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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Cheng Chu Aishwarya Hastak Fan Chen Luddy School of Informatics Computing and Engineering Indiana University Bloomington USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN’s effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Fréchet Inception Distance scores, and reductions of 5× in qubit counts, 5× in single-qubit gates, and 12× in two-qubit gates.

关键词： Visualization Scalability Qubit Logic gates Signal processing Generative adversarial networks Generators Speech processing Long short term memory Principal component analysis

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LSTM and BERT based transformers models for cyber threat intelligence for intent identification of social media platforms exploitation from darknet forums

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5277-5292页

作者： Sangher, Kanti Singh Singh, Archana Pandey, Hari Mohan Amity School of Engineering and Technology (ASET) Amity University Noida India Department of Computing and Informatics Bournemouth University Fern Barrow Poole BH12 5BB United Kingdom

Cybercriminals, terrorists, political activists, whistleblowers, and others are drawn to the darknet market and its use for illicit purposes. Various methods are employed to identify the people who are behind these identities and websites. Since DNMs are more recent than other platforms, there are more unexplored research possibilities in this field. Research has been done to identify the buying and selling of products connected to hacking from Darknet Marketplaces, the promotion of cyber threats in hacker’s forums and DNMs, and the supply chain elements of content related to cyber threats. The proposed research covers one of the most promising research areas: darknet markets and social media platforms exploitation tools and strategies. The research uses 6 DNMs publicly available data and then identified the most popular social media platform and intent of discussion based on the interaction available in form of the user remarks and comments. The research caters the social media platform and cybercrimes or threats associated to them, by help of the machine learning algorithms Logistic Regression, RandomForestClassifier, GradientBoostingClassifier, KNeighborsClassifier, XGBClassifier, Voting Classifier and Deep Learning based model LSTM and Transformer based Model used. In existing research, natural language processing techniques were employed to identify the kinds of commodities exchanged in these markets, while machine learning approaches were utilized to classify product *** proposed research work advanced and lighter version of BERT and LSTM model used yielding accuracy of 90.12% and 91.35% respectively. LSTM performed best to extract multiclass classification of actual intension of social media usage by intelligent analysis on hackers’ discussions. Strategies on social media platforms such as Facebook, twitter, Instagram, Snapchat to exploit them using darknet platforms also explored. This paper contributes on cyber threat intelligence that leverages

关键词： BERT CTI LSTM Machine learning Social media crimes Transformer based model

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YoNet: A Neural Network for Yoga Pose Classification

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SN Computer Science 2023年第2期4卷 198页

作者： Ashraf, Faisal Bin Islam, Muhammad Usama Kabir, Md Rayhan Uddin, Jasim Department of Computer Science and Engineering University of California Riverside CA United States School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA United States Department of Computing Science University of Alberta Edmonton AB Canada Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Cardiff Wales United Kingdom

Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. © 2023, The Author(s).

关键词： Deep learning Image classification Neural network Pose recognition Yoga pose

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Enabling data-driven anomaly detection by design in cyber-physical production systems

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Cybersecurity 2022年第3期5卷 34-51页

作者： Rui Pinto Gil Goncalves Jerker Delsing Eduardo Tovar Department of Informatics Engineering Faculty of EngineeringUniversity of PortoPortoPortugal EISLAB Lulea University of TechnologyLuleaSweden Research Centre in Real-Time and Embedded Computing Systems Polytechnic of Porto-School of EngineeringPortoPortugal

Designing and developing distributed cyber-physical production systems(CPPS)is a time-consuming,complex,and error-prone *** systems are typically heterogeneous,i.e.,they consist of multiple components implemented with different languages and development *** of the main problems nowadays in CPPS implementation is enabling security mechanisms by design while reducing the complexity and increasing the system’s *** the IEC 61499 standard is an excellent approach to tackle these challenges by enabling the design,deployment,and management of CPPS in a model-based engineering *** propose a method for CPPS design based on the IEC 61499 *** method allows designers to embed a bio-inspired anomaly-based host intrusion detection system(A-HIDS)in Edge *** A-HIDS is based on the incremental Dendritic Cell Algorithm(iDCA)and can analyze OPC UA network data exchanged between the Edge devices and detect attacks that target the CPPS’Edge *** study’s findings have practical implications on the industrial security community by making novel contributions to the intrusion detection problem in CPPS considering immune-inspired solutions,and cost-effective security by design system *** to the experimental data,the proposed solution can dramatically reduce design and code complexity while improving application maintainability and successfully detecting network attacks without negatively impacting the performance of the CPPS Edge devices.

关键词： Artificial immune systems Cyber-physical production systems IEC 61499 Function blocks Industrial Internet of Things Anomaly-based host intrusion detection system Model-based engineering

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Optimizing Convolutional Networks Using a Modified Metaheuristic for Apple Tree Leaf Disease Detection 32

Optimizing Convolutional Networks Using a Modified Metaheuri...

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32nd Telecommunications Forum, TELFOR 2024

作者： Petrovic, Aleksandar Jovanovic, Luka Kavitha, D. Bacanin, Nebojsa Zivkovic, Miodrag Malisic, Snezana Singidunum University Faculty of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia Simats Saveetha School of Engineering Department of Mathematics Chennai India

ISBN: (纸本)9798350391053

The global demand for food is continuously increasing. The use of artificial intelligence can vastly improve the field of agriculture. Computer vision can help detect issues with crops. This work focuses on detecting plant diseases in apple tree leaves through the analysis of images. The proposed approach consists of a convolutional neural network (CNN) optimized by a hybrid optimization algorithm. The optimized models showcase favorable outcomes with the best models attaining an accuracy of 0.772408. © 2024 IEEE.

