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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Yang, Qiang Liu, Yang Stuchbury-Wass, Jake Butkow, Kayla-Jade Panariti, Emeli Ma, Dong Mascolo, Cecilia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Faculty of Dentistry Oral and Craniofacial Sciences King's College London London United Kingdom School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798400713941

Improper toothbrushing practices persist as a primary cause of oral health issues such as tooth decay and gum disease. Despite the availability of high-end electric toothbrushes that offer some guidance, manual toothbrushes remain widely used due to their simplicity and convenience. We present SmarTeeth, an earable-based toothbrushing monitoring system designed to augment manual toothbrushing with functionalities typically offered only by high-end electric toothbrushes, such as brushing surface tracking. The underlying idea of SmarTeeth is to leverage in-ear microphones on earphones to capture toothbrushing sounds transmitted through the oral cavity to ear canals through facial bones and tissues. The distinct propagation paths of brushing sounds from various dental locations to each ear canal provide the foundational basis for our methods to accurately identify different brushing locations. By extracting customized features from these sounds, we can detect brushing locations using a deep-learning model. With only one registration session () for a new user, the average accuracy is 92.7% for detecting six regions and 75.6% for sixteen tooth surfaces. With three registration sessions (), the performance can be boosted to 98.8% and 90.3% for six-region and sixteen-surface tracking, respectively. A key advantage of using earphones for monitoring is that they provide natural auditory feedback to alert users when they are overbrushing or underbrushing. Comprehensive evaluation validates the effectiveness of SmarTeeth under various conditions (different users, brushes, orders, noise, etc.), and the feedback from the user study (N=13) indicates that users found the system highly useful (6.0/7.0) and reported a low workload (2.5/7.0) while using it. Our findings suggest that SmarTeeth could offer a scalable and effective solution to improve oral health globally by providing manual toothbrush users with advanced brushing monitoring capabilities. © 2025 Copyright held by the ow

关键词： Ear protectors

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Edge Intelligence for Wildlife Conservation: Real-Time Hornbill Call Classification Using TinyML

arXiv

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arXiv 2025年

作者： Hing, Kong Ka Behjati, Mehran Saleh, Vala Meng, Yap Kian Abdul Majeed, Anwar P.P. Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Department of Computing and Information Systems School of Engineering and Technology Sunway University 47500 Malaysia Department of Electrical Engineering Shiraz University of Technology Shiraz Iran Taicang Suzhou215400 China

Hornbills, an iconic species of Malaysia's biodiversity, face threats from habitat loss, poaching, and environmental changes, necessitating accurate and real-time population monitoring that is traditionally challenging and resource intensive. The emergence of Tiny Machine Learning (TinyML) offers a chance to transform wildlife monitoring by enabling efficient, real-time data analysis directly on edge devices. Addressing the challenge of wildlife conservation, this research paper explores the pivotal role of machine learning, specifically TinyML, in the classification and monitoring of hornbill calls in Malaysia. Leveraging audio data from the Xeno-canto database, the study aims to develop a speech recognition system capable of identifying and classifying hornbill vocalizations. The proposed methodology involves preprocessing the audio data, extracting features using Mel-Frequency Energy (MFE), and deploying the model on an Arduino Nano 33 BLE, which is adept at edge computing. The research encompasses foundational work, including a comprehensive introduction, literature review, and methodology. The model is trained using Edge Impulse and validated through real-world tests, achieving high accuracy in hornbill species identification. The project underscores the potential of TinyML for environmental monitoring and its broader application in ecological conservation efforts, contributing to both the field of TinyML and wildlife conservation. Copyright © 2025, The Authors. All rights reserved.

关键词： Taxonomies

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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2023 Wave Electronics and its Application in information and Telecommunication systems, WECONF 2023

作者： Tu, Le Anh Bogatyrev, V.A. Abramova, E.A. ITMO University Faculty of Software Engineering and Computer Technology Saint Peterburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia

ISBN: (纸本)9798350348293

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved. © 2023 IEEE.

关键词： Redundancy

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Characterizing Text Revisions to Better Support Collaborative

Characterizing Text Revisions to Better Support Collaborativ...

