nils-herrmann/clean_scirepeval huggingface dataset:
Air-ground localization and map augmentation using monocular dense reconstruction

We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.
Sequential Person Recognition in Photo Albums with a Recurrent Network

Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to issues such as non-frontal faces, changes in clothing, location, lighting. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people as a sequence prediction task. At the core of our work is a novel recurrent network architecture, in which relational information between instances labels and appearance are modeled jointly. In addition to relational cues, scene context is incorporated in our sequence prediction model with no additional cost. In this sense, our approach is a unified framework for modeling both contextual cues and visual appearance of person instances. Our model is trained end-to-end with a sequence of annotated instances in a photo as inputs, and a sequence of corresponding labels as targets. We demonstrate that this simple but elegant formulation achieves state-of-the-art performance on the newly released People In Photo Albums (PIPA) dataset.
Distributed generation intelligent islanding detection using governor signal clustering

One of the major protection concerns with distribution networks comprising distributed generation is unintentional islanding phenomenon. Expert diagnosis system is needed to distinguish network cut off from normal occurrences. An important part of synchronous generator is automatic load-frequency controller (ALFC). In this paper, a new approach based on clustering of input signal to governor is introduced. Self-organizing map (SOM) neural network is used to identify and classify islanding and non-islanding phenomena. Simulation results show that input signal to governor has different characteristics concern with islanding conditions and other disturbances. In addition, the SOM is able to identify and classify phenomena satisfactorily. Using proposed method, islanding can be detected after 200 ms.
CIDEr: Consensus-based image description evaluation

Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.
A systematic survey on automated concurrency bug detection, exposing, avoidance, and fixing techniques

Currently, concurrent programs are becoming increasingly widespread to meet the demands of the rapid development of multi-core hardware. However, it could be quite expensive and challenging to guarantee the correctness and efficiency of concurrent programs. In this paper, we provide a systematic review of the existing research on fighting against concurrency bugs, including automated concurrency bug exposing, detection, avoidance, and fixing. These four categories cover the different aspects of concurrency bug problems and are complementary to each other. For each category, we survey the motivation, key issues, solutions, and the current state of the art. In addition, we summarize the classical benchmarks widely used in previous empirical studies and the contribution of active research groups. Finally, some future research directions on concurrency bugs are recommended. We believe this survey would be useful for concurrency programmers and researchers.
Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies

In this paper, we explore a large multimodal dataset of about 65k albums constructed from a combination of Amazon customer reviews, MusicBrainz metadata and AcousticBrainz audio descriptors. Review texts are further enriched with named entity disambiguation along with polarity information derived from an aspect-based sentiment analysis framework. This dataset constitutes the cornerstone of two main contributions: First, we perform experiments on music genre classification, exploring a variety of feature types, including semantic, sentimental and acoustic features. These experiments show that modeling semantic information contributes to outperforming strong bag-of-words baselines. Second, we provide a diachronic study of the criticism of music genres via a quantitative analysis of the polarity associated to musical aspects over time. Our analysis hints at a potential correlation between key cultural and geopolitical events and the language and evolving sentiments found in music reviews.
Empathy circuits

The social neuroscientific investigation of empathy has revealed that the same neural networks engaged during first-hand experience of affect subserve empathic responses. Recent meta-analyses focusing on empathy for pain for example reliably identified a network comprising anterior insula and anterior midcingulate cortex. Moreover, recent studies suggest that the generation of empathy is flexibly supported by networks involved in action simulation and mentalizing depending on the information available in the environment. Further, empathic responses are modulated by many factors including the context they occur in. Recent work shows how this modulation can be afforded by the engagement of antagonistic motivational systems or by cognitive control circuits, and these modulatory systems can also be employed in efforts to regulate one's empathic responses.
Learning to Act by Predicting the Future

We present an approach to sensorimotor control in immersive environments. Our approach utilizes a high-dimensional sensory stream and a lower-dimensional measurement stream. The cotemporal structure of these streams provides a rich supervisory signal, which enables training a sensorimotor control model by interacting with the environment. The model is trained using supervised learning techniques, but without extraneous supervision. It learns to act based on raw sensory input from a complex three-dimensional environment. The presented formulation allows changing the agent’s goal at test time. We conduct extensive experiments in three-dimensional simulations based on the classical first-person game Doom. The results demonstrate that the presented approach outperforms sophisticated prior formulations, particularly on challenging tasks. The results also show that trained models successfully generalize across environments and goals. A model trained using the presented approach won the Full Deathmatch track of the Visual Doom AI Competition, which was held in previously unseen environments.
Platform or Wholesale? A Strategic Tool for Online Retailers to Benefit from Third-Party Information

