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international Journal of Interactive Mobile Technologies 2022年第24期16卷 81-106页

作者： Kanaker, Hasan Karim, Nader Abdel Awwad, Samer A. B. Ismail, Nurul H. A. Zraqou, Jamal Al ali, Abdulla M. F. Department of Cyber Security Isra University Amman Jordan Department of Intelligent Systems Al-Balqa Applied University Al-Salt Jordan Department of Quality Assurance Imam Abdulrahman Bin Fisal University Dammam Saudi Arabia Department of Computer Science and Information technology Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science University of Petra Amman Jordan Department of Courses Service Isra University Amman Jordan

Cloud computing technology is known as a distributed computing network, which consists of a large number of servers connected via the internet. This technology involves many worthwhile resources, such as applications, services, and large database storage. Users have the ability to access cloud services and resources through web services. Cloud computing provides a considerable number of benefits, such as effective virtualized resources, cost efficiency, selfservice access, flexibility, and scalability. However, many security issues are present in cloud computing environment. One of the most common security challenges in the cloud computing environment is the trojan horses. Trojan horses can disrupt cloud computing services and damage the resources, applications, or virtual machines in the cloud structure. Trojan horse attacks are dangerous, complicated and very difficult to be detected. In this research, eight machine learning classifiers for trojan horse detection in a cloud-based environment have been investigated. The accuracy of the cloud trojan horses detection rate has been investigated using dynamic analysis, Cukoo sandbox, and the Weka data mining tool. Based on the conducted experiments, the SMO and Multilayer Perceptron have been found to be the best classifiers for trojan horse detection in a cloud-based environment. Although SMO and Multilayer Perceptron have achieved the highest accuracy rate of 95.86%, Multilayer Perceptron has outperformed SMO in term of Receiver Operating Characteristic (ROC) area. © 2022, international Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Multilayer neural networks

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Digital Twin Enabled Runtime Verification for Autonomous Mobile Robots under Uncertainty

Digital Twin Enabled Runtime Verification for Autonomous Mob...

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international Workshop/symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： Joakim Schack Betzer Jalil Boudjadar Mirgita Frasheri Prasad Talasila Department of Electrical and Computer Engineering Aarhus University Denmark

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

As autonomous robots increasingly navigate complex and unpredictable environments, ensuring their reliable behavior under uncertainty becomes a critical challenge. This paper introduces a Digital Twin as a service approach to enable runtime monitoring and verification of an autonomous mobile robot and mitigate the impact posed by uncertainty in the deployment environment. The safety and performance properties are specified and synthesized as runtime monitors using TeSSLa. The integration of the executable digital twin, via the MQTT protocol, enables continuous monitoring and validation of the robot’s behavior in real-time. We explore different sources of uncertainties and analyze their impact on the robot safety and performance. Equipped with high computation resources, the cloud-located digital twin serves as a watch-dog model to estimate the actual state, checking the consistency of the robot’s actuations and approving or denying such actuations depending on the safety and performance properties. The experimental analysis demonstrated high efficiency of the proposed approach in ensuring the reliability and robustness of the autonomous robot behavior in uncertain environments by securing high alignment between the actual and expected speeds where the difference is reduced by up to 41% compared to the default robot navigation control.

关键词： Runtime Uncertainty Robot sensing systems Real-time systems Digital twins Safety Mobile robots Synchronization Monitoring Autonomous robots

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Melanoma Detection with Uncertainty Quantification

Melanoma Detection with Uncertainty Quantification

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IEEE international symposium on Biomedical Imaging

作者： SangHyuk Kim Edward Gaibor Brian Matejek Daniel Haehn Department of Computer Science University of Massachusetts Boston Boston Massachusetts USA Computer Science Laboratory SRI International Arlington Virginia USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Early detection of melanoma is crucial for improving survival rates. Current detection tools often utilize data-driven machine learning methods but often overlook the full integration of multiple datasets. We combine publicly available datasets to enhance data diversity, allowing numerous experiments to train and evaluate various classifiers. We then calibrate them to minimize misdiagnoses by incorporating uncertainty quantification. Our experiments on benchmark datasets show accuracies of up to 93.2% before and 97.8% after applying uncertainty-based rejection, leading to a reduction in misdiagnoses by over 40.5%. Our code and data are publicly available 1 1 Our GitHub repository, https: / /mpsych. org /melanoma, and a web-based interface 2 2 0ur Web interface, https: / /mpsych. gi thub. io/melanoma/ for quick melanoma detection of user-supplied images is also provided.

