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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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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

引用

11th International Conference on Electrical and Electronics Engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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TRAP: Tailored Robustness Assessment for Index Advisors via Adversarial Perturbation 40

TRAP: Tailored Robustness Assessment for Index Advisors via ...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhou, Wei Lin, Chen Zhou, Xuanhe Li, Guoliang Wang, Tianqing School of Informatics Shanghai Artificial Intelligence Laboratory Xiamen University China Tsinghua University Dept. of Computer Science Beijing China Huawei Company Beijing China

ISBN: (纸本)9798350317152

Many index advisors have recently been proposed to build indexes automatically to improve query performance. However, they mainly consider performance improvement in static scenarios. Their robustness, i.e., stable performance in dynamic scenarios (e.g., with minor workload changes), has not been well investigated. This paper addresses the challenges of assessing the index advisor's robustness from the following aspects. First, we introduce perturbation-based workloads for robustness assessment and identify three typical perturbation constraints that occur in real scenarios. Second, with the perturbation constraints, we formulate the generation of perturbed queries as a sequence-to-sequence problem and propose TRAP (Tailored Robustness assessment via Adversarial Perturbation) to pinpoint the performance loopholes of index advisors. Third, to generalize to various index advisors, we place TRAP in an opaque-box setting (i.e., with little knowledge of the index advisors' internal design), and we propose a two-phase training paradigm to efficiently train TRAP without elaborately annotated data. Fourth, we conduct comprehensive robustness assessments on standard benchmarks and real workloads for ten existing index advisors. Our findings reveal that these index advisors are vulnerable to the workloads generated by TRAP. Finally, based on the assessment results, we shed light on insights to enhance the robustness of different index advisors. For example, learning-based index advisors can benefit from adopting a fine-grained state representation and a candidate pruning strategy. © 2024 IEEE.

关键词： AI4DB Index Advisors Robustness Assessment

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Transforming Energy Efficiency in IoMT: Advanced Modulation Strategies for Sustainable Wireless Medical Sensor Networks 5

Transforming Energy Efficiency in IoMT: Advanced Modulation ...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Sadek, Ayoub Hadj Rattal, Salma Varshney, Gunjan Ar-Reyouchi, El Miloud Ghoumid, Kamal Mohammed I University Ensao Dept. of Electronics Informatics and Telecommunications Oujda Morocco Casablanca Morocco Jss University Noida Dept. of Robotics and Artificial Intelligence Noida India Abdelmalek Essaâdi University Dept. of Telecommunication and Computer Science Tetouan Morocco

ISBN: (纸本)9798350389609

The investigation focuses on advanced modulation strategies to optimize energy efficiency in Wireless Medical Sensor Networks (WMSNs) within the Internet of Medical Things (IoMT). The study addresses the balance between energy consumption and spectral efficiency, specifically targeting challenges associated with WMSN deployment in critical healthcare applications. The analysis rigorously evaluates energy consumption across various distances and modulation schemes, particularly in the presence of Additive White Gaussian Noise (AWGN) channels. Modulation techniques are categorized into linear and nonlinear types, allowing for a comprehensive examination of each class's energy efficiency. The objective is to minimize energy usage while ensuring reliable and secure communication in remote healthcare systems through the optimal selection of modulation schemes and transmission parameters. The results offer significant contributions to sustainable healthcare technology, enhancing the operational lifespan of medical sensors without compromising data transmission quality. © 2024 IEEE.

关键词： Gaussian noise (electronic)

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks 24

Carotid plaque stroke risk assessment using multiscale AM-FM...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Constantinou, Kyriacos P. Constantinou, Ioannis P. Pattichis, Marios S. Kyriacou, Efthyvoulos Pattichis, Constantinos S. University of Cyprus Dept. of Computer Science Nicosia Cyprus Istognosis Ltd Nicosia Cyprus University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States Cyprus University of Technology Dept. of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350339598

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%. © 2023 IEEE.

关键词： Frequency modulation

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The Integration of 6G and Blockchain into an Efficient AIoT-Based Smart Education Model 6

The Integration of 6G and Blockchain into an Efficient AIoT-...

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6th World Symposium on Communication Engineering, WSCE 2023

作者： Lioupa, Anna Memos, Vasileios A. Stergiou, Christos L. Ishibashi, Yutaka Psannis, Konstantinos E. School of Information Sciences University of Macedonia Dept. of Applied Informatics Thessaloniki Greece Graduate School of Engineering Nagoya Institute of Technology Dept. of Computer Science Nagoya Japan

ISBN: (纸本)9798350339505

The technological advances impact the future classrooms and the whole education system. The evolution of 6G promises faster speeds based on terahertz, wireless communication, the leverage of artificial intelligence (AL), virtually reality (VR) and generally a fully connected world. The use of applications of IoT and AIoT improves the education environment, but the intelligent network services undoubtedly gather, store and analyze big data which are vulnerable to attackers. As a solution we may consider the technology of blockchain. The trust and security of next generation intelligent networks are essential part of their design. In this article we suggest a data security system based on blockchain for AL applications in 6G networks. First, we present two Al-enabled applications, indoor positioning and mobile payment within the framework of 6G in the university area. We present the efficiency of blockchain in data security for Al-enabled systems using two case studies of indoor positioning system and mobile payment. The 6G system which is fully supported by Al and the integration of this in technology of blockchain, is ensured the security in future wireless communication. © 2023 IEEE.

