Predictive Modeling for Fair and Efficient Transaction Inclusion in Proof-of-Work Blockchain Systems

This dissertation investigates the strategic integration of Proof-of-Work(PoW)-based blockchains and ML models to improve transaction inclusion, and consequently molding transaction fees, for clients using cryptocurrencies such as Bitcoin. The research begins with an in-depth exploration of the Bitcoin fee market, focusing on the interdependence between users and miners, and the emergence of a fee market in PoW-based blockchains. Our observations are used to formalize a transaction inclusion pattern. To support our research, we developed the Blockchain Analytics System (BAS) to acquire, store, and pre-process a local dataset of the Bitcoin blockchain. BAS employs various methods for data acquisition, including web scraping, web browser APIs, and direct access to the blockchain using Bitcoin Core software. We utilize time-series data analysis as a tool for predicting future trends, and transactions are sampled on a monthly basis with a fixed interval, incorporating a notion of relative time represented by block-creation epochs. We create a comprehensive model for transaction inclusion in a PoW-based blockchain system, with a focus on factors of revenue and fairness. Revenue serves as an incentive for miners to participate in the network and validate transactions, while fairness ensures equal opportunity for all users to have their transactions included upon paying an adequate fee value. The ML architecture used for prediction consists of three critical stages: the ingestion engine, the pre-processing stage, and the ML model. The ingestion engine processes and transforms raw data obtained from the blockchain, while the pre-processing phase transforms the data further into a suitable form for analysis, including feature extraction and additional data processing to generate a complete dataset. Our ML model showcases its effectiveness in predicting transaction inclusion, with an accuracy of more than 90%. Such a model enables users to save at least 10% on transaction fees while maintaining a likelihood of inclusion above 80%. Furthermore, adopting such model based on fairness and revenue, demonstrates that miners' average loss is never higher than 1.3%. Our research proves the efficacy of a formal transaction inclusion model and ML prototype in predicting transaction inclusion. The insights gained from our study shed light on the underlying mechanisms governing miners' decisions, improving the overall user experience, and enhancing the trust and reliability of cryptocurrencies. Consequently, this enables Bitcoin users to better select suitable fees and predict transaction inclusion with notable precision, contributing to the continued growth and adoption of cryptocurrencies

When is Spring coming? A Security Analysis of Avalanche Consensus

Avalanche is a blockchain consensus protocol with exceptionally low latency and high throughput. This has swiftly established the corresponding token as a top-tier cryptocurrency. Avalanche achieves such remarkable metrics by substituting proof of work with a random sampling mechanism. The protocol also differs from Bitcoin, Ethereum, and many others by forming a directed acyclic graph (DAG) instead of a chain. It does not totally order all transactions, establishes a partial order among them, and accepts transactions in the DAG that satisfy specific properties. Such parallelism is widely regarded as a technique that increases the efficiency of consensus. Despite its success, Avalanche consensus lacks a complete abstract specification and a matching formal analysis. To address this drawback, this work provides first a detailed formulation of Avalanche through pseudocode. This includes features that are omitted from the original whitepaper or are only vaguely explained in the documentation. Second, the paper gives an analysis of the formal properties fulfilled by Avalanche in the sense of a generic broadcast protocol that only orders related transactions. Last but not least, the analysis reveals a vulnerability that affects the liveness of the protocol. A possible solution that addresses the problem is also proposed

When Is Spring Coming? A Security Analysis of Avalanche Consensus

Avalanche is a blockchain consensus protocol with exceptionally low latency and high throughput. This has swiftly established the corresponding token as a top-tier cryptocurrency. Avalanche achieves such remarkable metrics by substituting proof of work with a random sampling mechanism. The protocol also differs from Bitcoin, Ethereum, and many others by forming a directed acyclic graph (DAG) instead of a chain. It does not totally order all transactions, establishes a partial order among them, and accepts transactions in the DAG that satisfy specific properties. Such parallelism is widely regarded as a technique that increases the efficiency of consensus. Despite its success, Avalanche consensus lacks a complete abstract specification and a matching formal analysis. To address this drawback, this work provides first a detailed formulation of Avalanche through pseudocode. This includes features that are omitted from the original whitepaper or are only vaguely explained in the documentation. Second, the paper gives an analysis of the formal properties fulfilled by Avalanche in the sense of a generic broadcast protocol that only orders related transactions. Last but not least, the analysis reveals a vulnerability that affects the liveness of the protocol. A possible solution that addresses the problem is also proposed

