6,312 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
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
Annual SHOT Report 2018
SHOT is affiliated to the Royal College of PathologistsAll NHS organisations must move away from a blame culture towards a just and learning culture. All clinical and laboratory staff should be encouraged to become familiar with human factors and ergonomics concepts. All transfusion decisions must be made after carefully assessing the risks and benefits of transfusion therapy. Collaboration and co-ordination among staff is vital
The determinants of value addition: a crtitical analysis of global software engineering industry in Sri Lanka
It was evident through the literature that the perceived value delivery of the global software
engineering industry is low due to various facts. Therefore, this research concerns global
software product companies in Sri Lanka to explore the software engineering methods and
practices in increasing the value addition. The overall aim of the study is to identify the key
determinants for value addition in the global software engineering industry and critically
evaluate the impact of them for the software product companies to help maximise the value
addition to ultimately assure the sustainability of the industry.
An exploratory research approach was used initially since findings would emerge while the
study unfolds. Mixed method was employed as the literature itself was inadequate to
investigate the problem effectively to formulate the research framework. Twenty-three face-to-face online interviews were conducted with the subject matter experts covering all the
disciplines from the targeted organisations which was combined with the literature findings as
well as the outcomes of the market research outcomes conducted by both government and nongovernment institutes. Data from the interviews were analysed using NVivo 12. The findings
of the existing literature were verified through the exploratory study and the outcomes were
used to formulate the questionnaire for the public survey. 371 responses were considered after
cleansing the total responses received for the data analysis through SPSS 21 with alpha level
0.05. Internal consistency test was done before the descriptive analysis. After assuring the
reliability of the dataset, the correlation test, multiple regression test and analysis of variance
(ANOVA) test were carried out to fulfil the requirements of meeting the research objectives.
Five determinants for value addition were identified along with the key themes for each area.
They are staffing, delivery process, use of tools, governance, and technology infrastructure.
The cross-functional and self-organised teams built around the value streams, employing a
properly interconnected software delivery process with the right governance in the delivery
pipelines, selection of tools and providing the right infrastructure increases the value delivery.
Moreover, the constraints for value addition are poor interconnection in the internal processes,
rigid functional hierarchies, inaccurate selections and uses of tools, inflexible team
arrangements and inadequate focus for the technology infrastructure. The findings add to the
existing body of knowledge on increasing the value addition by employing effective processes,
practices and tools and the impacts of inaccurate applications the same in the global software
engineering industry
The Future of Work and Digital Skills
The theme for the events was "The Future of Work and Digital Skills". The 4IR caused a
hollowing out of middle-income jobs (Frey & Osborne, 2017) but COVID-19 exposed the digital gap as survival depended mainly on digital infrastructure and connectivity. Almost overnight, organizations that had not invested in a digital strategy suddenly realized the need for such a strategy and the associated digital skills. The effects have been profound for those who struggled to adapt, while those who stepped up have reaped quite the reward.Therefore, there are no longer certainties about what the world will look like in a few years from now. However, there are certain ways to anticipate the changes that are occurring and plan on how to continually adapt to an increasingly changing world. Certain jobs will soon be lost and will not come back; other new jobs will however be created. Using data science and other predictive sciences, it is possible to anticipate, to the extent possible, the rate at which certain jobs will be replaced and new jobs created in different industries. Accordingly, the collocated events sought to bring together government, international organizations, academia, industry, organized labour and civil society to deliberate on how these changes are occurring in South Africa, how fast they are occurring and what needs to change in order to prepare society for the changes.Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ)
British High Commission (BHC)School of Computin
Management controls, government regulations, customer involvement: Evidence from a Chinese family-owned business
This research reports on a case study of a family-owned elevator manufacturing company in China, where management control was sandwiched between the state policies and global customer production requirements. By analysing the role of government and customer, this thesis aimed to illustrate how management control operated in a family-owned business and to see how and why they do management control differently. In particular, it focused on how international production standards and existing Chinese industry policies translated into a set of the management control practices through a local network within the family-owned business I studied.
Based on an ethnographic approach to research, I spent six months in the field, conducted over 30 interviews, several conservations, and reviewed relevant internal documents to understand how management control (MC) techniques with humans cooperated in the company. I also understood how two layers of pressure have shaped company behaviour, and how a company located in a developing country is connecting with global network. I also found there is considerable tension among key actors and investigated how the company responded and managed it.
Drawing on Actor Network Theory (ANT), I analysed the interviews from key actors, examined the role of government regulations and customer requirements to see how management control being managed under two layers of pressure, i.e., the government regulations (e.g., labour, tax, environment control) and customer requirement (e.g., quality and production control). Management controls were an obligatory passage point (OPP), and transformation of those elements of Western production requirements and government requirements arrived at the Chinese local factory and influenced management control and budgeting.
