Miracle-dz/UniFAR-FTUs huggingface dataset:
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction

Accurate parsing of citation reference strings is crucial to automatically construct scholarly databases such as Google Scholar or Semantic Scholar. Citation field extraction (CFE) is precisely this task---given a reference label which tokens refer to the authors, venue, title, editor, journal, pages, etc. Most methods for CFE are supervised and rely on training from labeled datasets that are quite small compared to the great variety of reference formats. BibTeX, the widely used reference management tool, provides a natural method to automatically generate and label training data for CFE. In this paper, we describe a technique for using BibTeX to generate, automatically, a large-scale 41M labeled strings), labeled dataset, that is four orders of magnitude larger than the current largest CFE dataset, namely the UMass Citation Field Extraction dataset [Anzaroot and McCallum, 2013]. We experimentally demonstrate how our dataset can be used to improve the performance of the UMass CFE using a RoBERTa-based [Liu et al., 2019] model. In comparison to previous SoTA, we achieve a 24.48% relative error reduction, achieving span level F1-scores of 96.3%.
Investigation of the formation evolution of aluminum alloy 1050 structure during rolling in the spiral rollers and the longitudinal wedge mill

S. A. Mashekov, (mashekov.1957@mail.ru) M. R. Maulenova, Kazakh National Technical University, Almaty, Kazakhstan G. A. Smailova, Kazakh Agrarian University, Almaty, Kazakhstan A. M. Alshynova, Almaty Technological University, Almaty, Kazakhstan Zhauyt, Almaty University of Power Engineering and Telecommunications, Almaty, Kazakhstan N. S. Sembayev, Pavlodar State University, Pavlodar, Kazakhstan This article presents a new methodof obtaining the sheet steel with ultrafine-grained structure. The ultrafinegrained structure is obtained by implementing severe plastic deformation developed by a spiral roller. This paper investigates the stress-strain state (SSS) of the workpiece during rolling in a spiral roller and a longitudinal wedge mill (LWM). A rational technology of rolling aluminum alloy 1050 was developed and tested under the laboratory conditions. It was proved that the system achieves an equilibrium state by diffusion along the grain boundaries accelerated by the flow of vacancies formed during the deformation. It has been established that rolling aluminum alloy 1050in the spiral rollers and LWM leads to an increase in the strength and plastic properties of the sheet metal.
Musical Signal Digital Classification

— Automatic digital classification of musical signals has a variety of possible applications, being one of the most important ones music genre classification, which, for instance, may be used to automatically organize music huge databases. Fado became Intangible Cultural Heritage of Humanity in 2011, and this Portuguese music genre with unique characteristics, has the presence of a singer accompanied by a classic and a Portuguese guitars. This thesis presents an identification and automatic classification system for Fado. The main objective of this work was the development of a tool for Fado identification and separation from other genres, such as Classic, Jazz, Pop, Rock, Metal or Electronic. The develop system is based on three musical dimensions: rhythm, instrumentation and voice. These musical dimensions are assessed through low-level and mid-level features, extracted from the audio signal, that are related to timbre and rhythm (spectral centroid, spectral flux and analysis of the autocorrelation). The developed system may be seen as being composed of two main blocks: a features extractor and a binary classifier. The features extractor feeds information to the classifier that then produces a binary output: Fado or Non-Fado. It was necessary to compile a database with Fado excerpts and also with excerpts from the other six genres. Good results were obtained for the established database: 81% of the Fado excerpts were well classified and 84% of correct classifications for the other genres. The classifier showed 82% of accuracy for the compiled database. System performance was also evaluated with two different external databases.
Deep Q-learning with PCA and Prioritized Experience Replay for Trading in the Forex Market

This paper presents an approach combining Technical Analysis and Principal Component Analysis (PCA) with a deep Q-network (DQN) using Double Q-learning and Prioritized Experience Replay (PER) to generate trading decisions capable of achieving long-term ﬁnancial gains with low ﬁnancial risk. The proposed approach is tested with real data from ﬁve Forex markets with different characteristics from each other, using transaction costs. Two different state and reward functions are considered in the DQN algorithm and the most robust results are produced by a combination of a state representation using features produced by PCA and a reward function measuring the proﬁt percentage obtained by the generated trading signal in a trading period. The system uses on every test the same network topology and system’s hyperparameters values. The results achieved show that this approach outperforms the Buy and Hold (BnH) strategy in four of the ﬁve tested markets. In the EUR-USD market, this system achieves a rate of return of 19.21% while the BnH strategy only achieves 3.92%. Furthermore, it is concluded that the PCA technique, Prioritized Experience Replay and Double Q-learning are essential to the performance.
Dynamic Crack Propagation in a Bi-Metallic Nanolayer

