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Multifaceted Cyberbullying Detection using Deep Learning architectures and Semantic Embeddings in Social Media Discourse

Multifaceted Cyberbullying Detection using Deep Learning Arc...

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International Conference on computing and Networking Technology (ICCNT)

作者： Shanmukha Aditya G Ananya Aggarwal Karan S Deepa Gupta Suja Palaniswamy Manju Venugopalan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru India Mission San Jose High School Fremont CA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the Internet is growing into an effective communication tool. But the way the medium is used for targeted communication negatively often referred to as cyberbullying is a matter of concern. To build a model that would be able to identify such text orientations automatically, and block or impose a fine on the user would be great to make the internet a more cordial environment. this study focuses on designing a model for classifying tweets into one of the cyberbullying types and is designed as a six-class classification problem, the classes being: religion, age, ethnicity, gender, noncyberbullying, and others. Before building the model, multiple pre-processing steps are done and then the models are trained. Five different kinds of word embeddings were experimented with, along with five different neural network models. the embeddings used are Keras embedding layer, Glove, W2V, Fast text, and BERT. the neural network models used are BERT, CNN, LSTM, and Bi-LSTM. Multiple combinations of embeddings and neural network models are experimented and the results are discussed. Among the various models scrutinized, the hyperparameter finetuned BERT model distinguishes itself by delivering an outstanding performance, with an accuracy rate of $95 \%$. this breakthrough suggests that the hyperparameter finetuned BERT model could be instrumental in applications designed to combat the prevalence of cyberbullying and potentially prevent the adverse effects associated with such harmful conduct. *CRITICAL: Do Not Use Symbols, Special Characters, Footnotes, or Math in Paper Title or. ()

关键词： Accuracy Instruments Neural networks Semantics Buildings Cyberbullying Symbols Deep architecture Internet Long short term memory

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Optimizing Transformer-based Models for Radiation Estimation at high Energy Accelerator Facility

Optimizing Transformer-based Models for Radiation Estimation...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Jackie Wei Hongfang Zhang Adam Stavola Bence Budavari Chiman Kwan Hongyi Wu Jiang Li Piedmont Hills High School San Jose CA Department of Electrical and Computer Engineering Old Dominion University Norfolk VA Thomas Jefferson National Accelerator Facility Newport News VA Applied Research LLC Rockville MD Force Projection Sector Johs Hopkins University Applied Physics Laboratory Laurel MD Department of Electrical & Computer Engineering University of Arizona Tuscon AZ

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

At high energy accelerator facilities, it is important to control radiation exposure to ensure the safety of the staff and public. In this paper, we optimized various Transformer-based deep learning models to efficiently estimate radiation exposure at the thomas Jefferson National Accelerator Facility (JLab). We explored the effects of utilizing various model embedding types and training with either channel dependence or independence. the models were trained and optimized on radiation, energy, current, and beam loss accounting data collected from sensors at JLab. Our results demonstrated that the Transformer-based model utilizing temporal embedding and channel dependence achieved the best performance with an average R 2 score of 0.771. To the best of our knowledge, this work represents the first attempt to explore transformer deep learning models for radiation estimation at JLab and showed potential to further improve radiation estimation performance.

关键词： Deep learning Training Estimation Focusing Channel estimation Transformers Mobile communication Data models Sensors Safety

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Improving CNN Runtime Robustness Against Soft Errors by Dropout Layer Optimization

Improving CNN Runtime Robustness Against Soft Errors by Drop...

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Latin American Test Workshop, LATW

作者： Robert Limas Sierra Giuseppe Esposito Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Matteo Sonza Reorda Politecnico di Torino - Department of Control and Computer Engineering (DAUIN)

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Convolutional Neural Networks (CNNs) have shown exceptional effectiveness in complex and data-intensive domains such as image and video processing, conversational systems, and healthcare. Moreover, sectors like high-performance computing and safety-critical applications, including automotive, aerospace, and autonomous robotics, impose stringent requirements on energy efficiency, performance, and robustness. However, modern semiconductor technologies are increasingly vulnerable to faults, which can degrade CNN performance and potentially result in catastrophic failures. this work explores the impact of regularization techniques (dropout layer) in enhancing the inference robustness of CNN models against soft errors. We analyzed soft error impacts on five widely adopted CNN architectures, each trained with ten different dropout rates. Our experimental results reveal that optimizing the dropout rate during training can improve the in-field robustness of CNN models by up to 12% compared to baseline configurations under soft error conditions. Additionally, fine-tuning this architectural parameter can lead to accurary improvements of up to 10%.

