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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Bohan, Nolan Manzi, Miranda Yun, Mira Wentworth Institute of Technologuy School of Computing and Data Science BostonMA United States

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

Businesses are transitioning towards using cloud services for many of their networking and system needs. However, designing and implementing these cloud services can be costly for business owners. In this paper, we propose Network2U, a business cloud network infrastructure service built upon Google Cloud. Network2U can be fit into several types of business by extending two sample templates. Current templates include a school system where hundreds of students will connect to a network, with strict security requirements regarding student data. The other template is a hybrid workforce finance company, where its users will connect to the network from different locations, and customer data security is key. These templates provide the different business types with all the systems, networking and security services needed to run their business. Network2U is for small or medium business owners who cannot invest in the large cost of getting a business cloud network infrastructure implemented. © 2022 IEEE.

关键词： Web services

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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RokuControl-Conducting MITM Attacks on Roku 13

RokuControl-Conducting MITM Attacks on Roku

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13th IEEE Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2022

作者： Coles, Devin Peterson, Michael Park, Sunjae Yun, Mira Wentworth Institute of Technology School of Computing and Data Science BostonMA United States

ISBN: (数字)9781665463164

ISBN: (纸本)9781665463164

Smart TVs are increasingly common in many house-holds as an alternative to traditional cable television. Smart TVs utilize high-speed Internet and allow people to watch streaming video services in a TV format. Man-in-the-middle attacks (MITM) insert the attacker between two devices that are communicating with each other. In this paper, we implement a MITM attack on a Roku device by capturing packets and replaying them using a packet crafter. Through such unauthorized control, this paper shows how the security of these devices can be impacted. © 2022 IEEE.

关键词： Network security

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MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts 41

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Exp...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much ***, most neural solvers are only structured and trained independently on a specific problem, making them less generic and *** this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants ***, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in *** further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational ***, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark *** further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution *** source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright 2024 by the author(s)

关键词： Vehicle routing

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Optimized Reading for ADHD and Dyslexia Users In VR

Optimized Reading for ADHD and Dyslexia Users In VR

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2023 Congress in Computer science, Computer Engineering, and Applied computing, CSCE 2023

作者： Hoang, Kyler Deligiannidis, Leonidas School of Engineering Wentworth Institute of Technology BostonMA02115 United States School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350327595

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books. © 2023 IEEE.

关键词： ADHD Dyslexia Virtual Reality

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Indoor Navigation Using Ultra Wide Band

Indoor Navigation Using Ultra Wide Band

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2023 Intelligent computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Divya, B. Veeramakali, T. Revathi, S. Harisharajan, R.R. Adhirooban, D. Ebishdon, G.V. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India School of Computing Srm Institute of Science and Technology Dept of Data Science and Business Systems Tamilnadu Kattankulathur India

ISBN: (纸本)9798350394580

Indoor positioning is a significant and intriguing topic in navigation systems that present numerous use cases for investigation. Researchers are exploring technologies capable of providing accurate locations in indoor spaces where GNSS positioning systems are ineffective. In the numerous solutions available for indoor location services, Ultra-wideband (UWB) technology was chosen for its ability to provide high-precision positioning information for multiple points in real-time. This paper presents a UWB indoor positioning system that utilizes time of flight (TOF) algorithm. At times, time difference of arrival (TDOA) algorithms compute range measurements, this facilitates the trans-receiver (TAG) plane position algorithm's location determination. UWB technology was chosen because it is the latest technology available on Android 12 and further also offers high accuracy, resistance to interference and minimal signal reflection from walls. UltraWide Band(UWB) localization encounters various obstacles, such as multipath distortions, additional signal delays, time discrepancies in clocks, signal interference, and the computational resources necessary to determine the user's location. © 2023 IEEE.

