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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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Analysis of Sentiments on Russia-Ukraine Conflict using Machine Learning Techniques

Analysis of Sentiments on Russia-Ukraine Conflict using Mach...

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Contemporary computing and Communications (InC4), IEEE International Conference on

作者： Sakshi Negi Ishaan Dawar Rishabh Prakash Prashant Tiwari Megha Bhushan Ajay Kumar School of Computing DIT University Dehradun Uttarakhand India Department of Software Engineering DTU Delhi India Department of Information Technology DTU Delhi India CSIT Department KIET Group of Institutions Gaziabadh India

Nowadays, the internet has become an essential part of the current scenario. Due to the rapid expansion of the social network industry, people are using its platforms to share their opinions on current events. It is important to collect and evaluate consumer feedback on products, services, and other topics. It is of utmost importance to determine if an endorsement or criticism of war operations leads to the universal message as the crisis between Russia and Ukraine has worsened and more foreign powers are involved. The abundant writings on the critical connection between public opinion to international relations support the above-mentioned objective. This study focuses mainly on Twitter text data extracted by Twitter API during the Ukraine-Russia war timeline and is available on Kaggle. Sentimental analysis using various supervised machine learning techniques, including Random Forest, Xtreme Gradient Boosting, Support Vector Machine, and Logistic Regression. Also, the comparison of their performance based on various metrics, including the accuracy and precision of the data is presented. As a result, it investigates the opinions and reactions of the global world to Russia’s invasion of Ukraine.

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Comparing the Effect of Code Optimizations on Simulation Runtime Across Synchronous Cellular Automata Models of HIV

Comparing the Effect of Code Optimizations on Simulation Run...

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2021 Winter Simulation Conference, WSC 2021

作者： Li, Junjiang Giabbanelli, Philippe J. Koster, Till Miami University Department of Computer Science Software Engineering OH45056 United States Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

Models developed by domain experts occasionally struggle to achieve a sufficient execution speed. Improving performances requires expertise in parallel and distributed simulations, hardware, or time to profile performances to identify bottlenecks. However, end-users in biological simulations of the Human Immunodeficiency Virus (HIV) have repeatedly demonstrated that these resources are either not available or not sought, resulting in models that are developed through user-friendly languages and platforms, then used on workstations. This situation becomes problematic when performances cannot cope with the salient characteristics of the phenomenon that is modeled, as is the case with cellular automata (CA) models of HIV. In this paper, we optimize the Python code of CA models of HIV to scale the number of cells handled by a simulation on a workstation commonly available to end-users. We demonstrate this scalability via five HIV CA models and compare these results to assess how modeling choices can impact runtime. © 2021 IEEE.

关键词： Viruses

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Multimodal Deep Neural Networks for Digitized Document Classification

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Computer Systems Science & engineering 2024年第3期48卷 793-811页

作者： Aigerim Baimakhanova Ainur Zhumadillayeva Bigul Mukhametzhanova Natalya Glazyrina Rozamgul Niyazova Nurseit Zhunissov Aizhan Sambetbayeva Department of Computer Engineering Khoja Akhmet Yassawi International Kazakh-Turkish UniversityTurkistanKazakhstan Department of Computer and Software Engineering L.N.Gumilyov Eurasian National UniversityAstanaKazakhstan Department of Information and Computing Systems Non-Profit Joint Stock Company Abylkas Saginov Karaganda Technical UniversityKaragandaKazakhstan Department of Information Systems Al-Farabi Kazakh National UniversityAlmatyKazakhstan

As digital technologies have advanced more rapidly,the number of paper documents recently converted into a digital format has exponentially *** respond to the urgent need to categorize the growing number of digitized documents,the classification of digitized documents in real time has been identified as the primary goal of our study.A paper classification is the first stage in automating document control and efficient knowledge discovery with no or little human *** intelligence methods such as Deep Learning are now combined with segmentation to study and interpret those traits,which were not conceivable ten years *** learning aids in comprehending input patterns so that object classes may be *** segmentation process divides the input image into separate segments for a more thorough image *** study proposes a deep learning-enabled framework for automated document classification,which can be implemented in higher *** further this goal,a dataset was developed that includes seven categories:Diplomas,Personal documents,Journal of Accounting of higher education diplomas,Service letters,Orders,Production orders,and Student ***,a deep learning model based on Conv2D layers is proposed for the document classification *** the final part of this research,the proposed model is evaluated and compared with other machine-learning *** results demonstrate that the proposed deep learning model shows high results in document categorization overtaking the other machine learning models by reaching 94.84%,94.79%,94.62%,94.43%,94.07%in accuracy,precision,recall,F-score,and AUC-ROC,*** achieved results prove that the proposed deep model is acceptable to use in practice as an assistant to an office worker.

关键词： Document categorization deep learning machine learning classification digitization

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Consumer and Farmer Centric Subscription Based Organic Vegetable/Fruit Delivery System 3

Consumer and Farmer Centric Subscription Based Organic Veget...

