaging

作者： Bishal Thapaliya Esra Akbas Ram Sapkota Bhaskar Ray Vince Calhoun Jingyu Liu Department of Computer Science Georgia State University Atlanta USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the sub clusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

关键词： Biometrics Correlation Cognitive processes Biological system modeling Organizations Predictive models Functional magnetic resonance imaging Transformers Brain modeling Biomedical imaging

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Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility data with Large Language Models

arXiv

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arXiv 2024年

作者： Gong, Letian Lin, Yan Zhang, Xinyue Lu, Yiwen Han, Xuedi Liu, Yichen Guo, Shengnan Lin, Youfang Wan, Huaiyu School of Computer Science and Technology Beijing Jiaotong University China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing China Department of Computer Science Aalborg University Denmark

Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users’ intentions and preferences. Yet, existing models analyzing check-in sequences fail to consider the semantics contained in these sequences, which closely reflect human visiting intentions and travel preferences, leading to an incomplete comprehension. Drawing inspiration from the exceptional semantic understanding and contextual information processing capabilities of large language models (LLMs) across various domains, we present Mobility-LLM, a novel framework that leverages LLMs to analyze check-in sequences for multiple tasks. Since LLMs cannot directly interpret check-ins, we reprogram these sequences to help LLMs comprehensively understand the semantics of human visiting intentions and travel preferences. Specifically, we introduce a visiting intention memory network (VIMN) to capture the visiting intentions at each record, along with a shared pool of human travel preference prompts (HTPP) to guide the LLM in understanding users’ travel preferences. These components enhance the model’s ability to extract and leverage semantic information from human mobility data effectively. Extensive experiments on four benchmark datasets and three downstream tasks demonstrate that our approach significantly outperforms existing models, underscoring the effectiveness of Mobility-LLM in advancing our understanding of human mobility data within LBS contexts. © 2024, CC BY.

关键词： Semantics

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Critical Analysis and Performance Assessment of Reactive Ad Hoc Routing Protocols in Unmanned Aerial Vehicle Network

Critical Analysis and Performance Assessment of Reactive Ad ...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Vinti Gupta Abhinav Goel Seema Luthra Vandana Verma Harsh Dhiman Department of Computer Science Engineering - Data Science ABES Engineering collage Ghaziabad Uttar Pradesh India Department of Mathematics Graphic Era Deemed to be University Dehradun India CSE Department Galgotias College of Engineering and Technology Greater Noida Department of Computer Science And Engineering SRM Institute of Science and Technology Ghaziabad

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

When used as aerial relays in the Internet of Drones (IoD) network unmanned aerial vehicles (UAVs) provide several advantages in both the military and civilian sectors. Their collaboration creates a fascinating field for additional study. An autonomous network of unmanned aerial vehicles (UAVs) known as a Flying Ad-Hoc Network (FANET) is defined by its high mobility and frequent topology changes, which present routing issues. The 3Dimensional Improvised Clustering Algorithm (3DICA), 3D Improvised Trajectory Algorithm (3DITA), and Bio-Inspired Clustering Scheme for FANETs (BISCF) are three of the optimized routing protocols for FANETs that are examined in this work These techniques have been assessed using the MATLAB simulator. based on the following mobility models: Manhattan Grid Mobility Model (MGM), Random Waypoint Mobility (RWPM), Semi-Circular Random Movement (SCRM), Number of Hops, Reliability, Jitter, and Throughput across different speeds and mobility models (PRS). The results show that in every evaluation situation, 3DITA performs better than 3DICA and BISCF. This assessment is necessary to determine the best FANET routing strategy, which could result in more dependable and durable UAV communication systems.

关键词： Mobility models Clustering algorithms Jitter Autonomous aerial vehicles Routing Throughput Routing protocols Reliability MATLAB Vehicle dynamics

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Semanformer: Semantics-aware Embedding Dimensionality Reduction Using Transformer-Based Models

Semanformer: Semantics-aware Embedding Dimensionality Reduct...

引用

International Conference on Semantic Computing

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

In recent years, transformer-based models, particularly BERT (Bidirectional encoder Representations from Transformers), have revolutionized natural language processing tasks, achieving state-of-the-art performance in various domains. In the context of natural language processing (NLP) and linguistics, understanding the semantic aspects of text is crucial for tasks like information retrieval, sentiment analysis, machine translation, and many others. However, the high dimensionality of BERT embeddings presents challenges in real-world applications due to increased memory and computational requirements. Reducing the dimensionality of BERT embeddings would benefit many application downstream tasks by reducing the computational requirements. Although there are prevalently used dimensionality reduction methods which focus on feature representation with lower dimensions, their application on NLP tasks may not yield semantically correct results. We propose a novel framework named as semanformer (semantics-aware encoder-decoder dimensionality reduction method) that leverages transformer-based encoder-decoder model architecture to perform dimensionality reduction on BERT embeddings for a corpus while preserving crucial semantic information. To evaluate the effectiveness of our approach, we conduct a comprehensive use case evaluation on diverse text datasets by sentence reconstruction. Our experiments show that our proposed method achieves high sentence reconstruction accuracy (SRA) more than 83% compared to the traditional dimensionality reduction methods such as PCA (SRA < 66%) and t-SNE (SRA < 9%).

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