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CoVal-SGAN: A Complex-Valued Spectral GAN architecture for the effective audio data augmentation in construction sites

CoVal-SGAN: A Complex-Valued Spectral GAN architecture for t...

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ieee International conference on Fuzzy Systems (FUZZ-ieee) / ieee World Congress on Computational Intelligence (ieee WCCI) / International Joint conference on Neural networks (IJCNN) / ieee Congress on Evolutionary Computation (ieee CEC)

作者： Scarpiniti, Michele Mauri, Cristiano Comminiello, Danilo Uncini, Aurelio Lee, Yong-Cheol Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Via Eudossiana 18 I-00185 Rome Italy Louisiana State Univ Dept Construct Management Baton Rouge LA 70803 USA

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Generative audio data augmentation in a construction site is one of challenging research areas due to the high dissimilarity between work sounds of involved machines and equipment. However, it becomes necessary since the availability of audio data of critical work classes is often rare. Motivated by these considerations and demands, in this paper, we propose a complex-valued GAN architecture working with the audio spectrogram, named CoVal-SGAN, for an effective augmentation of audio data. Specifically, the proposed CoVal-SGAN exploits both the magnitude and phase information to improve the quality of the artificially generated audio signals and increase the overall performance of the underlying classifier. Numerical results, performed on the data recorded in real-world construction sites, along with the comparisons with available state-of-the-art approaches, show the effectiveness of the proposed idea by obtaining an improved accuracy.

关键词： Audio data augmentation Construction sites Complex-valued architectures Generative Adversarial networks GAN

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Combining Deep Convolutional Feature Extraction with Hyperdimensional Computing for Visual Object Recognition

Combining Deep Convolutional Feature Extraction with Hyperdi...

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作者： Luczak, Piotr Slot, Krzysztof Kucharski, Jacek Lodz Univ Technol Inst Appl Comp Sci Lodz Poland

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

The presented paper proposes a novel, hybrid neuromorphic computational architecture for visual data classification aimed at implementation in energy-efficient application-specific, FPGA or ASIC-based edge computing devices. The architecture combines a convolutional neural extractor that produces comprehensive representations of input patterns with a Hyperdimensional Computing (HDC) module that enables complex data analyses, including vector and vector sequence classification. As the biologically inspired HDC paradigm operates on holistic representations of concepts, we accordingly design a convolutional extractor to summarize various aspects of objects' appearance. As low energy consumption is the key design constraint, we assume that input images are delivered by energy-efficient dynamic vision sensors (event cameras). The extractor is pretrained using a three-head Convolutional Neural network (CNN). The different CNN heads: classifier, decoder, and clusterer implement optimization objectives essential for the "holographic" concept representation. Feature vectors produced by the extractor are projected onto hyperdimensional binary vectors using an encoding unit, and they are subject to classification in the HDC module. The neural extractor is trained in limited precision mode to account for ASIC/FPGA hardware constraints. We apply the proposed architecture to classify objects (pedestrians, cars, and cyclists) from two different traffic datasets: VIRAT and KAIST. We show that the proposed concept enables solving classification problems with an accuracy that matches the performance of deep neural classifiers while being feasible for implementation in energy-efficient application-specific hardware.

关键词： neuromorphic architectures convolutional neural networks hyperdimensional computing representation extraction

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GDenseMNet: Global Dense Multiscale Feature Learning network for Efficient COVID-19 Detection in CT Images

GDenseMNet: Global Dense Multiscale Feature Learning Network...

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作者： Joshi, Amogh Manoj Nayak, Deepak Ranjan VES Inst Technol Dept Elect & Telecom Engn Mumbai Maharashtra India Malaviya Natl Inst Technol Jaipur Dept Comp Sci & Engn Jaipur Rajasthan India