关键词： Training Accuracy Runtime computer architecture Medical services Robustness Hardware Convolutional neural networks Robots Optimization

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BERT in Cognition for Learner

BERT in Cognition for Learner

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International Conference on computing and Networking Technology (ICCNT)

作者： Souvik Ganguly Priyanka Chatterjee Kailash Pati Mandal School of Computer Science Inspiria Knowledge Campus Siliguri India Master in Computer Application Dr. B.C. Roy Engineering College Durgapur India Computer Science and Engineering National Institute of Technology Durgapur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Profound subject knowledge is necessary for education to spark creative or formative projects and boost individual’s dynamism. Acquiring a magnificent education is crucial because it permits us to live completely, make wise decisions, and overcome obstacles in life. A learner’s academic journey is continued in early education, which also permits them to become more adept at discriminating between inequity and fairness and equips them for a broader spectrum of activities in higher education. Alongside superficial constraints like threats or financial crises, learners must compose a plethora of delightful and demanding activities in addition to their studies. therefore, learners can experience moments of cognitive disorientation and progressively acquire cognitive ailments such as stress or depression, to name a couple. thus, this research suggests a novel Bi-directional Encoder Representations from Transformer (BERT) method for ascertaining learners’ cognitive declines. this research utilizes the BERT-Base architecture, and the hyper-tuning of the hyperparameters is carried out by the innovative Tweaked Shuffled Frog Leaping Algorithm technique. Although there is a high need for language models these days, there are currently no specific language models that can reliably specify a learner’s cognitive deficits according to psychological inqueries. thus, this work offers a novel strategy known as CogniBERT in response to this. Our approach confounds some of the weaknesses of the BERT methodology by choosing the right scaling factors during training. In terms of training, validation, and testing accuracies, the experimental result surpasses the other approaches, obtaining $\mathbf{9 9. 5 6 \%}, 98.97 \%$, and $99.29 \%$, respectively, when the suggested method is contrasted with a few state-of-the-art methods.

关键词： Training Psychology Bidirectional control Human factors computer architecture Depression Transformers Encoding Cognition Testing

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EfficientNet-V2-B3 Transfer Learning for high-Accuracy Indian Bird Species Classification

EfficientNet-V2-B3 Transfer Learning for High-Accuracy India...

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International Conference on computing and Networking Technology (ICCNT)

作者： Rudresh Pillai Neha Sharma Deepak Upadhyay Swati Devliyal Gurjot Kaur Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science &Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

this research paper presents a study on classifying images of Indian birds into 25 different types using the EfficientNet-V2-B3 transfer learning model. the research focuses on using deep learning techniques to accurately classify images of Indian birds, which is important for many ecological and conservation efforts. the “ Indian-Birds-Species-Image-Classification” dataset, which contains 37,500 descriptive images of 25 Indian birds, was used in the study. To train and evaluate the model, the data was split into training, validation, and testing sets. the EfficientNet-V2-B3 model was chosen for its efficiency and effectiveness in image classification, especially through transfer learning. During training, the model was trained on 24,000 images in training set and their weights were adjusted to learn the unique characteristics of Indian birds. the validation set contained 6000 images and was used to monitor the performance of the model and prevent overfitting. the pre-trained EfficientNetB3 model was adjusted throughout the training phase using stochastic gradient descent and backpropagation. Measurements like accuracy and loss were monitored throughout several epochs during the training and validation phases to get insight into the model’s convergence. A confusion matrix, which provides a thorough perspective of classification accuracy and misclassifications across species, was used to examine the model’s performance. the confusion matrix created from the test results provides information on performance standards for each bird species, including several performance metrics. In test phase, the model classified images of Indian birds by species with 97% accuracy. this result highlights the effectiveness of the transfer learning methodology and the EfficientNet-V2-B3 architecture’s adaptability for challenging classification problems. the implications for ornithology and biodiversity preservation are profound since this automated technique streamlines the procedures f

