nils-herrmann/clean_scirepeval huggingface dataset:
Distributed trajectory estimation with privacy and communication constraints: A two-stage distributed Gauss-Seidel approach

We propose a distributed algorithm to estimate the 3D trajectories of multiple cooperative robots from relative pose measurements. Our approach leverages recent results  which show that the maximum likelihood trajectory is well approximated by a sequence of two quadratic subproblems. The main contribution of the present work is to show that these subproblems can be solved in a distributed manner, using the distributed Gauss-Seidel (DGS) algorithm. Our approach has several advantages. It requires minimal information exchange, which is beneficial in presence of communication and privacy constraints. It has an anytime flavor: after few iterations the trajectory estimates are already accurate, and they asymptotically convergence to the centralized estimate. The DGS approach scales well to large teams, and it has a straightforward implementation. We test the approach in simulations and field tests, demonstrating its advantages over related techniques.
NOVEL COMPACT WAVEGUIDE DUAL CIRCULAR POLARIZER

A novel type of dual circular polarizer for simultaneously receiving and transmitting right-hand and left-hand circularly polarized waves is developed and tested. It consists of a H-plane T junction of rectangular waveguide, one circular waveguide as an Eplane arm located on top of the junction, and two metallic pins used for matching. The theoretical analysis and design of the three-physicalport and four-mode polarizer were researched by solving ScatteringMatrix of the network and using a full-wave electromagnetic simulation tool. The optimized polarizer has the advantages of a very compact size with a volume smaller than 0.6λ3, low complexity and manufacturing cost. A couple of the polarizer has been manufactured and tested, and the experimental results are basically consistent with the theories.
Network-Induced Constraints in Networked Control Systems—A Survey

Networked control systems (NCSs) have, in recent years, brought many innovative impacts to control systems. However, great challenges are also met due to the network-induced imperfections. Such network-induced imperfections are handled as various constraints, which should appropriately be considered in the analysis and design of NCSs. In this paper, the main methodologies suggested in the literature to cope with typical network-induced constraints, namely time delays, packet losses and disorder, time-varying transmission intervals, competition of multiple nodes accessing networks, and data quantization are surveyed; the constraints suggested in the literature on the first two types of constraints are updated in different categorizing ways; and those on the latter three types of constraints are extended.
Using a Single RGB Frame for Real Time 3D Hand Pose Estimation in the Wild

We present a method for the real-time estimation of the full 3D pose of one or more human hands using a single commodity RGB camera. Recent work in the area has displayed impressive progress using RGBD input. However, since the introduction of RGBD sensors, there has been little progress for the case of monocular color input. We capitalize on the latest advancements of deep learning, combining them with the power of generative hand pose estimation techniques to achieve real-time monocular 3D hand pose estimation in unrestricted scenarios. More specifically, given an RGB image and the relevant camera calibration information, we employ a state-of-the-art detector to localize hands. Given a crop of a hand in the image, we run the pretrained network of OpenPose for hands to estimate the 2D location of hand joints. Finally, non-linear least-squares minimization fits a 3D model of the hand to the estimated 2D joint positions, recovering the 3D hand pose. Extensive experimental results provide comparison to the state of the art as well as qualitative assessment of the method in the wild.
An End to End Model for Automatic Music Generation : Combining Deep Raw and Symbolic Audio Networks

We develop an approach to combine two types of music generation models, namely symbolic and raw audio models. While symbolic models typically operate at the note level and are able to capture long-term dependencies, they lack the expressive richness and nuance of performed music. Raw audio models train directly on raw audio waveforms, and can be used to produce expressive music; however, these models typically lack structure and long-term dependencies. We describe a work-in-progress model that trains a raw audio model based on the recently-proposed WaveNet architecture, but that incorporates the notes of the composition as a secondary input to the network. When generating novel compositions, we utilize an LSTM network whose output feeds into the raw audio model, thus yielding an end-to-end model that generates raw audio outputs combining the best of both worlds. We describe initial results of our approach, which we believe to show considerable promise for structured music generation.
Inducing Tree-Substitution Grammars

