10,779 research outputs found
Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints
Physical exercise is an essential component of rehabilitation programs that
improve quality of life and reduce mortality and re-hospitalization rates. In
AI-driven virtual rehabilitation programs, patients complete their exercises
independently at home, while AI algorithms analyze the exercise data to provide
feedback to patients and report their progress to clinicians. To analyze
exercise data, the first step is to segment it into consecutive repetitions.
There has been a significant amount of research performed on segmenting and
counting the repetitive activities of healthy individuals using raw video data,
which raises concerns regarding privacy and is computationally intensive.
Previous research on patients' rehabilitation exercise segmentation relied on
data collected by multiple wearable sensors, which are difficult to use at home
by rehabilitation patients. Compared to healthy individuals, segmenting and
counting exercise repetitions in patients is more challenging because of the
irregular repetition duration and the variation between repetitions. This paper
presents a novel approach for segmenting and counting the repetitions of
rehabilitation exercises performed by patients, based on their skeletal body
joints. Skeletal body joints can be acquired through depth cameras or computer
vision techniques applied to RGB videos of patients. Various sequential neural
networks are designed to analyze the sequences of skeletal body joints and
perform repetition segmentation and counting. Extensive experiments on three
publicly available rehabilitation exercise datasets, KIMORE, UI-PRMD, and
IntelliRehabDS, demonstrate the superiority of the proposed method compared to
previous methods. The proposed method enables accurate exercise analysis while
preserving privacy, facilitating the effective delivery of virtual
rehabilitation programs.Comment: 8 pages, 1 figure, 2 table
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
Recommended from our members
Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
The Viability and Potential Consequences of IoT-Based Ransomware
With the increased threat of ransomware and the substantial growth of the Internet of Things (IoT) market, there is significant motivation for attackers to carry out IoT-based ransomware campaigns. In this thesis, the viability of such malware is tested.
As part of this work, various techniques that could be used by ransomware developers to attack commercial IoT devices were explored. First, methods that attackers could use to communicate with the victim were examined, such that a ransom note was able to be reliably sent to a victim. Next, the viability of using "bricking" as a method of ransom was evaluated, such that devices could be remotely disabled unless the victim makes a payment to the attacker. Research was then performed to ascertain whether it was possible to remotely gain persistence on IoT devices, which would improve the efficacy of existing ransomware methods, and provide opportunities for more advanced ransomware to be created. Finally, after successfully identifying a number of persistence techniques, the viability of privacy-invasion based ransomware was analysed.
For each assessed technique, proofs of concept were developed. A range of devices -- with various intended purposes, such as routers, cameras and phones -- were used to test the viability of these proofs of concept. To test communication hijacking, devices' "channels of communication" -- such as web services and embedded screens -- were identified, then hijacked to display custom ransom notes. During the analysis of bricking-based ransomware, a working proof of concept was created, which was then able to remotely brick five IoT devices. After analysing the storage design of an assortment of IoT devices, six different persistence techniques were identified, which were then successfully tested on four devices, such that malicious filesystem modifications would be retained after the device was rebooted. When researching privacy-invasion based ransomware, several methods were created to extract information from data sources that can be commonly found on IoT devices, such as nearby WiFi signals, images from cameras, or audio from microphones. These were successfully implemented in a test environment such that ransomable data could be extracted, processed, and stored for later use to blackmail the victim.
Overall, IoT-based ransomware has not only been shown to be viable but also highly damaging to both IoT devices and their users. While the use of IoT-ransomware is still very uncommon "in the wild", the techniques demonstrated within this work highlight an urgent need to improve the security of IoT devices to avoid the risk of IoT-based ransomware causing havoc in our society. Finally, during the development of these proofs of concept, a number of potential countermeasures were identified, which can be used to limit the effectiveness of the attacking techniques discovered in this PhD research
Intermodal Terminal Subsystem Technology Selection Using Integrated Fuzzy MCDM Model
Intermodal transportation is the use of multiple modes of transportation, which can lead to
greater sustainability by reducing environmental impact and traffic congestion and increasing the
efficiency of supply chains. One of the preconditions for efficient intermodal transport is the efficient
intermodal terminal (IT). ITs allow for the smooth and efficient handling of cargo, thus reducing the
time, cost, and environmental impact of transportation. Adequate selection of subsystem technologies
can significantly improve the efficiency and productivity of an IT, ultimately leading to cost savings
for businesses and a more efficient and sustainable transportation system. Accordingly, this paper
aims to establish a framework for the evaluation and selection of appropriate technologies for IT
subsystems. To solve the defined problem, an innovative hybrid multi-criteria decision making
(MCDM) model, which combines the fuzzy factor relationship (FFARE) and the fuzzy combinative
distance-based assessment (FCODAS) methods, is developed in this paper. The FFARE method
is used for obtaining criteria weights, while the FCODAS method is used for evaluation and a
final ranking of the alternatives. The established framework and the model are tested on a real-life
case study, evaluating and selecting the handling technology for a planned IT. The study defines
12 potential variants of handling equipment based on their techno-operational characteristics and
evaluates them using 16 criteria. The results indicate that the best handling technology variant is
the one that uses a rail-mounted gantry crane for trans-shipment and a reach stacker for horizontal
transport and storage. The results also point to the conclusion that instead of choosing equipment
for each process separately, it is important to think about the combination of different handling
technologies that can work together to complete a series of handling cycle processes. The main
contributions of this paper are the development of a new hybrid model and the establishment of
a framework for the selection of appropriate IT subsystem technologies along with a set of unique
