nils-herrmann/clean_scirepeval huggingface dataset:
Multi-hierarchical semantic maps for mobile robotics

The success of mobile robots, and particularly of those interfacing with humans in daily environments (e.g., assistant robots), relies on the ability to manipulate information beyond simple spatial relations. We are interested in semantic information, which gives meaning to spatial information like images or geometric maps. We present a multi-hierarchical approach to enable a mobile robot to acquire semantic information from its sensors, and to use it for navigation tasks. In our approach, the link between spatial and semantic information is established via anchoring. We show experiments on a real mobile robot that demonstrate its ability to use and infer new semantic information from its environment, improving its operation.
Real-Time Specular Highlight Removal Using Bilateral Filtering

In this paper, we propose a simple but effective specular highlight removal method using a single input image. Our method is based on a key observation the maximum fraction of the diffuse color component (so called maximum diffuse chromaticity in the literature) in local patches in color images changes smoothly. Using this property, we can estimate the maximum diffuse chromaticity values of the specular pixels by directly applying low-pass filter to the maximum fraction of the color components of the original image, such that the maximum diffuse chromaticity values can be propagated from the diffuse pixels to the specular pixels. The diffuse color at each pixel can then be computed as a nonlinear function of the estimated maximum diffuse chromaticity. Our method can be directly extended for multi-color surfaces if edge-preserving filters (e.g., bilateral filter) are used such that the smoothing can be guided by the maximum diffuse chromaticity. But maximum diffuse chromaticity is to be estimated. We thus present an approximation and demonstrate its effectiveness. Recent development in fast bilateral filtering techniques enables our method to run over 200× faster than the state-of-the-art on a standard CPU and differentiates our method from previous work.
Face Recognition Using the Improved Bag of Words Model

Bag of words (BoW) model, which was originally used for document processing field, has been introduced to computer vision field recently and used in object recognition successfully. However, in face recognition, the order less collection of local patches in BoW model cannot provide strong distinctive information since the objects (face images) belong to the same category. A new framework for extracting facial features based on BoW model is proposed in this paper, which can maintain holistic spatial information. Experimental results show that the improved method can obtain better face recognition performance on face images of AR database with extreme expressions, variant illuminations, and partial occlusions.
SIFT Flow: Dense Correspondence across Different Scenes

While image registration has been studied in different areas of computer vision, aligning images depicting different scenes remains a challenging problem, closer to recognition than to image matching. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its neighbors in a large image collection consisting of a variety of scenes. For a query image, histogram intersection on a bag-of-visual-words representation is used to find the set of nearest neighbors in the database. The SIFT flow algorithm then consists of matching densely sampled SIFT features between the two images, while preserving spatial discontinuities. The use of SIFT features allows robust matching across different scene/object appearances and the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach is able to robustly align complicated scenes with large spatial distortions. We collect a large database of videos and apply the SIFT flow algorithm to two applications: (i) motion field prediction from a single static image and (ii) motion synthesis via transfer of moving objects.
Canonicalizing Open Knowledge Bases

Open information extraction approaches have led to the creation of large knowledge bases from the Web. The problem with such methods is that their entities and relations are not canonicalized, leading to redundant and ambiguous facts. For example, they may store {Barack Obama, was born, Honolulu and {Obama, place of birth, Honolulu}. In this paper, we present an approach based on machine learning methods that can canonicalize such Open IE triples, by clustering synonymous names and phrases.
 We also provide a detailed discussion about the different signals, features and design choices that influence the quality of synonym resolution for noun phrases in Open IE KBs, thus shedding light on the middle ground between "open" and "closed" information extraction systems.
Frame-Based Ontology Population with PIKES

We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames, i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.
DecoBrush: drawing structured decorative patterns by example

Structured decorative patterns are common ornamentations in a variety of media like books, web pages, greeting cards and interior design. Creating such art from scratch using conventional software is time consuming for experts and daunting for novices. We introduce DecoBrush, a data-driven drawing system that generalizes the conventional digital "painting" concept beyond the scope of natural media to allow synthesis of structured decorative patterns following user-sketched paths. The user simply selects an example library and draws the overall shape of a pattern. DecoBrush then synthesizes a shape in the style of the exemplars but roughly matching the overall shape. If the designer wishes to alter the result, DecoBrush also supports user-guided refinement via simple drawing and erasing tools. For a variety of example styles, we demonstrate high-quality user-constrained synthesized patterns that visually resemble the exemplars while exhibiting plausible structural variations.
ElasticFusion: Dense SLAM Without A Pose Graph

