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arXiv 2024年

作者： Clark, Joseph Thapliyal, Himanshu Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville United States

Peephole optimization of quantum circuits provides a method of leveraging standard circuit synthesis approaches into scalable quantum circuit optimization. One application of this technique partitions an entire circuit into a series of peepholes and produces multiple approximations of each partitioned subcircuit. A single approximation of each subcircuit is then selected to form optimized result circuits. We propose a series of improvements to the final phase of this architecture, which include the addition of error awareness and a better method of approximating the correctness of the result. We evaluated these proposed improvements on a set of benchmark circuits using the IBMQ FakeWashington simulator. The results demonstrate that our best-performing method provides an average reduction in Total Variational Distance (TVD) and Jensen-Shannon Divergence (JSD) of 18.2% and 15.8%, respectively, compared with the Qiskit optimizer. This also constitutes an improvement in TVD of 11.4% and JSD of 9.0% over existing solutions. © 2024, CC BY.

关键词： Quantum electronics

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Mental Health and Stress Prediction Using NLP and Transformer-Based Techniques

Mental Health and Stress Prediction Using NLP and Transforme...

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IEEE Symposium on Wireless Technology and Applications (ISWTA)

作者： Abhishek Pandey Sanjay Kumar Dept. of Computer Science and Engineering Delhi Technological University New Delhi India

ISBN: (数字)9798350351354

ISBN: (纸本)9798350351361

The integration of Natural Language Processing (NLP) and machine learning technologies into the field of mental health and stress prediction represents a significant advancement in the early detection and intervention of mental health issues. This paper explores the application of various NLP and transformer-based techniques, such as TF-IDF, n-grams, BERT, RoBERTa, and ALBERT, to analyze textual data from diverse sources such as social media platforms towards mental health and stress prediction. The developed methodology involves comprehensive data preprocessing, innovative feature extraction, and the application of deep learning models to understand the refinement of language indicative of mental health states. By comparing the effectiveness of different models and approaches, this paper aims to establish a robust framework for accurately predicting mental health conditions, thereby contributing to the broader goal of improving mental health care through technology. The simulation performed on a real-life dataset obtained from Reddit reveals that RoBERTa-based techniques perform better than the other methods.

关键词： Deep learning Social networking (online) data preprocessing Mental health Bidirectional control Predictive models Transformers

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A Machine Learning-Based Secure Face Verification Scheme and Its Applications to Digital Surveillance

arXiv

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arXiv 2024年

作者： Wang, Huan-Chih Wu, Ja-Ling Dept. Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Face verification is a well-known image analysis application and is widely used to recognize individuals in contemporary society. However, most real-world recognition systems ignore the importance of protecting the identity-sensitive facial images that are used for verification. To address this problem, we investigate how to implement a secure face verification system that protects the facial images from being imitated. In our work, we use the DeepID2 convolutional neural network to extract the features of a facial image and an EM algorithm to solve the facial verification problem. To maintain the privacy of facial images, we apply homomorphic encryption schemes to encrypt the facial data and compute the EM algorithm in the ciphertext domain. We develop three face verification systems for surveillance (or entrance) control of a local community based on three levels of privacy concerns. The associated timing performances are presented to demonstrate their feasibility for practical implementation. © 2024, CC BY.

关键词： Adversarial machine learning

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rajdeep Das Vaishnavi Sharma Priyanka Kumari Abhishek Raj Sudipta Das Indrajit De Dept. of Computer Science and Business Systems Institute of Engineering and Management Kolkata India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space.

