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Evaluating Llama-3.1 for Adverse Drug Event Entity and Relationship Extraction Across Prompting Techniques 3rd

Evaluating Llama-3.1 for Adverse Drug Event Entity and Rel...

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3rd international conference on Advances in computing Research, ACR 2025

作者： Prioleau, Howard Aryal, Saurav K. Burge, Legand Electrical Engineering and Computer Science Howard University 2400 6th St NW WashingtonDC20059 United States

ISBN: (纸本)9783031876462

Post-market adverse drug effects (ADEs) are often found in the clinical notes of Electronic Health Records. We evaluate open-weight large language model, Llama-3.1, for detection of ADEs, Named Entity Recognition, and Entity Linkage tasks across varying prompting techniques suited for text decomposition including, but not limited to zero-shot, few-shot and tree of thought. Finally, we propose a novel improvement to the tree of thought prompting technique which combines consistency and thought merging. Our proposed approach is then evaluated and compared with the other approaches on these tasks using standard metrics for each task (f1, precision and recall). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adverse Drug Events Artificial intelligence Clinical Notes Large language models Llama n2c2

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6th international conference on Computational Science and Technology, ICCST 2019

6th International Conference on Computational Science and Te...

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6th international conference on Computational Science and Technology, ICCST 2019

ISBN: (纸本)9789811500572

the proceedings contain 69 papers. the special focus in this conference is on Computational Science and Technology. the topics include: An Automated Driver’s Context Recognition Approach Using Smartphone Embedded Sensors;A Variable Sampling Interval EWMA X¯ Chart for the Mean with Auxiliary Information;Design of Cloud computing Load Balance System Based on SDN Technology;Analysis of Expert’s Opinion on Requirements Patterns for Software Product Families Framework Using GQM Method;a Multi-Layer Perceptron Model in Analyzing Parametric Classification of Students’ Assessment Results in K12;redefining the White-Box of k-Nearest Neighbor Support Vector Machine for Better Classification;vehicle Classification using Convolutional Neural Network for Electronic Toll Collection;social Media, Software Engineering Collaboration Tools and Software Company’s Performance;the MMUISD Gait Database and Performance Evaluation Compared to Public Inertial Sensor Gait Databases;a Sequential Approach to Network Intrusion Detection;Brief of Intrusion Detection systems in Detecting ICMPv6 Attacks;lake Chini Water Level Prediction Model using Classification Techniques;development of a Bi-level Web Connected Home Access System using Multi-Deep Learning Neural Networks;portrait of Indonesia’s Internet Topology at the Autonomous System Level;low Latency Deep Learning Based Parking Occupancy Detection By Exploiting Structural Similarity;sequential Constructive Algorithm incorporate with Fuzzy Logic for Solving Real World Course Timetabling Problem;an Investigation of Generality in two-layer Multi-agent Framework towards different domains;µ2: A Lightweight Block Cipher;tweet sentiment analysis using deep learning with nearby locations as features;quantifying attractiveness of incomplete-information multi-player game: case study using DouDiZhu;semantic Segmentation of Herbarium Specimens Using Deep Learning Techniques.

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Selection and Deletion Model under Discovering Valid and Invalid Test Case 6

Selection and Deletion Model under Discovering Valid and Inv...

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6th international conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2022

作者： Lawanna, thacha Wongwuttiwat, Jittima Ounchaoen, Rujira International College of Digital Innovation Chiang Mai University Chiang Mai Thailand Martin de Tours School of Management and Economics Digital Business Management Assumption University Samut Prakarn Thailand

ISBN: (纸本)9798350399615

Gathering the accurate test cases of any adopted program and eliminating unrelated ones follow the notion of retaining software to build a typical of a test suite by concerning with the judgment of test case formation, fixing bugs and methods of selection, deletion or minimization, while the lowest size and the ability of solving faults have been required for this work. therefore, several test case methods have been proposed to response the matters of keeping good cases or removing the bad groups that may drop the entire performance of a software. the proposed model comprises four main algorithms, which are clarifying, selecting, deleting, and collecting test cases. It gives lower percentage of size reduction than the regression test selection by 2%-4 % while proportion of solving fault is higher than random and regression approaches about 20%-70% and 0%-25% respectively. © 2022 IEEE.

关键词： clarifying colleting deleting selecting test case

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Optimizing UAV Jammer for Covert Communication of LEO Satellite System 10

Optimizing UAV Jammer for Covert Communication of LEO Satell...

