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IEEE Networking Letters 2022年第1期4卷 34-38页

作者： Cheng, Ray-Guang Yang, Chi-Ming Firmansyah, Bariq S. Harwahyu, Ruki Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia Department of Electrical Engineering University of Indonesia Depok16424 Indonesia

IEEE 802.11ax defines a new channel access scheme of OFDMA-based random access (UORA) for varying number of associated stations (STAs) to send their requests or data to the access point (AP) in a contention manner. This letter presents an analytical model to investigate the performance of UORA in transient condition under bursty arrivals with arbitrary distribution. The key performance metrics of access success probability;average access delay;cumulative distribution function (CDF) for the number of transmissions;and utilization of random access resource units (RA-RUs) were derived. Simulation results verified the accuracy of the proposed analytical model. © 2019 IEEE.

关键词： Analytical models

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Rabbits Approximate, Cows Compute Exactly! 47

Rabbits Approximate, Cows Compute Exactly!

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47th International Symposium on Mathematical Foundations of computer Science, MFCS 2022

作者： Komarath, Balagopal Pandey, Anurag Saurabh, Nitin IIT Gandhinagar India Department of Computer Science Saarland University Saarland Informatics Campus Germany Department of Computer Science and Engineering IIT Hyderabad India

ISBN: (纸本)9783959772563

Valiant, in his seminal paper in 1979, showed an efficient simulation of algebraic formulas by determinants, showing that VF, the class of polynomial families computable by polynomial-sized algebraic formulas, is contained in VDet, the class of polynomial families computable by polynomialsized determinants. Whether this containment is strict has been a long-standing open problem. We show that algebraic formulas can in fact be efficiently simulated by the determinant of tetradiagonal matrices, transforming the open problem into a problem about determinant of general matrices versus determinant of tetradiagonal matrices with just three non-zero diagonals. This is also optimal in a sense that we cannot hope to get the same result for matrices with only two non-zero diagonals or even tridiagonal matrices, thanks to Allender and Wang (Computational Complexity'16) which showed that the determinant of tridiagonal matrices cannot even compute simple polynomials like x1x2 + x3x4 + + x15x16. Our proof involves a structural refinement of the simulation of algebraic formulas by width-3 algebraic branching programs by Ben-Or and Cleve (SIAM Journal of Computing'92). The tetradiagonal matrices we obtain in our proof are also structurally very similar to the tridiagonal matrices of Bringmann, Ikenmeyer and Zuiddam (JACM'18) which showed that, if we allow approximations in the sense of geometric complexity theory, algebraic formulas can be efficiently simulated by the determinant of tridiagonal matrices of a very special form, namely the continuant polynomial. The continuant polynomial family is closely related to the Fibonacci sequence, which was used to model the breeding of rabbits. The determinants of our tetradiagonal matrices, in comparison, is closely related to Narayana's cows sequences, which was originally used to model the breeding of cows. Our result shows that the need for approximation can be eliminated by using Narayana's cows polynomials instead of continuant polyno

关键词： Matrix algebra

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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Real-Time Health Monitoring Based ESP8266 and Fuzzy Logic

Real-Time Health Monitoring Based ESP8266 and Fuzzy Logic

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computer Sciences, engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Fadhillah Azmi Nurul Khairina Amir Saleh Department of Electrical Engineering Universitas Medan Area Medan Indonesia Department of Informatics Engineering Universitas Medan Area Medan Indonesia Department of Computer and Informatics Engineering Politeknik Negeri Medan Medan Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

This study aims to develop a health monitoring system using an ESP8266 microcontroller and fuzzy logic to view real-time health data and predict the patients’ heart health conditions. The system is designed to monitor various physiological data, including heart rate and blood pressure, by utilizing sensors connected to the ESP8266 microcontroller device. Data obtained from the sensors will be transmitted wirelessly to a web-based platform, which facilitates remote monitoring and real-time data visualization. In addition, fuzzy logic is used to analyze the sensor data, classifying the health status into defined categories, including normal, moderate, and critical. This method facilitates a comprehensive understanding of the health situation, which offers patients a precise and clear description of their health status. The results of this study indicate that the combination of ESP8266 with fuzzy logic offers an effective, economical, and scalable solution for real-time health monitoring. This device can be applied for remote patient monitoring and early identification of possible heart disease problems.

