nils-herrmann/clean_scirepeval huggingface dataset:
ZStream: a cost-based query processor for adaptively detecting composite events

Composite (or Complex) event processing (CEP) systems search sequences of incoming events for occurrences of user-specified event patterns. Recently, they have gained more attention in a variety of areas due to their powerful and expressive query language and performance potential. Sequentiality (temporal ordering) is the primary way in which CEP systems relate events to each other. In this paper, we present a CEP system called ZStream to efficiently process such sequential patterns. Besides simple sequential patterns, ZStream is also able to detect other patterns, including conjunction, disjunction, negation and Kleene closure.
 Unlike most recently proposed CEP systems, which use non-deterministic finite automata (NFA's) to detect patterns, ZStream uses tree-based query plans for both the logical and physical representation of query patterns. By carefully designing the underlying infrastructure and algorithms, ZStream is able to unify the evaluation of sequence, conjunction, disjunction, negation, and Kleene closure as variants of the join operator. Under this framework, a single pattern in ZStream may have several equivalent physical tree plans, with different evaluation costs. We propose a cost model to estimate the computation costs of a plan. We show that our cost model can accurately capture the actual runtime behavior of a plan, and that choosing the optimal plan can result in a factor of four or more speedup versus an NFA based approach. Based on this cost model and using a simple set of statistics about operator selectivity and data rates, ZStream is able to adaptively and seamlessly adjust the order in which it detects patterns on the fly. Finally, we describe a dynamic programming algorithm used in our cost model to efficiently search for an optimal query plan for a given pattern.
Extrinsic Camera Parameter Estimation Based-on Feature Tracking and GPS Data

This paper describes a novel method for estimating extrinsic camera parameters using both feature points on an image sequence and sparse position data acquired by GPS. Our method is based on a structure-from-motion technique but is enhanced by using GPS data so as to minimize accumulative estimation errors. Moreover, the position data are also used to remove mis-tracked features. The proposed method allows us to estimate extrinsic parameters without accumulative errors even from an extremely long image sequence. The validity of the method is demonstrated through experiments of estimating extrinsic parameters for both synthetic and real outdoor scenes.
RWTH-PHOENIX-Weather: A Large Vocabulary Sign Language Recognition and Translation Corpus

This paper introduces the RWTH-PHOENIX-Weather corpus, a video-based, large vocabulary corpus of German Sign Language suitable for statistical sign language recognition and translation. In contrast to most available sign language data collections, the RWTH-PHOENIX-Weather corpus has not been recorded for linguistic research but for the use in statistical pattern recognition. The corpus contains weather forecasts recorded from German public TV which are manually annotated using glosses distinguishing sign variants, and time boundaries have been marked on the sentence and the gloss level. Further, the spoken German weather forecast has been transcribed in a semi-automatic fashion using a state-of-the-art automatic speech recognition system. Moreover, an additional translation of the glosses into spoken German has been created to capture allowable translation variability. In addition to the corpus, experimental baseline results for hand and head tracking, statistical sign language recognition and translation are presented.
Islamic star patterns from polygons in contact

We present a simple method for rendering Islamic star patterns based on Hankin’s “polygons-in-contact” technique. The method builds star patterns from a tiling of the plane and a small number of intuitive parameters. We show how this method can be adapted to construct Islamic designs reminiscent of Huff’s parquet deformations. Finally, we introduce a geometric transformation on tilings that expands the range of patterns accessible using our method. This transformation simplifies construction techniques given in previous work, and clarifies previously unexplained relationships between certain classes of star patterns.
Collateral , Type of Lender and Relationship Banking as Determinants of Credit Risk

