Reviewerly/exHarmony huggingface dataset:
A Formal Approach for the Analysis of the XRP Ledger Consensus Protocol

Distributed ledger technology is envisioned as one of the cornerstones of promising solutions for building the next generation of critical applications.However, there is still quite a bit of confusion and hype around the real security guarantees this technology offers.This is especially due to the fact that for the vast majority of existing blockchain-based consensus protocols it is really hard to find sufficiently detailed documentation that fully captures their behavior.A number of recent papers have formalized the behavior of Bitcoin-like protocols in order to rigorously study the security and privacy properties of their underlying structure, but surprisingly very little work has been devoted to the formalization of distributed ledger systems using BFTlike approaches.In this work, we focus on XRP Ledger, better known as Ripple, and take the first steps towards the complete formalization of its consensus protocol.To this end, we have investigated all the existing documentation and analyzed its source code.We present a formal description of its consensus protocol for every step.Furthermore, we provide an accurate view of its security guarantees in terms of safety and liveness and show how to increase the desired tolerance by changing the value of specific protocol parameters.
Parametric Optimization of Fuzzy Control Systems Based on Hybrid Particle Swarm Algorithms with Elite Strategy

This article is dedicated to the development and study of a hybrid multiagent method of parametric optimization of fuzzy control systems that combines the advantages of particle swarm algorithms and local search algorithms based on the elite strategy. The obtained method allows one to optimize effectively various parameters of fuzzy systems, finding the global optimum of the problem to be solved, and, at the same time, has the higher convergence rate compared with the basic particle swarm method. The study of the effectiveness of the proposed hybrid multiagent method is carried out at parametric optimization of a luzzy system of speed control of the multipurpose caterpillar mobile robot able to move along inclined and vertical ferromagnetic surfaces. In particular, in this paper, the problem of parametric optimization of the weight coefficients of the consequents of the rule base of the fuzzy speed controller of the mobile robot is solved, using the basic particle swarm method, as well as using two modifications of the developed method: the hybrid particle swarm method based on the elite strategy with gradient descent, the hybrid particle swarm method based on the elite strategy with the algorithm of extended Kalman filter. The analysis of the computer simulation results has showed that both modifications of the proposed hybrid method allow one to conduct optimization of the coefficients of the consequents of the rule base significantly more efficient compared with the basic particle swarm method. Also, a speed control system with an optimized vector of coefficients of the controller's rule base based on the considered modifications of the hybrid method of parametric optimization has higher control performance indicators than a system with an optimized vector of coefficients based on the basic particle swarm method. Thus, the studies conducted in this article confirm the high efficiency of the developed hybrid multiagent method based on the elite strategy, as well as the feasibility of its use for parametric optimization of fuzzy control systems of various types and configurations.
Trustee: Full Privacy Preserving Vickrey Auction on Top of Ethereum

The wide deployment of tokens for digital assets on top of Ethereum implies the need for powerful trading platforms. Vickrey auctions have been known to determine the real market price of items as bidders are motivated to submit their own monetary valuations without leaking their information to the competitors. Recent constructions have utilized various cryptographic protocols such as ZKP and MPC, however, these approaches either are partially privacy-preserving or require complex computations with several rounds. In this paper, we overcome these limits by presenting Trustee as a Vickrey auction on Ethereum which fully preserves bids’ privacy at relatively much lower fees. Trustee consists of three components: a front-end smart contract deployed on Ethereum, an Intel SGX enclave, and a relay to redirect messages between them. Initially, the enclave generates an Ethereum account and ECDH key-pair. Subsequently, the relay publishes the account’s address and ECDH public key on the smart contract. As a prerequisite, bidders are encouraged to verify the authenticity and security of Trustee by using the SGX remote attestation service. To participate in the auction, bidders utilize the ECDH public key to encrypt their bids and submit them to the smart contract. Once the bidding interval is closed, the relay retrieves the encrypted bids and feeds them to the enclave that autonomously generates a signed transaction indicating the auction winner. Finally, the relay submits the transaction to the smart contract which verifies the transaction’s authenticity and the parameters’ consistency before accepting the claimed auction winner. As part of our contributions, we have made a prototype for Trustee available on Github for the community to review and inspect it. Additionally, we analyze the security features of Trustee and report on the transactions’ gas cost incurred on Trustee smart contract.
Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer

