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6th International Conference on Recent Trends on Electronics, Information, Communication and Technology, RTEICT 2021

作者： Sethi, Anushka Kuchhal, Utkarsh Anjum Katarya, Rahul Delhi Technological University Department of Software Engineering Delhi India Delhi Technological University Department of Electronics and Communication Engineering Delhi India Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665435598

An Internet meme is a catchphrase, concept, or piece of media that is spread by means of the Internet, regularly through online media platforms and particularly for comical purposes. Detection of Online Hateful speech in the form of memes presents as a major challenge due to its multimodal nature. This study explores various methods for the classification of hateful memes on multimodal data using several pre-trained models like VGG19 and Xception in combination with machine learning models like Support Vector Machine and Naïve Bayes. We have experimented several techniques where one of them being integrated stacked model technique for the image modality. In our multimodal experiment approach, we have used early fusion technique and GloVe Embedding for which our model obtained 0.584 as the highest f1-score in a multimodal experiment consisting of an LSTM and VGG16 model. © 2021 IEEE.

关键词： Embeddings

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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An Image Classification Approach to Pre-Determine Extent of Development of Post-Operative Necrosis in Skin Flaps

An Image Classification Approach to Pre-Determine Extent of ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： G. Spacagna T. Fevens J. Barralet Department of Surgery McGill University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada Faculty of Dentistry McGill University Montreal Canada

Purpose - Clinicians and care workers currently cannot determine the final extent of necrosis once it begins. If the area is relatively small, it may heal like a normal wound, whereas re-operation may be the only option if it spreads. This paper represents work toward a predictive algorithm using full-thickness random rat skin flap photographs to determine whether the tissue will develop irretrievable *** - Using post-surgery images taken over a series of days of ischemic flaps, features were extracted, selected, and input into a classification algorithm to see if it could provide information on the future condition of the flaps. We split our data into two groups: flaps that underwent normal healing and slow healing. When consulting with a specialist, it was observed that the resulting dermal damage was not severe when a flap had ≤ 40% necrosis over its length on the final day. Three classifiers were implemented: K-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM).Results - The trained KNN was able to correctly determine whether a flap developed a necrotic area larger or less than 40% of its length with an accuracy of 91% using only images one day post-surgery. Under leave-one-out cross-validation, the Random Forest and SVM achieved accuracies of 82% and 79.5%, respectively, using images spanning ten days *** - We have shown that a classifier can accurately determine whether ischemic skin flaps will develop severe necrotic *** Relevance— An algorithm that assesses early timepoint images and predicts necrosis spread is not only a vital tool for patients and clinicians but is also an extremely important tool to accelerate research into necrosis reduction strategies that ultimately may find application for more life-threatening necrosis-related conditions.

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Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II

arXiv

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arXiv 2024年

作者： Wu, Rixin Wang, Ran Hao, Jie Wu, Qiang Wang, Ping Niyato, Dusit The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing Jiangsu210038 China The Department of Electrical Engineering and Computer Science Lassonde School of Engineering York University M3J 1P3 Canada The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learning (DRL) model to solve the entire multiobjective optimization problem. The Non-dominated sorting genetic algorithm-II (NSGA-II) method is then employed to optimize the outcomes produced by the WADRL, thereby mitigating the limitations of both approaches. Firstly, we design an MOVRPTW model to balance the minimization of travel cost and the maximization of customer satisfaction. Subsequently, we present a novel DRL framework that incorporates a transformer-based policy network. This network is composed of an encoder module, a weight embedding module where the weights of the objective functions are incorporated, and a decoder module. NSGA-II is then utilized to optimize the solutions generated by WADRL. Finally, extensive experimental results demonstrate that our method outperforms the existing and traditional methods. Due to the numerous constraints in VRPTW, generating initial solutions of the NSGA-II algorithm can be time-consuming. However, using solutions generated by the WADRL as initial solutions for NSGA-II significantly reduces the time required for generating initial solutions. Meanwhile, the NSGA-II algorithm can enhance the quality of solutions generated by WADRL, resulting in solutions with better scalability. Notably, the weight-aware strategy significantly reduces the training time of DRL while achieving better results, enabling a single DRL model to solve the entire multiobjective optimization problem. © 2024, CC BY.