关键词： Temperature sensors Cloud computing Atmospheric modeling Space shuttles data visualization Machine learning Humidity Agriculture Sensors Anomaly detection

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Quora Question Sincerity Detection Using BERT-Based Framework

Quora Question Sincerity Detection Using BERT-Based Framewor...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Drishti Arora Garima Aggarwal Farhan Asif Dept. of Computer Science and Engineering Amity University Noida Uttar Pradesh India

In the dynamic landscape of online knowledge exchange, platforms such as Quora have become vital arenas for diverse discussions and information dissemination. However, distinguishing sincere from insincere questions poses a challenge, impacting user experience and content credibility. The research seeks to utilize BERT-based frameworks to classify Quora questions, with a specific focus on discerning between sincere and insincere inquiries. The study underscores the significance of automated insincere content identification in fostering a trustworthy and constructive online environment on Quora, thereby contributing to the improvement of online interaction quality. The research delves into a comprehensive methodology, including data preprocessing, model training, fine-tuning, and performance evaluation. Extensive experimentation demonstrates that the proposed BERT-based approach achieves an impressive F1-score of 96.1%, outperforming existing methods. The contribution of the study lies in its potential to enhance online interactions by automating the identification of insincere content, fostering a conducive environment for meaningful conversations.

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Machine Learning based Anxiety Detection using Physiological Signals and Context Features

Machine Learning based Anxiety Detection using Physiological...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Arushi Jain Rajeev Kumar Dept. of Computer Science and Engineering National Institute of Technology Hamirpur Hamirpur India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

Anxiety is a common mental disorder that affects millions of people worldwide. Anxiety can be detected using physiological signals such as heart rate, skin conductance, blood pressure, and respiration. Machine learning is the ability to learn from data and forecast future events. Physiological signals can be analyzed and classified into different anxiety levels using machine learning. This paper aims to present the contribution of context features in detection of anxiety using physiological signals via machine learning. It is an imbalanced multiclassification problem using ECG and EDA signals with context features collected from 15 healthy individuals. These signals of sampling rate 700 Hz collected using RespiBAN from the Wearable Stress and Affect Detection (WESAD) dataset are used here with the 6-STAI questionnaire and the context collected about the subject during the experiment. Scores for each subject are calculated from this questionnaire which categorized subjects into different levels of anxiety. Multiple features are extracted from these signals and scaling is done on the extracted features. With the help of different data balancing techniques and different machine learning algorithms, this study will classify anxiety into three classes, namely, low, moderate and high. Classification of anxiety with fusion of multimodal physiological signals gave better results as compared to single modal physiological signals with an accuracy of 89.8% with ECG signal, 85.9% with EDA signal and 96.7% with ECG and EDA both using Gradient Tree Boosting algorithm with LOOCV after random oversampling. While, the best results are achieved when multimodal physiological signals are fused with context features, it gives the accuracy of 97.3% with Gradient Tree Boosting and LOOCV algorithm.

关键词： Machine learning algorithms Anxiety disorders Human factors Electrocardiography Feature extraction Boosting Physiology

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Zhuxuan Ma Hui Cao Yue Zhao Dept. of Electrical Engineering and Computer Science University of Arkansas Fayetteville AR USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components.

关键词： Inductance Circuit topology Bridge circuits Transportation Thermal conductivity Electric vehicle charging Topology Thermal stresses Stress Power transformer insulation

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A Rule-Based Approach for Interpretable Intensity-Modulated Radiation Therapy Treatment Selection

A Rule-Based Approach for Interpretable Intensity-Modulated ...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Xabier Gonzalez-Garcia Javier Fumanal-Idocin Joan M. Nunez Do Rio Humberto Bustince Dept. of Statistics Computer Science and Mathematics Public University of Navarre Spain University of Essex School of Computer Science and Electronic Engineering United Kingdom Dept. of Computer Science Multimedia and Telecommunications Universitat Oberta de Catalunya Spain

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

Artificial Intelligence (AI) methods are becoming essential in healthcare. In the context of Intensity-Modulated Radiation Therapy (IMRT), Knowledge-Based Planning (KBP) methodologies have enabled the modification of treatments in real-time to accommodate morphological changes in patients. KBP for IMRT is a data-driven approach that utilises real-time medical imaging to adjust the radiation dose for a patient as needed for the different stages of an illness. In this work we present an interpretable AI model that selects the best IMRT treatment alternatives and determines which is the best. We use an Adaptive Neuforuzzy Adaptive Inference System (ANFIS), which combines the potential of a neural network with the interpretability of a rule based system. We train the model in a supervised manner using the OpenKBP challenge data repository. For this purpose, we also developed a data augmentation method that is supported by Diffusion Probabilistic Models. This approach enables the generation of a wider spectrum of treatment qualities and aids regularisation. The primary advantage of this framework resides in its ability to offer explanations, which is essential in the deployment of medical procedures in real life. Moreover, it serves as a valuable means to test hypotheses concerning the quality of IMRT treatments. Our study reveals that the developed tool has substantial potential to establish itself as a reference in the realm of explainable IMRT treatment selection tools.

