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5th International Conference on Communications, information System and computer Engineering, CISCE 2023

作者： Jin, Jiayuan Jiang, Xiantao Song, Tian Richard Yu, Fei. Weili Liu, Jin Shanghai Maritime University Dept. Information Engineering Shanghai China Tokushima University Dept. Electrical and Electronics Engineering Tokushima Japan Carleton University Dept. Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9798350326796

A new Quadtree with nested multi-type tree(QTMT) partitioning method is adopted in the latest video coding standard Versatile Video Coding(VVC) to achieve effective encoding. Compared with Quadtree(QT) in HEVC, QTMT can provide enhanced encoding performance. However, this improved coding performance is obtained at the cost of additional computational load from recursive and nested searches for the best CU structure. A context similarity-enabled CU partitioning algorithm is proposed to achieve a balance between coding efficiency and compression quality in VVC. According to the difference between frames, the method of frame difference algorithm is used to calculate the possibility that the current block can follow the same subblock division mode of the previous frame, so as to skip the unnecessary division selection process. The experimental results show that the proposed method can save the encoding time up to 18.22% with about 0.91% BD-rate degradation on average compared with VVC software reference VTM-9.3 © 2023 IEEE.

关键词： Encoding (symbols)

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AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework

AirWave: Enhancing UAV Connectivity in Cellular Networks thr...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Zor, Emirhan Taskiran, Yusuf Dael, Fares A. Shayea, Ibraheem Rzayeva, Leila Syzdykova, Zuleikha Istanbul Technical University Electronic and Communication Engineering Istanbul Turkey İzmir Bakirçay University Management Information Systems İzmir Turkey Astana IT University Dept. of Intelligent Systems and Cybersecurity Astana Kazakhstan Astana IT University Dept. of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798350374100

This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates in-dept. investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications. © 2024 IEEE.

关键词： Reinforcement learning

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Hash Tag-Based Sentiment Analysis Using A Deep Learning Approach 10

Hash Tag-Based Sentiment Analysis Using A Deep Learning Appr...

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10th International Conference on Advanced Computing and Communication systems, ICACCS 2024

作者： Venkatesan, R. Sangeetha, M. Nivedha, S. Selvakumar, M. Chandru, K.S. Parthasarathi, P. Bannari Amman Institute of Technology Department of Information Technology Erode India K S Rangasamy College of Technology Department of Information Technology Namakkal India Hindusthan College of Engg and Technology Dept. of Computer Science & Engineering Coimbatore India Bannari Amman Institute of Technology Dept. Computer Science & Business Systems Erode India Bannari Amman Institute of Technology Dept. Computer Science and Engineering Erode India

ISBN: (纸本)9798350384369

Social commerce uses a variety of social networking sites like Facebook, Instagram, Twitter, Pinterest, and business Whatsapp as a platform to promote products and services. Since advertising, a product, or service in s-comm will reach as high as a possible number of users on networking sites. Ads will spread virally by sharing among friends and friends of friends. So that wider reachability is possible and shortly will get the customer for our business. The success of an s-comm campaign is measured by the degree to which consumers interact with the sellers through review comments, likes, and shares between friends. Customer behavior analysis is needed for s-comm marketing because social commerce can significantly boost economic growth. The sentiment analysis of customer recommendations was investigated using a dataset of women's s-comm apparel hashtags and reviews. So for such reviews are experimented with different machine learning algorithms (Decision tree, SVM, Random Forest, KNN, Naïve Bayes and LSTM). These models handled the common English reviews and compared the metrics (Accuracy, Sensitivity and Specificity) with one another. Instead of working with individual model, this paper introduces a novel hybrid approach that integrates CNN and Ada Boost LSTM algorithms to provide better results in sentiment analysis when compared to other machine learning algorithms mentioned above. While considering the hash tag with common review comments the hybrid system is out performed. The comparison analysis is done for performance matrices namely accuracy of 98%, specificity of 98.1%, and sensitivity of 98.1%. In the s-commerce transaction industry, this hybrid approach will help to create a deeper understanding of consumer sentiment analysis and psychologically attract clients. © 2024 IEEE.

关键词： Tweets

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Breaking Tester Stereotypes: who is testing and why it matters 24

Breaking Tester Stereotypes: who is testing and why it matte...

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Proceedings of the 37th International BCS Human-computer Interaction Conference

作者： Isabel Evans Chris Porter Mark Micallef Dept. of Computer Information Systems Faculty of ICT University of Malta Msida Malta Dept. of Computer Science

Testing is essential for successful delivery of software solutions and is not always performed by specialist testers. In earlier studies, we noted a wide diversity in the backgrounds of the testers participating in these studies, prompting our research question: Who is Testing? We conducted a qualitative study of over 70 industry testers, covering testers from multiple countries and domains, with information about their backgrounds, hobbies, roles and characteristics. We show testers are from a wide range of backgrounds, with differing needs, characteristics, and problem-solving preferences. Their roles in software projects are multi-faceted, requiring a high cognitive skill level. We discuss how to break stereotyping and best support diversity in testers’ backgrounds and *** consider whether software testers are different from other software practitioners, and how understanding tester personas helps support of testers and testing.

关键词： Software Testing

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Impact of programming Language Skills in programming Learning 15

Impact of Programming Language Skills in Programming Learnin...

