1,720,976 research outputs found
A Proof-of-Stake protocol for consensus on Bitcoin subchains
Full text linkAlthough the transactions on the Bitcoin blockchain have the main purpose of recording currency transfers, they can also carry a few bytes of metadata. A sequence of transaction metadata forms a subchain of the Bitcoin blockchain, and it can be used to store a tamper-proof execution trace of a smart contract. Except for the trivial case of contracts which admit any trace, in general there may exist inconsistent subchains which represent incorrect contract executions. A crucial issue is how to make it difficult, for an adversary, to subvert the execution of a contract by making its subchain inconsistent. Existing approaches either postulate that subchains are always consistent, or give weak guarantees about their security (for instance, they are susceptible to Sybil attacks). We propose a consensus protocol, based on Proof-of-Stake, that incentivizes nodes to consistently extend the subchain. We empirically evaluate the security of our protocol, and we show how to exploit it as the basis for smart contracts on Bitcoin
Artificial Intelligence Methods for Smart Cities
Full text linkIn recent years, the concept of smart cities has garnered increasing attention as urban areas grapple with the challenges of population growth, resource management, and infrastructure optimization [...
Investigating the Effectiveness of 3D Monocular Object Detection Methods for Roadside Scenarios
No full textMulti-scale deep learning ensemble for segmentation of endometriotic lesions
Full text linkUltrasound is a readily available, non-invasive and low-cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, both in terms of workload reduction and in increasing the overall accuracy of this type of examination and its outcome. However, although these techniques are spreading rapidly in a variety of domains, their application to endometriosis is still very limited. To fill this gap, we propose and evaluate a novel multi-scale ensemble approach for the automatic segmentation of endometriosis lesions from transvaginal ultrasounds. The peculiarity of the method lies in its high discrimination capability, obtained by combining, in a fusion fashion, multiple Convolutional Neural Networks trained on data at different granularity. The experimental validation carried out shows that: (i) the proposed method allows to significantly improve the performance of the individual neural networks, even in the presence of a limited training set; (ii) with a Dice coefficient of 82%, it represents a valid solution to increase the diagnostic efficacy of the ultrasound examination against such a pathology
CARgram: CNN-based accident recognition from road sounds through intensity-projected spectrogram analysis
Full text linkRoad surveillance systems play an important role in traffic monitoring and detecting hazardous events. In recent years, several artificial intelligence-based approaches have been proposed for this purpose, typically based on the analysis of the acquired video streams. However, occlusions, poor lighting conditions, and heterogeneity of the events may often reduce their effectiveness and reliability. To overcome the limitations mentioned, scientific and industrial research has therefore focused on integrating such solutions with audio recognition methods. By automatically identifying anomalous traffic sounds, e.g., car crashes and skids, they help reduce false positives and missed alarms. Following this trend, in this work, we propose an innovative pipeline for the analysis of intensity-projected audio spectrograms from streams of traffic sounds, which exploits both (i) a visual approach based on a custom, special-purpose Convolutional Neural Network for the identification of anomalous events on the sound signal; and, (ii) a novel multi-representational encoding of the input, which proved to significantly improve the recognition accuracy of the neural models. The validation results of the proposed pipeline on the public MIVIA dataset, with a 0.96% of false positive rate, showed to be the best performance against the stateof-the-art competitors. Notably, following such findings, a prototype implementation has been deployed on a real-world video surveillance infrastructure
Corporate risk stratification through an interpretable autoencoder-based model
Full text linkIn this manuscript, we propose an innovative early warning Machine Learning-based model to identify potential threats to financial sustainability for non-financial companies. Unlike most state-of-the-art tools, whose outcomes are often difficult to understand even for experts, our model provides an easily interpretable visualization of balance sheets, projecting each company in a bi-dimensional space according to an autoencoder-based dimensionality reduction matched with a Nearest-Neighbor-based default density estimation. In the resulting space, the distress zones, where the default intensity is high, appear as homogeneous clusters directly identified. Our empirical experiments provide evidence of the interpretability, forecasting ability, and robustness of the bi-dimensional space
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HUMAD 2024: International Workshop on Human-Centered Modeling and Adaptation for Digital Transformation
Full text linkWhile digital transformation brings broad positive impacts, its rapid propagation varies across territories, accentuating disparities not only in the social and organisational contexts but even in industrial sectors. Acknowledging this digital divide, the HUMAD workshop aimed to reshape industrial and digital landscapes for equity and accessibility, specifically emphasising the pivotal role of user modelling and artificial intelligence. Discussions centred on personalised user modelling and holistic, human-centred approaches tailored for an industrial context. The workshop explored applications in critical sectors, including smart cities, tourism, industrial production, healthcare, education, and well-being, focusing on delivering quality digital interactions and services tuned to industrial and human-centric needs. The objective was to elevate the quality of digital interactions and services, ensuring a more uniform and inclusive distribution of the benefits of industrial digital transformation. Beyond immediate concerns, HUMAD delved into user modelling’s potential to drive growth in disadvantaged territories. By comprehending the unique challenges these regions face, the aim was to cultivate sustainable solutions within an industrial framework. In the light of the above, we invited contributions that navigate the intricacies of user modelling, proposing innovative strategies to reduce disparities and develop a more equitable, human-centric digital future
Can Existing 3D Monocular Object Detection Methods Work in Roadside Contexts? A Reproducibility Study
No full textSailGenie: SAiling expertIse to knowLedge Graph through opEN Information Extraction
Full text linkThis work is focused on the sailing domain, for which several innovative technologies are being adopted to improve sailing efficiency, performance, and safety. In this context a knowledge graph could be used, for example, to represent information about different types of boats, sailing techniques, maritime safety, or weather conditions. Although numerous construction methods or ready-to-go knowledge graphs have been proposed in many fields, the sailing domain still needs to be explored. As the most effective methods rely on domain-specific datasets, the absence of suitable and available sailing datasets is one of the main challenges. Although several Open Information Extraction (OpenIE) methods may generate relevant triplets (the elementary units composing a knowledge graph) from arbitrary text without any additional information about its topic, such methods usually generate many incorrect triplets. In this paper, we aim (i) to address the aforementioned problem by proposing an innovative method that combines in an improved and strengthened way different OpenIE tools to generate proper triplets from domain-specific sources and, in particular, (ii) to build and release a suitable dataset for the sailing domain. Results confirm that our proposal can maximize the extracted information and infer unique information irretrievable by the classical OpenIE tools and, furthermore, that the generated dataset is significantly valuable for the sailing scenario
A Zero-Shot Strategy for Knowledge Graph Engineering Using GPT-3.5
Full text linkIn the recent digitization era, capturing, representing, and understanding knowledge is essential in countless real-world scenarios. Knowledge graphs emerged as a powerful tool for representing information through an adequately interconnected and interpretable structure in such a context. Nevertheless, generating proper knowledge graphs usually requires significant manual effort and domain expertise, resulting in graphs often affected by human subjectivity, limited scalability, or inability to capture implicit knowledge or handle heterogeneity. This paper proposes an innovative zero-shot strategy tailored to uncover reliable knowledge from text leveraging the recent highly effective generative large language models, with a particular focus on the GPT-3.5 model. Our proposal aims to create a suitable knowledge graph or improve existing ones by discovering missing qualitative triples. To assess the effectiveness of our methodology, we performed experiments on domain-specific datasets, confirming its potential for scalable and versatile knowledge discovery
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