关键词： Convolutional neural networks

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A Machine Learning-Based Privacy-Preserving Approach to Incorporate Distributed Generators in AC Optimal Power Flow

A Machine Learning-Based Privacy-Preserving Approach to Inco...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Kasif, Gibran Thondilege, Comganesha University of Westminster London School of Computer Science and Engineering United Kingdom Informatics Institute of Technology Department of Computing Colombo06 Sri Lanka

ISBN: (纸本)9798350341454

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model's performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues. © 2023 IEEE.

关键词： Music

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Blockchain-based Energy Trading among UAVs and Base Stations for Net-Zero 10

Blockchain-based Energy Trading among UAVs and Base Stations...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Ayaz, Ferheen Nekovee, Maziar Saeed, Nagham University of London City St. George's Department of Computer Science United Kingdom University of Sussex 6 G Lab School of Engineering and Informatics United Kingdom University of West London School of Computing and Engineering United Kingdom

ISBN: (纸本)9798350373011

The recent development in Internet-of-Things (IoT) networks utilizing Unmanned Aerial Vehicles (UAVs) have greatly enhanced system analysis and management. For example, an energy management system can exploit UAVs to intelligently predict output from renewable resources and estimate grid supply. However, UAVs are battery-operated and need to be charged regularly. It has become crucial to maintain supply and achieve net-zero with negligible emissions for high energy demanding IoT networks, such as those incorporating UAVs. A promising solution is to utilize backup batteries of cellular base stations (BSs) which often remain idle when grid supply is consistent. A P2P energy exchange between a BS seller and UAV buyer is possible in exchange of some incentives. However, BS operators may become selfish and not share their energy. Also, they can behave maliciously by selling energy at unreasonably high prices or sharing false information about the original source of energy. This paper proposes an energy trading system between 5G BSs and UAVs. A Reinforcement Learning (RL) algorithm is used by UAVs to select BS which maximizes incentives for both BSs and UAVs. Blockchain security increase energy exchange by 0.26 kWh and 4.9 kWh in presence of malicious and selfish BS seller respectively. © 2024 IEEE.

关键词： Sales

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CARE-30: A Causally Driven Multi-Modal Model for Enhanced 30-Day ICU Readmission Predictions

CARE-30: A Causally Driven Multi-Modal Model for Enhanced 30...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Linna Zhao, Leyi Luo, Zhanpeng Wang, Xuan Feng, Ziliang Lu, Li Sichuan University College of Computer Science Sichuan Chengdu China Indiana University Luddy School of Informatics Computing and Engineering BloomingtonIN United States

ISBN: (纸本)9798350337488

Accurate prediction of unplanned readmissions allows healthcare systems to adopt preventive measures, reducing these occurrences. Creating a model that accurately predicts readmissions while simultaneously providing insights that are clinically interpretable is a complex and demanding task. This interpretability is vital, as it informs clinicians about the underlying reasons behind a model's decisions, thus aiding in more informed clinical decision-making. In light of this, we introduce CARE-30 (Causal Analysis based Readmission Estimator within a 30-day time frame), a novel causal inference-based model, designed with clear cause-and-effect relationships, enhancing its potential acceptance and understanding among *** our approach, we generate a directed acyclic graph (DAG) to elucidate the latent causal relationships among pivotal clinical variables. Leveraging this causal graph, we can effectively apply the front-door criterion to construct the model and eliminate bias. By utilizing transformers, we integrate clinical texts, time-series data and categorical data into patient representation data as a robust input to the CARE-30 model. Our work signifies the effective application of knowledge from the field of causal inference in the domain of clinical medicine, and it also reflects the originality and impact of our research. CARE-30 unites causality and clinical prediction, unlocking new vistas in healthcare forecasting. Experiments demonstrated that our model outperforms all other methods in Accuracy and F1 scores. © 2023 IEEE.

关键词： Causal inference MIMIC-III multi-modal readmission prediction

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DeepDect: A Facial Deepfake Video Detection Application using Ensemble Learning 12

DeepDect: A Facial Deepfake Video Detection Application usin...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Tay, Wan Xin Chua, Hui Na Jasser, Muhammed Basheer Issa, Bayan Wong, Richard T.K. Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria

ISBN: (纸本)9798350391398

The emerging technology of deepfake video poses significant threats to information integrity and public trust. Deepfake videos come in various forms, including face swaps, lip-syncing, and full-body simulations. Detection algorithms may struggle to generalize across these different types of deepfakes, leading to lower effectiveness in real-world applications. An advanced deepfake detection system is developed using an ensemble approach, combining the strengths of three pre-trained Convolutional Neural Networks (CNNs)ResNeXt50, Xception, and EfficientNet-v2-with Long ShortTerm Memory (LSTM) layers to capture both spatial and temporal features from videos to improve detection accuracy. The ensemble model is further enhanced by incorporating additional features such as lip movements, eye blinks, head poses, and color contrasts to identify subtle inconsistencies in deepfake videos that are often ignored by traditional detection methods. The ensemble model achieves an accuracy of 84.06%, with a good balance between precision and recall, indicating its effectiveness in real-world applications. We developed DeepDect, a web application embedded with the ensemble model, to provide a user-friendly interface for deepfake detection by uploading videos or submitting URL links. The application includes educational content about deepfake techniques to raise user awareness about the potential dangers of deepfakes. © 2024 IEEE.

关键词： Convolutional neural networks

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