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共 945 条记录，以下是151-160 订阅

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Effective dimension in bandit problems under censorship 22

Effective dimension in bandit problems under censorship

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Proceedings of the 36th International Conference on Neural information Processing systems

作者： Gauthier Guinet Saurabh Amin Patrick Jaillet AWS AI Labs MIT and Dept. of Civil and Environmental Engineering & Laboratory for Information and Decision Systems MIT and Dept. of Electrical Engineering and Computer Science & Laboratory for Information and Decision Systems

ISBN: (纸本)9781713871088

In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem - a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision- making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost; followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents' willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information).

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

引用

International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Resource Allocation in Smart City Applications

Extended Adaptive Data-Rate (X-ADR) Technique for Optimal Re...

引用

IEEE International Symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Nikumani Choudhury Manik Gupta Moustafa M. Nasralla Satoshi Fujita Dept. of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Hyderabad India Dept. of Communications & Networks Engineering Prince Sultan University Riyadh Saudi Arabia Graduate School of Advanced Science and Engineering Hiroshima University Japan

With the rapid growth of urbanization, the need for sustainable, energy-efficient, and smart solutions for home, industry, governance, traffic, and in general, the need for improved quality of life and health has risen. As an enabling technology, the Internet of Things (IoT) must facilitate several advanced applications with varied QoS requirements for smart cities. In this regard, Long Range (LoRa) technologies can be an ideal communication protocol for resource-constrained IoT devices. The LoRa Wide Area Network (LoRaWAN) defines physical layer options and the medium access control (MAC) sub-layer protocols for facilitating low-power, low-rate communications among battery-operated wireless IoT devices. With the enormous number of IoT devices and growing QoS requirements, it is imperative to optimally allocate resources (bandwidth, spreading factor, and transmit power) to these constrained devices so that network lifetime can be improved. The Adaptive Data-Rate (ADR) technique is used by the Network Servers (NS) to adapt the transmission parameters of the devices optimally. This is based on several received parameters from the end devices as well as the network settings. In this work, we extend the ADR technique to the LoRa gateways to consider the class of LoRa devices and the frequency of transmissions further to extend the energy efficiency and lifespan of IoT devices. Additionally, there is a considerable delay in adapting to optimal settings for the end-devices resulting in excess power dissipation. Further, a novel mechanism is proposed to address the congestion issue in the network using the Spreading Factor parameter (SF).

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Learning Location Semantics and Dynamics for Traffic Origin-Destination Demand Prediction

Learning Location Semantics and Dynamics for Traffic Origin-...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Kuan-Hsuan Yung Josh Jia-Ching Ying Hui-Ting Lin Vincent S. Tseng Institute of Multimedia Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan Dept. Management Information Systems National Chung Hsing University Taichung Taiwan Dept. Computer Science and Information Engineering National Yang Ming Chiao Tung University Hsinchu City Taiwan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Traffic Origin-Destination (OD) Demand Prediction is pivotal for real-time ride-hailing services and government traffic management, aiming to anticipate traffic patterns and volumes between specific locations. Traditional grid-based traffic OD demand prediction methods, however, often fail to accurately capture the intricate and context-driven demand patterns inherent in modern urban transportation systems. In this paper, we propose an innovative location semantics and dynamics learning framework for capturing location semantics and dynamics to improving origin-destination traffic demand prediction. Departing from conventional grid-based methods, our approach incorporates a semantic location generation module that dynamically organizes semantic locations based on Points of Interest (POI). Our semantic location generation module is designed to form semantic locations according to POI types, spatial proximity, and demand patterns. This model captures hierarchical relationships and varying importance levels across POI types and domains. To learn the context and function of each area, we design a POI context extractor which can analyze contextual information of POIs within a specified radius. The POI Type Encoder utilizes advanced word embedding techniques to encode the POIs, enabling the model to comprehend the semantic significance of diverse locations and their influence on traffic patterns. Extensive experiments conducted on real-world New York City Taxi datasets demonstrate the superior performance of the proposed framework over state-of-the-art methods, as indicated by experiment results across all evaluation measurement. These findings affirm that our proposed framework could improve effectiveness by learning location semantics and dynamics.

