nils-herrmann/clean_scirepeval huggingface dataset:
A High-Voltage Pulse Generator for Corona Plasma Generation

This paper discusses a high-voltage pulse generator for producing corona plasma. The generator consists of three resonant charging circuits, a transmission line transformer, and a triggered spark-gap switch. Voltage pulses in the order of 30–100 kV with a rise time of 10–20 ns, a pulse duration of 100–200 ns, a pulse repetition rate of 1–900 pps, an energy per pulse of 0.5–12 J, and the average power of up to 10 kW have been achieved with total energy conversion efficiency of 80%–90%. Moreover, the system has been used in four industrial demonstrations on volatile organic compounds removal, odor emission control, and biogas conditioning.
Maturity Models in Supply Chain Sustainability : A Systematic Literature Review

A systematic literature review of supply chain maturity models with sustainability concerns is presented. The objective is to give insights into methodological issues related to maturity models, namely the research objectives; the research methods used to develop, validate and test them; the scope; and the main characteristics associated with their design. The literature review was performed based on journal articles and conference papers from 2000 to 2015 using the SCOPUS, Emerald Insight, EBSCO and Web of Science databases. Most of the analysed papers have as main objective the development of maturity models and their validation. The case study is the methodology that is most widely used by researchers to develop and validate maturity models. From the sustainability perspective, the scope of the analysed maturity models is the Triple Bottom Line (TBL) and environmental dimension, focusing on a specific process (eco-design and new product development) and without a broad SC perspective. The dominant characteristics associated with the design of the maturity models are the maturity grids and a continuous representation. In addition, results do not allow identifying a trend for a specific number of maturity levels. The comprehensive review, analysis, and synthesis of the maturity model literature represent an important contribution to the organization of this research area, making possible to clarify some confusion that exists about concepts, approaches and components of maturity models in sustainability. Various aspects associated with the maturity models (i.e., research objectives, research methods, scope and characteristics of the design of models) are explored to contribute to the evolution and significance of this multidimensional area.
New approach for liveness detection in fingerprint scanners based on valley noise analysis

Recently, research has shown that it is possible to spoof a variety of fingerprint scanners using some simple techniques with molds made from plastic, clay, Play-Doh, silicone or gelatin materials. To protect against spoofing, methods of liveness detection measure physiological signs of life from fingerprints ensuring only live fingers are captured for enrollment or authentication. In this paper, a new liveness detection method is proposed which is based on noise analysis along the valleys in the ridge-valley structure of fingerprint images. Unlike live fingers which have a clear ridge-valley structure, artificial fingers have a distinct noise distribution due to the material’s properties when placed on a fingerprint scanner. Statistical features are extracted in multiresolution scales using wavelet decomposition technique. Based on these features, liveness separation (live/non-live) is performed using classification trees and neural networks. We test this method on the dataset which contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different scanners. Also, we test this method on a second dataset which contains 28 live and 28 spoof (made from silicone) subjects. Results show that we can get approximately 90.9-100% classification of spoof and live fingerprints. The proposed liveness detection method is purely software based and application of this method can provide anti-spoofing protection for fingerprint scanners.
Cryo-CMOS for quantum computing

Cryogenic CMOS, or cryo-CMOS circuits and systems, are emerging in VLSI design for many applications, in primis quantum computing. Fault-tolerant quantum bits (qubits) in surface code configurations, one of the most accepted implementations in quantum computing, operate in deep sub-Kelvin regime and require scalable classical control circuits. In this paper we advocate the need for a new generation of deep-submicron CMOS circuits operating at deep-cryogenic temperatures to achieve the performance required in a fault-tolerant qubit system. We outline the challenges and limitations of operating CMOS in near-zero Kelvin regimes and we propose solutions. The paper concludes with several examples showing the suitability of integrating fault-tolerant.qubits with CMOS.
Rules of play: game design fundamentals