关键词： Reinforcement learning

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Discovering Authorization Business Rules toward Detecting Web applications Logic Flaws

Discovering Authorization Business Rules toward Detecting We...

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Information Technology (ACIT)

作者： Hamza Alkofahi David Umphress Heba Alawneh Department of Software Engineering Jordan University of Science and Technology Irbid Jordan Department of Computer Science and Software Engineering Auburn University Auburn USA Department of Cybersecurity Al Hussein Technical University Amman Jordan

ISBN: (数字)9798350320244

ISBN: (纸本)9798350320251

Aggressive integration of validation checks into web framework software has altered the attack surface of web applications by reducing the opportunity for traditional injection flaws. The hacking community's reaction has shifted to a more subtle - and more challenging to detect - form of attacks, that of discovering and exploiting underlying application business logic. The lack of accurate business rules defining the final application product extends its logical vulnerability surface. We propose a novel black-box approach for discovering authorization business rules in web applications through users' dynamic behavior. Allowing applications that lack formal specifications to be better tested for logic vulnerabilities. Our approach discovers groups using agglomerative hierarchical clustering based on different profiling techniques that capture users' actions and privileges. We also automated the process of identifying the optimal number of roles. The results indicated high quality and stability in discovering business rules, even in smaller datasets.

关键词： Authorization Closed box Behavioral sciences Formal specifications Data mining Information technology computer crime

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Fine feature reconstruction in point clouds by adversarial domain translation

Fine feature reconstruction in point clouds by adversarial d...

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Graphics Interface 2020, GI 2020

作者： Raina, Prashant Popa, Tiberiu Mudur, Sudhir Department of Computer Science and Software Engineering Concordia University

Point cloud neighborhoods are unstructured and often lacking in fine details, particularly when the original surface is sparsely sampled. This has motivated the development of methods for reconstructing these fine geometric features before the point cloud is converted into a mesh, usually by some form of upsampling of the point cloud. We present a novel data-driven approach to reconstructing fine details of the underlying surfaces of point clouds at the local neighborhood level, along with normals and locations of edges. This is achieved by an innovative application of recent advances in domain translation using GANs. We "translate" local neighborhoods between two domains: point cloud neighborhoods and triangular mesh neighborhoods. This allows us to obtain some of the benefits of meshes at training time, while still dealing with point clouds at the time of evaluation. By resampling the translated neighborhood, we can obtain a denser point cloud equipped with normals that allows the underlying surface to be easily reconstructed as a mesh. Our reconstructed meshes preserve fine details of the original surface better than the state of the art in point cloud upsampling techniques, even at different input resolutions. In addition, the trained GAN can generalize to operate on low resolution point clouds even without being explicitly trained on low-resolution data. We also give an example demonstrating that the same domain translation approach we use for reconstructing local neighborhood geometry can also be used to estimate a scalar field at the newly generated points, thus reducing the need for expensive recomputation of the scalar field on the dense point cloud. © 2020 Canadian Information Processing Society. All rights reserved.

关键词： Machine learning

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Main Confidentiality Challenges in Network Security: A Literature Review

Main Confidentiality Challenges in Network Security: A Liter...

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Information Technology (ACIT)

作者： Mahmoud Asassfeh Mohammad Rasmi Al-Mousa Waleed Amer Adai Mohammad Almomani Ala’a Al-Shaikh Suha Afaneh Mohammed Nayef Alrefai Ali Elrashidi Ghassan Samara Department of Cybersecurity Zarqa University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Software Enginereeing Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Nowadays, a big challenge is how to protect information from unwanted accesses. It constitutes a big challenge when we have different ways to access data. That is, a lot of information today travels through networks and our challenge today is to guarantee that traveled information should be secret that is not divulged by intruders. There are many different ways to assure that information being transmitted through a network is secret. In this paper, after we have shown in Section 2 a brief description of the security problems inside a local network, the most well-known network security models are presented. This paper provides a review of the confidentiality in network security. Confidentiality is a property that aims to ensure that only authorized users can access the required information and data while unauthorized users and third parties cannot learn it. Confidentiality components encompass the use of access controls, encryption with cryptographic techniques, and secure data communications. Confidentiality has encountered many threats and security risks during its use and deployment such as eavesdropping, network traffic analysis, passive attacks, deception and impersonation attacks, insecure user authentication, and weak password vulnerabilities. Therefore, strategies and measures should be applied and used to enhance the confidentiality and thus secure the communication of data across the internet and other means.