关键词： Indoor Positioning System(IPS) Time of Flight(TOF) Ultra Wide Band(UWB)

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Automated Counting System Based on Watershed Algorithm for Tilapia Fish Egg 1

Automated Counting System Based on Watershed Algorithm for T...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Hassan, Mohamad Ghozali Harun, Nor Hazlyna Md Azhar, Amiera Syazlin Md Pauzi, Siti Naquiah Ja'afar, Noor Ashri Mansor, Nur Suhaili School of Technology Management and Logistics Universiti Utara Malaysia Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Malaysia Institute for Advanced and Smart Digital Opportunities School of Computing Sintok Malaysia

ISBN: (纸本)9798350306156

Aquaculture is a significant component of Malaysia's fishery sector and makes a substantial contribution to the country's economy. It is the source of income and employment for the nation. Tilapia fish is one of the main species cultured in the aquaculture industry, as eggs produced by this fish are considered very valuable both in terms of quantity and quality. A standard egg selection procedure to calculate the fish egg properly has not yet been implemented. The process of fish egg selection plays a vital role in determining the accurate count of fish eggs. This study focuses on developing an automated counting system for tilapia fish eggs using an image processing technique concentrating on the watershed algorithm. This algorithm enables the image of the fish eggs to be clustered in shaded colors, thus enabling the total number of fish eggs to be automatically counted. The results show that the automated counting system for tilapia fish eggs works successfully as the accuracy obtained was above 90%. It is believed that an automated counting system can help fish breeders to perform fish egg counting automatically and more effectively and efficiently. © 2023 IEEE.

关键词： Fish

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Fire Detection System using Deep Learning Techniques 4

Fire Detection System using Deep Learning Techniques

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4th International Conference on Inventive Research in computing Applications, ICIRCA 2022

作者： Srividhya, S. Sindhu, S. Sornalakshmi, K. Hemavathi, D. Rajaram, V. School of Computing Srm Institute of Science and Technology Data Science and Business Systems Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

An image-based fire detection system is designed using an image dataset to train the model, and further, any image capturing device will be used for later prediction purposes. The training and prediction are done using deep learning techniques. Convolution Neural Network is used for implementation. As another fire identification innovation, picture fire location has as of late assumed a pivotal part in diminishing property misfortune and harm during a fire by disturbing clients at a beginning phase through early fire discovery. Picture fire location depends on an algorithmic investigation of pictures. Nonetheless, there is a lower precision, postponed recognition, and much calculation in like manner identification calculations, including physically and machine naturally separating picture highlights. The model is designed to alert the user via SMS and email along with a captured image of fire when a fire occurs. The proposed system in development aims at providing a detection system that can alert a user instantly at the time of a hazardous fire. The aim is to create an accurate model that does not result in false alerts. The model is trained using accurate image datasets to differentiate between a fire and a non-fire scenario. Existing models used the RGB-HIS colour space for their model. Particularly, it is used to obtain pre-detected areas rather than the traditional motion differential method. The centroid variety of candidates is used to measure the flame colour dispersions in subsequent frames. Multi-expert systems have the following;dispersion of colour and centroid movement are entrenched to classify fire based on testing various clips acquired by a surveillance camera in private confinement. © 2022 IEEE.

关键词： Fires

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Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure 10

Who Responded to Whom: The Joint Effects of Latent Topics an...

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10th SIGHAN Workshop on Chinese Language Processing, SIGHAN 2024

作者： Ji, Lu Chen, Lei Li, Jing Wei, Zhongyu Zhang, Qi Huang, Xuanjing School of Computer Science Fudan University China School of Data Science Fudan University China Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Intelligent and Complex Systems Fudan University China

ISBN: (纸本)9798891761551

Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts. © 2024 Association for Computational Linguistics

关键词： Computational linguistics

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Automatic Stress Recognition System with Deep Learning using Multimodal Psychological data

Automatic Stress Recognition System with Deep Learning using...

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2022 International Conference on Electronic Systems and Intelligent computing, ICESIC 2022

作者： Praveenkumar., S. Karthick, T. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665483858

There are numerous applications for detecting stress states from physiological inputs. Human stress detection may be used to enhance the human experience as well as monitor and prevent stress-related disorders. Previous research has leveraged existing machine learning techniques for automated stress identification. However, there are also some limitations. Deep learning allows you to discover basic patterns in your body's reactions that are often difficult to notice. This study offers a convolutional network-based approach for detecting human stress utilizing multimodal data sets obtained by smart sensors for physiological and motion, which can aid humans in avoiding health issues caused by stress. Sensor information like triaxial accelerates, heartbeat, body temperature, respiration, electromyographic (EMG) data for physiological states stressed and non-stressed, are included in the WESAD data collection. The use of Long short - term memory method and the DNN method to detect stress in people in real-time is investigated in this study. Our research is based on their physical reactions. The study's findings provide an assessment of both models' ability to predict real-time users' mental expressions based on physiological parameter fluctuation. © 2022 IEEE.

关键词： Deep neural networks

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