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Monocular UAV Motion Planning Method Based on Deep Reinforcement Learning

Monocular UAV Motion Planning Method Based on Deep Reinforce...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Hsueh, Man-Chen Ku, Chun Chen, Xiu-Zhi Chen, Yen-Lin Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798331521165

This paper presents a monocular UAV motion planning method based on the DRL method SDDPG. By processing image data with the self-proposed lightweight dept. estimation model and a pretrained encoder, the information is sent into an improved Actor-Critic Network, which addresses overestimation issues and enhances learning efficiency. The experiments were conducted in Unreal Engine 4 and Airsim, which demonstrate the improvement of the motion planning ability, enabling UAVs to autonomously navigate and safely reach the defined destination. © 2025 IEEE.

关键词： Drones

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EEG-Based Alzheimer’s Disease Detection Using Deep Learning Techniques

EEG-Based Alzheimer’s Disease Detection Using Deep Learning...

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IEEE Region 10 Humanitarian Technology Conference (R10-HTC)

作者： Ancy S.A. Jerrin Thomas Panachakel Dept. of Computer Science and Engineering College of Engineering Trivandrum India Dept. of Electronics and Comm. Engg. College of Engineering Trivandrum India

ISBN: (数字)9798350340204

ISBN: (纸本)9798350340211

Alzheimer’s disease (AD) is a neurological condition characterized by the degeneration of neuron cells, leading to cognitive impairment. This project investigates the effectiveness of EEG-based features for detecting Alzheimer’s Disease using deep learning techniques. The dataset comprises EEG recordings from healthy controls, Frontotemporal Dementia (FTD) patients, and patients diagnosed with AD. Data preprocessing involved segmenting the EEG signals into fixed-length epochs and filtering the baseline EEG into alpha, beta, and theta frequency bands. Features such as Mean Phase Coherence (MPC), Differential Entropy (DE), and Mean Squared Coherence (MSC) were computed pairwise for each epoch and frequency band. A feature matrix was constructed for each subject by stacking these features, which were then converted into images and used as input data for a ResNet50-based pre-trained model. The model was tested in leave-one-out cross-validation setting and achieved results comparable to the state-of-the-art in distinguishing between EEG recordings from healthy and AD-affected subjects.

关键词： Deep learning Computational modeling Coherence Brain modeling Feature extraction Electroencephalography Data models Recording Reliability Alzheimer's disease

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Leveraging Community Software in CS Education to Avoid Reinventing the Wheel 22

Leveraging Community Software in CS Education to Avoid Reinv...

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27th ACM Conference on Innovation and Technology in computer science Education, ITiCSE 2022

作者： Blanchard, Jeremiah Hott, John R. Berry, Vincent Carroll, Rebecca Edmison, Bob Glassey, Richard Karnalim, Oscar Plancher, Brian Russell, Seán University of Florida Dept. of Engineering Education GainesvilleFL United States University of Virginia Dept. of Computer Science CharlottesvilleVA United States LIRMM - Univ Montpellier CNRS Polytech Engineering School Montpellier France Full Sail University Dept. of Computer Science Winter ParkFL United States Virginia Tech Dept. of Computer Science BlacksburgVA United States KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden University of Newcastle School of Information and Physical Sciences CallaghanNSW Australia Barnard College Columbia University Dept. of Computer Science New York CityNY United States University College Dublin School of Computer Science Dublin Ireland

ISBN: (纸本)9781450392006

Historically, computing instructors and researchers have developed a wide variety of tools to support teaching and educational research, including exam and code testing suites and data collection solutions. Many are then community or individually maintained. However, these tools often find limited adoption beyond their creators. As a result, it is common for many of the same functionalities to be re-implemented by different instructional groups within the CS Education community. We hypothesize that this is due in part to accessibility, discoverability, and adaptability challenges, among others. Further, instructors often face institutional barriers to deployment, which can include hesitance of institutions to utilize community developed solutions that often lack a centralized authority. This working group will explore what solutions are currently available, what instructors need, and reasons behind the above-mentioned phenomenon. This will be accomplished via a literature review and survey to identify the tools that have been developed by the community;the solutions that are currently available and in use by instructors;what features are needed moving forward for classroom and research use;what support for extensions is needed to support further CS Education research;and what institutional challenges instructors and researchers are currently facing or have faced in the past in developing, deploying or otherwise using community software solutions. Finally, the working group will identify factors that limit adoption of solutions and ways to integrate and improve the accessibility, discoverability, and dissemination of existing community projects, as well as manage and overcome institutional challenges. © 2022 Owner/Author.