关键词： Authentication Telecommunication traffic Passwords Impersonation attacks Network security Encryption Data communication Information technology Systematic literature review Eavesdropping

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks Data models

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Flying sensor network optimization using bee intelligence for internet of things

Flying sensor network optimization using bee intelligence fo...

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Intelligent Systems Conference, IntelliSys 2020

作者： Salam, Abdu Javaid, Qaisar Ali, Gohar Ahmad, Fahad Ahmad, Masood Wahid, Ishtiaq Department of Computer Science and Software Engineering International Islamic University Islamabad Islamabad Pakistan Department of Information System and Technology Sur University Sur Oman Department of Computer Science Abdul Wali Khan University Mardan Mardan Pakistan

ISBN: (纸本)9783030551896

Flynig sensor network (FSN) for internet of things (IoT) consist of flying nodes called sensors and ground segments. The flying nodes may be operated manually or it may be automated devices. The flying segment of IoT has different nature compared to ordinary mobile sensor network. The flying speed and diverse directions of nodes make it harder to route the sensor information in a desired way. The data may be collected on the basis of contract opportunities. Here the timely delivery may not be guaranteed. To ensure the desired operation of the FSN, the delivery of data to the base station either deployed in the air or on the ground segment must be ensured in an efficient manner. In this paper, the mating intelligence of bees is used to ensure the delivery of data. The energy consumption is reduced by reducing the amount of control messages and transmitting redundant information. The network lifetime is increased. Simulation is conducted to evaluate the performance of the proposed scheme. The simulation results show that the proposed scheme outperforms existing schemes under consideration. © Springer Nature Switzerland AG 2021.

关键词： Internet of things

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Hierarchical Continual Learning for Domain-Knowledge Retention in Healthcare Federated Learning

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Choudhry, Imran Arshad Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia University of Central Punjab Faculty of Information Technology and Computer Science Department of Software Engineering Lahore Pakistan

Internet of Medical Things (IoMT) applications encounter issues with data protection, continual adaptation, and domain-specific knowledge retention, especially in consumer-centric IoMT scenarios. We overcome these obstacles and facilitate effective knowledge retention and task adaptation in IoMT applications. This study attempts to create a unique privacy-preserving federated learning framework that combines a hierarchical learning structure with Continual Learning (CL). Despite the advancements in Federated Learning (FL), current models have trouble integrating changing datasets in real-time while protecting privacy, as well as catastrophic forgetting, which occurs when previously learned knowledge is lost when adjusting to new tasks. We present a hierarchical learning framework that makes use of three levels of models -Junior Model (JM), Consultant Model (CM), and Senior Consultant Model (SCM) -to overcome these drawbacks. Each level of the model aids in archived retention and domain-knowledge adaptation. To guarantee that the model maintains valuable information over time and adapts to new tasks with ease, our method blends domain adaptation strategies with ongoing learning approaches like knowledge distillation and elastic weight consolidation (EWC). We compare the suggested methodology with current state-of-the-art (SOTA) models on healthcare datasets for tasks like illness diagnosis and medical image categorization. According to our findings, the hierarchical continual learning model performs better than SOTA techniques in terms of accuracy, task adaptability, and privacy protection. In the healthcare industry, our study sets a new standard for privacy-preserving, continuously adaptable federated learning systems, allowing for real-time, scalable IoMT applications that can adapt dynamically to a variety of changing datasets. © 1975-2011 IEEE.

关键词： Patient treatment

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