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Unveiling Veracity: An Integrated Semi-Supervised Algorithm for Fake News Detection

Unveiling Veracity: An Integrated Semi-Supervised Algorithm ...

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international conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Aman Malik Dayal Kumar Behera Amulya Ratna Swain Jhalak Hota Department of Computer Science & Information Systems Birla Institute of Technology and Science Hyderabad School of Computer Engineering KIIT University Bhubaneswar India

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In recent years, the pervasive dissemination of misinformation and deliberately falsified content, commonly referred to as “fake news,” has become a critical challenge in the realm of information dissemination. With the exponential growth of digital media and online platforms, the ease and speed at which misleading information can be generated and spread have increased substantially. Addressing the formidable task of detecting fake news has thus become an imperative within the field of information technology. The supervised model gives promising results. However, the availability of labelled data is critical in the realm of fake news detection. Leveraging unlabeled data alongside labelled data is an effective approach, but it requires careful handling. This paper explores the application of Semi-Supervised Learning for fake news detection by integrating Label Propagation and various ML models as base classifiers. By leveraging both labelled and unlabeled data, Label Propagation with CatBoost Classifier seeks to overcome traditional limitations, offering a promising avenue for bolstering the reliability of automated systems in distinguishing between authentic and deceptive information in the vast landscape of digital content.

关键词： measurement Technological innovation Logistic regression Accuracy Computational modeling Refining Semisupervised learning

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Assessing ChatGPT’s Capability to Generate Course Learning Outcomes

Assessing ChatGPT’s Capability to Generate Course Learning ...

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international conference on Information and computer Technologies (ICICT)

作者： Omaima Almatrafi Department of Information Systems King Abdulaziz University Jeddah Saudi Arabia

ISBN: (数字)9798350385625

ISBN: (纸本)9798350385632

ChatGPT is transforming higher education through the utilization of advanced neural networks and large language models to produce human-like content. However, its integration in various educational contexts has yet to be comprehensively evaluated. This study aims to assess ChatGPT’s ability to create course learning outcomes (CLOs) effectively, which remains unexplored. ChatGPT was instructed to generate 10-15 CLOs for 10 courses in the information systems department, based on the course title, description, and topics. The CLOs generated by ChatGPT were then evaluated using two different methods: 1) automatic evaluation metrics (BERTScore) to measure semantic similarity and 2) experts who provided a more comprehensive evaluation of the CLOs relevance to the course and diagnose the characteristic of poorly generated CLOs. The findings demonstrate that ChatGPT can serve as a supportive tool for instructors. The majority of the generated CLOs are either similar to or novel additions to those developed by humans. Nonetheless, approximately 20% of experts CLOs were missing and 22% of the produced CLOs were incorrect, mainly identified as irrelevant. Therefore, it is crucial to exercise caution when using this tool and to rely on human expertise to assess the CLOs generated by ChatGPT.

关键词： Productivity measurement Reviews Computational modeling Education Semantics Neural networks

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An Autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning

An Autoencoder-Based Constellation Design for AirComp in Wir...

引用

IEEE international conference on Communications (ICC)

作者： Yujia Mu Xizixiang Wei Cong Shen Charles L. Brown Department of Electrical and Computer Engineering University of Virginia

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scala-bility challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allows multiple edge devices to transmit uplink signals simultaneously, enabling the parameter server to directly decode the average global model. However, existing AirComp solutions are intrinsically analog, while modern wireless systems predominantly adopt digital modulations. Consequently, careful constellation designs are necessary to accurately decode the sum model updates without ambiguity. In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs. We leverage autoencoder network structures and explore the joint optimization of transmitter and receiver components. Our approach fills an important gap in the context of accurately decoding the sum signal in digital modulation-based AirComp, which can advance the deployment of FL in contemporary wireless systems.

关键词： Wireless communication Transmitters Federated learning Atmospheric modeling Digital modulation Computational modeling Receivers

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Utilizing Comprehensive Biological Network to Improve Accuracy of Computational Drug Repurposing

Utilizing Comprehensive Biological Network to Improve Accura...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Aleksandar Poleksic Department of Computer Science University of Northern Iowa Cedar Falls IA USA

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug repurposing can overcome challenges associated with expensive and time-consuming drug discovery pipelines by quickly finding new uses for existing drugs. Computational drug repurposing is founded on the principle that similar drugs treat similar diseases. Thus, the research in the field has traditionally focused on defining and implementing measures of drug and disease similarities that closely resemble similarities in the observed drug pharmacological profiles and disease pathways. In this study, we incorporate comprehensive data on biological relationships and interactions to arrive at a fast and accurate method for completing the existing knowledge on drug-disease associations and, in turn, find new indications for FDA approved drugs.

