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International Conference on Emerging Technologies, ICET

作者： Iqra Mueed Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure.

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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computer Systems Science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Numerical Simulation of Full-Scale Square Concrete Filled Steel Tubular (CFST) Columns Under Seismic Loading

Numerical Simulation of Full-Scale Square Concrete Filled St...

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International Conference on Modern Mechanics and Applications, ICOMMA 2020

作者： Phan, Hao D. Lin, Ker-Chun Phan, Hieu T. Faculty of Civil Engineering The University of Danang – University of Science and Technology Danang Viet Nam Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei Taiwan National Center for Research on Earthquake Engineering Taipei Taiwan Department of Computer Science and Software Engineering Miami University OxfordOH United States

ISBN: (纸本)9789811632389

This paper presents numerical investigations on the seismic behavior of full-scale square concrete filled steel tubular (CFST) columns. The main objective is to understand the seismic behavior and evaluate the seismic performance of these composite columns under high levels of axial compression. Finite element analysis (FEA) models in ABAQUS software were used to simulate a series of columns subjected to axial compression and cyclic lateral loading. The CFST columns were modeled using eight-node reduced integration brick elements (C3D8R) for the infilled concrete with confinement effect, and four-node reduced integration shell elements (S4R) for the steel tube with consideration of steel-concrete interaction and steel wall’s buckling. The feasibility of the FEA models has been validated by published experimental results. The validated FEA model was further extended to conduct parametric studies with various parameters including width-to-thickness ratio (B/t), concrete strength, and axial compression level. The numerical analysis results reveal that with the same B/t and constituent materials, the higher the axial compression was, the lower the shear strength and the deformation capacity were. Also, the higher axial compression led to earlier local buckling of the steel tube, especially, in the case of the thinner steel wall (B/t of 41.7). The thicker steel wall (B/t of 20.8) resulted in higher strength and larger deformation capacity of the column. Increasing concrete material strength significantly improved the column’s shear strength for both thinner and thicker steel walls, but it led to significant development in deformation for the column having thicker steel walls. This study also reveals that only the square CFST columns with B/t of 20.8 using medium material strengths satisfy the seismic performance demand for the building columns in high seismic zones (ultimate interstory drift ratio (IDRu) not less than 3% radian) under high axial compression (up to 55% of

关键词： Concrete confinement effect Finite element analysis (FEA) model High axial compression Local buckling Seismic performance Square CFST columns Width-to-thickness ratio (B/t)

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Semantic, Technical and Legal Interoperability of European Company Open Data in Practice: The STIRData Approach

Semantic, Technical and Legal Interoperability of European C...

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12th International Conference on Data Science, technology and Applications, DATA 2023

作者： Klımek, Jakub Chortaras, Alexandros Mısek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Charles University Faculty of Mathematics and Physics Department of Software Engineering Czech Republic School of Electrical and Computer Engineering National Technical University of Athens Greece Institute of Law and Technology Masaryk University Czech Republic Norwegian Digitalisation Agency Norway Brønnøysund Register Centre Norway

ISBN: (纸本)9789897586644

As part of the Open Data Directive, the European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data. The list also contains specific data items that must be included in these datasets. However, it does not prescribe any technical means of how the data should be published, severely hindering the interoperability of the datasets once they are published. One of the HVD topics is company data. In this practice report paper, we present results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on technical, semantic, and legal interoperability of open data from business registries, covering the company data HVDs topic. The results include a data architecture and a data specification to make the published data technically and semantically interoperable, and legal interoperability guidelines to ensure legal interoperability of the published data. Moreover, proof-of-concept transformations of data from selected European business registries are shown using open source tools and according to the specification. Finally, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. © 2023 by SCITEPRESS–Science and technology Publications, Lda.

关键词： Data consistency

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Partially Hidden Policy Attribute-Based Multi-Keyword Searchable Encryption with Verification and Revocation

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Liqing Xu, Shiguo Jin, Chunhua Zhang, Hao Weng, Jian Huaiyin Institute of Technology Faculty of Computer and Software Engineering Jiangsu Huai'an223003 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Jinan University College of Information Science and Technology Guangdong Guangzhou510632 China

Data security is becoming increasingly critical as outsourced data services flourish. An effective solution for ensuring data confidentiality in the cloud is attribute-based searchable encryption (ABSE). However, most extant ABSE solutions have not been able to simultaneously achieve rich expressive queries, result validation, attribute revocation, and policy hiding, and there are problems such as inflexibility in query methods and access control capabilities, insufficient scalability and practicality, and privacy leakage. In particular, it should be noted that most ABSE schemes fail to promise the privacy security of access policies and are susceptible to offline keyword guessing attack. Hidden policy-ABSE (HP-ABSE) has been proposed to tackle the issue of explicit attribute values in access policies potentially compromising privacy. Nevertheless, in most existing HP-ABSE schemes, an adversary may initiate attribute values guessing attack to speculate on the values of attribute within access strategy. To this end, this paper proposes partially hidden policy attribute-based multi-keyword searchable encryption with verification and revocation (PHP-ABMKSE-VR). PHP-ABMKSE-VR scheme is practical, secure, as well as effective, making it appropriate for real-world scenarios like smart healthcare, as demonstrated by the experimental outcomes and safety analysis. © 2002-2012 IEEE.