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Accurate and rapid diagnosis of COVID-19 is crucial for curbing its fast spread across the globe, with constant mutations leading to newer variants. Recent studies have exhibited that chest CT scans manifest clear radiological findings for the COVID-19 infected patients. Convolutional neural networks (CNN) have been used considerably for COVID-19 diagnosis;however, most CNN architectures demand a huge amount of parameters, resulting in overfitting on limited training data and a slower inference. Further, residual and densely connected neural networks such as ResNet and DenseNet have been proven to strengthen feature extraction and feature propagation but fail to fully discover both local and global representations. Moreover, few linearly stacked networks fall short in capturing and preserving multiscaled features from various receptive fields. This paper proposes a new CNN architecture called global dense multiscale feature learning network (GDenseMNet) for COVID-19 detection from CT images that effectively incorporates global dense connections while capturing multiscaled features. The GDenseMNet model comprises multiscale local feature extraction (MLF) blocks that capture local features of various size receptive fields using multiple filters and residual skip connections. The global dense connections between these blocks further enable global feature learning capability. The proposed architecture is lightweight, endto-end learnable, and validated using the SARS-CoV-2 CT-Scan dataset. Experimental results demonstrate that the GDenseMNet model achieves promising detection performance compared to state-of-the-art CNN approaches and hence, it can be utilized as an effective tool real-time COVID-19 diagnosis.

关键词： CNN GDenseMNet MLF block COVID-19 CT Scan

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Jupyter Notebook Attacks Taxonomy: Ransomware, Data Exfiltration, and Security Misconfiguration

Jupyter Notebook Attacks Taxonomy: Ransomware, Data Exfiltra...

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2024 Workshops of the International conference for High Performance Computing, networking, Storage and Analysis, SC Workshops 2024

作者： Cao, Phuong University of Illinois National Center for Supercomputing Applications Coordinated Science Laboratory Urbana-Champaign United States

ISBN: (纸本)9798350355543

open-science collaboration using Jupyter Notebooks may expose expensively trained AI models, high-performance computing resources, and training data to security vulnerabilities, such as unauthorized access, accidental deletion, or misuse. The ubiquitous deployments of Jupyter Notebooks (≈ 11 million public notebooks on Github have transformed collaborative scientific computing by enabling reproducible research. Jupyter is the main HPC's science gateway interface between AI researchers and supercomputers at academic institutions, such as the National Center for Supercomputing Applications (NCSA), national labs, and the industry. An impactful attack targeting Jupyter could disrupt scientific missions and business *** paper describes the network-based attack taxonomy of Jupyter Notebooks, such as ransomware, data exfiltration, security misconfiguration, and resource abuse for cryptocurrency mining. The open nature of Jupyter (direct data access, arbitrary code execution in multiple programming languages kernels) and its vast attack interface (terminal, file browser, untrusted cells) also attract attacks attempting to misuse supercomputing resources and steal state-of-the-art research artifacts. Jupyter uses encrypted datagrams of rapidly evolving WebSocket protocols that challenge even the most state-of-the-art network observability tools, such as *** envisage even more sophisticated AI-driven attacks can be adapted to target Jupyter, where defenders have limited visibility. In addition, the cryptographic design of Jupyter should be adapted to resist emerging quantum threats. On balance, this is the first paper to systematically describe the threat model against Jupyter Notebooks and lay out the design of auditing Jupyter to have better visibility against such attacks. © 2024 ieee.

关键词： attacks auditing cryptography jupyter network notebooks post-quantum provenance quantum ransomware security vulnerabilities

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AgileAFP: ML-enabled Agile Private/Public 5G/B5G Service and network Autonomic Federation Platform 14

AgileAFP: ML-enabled Agile Private/Public 5G/B5G Service and...

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14th International conference on Information and Communication Technology Convergence, ICTC 2023

作者： Choi, Taesang Kim, Jeongyoon Yoon, Sangsik Park, Moonkook Ko, Seungyu Chaparadza, Ranganai Etri Korea Republic of Mobigen Etsi Afi IPv6Forum Korea Republic of IN-Soft