On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, https://doi.org/10.1145/3528669.The transaction-rate bottleneck built into popular proof-of-work-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this paper, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions

Basilar invagination: surgical results

Basilar invagination (BI) is a congenital craniocervical junction (CCJ) anomaly represented by a prolapsed spine into the skull-base that can result in severe neurological impairment. In this paper, we retrospective evaluate the surgical treatment of 26 patients surgically treated for symptomatic BI. BI was classified according to instability and neural abnormalities findings. Clinical outcome was evaluated using the Nürick grade system. A total of 26 patients were included in this paper. Their age ranged from 15 to 67 years old (mean 38). Of which, 10 patients were male (38%) and 16 (62%) were female. All patients had some degree of tonsillar herniation, with 25 patients treated with foramen magnum decompression. Nine patients required a craniocervical fixation. Six patients had undergone prior surgery and required a new surgical procedure for progression of neurological symptoms associated with new compression or instability. Most of patients with neurological symptoms secondary to brainstem compression had some improvement during the follow-up. There was mortality in this series, 1 month after surgery, associated with a late removal of the tracheal cannula. Management of BI requires can provide improvements in neurological outcomes, but requires analysis of the neural and bony anatomy of the CCJ, as well as occult instability. The complexity and heterogeneous presentation requires attention to occult instability on examination and attention to airway problems secondary to concomitant facial malformations.Basilar invagination (BI) is a congenital craniocervical junction (CCJ) anomaly represented by a prolapsed spine into the skull-base that can result in severe neurological impairment. Materials and Methods: In this paper, we retrospective evaluate the sur527884sem informaçãosem informaçã

Intracranial extension of orbital inflammatory pseudotumor: a case report and literature review

Background: Orbital inflammatory pseudotumor is a rare inflammatory condition of unknown cause that may extend intracranially, usually as a dural-based infiltrate. Here we report the first case of orbital pseudotumor presenting with intra-axial Magnetic Resonance Imaging (MRI) changes. Case presentation: A 57-year-old white female, with a 3-month history of headache and right palpebral edema, presented with marked right temporal lobe edema with ominous MRI appearance, and ipsilateral alterations of orbital and periorbital structures. Following steroid therapy, both intracranial and orbital involvement dramatically improved. Conclusion: Orbital inflammatory pseudotumor with chronic inflammation may infrequently present with intracranial involvement, mimicking more aggressive diseases, even showing intra-axial enhancement after i.v. contrast administration in brain MRI. Awareness of this possibility may help neurologists to choose the appropriate therapeutic approach

Frequency encoding for simultaneous display of multimodality images.

An original method for simultaneous display of functional and anatomic images, based on frequency encoding (FE), merges color PET with T1-weighted MR brain images, and grayscale PET with multispectral color MR images. A comparison with two other methods reported in the literature for image fusion (averaging and intensity modulation techniques) was performed. Methods: For FE, the Fourier transform of the merged image was obtained summing the low frequencies of the PET image and the high frequencies of the MR image. For image averaging, the merged image was obtained as a weighted average of the intensities of the two images to be merged. For intensity modulation, the red, green and blue components of the color image were multiplied on a pixel- by-pixel basis by the grayscale image. A comparison of the performances of the three techniques was made by three independent observers assessing the conspicuity of specific MRI and PET information in the merged images. For evaluation purposes, images from seven patients and a computer-simulated MRI/PET phantom were used. Data were compared with a chi-square test applied to ranks. Results: For the depiction of MRI and PET information when merging color PET and T1-weighted MR images, FE was rated superior to intensity modulation and averaging techniques in a significant number of comparisons. For merging grayscale PET with multispectral color MR images, FE and intensity modulation were rated superior to image averaging in terms of both MRI and PET information. Conclusion: The data suggest that improved simultaneous evaluation of MRI and PET information can be achieved with a method based on FE