We present a novel approach to real-time dense visual SLAM. Our system is capable of capturing comprehensive dense globally consistent surfel-based maps of room scale environments explored using an RGB-D camera in an incremental online fashion, without pose graph optimisation or any postprocessing steps. This is accomplished by using dense frame-tomodel camera tracking and windowed surfel-based fusion coupled with frequent model refinement through non-rigid surface deformations. Our approach applies local model-to-model surface loop closure optimisations as often as possible to stay close to the mode of the map distribution, while utilising global loop closure to recover from arbitrary drift and maintain global consistency.
Smart Transmission Grid: Vision and Framework

A modern power grid needs to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. For these reasons, considerable activity has been carried out in the United States and Europe to formulate and promote a vision for the development of future smart power grids. However, the majority of these activities emphasized only the distribution grid and demand side leaving the big picture of the transmission grid in the context of smart grids unclear. This paper presents a unique vision for the future of smart transmission grids in which their major features are identified. In this vision, each smart transmission grid is regarded as an integrated system that functionally consists of three interactive, smart components, i.e., smart control centers, smart transmission networks, and smart substations. The features and functions of each of the three functional components, as well as the enabling technologies to achieve these features and functions, are discussed in detail in the paper.
Transferable Deep-CNN Features for Detecting Digital and Print-Scanned Morphed Face Images

Face biometrics is widely used in various applications including border control and facilitating the verification of travellers' identity claim with respect to his electronic passport (ePass). As in most countries, passports are issued to a citizen based on the submitted photo which allows the applicant to provide a morphed face photo to conceal his identity during the application process. In this work, we propose a novel approach leveraging the transferable features from a pre-trained Deep Convolutional Neural Networks (D-CNN) to detect both digital and print-scanned morphed face image. Thus, the proposed approach is based on the feature level fusion of the first fully connected layers of two D-CNN (VGG19 and AlexNet) that are specifically fine-tuned using the morphed face image database. The proposed method is extensively evaluated on the newly constructed database with both digital and print-scanned morphed face images corresponding to bona fide and morphed data reflecting a real-life scenario. The obtained results consistently demonstrate improved detection performance of the proposed scheme over previously proposed methods on both the digital and the print-scanned morphed face image database.
Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex

Traditional (univariate) analysis of functional MRI (fMRI) data relies exclusively on the information contained in the time course of individual voxels. Multivariate analyses can take advantage of the information contained in activity patterns across space, from multiple voxels. Such analyses have the potential to greatly expand the amount of information extracted from fMRI data sets. In the present study, multivariate statistical pattern recognition methods, including linear discriminant analysis and support vector machines, were used to classify patterns of fMRI activation evoked by the visual presentation of various categories of objects. Classifiers were trained using data from voxels in predefined regions of interest during a subset of trials for each subject individually. Classification of subsequently collected fMRI data was attempted according to the similarity of activation patterns to prior training examples. Classification was done using only small amounts of data (20 s worth) at a time, so such a technique could, in principle, be used to extract information about a subject's percept on a near real-time basis. Classifiers trained on data acquired during one session were equally accurate in classifying data collected within the same session and across sessions separated by more than a week, in the same subject. Although the highest classification accuracies were obtained using patterns of activity including lower visual areas as input, classification accuracies well above chance were achieved using regions of interest restricted to higher-order object-selective visual areas. In contrast to typical fMRI data analysis, in which hours of data across many subjects are averaged to reveal slight differences in activation, the use of pattern recognition methods allows a subtle 10-way discrimination to be performed on an essentially trial-by-trial basis within individuals, demonstrating that fMRI data contain far more information than is typically appreciated.
Collective Spammer Detection in Evolving Multi-Relational Social Networks

