nils-herrmann/clean_scirepeval huggingface dataset:
Design principles for origin-destination flow maps

Origin-destination flow maps are often difficult to read due to overlapping flows. Cartographers have developed design principles in manual cartography for origin-destination flow maps to reduce overlaps and increase readability. These design principles are identified and documented using a quantitative content analysis of 97 geographic origin-destination flow maps without branching or merging flows. The effectiveness of selected design principles is verified in a user study with 215 participants. Findings show that (a) curved flows are more effective than straight flows, (b) arrows indicate direction more effectively than tapered line widths, and (c) flows between nodes are more effective than flows between areas. These findings, combined with results from user studies in graph drawing, conclude that effective and efficient origin-destination flow maps should be designed according to the following design principles: overlaps between flows are minimized; symmetric flows are preferred to asymmetric flows; longer flows are curved more than shorter or peripheral flows; acute angles between crossing flows are avoided; sharp bends in flow lines are avoided; flows do not pass under unconnected nodes; flows are radially distributed around nodes; flow direction is indicated with arrowheads; and flow width is scaled with represented quantity. ARTICLE HISTORY Received 11 September 2016
Edge Detection Techniques-An Overview

In computer vision and image processing, edge detection concerns the localization of signiicant variations of the grey level image and the identiication of the physical phenomena that originated them. This information is very useful for applications in 3D diierentiation of the image. Diierentiation is an ill-conditioned problem and smoothing results in a loss of information. It is diicult to design a general edge detection algorithm which performs well in many contexts and captures the requirements of subsequent processing stages. Consequently, over the history of digital image processing a variety of edge detectors have been devised which diier in their mathematical and algorithmic properties. This paper is an account of the current state of our understanding of edge detection. We propose an overview of research in edge detection: edge deenition, properties of detectors, the methodology of edge detection, the mutual innuence between edges and detectors, and existing edge detectors and their implementation.
QR-code based Localization for Indoor Mobile Robot with validation using a 3D optical tracking instrument

This paper addresses effective artificial landmark based indoor localization technique for mobile robotics applications. In the proposed approach, QR-codes are strategically placed in the operating environment, attached to the ceiling; they are used as artificial landmarks for providing reference points to aid the robot localization. The vision processing, robot localization and navigation tasks are all carried out on an Android Smartphone mounted on the mobile robotic platform; by taking advantage of the computational power, the built-in sensing, and the communication capabilities of the Smartphone. The Smartphone detects and recognizes the QR-codes to calculate the approximate global location of the robot. The pixel coordinate information in the image of QR-code is also used to enhance the position estimate in the real-world environment. Furthermore, the QR-code orientation in the image view is used to calculate the mobile robot's heading direction. An NDI 3D optical tracking instrument is used for validation of the effectiveness and feasibility of the proposed method. This paper demonstrates successful implementation of the QR-code based localization strategy and the experimental evaluation of its performance in actual environment.
Consistency of the FastSLAM algorithm

This paper presents an analysis of FastSLAM - a Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping. It shows that the algorithm degenerates with time, regardless of the number of particles used or the density of landmarks within the environment, and would always produce optimistic estimates of uncertainty in the long-term. In essence, FastSLAM behaves like a non-optimal local search algorithm; in the short-term it may produce consistent uncertainty estimates but, in the long-term, it is unable to adequately explore the state-space to be a reasonable Bayesian estimator. However, the number of particles and landmarks does affect the accuracy of the estimated mean and, given sufficient particles, FastSLAM can produce good non-stochastic estimates in practice. FastSLAM also has several practical advantages, particularly with regard to data association, and would probably work well in combination with other versions of stochastic SLAM, such as EKF-based SLAM
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching

Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes. The techniques are essentially complementary: DfD can robustly handle repetitive textures that are problematic for stereo matching whereas stereo matching is insensitive to defocus blurs and can handle large depth range. In this paper, we present a unified learning-based technique to conduct hybrid DfD and stereo matching. Our input is image triplets: a stereo pair and a defocused image of one of the stereo views. We first apply depth-guided light field rendering to construct a comprehensive training dataset for such hybrid sensing setups. Next, we adopt the hourglass network architecture to separately conduct depth inference from DfD and stereo. Finally, we exploit different connection methods between the two separate networks for integrating them into a unified solution to produce high fidelity 3D disparity maps. Comprehensive experiments on real and synthetic data show that our new learning-based hybrid 3D sensing technique can significantly improve accuracy and robustness in 3D reconstruction.
Spatio-temporal context analysis within video volumes for anomalous-event detection and localization