Deformation behaviors of nanoscale metallic systems under mechanical loading conditions have received considerable attention in the recent years. Associated with mechanical deformation of the material systems are their fracture and failure. This paper focuses on the computational modeling of the dynamic crack propagation in a bi-metallic nanolayer. In particular, this paper focuses on the dynamic crack propagation in Nickel and NickelAluminum bi-metal nanolayer under tensile and cyclic loading conditions. Atomistic modeling analysis of crack propagation in Ni single crystal indicate the crack initially growing brittle and eventually undergoing a dynamic brittle-to-ductile transition with a spontaneous proliferation of dislocations from the crack tip following a roughening of the crack surfaces. Results for Ni-Al also showed an initial brittle crack propagation with planar cleavage of bonds between the two neighboring (001) planes defined by the initial seed crack. Findings from our study clearly indicate that the crack propagation in such systems is influenced by the interactions and atomic defects at the interfaces, and the associated deformation mechanisms at the nano scale.
Study about Risk Analysis of a Portfolio of Network Distribution Investments

This thesis studies the risk analysis of a group of projects. In order to do that, it begins by explaining how to study each project individually and how to calculate their Net Present Value (NPV) and their Value at Risk (VaR). Posteriorly, the same values are determined for a simple portfolio of non-correlated projects through convolutions. It can be concluded that the risk of a portfolio decreases with the increase in the number of similar projects that constitute the portfolio and that an approximation of the probability density function of each project to a Normal distribution is valid for portfolios with more than 20 projects. After that, the effect of correlations between projects in the return of a portfolio is explained as well as how it affects the previous risk analysis. After verifying that the correlation is an important factor in determining the risk of a portfolio and that its application to the projects that EDP made available is hard, a method of dealing with these difficulties and of calculating the correlations between projects is suggested. Finally, the proposed method is applied to the projects and the results are shown, yielding no correlation between projects with respect to the uncertainty primitives analysed and thus allowing to easily calculate the NPV and VaR of any portfolio through simple convolutions. The last section presents the results of some simulations of ways of selecting and optimizing portfolios that obey to certain imposed customizable conditions.
The structures of deoxy human haemoglobin and the mutant Hb Tyr[alpha]42His at 120 K

# 2000 International Union of Crystallography Printed in Denmark ± all rights reserved The structures of deoxy human haemoglobin and an arti®cial mutant (Tyr 42! His) have been solved at 120 K. While overall agreement between these structures and others in the PDB is very good, certain side chains are found to be shifted, absent from the electron-density map or in different rotamers. Non-crystallographic symmetry (NCS) is very well obeyed in the native protein, but not around the site of the changed residue in the mutant. NCS is also not obeyed by the water molecule invariably found in the -chain haem pocket in room-temperature crystal structures of haemoglobin. At 120 K, this water molecule disappears from one chain in the asymmetric unit but not the other. Received 14 February 2000 Accepted 26 April 2000
A new approach to the organization and tracking of Walking School Buses

Over the years, public health problems such as obesity have taken epidemic proportions in developed countries, the lack of physical activity being one of the biggest causes. Part of that lack of physical activity resides in the way children locomote in a daily basis. This is influenced by a growing tendency to develop car-oriented lifestyles, which leads to children usually being transported to school on their parents private cars. This tendency also leads to unseen levels of air pollution that exacerbate multiple health issues. Walking school bus (WSB) is a concept that tries to address those issues by pushing people to organize trips to school on foot. Multiple applications and systems try to support the organization of WSBs, but they all seem to fail when it comes to one very important point: real-time tracking and monitoring of the bus. This report presents an overview on existing related work about WSBs generic architecture, position and tracking technologies and approaches, privacy solutions and an analysis to existing applications and systems that support WSBs. It also presents our designed and implemented solution for a complete and efficient system that tackles the issues present in existing systems.
Incremental Sampling-based Algorithms for Open-loop Solutions of Pursuit-Evasion Games

Pursuit-evasion games have been used for modeling various forms of con-ﬂict arising between two agents modeled as dynamical systems. Although analytical solutions of some simple pursuit-evasion games are known, most interesting instances can only be solved using numerical methods, which require signiﬁcant ofﬂine computation prohibiting real-time implementations. In this paper, a class of pursuit-evasion games with separable dynamics is considered. A novel incremental sampling-based algorithm that computes the open-loop solution to the pursuit-evasion game is provided. It is shown that the algorithm has probabilistic completeness and soundness guarantees. The computational complexity of the algorithm is also analyzed. As opposed to many other numerical methods tailored to solve pursuit-evasion games, incremental sampling-based algorithms offer anytime properties, which allow their real-time implementations in online settings.
A Scalable IoT Video Data Analytics for Smart Cities