关键词： Drugs Accuracy Computational modeling systems biology Pipelines Machine learning Predictive models Biomedical measurement Drug discovery Diseases

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Stochastic Adaptations in Cellular Automata: Modifications to Conway's Game of Life

Stochastic Adaptations in Cellular Automata: Modifications t...

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Artificial Intelligence and Emerging Technology (Global AI Summit), international conference on

作者： Shreya Gupta Hitesh Shivkumar Reenu Rani School of Computer Sci. and Engg. Vellore Institute of Technology Vellore India School of Advanced Sciences Vellore Institute of Technology Vellore India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

The study extends Conway's Game of Life with two novel modifications to enhance its simulation capabilities for population dynamics. These extensions incorporate cellular degenerative properties and energy dynamics into the classical model. The effectiveness of these enhanced models was evaluated by comparing their outputs against empirical population data from three species of penguins in Antarctica. The findings demonstrate that the proposed modifications yield a more accurate representation of living populations, capturing the complexity of real-life biological systems more effectively than the original framework.

关键词： measurement Adaptation models Accuracy Biological system modeling Stochastic processes Automata Games Mathematical models Data models Artificial intelligence

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Dual Encoder-Decoder U-Net Architecture for Polyp Segmentation in Colonoscopy Images with Shuffle Attention and Conditional Random Fields

Dual Encoder-Decoder U-Net Architecture for Polyp Segmentati...

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IEEE international conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Lijin P. Santhosh Kumar G. Madhu S. Nair Department of Computer Science Artificial Intelligence & Computer Vision Lab Cochin University of Science and Technology Kochi Kerala India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Colonoscopy plays a pivotal role in detecting and diagnosing colorectal diseases, with polyp segmentation being a critical step for accurate diagnosis. In this study, we propose a novel approach for polyp segmentation in colonoscopy images, leveraging the Shuffle attention mechanism within the proposed architecture. Our method is rigorously evaluated across three diverse colonoscopy datasets, demonstrating promising results with an mean dice score of 0.93. Furthermore, to enhance segmentation accuracy, we employ a Conditional Random Field (CRF) post-processing method to refine the segmentation results. Through extensive experimentation and analysis, we showcase the effectiveness of our approach in achieving highly accurate polyp segmentation, thereby contributing to improved diagnostic outcomes in colorectal healthcare. Our method holds significant potential for enhancing computer-aided detection systems in clinical practice, facilitating early detection and treatment of colorectal abnormalities.

关键词： measurement Image segmentation Accuracy Computational modeling Colonoscopy computer architecture Medical services

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UKF-Based Sensor Fusion for joint-Torque Sensorless Humanoid Robots

UKF-Based Sensor Fusion for Joint-Torque Sensorless Humanoid...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Ines Sorrentino Giulio Romualdi Daniele Pucci Artificial and Mechanical Intelligence Italian Institute of Technology Genoa Italy School of Computer Science University of Manchester Manchester UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper proposes a novel sensor fusion based on Unscented Kalman Filtering for the online estimation of joint-torques of humanoid robots without joint-torque sensors. At the feature level, the proposed approach considers multimodal measurements (e.g. currents, accelerations, etc.) and non-directly measurable effects, such as external contacts, thus leading to joint torques readily usable in control architectures for human-robot interaction. The proposed sensor fusion can also integrate distributed, non-collocated force/torque sensors, thus being a flexible framework with respect to the underlying robot sensor suit. To validate the approach, we show how the proposed sensor fusion can be integrated into a two-level torque control architecture aiming at task-space torque-control. The performances of the proposed approach are shown through extensive tests on the new humanoid robot ergoCub, currently being developed at Istituto Italiano di Tecnologia. We also compare our strategy with the existing state-of-the-art approach based on the recursive Newton-Euler algorithm. Results demonstrate that our method achieves low root mean square errors in torque tracking, ranging from 0.05 Nm to 2.5 Nm, even in the presence of external contacts.

关键词： Torque Current measurement Torque control Humanoid robots Estimation Sensor fusion Robot sensing systems

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Stabilization of Neural Activity in Brain Circuit Model of Cerebral Cortex-Basal Ganglia by Chaotic Resonance Control

Stabilization of Neural Activity in Brain Circuit Model of C...

