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arXiv 2023年

作者： Mouhammad, Nina Daxenberger, Johannes Schiller, Benjamin Habernal, Ivan DIPF Leibniz Institute for Research and Information in Education Germany Summetix GmbH Germany Trustworthy Human Language Technologies Department of Computer Science Technical University of Darmstadt Germany

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included. Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given. © 2023, CC BY.

关键词： Crowdsourcing

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Outlook on foreign economic activities in the forestry sector of Russia and China

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International Journal of Environmental Studies 2023年第4期80卷 1041-1054页

作者： Grigorev, Vladimir Morkovina, Svetlana Tikhonov, Evgeniy Mikhaylenko, Ekaterina Khakhina, Anna Storodubtseva, Tamara Kruchinin, Igor Grigorev, Igor Faculty of Asian and African Studies Saint Petersburg State University Saint-Petersburg Russia Department of Management and Economics of Entrepreneurship G. F. Morozov Voronezh State University of Forestry and Technologies Voronezh Russia Department of Transport and Technological Machinery and Equipment Petrozavodsk State University Petrozavodsk Russia Department of Technologies and Transport-Technological Machines Ukhta State Technical University Ukhta Russia Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University Saint-Petersburg Russia Department of Industrial Transport Construction and Geodesy G. F. Morozov Voronezh State University of Forestry and Technologies Voronezh Russia Department of Transport and Road Construction University the Ural State Forestry University Ekaterinburg Russia Department of Technology and Equipment of Forest Complex Arctic State Agrotechnological University Yakutsk Russia

This paper examines the potential of the forest sector in China and Russia over the past decade to identify the main trends and prospects for developing international timber business between the countries. The research showed that further development of foreign economic cooperation between the countries requires reindustrialization of the Russian forest industry sector in processing and manufacturing wood products. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Industrial research

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Advancements in Chatbot Technology for Enhanced Customer Support in Online Retail

Advancements in Chatbot Technology for Enhanced Customer Sup...

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International Conference on science Technology Engineering and management (ICONSTEM)

作者： N Keerthi Kumar K. Maheswari A Abinaya J Ramya Pavan Kumar Ande Christu Paul Ramaian Department of Mechanical Engineering BMS Institute of Technology and Management Bengaluru Karnataka Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department ofComputer Science and Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Automobile Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In order to improve customer service and boost sales, this paper describes the creation of an ecommerce sales chatbot. In order to grasp natural language, the system employs machine learning. It is based on a chatbot that is modular. Framework. There are several parts to the system. A web-based framework for natural language teaching comes first. Chat bots are employed in a variety of industries, including e-commerce, healthcare, and education. The objective of this venture is to make Hebron, an online chatbot for the Contract College People group Shopping center. Python and Respond are both utilized in the chatbot's creation. Conventional customer service chatbots frequently copy human discussion, yet they have serious protection and data adaptability issues. Here, we present SuperAgent, a customer support chatbot that utilizes broad and promptly open internet business data.

关键词： Customer services Natural languages education Medical services Machine learning Chatbots Mathematics

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Software for Implementing the Directed Cone Optimization Method

Software for Implementing the Directed Cone Optimization Met...

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International Conference on Advanced computer Information technologies (ACIT)

作者： Bohdan Melnyk Petro Stakhiv Nataliya Melnyk Yuriy Franko Bohdan Seniv Yevgeniya Martsenyuk Department of Information Systems of Management Ivan Franko National University of Lviv Lviv Ukraine Department of Theoretical and General Electrical Engineering Lviv Polytechnic National University Lviv Ukraine Department of Computer Technologies Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Department of Financial Technologies Banking Business Ternopil Ukraine Department of Computer Science West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

The article is devoted to the development of software for the implementation of the stochastic optimization method. The classical method of the directed cone and its modification are considered. Its shortcomings in the process of searching for the global minimum are pointed out. To improve the search, it is proposed to use algorithms with adaptation of optimization parameters. The architecture of software implementation of optimization algorithms of the directed cone method is proposed. Appropriate software was developed. It was tested on the example of optimization of the structured cable system of the telecommunications network. For this, a corresponding optimization problem is formulated. The results of its solution and application of optimization algorithms are analyzed.

