19,136 research outputs found
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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
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Avaliação do potencial de desenvolvimento de coleções de moda com recurso à tecnologia CAD 3D - estudo de caso CLO 3D
Dissertação de mestrado em Design e Marketing de Produto Têxtil, Vestuário e AcessóriosA Indústria da moda sempre foi umas das indústrias mais competitivas e velozes do mercado,
onde a relação qualidade-rapidez é a chave do sucesso. Com a pandemia da COVID-19 esta
teve de se adaptar e responder prontamente Ă s novas necessidades do mercado. O
cancelamento de diversos desfiles por todo mundo, os vários confinamentos que fecharam
durante meses a população nas suas casas e por consequência o interesse pela a moda que
deixou de estar no topo dos interesses da população. A indústria foi obrigada a reagir e a
encontrar meios digitais que possibilitassem a deslocação pelo mundo em frações de segundo,
alterando aquele que era o seu processo produtivo até então. Alem disso, também a
comunicação das marcas foi obrigada a adaptar-se aos novos interesses da população. Todas
estas mudanças aceleraram aquela que já era apontada pelos especialistas como o futuro da
moda e da produção destes artigos, a aposta digital, nomeadamente o 3D na Indústria 4.0.
Esta dissertação de mestrado tem como objetivo analisar a eficácia e fiabilidade dos sistemas
CAD como resposta Ă s necessidades dos novos mercados. O desenvolvimento da mesma foi
possĂvel atravĂ©s da revisĂŁo da literatura onde a premissa Ă© as tecnologias de moda, assim
como de notĂcias e artigos cientĂficos escritos relativamente ao tema. O estudo explora as
diversas tecnologias usadas no contexto, revelando novos hábitos de trabalho e consumo e o
uso de softwares 3D para facilitar no fluxo dos mesmos.The fashion industry has always been one of the fastest and most competitive industries in
the market, where the quality-fastness relationship is the key to success. With the pandemic
of COVID-19 it had to adapt and respond to the needs of the market. The cancellation of
several fashion shows around the world, the several lockdowns that kept the population in
their homes for months, and consequently the interest in fashion that was no longer at the
top of the population's interests. The industry was forced to react and find digital means that
made it possible to move around the world in fractions of a second, changing what was its
production process until then. In addition, brand communication was also forced to adapt to
the new interests of the population. All these changes accelerated what was already pointed
out by experts as the future of fashion and the production of these articles, the digital bet,
namely 3D in Industry 4.0. This master's thesis aims to analyze the effectiveness and reliability
of CAD systems as a response to the needs of new markets. Its development was possible
through a literature review where the premise is fashion technologies, as well as news and
scientific articles written on the subject. The study explores the various technologies used in
the context, revealing new work and consumption habits and the use of 3D software to
facilitate their flow
BotMoE: Twitter Bot Detection with Community-Aware Mixtures of Modal-Specific Experts
Twitter bot detection has become a crucial task in efforts to combat online
misinformation, mitigate election interference, and curb malicious propaganda.
However, advanced Twitter bots often attempt to mimic the characteristics of
genuine users through feature manipulation and disguise themselves to fit in
diverse user communities, posing challenges for existing Twitter bot detection
models. To this end, we propose BotMoE, a Twitter bot detection framework that
jointly utilizes multiple user information modalities (metadata, textual
content, network structure) to improve the detection of deceptive bots.
