nils-herrmann/clean_scirepeval huggingface dataset:
Landscapes' Capacities to Provide Ecosystem Services - a Concept for Land-Cover Based Assessments

Landscapes differ in their capacities to provide ecosystem goods and services, which are the benefits humans obtain from nature. Structures and functions of ecosystems needed to sustain the provision of ecosystem services are altered by various human activities. In this paper, a concept for the assessment of multiple ecosystem services is proposed as a basis for discussion and further development of a respective evaluation instrument. Using quantitative and qualitative assessment data in combination with land cover and land use information originated from remote sensing and GIS, impacts of human activities can be evaluated. The results reveal typical patterns of different ecosystems‘ capacities to provide ecosystem services. The proposed approach thus delivers useful integrative information for environmental management and landscape planning, aiming at a sustainable use of services provided by nature. The research concept and methodological framework presented here for discussion have initially been applied in different case studies and shall be developed further to provide a useful tool for the quantification and spatial modelling of multiple ecosystem services in different landscapes. An exemplary application of the approach dealing with food provision in the Halle-Leipzig region in Germany is presented. It shows typical patterns of ecosystem service distribution around urban areas. As the approach is new and still rather general, there is great potential for improvement, especially with regard to a data-based quantification of the numerous hypotheses, which were formulated as base for the assessment. Moreover, the integration of more detailed landscape information on different scales will be needed in future in order to take the heterogeneous distribution of landscape properties and values into account. Therefore, the purpose of this paper is to foster critical discussions on the methodological development presented here.
Dual Circularly Polarized Broadside Beam Metasurface Antenna

This paper presents the design of a modulated metasurface (MTS) antenna capable to provide both right-hand (RH) and left-hand (LH) circularly polarized (CP) boresight radiation at Ku-band (13.5 GHz). This antenna is based on the interaction of two cylindrical-wavefront surface wave (SW) modes of transverse electric (TE) and transverse magnetic (TM) types with a rotationally symmetric, anisotropic-modulated MTS placed on top of a grounded slab. A properly designed centered circular waveguide feed excites the two orthogonal (decoupled) SW modes and guarantees the balance of the power associated with each of them. By a proper selection of the anisotropy and modulation of the MTS pattern, the phase velocities of the two modes are synchronized, and leakage is generated in broadside direction with two orthogonal linear polarizations. When the circular waveguide is excited with two mutually orthogonal TE11 modes in phase-quadrature, an LHCP or RHCP antenna is obtained. This paper explains the feeding system and the MTS requirements that guarantee the balanced conditions of the TM/TE SWs and consequent generation of dual CP boresight radiation.
Motion control of a novel spherical robot equipped with a flywheel

An omni-directional rolling spherical robot equipped with a high-rate flywheel (BYQ-V) is presented, the gyroscopic effects of high-rate flywheel can further enhance the dynamic stability of the spherical robot. This robot is designed for territory or lunar exploration in the future. The mechanical structure and control system of the robot are given particularly. Using the constrained Lagrangian method, the simplified dynamic model of the robot is derived under some assumptions, Moreover, a Linear Quadratic Regulator (LQR) controller and Percentage Derivative (PD) controller are designed to implement the pose and velocity control of the robot respectively, Finally, the dynamic model and the controllers are validated through simulation study and prototype experiment.
Solving stochastic differential equations and Kolmogorov equations by means of deep learning

Stochastic differential equations (SDEs) and the Kolmogorov partial differential equations (PDEs) associated to them have been widely used in models from engineering, finance, and the natural sciences. In particular, SDEs and Kolmogorov PDEs, respectively, are highly employed in models for the approximative pricing of financial derivatives. Kolmogorov PDEs and SDEs, respectively, can typically not be solved explicitly and it has been and still is an active topic of research to design and analyze numerical methods which are able to approximately solve Kolmogorov PDEs and SDEs, respectively. Nearly all approximation methods for Kolmogorov PDEs in the literature suffer under the curse of dimensionality or only provide approximations of the solution of the PDE at a single fixed space-time point. In this paper we derive and propose a numerical approximation method which aims to overcome both of the above mentioned drawbacks and intends to deliver a numerical approximation of the Kolmogorov PDE on an entire region [a, b]d without suffering from the curse of dimensionality. Numerical results on examples including the heat equation, the Black-Scholes model, the stochastic Lorenz equation, and the Heston model suggest that the proposed approximation algorithm is quite effective in high dimensions in terms of both accuracy and speed. 1 ar X iv :1 80 6. 00 42 1v 1 [ m at h. N A ] 1 J un 2 01 8
A study of link farm distribution and evolution using a time series of web snapshots