关键词： Training Deep learning Accuracy Biological system modeling Transfer learning Birds Loss measurement Standards Image classification Testing

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EQC: Ensembled Qantum computing for Variational Qantum Algorithms 49

EQC: Ensembled Qantum Computing for Variational Qantum Algor...

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49th IEEE/ACM International symposium on computer architecture, ISCA 2022

作者： Stein, Samuel Wiebe, Nathan Ding, Yufei Bo, Peng Kowalski, Karol Baker, Nathan Ang, James Li, Ang Pacifc Northwest National Laboratory RichlandWA United States University Of California Santa BarbaraCA United States University Of Toronto TorontoON Canada

ISBN: (纸本)9781450386104

Variational quantum algorithm (VQA), which is comprised of a classical optimizer and a parameterized quantum circuit, emerges as one of the most promising approaches for harvesting the power of quantum computers in the noisy intermediate scale quantum (NISQ) era. However, the deployment of VQAs on contemporary NISQ devices often faces considerable system and time-dependant noise and prohibitively slow training speeds. On the other hand, the expensive supporting resources and infrastructure make quantum computers extremely keen on high utilization. In this paper, we propose a virtualized way of building up a quantum backend for variational quantum algorithms: rather than relying on a single physical device which tends to introduce everchanging device-specifc noise with less reliable performance as time-since-calibration grows, we propose to constitute a quantum ensemble, which dynamically distributes quantum tasks asynchronously across a set of physical devices, and adjusts the ensemble confguration with respect to machine status. In addition to reduced machine-dependant noise, the ensemble can provide signifcant speedups for VQA training. With this idea, we build a novel VQA training framework called EQC-a distributed gradientbased processor-performance-aware optimization system-that comprises: (i) a system architecture for asynchronous parallel VQA cooperative training;(ii) an analytical model for assessing the quality of a circuit output concerning its architecture, transpilation, and runtime conditions;(iii) a weighting mechanism to adjust the quantum ensemble's computational contribution according to the systems' current performance. Evaluations comprising 500K times' circuit evaluations across 10 IBMQ NISQ devices using a VQE and a QAOA applications demonstrate that EQC can attain error rates very close to the most performant device of the ensemble, while boosting the training speed by 10.5× on average (up to 86× and at least 5.2×). EQC is available at https://

关键词： Quantum computers

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Natural Language Processing-Enhanced Machine Learning Framework for Comprehensive Phishing Email Identification

Natural Language Processing-Enhanced Machine Learning Framew...

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International Conference on computing and Networking Technology (ICCNT)

作者： Siva theja Kopparaju Cesar Chavarriaga Erick Galarreta Sajal Bhatia School of Computer Science and Engineering Sacred Heart University Fairfield USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Phishing emails remain a significant cybersecurity threat, bypassing traditional rule-based detection methods. this paper proposes a novel Machine Learning (ML) and Natural Language Processing (NLP) based approach for enhanced phishing email detection. the core of the model utilizes Term Frequency-Inverse Document Frequency (TF-IDF) for text vectorization and a Multinomial Naive Bayes (MNB) classifier. this combination facilitates the identification of semantic cues and enables dynamic adaptation to evolving phishing tactics. the model prioritizes a balance between sensitivity and specificity, ensuring accurate detection of phishing attempts while minimizing false positives. the proposed PED (Phishing Email Detection) model achieves an accuracy of ${9 7. 2 5 \%}$ demonstrating a high overall accuracy in correctly classifying both legitimate and phishing emails. high F1-scores for two email categories are indicative of the proposed model’s robust performance in balancing precision and recall. the research offers a practical solution for cybersecurity professionals, contributing to a more secure email environment.

