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Stability and Limit Cycles of Fuzzy Inferences in a Recurrent Petri-like Neural Network

Stability and Limit Cycles of Fuzzy Inferences in a Recurren...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lidia Ghosh Dipanjan Konar Amit Konar Atulya K. Nagar Dept. of Computer Science and Engineering Institute of Engineering and Management Kolkata Dept. of Electronics and Communication Engineering Institute of Radio Physics and Electronics Calcutta University Kolkata India ETCE Department Artificial Intelligence Laboratory Jadavpur University Kolkata India Dept. of Math and Computer Science Liverpool Hope University UK

This paper proposes a generic architecture of a recurrent fuzzy inferential neural network realized with Petri Nets. The proposed recurrent topology allows revision or updates of fuzzy singleton memberships of inferences, which may lead to limit cycles (sustained periodic oscillations) or stability of fuzzy inferences. Determining stability in such recurrent fuzzy neural network requires adaptation of fuzzy memberships for all propositions mapped at places of the Petri Net in parallel. The network is said to have attained stability, if after $k$ updates of memberships of the propositions, the steady-state values are reached for all the propositions of the network, where $k$ denotes the number of transitions in the Petri net. If fuzzy memberships of the propositions do not converge after $k$ updates, then the network yields sustained oscillations in memberships (called Limit Cycles). Detection of stability or limit cycles in such network requires users to wait for $k$ steps of fuzzy membership updates at all places of the network. To avoid the computational overhead for $k$ updating of memberships in the entire network, this paper makes an attempt to determine the condition of stability or limit cycles using Lyapunov stability theorem before the network is invoked for fuzzy membership updating and inference generation. The results of the analysis envisage that the condition of stability depends on the topological architecture and initial assignment of memberships at the places. The condition derived can be checked to test possible stability in the network. In case the condition for stability is not attainable, the network is expected to have limit cycles, which too can be detected without updating of memberships.

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Novel Switched-Capacitor DC-DC Converter Achieving Highest Rational Conversion Ratios Using Inter-Stage Feedback

Novel Switched-Capacitor DC-DC Converter Achieving Highest R...

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IEEE Workshop on computers in Power Electronics

作者： Nagesh Patle Bibhu Datta Sahoo Dept. of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley USA Dept. of Electronics and Electrical Communication Engg Indian Institute of Technology Kharagpur Kharagpur India

This paper proposes an inter-stage feedback-based switched-capacitor (IFSC) converter that can be configured to realize any rational voltage-conversion ratio (VCR) using a minimum number of 2:1 switched-capacitor (SC) stages. The converter enables efficient dynamic voltage scaling (DVS) operation over a larger voltage range by significantly increasing the resolution of voltage levels. Additionally, a restructuring algorithm has been proposed to achieve all VCRs without requiring any additional dedicated voltage stages. The converter extends the idea of the recursive switched-capacitor (RSC) topology, which spans $2^{N}$-1 ratios with N 2:1 SC cells. Comparative analysis shows that the proposed converter outperforms the negative-output feedback-based converter in terms of bottom-plate parasitic loss for most VCRs. To validate the concept, a fully-reconfigurable hardware prototype has been developed.

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Gül, G. Berkay On, M. Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

We investigate the impact of wavelength choice for the classical headers in quantum wrapper networking with O-band quantum payload. We show that a C-band header performs substantially better than an O-band header. ... 详细信息

ISBN: (纸本)9781957171296

关键词： Bandpass filters Beam splitters Bit error rate Entanglement distribution Quantum teleportation Raman scattering

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Forecast the Rating of Online Products from Customer Text Review based on Machine Learning Algorithms

Forecast the Rating of Online Products from Customer Text Re...

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2021 International Conference on Information and communication Technology for Sustainable Development, ICICT4SD 2021

作者： Hossain, Md. Iqbal Rahman, Maqsudur Ahmed, Tofael Touhidul Islam, A.Z.M. Port City International University Dept. of Computer Science and Engineering Chattogram Bangladesh Comilla University Dept. of Information and Communication Technology Cumilla Bangladesh University of Rajshahi Dept. of Electrical and Electronics Engineering Rajshahi Bangladesh