This paper analyses the determinants of the probability of default (PD) of bank loans. We focus the discussion on the role of a limited set of variables (collateral, type of lender and bank-borrower relationship) while controlling for the other explanatory variables. The study uses information on the more than three million loans entered into by Spanish credit institutions over a complete business cycle (1988 to 2000) collected by the Bank of Spain’s Credit Register (Central de Información de Riesgos). We find that collateralised loans have a higher PD, loans granted by savings banks are riskier and, finally, that a close bank-borrower relationship increases the willingness to take more risk.
Detecting highly overlapping community structure by greedy clique expansion

In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that the existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introduce a new community assignment algorithm called Greedy Clique Expansion (GCE). The algorithm identifies distinct cliques as seeds and expands these seeds by greedily optimizing a local fitness function. We perform extensive benchmarks on synthetic data to demonstrate that GCE’s good performance is robust across diverse graph topologies. Significantly, GCE is the only algorithm to perform well on these synthetic graphs, in which every node belongs to multiple communities. Furthermore, when put to the task of identifying functional modules in protein interaction data, and college dorm assignments in Facebook friendship data, we find that GCE performs competitively.
Feature Detection with Automatic Scale Selection

The fact that objects in the world appear in different ways depending on the scale of observation has important implications if one aims at describing them. It shows that the notion of scale is of utmost importance when processing unknown measurement data by automatic methods. In their seminal works, Witkin (1983) and Koenderink (1984) proposed to approach this problem by representing image structures at different scales in a so-called scale-space representation. Traditional scale-space theory building on this work, however, does not address the problem of how to select local appropriate scales for further analysis. This article proposes a systematic methodology for dealing with this problem. A framework is presented for generating hypotheses about interesting scale levels in image data, based on a general principle stating that local extrema over scales of different combinations of γ-normalized derivatives are likely candidates to correspond to interesting structures. Specifically, it is shown how this idea can be used as a major mechanism in algorithms for automatic scale selection, which adapt the local scales of processing to the local image structure. Support for the proposed approach is given in terms of a general theoretical investigation of the behaviour of the scale selection method under rescalings of the input pattern and by integration with different types of early visual modules, including experiments on real-world and synthetic data. Support is also given by a detailed analysis of how different types of feature detectors perform when integrated with a scale selection mechanism and then applied to characteristic model patterns. Specifically, it is described in detail how the proposed methodology applies to the problems of blob detection, junction detection, edge detection, ridge detection and local frequency estimation. In many computer vision applications, the poor performance of the low-level vision modules constitutes a major bottleneck. It is argued that the inclusion of mechanisms for automatic scale selection is essential if we are to construct vision systems to automatically analyse complex unknown environments.
Slotless Bearingless Disk Drive for High-Speed and High-Purity Applications

In this paper, a bearingless drive for high-speed applications with high purity and special chemical demands is introduced. To achieve high rotational speeds with low losses, a slotless bearingless disk drive with toroidal windings is used. We present the working principle of the bearingless drive as well as a model for calculating the achievable drive torque. An advantageous winding system for independent force and torque generation is proposed, which can be realized with standard inverter technology. Additionally, the winding inductances are examined to evaluate the dynamic properties of bearing and drive. The findings are verified with simulation results and the system performance is successfully demonstrated on an experimental prototype, which runs up to 20 000 rpm and is designed for an output power of 1 kW.
IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m of urban area in total.
Circularly Polarized Patch Antenna for Future 5G Mobile Phones

A circularly polarized patch antenna for future fifth-generation mobile phones is presented in this paper. Miniaturization and beamwidth enhancement of a patch antenna are the two main areas to be discussed. By folding the edge of the radiating patch with loading slots, the size of the patch antenna is 44.8% smaller than a conventional half wavelength patch, which allows it to be accommodated inside handsets easily. Wide beamwidth is obtained by surrounding the patch with a dielectric substrate and supporting the antenna by a metallic block. A measured half power beamwidth of 124° is achieved. The impedance bandwidth of the antenna is over 10%, and the 3-dB axial ratio bandwidth is 3.05%. The proposed antenna covers a wide elevation angle and complete azimuth range. A parametric study of the effect of the metallic block and the surrounding dielectric substrate on the gain at a low elevation angle and the axial ratio of the proposed antenna are presented.
OntoVPA - An Ontology-Based Dialogue Management System for Virtual Personal Assistants

