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Benefiting From Power of The Crowd: Contribution of Crowds to the Idea Evaluation Phase 28

Benefiting From Power of The Crowd: Contribution of Crowds t...

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Joint conference of the ieee 28th International conference on Engineering, Technology and Innovation (ICE/ITMC) / 31st conference of the International-Association-for-Management-of-Technology (IAMOT)

作者： Cubukcu, Ahmet Akarcay, Ozlem Ulusoy, Tuba Necmettin Erbakan Univ Dept Comp Forens Engn Konya Turkey KTO Karatay Univ Programming Program Dept Comp Konya Turkey Necmettin Erbakan Univ Dept Ind Engn Konya Turkey

ISBN: (纸本)9781665488174

The Covid-19 pandemic has put the world in a serious crisis by damaging the functioning of many systems (i.e.,health, economy, trade). Online communities have also tried to find solutions to minimize the negative effects caused by the Covid-19 pandemic. One of these communities conducted a program in Turkey to minimize the risky effects brought by the COVID-19 pandemic. The program took place over a three-month period (April 2020 to June 2020) during the open innovation platform powered by innoCentrum. In this process, an accelerator program was carried out to gather, evaluate, and implement innovative ideas. During the evaluation phase of an idea, the assessments of other entrepreneurs who submitted ideas were also taken into consideration by a rating system. The aim of this study is to reveal whether there is a relationship between the contribution levels of crowds (entrepreneurs) and the best ideas selected during the evaluation phase. While the result of the study shows that there is a significant relationship between the average scores given by the crowds with the scores given by the mentors who mainly evaluated the ideas, there is no significant relationship between the number of crowds' comments and the number of crowds' vote with the scores given by the mentors.

关键词： Covid-19 innovation entrepreneurship idea evaluation crowd wisdom innovation platform

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Off-Shelf Deep Learning architectures as Feature Extractor in CT COVID-19 Classification

Off-Shelf Deep Learning Architectures as Feature Extractor i...

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2022 International conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022

作者： El-Messiry, Haythem University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Ainsham University Faculty of Computer and Information Science Departement of Computer Science Egypt

ISBN: (纸本)9781665477093

Recently the chest imaging plays an important role in COVID-19 diagnosis compared to laboratory diagnosis such as RT-PCR. This paper investigates a robust algorithm to detect infected COVID-19 patients using computed tomography scans. The proposed algorithm utilizes a deep learning approach through applying an off shelf pretrained neural network to extract a feature map matrix from the first layer of convolutional neural network, which preserve the basic features related to geometry structures. Later the trained features are classified using a machine learning classifier as support vector machine algorithm to classify the tested images into two classes infected against not infected. The investigated algorithm was trained and tested using two open-source datasets. The results were experimented using five general pretrained off shelf CNN architectures, the performance of the pretrained algorithm was measured and evaluated for the extracted image features with popular five pretrained CNN and classification accuracy 90%. © 2022 ieee.

关键词： COVID-19

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Analysis and Optimization of Beyond Diagonal-RIS Assisted RSMA Multi-User MIMO network

Analysis and Optimization of Beyond Diagonal-RIS Assisted RS...

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2025 ieee Wireless Communications and networking conference, WCNC 2025

作者： Agham, Kunal Singh, Yatindra Nath Peruru, Subrahmanya Swamy Iit Kanpur Department of Electrical Engineering Kanpur208016 India

ISBN: (纸本)9798350368369

The beyond-diagonal reconfigurable intelligent surface (BD-RIS) represents the next advancement in reconfigurable intelligent surfaces (RIS), offering enhanced flexibility by supporting multiple modes and architectures. This study examines the spectral efficiency (SE) of a single-cell RSMA-based multi-user MIMO system assisted by two sector BD-RIS. The scattering matrix of BD-RIS and the power allocation between common and private precoders primarily determine the SE. We employ a two-step optimization process to determine these parameters. First, the scattering matrix is optimized by maximizing the total channel gain of the user equipment (UEs). Using the optimized scattering matrix, the power allocation for the common and private streams is then calculated by maximizing the SE of the system through a fractional programming technique, which gives a closed-form solution. The SE is computed using the optimized parameters and is numerically plotted to evaluate the performance of the system. The system is extensively analyzed across various precoding techniques, numbers of UEs, and BD-RIS elements, and is also compared to space division multiple access (SDMA). Numerical results show that BD-RIS outperforms both the traditional RIS and the scenario without RIS. © 2025 ieee.