In this paper, we propose an anomaly-detection approach applied for video surveillance in crowded scenes. This approach is an unsupervised statistical learning framework based on analysis of spatiotemporal video-volume configuration within video cubes. It learns global activity patterns and local salient behavior patterns via clustering and sparse coding, respectively. Upon the composition-pattern dictionary learned from normal behavior, a sparse reconstruction cost criterion is designed to detect anomalies that occur in video both globally and locally. In addition, a multiple scale analysis is employed for obtaining accurate anomaly localization, considering scale variations of abnormal events. This approach is verified on publically available anomaly-detection datasets and compared with other existing work. The experiment results demonstrate that it not only detects various anomalies more efficiently, but also locates anomalous regions more accurately. & 2015 Elsevier B.V. All rights reserved.
Answering English questions by computer: a survey

Fifteen experimental English language question-answering I systems which are programmed and operating are described ) arid reviewed. The systems range from a conversation machine ~] to programs which make sentences about pictures and systems s~ which translate from English into logical calculi. Systems are ~ classified as list-structured data-based, graphic data-based, ~! text-based and inferential. Principles and methods of opera~4 tions are detailed and discussed. It is concluded that the data-base question-answerer has > passed from initial research into the early developmental ~.4 phase. The most difficult and important research questions for ~i~ the advancement of general-purpose language processors are seen to be concerned with measuring meaning, dealing with ambiguities, translating into formal languages and searching large tree structures.
On-To-Knowledge Methodology (OTKM)

1 Institute AIFB, University of Karlsruhe Postfach, 76128 Karlsruhe, Germany http://www.aifb.uni-karlsruhe.de Contact: sure@aifb.uni-karlsruhe.de 2 Ontoprise GmbH Karlsruhe, Germany http://www.ontoprise.de 3 FZI Research Center for Information Technologies Knowledge Management Department (WIM) Karlsruhe, Germany http://www.fzi.de/wim 4 L3S Learning Lab Hannover/Karlsruhe, Germany http://www.learninglab.de
Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data

The ever increasing interest in semantic technologies and the availability of several open knowledge sources have fueled recent progress in the field of recommender systems. In this paper we feed recommender systems with features coming from the Linked Open Data (LOD) cloud - a huge amount of machine-readable knowledge encoded as RDF statements - with the aim of improving recommender systems effectiveness. In order to exploit the natural graph-based structure of RDF data, we study the impact of the knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation algorithm. In more detail, we investigate whether the integration of LOD-based features improves the effectiveness of the algorithm and to what extent the choice of different feature selection techniques influences its performance in terms of accuracy and diversity. The experimental evaluation on two state of the art datasets shows a clear correlation between the feature selection technique and the ability of the algorithm to maximize a specific evaluation metric. Moreover, the graph-based algorithm leveraging LOD-based features is able to overcome several state of the art baselines, such as collaborative filtering and matrix factorization, thus confirming the effectiveness of the proposed approach.
Recurrent neural network training with dark knowledge transfer

Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.
Dynamic particle image velocimetry study of the aortic flow field of contemporary mechanical bileaflet prostheses

The characteristics of mechanical bileaflet valves, the leaflets of which open at the outside first, differ significantly from those of natural valves, whose leaflets open at the center first, and this fact affects the flow field down-stream of the valves. The direction of jet-type flows, which is influenced by this difference in valve features, and the existence of the sinus of Valsalva both affect the flow field inside the aorta in different ways, depending on the valve design. There may also be an influence on the coronary circulation, the entrance to which resides inside the sinus of Valsalva. A dynamic particle image velocimetry (PIV) study was conducted to analyze the influence of the design of prosthetic heart valves on the aortic flow field. Three contemporary bileaflet prostheses, the St. Jude Medical (SJM) valve, the On-X valve (with straight leaflets), and the MIRA valve (with curved leaflets), were tested inside a simulated aorta under pulsatile flow conditions. A dynamic PIV system was employed to analyze the aortic flow field resulting from the different valve designs. The two newer valves, the On-X and the MIRA valves, open more quickly than the SJM valve and provide a wider opening area when the valve is fully open. The SJM valve’s outer orifices deflect the flow during the accelerating flow phase, whereas the newer designs deflect the flow less. The flow through the central orifice of the SJM valve has a lower velocity compared to the newer designs; the newer designs tend to have a strong flow through all orifices. The On-X valve generates a simple jet-type flow, whereas the MIRA valve (with circumferentially curved leaflets) generates a strong but three-dimensionally diffuse flow, resulting in a more complex flow field downstream of the aortic valve. The clinically more adapted 180° orientation seems to provide a less diffuse flow than the 90° orientation does. The small differences in leaflet design in the bileaflet valves generate noticeable differences in the aortic flow; the newer valves show strong flows through all orifices.
A Hybrid Approach for Named Entity Recognition in Indian Languages

