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The Quantum Interface Controller: A Full-Stack, Modular, and Scalable System for Qubit Readout and Manipulation

The Quantum Interface Controller: A Full-Stack, Modular, and...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Luis E. Ardila-Perez Marvin Fuchs Robert Gartmann Lukas Scheller Mario Schlösser Roger Heil Christian Roth Ilja Bekman Markus Jerger Rami Barends Stefan van Waasen Oliver Sander Institute for Data Processing and Electronics (IPE) Karlsruhe Institute of Technology (KIT) Karlsruhe Germany Integrated Computing Architectures Electronic Systems (ZEA-2) Forschungszentrum Jülich. GmbH Jülich Germany Functional Quantum Systems Peter Grünberg Institute (PGI-13) Forschungszentrum Jülich GmbH Jülich Germany

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computing has the potential to revolutionize computation by solving complex problems in a way that classical computers cannot. However, robust and scalable qubit manipulation and control electronics are needed to exploit this potential. Academic efforts in this area are sparse, especially in terms of scalability to hundreds or thousands of qubits. The mod-ular and scalable Quantum Interface Controller (QiController), a room-temperature qubit control architecture, addresses this gap. It is implemented using the Advanced Telecommunications computing Architecture (ATCA) and its dual-dual star topology. The system has two boards: a “Hub” with an AMD Xilinx RFSoC and a “Node” with peripheral FPGAs and DACs for low-latency flux control. This paper describes the system architecture and firmware developments for multi-board synchronization.

关键词： Electric potential Qubit Systems architecture Stars Computer architecture Telecommunication computing Topology System-on-chip Synchronization Field programmable gate arrays

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Front-running Attack Detection in Blockchain using Conditional Packing Generative AI

Front-running Attack Detection in Blockchain using Condition...

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ieee international conference on Big data

作者： Shagufta Henna Mohamed Amjath Department of Computing Atlantic Technological University Donegal Ireland

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Detecting front-running attacks in Ethereum blockchain transactions is crucial for maintaining security and integrity within decentralized ecosystems. However, existing models struggle to accurately model the complex distributions inherent in tabular data, particularly in the presence of class imbalance and mode collapse. This paper leverages the potentials of Conditional Tabular Generative Adversarial Networks and PacGAN, called a Conditional Packing GAN (cPacGAN), to address these challenges. cPacGAN effectively generates synthetic data that closely mimics the distribution of real transactions, thereby augmenting the dataset and improving the performance of front-running attack detection. PacGAN mitigates mode collapse by incorporating packed samples in the discriminator, improving the diversity of generated samples and improving the stability of the training process. Through experimental evaluations of a real-world Ethereum transactions dataset, cPacGAN demonstrates improved performance across all selected machine learning classifiers, particularly augmenting the effectiveness of Tabular Neural Networks (TabNet).

关键词： Training Generative AI Biological system modeling Neural networks Generative adversarial networks data models Stability analysis Blockchains Security Synthetic data

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BWG: An IOC Identification Method for Imbalanced Threat Intelligence datasets

BWG: An IOC Identification Method for Imbalanced Threat Inte...

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ieee international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Juncheng Lu Yiyang Zhao Yan Wang Jiyuan Cui Sanfeng Zhang School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

APT attacks are becoming increasingly complex and stealthy. To effectively counter APT attacks, modelling threat intelligence data based on graphs, identifying Indicators of Compromise (IOC) nodes, and providing early warnings have become new research hotspots. However, the problem of node category imbalance in such graph datasets restricts the identification capabilities of these methods. Therefore, this paper proposes a supervised graph data augmentation method. In the training phase, graph disentangled representation learning is utilized to perform feature embedding for minority class nodes, effectively alleviating the sparsity problem faced by traditional methods and effectively integrating neighbourhood information of minority class nodes at a higher semantic level. Additionally, two loss functions designed based on link prediction and prototype constraints enhance node type consistency and semantic consistency, respectively. Experimental results on the APT and PDNS datasets demonstrate that the proposed method outperforms other baseline models in identification performance; even in highly imbalanced scenarios, it surpasses the second-best model.

关键词： Training data privacy Computational modeling Semantics Disentangled representation learning Prototypes data augmentation data models Security

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Daily Routine Recognition from Longitudinal, Real-Life Wearable Sensor data for the Elderly

Daily Routine Recognition from Longitudinal, Real-Life Weara...