Dialogue management (DM) is a difficult problem. We present OntoVPA, an Ontology-Based Dialogue Management System (DMS) for Virtual Personal Assistants (VPA’s). The features of OntoVPA are offered as potential solutions to core DM problems. We illustrate OntoVPA’s solutions to these problems by means of a running VPA example domain. To the best of our knowledge, OntoVPA is the first commercially available, fully implemented DMS that employs ontologies, reasoning, and ontology-based rules for a) domain model representation and reasoning, b) dialogue representation and state tracking, and c) response generation. OntoVPA is a declarative, knowledge-based system which, consequently, can be customized to a new VPA domain by swapping in and out ontologies and rule bases, with very little to no conventional programming required. OntoVPA relies on its domainindependent (generic), but dialogue-specific upper-level ontologies and DM rules, which are implementing typical, re-occurring (and usually expensive to address) dialogue system core capabilities, such as anaphora (coreference) resolution, slotfilling, inquiring about missing slot values, and so on. We argue that ontologies and ontology-based rules provide a flexible and expressive framework for realizing DMS’s for VPA’s, with a potential to significantly reduce development time.
The challenge of simultaneous object detection and pose estimation: A comparative study

Detecting objects and estimating their pose remains as one of the major challenges of the computer vision research community. There exists a compromise between localizing the objects and estimating their viewpoints. The detector ideally needs to be viewinvariant, while the pose estimation process should be able to generalize towards the category-level. This work is an exploration of using deep learning models for solving both problems simultaneously. For doing so, we propose three novel deep learning architectures, which are able to perform a joint detection and pose estimation, where we gradually decouple the two tasks. We also investigate whether the pose estimation problem should be solved as a classification or regression problem, being this still an open question in the computer vision community. We detail a comparative analysis of all our solutions and the methods that currently define the state of the art for this problem. We use PASCAL3D+ and ObjectNet3D datasets to present the thorough experimental evaluation and main results. With the proposed models we achieve the state-of-the-art performance in both datasets.
Sentiment Analysis and Subjectivity

Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.
Vehicle Detection and Tracking in Car Video Based on Motion Model

This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. We probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. We have investigated videos of day and night on different types of roads, showing that our approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras.
Polarization Reconfigurable Aperture-Fed Patch Antenna and Array

This paper presents a reconfigurable aperture-fed patch antenna array for ±45° polarizations. First, a new method to realize the reconfigurable ±45° polarizations is proposed. It introduces controllable RF switches on a cross-aperture to excite a square patch for two orthogonal polarizations. The RF switches are controlled by two sets of DC biases, which could select the polarization through the reconfigurable aperture. Second, two patch antennas based on cross-aperture excitation are discussed. The first structure uses a split ground plane with four switches, while the other one employs a united ground plane with eight switches. Both antennas operate well as the single element. However, only the antenna with a united ground is suitable for developing a reconfigurable dual-polarized antenna array, owning to its simple DC biasing lines and the reconfigurable-controlling network. Both simulation and experiment studies were examined to verify the proposed design. The measured 10-dB impedance bandwidth of the array is 9.3%, which can cover the 2.4-GHz WLAN band. The desired radiation patterns were observed with a maximum gain of 13.5 dBi. The proposed antenna array has low cross-polarization and stable gain across the entire operating bandwidth.
Mining Twitter Data with Resource Constraints