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10th international conference on Information systems and computing Technology, ISCTech 2022

作者： Li, Xiaomeng Duan, Guodong Yan, Shaohu Zhao, Zhongling Shi, Jia Li, Zan School of Communication Engineering Xidian University Xi'an China The 29th Research Institute of CETC Chengdu China

ISBN: (纸本)9798350332933

With the development of satellite communications' their security issues have received widespread attention. We investigate the covert performance of the low earth orbit (LEO) satellite communication system in this paper, where the unmanned aerial vehicle (UAV) is regarded as a jammer. For practical consideration, we assume that the UAV can only obtain part of the environmental information, and the detection threshold of the eavesdropper on the ground is not known. It derives the covert rate and the probability of detection errors of the eavesdropper. Furthermore, the genetic algorithm (GA) is proposed to achieve the best covert rate by optimizing the power and trajectory scheme of UAV. the simulation results show that the UAV jamming solution generated by GA can effectively improve the covert performance of the LEO satellite communication system. © 2022 IEEE.

关键词： Genetic algorithms

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A Comprehensive Framework for Clustering in 6G Cell-Free Networks 17

A Comprehensive Framework for Clustering in 6G Cell-Free Net...

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17th IEEE international conference on Advanced Networks and Telecommunications systems, ANTS 2023

作者： De Faria, Afonso José Marcos Câmara Brito, José Spadoti, Danilo Henrique Institute of Systems Engineering and Information Technology - Iesti Federal University of Itajubá - Unifei MG Itajubá Brazil National Institute of Telecommunications - Inatel MG Santa Rita do Sapucaí Brazil

ISBN: (纸本)9798350307672

Efficient resource allocation is paramount to ensuring optimal quality of services and effective utilization of radio resources. this paper presents a novel framework designed for communication mode selection, admission control, and user association within the realm of next-generation radio access networks (NG RAN), tailored to accommodate the unique demands of 6G enabling technologies. these encompass diverse aspects such as heterogeneous three-dimensional networks, cutting-edge cell-free architectures, and the dynamic integration of artificial intelligence. the framework further embraces the synergy of cloud-RAN, edge computing, cloud computing, massive multiple-input-multiple-output (mMIMO), and advanced beamforming techniques. What sets this proposed framework apart is its integrated and holistic approach. In an asynchronous and parallel manner, it addresses the intricate algorithms governing access to radio resources in 6G cell-free RANs. A primary focus lies on enhancing Quality-of-Service (QoS) metrics while optimizing the utilization of scarce radio and natural resources. this comprehensive strategy also leads to a reduction in the complexity of handoff management and optimizes the overall lifetime of sessions. By seamlessly unifying these components, the framework represents a significant advancement towards a more efficient and responsive network infrastructure aligned with the evolving landscape of advanced wireless communication systems. © 2023 IEEE.

关键词： Radio access networks

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19th international Symposium on Visual computing, ISVC 2024

19th International Symposium on Visual Computing, ISVC 2024

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19th international Symposium on Visual computing, ISVC 2024

the proceedings contain 73 papers. the special focus in this conference is on Visual computing. the topics include: AFIDAF: Alternating Fourier and60;Image Domain Adaptive Filters as60;an60;Efficient Alternat...

ISBN: (纸本)9783031773914

the proceedings contain 73 papers. the special focus in this conference is on Visual computing. the topics include: AFIDAF: Alternating Fourier and Image Domain Adaptive Filters as an Efficient Alternative to Attention in ViTs;multi-Actor-Critic Deep Reinforcement Learning with Hindsight Experience Replay;improving Zero-Shot Template-Based 6D Pose Estimation with Geometric Features;contrastive Loss Based on Contextual Similarity for Image Classification;estimation of Global Illumination Using Cycle-Consistent Adversarial Networks;anisotropic Point Synthesis by Example;3D Fluid Shape Control by Direct Manipulation;an Epithelium-Inspired Deformation Modeling Framework for 4D Sheets;hybrid Voxel Formats for Efficient Ray Tracing;PIEPredict++: An Improved Pedestrian Intention Estimation Model Incorporating Comprehensive Environment Information;infant Video Interaction Recognition Using Monocular Depth Estimation;real-Time Predictor in Two-Players Fighting Game via Vision Transformer;explainable Action-Recognition Based Approach for Unsupervised Video Anomaly Detection;LORTSAR: Low-Rank Transformer for Skeleton-Based Action Recognition;Pedestrian Tracking Using Ankle-Level 2D-LiDAR Based on ByteTrack;Human Pose Estimation-Based ID Assignment Method in the Wild: A Real-Time Approach;RGB-T-UV Multi-modal Object Tracking Based on Transformer Network;evaluating the Impact of Dehazing algorithms on Visual Object Tracking Performance;MEM: Mask Enhancement Model for Video Object Segmentation;analysis Automation for High Explosive Breakout Symmetry;CLAP: Concave Linear APproximation for Quadratic Graph Matching;generalizing Neural Radiance Fields for Robust 6D Pose Estimation of Unseen Appearances;effectiveness of Residual Noise Based Methods for Single Image Based Morphing Attack Detection: A Comparative Study;black Box Adversarial Face Transformation Network;bi-Feature Selection Deep Learning-Based Techniques for Speech Emotion Recognition;activeConfusion: Efficient Cold-Start

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Exploiting Spatial and Temporal Features for Deep Learning Based Human Activity Recognition 29

Exploiting Spatial and Temporal Features for Deep Learning B...

