Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightly-Supervised Attribute Extraction

We introduce lightly-supervised methods for extracting entity attributes from natural language text. Using those methods, we are able to extract large number of attributes of different entities at fairly high precision from a large natural language corpus. We compare our methods against a standard pattern-based relation extractor, showing that our methods improve very considerably on that baseline extractor.
Finding the Fastest Tra jectory for Autonomous Student Formula

algorithm for finding the fastest trajectory for the autonomous student formula.The input will be a list of points delineating the track. The output will be the trajectory as a list of waypoints comprising position and velocity. As a proxy problem, find (1) the shortest trajectory, and (2) the trajectory minimizing the average curvature. Formulate the problem mathematically, use a suitable representation and an optimization solver. The dynamic model of the formula can be very simplistic. Validate the algorithm on a synthetic experiment, or optionally on the real autonomous formula. Abstract This thesis proposes a trajectory planning algorithm for an autonomous formula built for the international Formula Student competition. In particular, the thesis focuses on the situation where the vehicle’s environment is largely known and described by a SLAM–provided global map of cones delineating the track. We demonstrate a parametrization method based on a list of cones, that is robust to false detections. Next, we construct three optimization problems: the shortest path, the path minimizing the average curvature, and the fastest path that takes basic handling limits of the vehicle into account. The algorithm outputs both the path and its optimal speed proﬁle. Our work is validated on multiple sources of data, including real–world SLAM maps and the Formula Student Driverless simulator, where we successfully decreased lap times by half compared to a simple baseline algorithm.
A comparison of statistical emulation methodologies for multi-wave calibration of environmental models Comparing emulation methodologies

Expensive computer codes, particularly those used simulating environmental or geological processes such as climate models, require calibration (sometimes called tuning). When calibrating expensive simulators using uncertainty quantification methods, it is usually necessary to use a statistical model called an emulator in place of the computer code when running the calibration algorithm. Though emulators based on Gaussian processes are typically many orders of magnitude faster to evaluate than the simulator they mimic, many applications have sought to speed up the computations by using regression-only emulators within the calculations instead, arguing that the extra sophistication brought using the Gaussian process is not worth the ex-
SAR imaging of virtual vegetation based on a 3-D coherent scattering model

ABSTRACT In this article, we simulate the synthetic aperture radar (SAR) image of virtual trees using an image-processing algorithm based on the radar coherent scattering model. To make simulations much closer to real SAR imaging, the changes of antenna observation angle and frequency bandwidth of SAR are considered in the image processing, and the antenna pattern in the azimuth plane is also taken into consideration in the scattering model. The combination of the scattering model and the image-processing algorithm will contribute to understanding the imaging result of SAR response to forest structures and to optimizing the design of the SAR sensor. Furthermore, a height extraction method from the interferometry of airborne single-pass SAR is used to extract vegetation height. Finally, the scattering amplitude and height images of virtual trees in a stand are simulated. The simulation results demonstrate that the combination of the radar coherent scattering model and the image-processing algorithm provides a reasonable interpretation of the images according to the scattering mechanism.
Title : We Need Numbers !-Heuristic Evaluation during Demonstrations ( HED ) for Measuring Usability in IT System Procurement Year : 2016 Version : Post print

We introduce a new usability inspection method called HED (heuristic evaluation during demonstrations) for measuring and comparing usability of competing complex IT systems in public procurement. The method presented enhances traditional heuristic evaluation to include the use context, comprehensive view of the system, and reveals missing functionality by using user scenarios and demonstrations. HED also quantifies the results in a comparable way. We present findings from a real-life validation of the method in a large-scale procurement project of a healthcare and social welfare information system. We analyze and compare the performance of HED to other usability evaluation methods used in procurement. Based on the analysis HED can be used to evaluate the level of usability of an IT system during procurement correctly, comprehensively and efficiently. Author
Scheduling jobs across geo-distributed datacenters with max-min fairness

It has become routine for large volumes of data to be generated, stored, and processed across geographically distributed datacenters. To run a single data analytic job on such geo-distributed data, recent research proposed to distribute its tasks across datacenters, considering both data locality and network bandwidth across datacenters. Yet, it remains an open problem in the more general case, where multiple analytic jobs need to fairly share the resources at these geo-distributed data-centers. In this paper, we focus on the problem of assigning tasks belonging to multiple jobs across datacenters, with the specific objective of achieving max-min fairness across jobs sharing these datacenters, in terms of their job completion times. We formulate this problem as a lexicographical minimization problem, which is challenging to solve in practice due to its inherent multi-objective and discrete nature. To address these challenges, we iteratively solve its single-objective subproblems, which can be transformed to equivalent linear programming (LP) problems to be efficiently solved, thanks to their favorable properties. As a highlight of this paper, we have designed and implemented our proposed solution as a fair job scheduler based on Apache Spark, a modern data processing framework. With extensive evaluations of our real-world implementation on Amazon EC2, we have shown convincing evidence that max-min fairness has been achieved using our new job scheduler.
Supplementary File-Data preprocessing to mitigate bias: A maximum entropy based approach