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15th IEEE International Symposium on Embedded Multicore/Many-Core systems-on-Chip, MCSoC 2022

作者： Ibne Amin, Md. Faizul Rahman, Md. Mostafizer Watanobe, Yutaka Daniel, Muepu Mukendi The University of Aizu Dept. of Computer Science and Information Systems Aizu-Wakamatsu Japan

ISBN: (纸本)9781665464994

In this modern era of the internet and information technology, a mentionable amount of data is generated from different sources consistently which refers to big data. This huge amount of data not only draws great attention for further research but also helps to extract different knowledge and infor-mation in various areas. The information and Communication Technology (ICT) area is not apart from that, as the huge amount of data in this area is enhancing opportunities for further research and development. In the ICT, most of the courses especially programming-related courses are designed to improve practical skills. With the increasing demand for software engineering and other related fields, programming education or learning plays a vital role. However, in programming learning, the impact of programming language is also important to enrich the programming and technical skill. This paper aims to analyze the impact of programming language skills in programming learning by collecting real-world data from a programming course. In this paper, we used a dataset from submission logs of a programming course in an Online Judge (OJ) system. We selected the users randomly and considered single and multiple languages used for acceptance. Finally, we have presented the analysis of the overall acceptance rate, single and multiple languages used acceptance rate, and compared them. Moreover, the analytical result of this paper can help students and programmers as well as the improvement programming learning. © 2022 IEEE.

关键词： Big data

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Enhancing Grade Prediction via Course Attribute-Aware Student Similarity in Collaborative Filtering 35th

Enhancing Grade Prediction via Course Attribute-Aware Studen...

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35th International Florida Artificial Intelligence Research Society Conference, FLAIRS-35 2022

作者： Park, Young Dept. of Computer Science & Information Systems Bradley University IL United States

Collaborative filtering has been used in predicting personalized students’ grades in courses at higher educational institutions. The traditional collaborative filtering through student-to-student correlation is based on similarity between students with respect to their grades in all courses that they have taken. However, courses are not completely independent each other. Given a course, the grades of its prerequisite, corequisite and similar courses will affect the grade of the course more than the grades of other unrelated courses will. © 2022 by the authors. All rights reserved.

关键词： Collaborative filtering

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Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis

Privacy-Preserving Data Sharing in Agriculture: Enforcing Po...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kotal, Anantaa Elluri, Lavanya Gupta, Deepti Mandalapu, Varun Joshi, Anupam University of Maryland Baltimore County Dept. of C.S.E.E. Baltimore United States Texas A&m University - Central Texas Dept. of Computer Information Systems Killeen United States University of Maryland Baltimore County Dept. of Information Systems Baltimore United States

ISBN: (纸本)9798350324457

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the European Union's General Data Protection Regulation (GDPR), the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Synthetic data that retains the statistical properties of the original data but does not include actual individuals' information provides a suitable alternative to sharing sensitive datasets. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We

关键词： Big data in Agriculture Data Privacy Privacy Attacks Privacy Policy

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Create Combined Thermometric Datasets for Machine Learning in Medicine 4

Create Combined Thermometric Datasets for Machine Learning i...

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4th International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2022

作者： Polyakov, Maxim Khoperskov, Alexander Volgograd State University Dept. Information Systems and Computer Modeling Volgograd Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

Methods for processing medical measurements face a big problem of quality of initial data. Complicating factors are the small size of such samples and the data heterogeneity due to the difficulty o f controlling medical measurements. A promising approach may be the creation of combined dataset, when real measurements are supplemented by the results of simulation modeling of the measurement procedure. Such computer models should reproduce the main physical processes inside biological tissues or organs. As a result, we can basically control the quality of simulation data. The size of the combined dataset can be quite large if there is a good fit between the medical s ample a nd the results of the numerical simulations. This approach is used by us in the diagnosis of breast cancer based on the Microwave Radiometry method. We use various machine learning algorithms to classify 'Sick vs. Healthy' on the combined dataset. The analysis shows that the expansion of data due to the modeling results increases the efficiency of diagnosing breast cancer. © 2022 IEEE.

关键词： Quality control

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

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

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6th International Conference on Computing and Informatics, ICCI 2024

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

ISBN: (纸本)9798350373875

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. © 2024 IEEE.

关键词： Sales

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Security Enhancement of OTAA based Joining Procedure in LoRaWAN for Satellite Communication

Security Enhancement of OTAA based Joining Procedure in LoRa...

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IEEE Conference on Vehicular Technology (VTC)

作者： Jay Dave Nikumani Choudhury Dept. of Computer Science & Information Systems BITS India

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol that facilitates efficient and wide-range communication under low-power conditions. Over-the-air activation (OTAA) is a process recommended by Lo-Ra Wan v1.0.4 (latest version) that enables end devices to join the network with the help of the joining server and generate the session keys for further communications. However, OTAA is vulnerable to potential security threats due to unencrypted join request messages and the reuse of the same encryption keys. In this paper, we present a new security enhancement that addresses the aforesaid security issues. In the proposed scheme, we encrypt the join request message using a secret key to ensure data confidentiality. In addition, we incorporate the use of a random nonce in the proposed joining procedure to protect LoRaWAN against attacks related to the reuse of the same key. We show that the adversary cannot learn sensitive information from the join request message and reuse the AppKey to execute the eavesdropping and unauthorized activation attacks with non-negligible probability.

关键词： Wide area networks Nonce Vehicular and wireless technologies Data privacy Wireless networks LoRaWAN Encryption

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