关键词： Training Uncertainty Melanoma Machine learning Benchmark testing Generative adversarial networks Real-time systems Calibration Medical diagnostic imaging Software development management

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Predicting cancer drug response using an adapted Deep Neural Network model 53

Predicting cancer drug response using an adapted Deep Neural...

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IEEE international symposium on Circuits and systems (IEEE ISCAS)

作者： Li, Xiaofan Kalofonou, Melpomeni Imperial Coll London Dept Elect & Elect Engn London SW7 2BT England Imperial Coll London Ctr Bioinspired Technol Inst Biomed Engn London SW7 2BT England

ISBN: (纸本)9781728192017

Recent advancements in biotechnology have contributed to the concept of precision oncology through the application of machine learning algorithms. The proposed work focuses on the improvement of a novel Deep Learning model, known as Reference drug-based Deep Neural Network (RefDNN), applied to the prediction of cancer drug response. The model utilizes drug's structure similarity profiles (SSP) to describe the similarity between different reference cancer drugs and uses an SSP vector to weigh the pre-predicted drug response probability obtained by the use of Elastic Net (EN), with the weighted response to be the input of the Deep Neural Network. The prediction performance of RefDNN has been improved by adding a t-distributed stochastic neighbor embedding (t-SNE) based feature extraction estimator, through the integration of gene expression, copy number variants (CNV) and mutation data. This adaptation was used to characterise the model and customize the prediction procedure based on cell line data to provide more precise and time-efficient results. The performance of the proposed system was based on a 5-fold cross validation and was compared to the original RefDNN model, showing significant improvements in accuracy and reduction of the computational processing time.

关键词： Deep neural networks

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distributed System for Predicting Malicious Activity in computer networks 4

Distributed System for Predicting Malicious Activity in Comp...

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4th international Workshop on Intelligent Information Technologies and systems of Information Security, IntellTSIS 2023

作者： Liubinetskyi, Denys Kashtalian, Antonina Sochor, Tomas Selskyi, Andrii Klein, Olexandr Khmelnytskyi National University Institutska str. 11 Khmelnytskyi29016 Ukraine Prigo University Havirov Czech Republic

This article introduce a self-organized that based on deep learning (DL) algorithms. A new self-organized incremental neural network FG-SOINN presented in the Python programming language. This self-organized neural network (SOINN) algorithm generates packets of nodes and edges that are cleaned through fixed, user-defined time intervals. This leads to sudden changes in the network structure. This is due to the fact that large regions of the network are removed with a fixed period before the resumption of the training. Such "oblivion" conflicts with a more gradual oblivion on a large period of time, which is usually observed in natural cognitive systems. In a self-organized incremental algorithm, the removal of nodes and edges is determined by two parameters. These settings should be improved for each existing program through cross-checking. This article proves that FG-SOINN significantly eliminates this shortcoming, considering removal of nodes and edges as a necessary part of the process of study. Three concepts have been developed and implemented to form FG: Time of idle nodes and edges, reliability and utility of a particular node or edges. It is using these concepts that this algorithm and system will remove nodes and fins. The FG technique or node wear technique determines when and after which node of the period it will be removed. But if they are not updated in the specified time, they are simply deleted. These characteristics make it attractive for the dynamics of the environment, which requires long-term training. To ensure system scalability, the network will be guided by the n parameter. The algorithm starts its work and initiates the network with empty sets of ***, the nodes are added to the weight vectors. After that, the winner vector (nearest node) and the second winner vector will be searched. Distance measurement formulas will be used to determine the distance. The structure and results of the system demonstrate that this proposition can be ada

