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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Bozkurtlar, Mert Yen, Benjamin Itoyama, Katsutoshi Nakadai, Kazuhiro School of Engineering Tokyo Institute of Technology Department of Systems and Control Engineering 2-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Istanbul Technical University Faculty of Management Department of Industrial Engineering Maçka/Beşiktaş/Harbiye Istanbul34367 Turkey Istanbul Technical University Faculty of Computer and Informatics Engineering Department of Computer Engineering Sariyer/Ayazaga Istanbul34485 Turkey The Nakadai Lab Japan Honda Research Institute Japan Co. Ltd 8-1 Honcho Saitama Wako351-0188 Japan

ISBN: (纸本)9798350312072

This paper addresses the task of learning periodic information using deep neural networks to achieve real-time, environment-independent sound source localization. Previous papers showed phase data is the most significant cue in sound source localization tasks and the proposed vM-B DNN was validated to be able to handle such periodic information using on synthesized data. However, they haven't shown its effectiveness and robustness in realistic use cases. This paper introduces a more complex model based on residual networks and adapts vM-B activation function for convolutional layers for use cases that require real-time predictions in dynamically changing environments. © 2024 IEEE.

关键词： Deep neural networks

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Neural Evolutionary Architecture Search for Compact Printed Analog Neuromorphic Circuits

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年

作者： Zhao, Haibin Pal, Priyanjana Hefenbrock, Michael Wang, Yuhong Beigl, Michael Tahoori, Mehdi B. Chair for Pervasive Computing Systems Institute of Telematics Germany Chair of Dependable Nano Computing Institute of Computer Engineering Department of Informatics Germany RevoAI GmbH Germany

Printed electronics (PE) is an additive fabrication technology for manufacturing electronic circuits which not only allows for a highly flexible printing of arbitrary circuit patterns, but also produce soft, non-Toxic, and degradable electronics at an extremely low cost. These properties are unmatched by silicon based electronics, making PE an enabler of new application domains, e.g., fast moving consumer goods, wearables, and disposable healthcare devices. A particularly promising class of circuits in this technology is the printed analog neuromorphic circuits, offering efficient and highly tailored computational functionalities. In this work, we leverage the highly flexible fabrication process of PE to address the bottleneck of PE, i.e., the large feature sizes and low device counts. This issue is crucial, as it impairs the integration of printed circuits into target applications with limited footprint, such as smart band aids. We also propose an evolutionary algorithm (EA) to improve the circuit compactness through circuit architecture optimization. As baseline, we compare the proposed EA method with a state-of-The-Art pruning method and a modified area-Aware pruning method. All of them are able to optimize circuit architecture alongside the component values of printed neuromorphic circuits. Experimental simulation reveals that the proposed EA approach can effectively achieve compact circuits and outperform the pruning method by 3.1× lower area with no loss of accuracy. As a byproduct, the power is reduced by 3.0×, paving the way to energy-harvested printed systems. © 1982-2012 IEEE.

关键词： Flexible electronics

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Exploring Experiences with New Musical Instruments through Microphenomenology

Exploring Experiences with New Musical Instruments through M...

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International Conference on New Interfaces for Musical Expression, NIME 2022

作者： Reed, Courtney N. Nordmoen, Charlotte Martelloni, Andrea Lepri, Giacomo Robson, Nicole Zayas-Garin, Eevee Cotton, Kelsey Mice, Lia McPherson, Andrew School of Electronic Engineering & Computer Science Queen Mary University of London London United Kingdom Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany KTH Royal Institute of Technology Stockholm Sweden

This paper introduces micro-phenomenology, a research discipline for exploring and uncovering the structures of lived experience, as a beneficial methodology for studying and evaluating interactions with digital musical instruments. Compared to other subjective methods, micro-phenomenology evokes and returns one to the moment of experience, allowing access to dimensions and observations which may not be recalled in reflection alone. We present a case study of five micro-phenomenological interviews conducted with musicians about their experiences with existing digital musical instruments. The interviews reveal deep, clear descriptions of different modalities of synchronic moments in interaction, especially in tactile connections and bodily sensations. We highlight the elements of interaction captured in these interviews which would not have been revealed otherwise and the importance of these elements in researching perception, understanding, interaction, and performance with digital musical instruments. © 2022, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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Ant Colony Optimization for Service Composition in Multi-cloud Environments 15

