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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset 8

An Approach to Detect Abnormal Submissions for CodeWorkout D...

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8th Educational Data Mining in computer science Education Workshop, CSEDM 2024

作者： Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech Blacksburg VA Dept of Computer Science Utah State University Logan UT Intelligent Systems Program University of Pittsburgh Pittsburgh PA School of Informatics Computing and Cyber Systems North Arizona University Flagstaff AZ Dept. of Computer Science University of Virginia wCharlottesville VA

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 Copyright for this paper by its authors.

关键词： CS1 Dataset Cleaning Dataset Standards Educational Data Mining Introductory programming

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intelligent Constellation Generation based on Autoencoder Communication System 2

Intelligent Constellation Generation based on Autoencoder Co...

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Matsumoto, Kaisei Toma, Takao Oshiro, Shiho Wada, Tomohisa 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 Area of Computer Science and Intelligent Systems University of the Ryukyus Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

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

关键词： Quadrature phase shift keying

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Scalability in Autoencoder-based OFDM Communication System 2

Scalability in Autoencoder-based OFDM Communication System

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

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

ISBN: (纸本)9798350305142

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 , but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0. As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Clinical Score Estimation for Determining Oro-Facial Dysfunction Severity 23

Clinical Score Estimation for Determining Oro-Facial Dysfunc...

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2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023

作者： Ipapo, Trassandra Jewelle Del Rosario, Charlize Abu, Patricia Angela Alampay, Raphael Ateneo Laboratory for Intelligent Visual Environment Dept. of Information Systems and Computer Science Ateneo de Manila University Quezon City Philippines

ISBN: (纸本)9798400707742

Stroke and amyotrophic lateral sclerosis manifest symptoms that affect facial motion in patients. Tracking these movements and assessing the severity of the impairment can be achieved with facial alignment technology and classification algorithms. Using the Toronto NeuroFace Dataset consisting of patients and healthy individuals performing clinical examination tasks, this study focuses on score estimation of clinical examinations to determine oro-facial dysfunction severity. Facial landmarks extracted using the 2D FAN were used to determine features under range of motion, speed of motion, and symmetry. Speech language pathologist scores from the dataset were transformed using ordinal encoding, then oversampled using random oversampling. The features and transformed scores were fed into random forest classifier models to predict a score using a scale of 1 to 4 for each feature category. The results show that the proposed method is able to estimate oro-facial dysfunction severity and classify between healthy individuals and patients. The average performance of the model setups are comparable to that of the baseline in terms of accuracy ( © 2023 ACM.

关键词： Score Estimation Face Alignment Oro-facial Dysfunction Ordinal Classification

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Oversampling Facial Motion Features Using the Variational Autoencoder to Estimate Oro-facial Dysfunction Severity 1

Oversampling Facial Motion Features Using the Variational Au...

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1st International Conference on computer Graphics and Image Processing, CGIP 2023

作者： Ipapo, Trassandra Jewelle Del Rosario, Charlize Alampay, Raphael Abu, Patricia Angela Ateneo de Manila University Ateneo Laboratory for Intelligent Visual Environments Dept. of Information Systems and Computer Science Quezon City Philippines

ISBN: (纸本)9798350333183

Class imbalance, which negatively affects classification model performance, is a common problem with machine learning. Various oversampling methods have been developed as potential solutions to compensate for imbalanced data. SMOTE is one of the more common methods employed. However, deep generative models such as the variational autoencoder are showing promise as alternatives to traditional oversampling methods. This study investigated the potential of variational autoencoders in learning the distribution of the minority class and producing new observations of facial motion features extracted from an imbalanced medical dataset as well as to see the effects of oversampling before and after the train-test split. The effectiveness of the variational autoencoder was compared to SMOTE in increasing ordinal classification performance across the metrics of accuracy, accuracy±1, inter-rater reliability, specificity, and sensitivity with no oversampling serving as the baseline. The results show that the variational autoencoder has potential as an oversampling method for facial motion features in the context of oro-facial dysfunction estimation. Oversampling prior to the train-test split was also shown to improve classification performance. © 2023 IEEE.