ISBN: (纸本)9798350313277

Future networks in 5G and beyond will continue to expand in scale, complexity and interconnectivity, and will be highly shaped by distributed systems that serve various use cases that go beyond current use cases like mMTC and URLLC. This will be coupled with an increasing demand for autonomy in self-provisioning and self-management, network interoperability, and service federation on a very dynamic flexible way. Requirements on systems-of-systems architectures will become more relevant as multiple autonomous/semi-autonomous systems/networks (AN) adaptively seek to operate and interact with their peers. Federated Distributed open Platforms (DOPs) as peers for cross-industry sectors end-to-end (E2E) services innovation and delivery agility can meet such requirements. The use of federation and associated mechanisms is a promising technology for interconnecting systems, innovation and service delivery by the federating AN systems;and allowing asset sharing and extending traditional eco-systems and value-chains with further resources and stakeholders. However, such a concept of cross-industry E2E services innovation and delivery agility in the 5G & Beyond Era and the associated ecosystem that would emerge DO NOT EXIST yet. This paper proposes an architecture to enable cross-industry DOP network federation and our proof of concept implementation, AgileAFP © 2023 ieee.

关键词： Autonomic Management & Control (AMC) Autonomic/Autonomous networking (AN) Autonomics Levels Degrees of Autonomy in ANs Federations & Governance of ANs Multi-Layer Autonomics and AI/ML Frameworks

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P4Chain: A Multichain Approach for Real-Time Anomaly Traffic Detection in P4 network 14

P4Chain: A Multichain Approach for Real-Time Anomaly Traffic...

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14th ieee Annual Ubiquitous Computing, Electronics and Mobile Communication conference, UEMCON 2023

作者： Ajayi, Oluwaseyi Akinyemi, Temidayo Saadawi, Tarek Vaughn College of Aeronautics and Engineering Engineering Department NY United States City University of New York City College Department of Electrical Engineering NY United States

ISBN: (纸本)9798350304138

programming Protocol-independent Packet Processors (P4) is an open-source domain-specific language to aid the data plane devices in programming packet forwarding. It has a variety of constructs optimized for this purpose. With P4, one can program ASICs, PISA chips, FPGAs, and many network devices since the language constructs allow true independence in some aspects that openFlow could not support. However, there are some challenges facing this technology. The first challenge is that P4 does not account for malicious traffic detection in the data plane pipeline. 2. The controllers have no secure medium of attack signature exchange. This ongoing work presents a multichain solution for detecting malicious traffic and exchanging attack signatures among controllers. This architecture uses an Artificial Immune System (AIS) based Intrusion Detection System (IDS), which runs on a distributed blockchain network, to introspect the P4 data plane to analyze and detect anomaly traffic flows. This IDS resides on the SideChain smart contracts and constantly monitors the traffic flow at the data planes based on introspection. Once malicious traffic is detected on any SideChain, the signatures are extracted and passed through the signature forwarding node to the MainChain for real-time storage. The malicious signatures are sent to all controllers via the mainchain network. We minimize the congestion the solution can cause to the P4 network by utilizing a load balancer to serve the SideChain. To evaluate the performance, we evaluate the False Positive Rate (FPR), Detection Rate (DR), and Accuracy (ACC) of the IDS. We also compute the execution time, performance overhead, and scalability of the proposed solution. © 2023 ieee.

关键词： Smart contract

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A Rule Based Secure network System - Prevents Log4jshell and SSH Intrusions 6

A Rule Based Secure Network System - Prevents Log4jshell and...

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6th International conference on Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)

作者： Arsalan, M. A. Samid Suryaraman, S. Sujatha, G. SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India

ISBN: (纸本)9798350398441

There is a rapid growth in the field of technology and with this growth, the need for security is very much necessary to protect the privacy of everyone who uses their computers on a daily basis. Applications that use Java are everywhere around us and Log4j is a fast and reliable framework that is written in Java to log all the information which occurs in the application (mainly errors) which is beneficial to the developers. The log4j package is present under the Apache Software License, which is completely an open-source license which is certified by the open source initiative, implying that it was widely available to use and modify the same as per ones requirement [1]. As this particular java utility was used to maliciously launch RCE (Remote Code Execution) to execute commands that puts the user data at risk of theft and malicious intent, and the number of devices that used this particular API was in billions. As Java is the most-used programming language for IoT app development and typically includes Log4j. Since the Log4j vulnerability is very easy to exploit, this puts the other devices within the IoT product ecosystem at risk. The proposed model is that the honeypot deployed has a low interaction and that it not only alerts the security team but also analyses the payload pattern so that a rule can be created in the firewall to prevent such attacks in the future. The honeypot also detects SSH based attacks and alerts for any such potential attacks. This rule adds another layer of security on top of having a honeypot.

