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Neutrosophic Encoding and Decoding Algorithm for ASCII Code system

Neutrosophic Sets and systems 2024年 63卷 105-129页

作者： Salama, A.A. Tarek, Zahraa Darwish, Eman Yousif Elseuofi, Sherif Shams, Mahmoud Y. Dept. of Math and Computer Sci. Faculty of Science Port Said Univ. Egypt Dept. of Computer Science Faculty of Computers and information Mansoura University Egypt Dept. of Information System Higher Institute for Computers & Specific Studies Damietta Ras El-Bar Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt

Context and Background: This paper addresses the challenge of encoding and decoding numerical data by introducing innovative algorithms utilizing Neutrosophic ASCII codes and ASCII Neutrosophic codes. These codes serve to represent uncertain or imprecise character values through the incorporation of neutrosophic numbers, encompassing degrees of truth, falsity, and indeterminacy. Motivation: The study stems from the necessity to effectively represent uncertain or imprecise character values in numerical data. This is crucial in diverse applications where handling uncertain or ambiguous data is a prevalent concern. Hypothesis: We hypothesize that employing Neutrosophic ASCII codes and ASCII Neutrosophic codes in encoding and decoding numerical data can provide a robust solution for representing uncertain or imprecise character values. Methods: The encoding algorithm in this study transforms each character in the ASCII string into its corresponding ASCII code, utilizing either 7 or 8 bits based on the code type. This algorithm calculates the degree of truth, falsity, and indeterminacy for each bit, considering the uncertainty or ambiguity associated with the character. The resulting neutrosophic numbers are appended to create the Neutrosophic ASCII code or ASCII Neutrosophic code. The decoding algorithm partitions the code into groups of neutrosophic numbers, calculates the associated degrees of truth, falsity, and indeterminacy for each ASCII bit, and converts the neutrosophic numbers back to ASCII codes, reconstructing the original ASCII character string. Results: Our study yields a novel and effective approach for encoding and decoding numerical data, demonstrating the potential of Neutrosophic ASCII codes and ASCII Neutrosophic codes in representing uncertain or imprecise character values. Conclusions: The proposed algorithms offer a promising solution for handling uncertain or ambiguous data in numerical encoding and decoding. The specific and quantitative results

关键词： Decoding

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Optimizing Structural Modification Operation for B+-Tree on Byte-Addressable Devices 42

Optimizing Structural Modification Operation for B+-Tree on ...

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42nd IEEE International Conference on computer Design, ICCD 2024

作者： Hong, Dingze Hu, Jinlei Chen, Jianxi Feng, Dan Liu, Jian School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System of MoE China The Fifth Electronics Research Institute of Miit China

ISBN: (纸本)9798350380408

Persistent Memory (PM) offers both byte-address ability and non-volatility, making it well-suited for accelerating B+-Tree indexes. However, existing persistent B+-Tree indexes face significant performance challenges due to high structural modification operation (SMO) overhead. SMOs often result in costly item migrations and increased tail latency, which severely degrade the overall performance. In this paper, we present SSTree, a high-performance B+- Tree index specifically optimized to address SMO overhead. SSTree introduces three key innovations: (i) efficient leaf node expansion using a list of subnodes to postpone expensive node splits, (ii) delegated fingerprints to speed up search operations across subnodes, and (iii) proactive subnode compaction that employs out-of-place updates to optimize item organization. Our evaluation demonstrates that SSTree delivers up to 4.38× higher write throughput and up to 62× lower tail latency compared to state-of-the-art persistent B+-Tree indexes. © 2024 IEEE.

关键词： Trees (mathematics)

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Earthquake Prediction Using Deep Neural Networks 8

Earthquake Prediction Using Deep Neural Networks

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8th International Conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Bhargava, Bharat Pasari, Sumanta Birla Institute of Technology and Science Pilani Department of Computer Science and Information System Rajasthan Pilani India

ISBN: (纸本)9781665408165

Reliable prediction of earthquakes has numerous societal and engineering benefits. In recent years, the exponentially rising volume of seismic data has led to the development of several automatic earthquake detection algorithms through machine learning approaches. In this study, we propose a fully functional and efficient earthquake detector cum forecaster based on deep neural networks of long-short-term memory (LSTM) units. The model captures inherent temporal characteristics of earthquake data. For illustration, we consider an earthquake catalog from the Himalaya and its neighboring regions. The proposed LSTM model shows satisfactory performance for small to medium-sized earthquakes. We also implement a baseline artificial neural network (ANN) model to perform a suitable comparison. It is observed that both ANN and LSTM models fail to produce desired result for large events. © 2022 IEEE.

