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Mouse Dynamics-Based Online Fraud Detection System for Online Education Platforms 9th

Mouse Dynamics-Based Online Fraud Detection System for Onlin...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Subash, Aditya Song, Insu Lee, Ickjai Lee, Kyungmi James Cook Univ Coll Sci & Engn Singapore Singapore James Cook Univ Coll Sci & Engn Cairns Australia

ISBN: (纸本)9789819733019;9789819733026

Over the past decade, the online education sector has gained significant popularity causing a major shift from traditional face-to-face classes to online education. This shift has left the education sector with major vulnerabilities. Specifically online fraud, which includes identity theft and online assessment fraud. Conventional methods used for online fraud detection are one-time, invasive, and expensive. Behavioral biometric authentication systems based on mouse dynamics have emerged as a secure and inexpensive alternative to conventional methods due to their ability to continuously track user identity non-invasively. However, there are no studies on mouse behavioral biometric-based continuous user identification, for online fraud detection, in online education platforms. Furthermore, there is also a need to identify a better combination of features that yield satisfactory performance (>80% in accuracy, precision, and recall) for this specific application. Existing research in the field still relies on typical machine learning methods despite major developments made in deep learning. To solve the identified problems, we first identify a unique combination of mouse behavior features and collect application-oriented mouse behavior data. We then investigate the efficacy of the features using both deep learning and typical machine learning methods. Experimental results demonstrate that the identified feature set, termed as user behavior-centric feature set, not only yields satisfactory performance but is reliable and scalable to significantly larger datasets.

关键词： Online education platforms Mouse dynamics User behavior-centric features Online fraud detection Deep learning Robust Scalable

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Optimal Cut-Off Points for Pancreatic Cancer Detection Using Deep Learning Techniques 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Dzemyda, Gintautas Kurasova, Olga Medvedev, Viktor Suboniene, Ausra Gulla, Aiste Samuilis, Arturas Jagminas, Dziugas Strupas, Kestutis Vilnius Univ Inst Data Sci & Digital Technol Akad Str 4 LT-08412 Vilnius Lithuania Vilnius Univ Fac Med Inst Clin Med MK Ciurlionio Str 21-27 LT-03101 Vilnius Lithuania Vilnius Univ Fac Med Dept Radiol Nucl Med & Med Phys Inst Biomed Sci MK Ciurlionio Str 21-27 LT-03101 Vilnius Lithuania

ISBN: (数字)9783031456428

ISBN: (纸本)9783031456411;9783031456428

Deep learning-based approaches are attracting increasing attention in medicine. Applying deep learning models to specific tasks in the medical field is very useful for early disease detection. In this study, the problem of detecting pancreatic cancer by classifying CT images was solved using the provided deep learning-based framework. The choice of the optimal cut-off point is particularly important for an effective assessment of the results of the classification. In order to investigate the capabilities of the deep learning-based framework and to maximise pancreatic cancer diagnostic performance through the selection of optimal cut-off points, experimental studies were carried out using open-access data. Four classification accuracy metrics (Youden index, closest-to-(0,1) criterion, balanced accuracy, g-mean) were used to find the optimal cut-off point in order to balance sensitivity and specificity. This study compares different approaches for finding the optimal cut-off points and selects those that are most clinically relevant.

关键词： Pancreatic cancer deep learning convolutional neural networks cut-off point CT scans Youden index classification accuracy

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Blockchain and Beyond - A Survey on Scalability Issues 1

引用

15th International Conference on Autonomous systems

作者： Tominski, Oliver Drebinger, Martin Fernuniv Chair Commun Networks Unistr 27 D-58084 Hagen Germany

ISBN: (数字)9783031614187

ISBN: (纸本)9783031614170;9783031614187

Blockchains have gained significant popularity in certain areas over the past decade. The establishment of a distributed consensus among untrusted participants has set new standards and enabled protocols that were not possible before. The substantial critique of the Bitcoin network centers on its high energy consumption. However, its popularity has shown majorscalablity problems such as slow completion of transactions and the high transaction fees, particularly especially when transferring small amounts of money. After outlining the underlying structural reasons for these problems, we will briefly explain ideas for addressing them, using several projects as examples. Beyond that, we present an alternative concept utilizing Directed Acyclic Graphs (DAGs) which achieves a unified decentralized consensus in the network without using a traditional blockchain. The IOTA project has made significant progress with the so-called Tangle in version 2.0 and represents a promising approach for various application areas.

关键词： Blockchain

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Human-Robot Interaction Applied to Robotized Laparoscopic Suture 6th

Human-Robot Interaction Applied to Robotized Laparoscopic Su...