Magnetic Resonance features of pyogenic brain abscesses and differential diagnosis using morphological and functional imaging studies: a pictorial essay.

The aim of this paper is to illustrate the potential of magnetic resonance imaging (MRI) in diagnosis, differential diagnosis, treatment planning and evaluation of therapy effectiveness of pyogenic brain abscesses, through the use of morphological (or conventional) and functional (or advanced) sequences. Conventional MRI study is useful for the identification of lesions, to determine the location and morphology and allows a correct hypothesis of nature in the most typical cases. However, the differential diagnosis from other brain lesions such as non pyogenic abscesses or necrotic tumors (high-grade gliomas and metastases) is often only possible through the use of functional sequences, as the measurement of diffusion with apparent diffusion coefficient (DWI-ADC), proton magnetic resonance spectroscopy (1H-MRS) and perfusion weighted imaging (PWI), which complement the morphological sequences and provide essential information on structural, metabolic and hemodynamic characteristics allowing greater neuroradiological confidence. Modern diagnostic MRI of pyogenic brain abscesses cannot be separated from knowledge, integration and proper use of the morphological and functional sequences

Prototyping a ROOT-based distributed analysis workflow for HL-LHC: the CMS use case

The challenges expected for the next era of the Large Hadron Collider (LHC), both in terms of storage and computing resources, provide LHC experiments with a strong motivation for evaluating ways of rethinking their computing models at many levels. Great efforts have been put into optimizing the computing resource utilization for the data analysis, which leads both to lower hardware requirements and faster turnaround for physics analyses. In this scenario, the Compact Muon Solenoid (CMS) collaboration is involved in several activities aimed at benchmarking different solutions for running High Energy Physics (HEP) analysis workflows. A promising solution is evolving software towards more user-friendly approaches featuring a declarative programming model and interactive workflows. The computing infrastructure should keep up with this trend by offering on the one side modern interfaces, and on the other side hiding the complexity of the underlying environment, while efficiently leveraging the already deployed grid infrastructure and scaling toward opportunistic resources like public cloud or HPC centers. This article presents the first example of using the ROOT RDataFrame technology to exploit such next-generation approaches for a production-grade CMS physics analysis. A new analysis facility is created to offer users a modern interactive web interface based on JupyterLab that can leverage HTCondor-based grid resources on different geographical sites. The physics analysis is converted from a legacy iterative approach to the modern declarative approach offered by RDataFrame and distributed over multiple computing nodes. The new scenario offers not only an overall improved programming experience, but also an order of magnitude speedup increase with respect to the previous approach

Incidence Of Basilar Invagination In Patients With Tonsillar Herniation? A Case Control Craniometrical Study.

A retrospective case-control study based on craniometrical evaluation was performed to evaluate the incidence of basilar invagination (BI). Patients with symptomatic tonsillar herniation treated surgically had craniometrical parameters evaluated based on CT scan reconstructions before surgery. BI was diagnosed when the tip of the odontoid trespassed the Chamberlain's line in three different thresholds found in the literature: 2, 5 or 6.6 mm. In the surgical group (SU), the mean distance of the tip of the odontoid process above the Chamberlain's line was 12 mm versus 1.2 mm in the control (CO) group (p<0.0001). The number of patients with BI according to the threshold used (2, 5 or 6.6 mm) in the SU group was respectively 19 (95%), 16 (80%) and 15 (75%) and in the CO group it was 15 (37%), 4 (10%) and 2 (5%).72706-1