The prior distribution we construct has the following form. For C ∈ [0, 1], q C(α) = C · u(α) + (1− C) · w(α). (1) Here u is the uniform distribution over ω. The weight distribution w is obtained using Algorithm 1 to satisfy certain statistical rate constraints. To establish the correctness of Algorithm 1, we can estimate the representation rate and statistical rate of this distribution and formally prove Theorem 4.1. Theorem A.1 (Theorem 4.1). There is an algorithm (Algorithm 1) that, given the dataset S and a τ ∈ [0, 1], outputs a probability distribution w : S → [0, 1] such that
MOBILE ROBOTS A STUDY ON SENSING AND PERCEPTION SYSTEMS

Sensors traditionally used in mobile robotics provide raw data. Most of this data is processed and converted into information. This thesis aimed to study traditional sensing techniques using RGB and 3D sensors and propose a new sensing approach embedded in compact hardware, named the DeepSpatial sensor. To reach the goal, a study of point cloud processing in land robots and air ﬂights was carried out, and later the hardware and software architecture was proposed for a new sensing approach. The thesis consists of 4 papers published in journals, where each chapter presents the summary of each published paper. Este trabalho ´ e um resumo da tese “Mobile robots: a study on sensing and perception systems” apresentada ao Programa de P ´ os-Graduac¸ ˜ ao em Engenharia El ´ etrica e In-form ´ atica Industrial (CPGEI) da Universidade Tecnol ´ ogica Federal do Paran ´ a, no dia 18 de janeiro de 2021. A tese
Camera ! Verifier ! visual challenge ! sense ! Physical Environment ! Digital Signage ! Control Station ! DB ! video ! feed ! DB ! Trusted !

We propose a new way to verify the integrity and freshness of footage from security cameras by sending visual challenges to the area being monitored by the camera. We study the effectiveness of periodically updating plain text and QR code visual challenges, propose attack detection statistics for each of them, and study their performance under normal conditions (without attack) and against a variety of adversaries. Our implementation results show that visual challenges are an effective method to add defense-in-depth mechanisms to improve the trustworthiness of security cameras.
Frequency Compensation of Two Stage Op-Amp using Triode Mode Compensation Stage

A frequency compensation technique for the improvement of unity gain bandwidth (UGB) and power supply rejection ratio (PSRR) of two stage operational amplifiers is presented in this paper. Performance of proposed op-amp is compared with classical miller compensated op-amp and op-amp proposed by G. Blakiewicz [13]. The technique exploits the triode mode operation of a MOSFET in the compensation stage which leads to the improvement in the UGB and Power Supply Rejection Ratio (PSRR). Small signal analysis for these parameters is carried out and theoretical improvements are verified through simulations in cadence VIRTUSO environment using UMC 0. 18 μm CMOS process technology.
Magnetic Response of Rectangular and Circular Split Ring Resonator: A Research Study

In this paper absorption loss & negative magnetic permeability feature of split ring resonator (SRR) is demonstrated & discussed at length for rectangular as well as circular geometry in microwave frequency region. Reflection as well as transmission parameter of SRR for the afro mentioned geometry is presented. The transmission shows a dip for propagation along the direction of split ring resonator plane. From the scattered data, the nature of absorption loss for different substrate thickness is analyzed. The effective medium parameter μ and e are also computed out of which the negative μ behavior of split ring resonator is demonstrated in the specific frequency region, thus providing us an opportunity to create artificial Mu-negative medium (MNG) having similar behavior as that of gyro-tropic or gyro- magnetic materials.
Continuously amplified warming in the Alaskan Arctic: Implications for estimating global warming hiatus

Historically, in situ measurements have been notoriously sparse over the Arctic. As a consequence, the existing gridded data of surface air temperature (SAT) may have large biases in estimating the warming trend in this region. Using data from an expanded monitoring network with 31 stations in the Alaskan Arctic, we demonstrate that the SAT has increased by 2.19°C in this region, or at a rate of 0.23°C/decade during 1921–2015. Meanwhile, we found that the SAT warmed at 0.71°C/decade over 1998–2015, which is 2 to 3 times faster than the rate established from the gridded data sets. Focusing on the “hiatus” period 1998–2012 as identified by the Intergovernmental Panel on Climate Change (IPCC) report, the SAT has increased at 0.45°C/decade, which captures more than 90% of the regional trend for 1951–2012. We suggest that sparse in situ measurements are responsible for underestimation of the SAT change in the gridded data sets. It is likely that enhanced climate warming may also have happened in the other regions of the Arctic since the late 1990s but left undetected because of incomplete observational coverage.
DeepMPLS: Fast Analysis of MPLS Configurations Using Deep Learning

