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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment 4

Double Encryption Technique for Sharing and Storing the Imag...

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Anusha, P. Maruthi, R. Kumar, D. Maghesh Begum, H. Parveen Periyar Maniammai Institute of Science and Technology Department of Software Engineering Tamil Nadu Thanjavur India Hindustan Institute of Technology and Science Department of Computer Applications Tamil Nadu Chennai India

ISBN: (纸本)9798350343670

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end-to end encryption for sharing and storing files in the cloud environment. © 2024 IEEE.

关键词： Advanced Encryption Standard Cloud Cryptography Elliptic Curve Cryptography File Sharing

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WIP: A Systematic Approach for Outcome-Based Assessment 54

WIP: A Systematic Approach for Outcome-Based Assessment

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Belkhouche, F. Bendjilali, K. Shippensburg University of Pennsylvania Department of Computer Electrical and Software Engineering ShippensburgPA United States Raritan Valley Community College Department of Mathematics and Computer Science BranchburgNJ United States

ISBN: (纸本)9798350351507

This research-to-practice WIP paper describes methods for improving assessment. Assessment is crucial for pro-gram accreditation and continuous improvement in engineering education. Outcome-based learning is a recent student-centred approach that can be used perform assessment. Under this paradigm, clear outcomes related to the knowledge and skills that the students should have after completing the course are identified and assessed. The student attainments are measured against the outcomes using clear and well-defined metrics. In this paper, an outcome-based strategy is proposed and used for assessing courses in control systems. In order to improve assessment reliability, the learning outcomes are broken down into sub-outcomes, which are used as performance indicators. The advantage of the proposed method is twofold, first it provides more accurate and reliable metrics for measuring student attainment, and second, the assessment results are personalized, allowing to evaluate individual students and identify means to help them. This approach also allows to identify common mistakes and misconceptions. Data are collected for several semesters and analyzed. It turns out that the proposed approach allows to obtain more accurate and reliable assessment results. © 2024 IEEE.

关键词： engineering education

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models 16

VisioSLR: A Vision Data-Driven Framework for Sign Language V...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Ismail, Leila Shahin, Nada Tesfaye, Henok Hennebelle, Alain Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Transfer learning

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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CONTEXT AWARE FACIAL EXPRESSION RECOGNITION WITH DEEP LEARNING ARCHITECTURES

CONTEXT AWARE FACIAL EXPRESSION RECOGNITION WITH DEEP LEARNI...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Bano, Nudrat Asif, Muhammad Zia, Muhammad Azam Bilal, Hafiz Muhammad Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (纸本)9781837243235

Facial expression recognition (FER) plays a pivotal role in applications such as mental health diagnosis, security, marketing, human-robot interaction, healthcare, education, and gaming. However, challenges like varied facial poses, uneven lighting, and the presence of facial accessories often hinder accurate detection. Traditional methods frequently struggle with effective feature extraction and classification. To address these limitations, this study proposes a robust facial expression recognition architecture based on Convolutional Neural Networks (CNNs) coupled with advanced preprocessing techniques. The model effectively mitigates issues such as lighting variations and class imbalances while achieving enhanced recognition accuracy. A comprehensive evaluation using k-fold cross-validation was conducted on the CK+ dataset, renowned for its high-quality labeled images of primary emotions. The proposed model achieved an accuracy of 96%, significantly outperforming established benchmarks, including VGG-19 (90%), ResNet50 (92%), and MobileNet (94%). These results underscore the efficacy of the CNN-based approach in advancing FER accuracy. Future work will focus on extending this research to real-time facial expression detection, leveraging transfer learning to adapt the model to diverse datasets, and integrating emotion recognition with multimodal data such as speech and EEG signals to broaden its applicability across industries. © The Institution of engineering & Technology 2025.

关键词： CONVOLUTION NEURAL NETWORK DEEP LEARNING EMOTION DETECTION FACIAL EXPRESSION RECOGNITION

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Analysis of Decision-Making Strategies in Social Networks

Analysis of Decision-Making Strategies in Social Networks

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： Zafar, Remsha Zia, Muhammad Azam Asif, Muhammad Department of Computer Science University of Agriculture Faisalabad Faisalabad Pakistan Department of Computer and Software Engineering Information Technology University (ITU) Punjab Pakistan