Detecting unsolicited content and the spammers who create it is a long-standing challenge that affects all of us on a daily basis. The recent growth of richly-structured social networks has provided new challenges and opportunities in the spam detection landscape. Motivated by the Tagged.com social network, we develop methods to identify spammers in evolving multi-relational social networks. We model a social network as a time-stamped multi-relational graph where vertices represent users, and edges represent different activities between them. To identify spammer accounts, our approach makes use of structural features, sequence modelling, and collective reasoning. We leverage relational sequence information using k-gram features and probabilistic modelling with a mixture of Markov models. Furthermore, in order to perform collective reasoning and improve the predictive power of a noisy abuse reporting system, we develop a statistical relational model using hinge-loss Markov random fields (HL-MRFs), a class of probabilistic graphical models which are highly scalable. We use Graphlab Create and Probabilistic Soft Logic (PSL) to prototype and experimentally evaluate our solutions on internet-scale data from Tagged.com. Our experiments demonstrate the effectiveness of our approach, and show that models which incorporate the multi-relational nature of the social network significantly gain predictive performance over those that do not.
Earnings Quality : Evidence from the Field Ilia

We provide new insights into earnings quality from a survey of 169 CFOs of public companies and indepth interviews of 12 CFOs and two standard setters. Our key findings include (i) high-quality earnings are sustainable and are backed by actual cash flows; they also reflect consistent reporting choices over time and avoid long-term estimates; (ii) about 50% of earnings quality is driven by innate factors; (iii) about 20% of firms manage earnings to misrepresent economic performance, and for such firms 10% of EPS is typically managed; (iv) CFOs believe that earnings manipulation is hard to unravel from the outside but suggest a number of red flags to identify managed earnings; and (v) CFOs disagree with the direction the FASB is headed on a number of issues including the sheer number of promulgated rules, the top-down approach to rule making, the curtailed reporting discretion, the de-emphasis of the matching principle, and the over-emphasis on fair value accounting. We acknowledge excellent research assistance by Mengyao Cheng, Jivas Chakravarthy and Stephen Deason. We appreciate written comments on an earlier version of the paper from Mark Nelson, Paul Healy, Vic Anand and especially Michelle Hanlon and oral comments from workshop participants at Texas A&M University, Cornell University, Harvard Business School, Wharton School, Temple University, the 2012 Minnesota financial accounting conference and the Indian School of Business. We acknowledge helpful comments on a preliminary version of the survey instrument from workshop participants at Emory University and from Bob Bowen, Dave Burgstahler, Brian Bushee, Dan Collins, John Core, Patty Dechow, Mark DeFond, Jennifer Francis, Weili Ge, Jeff Hales, Michelle Hanlon, Gary Hecht, Kathryn Kadous, Mark Lang, Russ Lundholm, Mark Nelson, Stephen Penman, Kathy Petroni, Grace Pownall, Cathy Schrand, Terry Shevlin, Shyam Sunder, Terry Warfield, Ross Watts, Greg Waymire, Joe Weber and Jerry Zimmerman, and two anonymous standard-setters. We thank Dean Larry Benveniste and Trevor Harris for arranging interviews. We are grateful for CFO magazine’s help in this project, though the views expressed here do not necessarily reflect those of CFO. Finally, we thank David Walonick and Statpac, Inc. for timely and dedicated work in coding and delivering the survey.
Visual Learning of Arithmetic Operations

A simple Neural Network model is presented for endto-end visual learning of arithmetic operations from pictures of numbers. The input consists of two pictures, each showing a 7-digit number. The output, also a picture, displays the number showing the result of an arithmetic operation (e.g., addition or subtraction) on the two input numbers. The concepts of a number, or of an operator, are not explicitly introduced. This indicates that addition is a simple cognitive task, which can be learned visually using a very small number of neurons. Other operations, e.g., multiplication, were not learnable using this architecture. Some tasks were not learnable end-to-end (e.g., addition with Roman numerals), but were easily learnable once broken into two separate sub-tasks: a perceptual Character Recognition and cognitive Arithmetic sub-tasks. This indicates that while some tasks may be easily learnable end-to-end, other may need to be broken into sub-tasks.
Approximating Thin-Plate Splines for Elastic Registration: Integration of Landmark Errors and Orientation Attributes