With the increasing complexity of communication networks and the resulting threat of disruptions of mission critical services due to manual misconfiguration, automated verification is becoming a key element in today's network operation. In particular, it has recently been shown that a polynomial-time, automated verification of the policy-compliance of network configurations is possible for the important class of MPLS networks, even under failures. However, this approach, while providing polynomial runtimes, is still fairly slow in practice and only allows to detect but not fix configurations. This paper proposes a novel approach to speed up the analysis of network properties as well as to suggest configuration changes in case a network property is not satisfied. More specifically, our solution, DeepMPLS, allows to predict if a network property is satisfiable, and if not, aims to present a counter example. We also show that DeepMPLS may be used to propose new prefix-rewriting rules in the MPLS configuration in order to make it satisfiable. DeepMPLS can hence be used for fast predictions, before more rigorous analyses are performed. DeepMPLS is based on a new extension of graph-based neural networks. Our prototype implementation, using Tensorflow, achieves low execution times and high accuracies in real-world network topologies.
The Road to the Moon Went Through Western New York

The Apollo program was made possible by many contractors and government agencies throughout the United States. There were significant concentrations of effort in many regions, one of which was Western New York between Buffalo, Niagara Falls, and Rochester. Steering of all three stages of the Saturn V was provided by Moog thrust vector actuation. Contributions by Bell Aerosystems were especially notable, from the Lunar Landing Training Vehicle that gave the astronauts the training required to land on the Moon to the Lunar Module ascent engine that returned them to orbit afterwards. The ascent engine was one of the most critical items in the entire Apollo stack as it had to work – there was no backup. Once back in Lunar orbit the astronauts knew they could rendezvous and dock with the Command Module because it was proven during Project Gemini with the Gemini Agena Target Vehicle powered by Bell primary and secondary propulsion systems.
Real-time multicast routing with optimal network cost

Multicast routing for real-time applications has two important requirements: minimal network cost and shortest network delay. It is always difficult to meet one requirement without compromising the other. This paper presents a real-time multicast routing algorithm which minimizes overall network cost without letting the delay from source to any destination exceed a time constraint. The multicast routing with optimal overall network cost is an NP-complete problem. Our algorithm is based on the idea of the minimum spanning tree heuristic.
Stitcher Seed Files Initial File Pool Potentially Invalid Files File Repair ÷ Fragment ✓ Condition tainted by enum . type ? ✘

Many real-world programs take highly structured and complex files as inputs. The automated testing of such programs is non-trivial. If the test does not adhere to a specific file format, the program returns a parser error. For symbolic execution-based whitebox fuzzing the corresponding error handling code becomes a significant time sink. Too much time is spent in the parser exploring too many paths leading to trivial parser errors. Naturally, the time is better spent exploring the functional part of the program where failure with valid input exposes deep and real bugs in the program. In this paper, we suggest to leverage information about the file format and data chunks of existing, valid files to swiftly carry the exploration beyond the parser code. We call our approach Modelbased Whitebox Fuzzing (MoWF) because the file format input model of blackbox fuzzers can be exploited as a constraint on the vast input space to rule out most invalid inputs during path exploration in symbolic execution. We evaluate on 13 vulnerabilities in 8 large program binaries with 6 separate file formats and found that MoWF exposes all vulnerabilities while both, traditional whitebox fuzzing and model-based blackbox fuzzing, expose only less than half, respectively. Our experiments also demonstrate that MoWF exposes 70% vulnerabilities without any seed inputs. CCS Concepts: •Software and its engineering → Software testing and debugging; •Security and privacy→ Vulnerability scanners;
Mathematical Foundations of Computational Anatomy Geometrical and Statistical Methods for Biological Shape Variability Modeling

One main obstacle in building a sophisticated parametric model along an arbitary anatomical manifold is the lack of an easily available orthonormal basis. Although there are at least two numerical techniques available for constructing an orhonormal basis such as the Laplacian eigenfunction approach and the Gram-Smidth orthogonalization, they are computationally not so trivial and costly. We present a relatively simpler method for constructing an orthonormal basis for an arbitrary anatomical manifold. On a unit sphere, a natural orthonormal basis is the spherical harmonics which can be easily computed. Assuming the manifold is topologically equivalent to the sphere, we can establish a smooth mapping ζ from the manifold to the sphere. Such mapping can be obtained from various surface flattening techniques. If we project the spherical harmonics to the manifold, they are no longer orthonormal. However, we claim that there exists an orthonormal basis that is the function of spherical harmonics and the spherical mapping ζ. The detailed step by step procedures for the construction is given along with the numerical validation using amygdala surfaces as an illustration. As an application, we propose the pullback representation that reconstructs surfaces using the orthonormal basis obtained from an average template. The pullback representation introduces less inter-subject variability and thus requires far less number of coefficients than the traditional spherical harmonic representation. The source code used in the study is freely available at http://www.stat.wisc.edu/∼mchung/research/amygdala.
University of Glasgow at Medical Records Track: Experiments with Terrier.