关键词： community software educational tools

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Reconsider Time Series Analysis for Insider Threat Detection

Reconsider Time Series Analysis for Insider Threat Detection

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IEEE International Conference on Big Data

作者： Chia-Cheng Chen Hsing-Kuo Pao Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Insider threat detection (ITD) presents a significant challenge in cybersecurity, particularly within large and complex organizations. Traditionally, ITD has been overshadowed by the focus of external threats, resulting in less attention and development in this critical area. Conventional ITD approaches often rely heavily on event-driven approaches. On top of that, researchers developed various rule-based methods to conquer the tasks. Based on that, we often ignore the intrinsic temporal relationships that are naturally built in between events that occur in different moments. For instance, we may easily understand events with causality such as one anomalous event followed by another specific event to complete a malicious action; however, may not be aware of events that occur around 9 am every morning during working hours. In our opinion, we attempt to re-consider the temporal behavior to extract the information hidden in cyberspace activities. Specifically, some effective sentence embeddings can assist us in providing informative internal representations to summarize temporal behaviors in the temporal activity sequences to make the right judgment on insider threat detection. In this paper, we propose a novel methodology for insider threat detection that emphasizes temporal relationship modeling on top of already-matured event sequence analysis to effectively catch insider threats. The proposed approach leverages contrastive sentence embeddings to learn users’ intentions in sequences, followed by the deployment of a user-level and event-level Contrastive Learning (euCL) model to incorporate temporal behaviors with user behavior embeddings. To validate the proposed methodology, we conduct extensive analyses and experiments using the publicly available CERT dataset. The results demonstrate the effectiveness and robustness of the proposed method in detecting insider threats and identifying malicious scenarios, highlighting its potential for enhancing cybersecurity measu

关键词： Analytical models Sequences Time series analysis Semantics Organizations Threat assessment Robustness Data models computer security Standards

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Exploring Voice-Activated AI's Impact on Workplace Productivity with Virtual Assistants

Exploring Voice-Activated AI's Impact on Workplace Productiv...

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Integration of Emerging Technologies for the Digital World (ICIETDW), International Conference on

作者： Veeramalai Sankaradass S. Sudhakar Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Anna University Chennai Tamil Nadu India

ISBN: (数字)9798331541170

ISBN: (纸本)9798331541187

This paper presents a revolutionary computer desktop computer aide encouraged by GPT-3, a cutting-edge language version along with support discovering strategies. The chatbot functioning as a detailed device for computer individuals, effortlessly incorporates AI to give user-friendly support in jobs varying from standard procedures to progressed data administration and also setups changes. By using the capacities of GPT-3 the chatbot involves individuals in all-natural language discussions, supplying thorough reactions and also carrying out jobs with very little individual input. In addition, an assistance discovering based suggestion system adjusts to individual actions, continually refining ideas to maximize individual experience. This paper likewise represents a substantial innovation in individual computer showcasing the transformative possibility of AI in boosting performance coupled with individualizing the computer experience. With this cutting-edge technique we picture a future where AI-driven individual aides redefine the requirements of customer communication and also effectiveness in computer procedures.

关键词： Training Productivity Technological innovation Refining Employment Personal voice assistants Reinforcement learning Chatbots Reliability Standards

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OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework

OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sen...

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International Conference on Distributed Computing Systems Workshop

作者： Cheng-Wei Ching Chirag Gupta Zi Huang Liting Hu Dept. of Computer Science and Engineering University of California Santa Cruz USA Dept. of Computer Science Virginia Tech USA

Compressed data aggregation (CDA) over wireless sensor networks (WSNs) is task-specific and subject to environmental changes. However, the existing compressed data aggregation (CDA) frameworks (e.g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes. Additionally, they do not consider the performance of follow-up IoT data-driven deep learning (DL)-based applications. To address these shortcomings, we propose OrcoDCS, an IoT-Edge orchestrated online deep compressed sensing framework that offers high flexibility and adaptability to distinct IoT device groups and their sensing tasks, as well as high performance for follow-up applications. The novelty of our work is the design and deployment of IoT-Edge orchestrated online training framework over WSNs by leveraging an specially-designed asymmetric autoencoder, which can largely reduce the encoding overhead and improve the reconstruction performance and robustness. We show analytically and empirically that OrcoDCS outperforms the state-of-the-art DCDA on training time, significantly improves flexibility and adaptability when distinct reconstruction tasks are given, and achieves higher performance for follow-up applications.