The smart city is a comprehensive application of information resources and a high degree of information technology integration. With the technical support from IoT (Internet of things), smart city need to have three features of being instrumented, interconnected and intelligent. IoT provides the ability to manage, remotely monitor and control devices from massive streams of real-time data.Our model o ﬀ ers a scalable IoT video data analytics applications for Smart cities to end users, who can exploit scalability in both data storage and processing power to execute analysis on large or complex datasets. This model provides data analytics programming suites and environments in which developers and researchers can design scalable analytics services and applications. A cloud/edge-based automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number of camera devices and easy to integrate analytic application. The system automates the video analysis process and reduces manual intervention. The design of our model is developed to be easily extended for new kinds of IoT devices, message routing and queueing, and data analytics, to permit speciﬁc application to be programmed via the paradigm to be ﬂexible yet simple.
Vet July.indd

Heart murmurs can occasionally be auscultated in young dogs and cats; their discovery can be in conjunction with other signs of cardiac disease or it may be an incidental finding. The identification of heart murmurs can cause apprehension for owners, and depending on the location, intensity and characteristics, the veterinary professional can perform a list of di erentials and suggest the best diagnostic approach.
Non-Parametric Reasoning on Knowledge Bases

We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires no training , and is reminiscent of case-based reasoning in classical artiﬁcial intelligence (AI). Consider the task of ﬁnding a target entity given a source entity and a binary relation. Our approach ﬁnds multiple graph path patterns that connect similar source entities through the given relation, and looks for pattern matches starting from the query source. Using our method, we obtain new state-of-the-art accuracy, outperforming all previous models, on NELL-995 and FB-122. We also demonstrate that our model is robust in low data settings, outperforming recently proposed meta-learning approaches.
Integrated Pricing and Routing for Package Express Carriers

We consider an integrated pricing and routing problem on a service network motivated by environments encountered at package express carriers. The decision maker sets the price for each origin-destination market, which determines the demand that needs to be served. The demand can be routed along multiple paths in the service network if desirable. The objective is to maximize the revenues from serving demand minus the transportation costs incurred by serving demand given the capacities in the network. We propose two algorithms for the solution of this problem with theoretical convergence guarantees: (1) a Frank-Wolfe type algorithm, which requires the objective function to be smooth, and (2) a primal-dual algorithm using an online learning technique, which allows non-smooth objective functions. We show that both algorithms have a convergence rate of Õ(1/T ) where T is the number of iterations. Numerical experiments on randomly generated instances show that coordinating pricing and routing decisions can improve profits by more than 10%.
Analog to Digital CMOS Converter for Wireles BLE Sensors

This work details the design and implementation of an Incremental Delta-Sigma ADC for the purpose of converting the output signal of a temperature sensor with a precision of 0.25C. The conversion range specified for the ADC is from -40C to 80C. The chosen architecture is that of a secondorder Incremental ADC with a third order Sinc filter (Sinc) that computes the Modulator output into a 11-bit word. The Integration process is accomplished with switch capacitor circuits and two different Current Mirror Operational Transconductance Amplifiers (CM-OTA). The Converter takes advantage of the sensor output signals to attain a Vref signal. This feature makes the ADC specially designed for this purpose. The final simulations unveil that the ADC precision slightly increases when the temperature read by the sensor rises, starting at 0.3C and ending at 0.15C. The ADC has an ENOB of 10.97 bits, a power consumption of 3.17 μW and an implementation area of 0.049 mm (Modulator) and 0.01 mm (Decimation Filter).
Comparison of 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma

Individualisation of treatment is a major goal for improving radiotherapy outcomes of cancer patients. Statistical survival models based on medical imaging may serve as a tool towards achieving this objective. We created ensembles of Cox proportional hazards convolutional neural network (CNN) models from pre-treatment CT imaging data of head and neck cancer patients. Based on a multicentre cohort of 291 patients, we then compared model performance between 2D and 3D CNN architectures, data augmentation techniques and training sample sizes. In the absence of data augmentation, 3D-CNNs showed improved concordance index (C-index) values compared to 2D-CNNs, even with much smaller sample sizes (C-index 0.33 and 0.37, respectively). Data augmentation only improved performance for 2D-CNNs and, given suﬃcient training samples, enabled them to achieve performance comparable to 3D-CNNs (C-index 0.32 and 0.30, respectively).
DELIVERABLE SUBMISSION SHEET