In this paper, we study the overall link-based spam structure and its evolution which would be helpful for the development of robust analysis tools and research for Web spamming as a social activity in the cyber space. First, we use strongly connected component (SCC) decomposition to separate many link farms from the largest SCC, so called the core. We show that denser link farms in the core can be extracted by node filtering and recursive application of SCC decomposition to the core. Surprisingly, we can find new large link farms during each iteration and this trend continues until at least 10 iterations. In addition, we measure the spamicity of such link farms. Next, the evolution of link farms is examined over two years. Results show that almost all large link farms do not grow anymore while some of them shrink, and many large link farms are created in one year.
Learning saliency-based visual attention: A review

Humans and other primates shift their gaze to allocate processing resources to a subset of the visual input. Understanding and emulating the way that human observers freeview a natural scene has both scientific and economic impact. It has therefore attracted the attention from researchers in a wide range of science and engineering disciplines. With the ever increasing computational power, machine learning has become a popular tool to mine human data in the exploration of how people direct their gaze when inspecting a visual scene. This paper reviews recent advances in learning saliency-based visual attention and discusses several key issues in this topic. & 2012 Elsevier B.V. All rights reserved.
Matrix and Tensor Factorization Methods for Natural Language Processing

Tensor and matrix factorization methods have attracted a lot of attention recently thanks to their successful applications to information extraction, knowledge base population, lexical semantics and dependency parsing. In the first part, we will first cover the basics of matrix and tensor factorization theory and optimization, and then proceed to more advanced topics involving convex surrogates and alternative losses. In the second part we will discuss recent NLP applications of these methods and show the connections with other popular methods such as transductive learning, topic models and neural networks. The aim of this tutorial is to present in detail applied factorization methods, as well as to introduce more recently proposed methods that are likely to be useful to NLP applications.
Proposal and Implementation of MPLS Fuzzy Traffic Monitor

Multiprotocol Label Switched Networks need highly intelligent controls to manage high volume traffic due to issues of traffic congestion and best path selection. The work demonstrated in this paper shows results from simulations for building optimal fuzzy based algorithm for traffic splitting and congestion avoidance. The design and implementation of Fuzzy based software defined networking is illustrated by introducing the Fuzzy Traffic Monitor in an ingress node. Finally, it displays improvements in the terms of mean delay (42.0%) and mean loss rate (2.4%) for Video Traffic. Then, the resu1t shows an improvement in the terms of mean delay (5.4%) and mean loss rate (3.4%) for Data Traffic and an improvement in the terms of mean delay(44.9%) and mean loss rate(4.1%) for Voice Traffic as compared to default MPLS implementation. Multiprotocol Label Switched Networks; Fuzzy Traffic Monitor; Network Simulator; Ingress; Traffic Splitting; Fuzzy Logic Control System; Label setup System; Traffic Splitting System
Design of Braun Multiplier with Kogge Stone Adder & It's Implementation on FPGA

Multiplication is the basic building block for several DSP processors, Image processing and many other. Over the years the computational complexities of algorithms used in Digital Signal Processors (DSPs) have gradually increased. This requires a parallel array multiplier to achieve high execution speed or to meet the performance demands. A typical implementation of such an array multiplier is Braun design. Braun multiplier is a type of parallel array multiplier. The architecture of Braun multiplier mainly consists of some Carry Save Adders, array of AND gates and one Ripple Carry Adder. In this research work, a new design of Braun Multiplier is proposed and this proposed design of multiplier uses a very fast parallel prefix adder ( Kogge Stone Adder) in place of Ripple Carry Adder. The architecture of standard Braun Multiplier is modified in this work for reducing the delay due to Ripple Carry Adder and performing faster multiplication of two binary numbers. This research also presents a comparative study of FPGA implementation on Spartan2 and Spartartan2E for new multiplier design and standard braun multiplier. The RTL design of proposed new Braun Multiplier and standard braun multiplier is done using Verilog HDL. The simulation is performed using ModelSim. The Xilinx ISE design tool is used for FPGA implementation. Comparative result shows the modified design is effective when compared in terms of delay with the standard design.
AN EFFICIENT COMBINATION OF MULTI-CHANNEL ACOUSTIC ECHO CANCELLATION WITH A BEAMFORMING MICROPHONE ARRAY

For hands-free man-machine audio interfaces with multi-channel sound reproduction and automatic speech recognition (ASR), both a multi-channel acoustic echo canceller (M-C AEC) and a beamforming microphone array are necessary for sufficient recognition rates. Based on known strategies for combining single-channel AEC and adaptive beamforming microphone arrays, we discuss special aspects for the extension to multi-channel AEC and propose an efficient system that can be implemented on a regular PC.
Risk assessment for automatic lane change maneuvers on highways

