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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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A Trustable LSTM-Autoencoder Network for Cyberbullying Detection on Social Media Using Synthetic Data

A Trustable LSTM-Autoencoder Network for Cyberbullying Detec...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Akter, Mst Shapna Shahriar, Hossain Cuzzocrea, Alfredo Wu, Fan Rodriguez-Cardenas, Juanjose University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center of Cybersecurity United States University of Calabria IDEA Lab Rende Italy Tuskegee University Dept. of Computer Science Tuskegee United States Kennesaw State University Dept. of Information Technology Georgia United States

ISBN: (纸本)9798350324457

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models' performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. © 2023 IEEE.

关键词： Cyber-bullying Deep Learning Natural Language Processing Neural Networks

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Uncovering the Interpretation of Large Language Models

Uncovering the Interpretation of Large Language Models

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Fan Wu Dept. of Intelligent and Robotics Systems University of West Florida USA Center for Cyberxecurity University of West Florida USA iDEA Lab University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France Dept. of Computer Science Tuskegee University Tuskegee USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Recent research has shown growing interest in the arithmetic reasoning capabilities of large language models (LLMs), especially those built on the Transformer architecture. However, our understanding of the intrinsic processes within these models for arithmetic calculations remains scant. This study leverages causal mediation analysis to offer an in-dept. look at how Transformer-based LLMs approach complex arithmetic problems. We experimentally intervened on particular model activations and assessed the resulting shifts in prediction probabilities, allowing us to pinpoint which parameters are crucial for such reasoning tasks. We discovered that for complex arithmetic operations, information is channeled from mid-layer activations to the final token through enhanced attention mechanisms. Subsequently, Multi-Layer Perceptrons (MLP) modules synthesize this data, integrating it into the model's residual pathways. To validate these observations, we also evaluated the activation dynamics across different types of tasks, such as retrieving numbers from text and answering fact-based questions.

关键词： Computational modeling Large language models Predictive models Transformers Mathematical models Data models Cognition

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Quantum Adversarial Attacks: Developing Quantum FGSM Algorithm

Quantum Adversarial Attacks: Developing Quantum FGSM Algorit...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Mst Shapna Akter Hossain Shahriar Alfredo Cuzzocrea Fan Wu Dept. of Intelligent and Robotics Systems University of West Florida USA Center for CyberSecurity University of West Florida USA iDEA Lab University of Calabria Rende Italy Dept. of Computer Science University of Paris City Paris France Dept. of Computer Science Tuskegee University Tuskegee USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Quantum machine learning, noted for its remarkable advancements in enhancing computational speed and augmenting data processing efficacy, is acquiring considerable recognition within the scientific community. Despite the noteworthy advancements of quantum machine learning, it shares with its traditional counterpart a susceptibility to adversarial threats. This presents a significant challenge and underscores the need for robust countermeasures in this emerging field of study. Developing effective adversarial attack algorithms, such as the Quantum Fast Gradient Sign Method (QFGSM), is crucial to opening the path for exploring robust defense mechanisms against these threats. However, unlike traditional machine learning, the quantum field currently lacks the effective techniques to orchestrate adversarial attacks. This study introduces and assesses the QFGSM, a adversarial attack algorithm tailored for QNNs. Additionally, we evaluate the effect of random noise and its quantum version on these models, providing a comprehensive comparison to understand the efficacy of adversarial attack methods. The evaluation is conducted on both traditional Neural Networks (NNs) and QNNs using the ClaMP dataset, a cybersecurity-focused dataset, and involves performance metrics like Accuracy, Precision, Recall, and F1 score. Our findings underscore the differential resilience of NNs and QNNs under adversarial attacks and reveal the contrasting effects of FGSM, QFGSM, and random noise-based methods. Our study reveals that Quantum Neural Networks (QNNs) show significant vulnerability to our proposed Quantum Fast Gradient Sign Method (QFGSM) compared to random noise, indicating a need for quantum-specific defenses in the face of this advanced adversarial attack algorithm.

关键词： Measurement Quantum computing Machine learning algorithms Accuracy Noise Machine learning Artificial neural networks

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Accelerating Real-Time Imaging for Radiotherapy: Leveraging Multi-GPU Training with PyTorch 22

Accelerating Real-Time Imaging for Radiotherapy: Leveraging ...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Obe, Ruth Kaufmann, Brandt Baird, Kaelen Kadel, Sam Soltani, Yasmin Cham, Mostafa Gobbert, Matthias K. Barajas, Carlos A. Jiang, Zhuoran Sharma, Vijay R. Ren, Lei Peterson, Stephen W. Poif, Jerimy C. U. of Houston-Clear Lake Dept. of Computer Science and of Software Engineering United States U. of San Francisco Dept. of Mathematics and Statistics United States Skidmore College Dept. of Computer Science and of Mathematics United States Mount Holyoke College Dept. of Computer Science and of Psychology United States U. of Houston Dept. of Biomedical Engineering United States U. of Maryland Dept. of Information Systems Baltimore County United States U. of Maryland Dept. of Mathematics and Statistics Baltimore County United States Medical Physics Graduate Program Duke University United States U. of Maryland School of Medicine Dept. of Radiation Oncology United States U. of Maryland School of Medicine Department of Radiation Oncology United States U. of Cape Town Dept. of Physics South Africa H3D Inc. United States

