nils-herrmann/clean_scirepeval huggingface dataset:
Dense Reconstruction Using 3D Object Shape Priors

We propose a formulation of monocular SLAM which combines live dense reconstruction with shape priors-based 3D tracking and reconstruction. Current live dense SLAM approaches are limited to the reconstruction of visible surfaces. Moreover, most of them are based on the minimisation of a photo-consistency error, which usually makes them sensitive to specularities. In the 3D pose recovery literature, problems caused by imperfect and ambiguous image information have been dealt with by using prior shape knowledge. At the same time, the success of depth sensors has shown that combining joint image and depth information drastically increases the robustness of the classical monocular 3D tracking and 3D reconstruction approaches. In this work we link dense SLAM to 3D object pose and shape recovery. More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery. This leads to a system that allows for full scaled 3D reconstruction with the known object(s) segmented from the scene. The segmentation enhances the clarity, accuracy and completeness of the maps built by the dense SLAM system, while the dense 3D data aids the segmentation process, yielding faster and more reliable convergence than when using 2D image data alone.
Common Mode Noise Cancellation for Electrically Non-Contact ECG Measurement System on a Chair

Electrically non-contact ECG measurement system on a chair can be applied to a number of various fields for continuous health monitoring in daily life. However, the body is floated electrically for this system due to the capacitive electrodes and the floated body is very sensitive to the external noises or motion artifacts which affect the measurement system as the common mode noise. In this paper, the driven-seat-ground circuit similar to the driven-right-leg circuit is proposed to reduce the common mode noise. The analysis of this equivalent circuit is performed and the output signal waveforms are compared between with driven-seat-ground and with capacitive ground. As the results, the driven-seat-ground circuit improves significantly the properties of the fully capacitive ECG measurement system as the negative feedback
Strategies for successful CRM implementation

Customer relationship management (CRM) is an information system that tracks customers’ interactions with the firm and allows employees to instantly pull up information about the customers such as past sales, service records, outstanding records and unresolved problem calls. This paper aims to put forward strategies for successful implementation of CRM and discusses barriers to CRM in e-business and m-business.  The paper combines narrative with argument and analysis.  CRM stores all information about its customers in a database and uses this data to coordinate sales, marketing, and customer service departments so as to work together smoothly to best serve their customers’ needs.  The paper demonstrates how CRM, if used properly, could enhance a company’s ability to achieve the ultimate goal of retaining customers and gain strategic advantage over its competitors.
Experimental results on the nonlinear H∞ control via quasi-LPV representation and game theory for wheeled mobile robots

In this paper, nonlinear dynamic equations of a wheeled mobile robot (WMR) are described in the state-space form where the parameters are part of the state (angular velocities of the wheels). This representation, known as quasi-linear parameter varying (Quasi-LPV), is useful for control designs based on nonlinear Hinfin approaches. Two nonlinear Hinfin controllers that guarantee induced L2-norm, between input (disturbances) and output signals, bounded by an attenuation level γ are used to control a WMR. These controllers are solved via linear matrix inequalities (LMIs) and algebraic Riccati equation. Experimental results are presented, with a comparative study among these robust control strategies and the standard computed torque, plus proportional-derivative, controller.
Natural Language Processing

The Natural Language Processing group at the University of Szeged has been involved in human language technology research since 1998, and by now, it has become one of the leading workshops of Hungarian computational linguistics. Both computer scientists and linguists enrich the team with their knowledge, moreover, MSc and PhD students are also involved in research activities. The team has gained expertise in the fields of information extraction, implementing basic language processing toolkits and creating language resources. The Group is primarily engaged in processing Hungarian and English texts and its general objective is to develop language-independent or easily adaptable technologies. With the creation of the manually annotated Szeged Corpus and TreeBank, as well as the Hungarian WordNet, SzegedNE and other corpora it has become possible to apply machine learning based methods for the syntactic and semantic analysis of Hungarian texts, which is one of the strengths of the group. They have also implemented novel solutions for the morphological and syntactic parsing of morphologically rich languages and they have also published seminal papers on computational semantics, i.e. uncertainty detection and multiword expressions. They have developed tools for basic linguistic processing of Hungarian, for named entity recognition and for keyphrase extraction, which can all be easily integrated into large-scale systems and are optimizable for the specific needs of the given application. Currently, the group’s research activities focus on the processing of non-canonical texts (e.g. social media texts) and on the implementation of a syntactic parser for Hungarian, among others.
Image-Based Visual Servoing With Light Field Cameras

