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Enhancement of HRM Business Processes Within Organizations in the Context of Digitalization 4th

Enhancement of HRM Business Processes Within Organizations i...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Dvulit, Zoriana Maznyk, Liana Seliuchenko, Nadiia Doroshkevych, Kateryna Chyrkova, Yuliia Zalizna, Lyubov Lviv Polytech Natl Univ Dept Foreign Trade & Customs Lvov Ukraine Natl Univ Food Technol Labor Econ & Management Kyiv Ukraine Lviv Polytech Natl Univ Dept Business Econ & Investment Lvov Ukraine Lviv Polytech Natl Univ Dept Management & Int Business Lvov Ukraine

ISBN: (纸本)9783031832093;9783031832109

Human resources management (HRM) is one of the most significant business processes since the efficiency of the organization as a whole depends on its effectiveness. Digitalization causes changes in all areas of life, organizations' business processes, and labor market changes, which determines the study's relevance. Our study aims to substantiate approaches to improving the HRM business processes in organizations to increase the efficiency of personnel and business as a whole. Staff turnover is singled out as one of the most important factors affecting the level of labor productivity. It is justified that this phenomenon is due to the inefficiency of business processes of personnel enhancement, which can be improved by introducing a qualitative adaptation process. It has been proposed to single out two stages in adapting the business process: the initial stage (onboarding);the stage of corporate socialization. The notation "Adaptation of a new employee" has been developed, which allows you to identify problem areas, simplifies the search for new solutions to eliminate them, and ensures the optimization of the corresponding HRM business process in the organization. The novelty of the investigation is to substantiate approaches to improving the business process of adapting new employees in the context of digitalization, which will enable organizations to receive additional cost savings in recruiting, reduce turnover and increase staff enhancement, and therefore will contribute to the growth of labor productivity.

关键词： HRM Business Processes in the Organization Digitalization Labor Productivity Turnover Enhancement Optimization Adaptation Notation

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A Two-Stage Fine-Tuning Procedure to Improve the Performance of Language Models in Sensitive Health information Recognition and Normalization Tasks

A Two-Stage Fine-Tuning Procedure to Improve the Performance...

引用

2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Chiu, Pin-Sen Hou, Bo-Wei Chen, Yen-Ting Huang, Shi-Hao Natl Kaohsiung Univ Sci & Technol Appl Intelligence Lab Dept Elect Engn Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Intelligent Control & Comp Vis Lab Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Intelligent Automat Lab Kaohsiung 807618 Taiwan Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Broadband Mobile Wireless Lab Kaohsiung 807618 Taiwan

ISBN: (纸本)9789819779659;9789819779666

In recent years, artificial intelligence (AI) technology has experienced flourishing development, and large-scale language models (LMs) are increasingly recognized as a pivotal direction for the future of AI in the field of intelligent healthcare. In the realm of clinical medical applications, there is an increasing focus on eliminating patient privacy information from textual electronic health records within medical institutions. To address this, we have developed a two-stage fine-tuning procedure which employs the Pythia model for training purposes, enabling the model to effectively extract and normalize patient-related privacy information. Furthermore, ChatGPT is utilized for data augmentation to generate additional training data, enhancing the overall robustness of the system.

关键词： Natural Language Processing Sensitive Health information Pythia ChatGPT Language Model

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Joint Spectrum and Antenna Selection Diversity for V2V Links with Ground Reflections

Joint Spectrum and Antenna Selection Diversity for V2V Links...

引用

4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Robles, Ramiro Gutierrez Gaitan, Miguel Javanmardi, Gowhar Kurunathan, Harrison CISTER Res Ctr Porto Portugal Pontificia Univ Catolica Chile Santiago Chile Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783031834318;9783031834325

This paper addresses the study of a fading-rejection algorithm based on joint spectrum and antenna selection in a vehicle-to-vehicle (V2V) multiple antenna system. The central objective of this selective scheme is to provide resilience against the destructive effects of the superposition of line-of-sight (LOS) and ground-reflected signals. The paper also provides an extension to channels that combine such deterministic superposition of multiple paths and reflections with an uncorrelated double scattering component, which shows how the algorithm is also beneficial under more general channel modelling assumptions. A multiple-ray performance model is used to describe the deterministic signal interactions between multiple antennas across contiguous vehicles. The antenna selection component is shown to reject deterministic fading, particularly at short values of the inter-vehicular distance. By contrast, when the spectrum bands are correctly chosen, the spectrum selection component can exhibit gains for a wider range of inter-vehicular distances than its antenna selection counterpart. This indicates that both components of the proposed solution are, in some cases, complementary, and therefore, they should be considered together in V2V multiple antenna design. Derivation of the statistics of the selective scheme considering an additional double scattering stochastic channel component is here proposed. Simulation results from all expressions show important gains for a given range of inter-vehicular distances.

关键词： MIMO V2V Line of sight Two-Ray model

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Utilizing Large Language Models for Privacy Protection and Advancing Medical Digitization

Utilizing Large Language Models for Privacy Protection and A...