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international joint conference on Neural Networks (IJCNN)

作者： Hirotaka Doho Sou Nobukawa Haruhiko Nishimura Tetsuya Takahashi Faculty of Education Kochi University Kochi Japan Department of Computer Science Chiba Institute of Technology Narashino Japan Faculty of Informatics Yamato University Suita Japan Research Center for Child Mental Development Kanazawa University Kanazawa Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Fluctuations in nonlinear systems can enhance the synchronization with weak input signals. Chaotic resonance (CR) is one of such phenomena, caused by a system-intrinsic chaotic fluctuation. CR is observed in systems with chaos-chaos intermittency (CCI), where a chaotic orbit appears between separate regions. Based on the characteristics of CR, we previously proposed a novel method for controlling the chaotic state to an appropriate CR state by adopting a feedback signal from the system itself. This method is called the reduced-region-of-orbit (RRO) feedback method. The RRO feedback method was applied to discrete and continuous time chaotic systems, and its versatility was confirmed. Moreover, we have applied this method to frontal cortex neural system model, and the effectiveness for controlling the system behavior by inducing CR was confirmed. In this study, we examined the responsiveness of CCI to a weak periodic signal by extending the model to the brain circuit level composed of frontal cortex, basal ganglia and thalamus. As a result, we confirmed the effectiveness of the RRO feedback method for stabilizing the neural activity observed in the brain circuit model of cerebral cortex-basal ganglia.

关键词： Chaos Basal ganglia Perturbation methods Neural activity RLC circuits Brain modeling Rail to rail outputs

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AADS: An Automated Accident Detection and Nighttime Surveillance System Using Fine-Tuned YOLOv10 Deep Learning Techniques

AADS: An Automated Accident Detection and Nighttime Surveill...

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Intelligent Control, Computing and Communications (IC3), international conference on

作者： M. Karthikeyan Pravin Ramdas Kshirsagar Tan Kuan Tak Keshav Lalit Kamal Upreti Rituraj Jain Dept. of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Electronics Engg. J.D College of Engg & Mgmt. Nagpur MS India Engineering Cluster Singapore Institute of Technology Singapore Dept. of Computer Science & Engineering Manipal Institute of Technology Manipal Karnataka India Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Info. Tech. Marwadi University Rajkot Gujarat India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

computer vision-based surveillance is very important today's security systems to detect, track and regulate the security much better than standard cameras. However, like any other performance measurement systems they have potential pitfalls and technical, ethical, and legal implications must be well understood. The continuous rise in connection and interaction implies that safety of the public especially when navigating roads or operating in public domains is paramount. The conventional approaches to accident identification include observation or reporting from witnesses and always record slow and imprecise outcomes. With the improvement of AI and computer visions, especially with deep learning models such as YOLO, accident detection is changing. YOLO v10 which is incorporated in the surveillance systems, performs real time video analysis to provide object and pattern recognition of accidents including car accidents and incidents involving the pedestrians. When applied to the initial set of annotated accident images, the fine-tuning of the YOLO v10 model enhances its detection capability. The system is in watching the video frames that contain aberrations and issues and alarms are issued when the accidents happen and relayed to the monitoring stations or emergency departments for proper response. The optimized YOLOv10 here delivers a meaningful testing score of 72.3% mAP to outperform the regular YOLOv10 efficiency in incident detection.

关键词： YOLO Deep learning computer vision Surveillance Computational modeling Streaming media Cameras Real-time systems Time factors Accidents

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A Multi-layer Planar Beamfocusing Lens for 5G Communications

A Multi-layer Planar Beamfocusing Lens for 5G Communications

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Qing Xiong Shiwan Zhang Yuanliang Ni Yong Luo Key laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China

We propose a beamfocusing lens by employing layered phase-gradient metasurface, the lens can focus the vertically incident plane wave on the focal area. The metasurface is engineered with a series of subwavelength (about 0.33λ 0 ×0.33 λ 0 ) metallic elements etched on a triple-layer dielectric substrate. Each element can achieve a conditional phase shifts and a high transmission magnitude in the incident wavefront at 5 GHz. We obtain different phase shifts by adjusting the metallic size of the elements, and a 16016 array is simulated in HFSS. The electromagnetic wave is focused on the expected position, which demonstrates excellent performances of the lens in regulating transmitted electromagnetic wave.

关键词： Phased arrays computer science Communication systems 5G mobile communication Computational modeling Electromagnetic scattering Dielectric substrates

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