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29th IEEE international conference on Parallel and Distributed systems, ICPADS 2023

作者： Cao, Wenying Shi, Gaotao Zhang, Tieguan Tianjin University International Engineering Institute Tianjin China Tianjin University College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350330717

Human activity recognition using inertial sensors has gained significant popularity and widespread adoption in various fields, while deep learning has emerged as the dominant approach, playing a pivotal role in enhancing performance. Nevertheless, existing algorithms often neglect the heterogeneity of sensors and fail to effectively extract contextual features from long-term time series data, resulting in low accuracy in activity recognition, especially for complex activities. this paper proposes a novel method called FUsion Transformer hUman activity REcognition(FUTURE). FUTURE exploits the feature fusion mechanism and designs a multi-scaled DenseNet to extract the spatial features so that the relationship between heterogeneous sensors can be captured. Furthermore, a customized multi-head attention model with less computational complexity is employed in FUTURE to capture global dependencies within the sensor data. Experimental results on multiple datasets validate that FUTURE achieves an average recognition accuracy of 95% for complex activities and the evaluation against the state-of-the-art demonstrates the superior performance of the FUTURE model in accurately classifying complex activities. © 2023 IEEE.

关键词： Attention mechanism Feature fusion Human Activity Recognition Inertial sensor

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Element Selection with Wide Class of Optimization Criteria Using Non-Convex Sparse Optimization 48

Element Selection with Wide Class of Optimization Criteria U...

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48th IEEE international conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kawamura, Taiga Ueno, Natsuki Ono, Nobutaka Graduate School of Systems Design Tokyo Metropolitan University 6-6 Asahigaoka Tokyo Hino-shi191-0065 Japan

ISBN: (纸本)9781728163277

Element selection techniques for high-dimensional features have various applications in machine learning. In general, the problem of element selection is typically solved by greedy methods or convex relaxation methods. However, these algorithms are applicable to only a specific class of optimization criteria such as the minimization of the squared error loss between the original and restored data. To overcome this limitation, we propose an element selection algorithm based on non-convex sparse optimization that can be used with a wider class of optimization criteria than conventional algorithms. In the proposed method, an algorithm based on the alternating direction method of multipliers (ADMM) is de-rived by reformulating the element selection problem as a matrix optimization on a non-convex set. A numerical experiment demonstrated the effectiveness of the proposed method for element selection with a non-squared error loss compared with the conventional greedy method. © 2023 IEEE.

关键词： ADMM Dimensionality reduction element selection proximal operator sparse optimization

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19th international Symposium on Visual computing, ISVC 2024

19th International Symposium on Visual Computing, ISVC 2024

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19th international Symposium on Visual computing, ISVC 2024

ISBN: (纸本)9783031773884

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Development of a speech dataset for emotion recognition and analysis 10

Development of a speech dataset for emotion recognition and ...

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10th IEEE Smart World Congress, SWC 2024

作者： Wu, Yi Liu, Haoming Hainan University School of International Communication and Arts Haikou China The Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798331520861

In recent years, with the development of technologies such as signal processing, machine learning, and deep learning, speech emotion recognition has found wide applications in mental health, educational training, and social media analysis. However, current methods for speech emotion recognition mostly rely on data-driven models, and the lack of emotional speech datasets significantly hinders their progress. To address this issue, this paper proposes an emotional speech dataset consisting of 6,447 audio clips for emotion recognition and analysis. the dataset includes five emotional categories: happy, sad, surprised, fearful, and angry. In addition, four speech signal processing algorithms including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Mel-Frequency Cepstral Coefficients (MFCC), and Continuous Wavelet Transform (CWT) are utilized for feature extraction, resulting in the creation of four emotional feature image datasets. Furthermore, eleven state-of-the-art (SOTA) classification models are selected to establish a benchmark. Experimental results show that most classification models achieve accurate recognition performance on the proposed emotional speech dataset. It is believed that the proposed dataset and benchmark can promote the research and development of emotion recognition and analysis. © 2024 IEEE.

关键词： Wavelet transforms

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