In this paper we describe a hybrid system that applies maximum entropy model (MaxEnt), language specific rules and gazetteers to the task of named entity recognition (NER) in Indian languages designed for the IJCNLP NERSSEAL shared task. Starting with named entity (NE) annotated corpora and a set of features we first build a baseline NER system. Then some language specific rules are added to the system to recognize some specific NE classes. Also we have added some gazetteers and context patterns to the system to increase the performance. As identification of rules and context patterns requires language knowledge, we were able to prepare rules and identify context patterns for Hindi and Bengali only. For the other languages the system uses the MaxEnt model only. After preparing the one-level NER system, we have applid a set of rules to identify the nested entities. The system is able to recognize 12 classes of NEs with 65.13% f-value in Hindi, 65.96% f-value in Bengali and 44.65%, 18.74%, and 35.47% f-value in Oriya, Telugu and Urdu respectively.
Threshold Signatures, Multisignatures and Blind Signatures Based on the Gap-Diffie-Hellman-Group Signature Scheme

We propose a robust proactive threshold signature scheme, a multisignature scheme and a blind signature scheme which work in any Gap Diffie-Hellman (GDH) group (where the Computational Diffie-Hellman problem is hard but the Decisional Diffie-Hellman problem is easy). Our constructions are based on the recently proposed GDH signature scheme of Boneh et al.  Due to the nice properties of GDH groups and of the base scheme, it turns out that most of our constructions are much simpler, more efficient and have more useful characteristics than similar existing constructions. We support all the proposed schemes with proofs under the appropriate computational assumptions, using the corresponding notions of security.
Autonomous Agricultural Robot and its Row Guidance

A vision-based row guidance method is presented to guide a robot platform which is designed independently to drive through the row crops in a field according to the design concept of open architecture. Then, the offset and heading angle of the robot platform are detected in real time to guide the platform on the basis of recognition of a crop row using machine vision. And the control scheme of the platform is proposed to carry out row guidance. Finally, the preliminary experiments of row guidance were implemented in a vegetable field. Experimental results show that algorithms of row identification and row guidance are effective according to the parameters measured and analyzed such as the heading angle and the offset for row guidance and the difference between the motion trajectory of the robot and the expected trajectory. And the accuracy of row guidance is up to ± 35mm, which means that the robot can move with a sufficiently high accuracy.
Reconfigurable Antennas: Design and Applications

The advancement in wireless communications requires the integration of multiple radios into a single platform to maximize connectivity. In this paper, the design process of reconfigurable antennas is discussed. Reconfigurable antennas are proposed to cover different wireless services that operate over a wide frequency range. They show significant promise in addressing new system requirements. They exhibit the ability to modify their geometries and behavior to adapt to changes in surrounding conditions. Reconfigurable antennas can deliver the same throughput as a multiantenna system. They use dynamically variable and adaptable single-antenna geometry without increasing the real estate required to accommodate multiple antennas. The optimization of reconfigurable antenna design and operation by removing unnecessary redundant switches to alleviate biasing issues and improve the system's performance is discussed. Controlling the antenna reconfiguration by software, using Field Programmable Gate Arrays (FPGAs) or microcontrollers is introduced herein. The use of Neural Networks and its integration with graph models on programmable platforms and its effect on the operation of reconfigurable antennas is presented. Finally, the applications of reconfigurable antennas for cognitive radio, Multiple Input Multiple Output (MIMO) channels, and space applications are highlighted.
Using machine learning to draw inferences from pass location data in soccer