关键词： Adaptive systems Image color analysis Computational modeling Knowledge based systems data augmentation Real-time systems data models

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dept.Voting: A Few-Shot Point Cloud Classification Model Incorporating a Projection-Based Voting Mechanism

DepthVoting: A Few-Shot Point Cloud Classification Model Inc...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Yunhui Zhu Jiajing Chen Senem Velipasalar Electrical Engineering and Computer Science Dept. Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the significant progress in few-shot 2D image classification, few-shot 3D point cloud classification remains relatively under-explored, particularly in addressing the challenges posed by missing points in 3D point clouds. Most existing methods for few-shot 3D point cloud classification are point-based, and thus, highly sensitive to missing points. Despite recent attempts, such as ViewNet, which introduce projection-based backbones to increase robustness against missing points, the reliance on max pooling, to extract information from multiple images simultaneously, makes them prone to information loss. To address these limitations, we introduce dept.Voting, a novel projection-based approach, for few-shot 3D point cloud classification. Instead of extracting features from multiple projection images simultaneously, dept.Voting captures features from pairs of projection images (obtained from opposite view angles) separately, enhancing the extraction of more comprehensive information. These features are sent to multiple few-shot heads, which share parameters. To further refine predictions, dept.Voting incorporates a voting mechanism, allowing contribution and incorporating information from different pairs. We conduct extensive experiments on three datasets, namely ModelNet40, ModelNet40-C, and ScanObjectNN, along with cross-validation. Our proposed method consistently outperforms the state-of-the-art baselines on all datasets in terms of average accuracy with even higher margins on the challenging ScanObjectNN dataset.

关键词： Point cloud compression Solid modeling Three-dimensional displays Head Accuracy Feature extraction data augmentation

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MAD: A Meta-Learning Approach to Detect Advanced Persistent Threats using Provenance data in Industrial IoT

MAD: A Meta-Learning Approach to Detect Advanced Persistent ...

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computer Security Applications Conference Workshops (ACSAC Workshops), Annual

作者： Bikash Saha Nanda Rani Sandeep Kumar Shukla Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9798331532819

ISBN: (纸本)9798331532826

The increasing reliance on Industrial Internet of Things (IIoT) systems in critical infrastructure has made these environments prime targets for stealthy and prolonged Advanced Persistent Threats (APTs) that evade traditional security systems. This study introduces MAD (Meta-learning based APT Detection), a novel framework designed to detect APTs in IIoT environments by leveraging provenance data. This data provides a detailed view of system-level interactions, capturing subtle and persistent malicious behaviors. We evaluate MAD on the CICAPT-IIoT dataset, which simulates real-world APT scenarios with severe class imbalance due to the "low and slow" nature of APT attacks. MAD includes two variants: MAD v1, utilizing a multi-layer perceptron (MLP), and MAD v2, employing Model-Agnostic Meta-Learning (MAML) to improve detection of rare attack behaviors. MAD v1 achieves F1-score of 0.91 and outperforms the existing baselines. MAD v2 achieves F1-score of 0.95 surpassess existing baselines as well as MAD v1, which demonstrates its enhanced capability to detect rare APT activities despite the dominance of benign samples. This work combines provenance-based detection with meta-learning to address class imbalance and enhance APT detection in IIoT, demonstrating MAD’s potential in tackling emerging IIoT security challenges.

关键词： Metalearning Deep learning Adaptation models Adaptive systems Conferences Threat assessment Real-time systems Critical infrastructure computer security Industrial Internet of Things

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