We introduce an approach to elastic registration of tomographic images based on thin-plate splines. Central to this scheme is a well-de ned minimizing functional for which the solution can be stated analytically. In this work, we consider the integration of anisotropic landmark errors as well as additional attributes at landmarks. As attributes we use orientations at landmarks and we incorporate the corresponding constraints through scalar products. With our approximation scheme it is thus possible to integrate statistical as well as geometric information as additional knowledge in elastic image registration. On the basis of synthetic as well as real tomographic images we show that this additional knowledge can signi cantly improve the registration result. In particular, we demonstrate that our scheme incorporating orientation attributes can preserve the shape of rigid structures (such as bone) embedded in an otherwise elastic material. This is achieved without selecting further landmarks and without a full segmentation of the rigid structures.
Maximum Power Point Tracking of Multiple Photovoltaic Arrays: A PSO Approach

Multiple photovoltaic (PV) modules feeding a common load is the most common form of power distribution used in solar PV systems. In such systems, providing individual maximum power point tracking (MPPT) schemes for each of the PV modules increases the cost. Furthermore, its v-i characteristic exhibits multiple local maximum power points (MPPs) during partial shading, making it difficult to find the global MPP using conventional single-stage (CSS) tracking. To overcome this difficulty, the authors propose a novel MPPT algorithm by introducing a particle swarm optimization (PSO) technique. The proposed algorithm uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation. The validity of the proposed algorithm is demonstrated through experimental studies. In addition, a detailed performance comparison with conventional fixed voltage, hill climbing, and Fibonacci search MPPT schemes are presented. Algorithm robustness was verified for several complicated partial shading conditions, and in all cases this method took about 2 s to find the global MPP.
Friction from Reflectance: Deep Reflectance Codes for Predicting Physical Surface Properties from One-Shot In-Field Reflectance

One-shot in-field reflectance creates new opportunities for recognition and scene understanding. We present a model for reflectance patterns using a state-of-the-art representation that builds on key aspects of deep learning and binary embedding. We show that our approach of deep reflectance codes (DRC) provides an efficient representation of materials for recognition, for unsupervised material grouping and for the first of its kind friction-from-reflectance algorithm. Our novel framework uses angular reflectance patterns to go beyond recognition and estimate physical properties of the surface.
MarineBlue: A Low-cost Chess Robot

This paper describes the development of a chess-playing robot called MarineBlue. This robot consists of three components: a computer vision component to recognize chess board situations, a chess engine component to compute new moves and a robot control component to execute these moves by means of a robot arm. In the paper, we focus on the algorithms that have been used to implement the computer vision and robot control components. The MarineBlue robot is fully autonomous, in the sense that it can recognize the moves done by a user, calculate a move in response to the user’s move and control a robot arm to perform this calculated move. The robot that was used to develop MarineBlue is a low-cost, educational robot, which results in a cost-effective and compact chess-playing robot.
Synonymous Paraphrasing Using WordNet and Internet

We propose a method of synonymous paraphrasing of a text based on WordNet synonymy data and Internet statistics of stable word combinations (collocations). Given a text, we look for words or expressions in it for which WordNet provides synonyms, and substitute them with such synonyms only if the latter form valid collocations with the surrounding words according to the statistics gathered from Internet. We present two important applications of such synonymous paraphrasing: (1) style-checking and correction: automatic evaluation and computer-aided improvement of writing style with regard to various aspects (increasing vs. decreasing synonymous variation, conformistic vs. individualistic selection of synonyms, etc.) and (2) steganography: hiding of additional information in the text by special selection of synonyms. A basic interactive algorithm of style improvement is outlined and an example of its application to editing of newswire text fragment in English is traced. Algorithms of style evaluation and information hiding are also proposed.
LIUM SPKDIARIZATION: AN OPEN SOURCE TOOLKIT FOR DIARIZATION

This paper presents an open-source diarization toolkit which is mostly dedicated to speaker and developed by the LIUM. This toolkit includes hierarchical agglomerative clustering methods using well-known measures such as BIC and CLR. Two applications for which the toolkit has been used are presented: one is for broadcast news using the ESTER 2 data and the other is for telephone conversations using the MEDIA corpus.