Why should wait for some days to get or receive the rules of play game design fundamentals book that you order? Why should you take it if you can get the faster one? You can find the same book that you order right here. This is it the book that you can receive directly after purchasing. This rules of play game design fundamentals is well known book in the world, of course many people will try to own it. Why don't you become the first? Still confused with the way?
Friction Stir Welding of Aluminum Alloys : A Review

Friction stir welding is a solid state joining process. High strength aluminum alloys are widely used in aircraft and marine industries. Generally, the mechanical properties of fusion welded aluminum joints are poor. As friction stir welding occurs in solid state, no solidification structures are created thereby eliminating the brittle and eutectic phases common in fusion welding of high strength aluminum alloys. In this review the process parameters, microstructural evolution, and effect of friction stir welding on the properties of weld specific to aluminum alloys have been discussed. Aluminum alloys, Friction stir welding (FSW), Microstructure, Properties.
An Attention-based BI-GRU-CapsNet Model for Hypernymy Detection between Compound Entities

Named entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general entities, we name them compound entities. In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model consists of several important components. To avoid the out-of-vocabulary problem, English words or Chinese characters in compound entities are fed into the bidirectional gated recurrent units. An attention mechanism is designed to focus on the differences between two compound entities. Since there are some different cases in hypernymy relationship between compound entities, capsule network is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate the advantages of our model over the state-of-theart methods both on English and Chinese corpora of symptom and disease pairs.
Cable suspended planar robots with redundant cables: controllers with positive tensions

Cable-suspended robots are structurally similar to parallel actuated robots but with the fundamental difference that cables can only pull the end-effector but not push it. From a scientific point of view, this feature makes feedback control of cable-suspended robots more challenging than their counterpart parallel-actuated robots. In the case with redundant cables, feedback control laws can be designed to make all tensions positive while attaining desired control performance. This paper presents approaches to design positive tension controllers for cable suspended robots with redundant cables. Their effectiveness is demonstrated through simulations and experiments on a three degree-of-freedom cable suspended robots.
Quantitative Risk Management

Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives.
CHAPTER 14 Security and Fraud Detection in Mobile and Wireless Networks

The fusion of computer and telecommunication technologies has heralded the age of information superhighway over wireline and wireless networks. Mobile cellular communication systems and wireless networking technologies are growing at an ever-faster rate, and this is likely to continue in the foreseeable future. Wireless technology is presently being used to link portable computer equipment to corporate distributed computing and other sources of necessary information. Wide-area cellular systems and wireless LANs promise to make integrated networks a reality and provide fully distributed and ubiquitous mobile communications, thus bringing an end to the tyranny of geography. Higher reliability, better coverage and services, higher capacity, mobility management, power and complexity for channel acquisition, handover decisions, security management, and wireless multimedia are all parts of the potpourri. Further increases in network security are necessary before the promise of mobile telecommunication can be fulfilled. Safety and security management against fraud, intrusions, and cloned mobile phones, just to mention a few, will be one of the major issues in the next wireless and mobile generations. A “safe” system provides protection against errors of trusted users, whereas a “secure” system protects against errors introduced by impostors and untrusted users . Therefore, rather than ignoring the security concerns of potential users, merchants, and telecommunication companies need to acknowledge these concerns and deal with them in a straightforward manner. Indeed, in order to convince the public to use mobile and wireless technology in the next and future generations of wireless systems, telecom companies and all organizations will need to explain how they have addressed the security of their mobile/wireless systems. Manufacturers, M-business, service providers, and entrepreneurs who can visualize this monumental change and effectively leverage their experiences on both wireless and Internet will stand to benefit from it. Concerns about network security in general (mobile and wired) are growing, and so is research to match these growing concerns. Indeed, since the seminal work by D. Denning  in 1981, many intrusion-detection prototypes, for instance, have been created. Intrusion-detection systems aim at detecting attacks against computer systems and wired net-
Spectral State Compression of Markov Processes