关键词： Software architecture Computational modeling Software algorithms Optimization methods Software systems Linear programming Software Telecommunications Communication cables Optimization

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A Study About the Impact of Rewards on Student's Engagement with the Flipped Classroom Methodology

A Study About the Impact of Rewards on Student's Engagement ...

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IEEE education Engineering (EDUCON)

作者： Jaime Urquiza-Fuentes Department of Computer Science & Statistics ACTIVATE Teaching Innovation Group & Laboratory of Information Technologies in Education - LITE Rey Juan Carlos University Móstoles Madrid Spain

This work describes an incremental design of a course that implements the flipped classroom methodology. The last phase of this implementation was to include rewards based on self-evaluation tests that would impact the students' grades. This should foster students' engagement with the learning methodology. In this work, a deep analysis of the impact of the reward is performed. The analysis of the use of the out-class materials shows that the students' pace does not correspond to the flipped classroom methodology. The students' behaviour is more focused on achieving rewards rather than preparing in-class activities with out-class materials. It is hypothesized that, the main reasons for these behaviours could be that, on the one hand, since the reward is considered by students as another item of the course grade, they work to get the reward. On the other hand, students do not want to change their study methodology because getting involved in the flipped methodology requires from them a significant effort.

关键词： Resistance Electronic learning education Online services Engineering education Faces Videos

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Predicting Employee Turnover Using Decision Tree Models in RapidMiner for a Sustainable Human Resource management Approach 4

Predicting Employee Turnover Using Decision Tree Models in R...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Goyal, Nandita Narendra Kiran, P.B. Singhal, Nidhi Tyagi, Vibhuti Singh, Jagendra Manikandan, N. Ajay Kumar Garg Engineering College Department of Technology Technology Ghaziabad India Mittal School of Business Lovely Professional University Phagwara India Sanskar College of Engineering and Technology Department of Management Ghaziabad India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India Kalasalingam Business School Kalasalingam Academy of Research and Education Department of Business Administration Virudhunagar India

ISBN: (纸本)9798350306118

This research utilized Decision Tree Models in RapidMiner to delve into the multifaceted nature of employee turnover within the IT sector. Key insights were drawn from an analysis of diverse IT companies, revealing a series of influential factors. Tech Titan Corp. showcased the importance of work-life balance, even with competitive compensation. InnovateHub highlighted the dynamic career aspirations of younger employees. TechRemote Solutions emphasized the significance of geographical proximity, while AscendTech Enterprises pointed to the need for clear growth trajectories. NexaSoft Systems underlined that compensation, while critical, should be complemented with comprehensive benefits and recognition. In essence, this study affirms that addressing employee turnover necessitates a holistic approach, understanding the interplay of professional, personal, and organizational facets. The findings offer invaluable guidance for crafting effective, empathetic, and sustainable retention strategies in the evolving IT landscape. © 2023 IEEE.

关键词： Predictive analytics

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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Deep Convolutional Neural Network for Two-Dimensional Medical Image Segmentation Using a Learned Curriculum

Deep Convolutional Neural Network for Two-Dimensional Medica...

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Communications and Information technologies (GCCIT), Global Conference on

作者： Joyce Jacob J Satvik Vats Sunitha B K D.S. Wankhede N. V. Balaji Sorabh Sharma Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India School of Management - UG Management JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Radiologists greatly benefit from medical image segmentation algorithms when determining the severity of diseases, conducting clinical evaluations, and choosing the best course of therapy. Deep learning is quickly becoming the go-to method for medical picture segmentation because to both the proliferation of big data and improvements in medical imaging methods. The medical segmentation structure encounters various obstacles, such as a lack of labelled data, complicated picture representation, low contrast variation, high structural similarity among infection as well as background, and irregularities related to texture and morphology within homogeneous infected areas. For the purpose of training convolutional neural networks to segment deformable paired 3D medical images, we investigate several curriculum learning approaches. We believe our approach to training medical image segmentation algorithms via curriculum learning, which involves beginning with a simple setup and progressively increasing its complexity, is the first of its kind to aim at improving performance. First, we take a look at two methods for learning curricula that are already out there: curriculum dropout along with curriculum through smoothing. But we provide a fresh and easy way to do curriculum: start with intentionally blurry photographs and work your way up to crisper ones throughout training. Curriculum learning can outperform traditional training methods, according to our studies using a state-of-the-art deep learning model. Among the evaluated curriculum learning methodologies, we also demonstrate that curriculum through input blur offers the highest accuracy against time trade-off.