Knowledge transfer between tasks can improve the performance of learned models, but requires an accurate estimate of inter-task relationships to identify the relevant knowledge to transfer. These inter-task relationships are typically estimated based on training data for each task, which is inefficient in lifelong learning settings where the goal is to learn each consecutive task rapidly from as little data as possible. To reduce this burden, we develop a lifelong learning method based on coupled dictionary learning that utilizes high-level task descriptions to model inter-task relationships. We show that using task descriptors improves the performance of the learned task policies, providing both theoretical justification for the benefit and empirical demonstration of the improvement across a variety of learning problems. Given only the descriptor for a new task, the lifelong learner is also able to accurately predict a model for the new task through zero-shot learning using the coupled dictionary, eliminating the need to gather training data before addressing the task.
The Effect of Content-Equivalent Near-Duplicates on the Evaluation of Search Engines

Current best practices for the evaluation of search engines do not take into account duplicate documents. Dependent on their prevalence, not discounting duplicates during evaluation artificially inflates performance scores, and, it penalizes those whose search systems diligently filter them. Although these negative effects have already been demonstrated a long time ago by Bernstein and Zobel [4], we find that this has failed to move the community. In this paper, we reproduce the aforementioned study and extend it to incorporate all TREC Terabyte, Web, and Core tracks. The worst-case penalty of having filtered duplicates in any of these tracks were losses between 8 and 53 ranks.
Unsupervised Discourse Constituency Parsing Using Viterbi EM

In this paper, we introduce an unsupervised discourse constituency parsing algorithm. We use Viterbi EM with a margin-based criterion to train a span-based discourse parser in an unsupervised manner. We also propose initialization methods for Viterbi training of discourse constituents based on our prior knowledge of text structures. Experimental results demonstrate that our unsupervised parser achieves comparable or even superior performance to fully supervised parsers. We also investigate discourse constituents that are learned by our method.
Discerning payment patterns in Bitcoin from ransomware attacks

Purpose The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks. Design/methodology/approach The authors created an analytic framework – the Ransomware–Bitcoin Intelligence–Forensic Continuum framework – to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ransomware Bitcoin addresses was extracted to populate the framework, via the WalletExplorer.com programming interface. This data was then assembled in a representation of the target network for pattern analysis on the input (cash-in) and output (cash-out) side of the ransomware seed addresses. Different graph algorithms were applied to these networks. The results were compared to a “control” network derived from a Bitcoin charity. Findings The findings show discernible patterns in the network relating to the input and output side of the ransomware graphs. However, these patterns are not easily distinguishable from those associated with the charity Bitcoin address on the input side. Nonetheless, the collection profile over time is more volatile than with the charity Bitcoin address. On the other hand, ransomware output patterns differ from those associated charity addresses, as the attacker cash-out tactics are quite different from the way charities mobilise their donations. We further argue that an application of graph machine learning provides a basis for future analysis and data refinement possibilities. Research limitations/implications Limitations are evident in the sample size of data taken on ransomware campaigns and the “control” subject. Further analysis of additional ransomware campaigns and “control” subjects over time would help refine and validate the preliminary observations in this paper. Future research will also benefit from the application of more powerful computing resources and analytics platforms that scale with the amount of data being collected. Originality/value This research contributes to the maturity of the field by analysing ransomware-Bitcoin behaviour using the Ransomware–Bitcoin Intelligence–Forensic Continuum. By combining several different techniques to discerning patterns of ransomware activity on the Bitcoin network, it provides insight into whether a ransomware attack is occurring and could be used to trigger alerts to seek additional evidence of attack, or could corroborate other information in the system.
Review of prominent strategies for mapping CNNs onto embedded systems