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6th Iberian Robotics Conference (Robot)

作者： Galan-Cuenca, Alvaro Herrera-Lopez, Juan Maria Rollon, M. Reina, A. J. Garcia-Morales, Isabel Munoz, Victor Univ Malaga Dept Ingn Sistemas & Automat C Severo Ochoa 4Parque Tecnol Andalucia PTA Malaga 29071 Spain

ISBN: (纸本)9783031591662;9783031591679

Laparoscopic surgery is a minimally invasive surgery that makes small incisions in the patient's tissue during the procedure in order to reduce infection and postoperative time. When using robots in this task applied to laparoscopic suturing, one of the main challenges encountered is human-robot iteration. To address this problem, this paper presents a functional architecture where the recognition of the surgeon's gestures by means of Machine Learning models and force-position control techniques of the surgical instrument stand out.

关键词： surgical gestures recognition Machine Learning laparoscopic surgery controller robot-assisted suture human-robot interaction

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Investigation of Digital Value Stream Twins in Learning Factory Environments 14th

Investigation of Digital Value Stream Twins in Learning Fact...

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14th International Conference on Learning Factories (CLF)

作者： Wollert, Tim Henning, Volker Behrendt, Fabian Berndt, Michael Magdeburg Stendal Univ Appl Sci Doctoral Ctr Social Hlth & Econ Sci Magdeburg Germany Magdeburg Stendal Univ Appl Sci Magdeburg Germany

ISBN: (纸本)9783031654107;9783031654114

In today's dynamic and technology-driven business landscape, the pursuit of operational excellence has become increasingly reliant on innovative approaches. This paper investigates the application of the Digital Twin (DT) concept in terms of value streams within learning factory environments to enhance operational optimization and transparency. Value Stream Management (VSM) is an approach which aims to optimize the value flow through an organization. It focuses on mapping, analyzing, and improving value streams, encompassing all activities required to deliver a product to customers. Derived from the VSM framework, a Digital Value Stream Twin (DVST) as representation of the learning factory is introduced, capturing real-time data and enabling a dynamic visualization, monitoring and control of the value stream performance. By integrating data analytics, the DVSTT evolves over time, adapting to changes in operational conditions and supporting the identification of optimization opportunities. The research methodology involves a case study conducted within a FESTO learning factory, which conceptually demonstrates the benefits of utilizing DVST to enhance decision-making, identify bottlenecks, and streamline processes. Findings reveal that integrating the DVST into a learning factory environment empowers organizations to experiment with process improvements before implementing changes in actual operations. Finally, an outlook on development potentials of the introduced DVST is given.

关键词： Value Stream Management (VSM) 4.0 digital value stream twin (DVST) FESTO learning factory manufacturing execution system (MES)

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Energy Harvesting and Computation Offloading for UAV-Assisted MEC with NOMA in IoT Network 5th

Energy Harvesting and Computation Offloading for UAV-Assiste...

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Nguyen, Gia-Huy Nguyen, Anh-Nhat Le, Hien-Hieu Do, Tien-Dung FPT Univ Dept Comp Fundamentals Hanoi 10000 Vietnam

ISBN: (纸本)9789819720811;9789819720828

This study investigates an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) based on mobile edge computing (MEC) in Internet of Things (IoT) networks. Specifically, we consider two IoT device (ID) clusters with resource limits and a UAV with an MEC server operating as a wireless power transfer (WPT) station. We present a protocol termed time switching (TS)-UAV NOMA MEC energy harvesting (EH) (TS-UNME), which includes four phases: energy harvesting, task-offloading, task-computing, and task-downloading. For system performance evaluation, we propose a closed-form expression of successful computation probability (SCP) that accounts for imperfect channel state information (ICSI) across the Rayleigh fading channel. In addition, we present a system performance optimization problem, which optimizes SCP by establishing time switching ratio (TSR) and height of UAV. A particle swarm optimization (PSO)-based approach is utilized for solving the problem. Finally, Monte Carlo simulations confirm our analysis's precision.

关键词： Internet of things Unmanned aerial vehicles Energy harvesting Non-orthogonal multiple access Mobile edge computing Particle swarm optimization

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Software MARTA: Improvements in Cell Segmentation and CX43 Lateralization Assessment 9th

Software MARTA: Improvements in Cell Segmentation and CX43 L...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Gattari, Daniel Chan, Debora Diez, Emiliano Llamedo Soria, Mariano Rossi, Mario Univ Austral Fac Ingn Mariano Acosta 1611B1629WWA Pilar Argentina CONICET Univ Austral Inst Invest Med Traslac IIMT Av Juan Domingo Peron 1500B1629AHJ Pilar Argentina Consejo Nacl Invest Cient & Tecn Inst Expt Med & Biol Cuyo IMBECU Mendoza Argentina Natl Technol Univ Elect Dept Buenos Aires DF Argentina CONICET Univ Austral Inst Invest Med Traslac IIMT Genom Func & Ciencia Datos Buenos Aires DF Argentina

ISBN: (纸本)9789819735556;9789819735563

Cardiomyocytes' geometry and connexin 43 (CX43) amount and distribution are structural features that play a pivotal role in electrical conduction of the heart. Their quantitative assessment is of high interest in the study of arrhythmias, but it is usually hampered by the lack of automatic tools. MARTA (Myocyte Automatic Retrieval and Tissue Analyzer) is a software algorithm which can automatically segment cardiomyocytes in fluorescent microscopy images of cardiac tissue and evaluate the expression of CX43 and its degree of lateralization. This study aims to analyze one type of myocardial tissue section of rat treated in the original software's study, improve the algorithm's segmentation performance, and extend its evaluation to new images of rat cardiac tissue. Besides, a new metric to evaluate the CX43 lateralization is presented. The metric is based on the dispersion of the lateralization distribution instead of the mean value. The results suggest that the presented method doubles the precision of the original algorithm's segmentation, with a slight increase in sensitivity. However, further evaluation of the proposed segmentation algorithm and metric are required to assess the actual performance of the methodology.