关键词： Reconfigurable architectures

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An open Source Simulation Framework for Moving-network-Convoy Based Cellular V2X Communication in Intelligent Traffic Systems

An Open Source Simulation Framework for Moving-Network-Convo...

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ieee conference on Vehicular Technology (VTC)

作者： Venkatnarayanan Lakshminarasimhan Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Munich Germany

open source system level simulators play a crucial role in the early stage research and optimization of cellular V2X communication networks and cooperative intelligent transportation systems by providing for accurate virtual modelling, rapid evaluation of system level design and algorithm development, and by offering the possibility to build-up on previous research. Amidst the various architectures under consideration for future cellular V2X, small moving cells or moving network convoys are promising candidates, considering their inherent mobility. The orchestration of such architectures in combination with the availability of precise information about the vehicles from road side infrastructure open up new possibilities for a cost-optimal deployment and a resource-optimal operation of the cellular V2X networks. Towards this goal, we propose and present an open source simulation framework for moving-network-convoy based cellular V2X applications, and make it available for wider use at https://***/tum-air/ConvoyV2X

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Summary of the artifact accompanying the article "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs" 19

Summary of the artifact accompanying the article "Designing ...

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19th ieee International conference on Software Architecture (ICSA)

作者： Vale, Guilherme Correia, Filipe Figueiredo Guerra, Eduardo Martins Rosa, Thatiane de Oliveira Fritzsch, Jonas Bogner, Justus Univ Porto Fac Engn Porto Portugal Free Univ Bozen Bolzano Comp Sci Fac Bolzano Alto Adige Italy Univ Sao Paulo Inst Math & Stat Sao Paulo SP Brazil Univ Stuttgart Inst Software Engn Stuttgart Germany

ISBN: (纸本)9781665494939

This package provides all published resources used and produced in the context of the research study leading to the article \"Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs\" , presented in ICSA 2022’s technical track. It includes materials used to conduct the study as well as aggregated and anonymized data produced in its context. Making this package available intends to foster transparency and to support researchers attempting to replicate the study. The package complies with the Research Object Reviewed (ROR) and open Research Object (ORO) badges, awarded by the Artifact Evaluation Track at ICSA 2022, and is available under Creative Commons Attribution 4.0 International. The package is openly available in Zenodo [1] and the article is available in ICSA 2022’s proceedings [2] and as a pre-print [3] .

关键词： Software architecture conferences Microservice architectures

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Recovering Gropius Models with the Cluster Architecture Recovery Assistant

Recovering Gropius Models with the Cluster Architecture Reco...

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ieee International conference on Software Architecture Workshops (ICSAW)

作者： Sandro Speth Elias Müller Philipp Recke Niklas Krieger Steffen Becker Alexander Poth Olsi Rrjolli Institute of Software Engineering University of Stuttgart Stuttgart Germany Volkswagen Group IT - Test & Quality Assurance Volkswagen AG Wolfsburg Germany

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Modern software systems often follow a component-based architecture style, e.g., microservices. Despite many advantages of such an architectural style, cross-component issue management is a complex and time-consuming challenge. The Gropius issue management system enables cross-component issue management with respect to the issues' architectural dependencies but requires an architecture model of the system. While creating such a model for new projects is straightforward, it is often unfeasible for older projects due to the large modeling effort required. One solution to this challenge is to recover the system's architecture and map it to a Gropius model. However, as different projects can use different programming languages, the recovery must be as programming language-independent as possible. Therefore, in this work, we present the Cluster Architecture Recovery Assistant (CLARA), a framework that combines various established dynamic architecture recovery approaches and a new approach based on DNS scraping. CLARA allows recovering of a component-based architecture that is deployed on Kubernetes and maps this architecture to a Gropius model. We evaluated CLARA by applying it to an open-source microservice reference architecture and a microservice application of Volkswagen AG, and we compared the recovered architectures with the actual ones. The results indicate that CLARA can recover Gropius models effectively but show some limitations, e.g., detecting fine-grained relation types.