Convolutional neural networks (CNN) have turned into one of the key algorithms in machine learning for content classification of digital images. Nevertheless, the CNN computational complexity is considerable larger than classic algorithms, thus, CPU- or GPU-based platforms are generally used for CNN implementations in many applications, but often do not fulfill portable requirements due to resources, energy and real-time constrains. Therefore, there is a growing interest on real time processing solutions for object recognition using CNNs mainly implemented on embedded systems, which are limited both in resources and energy consumption. An updated review of prominent reported approaches for mapping CNNs onto embedded systems is described in this paper. Two main solutions trends for reducing the hardware CNN workload are distinguished through a deduced taxonomy. One is focused on algorithm level solutions to reduce the number of multiplications and CNN coefficients. On the other hand, hardware level solutions goal is to achieve processing time, power consumption and hardware resources reduction. Two dominant hardware level design strategies are pointed out as oriented to either reducing the energy consumption and resources utilization meeting real-time requirements or increasing the throughput at the expense of resources utilization. Finally, two identified design strategies for CNN hardware accelerators are proposed as opportunity research areas.
Risk based approach in scope of cybersecurity threats and requirements

Paper is focused on theoretical and practical considerations related to risk management and cyber security based on the cyber kill chain concept introduced by Lockheed Martin. Proposed approach of cyber risk management embedded on the cyber kill chain is new and not reflected in the available literature. Proposed risk management process of identifying, analyzing, evaluating, assessing and ultimately responding to cyber threats and monitoring risks in each stage of the cyber kill chain is the heart of proposed approach. The approach may be used in organizations which are going to implement security mechanisms to align with the in-force requirements or to reduce cyber risks to accepted level. The process of the risk assessment introduced by the authors follows with the description of the example risk evaluation method based on a continuous-time Markov chain as a model of the cyber kill chain.
Subband Modeling for Spoofing Detection in Automatic Speaker Verification

Spectrograms - time-frequency representations of audio signals - have found widespread use in neural network-based spoofing detection. While deep models are trained on the fullband spectrum of the signal, we argue that not all frequency bands are useful for these tasks. In this paper, we systematically investigate the impact of different subbands and their importance on replay spoofing detection on two benchmark datasets: ASVspoof 2017 v2.0 and ASVspoof 2019 PA. We propose a joint subband modelling framework that employs n different sub-networks to learn subband specific features. These are later combined and passed to a classifier and the whole network weights are updated during training. Our findings on the ASVspoof 2017 dataset suggest that the most discriminative information appears to be in the first and the last 1 kHz frequency bands, and the joint model trained on these two subbands shows the best performance outperforming the baselines by a large margin. However, these findings do not generalise on the ASVspoof 2019 PA dataset. This suggests that the datasets available for training these models do not reflect real world replay conditions suggesting a need for careful design of datasets for training replay spoofing countermeasures.
Improving Early Estimates of Large Earthquake's Final Fault Lengths and Magnitudes Leveraging Source Fault Structural Maturity Information

Abstract Earthquake early warning would be improved if the final size of an ongoing earthquake could be predicted early in the rupture process. Previous research relies largely on parameters derived from seismic waveforms, resulting in widely varied estimates of how much of the rupture must be complete before final sizes can be predicted. We demonstrate here that incorporating prior information on along‐strike variation in source fault structural maturity helps improve constraints on the earthquake's final size. Using surface slip profiles from 26 large continental earthquakes worldwide, we derive a generic empirical relation between earthquake slip, rupture length, and along‐strike variability in source fault maturity. Using this equation, we fit successive slip profiles developing during each earthquake and find that, on average, we predict the final length and magnitude of an earthquake once it has reached ~20% of its total length. Our findings thus demonstrate a clear determinism in the rupture process.
Deep learning applications in automatic needle segmentation in ultrasound‐guided prostate brachytherapy