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Activity and Behavior computing (ABC), international conference on

作者： Sayeda Shamma Alia Paula Lago Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798350375503

ISBN: (纸本)9798350375510

As our population continues to age, there is a growing need for tailored healthcare solutions for the elderly. Despite the promise of current wearable devices, the effectiveness of these devices in addressing complex healthcare needs, especially in identifying cognitive decline still requires extensive refinement. Analyzing Routines can be a potential solution as it can provide insights into cognitive health, potentially serving as an early indicator of cognitive decline or impairment. This paper presents a framework to identify routine and non-routine behaviors using the CASAS smartwatch dataset, a public real-life longitudinal dataset. This is of particular significance in health monitoring and understanding long-term behavior patterns among the elderly. Our work addresses challenges throughout the entire Routine recognition process, including noisy and incomplete data, class imbalance, reliance on accurate activity labeling, and the preprocessing overhead typically encountered in wearable sensor datasets. The findings of our study suggest that labeling the dataset based on cosine similarity and label propagation results in improved activity recognition accuracy compared to kNN for classes with fewer samples. However, kNN demonstrates the best overall performance, particularly for classes such as Work, Other, Hygiene, where it excels due to its abundance in the dataset. Moreover, our findings emphasize the importance of identifying the most occurring activity sequences, which influences routine identification. This underscores the importance of recognizing and modeling recurring activity patterns, highlighting their significance in the routine identification process. Understanding these patterns can have an impact on the healthcare of the elderly, enabling tailored interventions to support healthy aging at home. In the future, this research aims to contribute to improved monitoring, timely interventions, and ultimately, enhanced quality of life for older adults li

关键词： data analysis Accuracy Medical services Activity recognition Routing protocols Labeling Noise measurement

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MEC- Assisted Task offloading using Meta-Reinforcement Learning for B5G/6G Network

MEC- Assisted Task offloading using Meta-Reinforcement Learn...

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ieee international conference on Big data

作者： Priyadarshni Priyadarshni Praveen Kumar Shivani Tripathi Akshun Pratap Dubey Rajiv Misra Computer Science and Engineering Indian Institute of Technology Patna India Computer Science and Engineering B.P. Mandal College of Engineering Madhepura Bihar India

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The rapid growth of mobile data and computing demands has strained resource-constrained edge devices, particularly in supporting IoT applications. Mobile Edge computing (MEC) offloading alleviates these challenges by shifting complex tasks to edge-cloud servers, reducing computational burdens and enhancing efficiency. The integration of 5G and 6G technologies further enhances MEC by providing ultra-low latency and high-bandwidth connections. Despite the use of deep learning methods in task offloading, current approaches struggle with slow learning and adaptability issues. To address these challenges, we introduce a Deep Meta Reinforcement Learning based Offloading (Deep Meta-RL) Framework. Formulating the task offloading problem as a Markov Decision Process (MDP) enables us to leverage the Deep Meta-RL algorithm for precise offloading decisions, reinforcement learning’s decision-making, and meta-learning’s adaptability. Simulation results demonstrate that Deep Meta-RL significantly outperforms traditional DQN algorithms, achieving a 16.75% improvement in rewards and reducing latency by 20%.

关键词： Deep learning Multi-access edge computing Simulation Markov decision processes Decision making Reinforcement learning Big data Computational efficiency Servers Internet of Things

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cognitive Deep-Learning approach to enhance Problem-Solving Skills based on Knowledge Organization

Cognitive Deep-Learning approach to enhance Problem-Solving ...

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international conference on ICT in Business Industry & Government (ICTBIG)

作者： Karnam Akhil Bollineni Harshadha Nikhil Kota Harika Kalakata Jaswanth Kondabrolu Medipally Abhinay Department of CSE&CSBS VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad India Department of CSBS VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad India VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY Hyderabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Knowledge is a fundamental component of human cognition that exists in procedural, declarative, and non-declarative forms and plays a crucial role in influencing problem-solving capabilities. In the current environment of education and professional development, the capacity to solve complex problems effectively is of great importance. Graduates are often examined for problem-solving skills as they transition from education to the professional world. Despite the vast amount of knowledge available, graduates frequently face challenges applying their cognitive skills to real-world situations. This requires a deeper insight into how different types of knowledge and their organization affect problem-solving abilities. Recent studies have demonstrated how data mining techniques with access to surveys can potentially solve problems associated with Attention Deficit Disorder (ADD) and Absence Epilepsy. Research on data pattern analysis regarding depression, anxiety, and cognitive impairments has actually enabled researchers to design interventions in the form of prototypes in video games that may improve cognitive and motor functions. The aim of this study is essentially applying a similar approach to analyze the intricate relationship between knowledge organization, cognitive skills, and problem-solving efficacy among graduates. The data is collected through surveys from the graduates. Concepts of data mining are used to extract the meaningful patterns between knowledge and cognitive skills and their impact on the problem-solving ability to analyze how effectively the knowledge is being used and affected for and by solving different problems. The study mainly focuses on deep learning approaches that are capable of recognizing complex patterns, inferring relationships, and providing tailored recommendations. In conclusion, This research will contribute to a deeper understanding of knowledge acquisition, organization, and utilization, with significant implications for educat

关键词： Surveys Deep learning Video games Knowledge acquisition Education Epilepsy Organizations Knowledge management Problem-solving data mining