This paper proposes the first derivation, implementation, and experimental validation of light field image-based visual servoing. Light field image Jacobians are derived based on a compact light field feature representation that is close to the form measured directly by light field cameras. We also enhance feature detection and correspondence by enforcing light field geometry constraints, and directly estimate the image Jacobian without knowledge of point depth. The proposed approach is implemented over a standard visual servoing control loop, and applied to a custom-mirror-based light field camera mounted on a robotic arm. Light field image-based visual servoing is then validated in both simulation and experiment. We show that the proposed method outperforms conventional monocular and stereo image-based visual servoing under field-of-view constraints and occlusions.
Segmentation using a region-growing thresholding

Our research deals with a semi-automatic region-growing segmentation technique. This method only needs one seed inside the region of interest (ROI). We applied it for spinal cord segmentation but it also shows results for parotid glands or even tumors. Moreover, it seems to be a general segmentation method as it could be applied in other computer vision domains then medical imaging. We use both the thresholding simplicity and the spatial information. The gray-scale and spatial distances from the seed to all the other pixels are computed. By normalizing and subtracting to 1 we obtain the probability for a pixel to belong to the same region as the seed. We will explain the algorithm and show some preliminary results which are encouraging.
Texture mapping progressive meshes

Given an arbitrary mesh, we present a method to construct a progressive mesh (PM) such that all meshes in the PM sequence share a common texture parametrization. Our method considers two important goals simultaneously. It minimizes texture stretch (small texture distances mapped onto large surface distances) to balance sampling rates over all locations and directions on the surface. It also minimizes texture deviation (“slippage” error based on parametric correspondence) to obtain accurate textured mesh approximations. The method begins by partitioning the mesh into charts using planarity and compactness heuristics. It creates a stretch-minimizing parametrization within each chart, and resizes the charts based on the resulting stretch. Next, it simplifies the mesh while respecting the chart boundaries. The parametrization is re-optimized to reduce both stretch and deviation over the whole PM sequence. Finally, the charts are packed into a texture atlas. We demonstrate using such atlases to sample color and normal maps over several models.
Curiosity, Creativity, and Surprise as Analytic Tools: Grounded Theory Method

Grounded theory method (GTM) is increasingly used in HCI and CSCW research . GTM offers a rigorous way to explore a domain, with an emphasis on discovering new insights, testing those insights, and building partial understandings into a broader theory of the domain. The strength of the method—as a full method— is the ability to make sense of diverse phenomena, to construct an account of those phenomena that is strongly based in the data (“grounded” in the data), to develop that account through an iterative and principled series of challenges and modifi cations, and to communicate the end result to others in a way that is convincing and valuable to their own research and understanding. GTM is particularly appropriate for making sense of a domain without a dominant theory. It is not concerned with testing existing theories. Rather, GTM is concerned with the creation of theory, and with the rigorous and even ruthless examination of that new theory. Grounded Theory Method is exactly that—a method , or rather, a family of methods (Babchuk, 2010 )—for the development of theory. GTM makes explicit use of the capabilities that nearly all human share, to be curious about the world, to understand the world, and to communicate that understanding to others. GTM adds to these lay human capabilities a rigorous, scientifi c set of ways of inquiring, ways of thinking, and ways of knowing that can add power and explanatory strength to HCI and CSCW research. Curiosity, Creativity, and Surprise as Analytic Tools: Grounded Theory
Fast and Efficient Pedestrian Detection via the Cascade Implementation of an Additive Kernel Support Vector Machine