In this paper, we present two approaches to analyzing pass event data to uncover sometimes-nonobvious insights into the game of soccer. We illustrate the utility of our methods by applying them to data from the 2012–2013 La Liga season. We first show that teams are characterized by where on the pitch they attempt passes, and can be identified by their passing styles. Using heatmaps of pass locations as features, we achieved a mean accuracy of 87% in a 20-team classification task. We also investigated using pass locations over the course of a possession to predict shots. For this task, we achieved an area under the receiver operating characteristic (AUROC) of 0.785. Finally, we used the weights of the predictive model to rank players by the value of their passes. Shockingly, Cristiano Ronaldo and Lionel Messi topped the rankings.
Formal verification of ethical choices in autonomous systems

Autonomous systems such as unmanned vehicles are beginning to operate within society. All participants in society are required to follow specific regulations and laws. An autonomous system cannot be an exception. Inevitably an autonomous system will find itself in a situation in which it needs to not only choose to obey a rule or not, but also make a complex ethical decision. However, there exists no obvious way to implement the human understanding of ethical behaviour in computers. Even if we enable autonomous systems to distinguish between more and less ethical alternatives, how can we be sure that they would choose right? We consider autonomous systems with a hybrid architecture in which the highest level of reasoning is executed by a rational (BDI) agent. For such a system, formal verification has been used successfully to prove that specific rules of behaviour are observed when making decisions. We propose a theoretical framework for ethical plan selection that can be formally verified. We implement a rational agent that incorporates a given ethical policy in its plan selection and show that we can formally verify that the agent chooses to execute, to the best of its beliefs, the most ethical available plan.
AC Voltage Regulation by Switch Mode Buck-Boost Voltage Controller

Voltage sag is a serious power quality problem affecting domestic, industrial and commercial customers. Voltage sags may either decrease or increase in the magnitude of system voltage due to faults or change in loads. In this paper a switch mode AC BuckBoost regulator is proposed to maintain voltage across a medium size domestic appliance constant during long period of voltage deviation from the rated value. Such deviation may occur due to change in load or change in input voltage due to voltage sag of the system itself.
Evolving Evolutionary Algorithms Using Linear Genetic Programming

A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
Trolls just want to have fun

In two online studies (total N = 1215), respondents completed personality inventories and a survey of their Internet commenting styles. Overall, strong positive associations emerged among online commenting frequency, trolling enjoyment, and troll identity, pointing to a common construct underlying the measures. Both studies revealed similar patterns of relations between trolling and the Dark Tetrad of personality: trolling correlated positively with sadism, psychopathy, and Machiavellianism, using both enjoyment ratings and identity scores. Of all personality measures, sadism showed the most robust associations with trolling and, importantly, the relationship was specific to trolling behavior. Enjoyment of other online activities, such as chatting and debating, was unrelated to sadism. Thus cyber-trolling appears to be an Internet manifestation of everyday sadism.
ElixirFM -- Implementation of Functional Arabic Morphology

Functional Arabic Morphology is a formulation of the Arabic inflectional system seeking the working interface between morphology and syntax. ElixirFM is its high-level implementation that reuses and extends the Functional Morphology library for Haskell. Inflection and derivation are modeled in terms of paradigms, grammatical categories, lexemes and word classes. The computation of analysis or generation is conceptually distinguished from the general-purpose linguistic model. The lexicon of ElixirFM is designed with respect to abstraction, yet is no more complicated than printed dictionaries. It is derived from the open-source Buckwalter lexicon and is enhanced with information sourcing from the syntactic annotations of the Prague Arabic Dependency Treebank.
LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity

Predicting the popularity dynamics of Twitter hashtags has a broad spectrum of applications. Existing works have primarily focused on modeling the popularity of individual tweets rather than the underlying hashtags. As a result, they fail to consider several realistic factors contributing to hashtag popularity. In this paper, we propose Large Margin Point Process (LMPP), a probabilistic framework that integrates hashtag-tweet influence and hashtaghashtag competitions, the two factors which play important roles in hashtag propagation. Furthermore, while considering the hashtag competitions, LMPP looks into the variations of popularity rankings of the competing hashtags across time. Extensive experiments on seven real datasets demonstrate that LMPP outperforms existing popularity prediction approaches by a significant margin. Additionally, LMPP can accurately predict the relative rankings of competing hashtags, offering additional advantage over the state-of-the-art baselines.