21,381 research outputs found
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Federated Recommender Systems (FedRecs) are considered privacy-preserving
techniques to collaboratively learn a recommendation model without sharing user
data. Since all participants can directly influence the systems by uploading
gradients, FedRecs are vulnerable to poisoning attacks of malicious clients.
However, most existing poisoning attacks on FedRecs are either based on some
prior knowledge or with less effectiveness. To reveal the real vulnerability of
FedRecs, in this paper, we present a new poisoning attack method to manipulate
target items' ranks and exposure rates effectively in the top-
recommendation without relying on any prior knowledge. Specifically, our attack
manipulates target items' exposure rate by a group of synthetic malicious users
who upload poisoned gradients considering target items' alternative products.
We conduct extensive experiments with two widely used FedRecs (Fed-NCF and
Fed-LightGCN) on two real-world recommendation datasets. The experimental
results show that our attack can significantly improve the exposure rate of
unpopular target items with extremely fewer malicious users and fewer global
epochs than state-of-the-art attacks. In addition to disclosing the security
hole, we design a novel countermeasure for poisoning attacks on FedRecs.
Specifically, we propose a hierarchical gradient clipping with sparsified
updating to defend against existing poisoning attacks. The empirical results
demonstrate that the proposed defending mechanism improves the robustness of
FedRecs.Comment: This paper has been accepted by SIGIR202
Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks
The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity
Countermeasures for the majority attack in blockchain distributed systems
La tecnologÃa Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus caracterÃsticas únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraÃdo o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnologÃa. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en IngenierÃa de Sistemas y Computació
iDML: Incentivized Decentralized Machine Learning
With the rising emergence of decentralized and opportunistic approaches to
machine learning, end devices are increasingly tasked with training deep
learning models on-devices using crowd-sourced data that they collect
themselves. These approaches are desirable from a resource consumption
perspective and also from a privacy preservation perspective. When the devices
benefit directly from the trained models, the incentives are implicit -
contributing devices' resources are incentivized by the availability of the
higher-accuracy model that results from collaboration. However, explicit
incentive mechanisms must be provided when end-user devices are asked to
contribute their resources (e.g., computation, communication, and data) to a
task performed primarily for the benefit of others, e.g., training a model for
a task that a neighbor device needs but the device owner is uninterested in. In
this project, we propose a novel blockchain-based incentive mechanism for
completely decentralized and opportunistic learning architectures. We leverage
a smart contract not only for providing explicit incentives to end devices to
participate in decentralized learning but also to create a fully decentralized
mechanism to inspect and reflect on the behavior of the learning architecture
The Artist as Surveillant: The Use of Surveillance Technology in Contemporary Art
Artists have long been called observers, voyeurs, and watchers, and with a particular interest in human behavior and society, they frequently use unknowing passersby as their subjects for works. Curators and scholars explored how artists put citizens under surveillance with photography and videography, which dates back to the early 1900s, years before governments deployed surveillance systems. Since the 1980s, artists have explicitly explored surveillance technology and theory to alert viewers to the rise of surveillance. Today, this genre is called artveillance, a term coined by Andrea Mubi Brighenti in 2010 to categorize art that explicitly deals with surveillance. This genre developed parallel to the rise of mass surveillance which created the current-day surveillance state. Since artveillance dominates the contemporary art scene, I was interested in the history of surveillance technology and themes in art. Although that history is brief, there is a wealth of artworks and studies on the topic. This thesis explores artists who use surveillance technology, specifically close-circuit video, in their practice and how this work has changed over time compared to the rise of government surveillance systems. To properly examine the artwork, each artwork’s technological history and broader cultural context is considered, with careful attention to the artists’ intentions. The thesis starts in the 1970s with Bruce Nauman and Peter Campus’s closed-circuit video installations. The artists did not aim to create a surveillance area but wanted to explore the viewer’s identity with their moving image. In Chapter 2, Julia Scher and Lynn Hershman Leeson’s work from the 1980s and early 1990s is discussed. Created when state surveillance was on the rise, the artists’ work used surveillance technology to critique the systems. The third chapter explores surveillance in a post-9/11 state through Jill Magid and Laura Poitras’s work. The artists exploited and exposed government systems to show how the public’s privacy is invaded. Finally, the paper concludes with an investigation into the public’s relationship with video surveillance, which resembles an apathetic acceptance
Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems
WiGig networks and 60 GHz frequency communications have a lot of potential
for commercial and personal use. They can offer extremely high transmission
rates but at the cost of low range and penetration. Due to these issues, WiGig
systems are unstable and need to rely on frequent handovers to maintain
high-quality connections. However, this solution is problematic as it forces
users into bad connections and downtime before they are switched to a better
access point. In this work, we use Machine Learning to identify patterns in
user behaviors and predict user actions. This prediction is used to do
proactive handovers, switching users to access points with better future
transmission rates and a more stable environment based on the future state of
the user. Results show that not only the proposal is effective at predicting
channel data, but the use of such predictions improves system performance and
avoids unnecessary handovers.Comment: Submitted to IEEE Networ
Redefining Community in the Age of the Internet: Will the Internet of Things (IoT) generate sustainable and equitable community development?
There is a problem so immense in our built world that it is often not fully realized. This problem is the disconnection between humanity and the physical world. In an era of limitless data and information at our fingertips, buildings, public spaces, and landscapes are divided from us due to their physical nature. Compared with the intense flow of information from our online world driven by the beating engine of the internet, our physical world is silent. This lack of connection not only has consequences for sustainability but also for how we perceive and communicate with our built environment in the modern age. A possible solution to bridge the gap between our physical and online worlds is a technology known as the Internet of Things (IoT). What is IoT? How does it work? Will IoT change the concept of the built environment for a participant within it, and in doing so enhance the dynamic link between humans and place? And what are the implications of IoT for privacy, security, and data for the public good? Lastly, we will identify the most pressing issues existing in the built environment by conducting and analyzing case studies from Pomona College and California State University, Northridge. By analyzing IoT in the context of case studies we can assess its viability and value as a tool for sustainability and equality in communities across the world
Compressed-VFL: Communication-Efficient Learning with Vertically Partitioned Data
We propose Compressed Vertical Federated Learning (C-VFL) for
communication-efficient training on vertically partitioned data. In C-VFL, a
server and multiple parties collaboratively train a model on their respective
features utilizing several local iterations and sharing compressed intermediate
results periodically. Our work provides the first theoretical analysis of the
effect message compression has on distributed training over vertically
partitioned data. We prove convergence of non-convex objectives at a rate of
when the compression error is bounded over the course
of training. We provide specific requirements for convergence with common
compression techniques, such as quantization and top- sparsification.
Finally, we experimentally show compression can reduce communication by over
without a significant decrease in accuracy over VFL without compression
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