Online retailing is dominated by a channel structure in which a retailer either buys products from competing manufacturers and resells to consumers (wholesale scheme) or lets manufacturers sell directly to consumers on its platform for a commission (platform scheme). Easy access to publicly available third-party information such as product reviews that facilitate consumers’ purchase decisions is another distinctive and ubiquitous characteristic of online retailing. We show that retailers can use the upstream pricing scheme, wholesale or platform, as a strategic tool to benefit from third-party information. Information on the quality dimension homogenizes consumers’ perceived utility differences between competing products and increases the upstream competition, which benefits the wholesale-based retailer but hurts the platform-based retailer. Information on the fit dimension, in constrast, heterogenizes consumers’ estimated fits to the products and softens the upstream competition, which hurts the wholesale-based retailer but benefits the platform-based retailer. Consequently, when the precision of the third-party information is high (low), a retailer can benefit from third-party information by adopting the wholesale (platform) scheme if the quality dimension plays a dominant role and by adopting the platform (wholesale) scheme if the fit dimension is dominant. Furthermore, the effect of precision improvement on the retailer’s profit depends on the pricing-scheme choice and the relative importance of quality and fit attributes in consumers’ evaluations of products. For instance, when the fit dimension is dominant, increasing the precision can hurt the wholesale-based retailer but benefit the platform-based retailer.
A General-Purpose Query-Centric Framework for Querying Big Graphs

Pioneered by Google’s Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly “think like a vertex” programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing lightworkload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.
An accident prediction approach based on XGBoost

As an important threat to public security, urban fire accident causes huge economic loss and catastrophic collapse. Predicting and analyzing the interior rule of urban fire accident from its appearance needed to be solved in the field. In this paper, we propose a new urban fire accident prediction approach based on XGBoost. The method determines the predictive indexes in a quantitative and qualitative way from different characteristics in various kinds of fire accidents. For screening the features we need, we adopt the feature selection algorithm based on association rules. For data cleaning, we use a method based on Box-Cox transformation that transforms the continual response variables from the feature space for removing the dependencies on unobservable errors and the predictor variable to some extent. Then we use the data to train the model based on XGBoost to obtain the best prediction accuracy. Experiments show that the method provides a feasible solution to urban fire accident prediction. The method contributes to improving the public security situation, we have added the method and related model to the City in a box™, Shenzhen Aerospace Smart City System Technology Co., Ltd.
Natural Logic Inference for Emotion Detection

Current research on emotion detection focuses on the recognizing explicit emotion expressions in text. In this paper, we propose an approach based on textual inference to detect implicit emotion expressions, that is, to capture emotion detection as an logical inference issue. The approach builds a natural logic system, in which emotional detection are decomposed into a series of logical inference process. The system also employ inference knowledge from textural inference resources for reasoning complex expressions in emotional texts. Experimental results show the efficiency in detecting implicit emotional expressions.
Four Text Classification Algorithms Compared on a Dutch Corpus

We describe an experiment in applying text classification algorithms to Dutch texts. Four well-known learning algorithms, Rocchio’s algorithm, the Simple Bayesian Classifier (SBC), the Sleeping Experts (SE) and Winnow were implemented. They were tested on a corpus of articles from the Dutch newspaper NRC, pre-classified into four categories. The algorithms are compared on learning speed and error rate. We also investigated the effect of discarding terms, using either a dynamic stoplist or the Winnow heuristic.
Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.
A study of phase noise in CMOS oscillators

This paper presents a study of phase noise in two inductorless CMOS oscillators. First-order analysis of a linear oscillatory system leads to a noise shaping function and a new definition of Q. A linear model of CMOS ring oscillators is used to calculate their phase noise, and three phase noise phenomena, namely, additive noise, high-frequency multiplicative noise, and low-frequency multiplicative noise, are identified and formulated. Based on the same concepts, a CMOS relaxation oscillator is also analyzed. Issues and techniques related to simulation of noise in the time domain are described, and two prototypes fabricated in a 0.5m CMOS technology are used to investigate the accuracy of the theoretical predictions. Compared with the measured results, the calculated phase noise values of a 2-GHz ring oscillator and a 900-MHz relaxation oscillator at 5 MHz offset have an error of approximately 4 dB.
A Hierarchical Neural Autoencoder for Paragraphs and Documents

