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IEEE International Conference on Communications Workshops, ICC

作者： Akanksha Gupta Satyam Agarwal Sumit Chakravarty Dept. of Electrical Engineering Indian Institute of Technology Ropar Rupnagar India Dept. of Electrical and Computer Engineering Kennesaw State University Georgia USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Efficient communication for internet-of-things (IoT) devices in remote rural areas, where resources are limited, remains a significant challenge. A communication technique capable of long-range, low-power communication is essential. Chirp spread spectrum (CSS) combined with multi-antenna transmission could be a potential solution. However, it's challenging to implement a multiple-input multiple-output (MIMO) configuration in small nodes. In this paper, we delve into the concept of distributed transmission using CSS waveforms to achieve extended communication range while maintaining the same transmit power. Achieving perfect synchronization among transmitters is intricate and time-consuming. Therefore, we resort to non-coherent distributed transmission (NCDT). In the presence of asynchronous transmitters, symbol detection is carried out using a least square (LS) detector. Simulation results demonstrate the better performance of the proposed NCDT-CSS in terms of bit error rate (BER), making it an efficient solution with less power usage for IoT devices in rural communication.

关键词： Transmitters Chirp Conferences Simulation Bit error rate Symbols Receivers

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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

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Electronics, Computing, Communication and Control technology (ICECCC), International Conference on

作者： Aishwarya D Ponduru Sandeep Penmatsa V P S Akhilesh Varma Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets.

关键词： Support vector machines Accuracy Reviews Face recognition Computational modeling Feature extraction Vectors

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On Neurodevelopmental Disorder based on Brain computer Interface for Enhancing the Learning Process 53

On Neurodevelopmental Disorder based on Brain Computer Inter...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Jamil, Nuraini Alawa, Omar Samir Manar, Saad Mohammed Alaidarous, Saeed Alawi Adam, Abdulrahman Saeed Eldemerdash, Shehab Adel Belkacem, Abdelkader Nasreddine College of It Uae University Dept. of Computer Science and Software Eng. Al Ain United Arab Emirates College of Science Uae University Dept. of Biology Al Ain United Arab Emirates College of It Uae University Dept. of Information Technology Al Ain United Arab Emirates College of Science Uae University Dept. of Chemistry Al Ain United Arab Emirates College of It Uae University Dept. of Computer and Network Eng. Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

This paper discusses neurodevelopmental disorders and their effects on the brain and nervous system. Autism, attention deficit hyperactivity disorder (ADHD), dyslexia, and cerebral palsy are just a few examples of the many conditions classified as neurodevelopmental disorders. These conditions can potentially influence wide-ranging facets of a person's life, including communication skills, behavior, ability to learn (and thus overall educational achievement), and motor skills. Hereditary and environmental variables are thought to contribute to the development of various developmental disorders, although the exact causes are unknown. The effects of neurodevelopmental disorders on a student's educational opportunities are highly variable according to the particular condition and specific requirements of the individual. Students with dyslexia, for instance, may have trouble reading and writing, whereas those with ADHD may have difficulty paying attention and controlling their impulses. Autism, meanwhile, is characterized by social interaction and communication difficulties. Students with neurodevelopmental problems need to be identified and treated as soon as possible to obtain the appropriate assistance and accommodations to facilitate success in their educational endeavors. In the classroom, teachers have a significant opportunity to recognize and respond to the needs of particular students on an individual and group level. Brain-computer interfaces (BCIs) are an exciting new field of study with potentially substantial ramifications for how persons with neurodevelopmental problems fare in educational settings. In particular, BCIs could help students with neu-rodevelopmental problems to enhance their cognitive function, ability to communicate, and motor skills. Accordingly, this paper investigates the potential applications of BCIs for people with neurodevelopmental disorders. Specifically, we conducted a systematic review and a meta-analysis of the literature indexed

关键词： Brain computer interface Education Neurodevelopmental disease

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Jing Liao Yadong Wu Guijuan Wang Fupan Wang Huarong Chen Li Long Dept. Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint.

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Epileptic Seizure Detection Using Quantum Support Vector Classifier

Epileptic Seizure Detection Using Quantum Support Vector Cla...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Gopichand Meesala Barkha Soni Manish Pandey Nilay Khare Dept. of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding the dynamics of epileptic brain activity. It offers a non-invasive means for monitoring and detecting epileptic seizures. This paper explores the use of quantum machine learning algorithms to improve the precision and effectiveness of utilizing EEG data for epileptic seizure detection. Quantum machine learning, leveraging principles from quantum mechanics, holds promise for addressing these challenges by harnessing quantum computational power and exploiting quantum entanglement to process information more efficiently. Quantum features and quantum kernels are employed to capture intricate patterns within the EEG signals, allowing for improved discrimination between normal brain activity and seizure events. Results indicate that the Quantum Support Vector Classifier exhibit a satisfactory classification accuracy compared to traditional techniques despite having constraints with resources. The findings suggest that quantum computing holds great promise in revolutionizing the field of epilepsy diagnosis by providing more robust and accurate tools for distinguishing between epileptic and non-epileptic EEG patterns.