关键词： Cognitive systems

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

Sensing Together: Cooperative Task Adaptation and Scheduling...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor systems (MASS)

作者： Elizabeth Liri K. K. Ramakrishnan Koushik Kar Flavio Esposito Dept. of Computer Science and Engineering University of California Riverside Riverside CA USA Computer Science Dept. Saint Louis University St. Louis MO USA Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: distributed Task Adaptation (DTA) and distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： Performance evaluation Schedules Renewable energy sources Simulation Redundancy Soil moisture Smart systems Sensors Internet of Things Meteorology

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Acceleration Object Detection using FPGA platform based on Improved CNN

Acceleration Object Detection using FPGA platform based on I...

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Conference on Artificial Intelligence and Robotics and Asia-Pacific international symposium

作者： Mohammad Hossein Salehzadeh Vahid Rostami Mahdi Salamati Mohammad Hamidi Monfared Department of Computer and Information Technology Engineering Science and Research Tehran Branch Islamic Azad University Tehran Iran Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

Deep neural networks have emerged as a leading approach for object detection in recent years. However, their intensive computational requirements often make them unsuitable for embedded systems. The TinyYolo network is a widely-used deep neural network is designed to be simpler and smaller compared to its counterparts. Yet, it still presents challenges in FPGA implementations due to its large number of nodes. In this research, we focus on adapting the TinyYolo network for FPGA implementation. Our modified network comprises four deep neural sub-networks. One serves as the input, two as intermediate networks that extract both local and global features and the final one aggregates the extracted features. Based on these aggregated features, the target objects are detected. Our adaptation offers several advantages including Hardware-based implementation leading to increased computing power, Significant power savings compared to software implementations and Optimal parallel processing, power efficiency and memory usage making it suitable for embedded applications across various domains Our network implemented on the ZU3EG A484 chip achieved a processing speed of 58 FPS and consumed 4.105 W making it competitive with similar systems.

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distributed REAL-TIME SOIL SATURATION ASSESSMENT IN LEVEES USING A NETWORK OF WIRELESS SENSOR PACKAGES WITH CONDUCTIVITY PROBES

DISTRIBUTED REAL-TIME SOIL SATURATION ASSESSMENT IN LEVEES U...

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ASME 2024 international Mechanical Engineering Congress and Exposition, IMECE 2024

作者： Chowdhury, Puja Crews, James Mokhtar, Ayman Oruganti, Sai Durga Rithvik Van Wyk, Ryan Downey, Austin R.J. Flemming, Malichi Bakos, Jason D. Imran, Jasim Khan, Sadik Department of Mechanical Engineering University of South Carolina ColumbiaSC29208 United States Department of Computer Science and Engineering University of South Carolina ColumbiaSC29208 United States Department of Civil and Environmental Engineering University of South Carolina ColumbiaSC29208 United States Department of Mechanical Engineering Department of Civil and Environmental Engineering University of South Carolina ColumbiaSC29208 United States

ISBN: (纸本)9780791888698

Levees play a critical role in safeguarding communities and assets from flooding, acting as essential defenses against the devastating impacts of inundation. Yet, earthen levees are prone to breaches, especially in the face of swift floodwaters. distributed low-cost sensor networks offer the potential to generate spatial maps illustrating soil moisture levels. Long-term monitoring of these spatial maps could identify vulnerable zones in the levee while providing an understanding of how climate change affects levee stability. This study presents an investigation into spatial monitoring of soil saturation in levees using a wireless network of UAV-deployable sensing spike packages. The goal of this paper is to demonstrate the use of these sensors for assessing soil conductivity levels in sand-filled embankments. The obtained soil conductivity levels are crucial for determining soil saturation. The developed sensing spikes consist of a spike that penetrates the ground and measures conductivity between two electrically conductive contacts. The sensing spike consists of microprocessors for edge computing, and wireless data communication systems that report data to a way station in real-time. To validate the efficacy of the developed sensors, a flume test is developed as a replica of a levee and monitored under controlled water flow conditions. The analysis of data at different times revealed the progression of moisture throughout the earthen embankment. Initially, the soil is almost dry. As the controlled water flow proceeds, the soil becomes partially saturated, with the final stage showing a dominant presence of saturated soil. The collected data sampled at the measurement points is expanded to a continuous moisture profile using kriging. Gaussian kriging, also known as ordinary kriging, is one of the commonly used variants of the kriging method. In Gaussian kriging, the estimation of values at unsampled locations is based on a linear combination of nearby data points,