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2024 International Workshop on Deidentification of Electronic Medical Record Notes

作者： Ru, Zhu-Jian Panchal, Omkar Chen, Ching-Tai Jonnagaddala, Jitendra Dai, Hong-Jie Hsueh, Sheng-Chun Natl Kaohsiung Univ Sci & Technol Dept Elect Engn High Performance Distributed Syst Lab 415 Jiangong Rd Kaohsiung Taiwan Asia Univ Dept Bioinformat & Med Engn Taichung Taiwan CGD Hlth Pvt Ltd Hyderabad Telangana India Univ New South Wales Sydney NSW Australia NMC Royal Hosp Abu Dhabi U Arab Emirates Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Intelligent Syst Lab 415 Jiangong Rd Kaohsiung Taiwan

ISBN: (纸本)9789819779659;9789819779666

This research explores the utilization of artificial intelligence (AI) language generation models for the de-identification of medical case narratives, effectively anonymizing patient identifiers to prevent unauthorized disclosure of sensitive health information. This ensures that patient data remains unrecognizable even when accessed, offering two significant advantages: compliance with legal standards forbidding the indiscriminate release of medical records, and bolstered trust in healthcare providers which encourages patients to share necessary details for therapeutic and investigative purposes. Historically, de-identification processes necessitated the manual examination of records by specialists to identify and sanitize personal identifiers, it is a time-consuming task with potential for error. The digitalization of medical records now offers avenues for automated de-identification, simultaneously enhancing data accuracy and facilitating the ethical utilization of extensive datasets in healthcare research, public health strategy, and policymaking. The emergence of sophisticated generative AI technologies has augmented the capacity for comprehensive de-identification. Platforms such as ChatGPT and Bing have evolved to address complex human inquiries. Yet, employing these tools without proper safeguards potentially compromises de-identification objectives by risking data exposure. To advance the integrity of privacy measures and maintain data governance, this study fine-tunes a pre-established vast language AI model, Pythia, for localized training with state-of-the-art technology with rigorous privacy assurance.

关键词： Large language models Sensitive health information Fine tuning QLora

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Deep Learning-Based Instance Segmentation of Neural Progenitor Cell Nuclei in Fluorescence Microscopy Images 12th

Deep Learning-Based Instance Segmentation of Neural Progenit...

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Perez, Gabriel Cecilia Russo, Claudia Laura Palumbo, Maria David Moroni, Alejandro Consejo Nacl Invest Cient & Tecn Ctr Invest & Transferencia Noroeste Provincia Bue UNNOBA UNsADA Buenos Aires DF Argentina Univ Nacl Noroeste Provincia Buenos Aires UNNOBA Inst Invest Transferencia Tecnol ITT Buenos Aires DF Argentina Ctr Invest Bas Apl CIBA Buenos Aires DF Argentina

ISBN: (纸本)9783031708060;9783031708077

In this work, a Deep Learning-based machine vision model was developed for the detection, segmentation and counting of Neural Progenitor Cell nuclei from fluorescence microscopy images. The cells were obtained from adult mice and cultivated in vitro, with cellular nuclei labeled using DAPI dye. Convolutional neural networks for instance segmentation, specifically the Mask R-CNN model with ResNet-50 and ResNet-101 backbones, were trained to recognize the nuclei, and their results were evaluated. Nuclei labeling was implemented semiautomatically, applying a Superpixel technique and then refining the segmentations from a manual process, also using a pre-trained model, which allowed to assemble a dataset of 66 images with 6392 labels in total. The results obtained with the Resnet-50 backbone show that there is an effectiveness of 98.6% for between the specialist count and model-predicted count, in addition to having an mAP50 of 98.0%. This approach has the potential to significantly reduce the time and effort required to analyze large image sets, which is especially useful in studies that require repetitive and detailed cellular analysis.

关键词： Instance segmentation Deep Learning Fluorescence Microscopy Cell Nuclei

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Detection of Vitiligo Using Ensemble Learning 4th

Detection of Vitiligo Using Ensemble Learning

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Jan, Mutasim Hafiz, Abdul Mueed Univ Kashmir Dept E&C Engn Inst Technol Srinagar 190024 JK India

ISBN: (纸本)9783031837890;9783031837906

Vitiligo, a chronic skin disorder characterized by depigmentation, presents a significant challenge in accurate diagnosis and monitoring due to its diverse manifestations and subjective evaluation. To improve the identification and categorization of Vitiligo lesions from dermoscopic images, this study suggests a simple and robust method that combines deep learning models, such as VGG16 and InceptionV3, with traditional classifiers, such as Random Forest, K-Nearest Neighbors (KNN), and Logistic Regression. The methodology involves pre-processing techniques for image enhancement and feature extraction to optimize model performance. This analysis not only validates the efficacy of deep learning architectures in handling complex image data but also provides insights into the potential advantages of classical classifiers in scenarios with limited training data or computational resources. Moreover, the study explores the synergistic potential of ensemble learning techniques to further enhance classification accuracy beyond individual models. The study also looks into how feature extraction from deep learning models that have already been trained can improve the discriminatory ability of traditional classifiers. The classification accuracies of 93.63% with our VGG16 ensemble, and 97.07% with our InceptionV3 ensemble respectively, demonstrate the better performance of the proposed technique as compared to other state-of-the-art techniques. To the best of our knowledge, this is the first such work in this direction.