The findings suggest that management control systems are not only a set of technical procedures, but it is also about managing tensions. This understanding shows a linear perspective on MC practices rather than a social perspective. However, when we use ANT as a theoretical perspective, we see those actors who, being obliged and sandwiched, and controlled by external forces for them to follow. Consequently, human actors must work in an unavoidable OPP. This is the tension they face which constructed mundane practices of MC. Hence, MCs are managing such tensions. This study contributes to management control research by analysing management controls in terms of OPP, extends our understanding by illustrating the role of the government and customers, and our understanding of family-owned business from a management controls perspective in a developing country
DEEP REINFORCEMENT LEARNING AND MODEL PREDICTIVE CONTROL APPROACHES FOR THE SCHEDULED OPERATION OF DOMESTIC REFRIGERATORS
Excess capacity of the UK’s national grid is widely quoted to be reducing to around 4% over the coming years as a consequence of increased economic growth (and hence power usage) and reductions in power generation plants. There is concern that short term variations in power demand could lead to serious wide-scale disruption on a national scale. This is therefore spawning greater attention on augmenting traditional generation plants with renewable and localized energy storage technologies, and consideration of improved demand side responses (DSR), where power consumers are incentivized to switch off assets when the grid is under pressure. It is estimated, for instance, that refrigeration/HVAC systems alone could account for ~14% of the total UK energy usage, with refrigeration and water heating/cooling systems, in particular, being able to act as real-time ‘buffer’ technologies that can be demand-managed to accommodate transient demands by being switched-off for short periods without damaging their outputs. Large populations of thermostatically controlled loads (TCLs) hold significant potential for performing ancillary services in power systems since they are well-established and widely distributed around the power network. In the domestic sector, refrigerators and freezers collectively constitute a very large electrical load since they are continuously connected and are present in almost most households. The rapid proliferation of the ‘Internet of Things’ (IoT) now affords the opportunity to monitor and visualise smart buildings appliances performance and specifically, schedule the operation of the widely distributed domestic refrigerator and freezers to collectively improve energy efficiency and reduce peak power consumption on the electrical grid. To accomplish this, this research proposes the real-time estimation of the thermal mass of individual refrigerators in a network using on-line parameter identification, and the co-ordinated (ON-OFF) scheduling of the refrigerator compressors to maintain their respective temperatures within specified hysteresis bands—commensurate with accommodating food safety standards. Custom Model Predictive Control (MPC) schemes and a Machine Learning algorithm (Reinforcement Learning) are researched to realize an appropriate scheduling methodology which is implemented through COTS IoT hardware. Benefits afforded by the proposed schemes are investigated through experimental trials which show that the co-ordinated operation of domestic refrigerators can 1) reduce the peak power consumption as seen from the perspective of the electrical power grid (i.e. peak power shaving), 2) can adaptively control the temperature hysteresis band of individual refrigerators to increase operational efficiency, and 3) contribute to a widely distributed aggregated load shed for Demand Side Response purposes in order to aid grid stability. Comparative studies of measurements from experimental trials show that the co-ordinated scheduling of refrigerators allows energy savings of between 19% and 29% compared to their traditional isolated (non-co-operative) operation. Moreover, by adaptively changing the hysteresis bands of individual fridges in response to changes in thermal behaviour, a further 20% of savings in energy are possible at local refrigerator level, thereby providing benefits to both network supplier and individual consumer
Secure authentication and key agreement via abstract multi-agent interaction
Authentication and key agreement are the foundation for secure communication over the Internet. Authenticated Key Exchange (AKE) protocols provide methods for communicating parties to authenticate each other, and establish a shared session key by which they can encrypt messages in the session. Within the category of AKE protocols, symmetric AKE protocols rely on pre-shared master keys for both services. These master keys can be transformed after each session in a key-evolving scheme to provide the property of forward secrecy, whereby the compromise of master keys does not allow for the compromise of past session keys.
This thesis contributes a symmetric AKE protocol named AMI (Authentication via Multi-Agent Interaction). The AMI protocol is a novel formulation of authentication and key agreement as a multi-agent system, where communicating parties are treated as autonomous agents whose behavior within the protocol is governed by private agent models used as the master keys. Parties interact repeatedly using their behavioral models for authentication and for agreeing upon a unique session key per communication session. These models are evolved after each session to provide forward secrecy. The security of the multi-agent interaction process rests upon the difficulty of modeling an agent's decisions from limited observations about its behavior, a long-standing problem in AI research known as opponent modeling. We conjecture that it is difficult to efficiently solve even by a quantum computer, since the problem is fundamentally one of missing information rather than computational hardness.
We show empirically that the AMI protocol achieves high accuracy in correctly identifying legitimate agents while rejecting different adversarial strategies from the security literature. We demonstrate the protocol's resistance to adversarial agents which utilize random, replay, and maximum-likelihood estimation (MLE) strategies to bypass the authentication test. The random strategy chooses actions randomly without attempting to mimic a legitimate agent. The replay strategy replays actions previously observed by a legitimate client. The MLE strategy estimates a legitimate agent model using previously observed interactions, as an attempt to solve the opponent modeling problem.
This thesis also introduces a reinforcement learning approach for efficient multi-agent interaction and authentication. This method trains an authenticating server agent's decision model to take effective probing actions which decrease the number of interactions in a single session required to successfully reject adversarial agents. We empirically evaluate the number of interactions required for a trained server agent to reject an adversarial agent, and show that using the optimized server leads to a much more sample-efficient interaction process than a server agent selecting actions by a uniform-random behavioral policy.
Towards further research on and adoption of the AMI protocol for authenticated key-exchange, this thesis also contributes an open-source application written in Python, PyAMI. PyAMI consists of a multi-agent system where agents run on separate virtual machines, and communicate over low-level network sockets using TCP. The application supports extending the basic client-server setting to a larger multi-agent system for group authentication and key agreement, providing two such architectures for different deployment scenarios
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