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AI-based model for securing cognitive IoT devices in advance communication systems

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International Journal of Cognitive Computing in Engineering 2025年 6卷 351-359页

作者： Gaurav, Akshat Arya, Varsha Chui, Kwok Tai Gupta, Brij B. Ronin Institute MontclairNJ United States Asia University Taichung 413 Taiwan Dehradun India UCRD Chandigarh University Chandigarh India Hong Kong Metropolitan University Hong Kong SAR China Department of Computer Science and Information Engineering Asia University Taichung Taiwan University of Economics and Human Science Warsaw Poland Symbiosis International University Pune India School of Cybersecurity Korea University Seoul Korea Republic of

The pervasive integration of the Cognitive Internet of Things (CIoT) in advanced communication systems has significantly heightened security concerns due to inherent vulnerabilities that cyber attackers exploit. These security challenges are exacerbated by IoT devices’ limited computational resources and storage capacities, rendering traditional security protocols ineffective. This paper introduces a AI- based security model tailored explicitly for IoT environments within advanced communication systems. Our approach leverages AI techniques for cyber attack detection and mitigation. The effectiveness of our model is demonstrated through its capability to identify different types of cyberattacks with a precision of more than 99%. This study advances the security mechanisms for IoT devices and offers a scalable and efficient solution to safeguard user privacy and system integrity in sophisticated communication networks. © 2025 The Authors

关键词： Cyber attacks

Minion: A Technology Probe for Resolving Value Conflicts through Expert-Driven and User-Driven Strategies in AI Companion Applications

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arXiv 2024年

作者： Fan, Xianzhe Xiao, Qing Zhou, Xuhui Su, Yuran Lu, Zhicong Sap, Maarten Shen, Hong Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science City University of Hong Kong Hong Kong SAR China Language Technologies Institute Carnegie Mellon University PittsburghPA United States

AI companions based on large language models can role-play and converse very naturally. When value conflicts arise between the AI companion and the user, it may offend or upset the user. Yet, little research has examined such conflicts. We first conducted a formative study that analyzed 151 user complaints about conflicts with AI companions, providing design implications for our study. Based on these, we created Minion, a technology probe to help users resolve human-AI value conflicts. Minion applies a user-empowerment intervention method that provides suggestions by combining expert-driven and user-driven conflict resolution strategies. We conducted a technology probe study, creating 40 value conflict scenarios on *** and Talkie. 22 participants completed 274 tasks and successfully resolved conflicts 94.16% of the time. We summarize user responses, preferences, and needs in resolving value conflicts, and propose design implications to reduce conflicts and empower users to resolve them more effectively. Copyright © 2024, The Authors. All rights reserved.

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A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

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Intelligent Automation & Soft Computing 2023年第1期35卷 163-180页

作者： Yassir Edrees Almalki Maryam Zaffar Muhammad Irfan Mohammad Ali Abbas Maida Khalid K.S.Quraishi Tariq Ali Fahad Alshehri Sharifa Khalid Alduraibi Abdullah AAsiri Mohammad Abd Alkhalik Basha Alaa Alduraibi M.K.Saeed Saifur Rahman Department of Medicine Division of RadiologyMedical CollegeNajran UniversityNajran11001Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran11001Saudi Arabia Department of Chemical Engineering PIEAS UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University Islamabad(CUI)Sahiwal Campus57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraydah52571Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran11001Saudi Arabia Faculty of Human Medicine Zagazig University44631Egypt

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the *** X-ray is considered very useful and less time-consuming for monitoring COVID *** doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is *** address this issue,we proposed to utilize deep learning *** this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients *** performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray *** proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of *** system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 ***,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

关键词： Biomedical systems chest X-ray images CNN COVID-19 swin transformer image processing

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Farbod Siahkali Seyed Amirmahdi Alavi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Computer Science Islamic Azad University Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476249

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks. 1 https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition

关键词： Representation learning Image recognition Video tracking Machine learning algorithms Neural networks Real-time systems Automobiles

"It’s the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication Assistance

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arXiv 2024年

作者： Jang, JiWoong Moharana, Sanika Carrington, Patrick Begel, Andrew Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA United States

Autistic adults often experience stigma and discrimination at work, leading them to seek social communication support from coworkers, friends, and family despite emotional risks. Large language models (LLMs) are increasingly considered an alternative. In this work, we investigate the phenomenon of LLM use by autistic adults at work and explore opportunities and risks of LLMs as a source of social communication advice. We asked 11 autistic participants to present questions about their own workplace-related social difficulties to (1) a GPT-4-based chatbot and (2) a disguised human confederate. Our evaluation shows that participants strongly preferred LLM over confederate interactions. However, a coach specializing in supporting autistic job-seekers raised concerns that the LLM was dispensing questionable advice. We highlight how this divergence in participant and practitioner attitudes reflects existing schisms in HCI on the relative privileging of end-user wants versus normative good and propose design considerations for LLMs to center autistic experiences. © 2024, CC BY-NC-ND.

关键词： Computational linguistics

An EEG Method to Identify Image Preference With an Explicit/Implicit Task Brain-computer Interface

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IEEE Transactions on Affective Computing 2025年

作者： Li, Yulei Li, Shuyi Qi, Hongzhi Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300092 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China

Accurately determining an individual's preference for images remains a major challenge in the field of emotional research. This study proposes a novel paradigm for identifying individual image preferences using electroencephalography (EEG) signals and brain-computer interface (BCI). The paradigm involves both explicit and implicit tasks, where participants perform a typical event-related potential-based brain-computer interface(ERP-BCI) operation and their subjective image preferences are identified, respectively. Two experiments with a total of 27 participants demonstrate that event-related potential (ERP) signals during explicit BCI tasks are significantly influenced by target image preferences, enabling high-accuracy image preference recognition. Online experiments selecting positive and negative preference images from a candidate pool show top-1 accuracy approaching 100% and top-3 accuracy exceeding 90%. These results indicate the effectiveness of the proposed EEG-based image preference recognition paradigm, laying the groundwork for preference analysis applications. © 2010-2012 IEEE.

关键词： Brain mapping

I Can’t Share Code, but I need Translation - An Empirical Study on Code Translation through Federated LLM

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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks

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arXiv 2022年

作者： Haghpanah, Mohammad Amin Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Computer Science and Engineering University of Nevada Reno United States

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

It's the Same Conflict Every Time, on Repeat.

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It's the Same Conflict Every Time, on Repeat.

2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Chin Derix, Eleanor Leong, Tuck Wah Prior, Julia Interaction Design + Human Practice Lab University of Technology Sydney Australia Uts Australia School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781450380959

Family technology use can create or amplify conflict in parents' relationships - we found four key factors that contribute to this issue. We conducted a probe and interview study with eight sets of parents, to explore how and why technology use might cause conflict in their relationships. This paper presents data from two particular sets of parents to illustrate our findings. In doing so, it complements existing work that primarily focuses on parent-child relationships, and contributes to a more complete understanding of how family technology use can affect family dynamics. We also suggest directions for further work to address this issue of conflict between parents, associated with their family's use of technology. © 2021 ACM.

关键词： computer programming