关键词： Accuracy Semantics Urban areas Neural networks Prediction methods Traffic control Data processing

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions

Support Chatbot based on ChatGPT for Customer Relationship M...

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Computing and Informatics (ICCI), International Conference on

作者： Islam Samih Mohamed Mohamed Abdelsalam Ibrahim F. Moawad Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently.

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Enhancing Predictive Model Performance through Comprehensive Pre-processing and Hybrid Feature Selection: A Study using SVM 2

Enhancing Predictive Model Performance through Comprehensive...

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2nd International Conference on Self Sustainable Artificial Intelligence systems. ICSSAS 2024

作者： Thuraka, Bharadwaj Pasupuleti, Vikram Kodete, Chandra Shikhi Naidu, U. Ganesh Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin Northwest Missouri State University School of Computer Science and Information Systems MaryvilleMO64468 United States Eastern Illinois University School of Technology CharlestonIL61920 United States B V Raju Institute of Technology Department of Csbs Narsapur Telangana Medak India Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798350368413

This study aims to achieve the improved performance of heart disease prediction based on the increased level of preprocessing and rather effective feature selection approaches. Starting with data preparation in which the data is first cleaned up the work develops a class balance solution using Synthetic Minority Over-sampling Technique (SMOTE) while the Z-score function identifies outliers. As it will be described in CHAN section that applies Chi-square and ANOVA under a hybrid model, the useful characteristics for model training are identified and prioritized. Using a method derived from the medical records database, the study focuses on the heart health indices using Support Vector Machines (SVM) which is a modelling technique widely known for its effectiveness in handling increased numbers of variables and the relationship between the measurements. Measures such as accuracy, precision, recall and F1-score among others are used in the evaluation of the success of the approach. In particular, the results shed the light to Health Care applications where risk assessment and correct diagnosis is extremely critical;the findings also assert the important roles of strong preprocessing and selective feature selection strategies in enhancing the predictive model's performance. This work contributes to the development of approaches intended for stable and efficient modelling in medical decision support system. © 2024 IEEE.

关键词： Support vector machines

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Obmaaq: Ontology-Based Model for Automated Assessment of Short-Answer Questions

Obmaaq: Ontology-Based Model for Automated Assessment of Sho...

引用

Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Vidasha Ramnarain-Seetohul Vandana Bassoo Yasmine Rosunally Dept. of Information and Communication Technologies University Of Mauritius Reduit Mauritius Dept. of Electrical and Electronic Engineering University Of Mauritius Reduit Mauritius Dept. of Computer Science and Creative Technology University of the West of England London UK

Automated scoring of short answers is becoming increasingly important nowadays, as more classes are taking place online due to COVID-19 outbreaks. Over the past 50 years, different techniques have been applied to develop ways of assessing short answers automatically. The automated assessment of short answer questions has been used with varying kinds of questions in different domains. However, no interoperable tools have been developed to facilitate the automated assessment of short answers for common modules across educational institutions. In this paper, we present a novel system called OBMAAQ, which grades short answers for questions designed according to Bloom's revised taxonomy remember category. The proposed system uses publicly available domain ontologies to grade short answers in different subject areas. This method is unique as it does not use model answers since it relies solely on the domain ontologies by utilizing an ontology-based pruning technique to grade the students' responses. The purpose of the system is to allow teachers from different educational institutions to use a similar approach when assessing the short answers for common modules automatically. Two publicly available domain ontologies in the field of security and computer networks have been used to validate students' answers. Experimental results based on both datasets indicate some promising results even though it is less than the set thresholds. This paper discusses the lessons learned and future recommendations based on the OBMAAQ model.

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WAM systems. Comparative Analysis of information Support Quality

WAM Systems: Comparative Analysis of Information Support Qua...