关键词： Ensemble Learning VGG16 Inception CNNs Vitiligo

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Emotion-Aware Interfaces: Empirical Methods for Adaptive User Interface 1

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Haddad, Syrine Daassi, Olfa Belghith, Safya Univ Tunis El Manar Natl Engn Sch Tunis Tunis Tunisia Univ Carthage Natl Engn Sch Carthage Carthage Tunisia Lab Robot Informat & Complex Syst LR RISC ENIT Tunis Tunisia

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

Designing User Interfaces (UIs) that can interpret and respond to user emotions has evolved from a passing trend to a core tenet of design philosophy. As technology advances, it becomes increasingly important to predict users' emotional states during interface interactions. This paper proposes a cutting-edge deep learning model designed to predict a wide range of emotions in users, such as Happiness, Anger, Calmness, and Surprise. The research started with a thorough survey, gathering feedback from 72 users regarding UI designs tailored to various emotions. Using these insights, interfaces were developed to address different emotional states. Following the design phase, a user interaction study was conducted involving 29 participants interacting with these interfaces while their facial expressions were recorded. Subsequently, a predictive model for user emotions was developed, achieving an accuracy of 83%, showcasing its robustness and reliability in discerning and predicting user emotions based on interactions with UIs. This model was seamlessly integrated into a proposed real-time adaptive UI system. Notably, existing literature has traditionally relied on pre-existing software for emotion detection in adaptive systems. Our contribution is to demonstrate an Emotional Interface Adaptation System that uses a trained model in real-world circumstances to provide a dynamic and responsive user experience that adapts in real-time to users' emotional cues.

关键词： Emotion-driven GUI design Facial expression analysis Emotion adaptive systems

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ESert: An Enhanced Span-Based Model for Measurable Quantitative information Extraction from Medical Texts 5th

ESert: An Enhanced Span-Based Model for Measurable Quantitat...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhang, Qixuan Chen, Jiale Wang, Haitao Cao, Xinyu Jie, Wei Hao, Tianyong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China China Natl Inst Standardizat Beijing Peoples R China

ISBN: (纸本)9789819770069;9789819770076

Measurable quantitative information is one of typical quantitative information in unstructured texts. It consists of entities related to numerics, units and their relationships. How to accurately and efficiently extract measurable quantitative information from unstructured texts remains a challenge. This paper aims to propose an Enhanced span-based joint model named ESert, which uses an n-gram encoder to identify word boundaries with a positive and negative sampling mechanism to extract measurable quantitative information. The experiments evaluate the ESert based on a clinical quantification information dataset containing 1359 Chinese electronic medical records. The results show that our model achieves F1 scores of 97.97% and 97.28% in measurable quantitative information recognition and association, respectively, indicating the effectiveness of the proposed model.

关键词： Measurable quantitative information n-gram encoder positive and negative sampling mechanism

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Planning with Non-deterministic Actions in Jason 4th

Planning with Non-deterministic Actions in Jason

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4th Workshops on Agents and Robots for Reliable Engineered Autonomy

作者： Blondin, Josh Esfandiari, Babak Carleton Univ Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9783031731792;9783031731808

This paper proposes a planner which can take non-deterministic actions and generate AgentSpeak plans, based on an existing linear planner. It uses a pipeline transforming the initial specification of the non-deterministic planning problem into an AND-OR tree, which is then used to solve for non-deterministic post conditions and generate a contingency plan in AgentSpeak.

关键词： BDI AgentSpeak Agents Planning Non-Determinism AND-OR Tree FOND

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The Evolutional Treasury Hybrid Optimization System (ETHOS) 8th

The Evolutional Treasury Hybrid Optimization System (ETHOS)

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Loukeris, N. Chalamandaris, G. Eleftheriadis, I. Gikas, G. Univ West Attica Athens 12244 Greece Athens Univ Econ & Business Athens Greece Univ Macedonia Thessaloniki 54006 Greece

ISBN: (纸本)9783031856273;9783031856280

We develop an innovative method which takes into consideration advanced higher moments under a new perspective of the Portfolio Selection problem, supported by effective Computational Intelligence models. The Evolutional Treasury Hybrid Optimization System (ETHOS) mines hidden patterns from vast accounting data and financial statements restricting misguiding effects of noise or various forms of fraud, providing a new method portfolio selection optimally.

关键词： Recurrent Neural Networks Time-Lag Recurrent Neural Networks Jordan-Elman Neural Networks MLP Self Organized Feature Maps Hybrids Genetic Algorithms Isoelastic Utility function Higher moments Portfolio Optimization

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