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Real-Time Automatic Checkout via Prompt-Based Product Extraction and Cross-Domain Learning 23

Real-Time Automatic Checkout via Prompt-Based Product Extrac...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Pettersson, Tobias Riveiro, Maria Löfström, Tuwe University of Skövde Jönköping University Itab Shop Products Ab Jönköping Sweden Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Jönköping University Dept. of Computing Jönköping Sweden

ISBN: (纸本)9798350374889

Automatic checkout systems are designed to predict a complete shopping receipt using an image from the checkout area. These systems require high classification accuracy across numerous classes and must operate in real-time, despite domain differences between training data and real-world conditions. Building on recent advancements, we propose a method that outperforms current solutions and can be applied in real-time in automatic checkout systems. Our method leverages the Segment Anything Model to extract high-quality masks from lab product images, which are then transformed into synthetic checkout images and adapted to the real domain using contrastive unpaired translation. We train a product recognition model with data augmentation, named SCA+Y8, and further improve it through fine-tuning with pseudo-labels from unlabeled checkout images, resulting in an improved model called SCAFT+Y8. SCAFT+Y8 achieves a great increase in state-of-the-art performance, with an average receipt classification accuracy of 97.58%, and shows strong performance in smaller models, indicating the potential for deployment on low-cost edge devices. © 2024 IEEE.

关键词： Object detection

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Dual-Branch Neural Networks for Predicting Shared Bikes 17

Dual-Branch Neural Networks for Predicting Shared Bikes

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Dongsoo S. Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Indiana Uiversity-Purdue University Dept. of Electrical and Computer Engineering Indianapolis United States Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Bike sharing is efficient urban mobility method as a user determines the rental period upon his/her demand. A dock-based sharing system designates places (i.e., bike docks or stations) where a user rents or returns a bike. Due to regionally and temporally erratic nature of urban mobility demand, bike imbalance among stations inevitably occurs and a service operator rebalances bikes to prevent service impairment. The precise prediction of imbalance for each station is essential to optimize the rebalancing schedule. This paper presents Dual-Branch Neural Networks (DBNNs) employing Long Short-Term Memory (LSTM) to classify the imbalance level of each station along with the prediction of bike counts. To address the dual objectives, the model trains by backpropagating both classification and prediction losses to the dual output branches individually, and jointly updates the trainable parameters of LSTM layers. The results showcase that the proposed model predicts bike counts within 8% average error for next 24 hours, and achieves 94% accuracy of classification for the same time period. © 2023 IEEE.

关键词： Forecasting

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Comparative analysis of classification algorithm to authenticate user based on keystroke technique 16

Comparative analysis of classification algorithm to authenti...

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16th International Conference on Correlation Optics, COR 2023

作者： Abd Hamid, Nazirah Raja Bongsu, Raja Hasyifah Mohamed, Mohamad Afendee Abidin Bharun, Ahmad Faisal Amri Abdul Kadir, Mohd Fadzil Youzlan, Nurazizah Rusyn, Volodymyr Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu22200 Malaysia Dept. of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine

ISBN: (纸本)9781510671829

Many people are using their mobile or smartphone to store data and allow the user to access the internet and many online services. These situations are causing an intensification of cybercrimes, thus, to improve the security of the password required, the mobile phone came out with an alternative method, by using biometrics technology. This study firstly would be focusing on the implementation of typing patterns known as keystroke technique and secondly to conduct analysis to determine whether it is suitable to be implemented as an authentication technique by training and testing the dataset with several machine learning classifiers. From the seven classification techniques, three suitable classifiers have been identified and the results show that Random Forest with False Accepted Rate (FAR) of 0.2% and False Rejection Rate (FRR) of 5.31% had the best performance It can be concluded that keystroke technique has the potential to be a good method to authenticate a user with further research. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Authentication

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A Machine-Learning Based IoT Smart Home System to Detect and Reduce Urban Insecurity in Uganda: A Case of Kampala Metropolitan Area

A Machine-Learning Based IoT Smart Home System to Detect and...