ISBN: (纸本)9781665414609

An online product's rating is an essential metric to understand the acceptability of that product to users. Shoppers use the rating to measure the quality and excellence of the online product. It helps an online shopper to decide to buy a product or not. It also helps the producer to further modification of that product during reproduction. Sometimes people buy a product online and give a text review of buying products but apathy towards giving a number rating, commonly a star rating. But producers need to know the rating of products for analysis of their business. Producers can use this rating for business analysis and can drive better revenue to their business. We have used some supervised machine learning approach to predict rating based on customer text review and compared the results between Random Forest Classifier, XGBoost and Logistic Regression algorithm with TF-IDF Vectorizer from extensive and series of experiments. We applied the algorithms mentioned above on the dataset named 'GrammarandProductReviews' provided by Datafiniti. We analyzed the performance of each algorithm through the accuracy, precision, recall and f1-score. From the study, it is observed that Random Forest algorithm gained accuracy of 94% and precision, recall and f1-scores are 0.94, 0.94, and 0.94 respectively, which showed best compared to others. © 2021 IEEE.

关键词： Forecasting

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Designing Classical Header Bits for Quantum Wrapper Networking with O-band Quantum Signal

Designing Classical Header Bits for Quantum Wrapper Networki...

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2023 Laser Science, LS 2023

作者： Gül, G. On, M. Berkay Singh, S.K. Proietti, R. Kanter, G.S. Yoo, S.J.B. Kumar, P. Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Department of Electronics and Communication Eng. Indian Institute of Technology Roorkee247667 India Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States

关键词： Quantum electronics

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

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Informatics International Conference (BTS-I2C), Beyond Technology Summit on

作者： Amirul Mukmin Adnan Abdul Jalil Mahabintang Pallawabonang Esa Prakasa Yuyun Dept. of informatics engineering Handayani University of Makassar Makassar Indonesia Dept. of computer system Handayani University of Makassar Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesian

ISBN: (数字)9798331508579

ISBN: (纸本)9798331508586

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation; however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts.

关键词： Accuracy Error analysis Computational modeling Focusing Speech recognition Documentation Media Linguistics Synchronization Informatics

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SoftHealth: Softwarized 5G-Driven Network Slicing for Real-time e-Healthcare Applications using ML 14

SoftHealth: Softwarized 5G-Driven Network Slicing for Real-t...

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14th International Conference on communication systems and NETworkS, COMSNETS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G.S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed to Be University Tamil Nadu Thanjavur India Vanderbilt University Dept. of Electrical Engineering and Computer Science NashvilleTN United States Indian Institute of Technology Dept. of Electronics and Electrical Communication Engineering Kharagpur India

ISBN: (纸本)9781665421041

The recent years have whiteness the substandard situations of the modern healthcare system due to a fatal pandemic called COVID19. The rapid advancements of modern technology have disseminated the superficial benefits of medical infrastructure, but significant improvements are still extremely necessary over the massive e-healthcare system (mHS). Considering the fact of limited resources and unlimited demands, a highly stable end-to-end optimization model is required. Healthcare also struggles with real-time communication. The next-generation communication networks (e.g 5G and beyond) proficiently influence the network resource distribution for URLLC. In this work, we have envisioned a novel on-demand e-Healthcare dynamic network slice architecture that uses the ML algorithms at the edge server for real-time classification and access of the offloaded data from the central controller (vSDN-Control layer to Data plane layer). The comparative analysis over the datasets of patients consisting of special index parameters shows that our proposed model allows the end-user more efficient data accessibility over the conventional approaches. We have studied the model over the multi-classification ML models (kNN, DT and RF) and we have found an average improvement of 10% to 15% of average data offloading time efficiency from the local machines from the edge servers. This approach can be further extended as the QoS improvement of the healthcare data traffic over the dynamic network slice instances. We have kept the model simple but standard in nature. © 2022 IEEE.

关键词： Quality of service

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Human Activity Recognition in Free Living Using a Wearable Sensor: A Two-Stage Approach

Human Activity Recognition in Free Living Using a Wearable S...