关键词： Fuzzy logic Accuracy Microcontrollers Real-time systems Blood pressure Sensors Batteries Biomedical monitoring Monitoring Diseases

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Decoding Gender on Social Networks: An In-depth Analysis of Language in Online Discussions Using Natural Language Processing and Machine Learning

Decoding Gender on Social Networks: An In-depth Analysis of ...

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IEEE International Conference on Big Data

作者： Gerasimos Vonitsanos Andreas Kanavos Phivos Mylonas Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

In today’s digital era, the internet is an indispensable platform for self-expression, facilitating communication, idea sharing, and community formation. Language, a pivotal tool in these online interactive spaces, is vital in reflecting personal identities, notably gender identification. This paper investigates gender identification on online discussion platforms, recognizing the crucial role of language in reflecting personal identities. The study employs Natural Language Processing techniques and machine learning algorithms to analyze data from a public discussion website. Beginning with a comprehensive literature review, the research explores the nexus between gender and language in online and offline contexts. The methodology involves data gathering, extensive preprocessing, and in-depth exploratory analysis, employing statistical methods and graphical representations. The study then rigorously evaluates their accuracy and effectiveness by applying diverse algorithms and models for gender-based text categorization. Results indicate the superior performance of transformer models, particularly distilBERT, in categorizing gender accurately. Additionally, the research underscores the challenges of gender-neutral analysis, emphasizing the need for inclusive methodologies in non-binary gender classification. The study contributes to the broader field of gender studies, providing valuable insights for future research and discussions on the interplay of gender and language in online spaces.

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Sentiment Analysis for Financial News Using RNN-LSTM Network 4

Sentiment Analysis for Financial News Using RNN-LSTM Network

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Kohsasih, Kelvin Leonardi Hayadi, B. Herawan Robet Juliandy, Carles Pribadi, Octara Andi Universitas Potensi Utama Department of Computer Science Medan Indonesia STMIK TIME Department of Informatics Engineering Medan Indonesia

ISBN: (纸本)9781665453950

Identifying financial sector sentiment, primarily through the financial news, is crucial in financial investment decisions. Financial Sentiment Analysis (FSA) significantly affects the secondary market and provides a significant contribution. Long Short-Term Memory (LSTM) is a deep learning model, especially Recurrent Neural Network (RNN), a reasonably popular model designed for long-term constraints. Various methods have been proposed in previous studies, but the performance generated by the model in previous studies is still below 90%, so it has the opportunity to be improved. This study aims to present a sentiment analysis model by implementing the RNN-LSTM. The results showed that the model used for sentiment analysis of financial news reached 92.23% precision, 91.54% accuracy, 90.99% recall, and an f1 score of 91.61%. The model we built is helpful for understanding trends and opinions for making financial and other investment decisions. © 2022 IEEE.

关键词： Sentiment analysis

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Head Pose Feature for Prediction Pedestrian Intention to Crossing the Road Using LSTM 38

Head Pose Feature for Prediction Pedestrian Intention to Cro...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Sidharta, Hanugra Aulia Ilham Perdana, Muhammad Mulyanto Yuniarno, Eko Al Kindhi, Berlian Hery Purnomo, Mauridhi Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Muhammadiyah Malang Department of Informatics Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Automation Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350302196

Understanding pedestrian behaviour when crossing the road is an important key to the development of autonomous vehicles. Because pedestrians are considered Vul-nerable Road Users (VRUs), they are likely to be killed if they are involved in an accident. To ensure their safety, it is then necessary to predict the pedestrian's intention based on their behaviour. In this experiment, we propose head pose observation for predicting their intention, by observing pedestrians' head pose data, we can then predict their intention to cross the road. To achieve this purpose, we use human head detector and head pose extraction feature, and the resulting yaw, pitch and roll as three head pose features. To select the most optimal feature is important for predicting pedestrian intention, then we make 7 combination scenarios based on these three features and compare it with the same model. Based on this scenario, it is proved that all these three data are optimal to observe pedestrian intention. There are three behavioural annotation that have been used, there are crossing, not crossing and will crossing. We derive will crossing from the annotation looking and not crossing while waiting at the roadside. Prediction of pedestrian behaviour is done by using LSTM model, and the resulting precision on crossing and not crossing with 0.98, while will crossing is 0.94. © 2023 IEEE.

