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3rd International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2023

作者： Inoue, Shuichi Nobukawa, Sou Nishimura, Haruhiko Watanabe, Eiji Isokawa, Teijiro Graduate School of Information and Computer Science Chiba Institute of Technology Narashino Japan Yamato University Faculty of Informatics Osaka Japan National Institute for Basic Biology Okazaki Japan Graduate School of Engineering University of Hyogo Himeji Japan

ISBN: (纸本)9798350300604

Deep echo state network (Deep-ESN) model consists of multiple reservoir layers, that can respond to layer-specific different time-scales. This dynamical characteristic leads to enhance performance of ESN. However, neither the design guidelines for the hyperparameters of the network and individual neurons nor the mechanism to produce the diverse dynamical response have been clarified. In this study, we proposed an approach to generate the dynamical responses with different time-scales in each layer by adjusting the leaking rate of neurons. Through the evaluations time-series prediction task for different leaking rates, multiscale entropy analysis for each reservoir layer, and cross-correlation between adjacent layers, we found that when the leaking rate is set to low, the layer-specific dynamics with different time-scales are generated, as well as a mechanism whereby the signal propagates to subsequent layers with a delay, i.e., queue characteristic. These characteristics produce a high memory capacity. Consequently, diverse responses with delay leads to an enhancement of Deep-ESN functionality. © 2023 IEEE.

关键词： Deep learning

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Stylomech: Unveiling Authorship via Computational Stylometry in English and Romanized Sinhala

arXiv

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arXiv 2025年

作者： Faumi, Nabeelah Gunathilake, Adeepa Wickramanayake, Benura Dias, Deelaka Sumanathilaka, T.G.D.K. Informatics Institute of Technology Colombo006 Sri Lanka School of Mathematics and Computer Science Swansea University United Kingdom

With the advent of Web 2.0, the development in social technology coupled with global communication systematically brought positive and negative impacts to society. Copyright claims and Author identification are deemed crucial as there has been a considerable amount of increase in content violation owing to the lack of proper ethics in society. The Author's attribution in both English and Romanized Sinhala became a major requirement in the last few decades. As an area largely unexplored, particularly within the context of Romanized Sinhala, the research contributes significantly to the field of computational linguistics. The proposed author attribution system offers a unique approach, allowing for the comparison of only two sets of text: suspect author and anonymous text, a departure from traditional methodologies which often rely on larger corpora. This work focuses on using the numerical representation of various pairs of the same and different authors allowing for, the model to train on these representations as opposed to text, this allows for it to apply to a multitude of authors and contexts, given that the suspected author text, and the anonymous text are of reasonable quality. By expanding the scope of authorship attribution to encompass diverse linguistic contexts, the work contributes to fostering trust and accountability in digital communication, especially in Sri Lanka. This research presents a pioneering approach to author attribution in both English and Romanized Sinhala, addressing a critical need for content verification and intellectual property rights enforcement in the digital age. © 2025, CC BY.

关键词： Computational linguistics

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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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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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Tangles in IOTA to make crypto currency transactions free and secure

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International Journal of Cloud Computing 2022年第1期11卷 1-13页

作者： Anakath, A.S. Kannadasan, R. Prabakaran, N. Basha, M.S. Saleem Department of MCA EGS Pillai Engineering College Nagapattinam 611002 India School Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computing and Informatics Mazoon College Muscat133 Oman

Blockchain introduction has made a revolutionary change in the crypto currency around the world but it has not delivered on its promises of free and faster transaction confirmation. Serguei Popov's proposal of using tangles, a directed acyclic graph, which essentially is considered to be the successor of blockchains and offers the required features like machine to machine micro payment and fee-less transaction. It requires the user to approve the previous two transactions in the web to participate in the network. This essentially eliminates the miners and the mining part form the currency exchange and provides the user or participants to do their transactions fee-less. Since the participant verifies the previous two transactions it also contributes to the security of the tangle. In this paper, features of internet of things application (IOTA) and all the improvements in it using tangles are discussed along with how it contributed to the security and how it enables the participants to have fee-less transactions is also discussed. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Distributed ledger

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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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Text Analysis and Recognition of Emotional Content Using Deep Learning Methods and BERT

Text Analysis and Recognition of Emotional Content Using Dee...

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International Conference on computer and Information Science (ACIS)

作者： Efthymios Andrikakis Isidoros Perikos Michael Paraskevas Ioannis Hatzilygeroudis Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Computer Engineering & Informatics University of Patras and Computer Technology Institute and Press “Diophantus” Patras Greece

Recognizing the emotional content of Natural Language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. In this paper, deep learning, deep neural networks as well as Transformers were examined. Specifically, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated. The results of the study show that BERT achieved the best overall performance, getting an F1-score of 0.86 on the Twitter Data dataset. Also, the Bi-LSTM with Attention Mechanism and Bi-LSTM using Word2vec performed quite well, achieving F1-scores of 0.83 and 0.82, respectively.

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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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Cost-sensitive Computational Adequacy of Higher-order Recursion in Synthetic Domain Theory

arXiv

引用

arXiv 2024年

作者： Niu, Yue Sterling, Jonathan Harper, Robert National Institute of Informatics Tokyo Japan Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

We study a cost-aware programming language for higher-order recursion dubbed PCFcost in the setting of synthetic domain theory (SDT). Our main contribution relates the denotational cost semantics of PCFcost to its computational cost semantics, a new kind of dynamic semantics for program execution that serves as a mathematically natural alternative to operational semantics in SDT. In particular we prove an internal, cost-sensitive version of Plotkin’s computational adequacy theorem, giving a precise correspondence between the denotational and computational semantics for complete programs at base type. The constructions and proofs of this paper take place in the internal dependent type theory of an SDT topos extended by a phase distinction in the sense of Sterling and Harper. By controlling the interpretation of cost structure via the phase distinction in the denotational semantics, we show that PCFcost programs also evince a noninterference property of cost and behavior. We verify the axioms of the type theory by means of a model construction based on relative sheaf models of SDT. © 2024, CC BY.

关键词： Semantics

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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