Inducing a grammar from text has proven to be a notoriously ch allenging learning task despite decades of research. The primary reason for its difficulty is hat in order to induce plausible grammars, the underlying model must be capable of representing t he in ricacies of language while also ensuring that it can be readily learned from data. The majori ty f existing work on grammar induction has favoured model simplicity (and thus learnability) over representational capacity by using context free grammars and first order dependency grammars, w hich are not sufficiently expressive to model many common linguistic constructions. We propose a novel compromise by inferring a probabilistictree substitution grammar , a formalism which allows for arbitrarily large tree fragments and thereby better represent complex linguistic stru tu es. To limit the model’s complexity we employ a Bayesian non-parametric prior which biases the m odel towards a sparse grammar with shallow productions. We demonstrate the model’s efficacy on supervised phrase-structure parsing, where we induce a latent segmentation of the training treeba nk, and on unsupervised dependency grammar induction. In both cases the model uncovers interes ting latent linguistic structures while producing competitive results.
Controlling racial prejudice: social-cognitive goals affect amygdala and stereotype activation.

The malleability of stereotyping matters in social psychology and in society. Previous work indicates rapid amygdala and cognitive responses to racial out-groups, leading some researchers to view these responses as inevitable. In this study, the methods of social-cognitive neuroscience were used to investigate how social goals control prejudiced responses. Participants viewed photographs of unfamiliar Black and White faces, under each of three social goals: social categorization (by age), social individuation (vegetable preference), and simple visual inspection (detecting a dot). One study recorded brain activity in the amygdala using functional magnetic resonance imaging, and another measured cognitive activation of stereotypes by lexical priming. Neither response to photos of the racial out-group was inevitable; instead, both responses depended on perceivers' current social-cognitive goal.
ICDAR 2003 robust reading competitions: entries, results, and future directions

This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term ‘robust reading’ to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.
An Intelligent Secure and Privacy-Preserving Parking Scheme Through Vehicular Communications

There are always frustrations for drivers in finding parking spaces and being protected from auto theft. In this paper, to minimize the drivers' hassle and inconvenience, we propose a new intelligent secure privacy-preserving parking scheme through vehicular communications. The proposed scheme is characterized by employing parking lot RSUs to surveil and manage the whole parking lot and is enabled by communication between vehicles and the RSUs. Once vehicles that are equipped with wireless communication devices, which are also known as onboard units, enter the parking lot, the RSUs communicate with them and provide the drivers with real-time parking navigation service, secure intelligent antitheft protection, and friendly parking information dissemination. In addition, the drivers' privacy is not violated. Performance analysis through extensive simulations demonstrates the efficiency and practicality of the proposed scheme.
Fast LIDAR-based road detection using fully convolutional neural networks

In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.
Automated ontology construction for unstructured text documents

Ontology is playing an increasingly important role in knowledge management and the Semantic Web. This study presents a novel episode-based ontology construction mechanism to extract domain ontology from unstructured text documents. Additionally, fuzzy numbers for conceptual similarity computing are presented for concept clustering and taxonomic relation definitions. Moreover, concept attributes and operations can be extracted from episodes to construct a domain ontology, while non-taxonomic relations can be generated from episodes. The fuzzy inference mechanism is also applied to obtain new instances for ontology learning. Experimental results show that the proposed approach can effectively construct a Chinese domain ontology from unstructured text documents.
Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks

Mobile malware threats (e.g., on Android) have recently become a real concern. In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Such an evaluation is important for not only measuring the available defense against mobile malware threats, but also proposing effective, next-generation solutions. We developed DroidChameleon, a systematic framework with various transformation techniques, and used it for our study. Our results on 10 popular commercial anti-malware applications for Android are worrisome: none of these tools is resistant against common malware transformation techniques. In addition, a majority of them can be trivially defeated by applying slight transformation over known malware with little effort for malware authors. Finally, in light of our results, we propose possible remedies for improving the current state of malware detection on mobile devices.
An online sequence-to-sequence model for noisy speech recognition