关键词： Training Deep learning Image segmentation Accuracy Three-dimensional displays Smoothing methods Morphology Object detection Convolutional neural networks Biomedical imaging

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Leveraging Scalable Classifier Mining for Improved Heart Disease Diagnosis

Leveraging Scalable Classifier Mining for Improved Heart Dis...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Swetha, K.R. Ushasree, Linginedi Reddy, K. Satish Nisha, R. Shariff Parameshachari, B.D. Bgs Institute of Technology Adichunchanagiri University Dept of Cse Karnataka India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Hyderabad Institute of Technology and Management Department of Electronics and Communication Engineering Hyderabad India J.J College of Engineering and Technology Tamilnadu India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The scalable classifier mining technique proposed in this article aims to improve the accuracy and scalability of heart disease prediction models. This method employs a wide range of methodologies, including data preparation, feature selection, ensemble learning, and distributed computing, among others, to handle complicated patterns in large datasets quickly. By merging decision trees, random forests, and XGBoost classifiers with the Apache Spark distributed computing platform, the proposed method outperforms both traditional machine learning techniques and cutting-edge deep learning algorithms. The proposed method achieved an accuracy of 93.5%, which is higher than any of the conventional machine learning methods and comparable to or better than the most cutting-edge deep learning methods. By encompassing all of the essential processes, such as preprocessing, feature selection, ensemble learning, model merging, and distributed computing, this technique assures that the model is accurate, efficient, and easy to comprehend. As a result, it is an especially well-suited approach for clinical applications. The proposed technique has effectively shown its potential to increase the accuracy and scalability of heart disease prediction models, resulting in high accuracy, scalability, and interpretability. © 2023 IEEE.

关键词： Learning systems

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A Deep Learning-based Innovative Approach for Enhancing the Precision of Tool Wear Prediction 8

A Deep Learning-based Innovative Approach for Enhancing the ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Vinod, D. Syed, Karimulla Sampath Kumar, S. Babu, B. Sankara Pandey, Shatrudhan Loganathan, Raja Velur Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies SRM Nagar Kattankulathur Chennai India Koneru Lakshmaiah Education Foundation 522501 India Sri Eshwar College of Engineering Computer Science and Engineering Coimbatore India Department of Cse Telangana Bachupally India Birla Institute of Technology Department of Production and Industrial Engineering Mesra Ranchi835215 India Loyola Institute of Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350396638

Throughout the cutting operation, among the most crucial steps is that has a direct impact on machining accuracy and product quality is tool wear. If you can accurately forecast how quickly tools will wear out, you can make the necessary adjustments early on, minimizing downtime and maximizing product quality. The evaluation of cutting tool wears during the production stage is crucial. The main objective of this study is to track tool degradation over time to guarantee regular tool changes and prevent workpiece waste and potential machine damage from excessive tool wear or unexpected tool breakage. For this reason, it is necessary to develop a system that is both intelligent and capable of providing accurate solutions to these issues. The criteria of intelligent production are great, though, and traditional methods simply cannot match them. Because of this, a deep learning-based innovative approach is presented to advance the precision of tool wear prediction. Parallel convolutional neural networks were constructed and used to achieve multi-scale feature fusion. To improve the model's efficiency, the channel attention mechanism was integrated with the residual connection to take into account the relative importance of each feature map. To prove that the created approach is superior, several experiments were carried out to anticipate tool wear, and the findings are more reliable and accurate than those obtained using existing methods. To guarantee the excellence of the engine cylinder and the steadiness of the machining procedure, a tool wear monitoring system was created. Using the PHM 2010 milling cutter wear dataset, tests were performed to validate the model effect. Based on the experimental data, this model has an average RMSE of 2.67 and an average MAE of 2.5. The trial outcomes proved the effectiveness of the suggested approach in evaluating the tool's wear. © 2023 IEEE.

关键词： Cutting tools

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