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2020 IEEE International Conference on Problems of Infocommunications science and Technology, PIC S and T 2020

作者： Obod, Ivan Svyd, Iryna Maltsev, Oleksandr Zavolodko, Ganna Leonov, Sergiy Kharkiv National University of Radio Electronics Dept. of Microprocessor Technologies and Systems Kharkiv Ukraine National Technical University khPI Dept. of Information Systems Kharkiv Ukraine National Technical University khPI Dept. of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9781728191775

The paper considers ways to improve the quality of information support for airspace control systems.and air traffic control with the help of wide-area multi-position monitoring systems. A comparative analysis has shown that the accuracy of estimating air object coordinates by the wide area multilateration system under consideration is significantly higher than that provided by currently available wide area multilateration systems. It is shown that the introduction of active and passive methods for creating wide-area multi-position observation systems.significantly broadens the number of available air object coordinate estimation techniques and, as a result, enhances the quality of information support by providing its users with the opportunity of choosing an optimal estimation method for various primary measurements of received signal parameters. Active wide area multilateration systems.significantly expand the number of methods available for solving the problem of adequate information support to airspace control system users;use of secondary surveillance radar aircraft responders allows estimating air object coordinates by both single-position and multi-position method. © 2020 IEEE.

关键词： Air traffic control

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An Implementation of Answer Code Validation Program for Code Writing Problem in Java Programming Learning Assistant System

An Implementation of Answer Code Validation Program for Code...

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information and Education Technology (ICIET), International Conference on

作者： Khaing Hsu Wai Nobuo Funabiki Soe Thandar Aung Khin Thet Mon Htoo Htoo Sandi Kyaw Wen-Chung Kao Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

To assist Java programming learning of novice students, we have developed the web-based Java Programming Learning Assistant System (JPLAS). It provides various exercise problems to cultivate code reading and code writing skills to support studies at various levels. The code writing problem (CWP) asks a student to write a source code to pass the given test code where the correctness is verified by running them on JUnit. Previously, we implemented the answer platform for students to solve CWP assignments with the automatic runs. However, the teacher needs to manually run them one by one to verify a lot of source codes from students. In this paper, we implement the answer code validation program that automatically verifies all the source codes for one test code and reports the number of tests that each code passed with the CSV file. For evaluations, we applied the program to a total of 999 source codes from 12-43 students for a total of 45 test codes that were assigned in the Java programming course in Okayama university, including for final examinations. The application results confirm the validity and effectiveness of the proposal.

关键词： Java Codes Source coding Education Writing Proposals Programming profession

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An Image Analysis-Based Automated Method using Deep Learning for Grain Counting

An Image Analysis-Based Automated Method using Deep Learning...

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International Conference on Industrial and information systems. ICIIS

作者： Ramesh Ajikaran Ashen Iranga Hewarathna Vigneshwaran Palanisamy Charles Joseph Selvarajah Thuseethan Dept. of Computing and Infromation Systems Sabaragamuwa University of Sri Lanka Dept. of Software Engineering Sabaragamuwa University of Sri Lanka Dept. of Computer Science FAU Germany School of Information Technology Deakin University Australia

In the process of quality analysis of grains, number counting is one of the key steps for agricultural production. Traditional manual grain counting methods are time-consuming and subject to human error, and automated methods have the potential to improve accuracy and save time. In this study, we aimed to develop an image analysis-based method to automatically quantify the number of grains in a quicker manner. The 576 grain images were collected manually, and labelImg tagging tool used to annotate to generate a text file with their respective positions by drawing bounding boxes manually. The datasets consist were separated into three groups: training, validation, and test. For object detection, the YOLOv5, YOLOv4, and YOLOv3 algorithms represent cutting-edge deep learning frameworks. They replace the tedious and error-prone manual counting process by precisely identifying and counting grains in images obtained from agricultural fields. This technique helps to increase grain counting's precision and effectiveness. We believe this method will be extremely beneficial in guiding the development of high throughput systems.for counting the number of grains in other crops as it performs well with a wide range of backgrounds, picture sizes, grain sizes, as well as various quantities of grain crowding. When compared to the other two approaches, YOLOv4 performed well in terms of accuracy, speed, and robustness (97.65%), demonstrating that the suggested strategy is competitive with other cutting-edge deep networkst.

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