关键词： Automated quantification Segmentation Fluorescent microscopy CX43 Lateralization

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Smart Occupancy-Driven Control for Ventilation systems in Buildings with Privacy Concerns

Smart Occupancy-Driven Control for Ventilation Systems in Bu...

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Intelligent systems Conference (IntelliSys)

作者： Matcher, Krzysztof Boudjadar, Jalil Aarhus Univ Aarhus Denmark

ISBN: (纸本)9783031477171;9783031477188

Nowadays occupancy monitoring solutions are an intrinsic component of modern buildings to track and optimize the utilization of spaces and resources. Such solutions form a ground to the decision support of buildings management and rely on a large set of sensors that are commonly part of the LHVAC (lighting, heating, ventilation, and air-condition) systems. However, with the increasing privacy concerns it is becoming challenging to deploy seeing/hearing sensors. In this paper we propose an accurate occupancy estimator using machine learning (ML) and blind CO2 sensors to assess the actual occupancy state and predict future states. We also design a smart control system, relying on the occupancy module, to actuate the ventilation system in an energy efficient manner while maintaining the safety properties dictated by CO2 standards. In order to improve the prediction accuracy of ML models, we use the DBSCAN outlier to filter the input data and Z-Score to normalize such data. The system is fully implemented and tested on a data set of 8000 samples. The experimental results show that the prediction accuracy of our system is 96.6% with a considerable impact of the DBSCAN outlier. Furthermore, our smart control system enables an improvement of the energy performance and provides an efficient peak shaving to the energy consumption of the ventilation system.

关键词： Data analysis Machine learning Intelligent control Sensor systems Energy efficiency

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Security Assessment of an Internet of Things Device 12th

Security Assessment of an Internet of Things Device

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Cimpean, Daiana Alexandra Vochin, Marius-Constantin Craciunescu, Razvan-Eusebiu Dragulinescu, Ana-Maria-Claudia Boicescu, Laurentiu Natl Univ Sci & Technol Politehn Bucharest UNSTPB Bucharest 060042 Romania

ISBN: (纸本)9783031602269;9783031602276

The rapid advance of Internet of Things (IoT) and its immersion in every domain brought into attention, besides many of its technical, social, and economic advantages, a panoply of security vulnerabilities and attack vectors that threaten IoT interconnected devices. IoT devices face many security issues such as weak authentication, insufficient encryption, deficient device management, insecure interfaces, inadequate physical security, lack of standardization, privacy concerns, insecure networks, resource constraints, and non-compliance with security standards. This highlights the pressing need for comprehensive security measures that currently seem insufficient to address the evolving landscape of threats in the dynamic IoT ecosystem. This paper conducts a security evaluation of a physical IoT device through the penetration testing methodology. It then focuses on a known vulnerability from the Common Vulnerabilities and Exposures (CVE) database. It presents the execution of a brute force attack to uncover credentials and the device's buffer overflow vulnerability to cause a denial of service (DoS) on the device's server. Employing a hands-on approach, the research emphasizes the practical execution of these exploitation scenarios providing a step-by-step guide on how they were performed. Lastly, it delves into the development of a proof of concept (PoC) application created to automate the process of firmware analysis and running the buffer overflow exploit for this particular use case.

关键词： Exploit Privacy Brute-force Firmware analysis Penetration Testing

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A Unit Cell Element for Reflectarrays Based on the Bowtie Shape 9th

A Unit Cell Element for Reflectarrays Based on the Bowtie Sh...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Jabbar, Rosl A. Abdul Sayidmarie, Khalil H. Ninevah Univ Coll Elect Engn Dept Commun Engn Mosul Iraq

ISBN: (纸本)9789819733040;9789819733057

As the bowtie antenna is known for its wide band property, this shape is proposed here as a unit cell for reflectarray antennas. The bowtie shape is characterized by two parameters, namely width and height. To obtain variable phase, one of the dimensions of the bowtie element is changed, while the other one is kept constant. The CST Microwave Studio Suite was used to model and analyze the performance of the proposed unit cell that was simulated using waveguide port excitation. The obtained results of the reflection coefficient in phase and magnitude were compared to assess the performance of the proposed unit cell. The simulations showed that as the dimension is increased, the response moves to a lower frequency because elements of larger size resonate at lower frequency.

关键词： Reflectarrays Unit cell Phase response Bowtie antenna Microstrip antennas

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