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3rd International Conference on Advancements in computing, ICAC 2021

作者： Karunarathna, T.M.N.P. Kumari, M.M.A. Kulasinghe, W.D.K.A. Parana Liyanage, T.L. Kodagoda, Nuwan Suriyanwsa, Kushnara Kumari, Disna Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Kings Hospital Colombo Sri Lanka

ISBN: (纸本)9781665408622

For centuries, the economy of Sri Lanka has been backed by the agricultural sector. Even though it has contributed to the development of the nation on a large scale, the economies and living standards of the farmers have not been improved much. Most of the farmers remain poor mainly due to not knowing the market demand and the low buying prices of the middleman. Meanwhile, the consumers in Sri Lanka are buying non-organic vegetables and fruits which are highly contaminated with dangerous chemicals which in turn has contributed to the rapidly increasing non-communicable disease rates in both consumers and farmers. The proposed system is an attempt to solve these problems faced by both farmers and consumers by reducing the farmers-consumer knowledge gap and providing consumers a trustworthy and personalized subscription for organic vegetables/fruits with a donation platform. © 2021 IEEE.

关键词： Crops

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Unveiling Fake News with an Attention-Based CNN Model Using Hybrid Features

Unveiling Fake News with an Attention-Based CNN Model Using ...

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Information and Communication Technology (ICICT), International Conference on

作者： Francis Rudra D Cruze Md. Faruk Hosen Md. Suhag Ali Mohammad Sarwar Hossain Mollah Muhammad Shahin Uddin Monir Morshed Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology Univeristy Tangail Bangladesh Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

ISBN: (数字)9798331508227

ISBN: (纸本)9798331508234

Sophisticated detection mechanisms are required to protect the integrity of information in the digital age, as the widespread dissemination of false news on social media platforms has become a critical challenge. To tackle the practical issue of spreading false information, there is an urgent need for reliable systems that can effectively identify and counteract the propagation of fake news. This work presents a novel method for identifying fake news by combining sophisticated encoding techniques and utilizing deep learning architectures. Our approach integrates TF-IDF, CountVectorizer (CV), and Word2Vec (W2V) techniques for encoding. These techniques are coupled sequentially to capture the subtle details in fake news content successfully. Through the utilization of attention-based Convolutional Neural Network (CNN) models, we improve the comprehension and categorization of intricate textual material. Our methodology prioritizes scalability to efficiently analyze big datasets while retaining high detection accuracy in many situations and languages. Our approach has been thoroughly tested and proven to be strong and applicable in a wide range of false news situations. We have attained a state-of-the-art level of precision, with an accuracy of 99.11 %, by employing an attention-based Convolutional Neural Network (CNN) coupled CountVectorizer and Word2Vec embeddings,

关键词： Deep learning Accuracy Social networking (online) Scalability Organizations Feature extraction Encoding Convolutional neural networks Reliability Fake news

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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Attention-Based Multiscale Localization for Bone Age Assessment

Attention-Based Multiscale Localization for Bone Age Assessm...

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IEEE International Symposium on Biomedical Imaging

作者： Kyunghee Jung Toan Duc Nguyen Duc-Tai Le Junghyun Bum Chang-Hwan Son Hyunseung Choo Department of Superintelligence Engineering Sungkyunkwan University Korea College of Computing and Informatics Sungkyunkwan University Korea Sungkyun AI Research Institute Sungkyunkwan University Korea Department of Software Science & Engineering Kunsan National University Korea Department of Electrical and Computer Engineering Sungkyunkwan University Korea

Bone Age Assessment (BAA) is crucial for the biological maturity evaluation of children. Developing automated techniques of BAA has gained a lot of attention from both academia and medicine. This paper presents a novel deep-learning-based BAA method including refining and multi-scale processing of hand X-ray images. The refining step removes unnecessary background and noises in the images, resulting in a high-quality dataset. Such image refinement is beneficial for Region of Interest (ROI) localization step with self-attention mechanism. The localization model is trained with multiscale hand X-ray images separately to obtain multiple ROIs for each image. Eventually, the multiscale ROIs are used as complementary features of an image in training a regression model for BAA. We evaluate the performance of the proposed method using 2017 RSNA pediatric bone age challenge dataset. The results show mean absolute error of 3.52, which is 24.9% lower than state-of-the-art results.

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A Conceptual Framework for Boosting Research Activities Learning in Undergraduate Students

A Conceptual Framework for Boosting Research Activities Lear...

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Latin American Conference on Learning Objects and Technology (LACLO)

作者： Alvaro Fernández Del Carpio Leonardo Bermón Angarita Department of Software Engineering Universidad La Salle Arequipa Perú Computing Department Universidad Nacional de Colombia Manizales-Caldas Colombia

ISBN: (纸本)9781665465229

The research process plays a significant role in the university environment since it allows knowledge to be enriched, requiring the active participation of university professors and students. Such involvement is not carried out as expected, mainly since there are not enough mechanisms to foster the inclusion of professors and students. It is widely known that many members of the academic and student community lack training or a knowledge base regarding the development of scientific research. This paper proposes a conceptual framework for research that enables to get knowledge in research activities targeted at university students and professors to improve individual and institutional productivity in research. The proposed framework is developed using a methodology of 5 phases: 1) literature review; 2) study of methodological frames; 3) definition of framework components; 4) implementation plan; and 5) validation of the framework. The approach of the framework has been evaluated by experts with extensive and recognized experience in developing research and managing research directions in universities. According to the results of the evaluation by experts, the framework enables to set the path to develop and increase the capabilities in research of the community of professors and students. However, being a conceptual framework, it requires yet more detail in the specification of each process to be implemented appropriately.

关键词： Adaptation models Monitoring Biomedical monitoring Training software engineering Productivity Knowledge based systems

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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