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2022 International Conference on Digital Transformation and Intelligence, ICDI 2022

作者： Ping, Tan Ping Verspoor, Karin Miller, Timothy Universiti Malaysia Sarawak Faculty of Computer Science and Information Technology Sarawk Malaysia School of Computing Technologies Royal Melbourne Institute of Technology Melbourne Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9798350397000

Despite advancement in collaborative writing tools, the track changes capability in modern editors remains limited to highlighting syntactic changes, with authors still required to manually read through each of the revisions. We envision a collaborative authoring system where an author could accept all minor edits first and then focus on the substantial changes. To support this, we define the task of significant revision identification as the task of identifying the revisions between two versions of a text according to one of four categories, i.e. formal, meaning preserving, micro-and macro-structure. Micro-structure change corresponds to minor meaning change while macro-structure change corresponds to major meaning change. Our main contribution is to define a computational approach to this task, by framing the task as bi-directional entailment between the original and revised sentences. An existing recognition of textual entailment (RTE) system is applied to evaluate whether the revised texts entails. We evaluate the approach through a novel corpus consisting of multiple versions of drafts of academic papers written by multiple authors, which were annotated with the four revision types by both authors and non-authors of the papers. The proposed bi-directional textual entailment approach performs better than baseline edit distance approaches, which is similar to the current track changes capability built into most word processors. © 2022 IEEE.

关键词： Text processing

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Cluster Availability Model with information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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2023 Wave Electronics and its Application in information and Telecommunication systems, WECONF 2023

作者： Bogatyrev, V.A. Bogatyrev, S.V. Sivov, V.V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia ITMO University Faculty of Software Engineering and Computer Technology Saint-Petersburg Russia

ISBN: (纸本)9798350348293

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node. © 2023 IEEE.

关键词： Fault tolerance

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Invalidator: Automated Patch Correctness Assessment via Semantic and Syntactic Reasoning

arXiv

引用

arXiv 2023年

作者： Le-Cong, Thanh Luong, Duc-Minh Le, Xuan Bach D. Lo, David Tran, Nhat-Hoa Quang-Huy, Bui Huynh, Quyet-Thang School of Computing and Information Systems The University of Melbourne Australia School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam School of Computing and Information Systems Singapore Management University Singapore

Automated program repair (APR) has been gaining ground recently. However, a significant challenge that still remains is patch overfitting, in which APR-generated patches plausibly pass the validation test suite but fail to generalize. A common practice to assess the correctness of APR-generated patches is to judge whether they are equivalent to ground-truth, i.e., developer-written patches, by either generating additional test cases or employing human manual inspections. The former often requires the generation of at least one test witnessing the behavioral differences between the APR-patched and developer-patched programs. Searching for the witnessing test, however, can be difficult as the search space can be enormous. Meanwhile, the latter is prone to human biases and requires repetitive and expensive manual effort. In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behav

关键词： Syntactics

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of Computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for Imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction Imbalance data Explainable AI Cybersecurity

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Towards Optimization of Math Pedagogical Techniques and Behaviors Using Decision and Ensemble Trees 4

Towards Optimization of Math Pedagogical Techniques and Beha...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Fawy, Mohamed Hamza, Meer Aziz, Rasha Abd El Yafi, Eiad Technology and Maritime Transport College of Management and Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport College of Computing and Information Technology Arab Academy for Science Alexandria Egypt Technology and Maritime Transport Arab Academy for Science Businese Information Systems Department Alexandria Egypt University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9798350348248

Professional development programs for educators are thought to be generally ineffective and inefficient in raising student enjoyment and performance. As a result, new educators often express that the first years in their career are challenging and lacking in appropriate and effective support. New educators may frequently experience stress and anxiety from those factors as well as the inherent challenges of teaching young learners. The objectives of this paper and further research are to derive the most effective teaching techniques and behaviors using decision and ensemble tree methods. Six different models were trained from data collected on Egyptian preparatory language school mathematics teachers and their respective students. Two decision trees, two random forests and two gradient boosted trees are included, one set for classification tasks and the other for regression. The Student Evaluation Index (SEI) employed and generated by the student survey responses in this paper are used to derive the importance rankings of teaching techniques and behaviors. The tree structures can also be used to help teachers create improvement programs that will increase the performance and enjoyment of their students. The results of SEI prediction utilizing our tree methods are found to be critically dependent on techniques like Visualization and the use of native Language. It stands to reason that preparatory language school teachers in Egypt should emphasize the use of visual aids while instructing students in their native language. Additional attributes of a popular or effective teachers include inspirational behavior and the usage of practice problem sets such as homework. © 2023 IEEE.

关键词： Students

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