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Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Machine Fault Diagnosis Using EMD-Gammatone Texture Represen...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Mahe Zabin Anika Nahian Binte Kabir Muhammad Khubayeeb Kabir Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science & Technology KAIST Daejeon South Korea Department of Computer Science and Engineering Brac University Dhaka Bangladesh Artificial Intelligence and Big Data Department Woosong University Daejeon South Korea

Machine fault diagnosis involves the intricate process of detecting and isolating faults, which is particularly challenging due to various sources of noise and the complex nature of the faults. In automatic fault diagnosis, feature engineering becomes daunting because of the time-varying characteristics of the fault signals. As artificial intelligence continues to advance, deep learning models have been increasingly employed for machine fault classification. However, a significant limitation of state-of-the-art models is their high computational complexity, making them unsuitable for deployment on portable devices. In this paper, a lightweight fault diagnosis model is proposed that consists of a self-attention SqueezeN et architecture along with a hybrid texture representation technique using empirical mode decomposition (EMD) and gammatone-spectrogram (GS) filter. In the model, initially, the dominant signal is extracted from the ID audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD and then the dominant signals are converted to 2D texture maps applying the GS filter. Then, the generated texture maps are fed to the modified self-attention SqueezeNet classifier, with reduced model width and depth as input for training and validation. In the experimental evaluation, two public benchmark datasets- ToyADMOS and MIMII are used to validate the model. Finally, the model classifies the machine audio faults using the trained features. The experimental results demonstrated that the hybrid EMD-Gammatone texture imaging outperforms the other state-of-the-art methods like MFCC, Gammatone, and Hilbert Huang Transform with a self-attention (SA) based SqueezeNet architecture. The EMD-Gammatone spectrum-based feature extraction accurately detected the faults by exhibiting an accuracy of 89.32% and 96.46% for MIMII and ToyADMOS datasets, respectively. In addition, with the EMD-Gammatone Spectrogram images, the proposed model outperforms the conventional

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Efficient Network Embedding by Approximate Equitable Partitions

Efficient Network Embedding by Approximate Equitable Partiti...

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ieee international conference on data Mining (ICDM)

作者： Giuseppe Squillace Mirco Tribastone Max Tschaikowski Andrea Vandin IMT Lucca Italy Aalborg University Denmark Sant'Anna Pisa Italy DTU Denmark

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Structural network embedding is a crucial step in enabling effective downstream tasks for complex systems that aim to project a network into a lower-dimensional space while preserving similarities among nodes. We introduce a simple and efficient embedding technique based on approximate variants of equitable partitions. The approximation consists in introducing a user-tunable tolerance parameter relaxing the otherwise strict condition for exact equitable partitions that can be hardly found in real-world networks. We exploit a relationship between equitable partitions and equivalence relations for Markov chains and ordinary differential equations to develop a partition refinement algorithm for computing an approximate equitable partition in polynomial time. We compare our method against state-of-the-art embedding techniques on benchmark networks. We report comparable-when not superior-performance for visualization, classification, and regression tasks at a cost between one and three orders of magnitude smaller using a prototype implementation, enabling the embedding of large-scale networks that could not be efficiently handled by most of the competing techniques.

关键词： Costs Prototypes Ordinary differential equations Approximation algorithms Polynomials Iterative algorithms Partitioning algorithms Classification algorithms data mining complex systems

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MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Huiting Li Weiyu Zhang Yong Shang Wenpeng Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. Extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods.

关键词： Drugs Computational modeling Biological system modeling Contrastive learning Benchmark testing Feature extraction data models Bipartite graph Bioinformatics Periodic structures

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An Empirical Analysis of Realistic Noise in Quantum Neural Networks for Medical Classifications of Tabular, Signal and Imaging data

An Empirical Analysis of Realistic Noise in Quantum Neural N...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Philipp Moser Alexander Maletzky Michael Giretzlehner Research Unit Medical Informatics RISC Software GmbH Hagenberg Austria

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum machine learning has gained significant momentum in recent years, particularly with the use of parameterized quantum circuits that exhibit a certain level of resilience to noise in current NISQ devices. However, the real-world applicability of QML remains an active field of research as transitioning from idealized simulations to real quantum hardware introduces challenges related to data size and noise. Our study empirically analyzes the performance of popular quantum neural networks in ideal, noise-free conditions, but also in the presence of realistic noise, which was modeled to mimic the noisy behavior of current IBM quantum devices. We carefully selected three medical classification tasks that cover common data modalities with varying numbers of features-from tabular cell data to signal sampling points and image pixels. Our findings statistically show that while complex parameterized circuits perform well in noise-free simulations, their performance significantly deteriorates with increasing qubit numbers under noisy conditions. Our study suggests that parameterized quantum circuits of moderate complexity are a favorable choice for future experiments on noisy quantum devices.

关键词： Performance evaluation Analytical models Noise Neural networks Signal sampling data models Noise measurement Integrated circuit modeling Quantum circuit Biomedical imaging

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