12,022 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
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
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
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
An Experimental Study on Sentiment Classification of Moroccan dialect texts in the web
With the rapid growth of the use of social media websites, obtaining the
users' feedback automatically became a crucial task to evaluate their
tendencies and behaviors online. Despite this great availability of
information, and the increasing number of Arabic users only few research has
managed to treat Arabic dialects. The purpose of this paper is to study the
opinion and emotion expressed in real Moroccan texts precisely in the YouTube
comments using some well-known and commonly used methods for sentiment
analysis. In this paper, we present our work of Moroccan dialect comments
classification using Machine Learning (ML) models and based on our collected
and manually annotated YouTube Moroccan dialect dataset. By employing many text
preprocessing and data representation techniques we aim to compare our
classification results utilizing the most commonly used supervised classifiers:
k-nearest neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and
deep learning (DL) classifiers such as Convolutional Neural Network (CNN) and
Long Short-Term Memory (LTSM). Experiments were performed using both raw and
preprocessed data to show the importance of the preprocessing. In fact, the
experimental results prove that DL models have a better performance for
Moroccan Dialect than classical approaches and we achieved an accuracy of 90%.Comment: 13 pages, 5 tables, 2 figure
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
Perfect is the enemy of test oracle
Automation of test oracles is one of the most challenging facets of software
testing, but remains comparatively less addressed compared to automated test
input generation. Test oracles rely on a ground-truth that can distinguish
between the correct and buggy behavior to determine whether a test fails
(detects a bug) or passes. What makes the oracle problem challenging and
undecidable is the assumption that the ground-truth should know the exact
expected, correct, or buggy behavior. However, we argue that one can still
build an accurate oracle without knowing the exact correct or buggy behavior,
but how these two might differ. This paper presents SEER, a learning-based
approach that in the absence of test assertions or other types of oracle, can
determine whether a unit test passes or fails on a given method under test
(MUT). To build the ground-truth, SEER jointly embeds unit tests and the
implementation of MUTs into a unified vector space, in such a way that the
neural representation of tests are similar to that of MUTs they pass on them,
but dissimilar to MUTs they fail on them. The classifier built on top of this
vector representation serves as the oracle to generate "fail" labels, when test
inputs detect a bug in MUT or "pass" labels, otherwise. Our extensive
experiments on applying SEER to more than 5K unit tests from a diverse set of
open-source Java projects show that the produced oracle is (1) effective in
predicting the fail or pass labels, achieving an overall accuracy, precision,
recall, and F1 measure of 93%, 86%, 94%, and 90%, (2) generalizable, predicting
the labels for the unit test of projects that were not in training or
validation set with negligible performance drop, and (3) efficient, detecting
the existence of bugs in only 6.5 milliseconds on average.Comment: Published in ESEC/FSE 202
Evaluating the Code Quality of AI-Assisted Code Generation Tools: An Empirical Study on GitHub Copilot, Amazon CodeWhisperer, and ChatGPT
Context: AI-assisted code generation tools have become increasingly prevalent
in software engineering, offering the ability to generate code from natural
language prompts or partial code inputs. Notable examples of these tools
include GitHub Copilot, Amazon CodeWhisperer, and OpenAI's ChatGPT.
Objective: This study aims to compare the performance of these prominent code
generation tools in terms of code quality metrics, such as Code Validity, Code
Correctness, Code Security, Code Reliability, and Code Maintainability, to
identify their strengths and shortcomings.
Method: We assess the code generation capabilities of GitHub Copilot, Amazon
CodeWhisperer, and ChatGPT using the benchmark HumanEval Dataset. The generated
code is then evaluated based on the proposed code quality metrics.
Results: Our analysis reveals that the latest versions of ChatGPT, GitHub
Copilot, and Amazon CodeWhisperer generate correct code 65.2%, 46.3%, and 31.1%
of the time, respectively. In comparison, the newer versions of GitHub CoPilot
and Amazon CodeWhisperer showed improvement rates of 18% for GitHub Copilot and
7% for Amazon CodeWhisperer. The average technical debt, considering code
smells, was found to be 8.9 minutes for ChatGPT, 9.1 minutes for GitHub
Copilot, and 5.6 minutes for Amazon CodeWhisperer.
Conclusions: This study highlights the strengths and weaknesses of some of
the most popular code generation tools, providing valuable insights for
practitioners. By comparing these generators, our results may assist
practitioners in selecting the optimal tool for specific tasks, enhancing their
decision-making process
Does Internet use connect smallholder farmers to a healthy diet? Evidence from rural China
IntroductionUndernutrition and micronutrient malnutrition remain problems of significant magnitude among small-scale subsistence farmers, posing a serious threat to their health and well-being. Developing a healthy diet can effectively reduce this threat. Fortunately, the Internet can speed up the process.MethodsBased on survey data from 5,114 farm households in nine provinces in China, this study quantitatively assesses the impact of Internet use on the dietary quality of smallholder farmers using OLS regression models and PSM models.Results/Discussion(1) Internet use can significantly contribute to dietary diversity and dietary rationality among smallholder farmers, thus optimizing their dietary structure. (2) Internet use significantly increased the average consumption amounts of milk and its products (2.9 g), fruits (21.5 g), eggs (7.5 g), and vegetables (27.1 g), while also decreasing the intake of salts (1.5 g) and oil (3.8 g). (3) The pull of internet use to improve diet quality is more significant for smallholder households with lower levels of education, older heads of households, and higher household incomes. (4) A possible mechanism is that Internet use increases household income and information access skills of rural residents, thus improving their dietary quality. In summary, governments should further promote Internet penetration in rural areas for health purposes
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