Model reduction of the Markov process is a basic problem in modeling statetransition systems. Motivated by the state aggregation approach rooted in control theory, we study the statistical state compression of a finite-state Markov chain from empirical trajectories. Through the lens of spectral decomposition, we study the rank and features of Markov processes, as well as properties like representability, aggregatability and lumpability. We develop a class of spectral state compression methods for three tasks: (1) estimate the transition matrix of a low-rank Markov model, (2) estimate the leading subspace spanned by Markov features, and (3) recover latent structures of the state space like state aggregation and lumpable partition. The proposed methods provide an unsupervised learning framework for identifying Markov features and clustering states. We provide upper bounds for the estimation errors and nearly matching minimax lower bounds. Numerical studies are performed on synthetic data and a dataset of New York City taxi trips. ∗Anru Zhang is with the Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, ; Mengdi Wang is with the Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544, E-mail: mengdiw@princeton.edu.
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, which hinders their wide deployment to many resource restricted usages such as usages on always-on devices, battery-powered low-end devices, etc. This paper considers the resource and accuracy trade-off for resource-restricted usages during designing the whole object detection framework. Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD introduces two innovative and ultra-efficient architecture blocks: depthwise dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN) based front-end. We conduct extensive experiments on three famous benchmarks (PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO, MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD outperforms these solutions in all the three metrics (parameter-size, FLOPs, accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts result with such a low resource requirement.∗
Generation of multi-LOD 3D city models in CityGML with the procedural modelling engine Random3Dcity

The production and dissemination of semantic 3D city models is rapidly increasing benefiting a growing number of use cases. However, their availability in multiple LODs and in the CityGML format is still problematic in practice. This hinders applications and experiments where multi-LOD datasets are required as input, for instance, to determine the performance of different LODs in a spatial analysis. An alternative approach to obtain 3D city models is to generate them with procedural modelling, which is—as we discuss in this paper— well suited as a method to source multi-LOD datasets useful for a number of applications. However, procedural modelling has not yet been employed for this purpose. Therefore, we have developed RANDOM3DCITY, an experimental procedural modelling engine for generating synthetic datasets of buildings and other urban features. The engine is designed to produce models in CityGML and does so in multiple LODs. Besides the generation of multiple geometric LODs, we implement the realisation of multiple levels of spatiosemantic coherence, geometric reference variants, and indoor representations. As a result of their permutations, each building can be generated in 392 different CityGML representations, an unprecedented number of modelling variants of the same feature. The datasets produced by RANDOM3DCITY are suited for several applications, as we show in this paper with documented uses. The developed engine is available under an open-source licence at Github at http://github.com/tudelft3d/Random3Dcity.
Map-matching algorithm for large-scale low-frequency floating car data

Map-matching algorithm for large-scale low-frequency floating car data Bi Yu Chen, Hui Yuan, Qingquan Li, William H.K. Lam, ShihLung Shaw & Ke Yan a State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China b School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China c Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, China d Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong e Department of Geography, The University of Tennessee, Knoxville, TN 37996-0925, USA f International School of Software, Wuhan University, Wuhan 430079, China Published online: 02 Sep 2013.
RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving

For autonomous driving, vehicle detection is the prerequisite for many tasks like collision avoidance and path planning. In this letter, we present a real-time three-dimensional (RT3D) vehicle detection method that utilizes pure LiDAR point cloud to predict the location, orientation, and size of vehicles. In contrast to previous 3-D object detection methods, we used a pre-RoIpooling convolution technique that moves a majority of the convolution operations to ahead of the RoI pooling, leaving just a small part behind, so that significantly boosts the computation efficiency. We also propose a pose-sensitive feature map design which can be strongly activated by the relative poses of vehicles, leading to a high regression accuracy on the location, orientation, and size of vehicles. Experiments on the KITTI benchmark dataset show that the RT3D is not only able to deliver competitive detection accuracy against state-of-the-art methods, but also the first LiDAR-based 3-D vehicle detection work that completes detection within 0.09 s which is even shorter than the scan period of mainstream LiDAR sensors.
End-to-end Learning of Multi-sensor 3D Tracking by Detection

In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.
APPROXIMATE METHOD FOR PERFORMANCE-BASED SEISMIC ASSESSMENT OF STEEL MOMENT-RESISTING FRAMES