关键词： Adaptation models Accuracy Phishing Semantics Machine learning Sensitivity and specificity Natural language processing Electronic mail Bayes methods computer crime

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An Efficient Approach for Pedestrian Detection

An Efficient Approach for Pedestrian Detection

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International Conference on computing and Networking Technology (ICCNT)

作者： Anugrah Srivastava Tapas Badal Ankush Goyal Advait Naik SCSET Bennett University Greater Noida India Computer Engineering Vivekanand Education Society’s Institute of Technology Mumbai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Pedestrian detection is a significant field within computer vision, closely related to object detection, with applications spanning surveillance, autonomous driving, optical character recognition (OCR), and face recognition. this paper focuses on detecting pedestrians and cyclists using images captured from surveillance cameras and vehicle-mounted cameras. Our application involves detecting objects in complex environments where the distinction between objects and the background is subtle. the detection model must achieve high accuracy and precision. We have employed the Faster R-CNN model for this task. Although Faster R-CNN offers higher accuracy compared to models like YOLO, it operates at a lower speed. the enhanced precision of Faster R-CNN makes it well-suited for our needs, despite the trade-off in processing time. Our implementation includes data preparation, model training with fine-tuning on a custom dataset, evaluation, and calculation of the mean number of bounding boxes. this approach ensures robust detection performance, which is crucial for the intended applications.

关键词： YOLO Training Accuracy Pedestrians Surveillance Optical character recognition Cameras Robustness Autonomous vehicles Tuning

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Luthien: A Parallel PIC Code for Modeling the Interaction of Focused Electron Beams with Plasma 15th

Luthien: A Parallel PIC Code for Modeling the Interaction of...

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15th International Scientific Conference on Parallel Computational Technologies (PCT)

作者： Berendeev, Evgeny Volchok, Evgeniia Inst Computat Math & Math Geophys SB RAS Novosibirsk Russia

ISBN: (纸本)9783030816919;9783030816902

the paper presents a parallel PIC code for simulating the electromagnetic emission from a focus of electron beams injected into a plasma. the code aims to study the method of efficient generation of terahertz radiation by kiloampere electron beams capable of focusing to mm-scale spots. Luthien uses an axially symmetric 2D3V kinetic plasma model based on the particle-in-cell method and implements open boundary conditions allowing for the continuous injection of high-power colliding electron beams into a plasma. the article discusses in detail the mathematical model used and describes the structure of the code, the basic numerical algorithms, and their parallel implementation. the performance of the code on various parallel computing systems is studied by considering as an example the collision of two Gaussian electron beams with different focal spot sizes in a plasma.

关键词： high-performance computing Kinetic plasma simulations Particle-in-cell method Beam-plasma interaction Sources of terahertz electromagnetic radiation

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Deep Learning Approaches for Multi Class Leather Texture Defect Classifcation

Deep Learning Approaches for Multi Class Leather Texture Def...

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International Conference on computing and Networking Technology (ICCNT)

作者： Tanjila Akter Abu Salaho As Samman Anamika Hossain Lily Md. Sadekur Rahman Nuzhat Noor Islam Prova Md Imran Kabir Joy Department of CSE Daffodil International University Dahaka Bangladesh Department of BBA North South University Dahaka Bangladesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Leather is still a preferred item high end consumer item. It is loved for its durability and luxurious texture. However, to maintain a perfect quality standards inspection are needed for detect any imperfections lurking on the leather surface. But traditional inspection approach is very time consuming and skilled worker are required. But a deep learning approach can be an innovative approach to automating the classification of defect. Our methodology combines traditional techniques such as edge detection and statistical analysis with artificial neural networks. Different types of deep learning model are applied to compare the accuracy of the model. they are CNN, MobileNet, Softmax, DensNet, ResNet, VGG. DenseNet show the highest accuracy of 89.78%. Our model is trained and evaluated with a dateset consist of 3637 lather patches belonging to 6 classes with $227 \times 227$ pixel. this revolutionizes quality control process within the leather industry.

关键词： Deep learning Industries Analytical models Accuracy Statistical analysis Process control computer architecture Quality control Inspection Standards

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