关键词： Hands Computer languages Adaptation models Software architecture Instruments Source coding Microservice architectures Computer architecture programming Standards

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BobGAT: Towards Inferring Software Bill of Behavior with Pre-Trained Graph Attention networks

BobGAT: Towards Inferring Software Bill of Behavior with Pre...

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ieee International conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Justin Allen Geoff Sanders Lawrence Livermore National Lab Livermore U.S.

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

In recent years, a variety of Graph Neural network (GNN) and Natural Language Processing (NLP) techniques have been proposed for binary analysis tasks including assembly embedding generation and binary similarity. However, a majority of these techniques were developed and tested on rather restrictive datasets or tasks, generally limited to one compiler, a limited number of compiler flags, and a small or artificially augmented dataset ill-suited for the semantic understanding of binaries required to generate a software bill of behavior. To that end, we have scraped a new programming competition dataset an order of magnitude larger than previous largest datasets, as well as designed a novel GNN architecture tailor-made for Control Flow Graph (CFG) analysis. We achieve over 92% accuracy on a complex 8000+ class programming problem classification task and perform extensive ablation studies showing the increased efficacy our model architectures provide and the necessity of proper dataset construction and deduplication for generalizability. We pre-train our models on an embedding task designed to improve our models’ semantic understanding of binaries and showcase their ability to group similar binaries together by behavior. Finally, we train our model as-is on a popular malware benchmark dataset and achieve near state-of-the-art performance with little effort: 99.23% accuracy on the Microsoft Malware Classification Challenge (Big 2015) dataset. Our data preparation tools are open-sourced, well-documented and tested, and pip-installable.

关键词： Training Accuracy Semantics Computer architecture programming Malware Graph neural networks Natural language processing Flow graphs Assembly

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Design of a Reference Architecture for Serverless IoT Systems

Design of a Reference Architecture for Serverless IoT System...

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ieee International conference on Omni-Layer Intelligent Systems (ieee COINS)

作者： Shah, Neel Pradip Otto Friedrich Univ Bamberg WIAI Fac Bamberg Germany

ISBN: (纸本)9781665431569

The Internet of Things (IoT) proposes the concept of a global and pervasive network of heterogeneous devices that interact with the users and their surroundings improving work efficiency and quality of life. From the conventional software systems development perspective, a good system design and architecture are critical. Its importance increases many-fold when the domain's objective is to connect an infinite set of heterogeneous devices, i.e., smaller systems should integrate and interoperate with each other with no or low modifications. Therefore, a consensus from a design perspective must be achieved in the community and the corporations working in the IoT domain to achieve this integrability and interoperability. One way to document this consensus is a reference architecture. This paper proposes a reference architecture that will act as a blueprint in developing Serverless IoT systems that integrate and interoperate with each other easily. The motivation behind selecting the serverless computing paradigm for IoT systems is that the system engineers can incorporate more decentralization. Thus, a significant amount of tasks will be handled at the Edge and the Fog layer. Only the heavy functions will be delegated to the Cloud, resulting in more efficient and inexpensive systems. Also, the event-driven programming model at the core of serverless computing, its advantages in scalability, focus on the core business logic, and high cohesion becomes necessary and valuable tools in the toolbox for efficiently developing IoT systems.

关键词： Reference architectures Software architectures IoT Systems IoT architectures Serverless Systems

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Mapping Spiking Neural networks to Heterogeneous Crossbar architectures using Integer Linear programming

Mapping Spiking Neural Networks to Heterogeneous Crossbar Ar...

