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2021 International Conference on Information Technology, ICIT 2021

作者： Mohammad, Zeyad Alkhatib, Ahmad Aa Lafi, Mohammed Abusukhon, Ahmad Albashish, Dheeb Atwan, Jaffar Al-Zaytoonah University of Jordan IT Faculty Computer Science Dept. Amman Jordan Al-Zaytoonah University of Jordan Department of Software Engineering Amman Jordan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of Information and Communication Technolgy Computer Science Department Sa lt Jordan Al-Balqa Applied University Prince Abdullah Bin Ghazi Faculty of ICT CIS Department Sa lt Jordan

ISBN: (纸本)9781665428705

Authenticated key exchange protocols are a fundamental cryptography building block for establishing a secret shared key among participating parties over an open network model. Mohamed et al. proposed a tightly-secure authenticated key exchange without NAXOS approach based on decision linear problem in order to solve an open problem for designing an authenticated key exchange protocol without using NAXOS trick. Mohamed et al.'s protocol has tight security proof in the eCK model under the random oracle problem and its security claims verified by using the games sequence tool. However, we show that Mohamed et al.'s protocol cannot withstand the key compromise impersonation attacks and man in the middle attacks. Furthermore, Mohamed et al.'s protocol cannot provide an implicit entity authentication property since it is a basic security property for an authenticated key exchange protocol. Therefore, the protocol is not secure in the eCK model. © 2021 IEEE.

关键词： Cryptography

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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International Conference on Emerging Technologies, ICET

作者： Usama Arshad Hamza Hasan Ellahie Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-dept. exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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International Conference on Emerging Technologies, ICET

作者： Omar Kashif Majeed Zain ul Abideen Usama Arshad Raja Hashim Ali Abdullah Habib Rafay Mustafa Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty.

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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International Conference on Emerging Technologies, ICET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta Ashokkumar S. R Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments & Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer and Communication Engineering Centre for Blockchain and Cyber security Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure.

关键词： Sentiment analysis IEEE merchandise Customer services Decision making Power control Customer satisfaction Process control

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BMEAUT at SemEval-2020 Task 2: Lexical entailment with semantic graphs 14

BMEAUT at SemEval-2020 Task 2: Lexical entailment with seman...

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14th International Workshops on Semantic Evaluation, SemEval 2020

作者： Kovács, Ádám Gémes, Kinga Kornai, Andras Recski, Gábor Dept. of Automation and Applied Informatics Budapest University of Technology and Economics Hungary SZTAKI Institute of Computer Science TU Wien Austria

ISBN: (纸本)9781952148316

In this paper we present a novel rule-based, language independent method for determining lexical entailment relations using semantic representations built from Wiktionary definitions. Combined with a simple WordNet-based method our system achieves top scores on the English and Italian datasets of the Semeval-2020 task "Predicting Multilingual and Cross-lingual (graded) Lexical Entailment" (Glavaš et al., 2020). A detailed error analysis of our output uncovers future directions for improving both the semantic parsing method and the inference process on semantic graphs. reported in this document. © 2020 14th International Workshops on Semantic Evaluation, SemEval 2020 - co-located 28th International Conference on Computational Linguistics, COLING 2020, Proceedings. All rights reserved.

关键词： Semantics

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A silicon-based ion trap chip protected from semiconductor charging

arXiv

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arXiv 2024年

作者： Chung, Daun Choi, Kwangyeul Lee, Woojun Kim, Chiyoon Shon, Hosung Park, Jeonghyun Cho, Beomgeun Lee, Kyungmin Kim, Suhan Yoo, Seungwoo Jung, Eui Hwan Jung, Changhyun Kang, Jiyong Kim, Kyunghye Berkis, Roberts Northup, Tracy Cho, Dong-Il Kim, Taehyun Dept. of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Dept. of Experimental Physics University of Innsbruck Technikerstraße 25 Innsbruck6020 Austria Dept. of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of

Silicon-based ion trap chips can benefit from existing advanced fabrication technologies, such as multi-metal layer techniques for two-dimensional architectures and silicon photonics for the integration of on-chip optical components. However, the scalability of these technologies may be compromised by semiconductor charging, where photogenerated charge carriers produce electric potentials that disrupt ion motion. Inspired by recent studies on charge distribution mechanisms in semiconductors, we developed a silicon-based chip with gold coated on all exposed silicon surfaces. This modification significantly stabilized ion motion compared to a chip without such metallic shielding, a result that underscores the detrimental effects of exposed silicon. With the mitigation of background silicon-induced fields to negligible levels, quantum operations such as sideband cooling and two-ion entangling gates, which were previously infeasible with the unshielded chip, can now be implemented. © 2024, CC BY.

关键词： Silicon photonics

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Retinal Blood Vessel Segmentation and Analysis using Lightweight Spatial Attention based CNN and Data Augmentation

Retinal Blood Vessel Segmentation and Analysis using Lightwe...

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IEEE Calcutta Conference (CALCON)

作者： Srinjoy Bhuiya Soumik Roy Choudhury Geetanjali Aich Muskaan Maurya Anindya Sen Dept. of Computer Science and Engineering Heritage Institute of Technology Kolkata India Dept. of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Kolkata India Center for Soft Computing Research Indian Statistical Institute Kolkata India Dept. of Electronics and Communication Engineering Heritage Institute of Technology Kolkata India

The exact detection and creation of treatment regimens for conditions like diabetic retinopathy and hypertensive retinopathy depend on the segmentation of retinal blood vessels. Methods based on deep learning have been employed in the last ten years to segment blood vessels in fundus images. Due to the lack of uniform data in large quantities, the wide range of brightness and anatomical structures of the fundus images that are available, and the variety of shapes and sizes of the vessels in the tree-like vascular structure, it is still difficult to accurately segment all the vessels in a retinal fundus image. In this study, we present a unique lightweight CNN with an encoder-decoder structure for real-time and precise segmentation of blood vessels. The most popular retina datasets, DRIVE and CHASE, were used to train and evaluate the model. With an accuracy of 96.3% and 78.45% f1 score with respect to the DRIVE dataset and accuracy of 97.14% and 82.79% f1 score with respect to the CHASE dataset, we can observe that the model is lightweight and has provided comparable performance. Additionally, the suggested model runs faster with an average inference time of 0.0059 seconds and has fewer parameters than state-of-the-art models currently in use.

关键词： Deep learning Image segmentation Retinopathy Shape Image color analysis Blood vessels Retina

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