A wide range of approximate methods has been historically proposed for performance-based assessment of frame buildings in the aftermath of an earthquake. Most of these methods typically require a detailed analytical model representation of the respective building in order to assess its seismic vulnerability and post-earthquake functionality. This paper proposes an approximate method for estimating story-based engineering demand parameters (EDPs) such as peak story drift ratios, peak floor absolute accelerations, and residual story drift ratios in steel frame buildings with steel moment-resisting frames (MRFs). The proposed method is based on concepts from structural health monitoring, which does not require the use of detailed analytical models for structural and non-structural damage diagnosis. The proposed method is able to compute story-based EDPs in steel frame buildings with MRFs with reasonable accuracy. Such EDPs can facilitate damage assessment/control as well as building-specific seismic loss assessment. The proposed method is utilized to assess the extent of structural damage in an instrumented steel frame building that experienced the 1994 Northridge earthquake.
The Language Experience and Proficiency Questionnaire (LEAP-Q): assessing language profiles in bilinguals and multilinguals.

To develop a reliable and valid questionnaire of bilingual language status with predictable relationships between self-reported and behavioral measures.



In Study 1, the internal validity of the Language Experience and Proficiency Questionnaire (LEAP-Q) was established on the basis of self-reported data from 52 multilingual adult participants. In Study 2, criterion-based validity was established on the basis of standardized language tests and self-reported measures from 50 adult Spanish-English bilinguals. Reliability and validity of the questionnaire were established on healthy adults whose literacy levels were equivalent to that of someone with a high school education or higher.



Factor analyses revealed consistent factors across both studies and suggested that the LEAP-Q was internally valid. Multiple regression and correlation analyses established criterion-based validity and suggested that self-reports were reliable indicators of language performance. Self-reported reading proficiency was a more accurate predictor of first-language performance, and self-reported speaking proficiency was a more accurate predictor of second-language performance. Although global measures of self-reported proficiency were generally predictive of language ability, deriving a precise estimate of performance on a particular task required that specific aspects of language history be taken into account.



The LEAP-Q is a valid, reliable, and efficient tool for assessing the language profiles of multilingual, neurologically intact adult populations in research settings.
The dynamic analysis of WannaCry ransomware

The global ransomware cyberattacks cripples the national hospital system across the United Kingdom, and causes waves of appointments and operations to be cancelled. Similar attacking methods have come to sweep over the world. Such trend of highprofile cyberattack sheds the lights on rapid defence through the malware information sharing platform. A complete malware analysis process is quite a time-consuming campaign. The dynamic analysis of WannaCry ransomware explores behavioural indicators and extracts important IOCs (Indicators of Compromise). Utilizing Yara tool to create customized patterns is useful for malware information sharing mechanism. Also, such mechanism help reduce time and human resource spent on detecting or finding similar malware families. We aim to generate effective cyber threat intelligence by formulating collected IOCs into structured formations. The positive effects show on immediate defensive response to security breaches, and meanwhile the integrated information security protection is consolidated.
Developing MOBA games using the Unity game engine

MOBA (Multiplayer Online Battle Arena) games are currently one of the most popular online video game genres. This paper discusses implementation of a typical MOBA game prototype for Windows platform in a popular game engine Unity 5. The focus is put on using the built-in Unity components in a MOBA setting, developing additional behaviours using Unity's Scripting API for C# and integrating third party components such as the networking engine, 3D models, and particle systems created for use with Unity and available through the Unity Asset Store. A brief overview of useful programming design patterns as well as design patterns already used in Unity is given. Various game state synchronization mechanisms available in the chosen networking engine, Photon Unity Networking, and their usage when synchronizing different types of game information over multiple clients are also discussed. The implemented game retains most of the main features of the modern MOBA games such as heroes with different play styles, skills, team versus team competition, resource collection and consumption, varied maps and defensive structures. The paper concludes with comments on Unity 5 as a MOBA game development environment and execution engine.