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2025 Design, Automation and Test in Europe conference, DATE 2025

作者： Pohl, Devin Young, Aaron Asifuzzaman, Kazi Miniskar, Narasinga Rao Vetter, Jeffrey S. Georgia Institute of Technology United States Oak Ridge National Lab United States

ISBN: (纸本)9783982674100

Advances in novel hardware devices and architectures allow Spiking Neural network (SNN) evaluation using ultra-low power, mixed-signal, memristor crossbar arrays. As individual network sizes quickly scale beyond the dimensional capabilities of single crossbars, networks must be mapped onto multiple crossbars. Crossbar sizes within modern Memristor Crossbar architectures (MCAs) are determined predominately not by device technology but by network topology;more, smaller crossbars consume less area thanks to the high structural sparsity found in larger, brain-inspired SNNs. Motivated by continuing increases in SNN sparsity due to improvements in training methods, we propose utilizing heterogeneous crossbar sizes to further reduce area consumption. This approach was previously unachievable as prior compiler studies only explored solutions targeting homogeneous MCAs. Our work improves on the state-of-the-art by providing Integer Linear programming (ILP) formulations supporting arbitrarily heterogeneous architectures. By modeling axonal interactions between neurons, our methods produce better mappings while removing inhibitive a priori knowledge requirements. We first show a 16.7-27.6% reduction in area consumption for square-crossbar homogeneous architectures. Then, we demonstrate 66.9-72.7% further reduction when using a reasonable configuration of heterogeneous crossbar dimensions. Next, we present a new optimization formulation capable of minimizing the number of inter-crossbar routes. When applied to solutions already near-optimal in area, an 11.9-26.4% routing reduction is observed without impacting area consumption. Finally, we present a profile-guided optimization capable of minimizing the number of runtime spikes between crossbars. Compared to the best-area-then-route optimized solutions, we observe a further 0.5-14.8% inter-crossbar spike reduction while requiring 1-3 orders of magnitude less solver time. © 2025 EDAA.

关键词： Linear programming

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An Autonomous Self-learning and Self-adversarial Training Neural Architecture for Intelligent and Resilient Cyber Security Systems 1

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24th International conference on Engineering Applications of Neural networks (EANN)

作者： Demertzis, Konstantinos Iliadis, Lazaros Hellen Open Univ Sch Sci & Technol Informat Studies Patras Greece Democritus Univ Thrace Sch Engn Fac Math Programming & Gen Courses Dept Civil Engn Xanthi Greece

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Cybersecurity systems have become increasingly important as businesses and individuals rely more on technology. However, the increasing complexity of these systems and the evolving nature of cyber threats require innovative solutions to protect against cyber attacks. One promising approach is the idea of autonomous self-learning and auto-training neural architectures. Autonomous self-learning refers to the ability of the system to adapt to new threats and learn from past experiences without human intervention. Auto-training, on the other hand, refers to the ability of the system to improve its performance over time by automatically adjusting its parameters and algorithms. This research proposes an autonomous Self-Learning and Self-Adversarial Training (SLSAT) neural architecture for intelligent and resilient cyber security systems. It is an extension of the next-generation Continuous-Time Reservoir Computing (CTRC) that was proposed by the authors recently. The CTRC is a time-series anomaly detection system controlled by time-varying differential equations. It uses Reinforcement Learning (RL) to dynamically fine-tune the reservoir computing parameters in order to identify the aberrant changes in the data. The proposed method in this research improves the CTRC's architecture by including a Conditional Tabular Generative Adversarial network (CTGAN). Specifically, including CTGAN allows the SLSAT architecture to generate synthetic data based on the identified abnormalities to improve the model's performance and adapt to new and evolving threats without manual intervention. This, as proved experimentally, helps the model identify aberrant changes in the data and fend off poison and zero-day attacks.

关键词： Reservoir Computing Continuous-Time Reservoir Computing Cyber Defense Time Series Analysis

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