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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Shreenidhi, H.S. Prabakar, Sachin Kumar, P Ashish Faculty of Engineering Technology Bengaluru India Computer Science and Engineering in Cloud Technology and Information Securiy Faculty of Engineering Technology India

ISBN: (纸本)9781665429214

The internet of things widely used nowadays. All things are connected to internet. It involves the creation of vulnerability scanner tool for IoT device which can be run in the system to detect vulnerability. With help of software and system configuration threats are detected. Which can lead to possible vulnerability and we also wrote attack script which performs an attack on the system to detect if the attack can be done on the system by using software tool configuration error identified. The proposed system identify the vulnerability of the architecture leads to Bruteforce attack and with the help of attacks script are able to find out if there are any open ports which could be used to exploit the system and also perform Dos DDos attack to check if the system is vulnerable for it along with it we also check if the data stored in the IoT device is encrypted or not. Once the scan is complete a detailed report is sent to the user Email. © 2021 IEEE.

关键词： Internet of things

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Navigating Urban Skies: Obstacle Avoidance Strategies for Quadrotor MAVs 13

Navigating Urban Skies: Obstacle Avoidance Strategies for Qu...

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13th International Physics Seminar 2024, IPS 2024

作者： Pebrianti, Dwi Kee, Wong Jack Bayuaji, Luhur Solichin, Achmad Department of Mechanical & Aerospace Engineering International Islamic University Malaysia Jl. Gombak Kuala Lumpur53100 Malaysia Faculty of Information Technology Universitas Budi Luhur Jl. Ciledug Raya Jakarta12260 Indonesia Faculty of Electrical & Electronics Engineering Universiti Malaysia Pahang Al-Sultan Abdullah Pekan26600 Malaysia Faculty of Data Science & Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai71800 Malaysia

Micro Aerial Vehicles (MAVs) has gained attentions since more than two decades ago starting from the applications in air combat up to civil applications such as packet deliveries, environmental monitoring, and surveillance. In an environment such as cities that grows denser, navigation and control for drones becomes challenging to ensure safe navigation around buildings and other obstacles. This study proposes an approach for obstacle avoidance for MAVs by using ultrasonic sensors. Four sensors are strategically positioned to cover the front, right, back, and left directions. Additionally, a downward-facing sensor measures the quadrotor's height above ground. Our goal is to develop autonomous MAV that can avoid obstacles, ensuring safe flight even in complex urban landscapes. The scenario implemented in the study is by introducing obstacle in any directions. When an obstacle is detected by the ultrasonic sensor, the signal will be sent to microcontroller and the attitude of the MAVs, roll or pitch will be adjusted to avoid the obstacle by moving to the counter direction of the obstacle. We conducted 20 trials of experiments by varying the gain values of Proportional Integral Derivative (PID) values, we fine-tune our obstacle avoidance algorithm. Modifications include optimizing roll and pitch adjustments, refining detection height thresholds, and implementing countermeasures after obstacle clearance. The results show that our proposed method has 10% overshoot when detecting any obstacles in different directions to avoid the obstacles. Our findings contribute to the advancement of safe and efficient urban drone operations, bridging the gap between technology and real-world challenges. © Published under licence by IOP Publishing Ltd.

关键词： Drones

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Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

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Evolving Systems 2024年第2期15卷 669-686页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Garfan, Salem Albahri, A.S. Zaidan, B.B. Chen, Juliana Perak Malaysia Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunrlin Douliou64002 Taiwan Discipline of Nutrition and Dietetics Susan Wakil School of Nursing and Midwifery Faculty of Medicine and Health The University of Sydney CamperdownNSW2006 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Charles Perkins Centre The University of Sydney CamperdownNSW2006 Australia

Mobile apps for sign language are a fascinating area of research that merits a lot of attention. These apps are widely used due to their affordability and usability. Nevertheless, the quality of these apps needs to be evaluated if they are to have a significant influence. The outcomes of app quality assessment can also stimulate app development efforts and improvements to their functionality and accuracy. Therefore, to gain more in depth understanding, four academic databases (science Direct, IEEE, Association for Computing Machinery, and Web of science) were searched as part of a systematic literature review. The review highlighted that sign language apps require further development to support users in achieving the greatest possible usage and learning experience. We present a solution framework based on a quality assessment criterion for sign language mobile apps that could be adopted by academics and developers. Alternative identification, criteria, and development are the three primary aspects of the framework. The results demonstrate that the new assessment approach can establish a detailed set of criteria for sign language mobile app development. Future academics could gain a thorough understanding of mobile app development criteria from this study. The results are advantageous and open up a new field where researchers and developers could collaborate on sign language mobile app development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Audition

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on information technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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Safe LLM-Controlled Robots with Formal Guarantees via Reachability Analysis

arXiv

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arXiv 2025年

作者： Hafez, Ahmad Akhormeh, Alireza Naderi Hegazy, Amr Alanwar, Amr Technical University of Munich TUM School of Computation Information and Technology Department of Computer Engineering Germany German university in Cairo Faculty of Media Engineering and Technology Department of Computer Science and Engineering Egypt

