arXiv subjects

Ala Al-Fuqaha

Publications and source records attributed to Ala Al-Fuqaha.

At least 37 records · Page 2Linked to original sources

arXiv2023

Metaverse for Wireless Systems: Architecture, Advances, Standardization, and Open Challenges

The growing landscape of emerging wireless applications is a key driver toward the development of novel wireless system designs. Such a design can be based on the metaverse that uses a virtual model of the physical world systems along with other schemes/technologies (e.g., optimization theory, machine learning, and blockchain). A metaverse using a virtual model performs proactive intelligent analytics prior to a user request for efficient management of the wireless system resources. Additionally, a metaverse will enable self-sustainability to operate wireless systems with the least possible intervention from network operators. Although the metaverse can offer many benefits, it faces some challenges as well. Therefore, in this tutorial, we discuss the role of a metaverse in enabling wireless applications. We present an overview, key enablers, design aspects (i.e., metaverse for wireless and wireless for metaverse), and a novel high-level architecture of metaverse-based wireless systems. We discuss metaverse management, reliability, and security of the metaverse-based system. Furthermore, we discuss recent advances and standardization of metaverse-enabled wireless system. Finally, we outline open challenges and present possible solutions.

cs.NI↗

arXiv2023

Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques

This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring Autonomous Vehicle (AV) user comfort and psychoanalysis are listed here. At the same time, this article introduces the technology related to the structure of automatic driving and the reaction time of automatic driving. 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. The function of the sensor is affected by various factors. Since the sensor of automatic driving mainly senses the environment around a vehicle, including "the weather" which introduces the challenges and limitations of second-hand sensors in autonomous vehicles under different weather conditions. The comfort and safety of autonomous driving are also factors that affect the development of autonomous driving technologies. This article further analyzes the impact of autonomous driving on the user's physical and psychological states and how the comfort factors of autonomous vehicles affect the automotive market. Also, part of our focus is on the benefits and shortcomings of autonomous driving. The goal is to present an exhaustive overview of the most relevant technical matters to help researchers and application developers comprehend the different comfort factors and systems of autonomous driving. Finally, we provide detailed automated driving comfort use cases to illustrate the comfort-related issues of autonomous driving. Then, we provide implications and insights for the future of autonomous driving.

cs.RO↗

arXiv2023

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. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-time adaptation with a systematic active fine-tuning (SAF) layer that is characterized by three key aspects: a continuity aspect that adapts to ever-present data distribution shifts; intelligence aspect that recognizes the importance of fine-tuning as a distribution-shift-aware process that occurs at the appropriate time to address the recently detected data distribution shifts; and cost-effectiveness aspect that involves budgeted human-machine collaboration to make relabeling cost-effective and practical for diverse smart city applications. Our empirical results show that our proposed approach outperforms the traditional test-time adaptation by a factor of two.

cs.LG↗

arXiv2023

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices {is} selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed while equipping every user with a customized model tailored to its data distribution.

cs.NI↗

arXiv2023

Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach

Prediction of taxi service demand and supply is essential for improving customer's experience and provider's profit. Recently, graph neural networks (GNNs) have been shown promising for this application. This approach models city regions as nodes in a transportation graph and their relations as edges. GNNs utilize local node features and the graph structure in the prediction. However, more efficient forecasting can still be achieved by following two main routes; enlarging the scale of the transportation graph, and simultaneously exploiting different types of nodes and edges in the graphs. However, both approaches are challenged by the scalability of GNNs. An immediate remedy to the scalability challenge is to decentralize the GNN operation. However, this creates excessive node-to-node communication. In this paper, we first characterize the excessive communication needs for the decentralized GNN approach. Then, we propose a semi-decentralized approach utilizing multiple cloudlets, moderately sized storage and computation devices, that can be integrated with the cellular base stations. This approach minimizes inter-cloudlet communication thereby alleviating the communication overhead of the decentralized approach while promoting scalability due to cloudlet-level decentralization. Also, we propose a heterogeneous GNN-LSTM algorithm for improved taxi-level demand and supply forecasting for handling dynamic taxi graphs where nodes are taxis. Extensive experiments over real data show the advantage of the semi-decentralized approach as tested over our heterogeneous GNN-LSTM algorithm. Also, the proposed semi-decentralized GNN approach is shown to reduce the overall inference time by about an order of magnitude compared to centralized and decentralized inference schemes.