This paper presents a risk assessment algorithm for automatic lane change maneuvers on highways. It is capable of reliably assessing a given highway situation in terms of the possibility of collisions and robustly giving a recommendation for lane changes. The algorithm infers potential collision risks of observed vehicles based on Bayesian networks considering uncertainties of its input data. It utilizes two complementary risk metrics (time-to-collision and minimal safety margin) in temporal and spatial aspects to cover all risky situations that can occur for lane changes. In addition, it provides a robust recommendation for lane changes by filtering out uncertain noise data pertaining to vehicle tracking. The validity of the algorithm is tested and evaluated on public highways in real traffic as well as a closed high-speed test track in simulated traffic through in-vehicle testing based on overtaking and overtaken scenarios in order to demonstrate the feasibility of the risk assessment for automatic lane change maneuvers on highways.
Learning to Generate Structured Queries from Natural Language with Indirect Supervision

Generating structured query language (SQL) from natural language is an emerging research topic. This paper presents a new learning paradigm from indirect supervision of the answers to natural language questions, instead of SQL queries. This paradigm facilitates the acquisition of training data due to the abundant resources of question-answer pairs for various domains in the Internet, and expels the difficult SQL annotation job. An endto-end neural model integrating with reinforcement learning is proposed to learn SQL generation policy within the answerdriven learning paradigm. The model is evaluated on datasets of different domains, including movie and academic publication. Experimental results show that our model outperforms the baseline models.
[Gender differences in action Fenotropil and its structural analog--compound RGPU-95 on anxiety-depressive behavior animals].

Fenotropil and its structural analog--compound RGPU-95 to a greater extent reduce the severity of anxious and depressive behavior in male rats than in females. On expression of the anxiolytic compound RGPU-95 significantly exceeds Fenotropil, but inferior to Diazepam; of antidepressant activity--comparable to Melipramin and exceeds Fenotropil.
Number 4 , August 1988 Collision Detection and Response for Computer Animation

When several objects are moved about by computer animarion, there is the chance that they will interpenetrate. This is often an undesired state, particularly if the animation is seeking to model a realistic world. Two issues are involved: detecting that a collision has occurred, and responding to it. The former is fundamentally a kinematic problem, involving the positional relationship of objects in the world. The latter is a dynamic problem, in that it involves predicting behavior according to physical laws. This paper discusses collision detection and response in general, presents two collision detection algorithms, describes modeling collisions of arbitrary bodies using springs, and presents an analytical collision response algorithm for articulated rigid bodies that conserves linear and angular momentum.
EEG-based emotion recognition during watching movies

This study aims at finding the relationship between EEG signals and human emotions. EEG signals are used to classify two kinds of emotions, positive and negative. First, we extracted features from original EEG data and used a linear dynamic system approach to smooth these features. An average test accuracy of 87.53% was obtained by using all of the features together with a support vector machine. Next, we reduced the dimension of features through correlation coefficients. The top 100 and top 50 subject-independent features were achieved, with average test accuracies of 89.22% and 84.94%, respectively. Finally, a manifold model was applied to find the trajectory of emotion changes.
BitTorrent Darknets

A private BitTorrent site (also known as a "Bit Torrent darknet") is a collection of torrents that can only be accessed by members of the darknet community. The private BitTorrent sites also have incentive policies which encourage users to continue to seed files after completing downloading. Although there are at least 800 independent BitTorrent darknets in the Internet, they have received little attention in the research community to date. We examine BitTorrent darknets from macroscopic, medium-scopic and microscopic perspectives. For the macroscopic analysis, we consider 800+ private sites to obtain a broad picture of the darknet landscape, and obtain a rough estimate of the total number of files, accounts, and simultaneous peers within the entire darknet landscape. Although the size of each private site is relatively small, we find the aggregate size of the darknet landscape to be surprisingly large. For the medium-scopic analysis, we investigate content overlap between four private sites and the public BitTorrent ecosystem. For the microscopic analysis, we explore in-depth one private site and examine its user behavior. We observe that the seed-to-leecher ratios and upload-to-download ratios are much higher than in the public ecosystem. The macroscopic, medium-scopic and microscopic analyses when combined provide a vivid picture of the darknet landscape, and provide insight into how the darknet landscape differs from the public BitTorrent ecosystem.
Graph-based 2D road representation of 3D point clouds for intelligent vehicles