Ant Colony Optimization for Service Composition in Multi-clo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Maurya, Mahesh Vidhya, G. Akram, Faiz Saravanan, M. Venkata Ramana, K. Rajakumari, S. K.C. College of Engineering Department of Computer Engineering Mith Bunder road Thane East Maharashtra Kopri India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Jimma Institute of Technology Jimma University Faculty of Computing and Informatics Department of Computer Science Oromia Region Jimma Ethiopia Sri Eshwar College of Engineering Department of Ece Tamilnadu Coimbatore India Qis College of Engineering and Technology Department of Cse Andrapradesh Ongole India R.M.D. Engineering College Department of Mathematics India

ISBN: (纸本)9798350370249

the Ant Colony Optimization (ACO) algorithm is a famous metaheuristic technique that has been successfully applied in various optimization issues. It mimics the foraging conduct of ants to locate the shortest path among meals assets and their colony. Recently, ACO has been increasingly more applied for provider composition in multi-cloud environments. This involves the composition of offerings from different cloud carriers to satisfy the service necessities of a person. By way of using ACO, services can be correctly selected and composed from a couple of carriers, considering elements including cost, excellent, and availability. ACO has proven promising outcomes in enhancing the overall performance and reliability of carrier composition in multi-cloud environments, making it a precious technique in todays tremendously distributed computing landscape. © 2024 IEEE.

关键词： Distributed cloud

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An Effective Deep Learning Technique for Analyzing COVID - 19 Using X-Ray Images 15th

An Effective Deep Learning Technique for Analyzing COVID - 1...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Uma Maheswari, V. Aluvalu, Rajanikanth Guduri, Manisha Kantipudi, M. V. V. Prasad Department of Computer Science and Engineering Chaitanya Bharati Institute of Technology Telangana Hyderabad India Pune India School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States University Pune412115 India

ISBN: (纸本)9783031810794

The COVID - 19 disease is a extremely contagious viral infection with significant global health insinuations. COVID has a negative impact on the world economy. If positive patients are identified early, the proliferation of this pandemic infection can be slackened. COVID-19 disease prophecy is useful for finding the people with health issues who are at risk of COVID. The Deep Learning and machine learning approaches for COVID prediction using X-ray images have the possibility to be tremendously helpful in addressing the shortage of clinicians and doctors in rural areas. This research presents a deep layered Convolutional Neural Network (CNN) for recognizing the COVID-19 patients by training the system with the datasets of around 6000 chest X-ray images collected from various sources. We are categorized them as normal or COVID - impacted. Our model trained the X-ray images of chest to find these patients. And it also predicted well with the help of image analysis and deep learning by showcasing the better results comparing with the state-of-the-art methods in terms of the average accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： COVID-19

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs 1

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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1st IEEE International Conference on Cryptography, informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Secure Blockchain Integration Approach for Knowledge Discovery in Industrial Internet of Things

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2025年 6卷 4772-4787页

作者： Rohan Tripathi Ritam Rao Vivekananda Bhat K. Abhishek Kumar Pandey Ashok Kumar Das Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea

The field of Industrial Internet of Things (IIoT) is now being duly recognized as a revolutionary area in industrial automation. With the budding interest of knowledge discovery that helps the businesses in applying technology on a large scale, researchers are now faced with several major security concerns. Blockchain integration is one of the suggested applications of technology that could help secure the data during transmission, storage, and knowledge discovery. Moreover, by integrating smart contracts, a secure architecture could also assimilate accountability during data exchange. Thus, we propose a four-layer security architecture that isolates IIoT devices from user-oriented layers and maintains a record of all IIoT devices registered in the organization to prevent malicious devices from corrupting the database and discovery process. Further, by choosing proof of authority (PoA), we ensure the fair functioning of the Blockchain nodes. Through proxy re-encryption for clients, PoA consensus for Blockchain nodes, and Whitelist-based access control for IIoT Devices, we ensure the legitimacy of all participating nodes. We also implement a prototype using a private Ethereum network with proof of authority consensus and present the time taken for the entire exchange and the gas (ethereum currency) consumed per exchange by the contracts. Also we implemented the secure knowledge discovery to understand the significance of the developed scheme. The results show that the exchange can be implemented in an IIoT environment and work with a reasonable amount of resource consumption.