关键词： Statistical tests

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Enhancing Communication through Speech to Sign Language Converter 2

Enhancing Communication through Speech to Sign Language Conv...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Gayana, M.N. Rumaiz, Mohammed Hussain, Shaz Rasheed Shravya, A. Acharya, Shreenath Parveen, Sana Systems St Joseph Engineering College Dept. of Intelligent Computing & Business Mangaluru India St Joseph Engineering College Dept. of Computer Science and Engineering Mangaluru India

ISBN: (纸本)9798350314045

There are millions of people suffering from speaking and hearing disabilities. Sign language is the only way to communicate for such people to convey their message. The advancement in technology has highly impacted this section of society, hence there is a need to deal with them. This paper describes the Application, "Speech to Sign Language Converter"for communicating with people having hearing and speaking impairments. The primary function of the Application is to convert input sentences into Indian Sign Language (ISL) actions in real time. It takes input in the form of speech or text and produces corresponding sign language using an Avatar which acts based on SiGML code. It can also be used to convert PDF based texts into sign language. In addition, the users can learn Indian Sign Language (ISL) using the tutorials provided within it. The application focuses mainly on converting local languages such as Kannada into ISL. © 2023 IEEE.

关键词： Audition

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sharma, Rohit Dahatonde, Shubham Acharjya, Srimanta Kumar Verma, Sourabh Singh Dhaka, Vijaypal Singh Rawat, Ramesh Singh School Of Computing & Intelligent Systems Manipal University Jaipur India C. V Raman Global University Department of Computer Science and engineering Bhubaneswar India Graphic Era deemed to be University Dept. of computer Science and Engg Uttrakhand Deharadun India

ISBN: (纸本)9798350379716

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency. © 2024 IEEE.

关键词： Satellite imagery

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Uncertainty-Aware Deep Learning for Segmenting Ultrasound Images of Breast Tumours

Uncertainty-Aware Deep Learning for Segmenting Ultrasound Im...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Munia, Afsana Ahmed Hossain, Ibrahim Jalali, Seyed Mohammad Tabarisaadi, Pegah Rahman, Ashikur Nahavandi, Saeid Deakin University Institute for Intelligent Systems Research and Innovation VIC3216 Australia Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350337020

Precise image segmentation is one of the dominant factors in disease diagnosis. A typical application is the segmentation of breast ultrasound images, allowing radiologists to suggest what to do next. After emerging deep learning technology especially convolutional neural networks (CNNs), the image segmentation model achieved state-of-the-art performance in various medical applications such as cancer detection and classification, lung node segmentation, cell segmentation and so on. However, despite these successes, a big question arises: to what extent is the model certain about the predicted result? Generally, most deep learning models focus on high accuracy but not on uncertainty of predicted results, which is not enough to make a critical real-life decision such as a disease diagnosis, where a wrong decision can be life-threatening. Hence for making a crucial decision, it is essential that the predicted result will provide not only accuracy but also estimate model uncertainty. Our contribution to this research is to build a system that predicts pixel-wise semantic segmentation and provides uncertainty estimation of the predicted results. It is achieved by adding a dropout layer during training and using Monte Carlo dropout in inference. We evaluate our model with the breast ultrasound image dataset (BUSI) and compare the results with a few other state-of-the-art methods where our method outperforms others in terms of IoU. © 2023 IEEE.

关键词： Breast cancer Deep learning Semantic segmentation Ultrasound images Uncertainty quantification

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, Engineering, science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Controlled Dropout for Uncertainty Estimation

Controlled Dropout for Uncertainty Estimation

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Hasan, Mehedi Hossain, Ibrahim Rahman, Ashikur Nahavandi, Saeid Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3220 Australia Bangladesh University of Engineering and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350337020

Uncertainty quantification in a neural network is one of the most discussed topics for safety-critical applications. Though Neural Networks (NNs) have achieved state-of-the-art performance for many applications, they still provide unreliable point predictions, which lack information about uncertainty estimates. Among various methods to enable neural networks to estimate uncertainty, Monte Carlo (MC) dropout has gained much popularity in a short period due to its simplicity. In this study, we present a new version of the traditional dropout layer where we are able to fix the number of dropout configurations. As such, each layer can take and apply the new dropout layer in the MC method to quantify the uncertainty associated with NN predictions. We conduct experiments on both toy and realistic datasets and compare the results with the MC method using the traditional dropout layer. Performance analysis utilizing uncertainty evaluation metrics corroborates that our dropout layer offers better performance in most cases. © 2023 IEEE.

关键词： dropout Monte Carlo dropout neural network Uncertainty estimation

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