关键词： Long short-term memory

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Video key concept extraction using Convolution Neural Network 3

Video key concept extraction using Convolution Neural Networ...

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3rd IEEE International Conference on AI in Cybersecurity, ICAIC 2024

作者： Sardar, Tanvir H Amin Hazarika, Ruhul Pandey, Bishwajeet Guru Prasad, M.S. Mahmudul Hassan, Sk Dodmane, Radhakrishna Gohel, Hardik Dept. of Computer Science & Engg Bengaluru India Manipal Institute of Technology Department of Information Technology Bengaluru India Astana It University Department of Intelligent System and Cyber Security Kazakhstan Dept. of Computer Science & Engg. Dehradun India VIT-AP School of Computer Science and Engineering India Nmam Institute of Technology Department of Computer Science & Engg. Nitte India University of Houston Department of Cse Victoria United States

ISBN: (纸本)9798350381856

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video. © 2024 IEEE.

关键词： Speech recognition

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Attention-stacking adaptive fuzzy neural networks for autism diagnosis

Attention-stacking adaptive fuzzy neural networks for autism...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Shuzhan Xiao, Lei Huang, Hui Hu, Zhongyi Wenzhou University Institute of Intelligent Information System Wenzhou University Key Laboratory of Intelligent Image Processing and Analysis College of Computer Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350386226

The current diagnostic methods for Autism Spectrum Disorder (ASD) based on Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) face two significant challenges. Firstly, the functional connectivity networks (FCNs) of the brain are subject to uncertainty due to noise interference during the rs-fMRI acquisition process. Secondly, variations in equipment parameters across different imaging sites lead to diverse distributions of collected features. To address these issues, this paper introduces a novel Multi-Task Stacked Adaptive Fuzzy Neural Network (AT-STTSK) specifically designed for Autism Spectrum Disorder (ASD) classification. The proposed network seamlessly integrates a Multi-Output TSK (MO-TSK) fuzzy system, a Stacked Autoencoder (SAE), and a Transformer. This integration aims to reduce the impact of uncertainty factors, enhance the model's interpretability, and leverage the multi-head attention mechanisms to capture both local and global information, thus strengthening feature learning. Additionally, we explore the utilization of a Contrastive Learning (CL) strategy to further optimize the model. By incorporating these advancements, our AT-STTSK network offers a promising approach for improving the accuracy and reliability of ASD diagnosis based on rs-fMRI data. The validity of the method in this paper is verified on ABIDE dataset, and the result shows that the accuracy rate can reach 85.7% at most. © 2024 IEEE.

关键词： Diseases

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BEACON: Block-wise Efficient Architecture Co-optimization for DNN-HW-Mapping with Zero-cost Evaluation and Progressive Distillation 37

BEACON: Block-wise Efficient Architecture Co-optimization fo...

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37th IEEE International system-on-Chip Conference, SOCC 2024

作者： Yang, Jiun-Kai Chen, Yao-Hua Huang, Chih-Tsun National Tsing Hua University Department of Computer Science Hsinchu Taiwan Industrial Technology Research Institute Electronic and Optoelectronic System Research Lab. Hsinchu Taiwan

ISBN: (纸本)9798350377569

This paper presents the BEACON (block-wise efficient architecture co-optimization) framework, a novel approach to adeptly address the co-optimization challenge of deep neural networks (DNNs), hardware configurations of accelerator architectures, and their mapping strategies. The framework leverages our efficient block-wise zero-cost estimation to accelerate network performance evaluation in a complex macro search space. Additionally, the proposed block-wise progressive distillation enhances the accuracy of co-optimized neural networks. By incorporating an existing hardware-mapping tool, experimental results demonstrate that the BEACON framework reduces the energy-delayproduct by 1.9-22.0 times with a 0.32% accuracy improvement on CIFAR-100, compared with previous optimization works of hardware and its mappings. © 2024 IEEE.