Natural language generation of coherent long texts like paragraphs or longer documents is a challenging problem for recurrent networks models. In this paper, we explore an important step toward this generation task: training an LSTM (Longshort term memory) auto-encoder to preserve and reconstruct multi-sentence paragraphs. We introduce an LSTM model that hierarchically builds an embedding for a paragraph from embeddings for sentences and words, then decodes this embedding to reconstruct the original paragraph. We evaluate the reconstructed paragraph using standard metrics like ROUGE and Entity Grid, showing that neural models are able to encode texts in a way that preserve syntactic, semantic, and discourse coherence. While only a first step toward generating coherent text units from neural models, our work has the potential to significantly impact natural language generation and summarization1.
Open domain event extraction from twitter

Tweets are the most up-to-date and inclusive stream of in- formation and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter's unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal-- the first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.
Fundamental Limits of Wideband Localization— Part I: A General Framework

The availability of position information is of great importance in many commercial, public safety, and military applications. The coming years will see the emergence of location-aware networks with submeter accuracy, relying on accurate range measurements provided by wide bandwidth transmissions. In this two-part paper, we determine the fundamental limits of localization accuracy of wideband wireless networks in harsh multipath environments. We first develop a general framework to characterize the localization accuracy of a given node here and then extend our analysis to cooperative location-aware networks in Part II. In this paper, we characterize localization accuracy in terms of a performance measure called the squared position error bound (SPEB), and introduce the notion of equivalent Fisher information (EFI) to derive the SPEB in a succinct expression. This methodology provides insights into the essence of the localization problem by unifying localization information from individual anchors and that from a priori knowledge of the agent's position in a canonical form. Our analysis begins with the received waveforms themselves rather than utilizing only the signal metrics extracted from these waveforms, such as time-of-arrival and received signal strength. Hence, our framework exploits all the information inherent in the received waveforms, and the resulting SPEB serves as a fundamental limit of localization accuracy.
Particle Systems - a Technique for Modeling a Class of Fuzzy Objects

This paper introduces particle systems--a method for modeling fuzzy objects such as fire, clouds, and water. Particle systems model an object as a cloud of primitive particles that define its volume. Over a period of time, particles are generated into the system, move and change form within the system, and die from the system. The resulting model is able to represent motion, changes of form, and dynamics that are not possible with classical surface-based representations. The particles can easily be motion blurred, and therefore do not exhibit temporal aliasing or strobing. Stochastic processes are used to generate and control the many particles within a particle system. The application of particle systems to the wall of fire element from the Genesis Demo sequence of the film Star Trek II: The Wrath of Khan [10] is presented.
Mesh saliency

Research over the last decade has built a solid mathematical foundation for representation and analysis of 3D meshes in graphics and geometric modeling. Much of this work however does not explicitly incorporate models of low-level human visual attention. In this paper we introduce the idea of mesh saliency as a measure of regional importance for graphics meshes. Our notion of saliency is inspired by low-level human visual system cues. We define mesh saliency in a scale-dependent manner using a center-surround operator on Gaussian-weighted mean curvatures. We observe that such a definition of mesh saliency is able to capture what most would classify as visually interesting regions on a mesh. The human-perception-inspired importance measure computed by our mesh saliency operator results in more visually pleasing results in processing and viewing of 3D meshes. compared to using a purely geometric measure of shape. such as curvature. We discuss how mesh saliency can be incorporated in graphics applications such as mesh simplification and viewpoint selection and present examples that show visually appealing results from using mesh saliency.
DeFlaker: Automatically Detecting Flaky Tests

Developers often run tests to check that their latest changes to a code repository did not break any previously working functionality. Ideally, any new test failures would indicate regressions caused by the latest changes. However, some test failures may not be due to the latest changes but due to non-determinism in the tests, popularly called flaky tests. The typical way to detect flaky tests is to rerun failing tests repeatedly. Unfortunately, rerunning failing tests can be costly and can slow down the development cycle.
 We present the first extensive evaluation of rerunning failing tests and propose a new technique, called DeFlaker, that detects if a test failure is due to a flaky test without rerunning and with very low runtime overhead. DeFlaker monitors the coverage of latest code changes and marks as flaky any newly failing test that did not execute any of the changes. We deployed DeFlaker live, in the build process of 96 Java projects on TravisCI, and found 87 previously unknown flaky tests in 10 of these projects. We also ran experiments on project histories, where DeFlaker detected 1, 874 flaky tests from 4, 846 failures, with a low false alarm rate (1.5%). DeFlaker had a higher recall (95.5% vs. 23%) of confirmed flaky tests than Maven's default flaky test detector.