ISBN: (纸本)9798350345346

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors. This helps to mit-igate unnecessary radiation exposure in healthy tissues. Real-time imaging of prompt gamma rays with Compton cameras has been suggested to improve therapy efficacy. However, the camera's non-zero time resolution leads to incorrect interaction classifications and noisy images that are insufficient for accurately assessing proton delivery in patients. To address the challenges posed by the Compton camera's image quality, machine learning techniques are employed to classify and refine the generated data. These machine-learning techniques include recurrent and feedforward neural networks. A PyTorch model was designed to improve the data captured by the Compton camera. This decision was driven by PyTorch's flexibility, powerful capabilities in handling sequential data, and enhanced G PU usage. This accelerates the model's computations on large-scale radiotherapy data. Through hyperparameter tuning, the validation accuracy of our PyTorch model has been improved from an initial 7 % to over 60 %. Moreover, the PyTorch Distributed Data Parallelism strategy was used to train the RNN models on multiple G PU s, which significantly reduced the training time with a minor impact on model accuracy. © 2023 IEEE.

关键词： Clas-sification Compton camera Distributed Data Parallelism Proton beam therapy PyTorch Recurrent neural network

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Attaching Microlearning to Existing Habits

Attaching Microlearning to Existing Habits

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Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable computers

作者： Takumi Nakai Andrew Vargo Koichi Kise Dept. of Computer Science and Intelligent Systems Osaka Prefecture University Japan Graduate School of Informatics Osaka Metropolitan University Japan

ISBN: (纸本)9781450394239

To take advantage of microlearning, it is desirable for learners to make microlearning a habit. Most of the conventional methods for making microlearning a habit are intended to form a habit from scratch, but attempts to form a habit from scratch require a lot of motivation and workload from the learner. In this study, we propose habit-attached microlearning as a method to make microlearning a habit with less burden. This method designs a mechanism in which a microlearning task is added to the learner’s existing habits. In this study, we focused on web browsing on smartphones as a habit to attach to learning, and developed a web browsing application for learning English vocabulary. This paper reports on a controlled experiment conducted to evaluate the learning and habit-forming effects of the developed application.

关键词： Vocabulary building Web browsing Microlearning Habituation

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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset

arXiv

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arXiv 2024年

作者： Hicks, Alex Shi, Yang Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech BlacksburgVA United States Dept of Computer Science Utah State University LoganUT United States Lekshmi-Narayanan Intelligent Systems Program University of Pittsburgh PittsburghPA United States School of Informatics Computing and Cyber Systems North Arizona University FlagstaffAZ United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024, CC BY-NC-SA.

关键词： Students

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Enhancing HIPAA Compliance in AI-driven mHealth Devices Security and Privacy

Enhancing HIPAA Compliance in AI-driven mHealth Devices Secu...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： ABM Kamrul Islam Riad Md Abdul Barek Md Mostafizur Rahman Mst Shapna Akter Tahia Islam Md Abdur Rahman Md Raihan Mia Hossain Shahriar Fan Wu Sheikh Iqbal Ahamed Department of Intelligent Systems and Robotics University of West Florida USA Dept. of Cybersecurity and Information Technology University of West Florida USA Center for Cybersecurity University of West Florida USA Department of Computer Science Georgia State University USA Dept. of Computer Science Tuskegee University USA Department of Computer Science Marquette University USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

-The integration of Artificial Intelligence (AI) in mobile health (mHealth) devices offers significant advancements in patient care but also raises complex security and privacy concerns for sensitive health data. This paper explores the evolving landscape of Health Insurance Portability and Accountability Act (HIP AA) compliance within the context of AI-based mHealth devices, focusing on anticipated challenges in 2024. It examines the current state of HIP AA compliance in AI-driven mHealth, identifying potential vulnerabilities and gaps among mHealth devices in considering privacy and security. The study outlines key security and privacy considerations specific to AI-powered mHealth technologies, emphasizing risks such as data breaches, ransomware attack and unauthorized access. The paper concludes by highlighting the importance of continual adaptation to the dynamic nature of AI technologies and offers insights for stakeholders to navigate the regulatory landscape and ensure the responsible deployment of AI in healthcare.

关键词： Privacy Data privacy Navigation Software Security Stakeholders Ransomware

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Online Task Scheduling Algorithm with Complex Dependencies in Edge Computing 12

Online Task Scheduling Algorithm with Complex Dependencies i...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Shi, Lei Ma, Zhaoxing Fan, Yuqi Shi, Yi Ding, Xu Li, Zhehao School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Procince Hefei University of Technology Hefei 230009 China Virginia Tech Dept. of ECE BlacksburgVA24061 United States

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. © 2022 The Authors. Published by Elsevier B.V.

关键词： Dependent task Edge computing Online scheduling

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