关键词： Cost benefit analysis

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Curve Monitoring and Alert system for Smart Transportation 1

Curve Monitoring and Alert System for Smart Transportation

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Natarajan, Yuvaraj Sri Preethaa K, R. Viswanathan, Shrinithi Jayarathinam, Sandhiya Balasubramaniam, Vikas Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kyungpook National University Department of Robot and Smart System Engineering Daegu Korea Republic of

ISBN: (纸本)9798350372748

The proposed IoT-based Smart Curve Monitoring and Alert system is designed to improve road safety through the fusion of embedded systems, machine learning and edge computing technologies. The proposed system is primarily powered by ESP32 microcontroller, continuously gathers data from an array of sensors, including accelerometers, gyroscopes and a potentiometer to set the speed limit. This collected data is then subjected to analysis by a Convolutional Neural Network (CNN) model, which has been trained to recognize driving patterns, especially when navigating curves. Here, CNN classifies the vehicle's behavior as safe and risky, and if a risky behavior is identified, it triggers an alert and dispatches relevant data to a NodeMCU device. The NodeMCU plays a crucial role in the system by monitoring the communication tasks. It receives the alert from the ESP32, validates the vehicle's speed against the preset limit, and, if necessary, activates a GSM module to send an immediate notification to designated guardian. To ensure real-time monitoring and awareness, all pertinent information will be displayed on the LCD screen, including the vehicle's behavior classification, current speed, speed limit and alert status. By addressing reckless driving behaviors, particularly on curves, the system contributes significantly to safer roads and reinforces the importance of responsible driving practices. © 2024 IEEE.

关键词： Convolution

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Non-IID federated learning via random exchange of local feature maps for textile IIoT secure computing

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science China(Information sciences) 2022年第7期65卷 16-31页

作者： Bo PENG Mingmin CHI Chao LIU School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Zhongshan Pool Net Technology Co.Ltd. Zhongshan Fudan Joint Innovation Center Zhengzhou Zhongke Institute of Integrated Circuit and System Application

With the fast development of artificial intelligence(AI) and industrial Internet of Things(IIoT)technologies, it is challenging to deal with the problems of data privacy protection and secure *** recent years, federated learning(FL) is proposed to attack the challenges of learning shared models collaboratively while protecting security based on the data from cross-domain clients. However, data in the real environment is usually not independent and identically distributed(Non-IID) due to the differences in business, working environments, and data acquisition, and thus classic federated methods suffer from significant performance degradation. In this paper, a novel federated framework is proposed for secure textile fiber identification(Fed TFI) via cross-domain texture representation based on high-definition fabric images. In addition to sharing the gradient of Fed TFI, the local patch of feature maps between crossdomain clients is randomly exchanged to build a richer image texture feature distribution while protecting data security simultaneously for secure computing. Furthermore, a texture embedding layer is designed to provide a joint representation through similarity measure between triplet samples in low-dimensional *** verify the effectiveness of the proposed framework, two textile image datasets, i.e., one public and the other we collected, are utilized to construct four Non-IID scenarios, including label skew, feature skew, and two combined skew scenarios. The experimental results confirm the effectiveness of our model to obtain better detection accuracies than benchmarks in four Non-IID scenarios by keeping data privacy for secure computing in fabric IIoT.

关键词： federated learning secure computing industrial Internet of Things (IIoT) machine vision texture encoding image classification Non-IID

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Avionics system Architectures for Software-Defined Spacecraft 1

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11th EAI International Conference on MOBILe Wireless MiddleWARE, Operating systems and Applications, MOBILWARE 2022

作者： He, Xiongwen Li, Jionghui Bai, Fan Jia, Xiaoyu Huang, Xiaofeng Xu, Mingwei Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing Institute of Spacecraft System Engineering Beijing100094 China

ISBN: (数字)9783031344978

ISBN: (纸本)9783031344961

Developing upon open system architecture, Software-Defined Spacecraft, as a new generation of spacecraft, can support payload plug-and-play, application software loading as needed, and system function reconfiguration on demand. Therefore, the Software-Defined Spacecraft is able to solve the design and application limitations of traditional specific-designed spacecraft, making it become a significant developing trend of future spacecraft. Regarding to the software defined spacecraft bus, this paper analyzes the requirements for avionics system on the aspects of space field development, technology development and corresponding applications. Then, combining with the development status, the avionics system architecture of software defined spacecraft is proposed, along with the related key technologies. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Application programs

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective 7

Toward Digital Transformation Adoption: A Conceptual Framewo...

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7th Information systems International Conference, ISICO 2023

作者： Marcel Gaol, Ford Lumban Supangkat, Suhono Harso Ranti, Benny Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. © 2023 The Authors. Published by Elsevier B.V.

关键词： Failure rate

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