The deployment of Large Language Models (LLMs) in robotic systems presents unique safety challenges, particularly in unpredictable environments. Although LLMs, leveraging zero-shot learning, enhance human-robot interaction and decision-making capabilities, their inherent probabilistic nature and lack of formal guarantees raise significant concerns for safety-critical applications. Traditional model-based verification approaches often rely on precise system models, which are difficult to obtain for real-world robotic systems and may not be fully trusted due to modeling inaccuracies, unmodeled dynamics, or environmental uncertainties. To address these challenges, this paper introduces a safety assurance framework for LLM-controlled robots based on data-driven reachability analysis, a formal verification technique that ensures all possible system trajectories remain within safe operational limits. Our framework specifically investigates the problem of instructing an LLM to navigate the robot to a specified goal and assesses its ability to generate low-level control actions that successfully guide the robot safely toward that goal. By leveraging historical data to construct reachable sets of states for the robot-LLM system, our approach provides rigorous safety guarantees against unsafe behaviors without relying on explicit analytical models. We validate the framework through experimental case studies in autonomous navigation and task planning, demonstrating its effectiveness in mitigating risks associated with LLM-generated commands. This work advances the integration of formal methods into LLM-based robotics, offering a principled and practical approach to ensuring safety in next-generation autonomous systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Human robot interaction

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Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

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computers, Materials & Continua 2022年第2期70卷 2761-2775页

作者： Abdul Hanan Ashraf Muhammad Imran Abdulrahman M.Qahtani Abdulmajeed Alsufyani Omar Almutiry Awais Mahmood Muhammad Attique Mohamed Habib Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science and TechnologyIslamabad44000Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan College of Computing and Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Faculty of Engineering Port Said UniversityPort Fuad CityEgypt

In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting *** is why an automated weapon detection system is *** automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good ***,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection *** models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance *** research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key *** proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur *** proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 *** results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are *** reached 0.010 s per frame compared to the 0.17 s of the Faster *** is promising to be used in the field of security and weapon detection.

关键词： Video surveillance weapon detection you only look once convolutional neural networks

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DURRNET: Deep Unfolded Single Image Reflection Removal Network with Joint Prior

DURRNET: Deep Unfolded Single Image Reflection Removal Netwo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jun-Jie Huang Tianrui Liu Jingyuan Xia Meng Wang Pier Luigi Dragotti College of Computer Science and Technology National University of Defense Technology China School of Computer Science and Information Engineering Hefei University of Technology China Department of Electrical and Electronic Engineering Imperial College London UK

Single image reflection removal (SIRR) problem can be interpreted as a canonical blind source separation problem and is highly ill-posed. A parameter effective, fast learning and interpretable reflection removal algorithm is essential for many vision analysis applications. In this paper, we propose a novel model-inspired and learning-based SIRR method called Deep Unfolded Reflection Removal Network (DURRNet). It combines the merits of both model-based and learning-based paradigms, leading to a more interpretable and effective deep architecture. To achieve this, we first propose a model-based optimization approach and then obtain DURRNet by unfolding an iterative step into a Unfolded Separation Block (USB) based on proximal gradient descent. Key features of DURR-Net include the use of Invertible Neural Networks to impose the transform-based exclusion prior on the basis of natural image prior, as well as a coarse-to-fine architecture to fine-grain the reflection removal process. Extensive experiments on public datasets demonstrate that DURRNet achieves state-of-the-art results not only visually, quantitatively, but also effectively.

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Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks

arXiv

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arXiv 2023年

作者： Buszydlik, Aleksander Dobiczek, Karol Okon, Michal Teodor Skublicki, Konrad Lippmann, Philip Yang, Jie Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands

We consider the problem of red teaming LLMs on elementary calculations and algebraic tasks to evaluate how various prompting techniques affect the quality of outputs. We present a framework to procedurally generate numerical questions and puzzles, and compare the results with and without the application of several red teaming techniques. Our findings suggest that even though structured reasoning and providing worked-out examples slow down the deterioration of the quality of answers, the gpt-3.5-turbo and gpt-4 models are not well suited for elementary calculations and reasoning tasks, also when being red teamed. Copyright © 2023, The Authors. All rights reserved.

关键词： Deterioration

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Adaptive Graph Structure Learning Neural Rough Differential Equations for Multivariate Time Series Forecasting

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IEEE Transactions on Big Data 2025年

作者： Su, Yuming Ma, Tinghuai Rong, Huan Wahab, Mohamed Magdy Abdel Nanjing University of Information Science & Technology The School of Cyber Science and Engineering School of Computer Science Nanjing210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China Nanjing University of Information Science & Technology School of Software Nanjing210044 China Nanjing University of Information Science & Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Cairo University Faculty of Science Cario 12613 Egypt

Multivariate time series forecasting has extensive applications in urban computing, such as financial analysis, weather prediction, and traffic forecasting. Using graph structures to model the complex correlations among variables in time series, and leveraging graph neural networks and recurrent neural networks for temporal aggregation and spatial propagation stage, has shown promise. However, traditional methods' graph structure node learning and discrete neural architecture are not sensitive to issues such as sudden changes, time variance, and irregular sampling often found in real-world data. To address these challenges, we propose a method called Adaptive Graph structure Learning neural Rough Differential Equations (AGLRDE). Specifically, we combine dynamic and static graph structure learning to adaptively generate a more robust graph representation. Then we employ a spatio-temporal encoderdecoder based on Neural Rough Differential Equations (Neural RDE) to model spatio-temporal dependencies. Additionally, we introduce a path reconstruction loss to constrain the path generation stage. We conduct experiments on six benchmark datasets, demonstrating that our proposed method outperforms existing state-of-the-art methods. The results show that AGLRDE effectively handles aforementioned challenges, significantly improving the accuracy of multivariate time series forecasting. © 2015 IEEE.

关键词： Graph neural networks

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