关键词： Climate change

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Load Balancing in Large WiFi networks Using DQL-MultiMDP with Constrained Clustering

Load Balancing in Large WiFi Networks Using DQL-MultiMDP wit...

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international symposium on Modeling, Analysis and Simulation of computer and Telecommunication systems (MASCOTS)

作者： Mohamed Bellouch Lynda Zitoune Iyad Lahsen-Cherif Véronique Vèque CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Université Paris-Saclay Gif-sur-Yvette France CS department INPT Rabat Morocco

ISBN: (数字)9798331531300

ISBN: (纸本)9798331531317

Developing efficient load-balancing techniques remains a persistent research challenge as modern WiFi networks evolve into increasingly complex environments, incorporating new enhancements in their standards. For instance, DQL-MultiMDP is a load-balancing algorithm that learns an optimal STA-to-AP association policy to ensure user fairness and optimize network performance in dense and dynamic WiFi networks. The algorithm leverages a Multi-Markov Decision Process (MultiMDP) strategy to accommodate the fluctuating number of devices caused by their switching on/off. However, scalability challenges arise due to the exponential expansion of the action space. In this paper, we propose a divide-and-conquer approach that extends the algorithm to operate in extremely large deployments: a dynamic partitioning mechanism divides the network into clusters and assigns a sub-controller to manage the STA-to-AP association in each cluster independently, and a coordination mechanism enables them to exchange their training updates. Experimental investigations validate the effectiveness of the approach and motivate future work.

关键词： Training Performance evaluation Heuristic algorithms Scalability Switches Telecommunications Partitioning algorithms Wireless fidelity Standards Load modeling

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Prediction of MRI-Induced Power Absorption in Patients with DBS Leads

Prediction of MRI-Induced Power Absorption in Patients with ...

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Annual IEEE symposium on computer-Based Medical systems

作者： Yalcin Tur Jasmine Vu Selam Waktola Alpay Medetalibeyoglu Laleh Golestanirad Ulas Bagci Computer Science Stanford University Stanford CA USA Radiology and BME Northwestern University Chicago IL USA Radiology Northwestern University Chicago IL USA BME and Radiology Northwestern University Chicago IL USA ECE BME and Radiology BME and Radiology Northwestern University

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

The interaction between deep brain stimulation (DBS) systems and magnetic resonance imaging (MRI) can induce tissue heating in patients. While electromagnetic (EM) simulations can be used to estimate the specific absorption rate (SAR) values in the presence of an implanted DBS system, they are computationally expensive. To address this drawback, we predict local SAR values in the tips of DBS leads with machine learning based efficient algorithms, specifically XgBoost and deep learning. We significantly outperformed the previous state of the art, and adapted new machine learning models based on Residual networks family as well as XgBoost models. We observed that already extracted limited features are better suited for ensemble learning via XgBoost than deep networks due the small-data regime. Although we conclude that boosting gradient algorithm is more suitable for this non-linear regression problem due to structured nature of the data and small data regime, we found that width plays a more critical role than depth in network design and it has a strong potential for future research. Our experimental results, using a dataset of 260 instances that are patient-derived and artificial, reached an outstanding RMSE of 17.8 W/kg with XgBoost, 78 W/kg with deep networks, given that the previous study on this problem reached a state-of-the-art root mean square error value (RMSE) of 168 W/kg.

关键词： Deep learning Adaptation models Machine learning algorithms Magnetic resonance imaging Computational modeling Deep brain stimulation Feature extraction

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