cs.LG↗

arXiv2023

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. The combination of artificial intelligence and extended reality (AI-XR) technologies has the potential to create a surgical metaverse, a virtual environment where surgeries can be planned and performed. This paper aims to provide insight into the various potential applications of an AI-XR surgical metaverse and the challenges that must be addressed to bring its full potential to fruition. It is important for the community to focus on these challenges to fully realize the potential of the AI-XR surgical metaverses. Furthermore, to emphasize the need for secure and robust AI-XR surgical metaverses and to demonstrate the real-world implications of security threats to the AI-XR surgical metaverses, we present a case study in which the ``an immersive surgical attack'' on incision point localization is performed in the context of preoperative planning in a surgical metaverse.

cs.AI↗

arXiv2023

Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models

Recent works have shown that deep learning (DL) models can effectively learn city-wide crowd-flow patterns, which can be used for more effective urban planning and smart city management. However, DL models have been known to perform poorly on inconspicuous adversarial perturbations. 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. In this paper, we perform a rigorous analysis of the adversarial vulnerabilities of DL-based crowd-flow prediction models under multiple threat settings, making three-fold contributions. (1) We propose CaV-detect by formally identifying two novel properties - Consistency and Validity - of the crowd-flow prediction inputs that enable the detection of standard adversarial inputs with 0% false acceptance rate (FAR). (2) We leverage universal adversarial perturbations and an adaptive adversarial loss to present adaptive adversarial attacks to evade CaV-detect defense. (3) We propose CVPR, a Consistent, Valid and Physically-Realizable adversarial attack, that explicitly inducts the consistency and validity priors in the perturbation generation mechanism. We find out that although the crowd-flow models are vulnerable to adversarial perturbations, it is extremely challenging to simulate these perturbations in physical settings, notably when CaV-detect is in place. We also show that CVPR attack considerably outperforms the adaptively modified standard attacks in FAR and adversarial loss metrics. We conclude with useful insights emerging from our work and highlight promising future research directions.

cs.LG↗

arXiv2023

Topic Modeling Based on Two-Step Flow Theory: Application to Tweets about Bitcoin

Digital cryptocurrencies such as Bitcoin have exploded in recent years in both popularity and value. By their novelty, cryptocurrencies tend to be both volatile and highly speculative. The capricious nature of these coins is helped facilitated by social media networks such as Twitter. However, not everyone's opinion matters equally, with most posts garnering little to no attention. Additionally, the majority of tweets are retweeted from popular posts. We must determine whose opinion matters and the difference between influential and non-influential users. This study separates these two groups and analyzes the differences between them. It uses Hypertext-induced Topic Selection (HITS) algorithm, which segregates the dataset based on influence. Topic modeling is then employed to uncover differences in each group's speech types and what group may best represent the entire community. 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. There were 2559 opinion leaders (0.72% of users) who accounted for 80% of the authority and the majority (99.28%) users for the remaining 20% out of a total of 355,139 users.

cs.SI↗

arXiv2022

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). The advancements in different technologies like augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization, etc.), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users' privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community.