Furthermore, BotMoE incorporates a community-aware Mixture-of-Experts (MoE)
layer to improve domain generalization and adapt to different Twitter
communities. Specifically, BotMoE constructs modal-specific encoders for
metadata features, textual content, and graphical structure, which jointly
model Twitter users from three modal-specific perspectives. We then employ a
community-aware MoE layer to automatically assign users to different
communities and leverage the corresponding expert networks. Finally, user
representations from metadata, text, and graph perspectives are fused with an
expert fusion layer, combining all three modalities while measuring the
consistency of user information. Extensive experiments demonstrate that BotMoE
significantly advances the state-of-the-art on three Twitter bot detection
benchmarks. Studies also confirm that BotMoE captures advanced and evasive
bots, alleviates the reliance on training data, and better generalizes to new
and previously unseen user communities.Comment: Accepted at SIGIR 202
Adjacent LSTM-Based Page Scheduling for Hybrid DRAM/NVM Memory Systems
Recent advances in memory technologies have led to the rapid growth of hybrid systems that combine traditional DRAM and Non Volatile Memory (NVM) technologies, as the latter provide lower cost per byte, low leakage power and larger capacities than DRAM, while they can guarantee comparable access latency. Such kind of heterogeneous memory systems impose new challenges in terms of page placement and migration among the alternative technologies of the heterogeneous memory system. In this paper, we present a novel approach for efficient page placement on heterogeneous DRAM/NVM systems. We design an adjacent LSTM-based approach for page placement, which strongly relies on page accesses prediction, while sharing knowledge among pages with behavioral similarity. The proposed approach leads up to 65.5% optimized performance compared to existing approaches, while achieving near-optimal results and saving 20.2% energy consumption on average. Moreover, we propose a new page replacement policy, namely clustered-LRU, achieving up to 8.1% optimized performance, compared to the default Least Recently Used (LRU) policy
Highly selective PtCo bimetallic nanoparticles on silica for continuous production of hydrogen from aqueous phase reforming of xylose
Hydrogen (H2) is a promising energy vector for mitigating greenhouse gas emissions. Lignocellulosic biomass waste has been introduced as one of the abundant and carbon-neutral H2 sources. Among those, xylose with its short carbon chain has emerged attractive, where H2 can be catalytically released in an aqueous reactor. In this study, a composite catalyst system consisting of silica (SiO2)-supported platinum (Pt)-cobalt (Co) bimetallic nanoparticles was developed for aqueous phase reforming of xylose conducted at 225 °C and 29.3 bar. The PtCo/SiO2 catalyst showed a significantly higher H2 production rate and selectivity than that of Pt/SiO2, whereas Co/SiO2 shows no activity in H2 production. The highest selectivity for useful liquid byproducts was obtained with PtCo/SiO2. Moreover, CO2 emissions throughout the reaction were reduced compared to those of monometallic Pt/SiO2. The PtCo bimetallic nanocatalyst offers an inexpensive, sustainable, and durable solution with high chemical selectivity for scalable reforming of hard-to-ferment pentose sugars
Large-scale rollout of extension training in Bangladesh: Challenges and opportunities for gender-inclusive participation
Despite the recognized importance of women’s participation in agricultural extension services, research continues to show inequalities in women’s participation. Emerging capacities for conducting large-scale extension training using information and communication technologies (ICTs) now afford opportunities for generating the rich datasets needed to analyze situational factors that affect women’s participation. Data was recorded from 1,070 video-based agricultural extension training events (131,073 farmers) in four Administrative Divisions of Bangladesh (Rangpur, Dhaka, Khulna, and Rajshahi). The study analyzed the effect of gender of the trainer, time of the day, day of the week, month of the year, Bangladesh Administrative Division, and venue type on (1) the expected number of extension event attendees and (2) the odds of females attending the event conditioned on the total number of attendees. The study revealed strong gender specific training preferences. Several factors that increased total participation, decreased female attendance (e.g., male-led training event held after 3:30 pm in Rangpur). These findings highlight the dilemma faced by extension trainers seeking to maximize attendance at training events while avoiding exacerbating gender inequalities. The study concludes with a discussion of ways to mitigate gender exclusion in extension training by extending data collection processes, incorporating machine learning to understand gender preferences, and applying optimization theory to increase total participation while concurrently improving gender inclusivity
Semantic Segmentation Enhanced Transformer Model for Human Attention Prediction
Saliency Prediction aims to predict the attention distribution of human eyes
given an RGB image. Most of the recent state-of-the-art methods are based on
deep image feature representations from traditional CNNs. However, the
traditional convolution could not capture the global features of the image well
due to its small kernel size. Besides, the high-level factors which closely
correlate to human visual perception, e.g., objects, color, light, etc., are
not considered. Inspired by these, we propose a Transformer-based method with
semantic segmentation as another learning objective. More global cues of the
image could be captured by Transformer. In addition, simultaneously learning
the object segmentation simulates the human visual perception, which we would
verify in our investigation of human gaze control in cognitive science. We
build an extra decoder for the subtask and the multiple tasks share the same
Transformer encoder, forcing it to learn from multiple feature spaces. We find
in practice simply adding the subtask might confuse the main task learning,
hence Multi-task Attention Module is proposed to deal with the feature
interaction between the multiple learning targets. Our method achieves
competitive performance compared to other state-of-the-art methods
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
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