ISBN: (纸本)9781837243235

In the era of social media, platforms have become integral to various domains, particularly business, where trends significantly influence decision-making processes. Despite numerous studies, effective decision-making in social networks remains a challenge. This study addresses these issues by proposing a novel model for analyzing decision-making strategies. A publicly available dataset containing social media user reviews of various products, including attributes such as identification, labels, country, and sentiment, is utilized. The dataset undergoes preprocessing and normalization, incorporating techniques such as tokenization, lemmatization, stop-word removal, and punctuation elimination. Deep learning methodologies are applied for model development and analysis, leveraging Python and the PyCharm framework. The proposed model is rigorously validated using state-of-the-art techniques and evaluated through extensive testing to measure its performance in terms of accuracy. Comparative analysis with recent methods underscores the effectiveness of the model. The findings of this study offer valuable insights for improving decision-making strategies, guiding new product development, and integrating diverse analytical models in social network contexts. © The Institution of engineering & Technology 2025.

关键词： big data Deep Learning sentiment analysis Social networks Twitter

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Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

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Utilizing the Blockchain Technology for Higher Education in the Era of Pandemics: A Systematic Review 14

Utilizing the Blockchain Technology for Higher Education in ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Al-Tawara, Feda'a Qasaimeh, Malik Jarad, Dania Al-Qassas, Raad S. Princess Sumaya University for Technology Department of Software Engineering Amman Jordan Jordan University of Science and Technology Department of Software Engineering Department of Cyber Security Irbid Jordan Princess Sumaya University for Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798350307870

The availability of important characteristics such as decentralization, permanence, anonymity, and audacity has driven interest in blockchain technology more recently than ever before. This technology has been employed in a variety of applications, including education. Therefore, this research is a systematic review of research that investigates the applications of blockchain technology in higher education institutions in addition to those that present the role of this technology in combating the Corona pandemic, as it focuses on the following main topics: (1) blockchain-based applications and systems that can be used in higher education institutions;and (2) the current state of blockchain challenges in this area that need to be addressed in the future. A detailed analysis of the results of each topic as well as an extensive discussion were conducted based on the results. This review also provides insight into other areas that could benefit from blockchain technology during and after the pandemic. © 2023 IEEE.

关键词： Blockchain

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Accurate Anomaly Detection in Medical Images using Transfer Learning and Data Optimization: MRI and CT as Case Studies 5

Accurate Anomaly Detection in Medical Images using Transfer ...

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5th International Conference on Neural Networks and Neurotechnologies, NeuroNT 2024

作者： Nizamli, Yasser Filatov, Anton Yu. Fadel, Weaam Shichkina, Yulia A. Saint Petersburg Electrotechnical University 'LETI' Department of Software Engineering and Computer Applications Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science Saint Petersburg Russia

ISBN: (纸本)9798350363739

Today, medical imaging techniques are widely used to detect a variety of human conditions and diseases. To speed up the diagnostic process, systems are often automated using deep learning methods, which have been proven to yield outstanding results. However, these models may be limited to a specific domain, prone to overfitting, and difficult to update. In this paper, we present a transfer learning approach that achieves high accuracy for detecting anomalies in both MRI and CT images. Our system utilizes the ConvNeXt network, inspired by the Swin Transformer architecture, as a high-level feature extractor from processed and augmented medical images. The resulting data is then optimized using the Edited Nearest Neighbors algorithm, which selects the most effective observations and discards the rest. Finally, the data is passed to the K-Nearest Neighbors classifier to detect anomalies in numerically represented images. Our model achieves high performance for two different types of medical images and can easily be updated without the need for retraining. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Interference cancellation in RIS-assisted FD relay transmission 14

Interference cancellation in RIS-assisted FD relay transmiss...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Kim, Donghyeon Choi, Wooyeol Chun, Chanjun Kim, Yohan Kim, Yonggang Chosun University Department of Computer Engineering Korea Republic of Dongseo University Department of Software Korea Republic of Kongju National University Division of Computer Science and Engineering Korea Republic of

ISBN: (纸本)9798350335385

Reconfigurable intelligent surface (RIS) is one of the promising technology for the next-generation wireless networks. The RIS reflects the received signal with phase shift introduced by reflecting elements without analog to digital conversion. In relay transmission scenario, RIS could be exploited to enhance the network coverage and improve transmission performance. In this paper, we consider RIS-assisted full-duplex (FD) relay transmission scenario. In the considered scenario, RIS and FD relay node performs relay transmission. Although the beamformed signal could be generated by phase shift of RIS, the interference from RIS to FD relay node may degrade the throughput performance. The proposed method cancels both of interference from RIS and self-interference at the FD relay node by estimating interference channels in the preparation phase before data transmission. © 2023 IEEE.

关键词： Analog to digital conversion

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