关键词： Honeypot Log4j Log4jshell Firewall SSH (Secure Shell) Header fields HTTP (Hypertext Transfer Protocol) Requests RCE (Remote Code Execution) Alert System

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Multiclass Artificial Neural network based Facial Expression Recognition 2

Multiclass Artificial Neural Network based Facial Expression...

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2nd International conference for Innovation in Technology, INOCON 2023

作者： Begum, Nazmin Mustafa, A. Syed Visvesvaraya Technological University Department of ISE H.K.B.K College of Engineering Karnataka India

ISBN: (纸本)9798350320923

Facial emotion recognition has emerged as one of the important research area from past few decades. It has wide variety of applications such as marketing research, medical field, security, criminal interrogations, etc. This paper provides a deep learning approach for face and emotion recognition that may be used to create a system that can accurately identify and recognize people.. Computer vision can find and recognize the human face and its emotions along with the numerous algorithms but to get the best results we are using a deep learning technique. The task is divided into three parts, the first of which is the detection of a human face from a camera, and the second of which is the analysis of the acquired data. Keras convolutional neural network was used to support the features and database used. Human faces are validated in the last phase to categorize human emotions such as Happy;neutral, furious, sad, disgusted, and surprised are all emotions that can be felt ising multiclass artificial neural network approach. Face detection, identification, and emotion categorization are the three main aims of the proposed study. For computer vision approaches, the open CV library, dataset, and python programming are employed in support of this work. © 2023 ieee.

关键词： Emotion Recognition

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Net Separation-Oriented Printed Circuit Board Placement via Margin Maximization 22

Net Separation-Oriented Printed Circuit Board Placement via ...

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27th Asia and South Pacific Design Automation conference (ASP-DAC)

作者： Cheng, Chung-Kuan Ho, Chia-Tung Holtz, Chester Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92037 USA

ISBN: (纸本)9781665421355

Packaging has become a crucial process due to the paradigm shift of More than Moore. Addressing manufacturing and yield issues is a significant challenge for modern layout algorithms. We propose to use printed circuit board (PCB) placement as a benchmark for the packaging problem. A maximum-margin formulation is devised to improve the separation between nets. Our framework includes seed layout proposals, a coordinate descent-based procedure to optimize routability, and a mixed-integer linear programming method to legalize the layout. We perform an extensive study with 14 PCB designs and an open-source router. We show that the placements produced by NSplace improve routed wirelength by up to 25%, reduce the number of vias by up to 50%, and reduce the number of DRVs by 79% compared to manual and wirelength-minimal placements.

关键词： Design automation Layout Printed circuits Manuals Packaging Benchmark testing Mixed integer linear programming

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Performance Portability of programming Strategies for Nearest-Neighbor Communication with GPU-Aware MPI

Performance Portability of Programming Strategies for Neares...

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2023 International conference on High Performance Computing, network, Storage, and Analysis, SC Workshops 2023

作者： White, James Buford Hewlett Packard Enterprise United States

ISBN: (纸本)9798400707858

To better advise HPC application developers, we have implemented Faces, a nearest-neighbor microbenchmark that quantifies performance trade-offs. The Faces experiments presented here explore the following design choices: 1) fewer dependent messages versus more independent messages, 2) fewer fused GPU kernels versus more simple kernels, 3) number of GPU streams, 4) size of GPU thread blocks, and 5) linear versus blocked ordering of MPI ranks. We present weak-scaling performance of a latency-sensitive "small"per-rank domain and of a bandwidth-sensitive "large"per-rank domain, and we compare results for two high-performance computers with contrasting CPU, GPU, and interconnect architectures: Summit and Frontier. We find that using more independent messages tends to give better performance than using few dependent messages. We identify performance-portability recommendations for GPU streams and synchronization, but other aspects of performance show complicated dependence on problem size and computer. © 2023 ACM.

关键词： Graphics processing unit

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