FP7-ICT Collaborative Project ICT-2013-611233 PHEME Deliverable D6.2.1 (WP6) The deliverable describes the results of Task 6.4 in WP6 on preliminary evaluations of the PHEME algorithms and their integration. Following the description of work, the datasets created in Task 2.1, WP7, and WP8, are used for iterative development and parameter tuning of the PHEME content analytics methods from WP3 and WP4, as well as for testing their integration into a processing pipeline. The scalability of the integrated tools (Task 6.3) will be evaluated on the large-scale datasets collected in PHEME, as well as on historical data. Keyword list: Evaluation, language processing, rumour detection, systems integration, performance evaluation Copyright c © 2016 University of Sheffield Project Delivery Date Contractual Date Nature Reviewed By Web links Dissemination PHEME No. 611233 30 October 2015 30 September 2015 Report Kalina Bontcheva http://www.pheme. eu PU
Regular (k, R, 1)-packings with max (R) = 3 and their locally repairable codes

The concept of a locally repairable code (LRC) was introduced to protect the data from disk failures in large-scale storage systems. In this paper, we consider the LRCs with multiple disjoint repair sets and each repair set contains exactly one check symbol. By using several structures from combinatorial design theory, such as balanced incomplete block design, cyclic packing, group divisible design, near-Skolem sequence and Langford sequence, we construct several infinite classes of LRCs with the size of each repair set at most 3, which are optimal with respect to the bound proposed by Rawat et al. in 2016.
Services Load Balancer for a Software Defined IXP

The advent of Software Defined Networking (SDN) technology opens new opportunities to scale interdomain routing. Industrial Scale SDX (iSDX) is an implementation of an Internet Exchange Point (IXP) using SDN to emulate the usual features of an IXP. For a new technology to be useful, it cannot simply emulate the functionality of an old one; it has to excel at the old tasks and bring in new capabilities. Our work proposes an extension to iSDX, in order to provide added value to the customers, with an implementation of a Services Load Balancer.
Detection of interaction intention with gaze and gesture analysis

This paper proposes a method to give a robots the ability to detect if humans users are willing to engage in interaction. We denote this skill Interaction Intention . It is possible to detect interaction intention by using nonverbal cues, verbal cues or even both. In the context of this work, it was studied the use of nonverbal communication to detect the interaction intent. The interaction intention detector can be used to help a robot choosing an interlocutor in a group of people. Our approach is based on a multimodal system that takes into account the gaze direction and gestures performed by a user. Gaze and gestures combined can be used to understand if a person wants to interact or not. However, it is necessary to synchronize and fuse them into a robust, fast and accurate detector. We trained and evaluated or detector from experiments with a total of 71 participants. The detector of interaction intent was capable of detecting if a person wants to interact with the robot or not with an accuracy of 91 . 1%.
4G Network Patch using Drones

The main goal of this thesis is to study and propose a new approach to network patching in mobile networks. The developed solution implements network patching making use of UAVs. The biggest challenge is to define the best positions to deploy the UAVs. A genetic algorithm was developed to tackle this problem. This algorithm takes advantage of the non-uniform distribution of data rate requirements among users to better position the UAVs. The UAVs make use of distant base stations to provide extra coverage or capacity in a given area. A simulator was developed to access the quality of the positions. The developed algorithm showed to be an effective way to compute the positions of the UAVs. Two metrics were analyzed to measure the effectiveness of the algorithm: the number of served users, and the total data rate served. Compared to other existing solutions, the algorithm was able to keep the number of served users in every scenario tested, and increase up to 24% the data rate provided.
Verb Usage in Egyptian Movies, Serials, and Blogs: A Case for Register Variation

Verb Usage in Egyptian Movies, Serials, and Blogs: A Case for Register Variation Michael G. White Department of Linguistics, BYU Master of Arts This thesis contributes to the discussion of register variation within Egyptian Arabic by focusing on the usage of verbs in blogs and transcripts of movies and television. Register variation has been extensively researched for English as well as several other languages; yet, the lexical and grammatical features that distinguish registers of Egyptian Arabic have not been analyzed. Several challenges have prevented such an analysis, among them the perceived lack of an automatic annotator and the uncertainty of results. In order to overcome these challenges, two corpora were compiled: one containing texts from blogs and the other transcripts of movies and television shows. With each corpus representing a potential register of the dialect, the verbs in each corpus were lemmatized and semi-automatically annotated for either aspect or mood. The verbs were then counted according to lemma, aspect, and mood in order to determine the extent of variance between the two corpora. The effectiveness of the state-of-the-art automatic annotator was also evaluated by comparing the counts it provided to those produced from corrections of its output. This thesis found that verbs are in fact used differently in the two corpora suggesting register variation and identified potential verbal features characteristic of each register. It also found that the automatic tagger produced counts that lead to the same conclusions as the corrected annotation.