For reliable driving assistance or automated driving, pedestrian detection must be robust and performed in real time. In pedestrian detection, a linear support vector machine (linSVM) is popularly used as a classifier but exhibits degraded performance due to the multipostures of pedestrians. Kernel SVM (KSVM) could be a better choice for pedestrian detection, but it has a disadvantage in that it requires too much more computation than linSVM. In this paper, the cascade implementation of the additive KSVM (AKSVM) is proposed for the application of pedestrian detection. AKSVM avoids kernel expansion by using lookup tables, and it is implemented in cascade form, thereby speeding up pedestrian detection. The cascade implementation is trained by a genetic algorithm such that the computation time is minimized, whereas the detection accuracy is maximized. In experiments, the proposed method is tested with the INRIA dataset. The experimental results indicate that the proposed method has better detection accuracy and reduced computation time compared with conventional methods.
Robust Systems for Preposition Error Correction Using Wikipedia Revisions

We show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. We present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new corpus is automatically extracted from Wikipedia revisions and contains over one million instances of preposition corrections.
Empathy for positive and negative emotions in the gustatory cortex

Anterior insula and adjacent frontal operculum (hereafter referred to as IFO) are active during exposure to tastants/odorants (particularly disgusting ones), and during the viewing of disgusted facial expressions. Together with lesion data, the IFO has thus been proposed to be crucial in processing disgust-related stimuli. Here, we examined IFO involvement in the processing of other people's gustatory emotions more generally by exposing participants to food-related disgusted, pleased and neutral facial expressions during functional magnetic resonance imaging (fMRI). We then exposed participants to pleasant, unpleasant and neutral tastants for the purpose of mapping their gustatory IFO. Finally, we associated participants' self reported empathy (measured using the Interpersonal Reactivity Index, IRI) with their IFO activation during the witnessing of others' gustatory emotions. We show that participants' empathy scores were predictive of their gustatory IFO activation while witnessing both the pleased and disgusted facial expression of others. While the IFO has been implicated in the processing of negative emotions of others and empathy for negative experiences like pain, our finding extends this concept to empathy for intense positive feelings, and provides empirical support for the view that the IFO contributes to empathy by mapping the bodily feelings of others onto the internal bodily states of the observer, in agreement with the putative interoceptive function of the IFO.
Learn to Solve Algebra Word Problems Using Quadratic Programming

This paper presents a new algorithm to automatically solve algebra word problems. Our algorithm solves a word problem via analyzing a hypothesis space containing all possible equation systems generated by assigning the numbers in the word problem into a set of equation system templates extracted from the training data. To obtain a robust decision surface, we train a log-linear model to make the margin between the correct assignments and the false ones as large as possible. This results in a quadratic programming (QP) problem which can be efficiently solved. Experimental results show that our algorithm achieves 79.7% accuracy, about 10% higher than the state-of-the-art baseline (Kushman et al., 2014).
Blockchain-based Data Management and Analytics for Micro-insurance Applications

In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application. This application allows drivers who rarely use cars to only pay insurance premium for particular trips they would like to travel. One of the key challenges from database perspective is how to ensure all the data pertaining to the actual trip and premium payment made by the users are transparently recorded so that every party in the insurance contract including the driver, the insurance company, and the financial institution is confident that the data are tamper-proof and traceable. 
 Another challenge from information retrieval perspective is how to perform entity matching and pattern matching on customer data as well as their trip and claim history recorded on the blockchain for intelligent fraud detection. Last but not least, the drivers' trip history, once have been collected sufficiently, can be much valuable for the insurance company to do offline analysis and build statistics on past driving behaviour and past vehicle runtime. These statistics enable the insurance company to offer the users with transparent and individualized insurance quotes. Towards this end, we develop a blockchain-based solution for micro-insurance applications that transparently keeps records and executes smart contracts depending on runtime conditions while also connecting with off-chain analytic databases.
ANN Approach Based On Back Propagation Network and Probabilistic Neural Network to Classify Brain Cancer