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Comparative Advances in Financial Sentiment Analysis:A Review of BERT,FinBert, and Large Language Models

Comparative Advances in Financial Sentiment Analysis:A Revie...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Manish Barath Mahendran Aswin Kumar Gokul Poornima Lakshmi S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Capturing market sentiments and supporting well-informed financial decision-making depend on the developing field of Financial Sentiment Analysis (FSA). Natural language processing (NLP) has made significant strides in comprehending and categorizing sentiment in intricate financial texts, especially with the use of Large Language Models (LLMs). The application of various LLMs, such as Bidirectional Encoder Representations form Transformers(BERT) and Financial BERT (FinBERT), as well as distilled models, such as DistilBERT and DistilRoBERTa, on a variety of financial datasets, including Financial Phrase Bank and LexisNexis news articles, was the main focus of this review article. highlighting different approaches such as model fine-tuning, zero-shot and few-shot learning, and prompt engineering. The study focuses on practical model predictions through a case study of sentimental analysis of cryptocurrencies. While FinBERT, a financial variant of BERT, exhibits high accuracy and robustness, other LLMs exhibit varying degrees of success based on the dataset and domain requirements. The analysis concentrates on the difficulties, compromises, and potential paths for improving LLMs for financial sentiment analysis.

关键词： Sentiment analysis Technological innovation Reviews Large language models Decision making Bidirectional control Market research Encoding Risk management Indexing

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Towards Quality Assurance of Natural Language in Code

Towards Quality Assurance of Natural Language in Code

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International Conference on Software engineering - Companion, ICSE, Companion

作者： Renyi Zhong Dept. of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

ISBN: (数字)9798331536831

ISBN: (纸本)9798331536848

The quality of code in software development is critical to ensuring robust, functional, and maintainable applications. While static analysis techniques can improve code quality, they often overlook the natural language (NL) components of code, such as comments and logging statements, which play an essential role in program development and maintenance. In this thesis, we aim to address quality assurance for these NL elements through two primary objectives. First, we plan to propose a new benchmark to facilitate consistent comparisons among existing methods. Second, we tackle logging statement quality by identifying a broader range of defect types and introducing an automated detection and repair tool. This work addresses critical limitations in current research, which often focuses on single defect types and lacks repair functionality, by enabling a comprehensive and practical approach to NL quality in code. Our contributions provide a foundation for ongoing improvements in code maintainability and contribute to a more reliable software development lifecycle.

关键词： Codes Quality assurance Large language models Natural languages Static analysis Maintenance engineering Software reliability Maintenance Software development management Software engineering

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Child and Women Safety Measures using iBeacon Technology

Child and Women Safety Measures using iBeacon Technology

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： Shanker Shalini Keerthana E Nishanthi P Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

A smart real-time tracking system featuring a compact wearable device is designed to enhance the safety of children and women by utilizing IoT-driven technology and a secure database for efficient movement monitoring. This system operates on four key pillars: outdoor localization, cross-platform communication, server integration, and user engagement. The tracking mechanism comprises a wearable device embedded with an iBeacon and a built-in MEMS accelerometer, seamlessly linked to a smartphone application. The system begins with sensor calibration to establish reference values, followed by continuous tracking of sensor variations, particularly accelerometer data, to detect motion patterns with precision. Once paired with the designated Android application, the wearable device analyze movement data and transmits GPS coordinates to a centralized server, ensuring real-time location tracking of both the device and the associated iBeacon. Planned future improvements include the integration of Google Maps for enhanced real-time visualization, interactive monitoring, geofencing alerts, and emergency response notifications, making the system more robust and responsive to safety concerns.

关键词： Accelerometers Tracking Web services Real-time systems Threat assessment Software Safety Servers Wearable devices Monitoring

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An Artificial Intelligence based Machine Learning Approach for Automatic Blood Glucose Level Identification of Diabetes Patients

An Artificial Intelligence based Machine Learning Approach f...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Maguluri, Lakshmana Phaneendra Santhosh, K. Meenakshi, K. Farooqui, Muhammad Ahtesham Parveen Sultana, H. Saravanan, V. Department of Computer Science and Engineering Andhra Pradesh Guntur India Kombolcha Institute of Technology Wollo University Department of Mechatronics Ethiopia Cmr Institute of Technology Department of Mathematics Bengaluru Karnataka India Sam Global University Dept. of Computer Science & Engineering Madhya Pradesh Raisen India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Dambi Dollo University Department of Computer Science Dambi Dollo Ethiopia

ISBN: (纸本)9798350323887

In general, treatments and medications for diabetics are usually prescribed based on the level of glucose-level in the human-blood. Blood glucose-level was calculated by performing two separate tests: before meals and after meals. The practical functions present in this test enable physicians to carry out appropriate treatment modalities. This paper introduces an improved method of running a machine learning system. Its main task is to accurately analyze the given input data and calculate the correct point of blood-glucose in the blood of diabetics. It was designed to do so and then list the appropriate control methods and drugs and share that data with the user in a paperless digital manner. Thus it is enough for patients to go to laboratories and do tests. Only their results will be calculated and sent to them for treatment. © 2023 IEEE.

关键词： Glucose

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