Generative models have long been the dominant approach for speech recognition. The success of these models however relies on the use of sophisticated recipes and complicated machinery that is not easily accessible to non-practitioners. Recent innovations in Deep Learning have given rise to an alternative – discriminative models called Sequence-to-Sequence models, that can almost match the accuracy of state of the art generative models. While these models are easy to train as they can be trained end-to-end in a single step, they have a practical limitation that they can only be used for offline recognition. This is because the models require that the entirety of the input sequence be available at the beginning of inference, an assumption that is not valid for instantaneous speech recognition. To address this problem, online sequence-to-sequence models were recently introduced. These models are able to start producing outputs as data arrives, and the model feels confident enough to output partial transcripts. These models, like sequence-to-sequence are causal – the output produced by the model until any time, t, affects the features that are computed subsequently. This makes the model inherently more powerful than generative models that are unable to change features that are computed from the data. This paper highlights two main contributions – an improvement to online sequence-to-sequence model training, and its application to noisy settings with mixed speech from two speakers.
Euro Banknote Recognition System Using a Three - layered Perceptron and RBF Networks

We propose an Euro banknote recognition system using two types of neural networks; a three-layered perceptron and a Radial Basis Function (RBF) network. A three-layered perceptron is well known method for pattern recognition and is also a very effective tool for classifing banknotes. An RBF network has a potential to reject invalid data because it estimates the probability distribution of the sample data. We use a three-layered perceptron for classification and several RBF networks for validation. The proposed system has two advantages over the system using only one RBF network. The feature extraction area can be simply defined, and the calculation cost does not increase when the number of classes increases. We also propose to use infra-red (IR) and visible images as input data to the system since Euro banknotes have quite significant features in IR images. We have tested our system in terms of acceptance rates for valid banknotes and rejection rates for invalid data.
On-body localization of wearable devices: An investigation of position-aware activity recognition

Human activity recognition using mobile device sensors is an active area of research in pervasive computing. In our work, we aim at implementing activity recognition approaches that are suitable for real life situations. This paper focuses on the problem of recognizing the on-body position of the mobile device which in a real world setting is not known a priori. We present a new real world data set that has been collected from 15 participants for 8 common activities were they carried 7 wearable devices in different positions. Further, we introduce a device localization method that uses random forest classifiers to predict the device position based on acceleration data. We perform the most complete experiment in on-body device location that includes all relevant device positions for the recognition of a variety of different activities. We show that the method outperforms other approaches achieving an F-Measure of 89% across different positions. We also show that the detection of the device position consistently improves the result of activity recognition for common activities.
RC-NET: A General Framework for Incorporating Knowledge into Word Representations

Representing words into vectors in continuous space can form up a potentially powerful basis to generate high-quality textual features for many text mining and natural language processing tasks. Some recent efforts, such as the skip-gram model, have attempted to learn word representations that can capture both syntactic and semantic information among text corpus. However, they still lack the capability of encoding the properties of words and the complex relationships among words very well, since text itself often contains incomplete and ambiguous information. Fortunately, knowledge graphs provide a golden mine for enhancing the quality of learned word representations. In particular, a knowledge graph, usually composed by entities (words, phrases, etc.), relations between entities, and some corresponding meta information, can supply invaluable relational knowledge that encodes the relationship between entities as well as categorical knowledge that encodes the attributes or properties of entities. Hence, in this paper, we introduce a novel framework called RC-NET to leverage both the relational and categorical knowledge to produce word representations of higher quality. Specifically, we build the relational knowledge and the categorical knowledge into two separate regularization functions, and combine both of them with the original objective function of the skip-gram model. By solving this combined optimization problem using back propagation neural networks, we can obtain word representations enhanced by the knowledge graph. Experiments on popular text mining and natural language processing tasks, including analogical reasoning, word similarity, and topic prediction, have all demonstrated that our model can significantly improve the quality of word representations.
Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images