Purpose High‐Dose‐Rate (HDR) brachytherapy is one of the most effective ways to treat the prostate cancer, which is the second most common cancer in men worldwide. This treatment delivers highly conformal dose through the transperineal needle implants and is guided by a real time ultrasound (US) imaging system. Currently, the brachytherapy needles in the US images are manually segmented by physicists during the treatment, which is time consuming and error prone. In this study, we propose a set of deep learning–based algorithms to accurately segment the brachytherapy needles and locate the needle tips from the US images. Methods Two deep neural networks are developed to address this problem. First, a modified deep U‐Net is used to segment the pixels belonging to the brachytherapy needles from the US images. Second, an additional VGG‐16–based deep convolutional network is combined with the segmentation network to predict the locations of the needle tips. The networks are trained and evaluated on a clinical US images dataset with labeled needle trajectories collected in our hospital (Institutional Review Board approval (IRB 41755)). Results The evaluation results show that our method can accurately extract the trajectories of the needles with a resolution of 0.668 mm and 0.319 mm in x and y direction, respectively. 95.4% of the x direction and 99.2% of the y direction have error ≤ 2 mm. Moreover, the position resolutions of the tips are 0.721, 0.369, and 1.877 mm in x , y , and z directions, respectively, while 94.2%, 98.3%, and 67.5% of the data have error ≤ 2 mm. Conclusions This paper proposed a neural network‐based algorithm to segment the brachytherapy needles from the US images and locate the needle tip. It can be used in the HDR brachytherapy to help improve the efficiency and quality of the treatments.
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking

Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user's goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system's inform operations; (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.
A Different Web Analytics Perspective Through Copy to Clipboard Heatmaps

Heatmaps are widely used in web analytics to visualize certain user activities within web pages, including mouse clicks, mouse moves and page scrolling. We propose Copy to Clipboard Heatmaps (CTCHs), to visualize what users copy from web pages. We present an implementation of CTCHs, demonstrate various types of useful information that CTCHs expose in technical-educational web pages, and discuss several possible uses.
A Multi-FoV Viewport-Based Visual Saliency Model Using Adaptive Weighting Losses for 360$^\circ$ Images

360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> media allows observers to explore the scene in all directions. The consequence is that the human visual attention is guided by not only the perceived area in the viewport but also the overall content in 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> . In this paper, we propose a method to estimate the 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> saliency map which extracts salient features from the entire 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> image in each viewport in three different Field of Views (FoVs). Our model is first pretrained with a large-scale 2D image dataset to enable the interpretation of semantic contents, then fine-tuned with a relative small 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> image dataset. A novel weighting loss function attached with stretch weighted maps is introduced to adaptively weight the losses of three evaluation metrics and attenuate the impact of stretched regions in equirectangular projection during training process. Experimental results demonstrate that our model achieves better performance with the integration of three FoVs and its diverse viewport images. Results also show that the adaptive weighting losses and stretch weighted maps effectively enhance the evaluation scores compared to the fixed weighting losses solutions. Comparing to other state of the art models, our method surpasses them on three different datasets and ranks the top using 5 performance evaluation metrics on the Salient360! benchmark set. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FannyChao/MV-SalGAN360</uri> .
Formalizing Bachmair and Ganzinger’s Ordered Resolution Prover

We present an Isabelle/HOL formalization of the first half of Bachmair and Ganzinger’s chapter on resolution theorem proving, culminating with a refutationally complete first-order prover based on ordered resolution with literal selection. We developed general infrastructure and methodology that can form the basis of completeness proofs for related calculi, including superposition. Our work clarifies fine points in the chapter, emphasizing the value of formal proofs in the field of automated reasoning.
Post-Processing Protocol for Physical-Layer Key Generation and Distribution in Fiber Networks

The unique and random physical-layer properties of optical fiber channels have been exploited for a simple and cost-effective secure key generation and distribution (SKGD). However, state-of-the-art SKGD approaches focused mainly on initial key extraction, while the post-processing protocol has to be explored to guarantee error-free operation, effective key generation rate (KGR) and high key secrecy. Here we propose a full post-processing protocol for SKGD in fiber networks. A lossy quantizer is optimized to achieve the trade-off between the KGR and key disagreement rate. The distributed source coding based information reconciliation is used to remove quantization errors. The privacy amplification is applied to improve the randomness and secrecy of the generated key using hashing functions. The post-processing protocol provides a SKGD solution of error-free, high randomness and high secrecy in fiber networks.