Comprehensive situational awareness is paramount to the effectiveness of proprietary navigational and higher-level functions of intelligent vehicles. In this paper, we address a graph-based approach for 2D road representation of 3D point clouds with respect to the road topography. We employ the gradient cues of the road geometry to construct a Markov Random Filed (MRF) and implement an efficient belief propagation (BP) algorithm to classify the road environment into four categories, i.e. the reachable region, the drivable region, the obstacle region and the unknown region. The proposed approach can overcome a wide variety of practical challenges, such as sloped terrains, rough road surfaces, rolling/pitching of the host vehicle, etc., and represent the road environment accurately as well as robustly. Experimental results in typical but challenging environments have substantiated that the proposed approach is more sensitive and reliable than the conventional vertical displacements analysis and show superior performance against other local classifiers.
Super-resolution Photoacoustic Microscopy Using Near-Field Localization by a Plasmonic Metal Nanoaperture: A Simulation Study

Super-resolution microscopy (SRM) is becoming increasingly important to study nanoscale biological structures. Two most widely used devices for SRM are super-resolution fluorescence microscopy (SRFM) and electron microscopy (EM). For biological living samples, however, SRFM is not preferred since it requires exogenous agents and EM is not preferred since vacuum is required for sample preparation. To overcome these limitations of EM and SFRM, we present a simulation study of super-resolution photoacoustic microscopy (SR-PAM). To break the diffraction limit of light, we investigated a sub-10 nm near-field localization by focusing femtosecond laser pulses under the plasmonic nanoaperture. Using this near-field localization as a light source, we numerically studied the feasibility of the SR-PAM with a k-wave simulation toolbox in MATLAB. In this photoacoustic simulation, we successfully confirmed that the SR-PAM could be a potential method to resolve and image nanoscale structures.
An Effective Verification Strategy for Testing Distributed Automotive Embedded Software Functions: A Case Study

The share and importance of software within automotive vehicles is growing steadily. Most functionalities in modern vehicles, especially safety related functions like advanced emergency braking, are controlled by software. A complex and common phenomenon in today’s automotive vehicles is the distribution of such software functions across several Electronic Control Units (ECUs) and consequently across several ECU system software modules. As a result, integration testing of these distributed software functions has been found to be a challenge. The automotive industry neither has infinite resources, nor has the time to carry out exhaustive testing of these functions. On the other hand, the traditional approach of implementing an ad-hoc selection of test scenarios based on the tester’s experience, can lead to test gaps and test redundancies. Hence, there is a pressing need within the automotive industry for a feasible and effective verification strategy for testing distributed software functions.  Firstly, to identify the current approach used to test the distributed automotive embedded software functions in literature and in a case company. Secondly, propose and validate a feasible and effective verification strategy for testing the distributed software functions that would help improve test coverage while reducing test redundancies and test gaps.  To accomplish the objectives, a case study was conducted at Scania CV AB, Södertälje, Sweden. One of the data collection methods was through conducting interviews of different employees involved in the software testing activities. Based on the research objectives, an interview questionnaire with open-ended and close-ended questions has been used. Apart from interviews, data from relevant artifacts in databases and archived documents has been used to achieve data triangulation. Moreover, to further strengthen the validity of the results obtained, adequate literature support has been presented throughout. Towards the end, a verification strategy has been proposed and validated using existing historical data at Scania.  The proposed verification strategy to test distributed automotive embedded software functions has given promising results by providing means to identify test gaps and test redundancies. It helps establish an effective and feasible approach to capture function test coverage information that helps enhance the effectiveness of integration testing of the distributed software functions.
A CRF Based Machine Learning Approach for Biomedical Named Entity Recognition

The amount of biomedical textual information available in the web becomes more and more. It is very difficult to extract the right information that users are interested in considering the size of documents in the biomedical literatures and databases. It is nearly impossible for human to process all these data and it is even difficult for computers to extract the information since it is not stored in structured format. Identifying the named entities and classifying them can help in extracting the useful information in the unstructured text documents. This paper presents a new method of utilizing biomedical knowledge by both exact matching of disease dictionary and adding semantic concept feature through UMLS semantic type filtering, in order to improve the human disease named entity recognition by machine learning. By engineering the concept semantic type into feature set, we demonstrate the importance of domain knowledge on machine learning based disease NER. The background knowledge enriches the representation of named entity and helps to disambiguate terms in the context thereby improves the overall NER performance.
A comparison of methods for sketch-based 3D shape retrieval

1077-3142/$ see front matter 2013  This paper has been recommended for acceptance by Nicu Sebe. ⇑ Corresponding author. Address: 601 University Drive, Department of Computer Science, Texas State University, San Marcos, TX 78666, United States.  E-mail address: lu@txstate.edu (Y. Lu). Bo Li , Yijuan Lu a,⇑, Afzal Godil , Tobias Schreck , Benjamin Bustos , Alfredo Ferreira , Takahiko Furuya , Manuel J. Fonseca , Henry Johan , Takahiro Matsuda , Ryutarou Ohbuchi , Pedro B. Pascoal , Jose M. Saavedra d,