Social media analysis constitutes a scientific field that is rapidly gaining ground due to its numerous research challenges and practical applications, as well as the unprecedented availability of data in real time. Several of these applications have significant social and economical impact, such as journalism, crisis management, advertising, etc. However, two issues regarding these applications have to be confronted. The first one is the financial cost. Despite the abundance of information, it typically comes at a premium price, and only a fraction is provided free of charge. For example, Twitter, a predominant social media online service, grants researchers and practitioners free access to only a small proportion (1%) of its publicly available stream. The second issue is the computational cost. Even when the full stream is available, off the shelf approaches are unable to operate in such settings due to the real-time computational demands. Consequently, real world applications as well as research efforts that exploit such information are limited to utilizing only a subset of the available data. In this paper, we are interested in evaluating the extent to which analytical processes are affected by the aforementioned limitation. In particular, we apply a plethora of analysis processes on two subsets of Twitter public data, obtained through the service's sampling API's. The first one is the default 1% sample, whereas the second is the Garden hose sample that our research group has access to, returning 10% of all public data. We extensively evaluate their relative performance in numerous scenarios.
Metric Learning and Manifolds Metric Learning and Manifolds : Preserving the Intrinsic Geometry

A variety of algorithms exist for performing non-linear dimension reduction, but these algorithms do not preserve the original geometry of the data except in special cases. In general, in the low-dimensional representations obtained, distances are distorted, as well as angles, areas, etc. This paper proposes a generic method to estimate the distortion incurred at each point of an embedding, and subsequently to correct distances and other intrinsic geometric quantities back to their original values (up to sampling noise). Our approach is based on augmenting the output of an embedding algorithm with geometric information embodied in the Riemannian metric of the manifold. The Riemannian metric allows one to compute geometric quantities (such as angle, length, or volume) for any coordinate system or embedding of the manifold. In this work, we provide an algorithm for estimating the Riemannian metric from data, consider its consistency, and demonstrate the uses of our approach in a variety of examples.
Less guilty by reason of adolescence: developmental immaturity, diminished responsibility, and the juvenile death penalty.

The authors use a developmental perspective to examine questions about the criminal culpability of juveniles and the juvenile death penalty. Under principles of criminal law, culpability is mitigated when the actor's decision-making capacity is diminished, when the criminal act was coerced, or when the act was out of character. The authors argue that juveniles should not be held to the same standards of criminal responsibility as adults, because adolescents' decision-making capacity is diminished, they are less able to resist coercive influence, and their character is still undergoing change. The uniqueness of immaturity as a mitigating condition argues for a commitment to a legal environment under which most youths are dealt with in a separate justice system and none are eligible for capital punishment.
Reverse Engineering of Network Signatures

Network-based intrusion detection systems analyze networ k traffic looking for evidence of attacks. The analysis is usually performed using signatures, which are rules that describe what traffic should be conside red as malicious. If the signatures are known, it is possible to either craft an attack to avoid detection or to send synthetic traffic that will match the signature to over-stimulate the network sens or causing a denial of service attack. To prevent these attacks, commercial systems usually do not publish their si gnature sets and their analysis algorithms. This paper describes a reverse engineering process and a reverse engin e ring tool that are used to analyze the way signatures are matched by network-based intrusion detection systems. The results of the analysis are used to either generate variations of attacks that evade detection or produce non-m alicious traffic that over-stimulates the sensor. This shows that security through obscurity does not work. That is , keeping the signatures secret does not necessarily increase the resistance of a system to evasion and over-stim ula on attacks.
Design, simulation and implementation of a self-oscillating control circuit to drive series resonant inverter feeding a brazing induction furnace

This research deals with the design and simulation of induction furnace power source (inverter) using MATLAB package. This source designed to lock on the resonant frequency of the load by using self-oscillating technique, also it has the capability to control the power supplied to the load using phase shift pulse width modulation (PSPWM) technique. These characteristics used to overcome the load nonlinear behavior during the brazing process and to achieve soft switching of the inverter elements. Also, the inverter has the capability to operate with or without load (workpiece). The implemented prototype operates at a frequency range (50-100)kHz and 10kW was successfully used for brazing two copper workpieces.