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International Conference on Advances in Big Data, computing and Data Communication Systems (icABCD)

作者： Edward Miiro Ismael Kato Department of Computer Science & Engineering Faculty of Computing & Informatics Makerere University Business School Kampala Uganda Dept. of Applied Computing & Information Technology Faculty of Computing & Informatics Makerere University Business School Kampala Uganda

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

This study sought to develop and test a machine learning based prototype of a smart home security system for Kampala Metropolitan area in Uganda. The researchers used a Design Science Research (DSR) method to execute the project. The researchers identified a problem- which was high level of insecurity and crimes around homes in Kampala and its metropolitan areas, defined objectives, collected requirements, designed and developed the artifact, then demonstrated, evaluated, and communicated the artifact to stakeholders. The team created a working prototype of a home-security system that was later tested, demonstrated, validated among potential users. Practically, this project was implemented in a simulated environment using Tinkercad Arduino software. The developed system prototype was simulated on screen and tested on at least one home to ensure its effectiveness in reducing unauthorized entry, robbery, and other forms of crimes that are widely prevalent in Kampala today. One limitation of the project was that Arduino board used was quite limited on the number of pins that were available for use. The initial project idea had featured a temperature sensor to that was intended to regulate the temperature in the home by making use of the fan. However, the researchers did not implement this due to the limited pins and the capacity of the Arduino Uno R3 board that was used. Higher capacity industrial tools could provide a more realistic design of a similar system. The project simulated an environment of a smart home security system that can be used to detect and reduce crime. This study is part of a wider project to test ML and IoT systems in building secure smart homes. The authors envisage the application on more advanced ML algorithms and techniques in improving the system.

关键词： Temperature sensors Urban areas Prototypes Smart homes Machine learning Software Pins

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, Computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

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. © 2023 IEEE.

关键词： Sentiment analysis

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Two Efficient Training Strategies for DyDaSL: A Dynamic Data Stream Learner Framework with Semi-Supervised Learning

Two Efficient Training Strategies for DyDaSL: A Dynamic Data...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Arthur Gorgonio Anne Canuto Arthur Medeiros Karliane Vale Flavius Gorgonio Dept. of Informatics and Applicated Mathematics (DIMAp) Natal Brazil Dept. of Computing and Technology (DCT) Caico Brazil

Processing instances quickly and updating models when a concept drift is detected is crucial in the data stream context. However, this process becomes even more challenging in the semi-supervised scenario in which only a few labelled instances are available. A semi-supervised framework called Dynamic Data Stream Learner (DyDaSL) has been proposed in literature to address this issue. This framework generates a fixed size ensemble in the first iteration to classify the remaining data. This paper proposes two extensions for DyDaSL, aiming to optimise the ensemble generation process. In both extensions, the ensemble size starts with only one classifier and it is increased throughout the learning process. In the first extension, a new classifier is included after a stream chunk is presented, while the second extension includes a new classifier when a drift is detected. In order to assess the feasibility of the proposed methods, an empirical analysis is conducted. As a result, these two proposed methods outperformed the standard variation in 128 out of 180 cases (71.11%), showing their effectiveness in all evaluated metrics.

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Color-based image processing techniques for laser range finder: a comparative study on air and water distance detection 16

Color-based image processing techniques for laser range find...

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16th International Conference on Correlation Optics, COR 2023

作者： Multajam, Rizki Ayob, Ahmad Faisal Mohamad Sanjaya, W. S. Mada Sambas, Aceng Rusyn, Volodymyr Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia Dept. of Physics Faculty of Science and Technology Universitas Islam Negeri Sunan Gunung Djati Bandung40614 Indonesia Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Campus Besut Terengganu22200 Malaysia Dept. of Mechanical Engineering Universitas Muhammadiyah Tasikmalaya Tasikmalaya46196 Indonesia Dept. of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi58000 Ukraine

ISBN: (纸本)9781510671829

In this paper, we studied comparative distance detection using a Laser Rangefinder equipped with color detection and image processing techniques in air and water environments. The main objective of this study is to evaluate the performance and accuracy of the distance detection methods in these two mediums. Furthermore, color detection is employed in the air environment to identify objects within the captured images, followed by image processing techniques such as edge detection and contour analysis to calculate the distances between the objects and the Laser Range Finder. In the water environment, challenges arise due to the refractive properties of water, which can distort object appearances. To overcome this, we employ an underwater Laser Range Finder system, along with color detection and image processing methods adjusted for the refractive index of water. The findings show that the distance measurement in the air has a small error value of 0.63 %. Meanwhile, measuring distances in water has a high error value of 35.18 %. These results indicate that measurements in the air perform better than measurements in water. Water's scattering and refractive properties caused significant deviations and higher error values. The light intensity fluctuations had minimal impact on measurements in water but were significant in water. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Refractive index