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International Conference on Awareness Science and Technology (iCAST)

作者： Tianhui Li Xue Zhou Kota Kokubun Wenxi Chen Division of Information System The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer and Information Systems The University of Aizu Aizu-Wakamatsu Japan Dept. of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

A novel two-stage approach for human activity recognition (HAR) using a wearable sensor is proposed in this study. The goal is to achieve both coarse-level and fine-level recognition of activities. The study focuses on five common activities: desk-working, driving, walking, downstairs, and upstairs. Three-axis acceleration signals were collected using a wrist-worn accelerometer sensor, and the activities were annotated during the participant’s daily routines. The acceleration data were preprocessed through segmentation, feature extraction, and normalization. In the first stage, a traditional machine learning model (decision tree) was used for coarse-level classification, distinguishing between activities with exercise and without exercise. In the second stage, deep learning models were employed for fine-level classification to recognize specific activities within the coarse classes. The performance of the models was evaluated using accuracy, sensitivity, and specificity. The proposed approach showed promising results with accuracies of 93.53%, 93.52%, 85.70%, 98.11%, and 85.91% respectively for desk-working, driving, walking, downstairs, and upstairs, demonstrating the effectiveness of the two-stage strategy for HAR. The proposed two-stage approach outperforms the one-stage method (decision tree classifier and deep learning model), particularly in imbalanced data, achieving a graceful equilibrium between sensitivity and specificity. The study highlights the importance of considering the complexity and variability of daily activities for accurate classification and resource optimization.

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Learning on Transformers is Provable Low-Rank and Sparse: A One-layer Analysis

Learning on Transformers is Provable Low-Rank and Sparse: A ...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hongkang Li Meng Wang Shuai Zhang Sijia Liu Pin-Yu Chen Dept. Electrical Computer and System Engineering Rensselaer Polytechnic Institute Troy NY USA Dept. Data Science New Jersey Institute of Technology Newark NJ USA Dept. Computer Science and Engineering Michigan State University East Lansing MI USA IBM Thomas J. Watson Research Center Yorktown Heights NY USA

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

Efficient training and inference algorithms, such as low-rank adaption and model pruning, have shown impressive performance for learning Transformer-based large foundation models. However, due to the technical challenges of the non-convex optimization caused by the complicated architecture of Transformers, the theoretical study of why these methods can be applied to learn Transformers is mostly elusive. To the best of our knowledge, this paper shows the first theoretical analysis of the property of low-rank and sparsity of one-layer Transformers by characterizing the trained model after convergence using stochastic gradient descent. By focusing on a data model based on label-relevant and label-irrelevant patterns, we quantify that the gradient updates of trainable parameters are low-rank, which de-pends on the number of label-relevant patterns. We also analyze how model pruning affects the generalization while improving computation efficiency and conclude that proper magnitude-based pruning has a slight effect on the testing performance. We implement numerical experiments to support our findings.

关键词： Training Analytical models Adaptation models Computational modeling Neurons Stochastic processes Signal processing algorithms

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A New Technique to Mitigate DHCPv6 Starvation Attack and Authenticate Clients using DUID

A New Technique to Mitigate DHCPv6 Starvation Attack and Aut...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Asaduzzaman Jony Muhammad Nazrul Islam Dept. of Information and Communication Technology (ICT) Bangladesh University of Professionals (BUP) Mirpur Cantonment Dhaka Bangladesh Dept. of Computer Science and Engineering (CSE) Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Dynamic Host Configuration Protocol for IPv6 (DHCPv6) is susceptible to DHCPv6 starvation attack due to the lack of authentication and security mechanisms. RSA authentication was proposed to secure DHCPv6 which increases the DHCPv6 packet size by adding certificate option. Moreover, suggested method generates digital signatures using SHA-1, which is vulnerable to hash collision attack. Cryptography encryption based DHCPv6 server was suggested which is very difficult to deploy since it takes longer to process DHCPv6 requests and consumes more hardware resources. Finally, MAC address whitelisting based DHCPv6 client authentication was introduced which can be easily evaded by MAC spoofing attacks. Therefore, the objective of this study is to propose a secured DHCPv6 server having multilayered security architecture to prevent starvation attack and authenticate clients before assigning IPv6 address. First, a thorough analysis of the DHCPv6 starvation attack was demonstrated that exhausts hardware resources of the DHCPv6 server and causes DHCPv6 service outages and network instability. Then, a DHCPv6 server was proposed that allows only a predetermined number of DHCPv6 requests per minute to prevent DHCPv6 starvation attack and also offered a robust client authentication technique using client’s MAC address and uniquely generated DHCP Unique Identifier (DUID) before allocating IPv6 address. Finally, the effectiveness of proposed DHCPv6 server was evaluated in EVE-NG network simulation environment and found that it successfully prevents DHCPv6 starvation attack and authenticate clients.

关键词： Phase shift keying Protocols Authentication Production Hardware Encryption Servers

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