cs.AI↗

arXiv2022

Social Media as an Instant Source of Feedback on Water Quality

This paper focuses on an important environmental challenge; namely, water quality by analyzing the potential of social media as an immediate source of feedback. The main goal of the work is to automatically analyze and retrieve social media posts relevant to water quality with particular attention to posts describing different aspects of water quality, such as watercolor, smell, taste, and related illnesses. To this aim, we propose a novel framework incorporating different preprocessing, data augmentation, and classification techniques. In total, three different Neural Networks (NNs) architectures, namely (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and (iii) custom Long short-term memory (LSTM) model, are employed in a merit-based fusion scheme. For merit-based weight assignment to the models, several optimization and search techniques are compared including a Particle Swarm Optimization (PSO), a Genetic Algorithm (GA), Brute Force (BF), Nelder-Mead, and Powell's optimization methods. We also provide an evaluation of the individual models where the highest F1-score of 0.81 is obtained with the BERT model. In merit-based fusion, overall better results are obtained with BF achieving an F1-score score of 0.852. We also provide comparison against existing methods, where a significant improvement for our proposed solutions is obtained. We believe such rigorous analysis of this relatively new topic will provide a baseline for future research.

cs.CL↗

arXiv2022

Mobile-IRS Assisted Next Generation UAV Communication Networks

Prior research on intelligent reflection surface (IRS)-assisted unmanned aerial vehicle (UAV) communications has focused on a fixed location for the IRS or mounted on a UAV. The assumption that the IRS is located at a fixed position will prohibit mobile users from maximizing many wireless network benefits, such as data rate and coverage. Furthermore, assuming that the IRS is placed on a UAV is impractical for various reasons, including the IRS's weight and size and the speed of wind in severe weather. Unlike previous studies, this study assumes a single UAV and an IRS mounted on a mobile ground vehicle (M-IRS) to be deployed in an Internet-of-Things (IoT) 6G wireless network to maximize the average data rate. Such a methodology for providing wireless coverage using an M-IRS assisted UAV system is expected in smart cities. In this paper, we formulate an optimization problem to find an efficient trajectory for the UAV, an efficient path for the M-IRS, and users' power allocation coefficients that maximize the average data rate for mobile ground users. Due to its intractability, we propose efficient techniques that can help in finding the solution to the optimization problem. First, we show that our dynamic power allocation technique outperforms the fixed power allocation technique in terms of network average sum rate. Then we employ the individual movement model (Random Waypoint Model) in order to represent the users' movements inside the coverage area. Finally, we propose an efficient approach using a Genetic Algorithm (GA) for finding an efficient trajectory for the UAV, and an efficient path for the M-IRS to provide wireless connectivity for mobile users during their movement. We demonstrate through simulations that our methodology can enhance the average data rate by 15\% on average compared with the static IRS and by 25\% on average compared without the IRS system.

cs.IT↗

arXiv2022

Advancing Education Through Extended Reality and Internet of Everything Enabled Metaverses: Applications, Challenges, and Open Issues

Metaverse has evolved as one of the popular research agendas that let the users learn, socialize, and collaborate in a networked 3D immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive review of the various educational use cases and explore how enabling technologies such as Extended reality (XR) and Internet of Everything (IoE) will play a major role in educational services in future metaverses. Secondly, we provide an overview of metaverse-based educational applications focusing on education, training, and skill development and analyze the technologies they are built upon. We identify common research problems and future research directions in the domain. The paper also identifies core ethical considerations of metaverse for education and potential pitfalls. We believe this survey can fully demonstrate the versatility of metaverse-driven education, which could serve as a potential guideline for the researchers.

cs.CY↗

arXiv2022

Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data

Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However, FL faces many challenges, including the difficulty to obtain high accuracy, high communication cost between clients and the server, and security attacks related to adversarial ML. To tackle these three challenges, we propose an FL algorithm inspired by evolutionary techniques. The proposed algorithm groups clients randomly in many clusters, each with a model selected randomly to explore the performance of different models. The clusters are then trained in a repetitive process where the worst performing cluster is removed in each iteration until one cluster remains. In each iteration, some clients are expelled from clusters either due to using poisoned data or low performance. The surviving clients are exploited in the next iteration. The remaining cluster with surviving clients is then used for training the best FL model (i.e., remaining FL model). Communication cost is reduced since fewer clients are used in the final training of the FL model. To evaluate the performance of the proposed algorithm, we conduct a number of experiments using FEMNIST dataset and compare the result against the random FL algorithm. The experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of accuracy, communication cost, and security.