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I/O Circuit and Sub-5V ESD Protection for Advanced Bonding Interfaces

I/O Circuit and Sub-5V ESD Protection for Advanced Bonding I...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Krilcic, M. Baric, A. Lin, S.-H. Markovic, T. Pantano, N. Simicic, M. University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb Croatia Imec Kapeldreef 75 Leuven 3001 Belgium Vrije Universiteit Brussel Dept. of Electronics and Informatics Brussel Belgium Ku Leuven Dept. of Electrical Engineering (ESAT) Leuven Belgium

ISBN: (纸本)9798331504779

This study investigates the impact of electrostatic discharge (ESD) protection on the performance of I/O circuits in 2.5D-chiplet and 3D integrated architectures, focusing on 12-nm FinFET devices. Using 1-ns-pulse ESD stress measurements and S-parameter analysis, it assesses trade-offs between ESD protection effectiveness and the corresponding capacitance and layout area penalties. The findings highlight the challenges of optimizing chiplet interconnects while meeting evolving protection standards. However, as ESD protection capacitance exceeds interconnect capacitance, it introduces severe RC delay penalties, making further reductions in protection levels essential for maintaining system efficiency and integration feasibility. © 2025 IEEE.

关键词： Chiplets ESD protection FinFET I/O design VF- TLP TDT

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Protection of Network Security Selector Secrecy in Outsourced Network Testing 32

Protection of Network Security Selector Secrecy in Outsource...

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32nd International Conference on Computer Communications and Networks, ICCCN 2023

作者： Alasmari, Sultan Wang, Weichao Lu, Aidong Wang, Yu Cs and It College Majmaah University Saudi Arabia Univ. North Carolina Charlotte Computing & Informatics CharlotteNC United States Temple University Dept. of Cs PhiladelphiaPA United States

ISBN: (纸本)9798350336184

With the emergence and fast development of cloud computing and outsourced services, more and more companies start to use managed security service providers (MSSP) as their security service team. This approach can save the budget on maintaining its own security teams and depend on professional security persons to protect the company infrastructures and intellectual property. However, this approach also gives the MSSP opportunities to honor only a part of the security service level agreement. To prevent this from happening, researchers propose to use outsourced network testing to verify the execution of the security policies. During this procedure, the end customer has to design network testing traffic and provide it to the testers. Since the testing traffic is designed based on the security rules and selectors, external testers could derive the customer network security setup, and conduct subsequent attacks based on the learned knowledge. To protect the network security configuration secrecy in outsourced testing, in this paper we propose different methods to hide the accurate information. For Regex-based security selectors, we propose to introduce fake testing traffic to confuse the testers. For exact match and range based selectors, we propose to use NAT VM to hide the accurate information. We conduct simulation to show the protection effectiveness under different scenarios. We also discuss the advantages of our approaches and the potential challenges. © 2023 IEEE.

关键词： Network security

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Tourette Syndrome's Coprolalia Explanation using Bipartite Graph Matching of Thoughts and Game Theoretic Model for Symptoms Minimisation 7

Tourette Syndrome's Coprolalia Explanation using Bipartite G...

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7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Spyrou, Evangelos D. Nestoris, Christoforos Stylios, Chrysostomos University of Ioannina Laboratory of Knowledge and Intelligent Computing Dept. of Informatics and Telecommunications Arta Greece Psychiatrist Pavlou Mela 26 Thessaloniki54622 Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

Coprolalia is a symptom of Tourette's syndrome, which is essentially involuntary cursing. The obscene words usually have a meaning, which is associated with offending social acquaintances often based on locality or sexuality. This results in a patient's social isolation and increased anxiety. The patient finds herself stopping things that she would do in her everyday life. In this work, we aim to explain the Coprolalia symptom with the means of bipartite graph matching. We assume that the brain network performs some kind of rerouting of thoughts, which results in the association of normal thoughts with their matched obscene ones. With this we aim to decode the operation that results in Coprolalia, in order for the patient to be informed of this behaviour. Furthermore, we propose a game-theoretic model based on the Battle of the Sexes, which has two equilibria in the pure strategies, namely to jam the intrusive coprolalic thoughts or to pass through the brain. In this way, we provide a mechanism for the patient to stand in social activities, in which the symptoms are severe. Moreover, we align with Cognitive Behavioural Therapy (CBT), with which intrusive thoughts pass through the brain with the aim of deteriorating. © 2022 IEEE.

关键词： Game theory

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