关键词： Differential privacy

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A Method for Depth Camera Calibration Based on Motion Capture System

A Method for Depth Camera Calibration Based on Motion Captur...

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Jianchu Lin Ming Zhao Guojun Yin Haiping Zhou Toshboev Hudoyberdi Bo Jiang Faculty of Computer and Software Engineering Huaiyin Institue of Technology Huai’an China Faculty of Art and Design Huaiyin Institue of Technology Huai’an China Jiangsu Eazytec Co. LTD Yixin China

With the rapid development of 3D computer vision technology, depth cameras have been widely used. Depth camera calibration is mainly constrained by two aspects: (1) alignment of depth data, and (2) distortion in RGB images. Under the condition of unknown camera intrinsic parameters, a method for RGBD commercial camera calibration is studied based on an optical motion capture system. The proposed approach utilizes the projection from the motion capture space to the depth camera’s field of view, establishing a coupled parameter model for the camera’s CCD photosensitive elements to improve the calibration of the depth camera. Experimental results demonstrate a 33% reduction in errors in the central region of the camera’s field of view compared to commonly Least Squares (LS) calibration methods. Moreover, the method exhibits faster computation speed, enabling efficient real-time calibration of depth cameras with unknown parameters in practice.

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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TAVP: Task-Adaptive Visual Prompt for Cross-domain Few-shot Segmentation

arXiv

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arXiv 2024年

作者： Yang, Jiaqi Zhang, Yaning Hu, Jingxi He, Xiangjian Shen, Linlin Qiu, Guoping School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan China Science & Software Engineering Guang Dong Shen Zhen China

While large visual models (LVM) demonstrated significant potential in image understanding, due to the application of large-scale pre-training, the Segment Anything Model (SAM) has also achieved great success in the field of image segmentation, supporting flexible interactive cues and strong learning capabilities. However, SAM’s performance often falls short in cross-domain and few-shot applications. Previous work has performed poorly in transferring prior knowledge from base models to new applications. To tackle this issue, we propose a task-adaptive auto-visual prompt framework, a new paradigm for Cross-dominan Few-shot segmentation (CD-FSS). First, a Multi-level Feature Fusion (MFF) was used for integrated feature extraction as prior knowledge. Besides, we incorporate a Class Domain Task-Adaptive Auto-Prompt (CDTAP) module to enable class-domain agnostic feature extraction and generate high-quality, learnable visual prompts. This significant advancement uses a unique generative approach to prompts alongside a comprehensive model structure and specialized prototype computation. While ensuring that the prior knowledge of SAM is not discarded, the new branch disentangles category and domain information through prototypes, guiding it in adapting the CD-FSS. Comprehensive experiments across four cross-domain datasets demonstrate that our model outperforms the state-of-the-art CD-FSS approach, achieving an average accuracy improvement of 1.3% in the 1-shot setting and 11.76% in the 5-shot setting. © 2024, CC BY.

关键词： Semantic Segmentation

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Detection of Toxic Content on Social Networking Platforms Using Fine Tuned ULMFiT Model

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Intelligent Automation & Soft Computing 2023年第1期35卷 15-30页

作者： Hafsa Naveed Abid Sohail Jasni Mohamad Zain Noman Saleem Rao Faizan Ali Shahid Anwar Department of Software Engineering Faculty of ScienceUniversity of LahorePakistan Department of Computer Science COMSATS University IslamabadLahore CampusPakistan Institute for Big Data Analyics and Artifcial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversii Tekoogi MARA40450Shah AlamSelangorMalaysia TechnoGenics SMC PVT LTD LahorePakistan Department of Computer and Infomation Science Universiti Teknologi PETRONASBandar Ser IskandarTronohPerakMalaysia Depatment of Information Engineering Technology National Skils University IslamabadSector H-8/1Faiz AhmedFaiz RoadIslamabadPakistan

Question and answer websites such as Quora,Stack Overflow,Yahoo Answers and Answer Bag are used by *** users post questions on these websites to get the answers from domain specific *** websites are multilingual meaning they are available in many different *** problem for these types of websites is to handle meaningless and irrelevant *** this paper we have worked on the Quora insincere questions(questions which are based on false assumptions or questions which are trying to make a statement rather than seeking for helpful answers)dataset in order to identify user insincere questions,so that Quora can eliminate those questions from their platform and ultimately improve the communication among users over the ***,a research was carried out with recurrent neural network and pretrained glove word embeddings,that achieved the F1 score of *** proposed study has used a pre-trained ULMFiT *** model has outperformed the previous model with an F1 score of 0.91,which is much higher than the previous studies.

关键词： Machine learning text mining quora mining artificial intelligence natural language processing

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