In this paper, a new computer tomography (CT) lung nodule computer-aided detection (CAD) method is proposed for detecting both solid nodules and ground-glass opacity (GGO) nodules (part solid and nonsolid). This method consists of several steps. First, the lung region is segmented from the CT data using a fuzzy thresholding method. Then, the volumetric shape index map, which is based on local Gaussian and mean curvatures, and the ldquodotrdquo map, which is based on the eigenvalues of a Hessian matrix, are calculated for each voxel within the lungs to enhance objects of a specific shape with high spherical elements (such as nodule objects). The combination of the shape index (local shape information) and ldquodotrdquo features (local intensity dispersion information) provides a good structure descriptor for the initial nodule candidates generation. Antigeometric diffusion, which diffuses across the image edges, is used as a preprocessing step. The smoothness of image edges enables the accurate calculation of voxel-based geometric features. Adaptive thresholding and modified expectation-maximization methods are employed to segment potential nodule objects. Rule-based filtering is first used to remove easily dismissible nonnodule objects. This is followed by a weighted support vector machine (SVM) classification to further reduce the number of false positive (FP) objects. The proposed method has been trained and validated on a clinical dataset of 108 thoracic CT scans using a wide range of tube dose levels that contain 220 nodules (185 solid nodules and 35 GGO nodules) determined by a ground truth reading process. The data were randomly split into training and testing datasets. The experimental results using the independent dataset indicate an average detection rate of 90.2%, with approximately 8.2 FP/scan. Some challenging nodules such as nonspherical nodules and low-contrast part-solid and nonsolid nodules were identified, while most tissues such as blood vessels were excluded. The method's high detection rate, fast computation, and applicability to different imaging conditions and nodule types shows much promise for clinical applications.
The Well-Played MOBA: How DotA 2 and League of Legends use Dramatic Dynamics

This paper will analyse the two most popular games within the MOBA genre, DotA 2 and League of Legends, as performance-designed spaces. By analysing MOBAs as performance and using Marc LeBlanc’s (2006) Tools for Creating Dramatic Game Dynamics as an aesthetic framework the aim is to posit a greater understanding of the ways in which e-Sports and MOBAs specifically can be designed in order to create dramatic tension within the increasing variety of available viewing platforms. In this way, this paper helps present new ways to think about how games can be designed/structured in order to be satisfyingly performed and consumed through increasingly diverse viewing methods.
Towards Generic Text-Line Extraction

Text-line extraction is the backbone of document image analysis. Since decades, a large number of text-line finding methods have been proposed, where these methods rely on certain assumptions about a target class of documents with respect to writing styles, digitization methods, intensity values, and scripts. There is no generic text-line finding method that can be robustly applied to a large variety of simple and complex document images. We introduced the ridge-based text-line finding method, and published its initial results for curled text-line detection on camera-captured document images. In this paper, we demonstrates our ridge-based method as a generic text-line finding approach that can be robustly applied on a diverse collection of simple and complex document images. The comprehensive performance evaluation of the ridge-based method and its comparison with several state-of-the-art methods is presented in the paper. For this purpose, diverse categories of publicly available and standard datasets have been selected: UWIII (scanned, printed English script), DFKI-I (camera-captured, printed English script), UMD (handwritten Chinese, Hindi, and Korean scripts), ICDAR2007 handwritten segmentation contest (handwritten English, French, German and Greek scripts), Arabic/Urdu (scanned, printed script), and Fraktur (scanned, calligraphic German script). Experiments on these datasets show that the ridge-based method achieves better text-line extraction results as those of the best performing, domain-specific text-line finding methods. Firstly, these results show that the ridge-based method is a generic text-line extraction method. Secondly, these results are also helpful for the community to assess the advantages of this method.
Please, tell me about yourself: automatic personality assessment using short self-presentations

Personality plays an important role in the way people manage the images they convey in self-presentations and employment interviews, trying to affect the other"s first impressions and increase effectiveness. This paper addresses the automatically detection of the Big Five personality traits from short (30-120 seconds) self-presentations, by investigating the effectiveness of 29 simple acoustic and visual non-verbal features. Our results show that Conscientiousness and Emotional Stability/Neuroticism are the best recognizable traits. The lower accuracy levels for Extraversion and Agreeableness are explained through the interaction between situational characteristics and the differential activation of the behavioral dispositions underlying those traits.