Search Results for author:
Found 55 papers, 5 papers with code
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.
Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach
Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple users.
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
Membership Inference Attacks on DNNs using Adversarial Perturbations
Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.
Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?
Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science.
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored.
Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin
We found differences in language and interest between these two groups regarding Bitcoin and that the opinion leaders of Twitter are not aligned with the majority of users.
Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach
This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization.
Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data.
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s).
Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
The remaining cluster with surviving clients is then used for training the best FL model (i. e., remaining FL model).
Social Media as an Instant Source of Feedback on Water Quality
We also provide an evaluation of the individual models where the highest F1-score of 0. 81 is obtained with the BERT model.
NLP Techniques for Water Quality Analysis in Social Media Content
This paper presents our contributions to the MediaEval 2021 task namely "WaterMM: Water Quality in Social Multimedia".
Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021
The Visual Sentiment Analysis task is being offered for the first time at MediaEval.
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions
These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing.
Explainable Event Recognition
The results of the study indicate that 78%, 84%, and 78% of the model decisions on natural disasters, sports, and social events datasets, respectively, are based onevent-related objects or regions.
Client Selection Approach in Support of Clustered Federated Learning over Wireless Edge Networks
Extensive experiments show that the proposed approach lowers the training time and accelerates the convergence rate by up to 50% while imbuing each client with a specialized model that is fit for its local data distribution.
Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning
Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL.
Deep Reinforcement Learning for Radio Resource Allocation and Management in Next Generation Heterogeneous Wireless Networks: A Survey
In this paper, we conduct a systematic in-depth, and comprehensive survey of the applications of DRL techniques in RRAM for next generation wireless networks.
The Duo of Artificial Intelligence and Big Data for Industry 4.0: Review of Applications, Techniques, Challenges, and Future Research Directions
In this paper, we provide a comprehensive overview of different aspects of AI and Big Data in Industry 4. 0 with a particular focus on key applications, techniques, the concepts involved, key enabling technologies, challenges, and research perspective towards deployment of Industry 5. 0.
Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort.
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed.
Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset
In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews.
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency).
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges.
Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case
On the binary classification, the BoW and BERT based solutions obtained an average F1-score of . 666% and . 693%, respectively.
Floods Detection in Twitter Text and Images
The extracted features are then used to train multiple individual classifiers whose scores are then combined in a late fusion manner achieving an F1-score of 0. 75%.
Flood Detection via Twitter Streams using Textual and Visual Features
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter.
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services
Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices.
Visual Sentiment Analysis from Disaster Images in Social Media
While sentiment analysis of text streams has been widely explored in literature, sentiment analysis from images and videos is relatively new.
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important.
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior.
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case
Sentiment analysis aims to extract and express a person's perception, opinions and emotions towards an entity, object, product and a service, enabling businesses to obtain feedback from the consumers.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.
Secure and Robust Machine Learning for Healthcare: A Survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images.
Exploiting Unlabeled Data in Smart Cities using Federated Learning
The algorithm is divided into two phases where the first phase trains a global model based on the labeled data.
Sentiment Analysis from Images of Natural Disasters
Social media have been widely exploited to detect and gather relevant information about opinions and events.
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Social Media andsatellites.
Active Learning for Event Detection in Support of Disaster Analysis Applications
Disaster analysis in social media content is one of the interesting research domains having abundance of data.
Black-box Adversarial ML Attack on Modulation Classification
We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings.
A Survey on the Use of Preferences for Virtual Machine Placement in Cloud Data Centers
With the rapid development of virtualization techniques, cloud data centers allow for cost effective, flexible, and customizable deployments of applications on virtualized infrastructure.
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?
We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.
Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services
In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent.
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
We argue that semi-supervision is a must for smart city to address this challenge.
SDN Flow Entry Management Using Reinforcement Learning
Modern information technology services largely depend on cloud infrastructures to provide their services.
Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks
This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e. g., vision impairments, physical disabilities, etc.
Deep Learning for IoT Big Data and Streaming Analytics: A Survey
The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced.
Role of Deep LSTM Neural Networks And WiFi Networks in Support of Occupancy Prediction in Smart Buildings
In this paper we propose to replace sensor technology with time series models that can predict the number of occupants at a given location and time.
Parameters Optimization of Deep Learning Models using Particle Swarm Optimization
The number of hidden layers and the number of neurons in each layer of a deep machine learning network are two key parameters, which have main influence on the performance of the algorithm.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