关键词： Forecasting

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QG-SKI: Question Classification and MCQ Question Generation Using Sequential Knowledge Induction 22nd

QG-SKI: Question Classification and MCQ Question Generation ...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Dhanvardini, R. Deepak, Gerard Santhanavijayan, A. Health Care Informatics Domain Optum-United Health Groups Hyderabad India Department of Computer Science Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031274084

E-Learning has emerged as the most effective way of getting information in a range of sectors in the contemporary age. The utilisation of electronic content to provide education and development is referred to as e-learning. While the broadening internet has a plethora of e-learning tools, knowledge acquisition is not just an aspect that adds to an individual's enrichment. Assessment and evaluation are crucial parts of every learning system. Due to more complex assessments and quicker inspection, multiple choice questions are becoming extremely prevalent in current evaluations. However, establishing a diversified pool of MCQs relevant to a certain subject matter presents a hurdle. Manually creating high-quality MCQ exams is a time-consuming and arduous procedure that demands skill. As a result, research has concentrated on the automated construction of well-structured MCQ-based tests. This paper presents a paradigm using natural language processing based on semantic similarity and dynamic ontology. The proposed QG-SKI model uses LOD Cloud and Wiki Data to generate Ontologies dynamically and Knowledge reservoir is performed. The dataset is analysed using TF-IDF algorithm and the Semantic Similarity and Semantic Dissimilarity are computed using Shannon’s entropy, Jaccard Similarity and Normalised Google Distance. These algorithms are executed for a multitude of degrees and levels to generate a similar of similar instances. The suggested model has a 98.15% accuracy and outperforms previous baseline models by dampening resilience. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： E-learning

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Stress Detection Using EEG Signals: A SVM-Based Classification and Feature Selection Approach

Stress Detection Using EEG Signals: A SVM-Based Classificati...

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Electrical Power, Electronics, Communications, Controls and informatics Seminar (EECCIS)

作者： Fi Imanur Sifaunnufus Fitra Abdurrachman Bachtiar Barlian Henryranu Prasetio Department of Computer Engineering Faculty of Computer Science Brawijaya University Malang Indonesia Department of Informatics Engineering Faculty of Computer Science Brawijaya University Malang Indonesia

ISBN: (数字)9798350379914

ISBN: (纸本)9798350379921

Stress is a problem with various adverse effects, such as mental disorders and suicide. With these problems arising, a stress detection system is needed as an initial examination of the level of stress experienced. Generally, stress detection is done with a stress test at a hospital. However, medical examinations done in the hospital require high costs. Another alternative to examine stress levels is by using a biosignal electroencephalogram (EEG), which provides more objective and accurate results. In addition, there is a need to develop a portable device that can be used to detect stress. This study tries to detect stress levels using EEG Signals. The data used in this study is primary data that simulates several stress-level activities. In total, 55 subjects participated in this stress detection research. The received signal goes through preprocessing to clean it from noise and artifacts. The data will be extracted using Power Spectral Density (PSD) into power value for each signal frequency. The preprocessed input signals are then classified into indications of stress levels. This study uses Random Forest, SVM, and Decision Tree. The model is stored on a portable system with Raspberry Pi as the main component. EEG signals are taken using electrodes on Muse 2 and sent to the system. The classification model generates predicted stress levels and is displayed on a 16×2 LCD. SVM as the best model test result achieved a testing accuracy of 1, and an average computation time of 0.92.

关键词： Support vector machines Seminars Accuracy Hospitals Computational modeling Human factors Brain modeling Electroencephalography Random forests Testing

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Machine Learning assisted Indoor Visible Light Communications Radio Environment Maps

Machine Learning assisted Indoor Visible Light Communication...

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2022 Panhellenic Conference on Electronics and Telecommunications, PACET 2022

作者： Serpi, Helena Politi, Christina Tanya University of Peloponnese Department of Informatics and Telecommunications Tripolis Greece University of Peloponnese Department of Electrical and Computer Engineering Patras Greece

ISBN: (数字)9798350399585

ISBN: (纸本)9798350399585

Visible light communications (VLC) have been proposed as reliable high-capacity wireless optical communication systems for indoor access networks. This technology comprises a solution that does not harm human tissues and has been especially suggested for ultra-reliable environments like hospitals. In this paper we utilize machine learning for investigating the wireless optical channel in indoοr areas where ultra-reliable communications are needed and hence signal coverage should be guaranteed. It is proven that ML methods can reliably and efficiently predict the Radio Environment Map (REM) of a VLC enabled communication system. © 2022 IEEE.

关键词： Machine learning

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