This paper presents the artificial neural network approach namely Back propagation network (BPNs) and probabilistic neural network (PNN). It is used to classify the type of tumor in MRI images of different patients with Astrocytoma type of brain tumor. The image processing techniques have been developed for detection of the tumor in the MRI images. Gray Level Co-occurrence Matrix (GLCM) is used to achieve the feature extraction. The whole system worked in two modes firstly Training/Learning mode and secondly Testing/Recognition mode.
Effects of X Capacitors on EMI Filter Effectiveness

The suppression mechanism of the differential-mode noise of an X capacitor in offline power supplies is, for the first time, attributed to two distinct concepts: 1) impedance mismatch (regarding a line impedance stabilization network or mains and the equivalent power supply noise source impedance) and 2) C(dv/dt) noise current balancing (to suppress mix-mode noise). The effectiveness of X capacitors is investigated with this theory, along with experimental supports. Understanding of the two aforementioned mechanisms gives better insight into filter effectiveness, which may lead to a more compact filter design.
End-to-end joint learning of natural language understanding and dialogue manager

Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our proposed model significantly outperforms the state-of-the-art pipeline models for both NLU and SAP, which indicates that our joint model is capable of mitigating the affects of noisy NLU outputs, and NLU model can be refined by error flows backpropagating from the extra supervised signals of system actions.
A Multi-tenant Architecture for Business Process Executions

Cloud computing, as a concept, promises cost savings to end-users by letting them outsource their non-critical business functions to a third party in pay-as-you-go style. However, to enable economic pay-as-you-go services, we need Cloud middleware that maximizes sharing and support near zero costs for unused applications. Multi-tenancy, which let multiple tenants (user) to share a single application instance securely, is a key enabler for building such a middleware. On the other hand, Business processes capture Business logic of organizations in an abstract and reusable manner, and hence play a key role in most organizations. This paper presents the design and architecture of a Multi-tenant Workflow engine while discussing in detail potential use cases of such architecture. Primary contributions of this paper are motivating workflow multi-tenancy, and the design and implementation of multi-tenant workflow engine that enables multiple tenants to run their workflows securely within the same workflow engine instance without modifications to the workflows.
Rotation-Invariant Features for Multi-Oriented Text Detection in Natural Images

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes.
The effects of computer games on primary school students' achievement and motivation in geography learning

The implementation of a computer game for learning about geography by primary school students is the focus of this article. Researchers designed and developed a three-dimensional educational computer game. Twenty four students in fourth and fifth grades in a private school in Ankara, Turkey learnt about world continents and countries through this game for three weeks. The effects of the game environment on students’ achievement and motivation and related implementation issues were examined through both quantitative and qualitative methods. An analysis of pre and post achievement tests showed that students made significant learning gains by participating in the game-based learning environment. When comparing their motivations while learning in the game-based learning environment and in their traditional school environment, it was found that students demonstrated statistically significant higher intrinsic motivations and statistically significant lower extrinsic motivations learning in the game-based environment. In addition, they had decreased focus on getting grades and they were more independent while participating in the game-based activities. These positive effects on learning and motivation, and the positive attitudes of students and teachers suggest that computer games can be used as an ICT tool in formal learning environments to support students in effective geography learning.
3D Head Pose Estimation from Multiple Distant Views

A method for human head pose estimation in multicamera environments is proposed. The method computes the textured visual hull of the subject and unfolds the texture of the head on a hypothetical sphere around it, whose parameterization is iteratively rotated so that the face eventually occurs on its equator. This gives rise to a spherical image, in which face detection is simplified, because exactly one frontal face is guaranteed to appear in it. In this image, the face center yields two components of pose (yaw, pitch), while the third (roll) is retrieved from the orientation of the major symmetry axis of the face. Face detection applied on the original images reduces the required iterations and anchors tracking drift. The method is demonstrated and evaluated in several data sets, including ones with known ground truth. Experimental results show that the proposed method is accurate and robust to distant imaging, despite the low-resolution appearance of subjects.