criteria for their evaluation and selection
Inovação, empreendedorismo e desenvolvimento económico em África: Uma abordagem pós-positivista e "topo da pirâmide" para Moçambique
Esta tese desenvolve uma investigação abrangente sobre o empreendedorismo africano, revisitando o seu quadro concetual tradicional e posicionando-o enquanto elemento fundamental das estratégias de desenvolvimento para a África Subsariana (ASS). Explorados os diferentes impactos do empreendedorismo de oportunidade e do empreendedorismo de necessidade na região, efetuou-se uma pesquisa sobre a situação dos vários países da ASS que participaram no Global Entrepreneurship Monitor na última década, com vista a compor o status quo hipotético do empreendedorismo regional, ao qual juntámos um estudo empírico original e com elementos metodológicos inovadores sobre a atividade empreendedora em Moçambique. O alcance das estratégias empreendedoras implementadas na ASS é avaliado através de um estudo dos polos africanos de inovação tecnológica e do empreendedorismo digital que neles tem vindo recentemente a emergir, a que juntámos um levantamento original do tech hub de Maluana. Por fim, a partir destes casos e de uma leitura política das opções económicas do estado moçambicano com impacto sobre o ecossistema empreendedor, desenvolve-se uma proposta de teoria da mudança, numa lógica pós-positivista, para suportar medidas de política pública desejáveis para a eclosão de um empreendedorismo de “topo da pirâmide” em Moçambique.This thesis develops a comprehensive investigation of African entrepreneurship, revisiting its traditional conceptual framework and positioning it as a fundamental element in development strategies for Sub-Saharan Africa (SSA). Exploring the different impacts of opportunity entrepreneurship and necessity entrepreneurship in the region, an analysis was carried out on the situation of the various SSA countries that participated in the Global Entrepreneurship Monitor in the last decade, with a view to composing the hypothetical status quo of the entrepreneurship in the region, to which we added an original empirical study with innovative methodological elements on entrepreneurial activity in Mozambique. The reach of entrepreneurial strategies implemented in the SSA is assessed through a study of the African tech hubs, or innovation hubs, and the digital entrepreneurship that has recently emerged there, to which we have added an original survey of the Maluana tech hub. Finally, based on these cases and on a political reading of the economic options of the Mozambican government with an impact on the entrepreneurial ecosystem, a proposal for a theory-of-change is developed, within a post-positivist approach, to support desired public policy measures for the emergence of a “top of the pyramid” entrepreneurship in Mozambique
Countermeasures for the majority attack in blockchain distributed systems
La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características 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 extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. 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 Ingeniería de Sistemas y Computació
Coincidental Generation
Generative AI models are emerging as a versatile tool across diverse
industries with applications in synthetic data generation computational art
personalization of products and services and immersive entertainment Here we
introduce a new privacy concern in the adoption and use of generative AI models
that of coincidental generation Coincidental generation occurs when a models
output inadvertently bears a likeness to a realworld entity Consider for
example synthetic portrait generators which are today deployed in commercial
applications such as virtual modeling agencies and synthetic stock photography
We argue that the low intrinsic dimensionality of human face perception implies
that every synthetically generated face will coincidentally resemble an actual
person all but guaranteeing a privacy violation in the form of a
misappropriation of likeness
Critical Review on Internet of Things (IoT): Evolution and Components Perspectives
Technological advancement in recent years has transformed the internet to a network where everything is linked, and everyday objects can be recognised and controlled. This interconnection is popularly termed as the Internet of Things (IoT). Although, IoT remains popular in academic literature, limited studies have focused on its evolution, components, and implications for industries. Hence, the focus of this book chapter is to explore these dimensions, and their implications for industries. The study adopted the critical review method, to address these gaps in the IoT literature for service and manufacturing industries. Furthermore, the relevance for IoT for service and manufacturing industries were also discussed. While the impact of IoT in the next five years is expected to be high by industry practitioners, experts consider the current degree of its implementation across industry to be on the average. This critical review contributes theoretically to the literature on IoT. In effect, the intense implementation of the IoT, IIoT and IoS will go a long way in ensuring improvements in various industries that would in the long run positively impact the general livelihood of people as well as the way of doing things. Practical implications and suggestions for future studies have been discussed
Deep Transfer Learning Applications in Intrusion Detection Systems: A Comprehensive Review
Globally, the external Internet is increasingly being connected to the
contemporary industrial control system. As a result, there is an immediate need
to protect the network from several threats. The key infrastructure of
industrial activity may be protected from harm by using an intrusion detection
system (IDS), a preventive measure mechanism, to recognize new kinds of
dangerous threats and hostile activities. The most recent artificial
intelligence (AI) techniques used to create IDS in many kinds of industrial
control networks are examined in this study, with a particular emphasis on
IDS-based deep transfer learning (DTL). This latter can be seen as a type of
information fusion that merge, and/or adapt knowledge from multiple domains to
enhance the performance of the target task, particularly when the labeled data
in the target domain is scarce. Publications issued after 2015 were taken into
account. These selected publications were divided into three categories:
DTL-only and IDS-only are involved in the introduction and background, and
DTL-based IDS papers are involved in the core papers of this review.
Researchers will be able to have a better grasp of the current state of DTL
approaches used in IDS in many different types of networks by reading this
review paper. Other useful information, such as the datasets used, the sort of
DTL employed, the pre-trained network, IDS techniques, the evaluation metrics
including accuracy/F-score and false alarm rate (FAR), and the improvement
gained, were also covered. The algorithms, and methods used in several studies,
or illustrate deeply and clearly the principle in any DTL-based IDS subcategory
are presented to the reader
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