In our participation in the TREC 2011 Medical Records track, we investigate (1) novel voting-based approaches for identifying relevant patient visits from an aggregate of relevant medical records, (2) the effective handling of negated language in records and queries, and (3) the adoption of medical-domain ontologies for improving the representation of queries, all within the context of our Terrier information retrieval platform.
Computational Photography

Computational photography is an emergent multidisciplinary field that combines topics in optics, computer graphics, image and signal processing. The objective is usually the development of new algorithms or systems to capture images overcoming the limitations of conventional techniques. Images are not simply captured as 2D projections of a scene, they are rather coded before reaching the sensor in a way that can later be decoded and interpreted. This coding-decoding process allows to recover information lost in the traditional capture process. In the present text we will go through the fundamentals and will then offer some examples of recent research projects, a representative cross-section covering some of the most relevant results produced. This cross-section will include plenoptic imaging, coded apertures, novel analysis of light transport and recent advances in ultra-fast imaging, as well as an introduction to computational displays.
OPT2020: 12th Annual Workshop on Optimization for Machine Learning Adaptivity of Stochastic Gradient Methods for Nonconvex Optimization

Adaptivity is an important yet under-studied property in modern optimization theory. The gap between the state-of-the-art theory and the current practice is striking in that algorithms with desirable theoretical guarantees typically involve drastically different settings of hyperparameters, such as step-size schemes and batch sizes, in different regimes. Despite the appealing theoretical results, such divisive strategies provide little, if any, insight to practitioners to select algorithms that work broadly without tweaking the hyperparameters. In this work, blending the “geometrization” technique introduced by [27] and the SARAH algorithm of [39], we propose the Geometrized SARAH algorithm for non-convex finite-sum and stochastic optimization. Our algorithm is proved to achieve adaptivity to both the magnitude of the target accuracy and the Polyak-Łojasiewicz (PL) constant, if present. In addition, it achieves the best-available convergence rate for non-PL objectives simultaneously while outperforming existing algorithms for PL objectives.
A Model-Free Robust Control Approach for Robot Manipulator

A model-free robust control (MFRC) approach is proposed for position control of robot manipulators in the state space. The control approach is verified analytically to be robust subject to uncertainties including external disturbances, unmodeled dynamics, and parametric uncertainties. There is a high flexibility to work on different systems including actuators by the use of the proposed control approach. The proposed control approach can guarantee the robustness of control system. A PUMA 560 robot driven by geared permanent magnet dc motors is simulated. The simulation results show a satisfactory performance for control system under technical specifications. Keywords—Model-free, robust control, position control, PUMA 560.
Casper*: Query processing for location services without compromising privacy

In this article, we present a new privacy-aware query processing framework, Capser*, in which mobile and stationary users can obtain snapshot and/or continuous location-based services without revealing their private location information. In particular, we propose a privacy-aware query processor embedded inside a location-based database server to deal with snapshot and continuous queries based on the knowledge of the user's cloaked location rather than the exact location. Our proposed privacy-aware query processor is completely independent of how we compute the user's cloaked location. In other words, any existing location anonymization algorithms that blur the user's private location into cloaked rectilinear areas can be employed to protect the user's location privacy. We first propose a privacy-aware query processor that not only supports three new privacy-aware query types, but also achieves a trade-off between query processing cost and answer optimality. Then, to improve system scalability of processing continuous privacy-aware queries, we propose a shared execution paradigm that shares query processing among a large number of continuous queries. The proposed scalable paradigm can be tuned through two parameters to trade off between system scalability and answer optimality. Experimental results show that our query processor achieves high quality snapshot and continuous location-based services while supporting queries and/or data with cloaked locations.
Understanding vehicular traffic behavior from video: a survey of unsupervised approaches

Abstract. Recent emerging trends for automatic behavior analysis and understanding from infrastructure video are reviewed. Research has shifted from high-resolution estimation of vehicle state and instead, pushed machine learning approaches to extract meaningful patterns in aggregates in an unsupervised fashion. These patterns represent priors on observable motion, which can be utilized to describe a scene, answer behavior questions such as where is a vehicle going, how many vehicles are performing the same action, and to detect an abnormal event. The review focuses on two main methods for scene description, trajectory clustering and topic modeling. Example applications that utilize the behavioral modeling techniques are also presented. In addition, the most popular public datasets for behavioral analysis are presented. Discussion and comment on future directions in the field are also provided.