关键词： Blockchain

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Leveraging the Pre-Trained Whisper Model and Levenshtein Distance for Audio Buginesse Transcription

Leveraging the Pre-Trained Whisper Model and Levenshtein Dis...

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2024 Beyond Technology Summit on informatics International Conference, BTS-I2C 2024

作者： Mukmin, Amirul Adnan Jalil, Abdul Pallawabonang, Mahabintang Prakasa, Esa Yuyun Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Handayani University of Makassar Dept. of computer system Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331508579

This research aims to transcribe audio into text using the pre-trained Whisper model, focusing on Bugis, a low-resource language. A key challenge in transcribing local languages like Bugis is the limited availability of language model. To address this, we implemented an algorithmic correction approach that does not require extensive datasets or computationally demanding customizations. The Whisper model provides an initial transcription, serving as a foundation;however, the output often lacks accuracy due to the absence of specific adjustments for Bugis. To enhance accuracy, we applied the Levenshtein distance algorithm to correct word errors by comparing transcription results with entries in a Bugis language dataset based on similarity. Although this method is not fully precise and depends on the completeness of the reference dataset, evaluation results indicate that it effectively lowers the word error rate (WER) and improves transcription accuracy. This research produces SRT files containing transcripts that have been corrected using the Levenhstein distance algorithm, which not only improves accessibility but also contributes to the preservation of Bugis language through digital media. This approach is expected to provide a practical solution to improve transcription accuracy and support more accurate documentation in Bugis language preservation efforts. © 2024 IEEE.

关键词： Speech recognition

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Assessing Swapping Policies as a Detailed Placement Approach

Assessing Swapping Policies as a Detailed Placement Approach

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South Eastern European Design Automation, computer Engineering, computer Networks and Social Media Conference (SEEDA-CECNSM)

作者： Ioannis Arvanitakis George K. Kranas Michael Dossis Athanasios Kakarountas Antonios N. Dadaliaris Dept. of Informatics and Telecommunications University of Thessaly Lamia Greece Computer Science & Biomedical Informatics Dept. University of Thessaly Lamia Greece Computer Science Dept. University of Western Macedonia Kastoria Greece

Detailed placement plays a crucial role in the physical design of integrated circuits, impacting factors such as area, power consumption, and performance. Swap-based placement algorithms have gained significant attention in recent years due to their effectiveness in improving the quality of placement solutions. This paper presents a comprehensive study of swap-based detailed placement algorithms for standard cell design, highlighting their principles, advantages, challenges, and recent advancements. In this paper we present results of swap-based algorithms, using different approaches for standard cells selection and how these affect the final solution.

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Automated segmentation and classification of the atherosclerotic carotid plaque in ultrasound videos 24

Automated segmentation and classification of the atheroscler...

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Liapi, Georgia D. Gemenaris, Michalis Loizou, Christos P. Kyriacou, Efthyvoulos Constatninou, Kyriacos P. Nicolaides, Andrew Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Dept. of Computer Science University of Cyprus Nicosia Cyprus Vascular Screening and Diagnostic Centre Nicosia Cyprus

ISBN: (纸本)9798350339598

The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selection of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selected per CUS video, and the CC's consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selection. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p © 2023 IEEE.

关键词： Frequency modulation

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Spread Spectrum Steganography in Audio Containers 5

Spread Spectrum Steganography in Audio Containers

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhelezov, Stanimir Kordov, Krasimir Nachev, Atanas Pavlova, Daniela Konstantin Preslavsky University of Shumen Dept. Computer Science and Technologies Shumen Bulgaria Konstantin Preslavsky University of Shumen Dept. Computer Informatics Shumen Bulgaria University of Library Studies and Information Technologies Sofia Bulgaria

ISBN: (纸本)9798350309126

In this paper, attention is paid to audio containers and the possibilities of using them as carriers of steganographic information. One of the most proven methods for increasing the reliability of stegoalgorithms is distributed embedding. An embedding method based on the use of a pseudo-random sequence generator and an algorithm for its implementation are proposed. The proposed method is investigated and the results presented show that it meets standard reliability metrics and is fully suitable for use in real-world settings. © 2023 IEEE.

关键词： Containers

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