cs.DC↗

arXiv2021

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

As the globally increasing population drives rapid urbanisation in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace more and more data-driven artificial intelligence services, it is worth remembering that technology can facilitate prosperity, wellbeing, urban livability, or social justice, but only when it has the right analog complements (such as well-thought out policies, mature institutions, responsible governance); and the ultimate objective of these smart cities is to facilitate and enhance human welfare and social flourishing. Researchers have shown that various technological business models and features can in fact contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In the light of these observations, addressing the philosophical and ethical questions involved in ensuring the security, safety, and interpretability of such AI algorithms that will form the technological bedrock of future cities assumes paramount importance. Globally there are calls for technology to be made more humane and human-centered. 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. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.

cs.CY↗

arXiv2021

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". The task aims at analyzing social media posts relevant to water quality with particular focus on the aspects like watercolor, smell, taste, and related illnesses. To this aim, a multimodal dataset containing both textual and visual information along with meta-data is provided. Considering the quality and quantity of available content, we mainly focus on textual information by employing three different models individually and jointly in a late-fusion manner. These models include (i) Bidirectional Encoder Representations from Transformers (BERT), (ii) Robustly Optimized BERT Pre-training Approach (XLM-RoBERTa), and a (iii) custom Long short-term memory (LSTM) model obtaining an overall F1-score of 0.794, 0.717, 0.663 on the official test set, respectively. In the fusion scheme, all the models are treated equally and no significant improvement is observed in the performance over the best performing individual model.

cs.CL↗

arXiv2021

Deep Models for Visual Sentiment Analysis of Disaster-related Multimedia Content

This paper presents a solutions for the MediaEval 2021 task namely "Visual Sentiment Analysis: A Natural Disaster Use-case". The task aims to extract and classify sentiments perceived by viewers and the emotional message conveyed by natural disaster-related images shared on social media. The task is composed of three sub-tasks including, one single label multi-class image classification task, and, two multi-label multi-class image classification tasks, with different sets of labels. In our proposed solutions, we rely mainly on two different state-of-the-art models namely, Inception-v3 and VggNet-19, pre-trained on ImageNet, which are fine-tuned for each of the three task using different strategies. Overall encouraging results are obtained on all the three tasks. On the single-label classification task (i.e. Task 1), we obtained the weighted average F1-scores of 0.540 and 0.526 for the Inception-v3 and VggNet-19 based solutions, respectively. On the multi-label classification i.e., Task 2 and Task 3, the weighted F1-score of our Inception-v3 based solutions was 0.572 and 0.516, respectively. Similarly, the weighted F1-score of our VggNet-19 based solution on Task 2 and Task 3 was 0.584 and 0.495, respectively.

cs.CV↗

arXiv2021

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. The main purpose of the task is to predict the emotional response to images of natural disasters shared on social media. Disaster-related images are generally complex and often evoke an emotional response, making them an ideal use case of visual sentiment analysis. We believe being able to perform meaningful analysis of natural disaster-related data could be of great societal importance, and a joint effort in this regard can open several interesting directions for future research. The task is composed of three sub-tasks, each aiming to explore a different aspect of the challenge. In this paper, we provide a detailed overview of the task, the general motivation of the task, and an overview of the dataset and the metrics to be used for the evaluation of the proposed solutions.

cs.CL↗

arXiv2021

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

The growing use of social media has led to the development of several Machine Learning (ML) and Natural Language Processing(NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these MLand NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this paper, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications, namely (i) rumors detection, (ii) satires detection, (iii) clickbait & spams identification, (iv) hate speech detection, (v)misinformation detection, and (vi) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

cs.CL↗