关键词： Support vector machines Dimensionality reduction Brain Machine learning algorithms Accuracy Quantum entanglement Qubit Epilepsy Electroencephalography Vectors

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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Development of Vulnerable Web Application Based on OWASP API Security Risks 23

Development of Vulnerable Web Application Based on OWASP API...

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23rd International Electronics Symposium, IES 2021

作者： Idris, Muhammad Syarif, Iwan Winarno, Idris Politeknik Negeri Batam Dept. of Information Technology Indonesia Politeknik Elektronika Negeri Surabaya Dept. of Information and Computer Indonesia

ISBN: (纸本)9781665443463

APIs are critical for digital transformation as well as the establishment and development of new business models. They are the foundation of application economics which allows for quicker, better, and less expensive development. In security perspective, OWASP released its first API security report in 2019 which finally differentiate the security risk categories between API and web application. In recent years, there have been many incidents of cyber attacks related to API, while the implementation of the API itself is growing in popularity among organizations. Therefore, the need to understand APIs from a security perspective should be taken seriously and considered as an integral part of the software development life cycle. In this research, we proposed an API security learning environment called Vulnerable Academic information System (VAIS) based on the OWASP API Security Risks with containerization deployment plan and gamification technique to provide a fun yet challenging environment for people in understanding the API security in a legal environment. © 2021 IEEE.

关键词： Software design

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Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

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International Conference on Inventive Computation Technologies (ICICT)

作者： Mugunth D Maheshwaran R Maheswari M S. L. Jany Shabu J. Refonaa Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

A data-driven application, Yelp has always served and will always serve as one. It is one of the first companies that allows local businesses to receive reviews from their customers. Communities have always developed it in cooperation with each other. According to Yelp, the data-set contains information regarding businesses, reviews, users, and check-ins, and has continuously been updated since 2015. A project titled "Analyzing Yelp Open-Source Data-set in Azure Data Bricks" will analyze this data-set which has been made available as an open source for sentiment analysis and descriptive analysis. This study analyzes local business performance, business distribution, review ratings, and other factors, as well as check-in rates in American business locations over time. This analysis shows that the Yelp is losing reviews, tips, elite users, and check-ins.

关键词： Sentiment analysis Reviews Decision making Data visualization Machine learning Companies Data science

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Dynamic Ineffectuality-based Clustered Architectures

arXiv

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arXiv 2023年

作者： Kalayappan, Rajshekar Chandran, Sandeep Dept. of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Dept. of Computer Science and Engineering Indian Institute of Technology Palakkad Palakkad India

The direction of conditional branches is predicted correctly in modern processors with great accuracy. We find several instructions in the dynamic instruction stream that contribute only towards computing the condition of these instructions. Hence, when the predicted direction of conditional branches is indeed correct, these instructions become Ineffectual – the functional state of the program would not be different had these instructions been dropped. However, the execution of ineffectual instructions cannot be avoided altogether because it is possible that the prediction of the branch direction is wrong. In this work, we determine all sources of ineffectuality in an instruction stream such as conditional branches, predicated instructions, indirect jumps and dynamically dead instructions. Then, we propose a technique to steer the ineffectual instructions away from the primary execution cluster so that effectual instructions can execute uncontended. We find that such ineffectuality-based clustering of instructions naturally simplifies the design and avoids several caveats of a clustered architecture. Finally, we propose a technique to detect instances when instructions were incorrectly marked as ineffectual, say due to a branch misprediction, and recover the pipeline. The empirical evaluation of the proposed changes on the SPEC CPU2017 and GAPBS benchmarks show performance uplifts of up to 4.9% and 10.3% on average respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Corpus of Human-Labeled Indirect Harassment

Corpus of Human-Labeled Indirect Harassment

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International Conference on Emerging Technologies, ICET

作者： Ebtihaj Abdullah Waseem Ullah Khan Safdar Nawaz Khan Marwat Salman Ahmed Dept. of Computer Systems Engg University of Engineering & Technology Peshawar Pakistan Faculty of Computer Science and Engg GIK Institute of Engineering Sciences & Technology Swabi Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

In an age of information and digital communication, social media platforms are becoming essential spaces where individuals can voice their thoughts, exchange ideas, and take part in discussions on a variety of subjects. High quality and high value datasets of such ideas and discussions in textual and formatted form are fundamental to the research aspects of Artificial Intelligence and data-science. In this paper, we present a dataset of indirect harassment. There are approximately 10,700 tweets with binary labels. The labels were assigned by a team of researchers collectively. The dataset contains approximately 19 percent positive indirect harassment labels and 81 percent negative indirect harassment labels. The data is useful for training and running machine and deep learning models to detect indirect and direct harassment. The ease in understanding the data for researchers and other peers is also very crucial. The corpus mentioned in this paper is easy to understand via the use of a binary label. The data is simplistic in nature because there are only two columns with one being the text and the other being the indirect label. This work was necessary because it was a component of a larger project that also needed this kind of dataset.

关键词： Deep learning Training Social networking (online) Oral communication Data science Digital communication information age Data models

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