关键词： Blockchains Industrial Internet of Things computer architecture Security Knowledge discovery computer science Cloud computing Prototypes Authentication Analytical models

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13571-13584页

作者： Liu, Wei Liu, Jierong Peng, Tao Wang, Guojun Balas, Valentina Emilia Geman, Oana Chiu, Hung-Wen School of Information and Communication Engineering Hainan University Hainan Haikou570228 China School of Computer Science and Cyber Engineering Guangzhou University Guangdong Guangzhou510006 China College of Biomedical Information and Engineering Hainan Medical University Street Hainan Haikou571199 China Department of Automation and Applied Informatics Aurel Vlaicu University of Arad Arad310130 Romania Department of Health and Human Development Stefan cel Mare University of Suceava Suceava720229 Romania Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Network layers

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IoT-Fog Computing Sustainable System for Smart Cities: A Queueing-based Approach 1

IoT-Fog Computing Sustainable System for Smart Cities: A Que...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Goswami, Veena Sharma, Bhisham Patra, Sudhansu Shekhar Chowdhury, Subrata Barik, Rabindra Kumar Dhaou, Imed Ben School of Computer Applications KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

In the current scenarios there is a lot of development in the networking sector. Additionally needed are quick operations and the capability to solve complex issues. From several technical angles, IoT is being promoted to meet these developments. Implementations of IoT confront difficulties in handling such enormous volume of data including issues with its Quality of Services (QoS) necessities, privacy with security and the variety of networking elements. In smart cities, a vast volume of data is generated through IoT devices. They need to be processed near the edge devices due to latency issues, especially in the case of critical data-sensitive applications. Fog computing, a new technological paradigm, delivers a collection of networking essential services nearer to the client than cloud computing does. Fog computing overrides the cloud computing in the areas such as networking infrastructure scalability, latency reduction, network service dependability, and network device security. The technical approach to provide the highest degree of computing service has advanced by contributing cloud-assisted network services nearer to the end user/customer level. Fog servers can malfunction in a variety of circumstances. In this study, we describe the fog system as a machine-repair problem, where repair work is performed at a certain pace as soon as the Virtual Machine (VM) malfunctions. To analyze the system, numerous numerical analyses have been conducted. © 2023 IEEE.

关键词： Fog computing

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Solving Max-3SAT Using QUBO Approximation

Solving Max-3SAT Using QUBO Approximation

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Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Sebastian Zielinski Jonas Nüßlein Michael Kölle Thomas Gabor Claudia Linnhoff-Popien Sebastian Feld Institute for Informatics LMU Munich Munich Germany Quantum & Computer Engineering Delft University of Technology Delft The Netherlands

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

As contemporary quantum computers do not possess error correction, any calculation performed by these devices can be considered an involuntary approximation. To solve a problem on a quantum annealer, it has to be expressed as an instance of Quadratic Unconstrained Binary Optimization (QUBO). In this work, we thus study whether systematically approximating QUBO representations of the MAX-3SAT problem can improve the solution quality when solved on contemporary quantum hardware, compared to using exact, non-approximated QUBO representations. For a MAX-3SAT instance consisting of a 3SAT formula with $n$ variables and $m$ clauses, we propose a method of systematically creating approximate QUBO representations of dimension $(n\times n)$ , which is significantly smaller than the QUBO matrices of any exact, non-approximated MAX-3SAT QUBO transformation. In an empirical evaluation, we demonstrate that using our QUBO approximations for solving MAX-3SAT problems on D-Wave's quantum annealer Advantage_System6.4 can yield better results than using state-of-the-art exact QUBO transformations. Furthermore, we demonstrate that using naive QUBO approximation methods, based on removing values from exact $(n+m)\times(n+m)-\mathbf{dimensional}$ QUBO representations of MAX-3SAT instances, is ineffective.

关键词： computers Performance evaluation Annealing Systematics Qubit Quantum annealing Hardware Error correction Approximation methods Optimization

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