nils-herrmann/clean_scirepeval huggingface dataset:
Numerical Solution of the Navier-Stokes Equations

A finite-difference method for solving the time-dependent NavierStokes equations for an incompressible fluid is introduced. This method uses the primitive variables, i.e. the velocities and the pressure, and is equally applicable to problems in two and three space dimensions. Test problems are solved, and an application to a three-dimensional convection problem is presented.  The equations of motion of an incompressible fluid are dtUi 4UjdjUi = — — dip + vV2Ui + Ei} ( V2 = Yl d2 ) , djUj = 0 , PO \ 3 ' where Ui are the velocity components, p is the pressure, p0 is the density, Ei are the components of the external forces per unit mass, v is the coefficient of kinematic viscosity, t is the time, and the indices i, j refer to the space coordinates Xi, x¡, i, j = 1, 2, 3. d, denotes differentiation with respect to Xi, and dt differentiation with respect to the time t. The summation convention is used in writing the equations. We write , Uj , Xj , _ ( d \ Ui u ' Xi " d ' p \povur
Video Segmentation with Background Motion Models

Many of today’s most successful video segmentation methods use long-term feature trajectories as their first processing step. Such methods typically use spectral clustering to segment these trajectories, implicitly assuming that motion is translational in image space. In this paper, we explore the idea of explicitly fitting more general motion models in order to classify trajectories as foreground or background. We find that homographies are sufficient to model a wide variety of background motions found in real-world videos. Our simple approach achieves competitive performance on the DAVIS benchmark, while using techniques complementary to state-of-the-art approaches.
Bethe free energy, Kikuchi approximations, and belief propagation algorithms

This is an updated and expanded version of TR2000-26, but it is still in draft form. More importantly, our analysis lets us build on the progress made in statistical physics since Bethe’s approximation was introduced in 1935. Kikuchi and others have shown how to construct more accurate free energy approximations, of which Bethe’s approximation is the simplest. Exploiting the insights from our analysis, we derive generalized belief propagation (GBP) versions of these Kikuchi approximations. These new message passing algorithms can be significantly more accurate than ordinary BP, at an adjustable increase in complexity. We illustrate such a new GBP algorithm on a grid Markov network and show that it gives much more accurate marginal probabilities than those found using ordinary BP. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved.
Avocado: A Distributed Virtual Reality Framework

We present Avocado, our object-oriented framework for the development of distributed, interactive VE applications. Data distribution is achieved by transparent replication of a shared scene graph among the participating processes of a distributed application. A sophisticated group communication system is used to guarantee state consistency even in the presence of late joining and leaving processes. We also describe how the familiar dataflow graph found in modern stand-alone 3D-application toolkits extends nicely to the distributed case.
An implementation of AES algorithm Based on FPGA

An implementation of high speed AES algorithm based on FPGA is presented in this paper in order to improve the safety of data in transmission. The mathematic principle, encryption process and logic structure of AES algorithm are introduced. So as to reach the porpose of improving the system computing speed, the pipelining and papallel processing methods were used. The simulation results show that the high-speed AES encryption algorithm implemented correctly. Using the method of AES encryption the data could be protected effectively.
Topic Detection and Extraction in Chat

Internet-based Chat environments such as Internet relay Chat and instant messaging pose a challenge for data mining and information retrieval systems due to the multi-threaded, overlapping nature of the dialog and the nonstandard usage of language. In this paper we present preliminary methods of topic detection and topic thread extraction that augment a typical TF-IDF-based vector space model approach with temporal relationship information between posts of the Chat dialog combined with WordNet hypernym augmentation. We show results that promise better performance than using only a TF-IDF bag-of-words vector space model.
A substrate integrated waveguide leaky wave antenna radiating from a slot in the broad wall

This paper presents the application of a substrate integrated waveguide (SIW) for the design of a leaky wave antenna radiating from a slot in the broad wall. The antenna radiates into a beam split into two main lobes and its gain is about 7 dB at 19 GHz. The characteristics and radiation aspects of the antenna are discussed here. The measured antenna characteristics are in good agreement with those predicted by the simulation. Due to the SIW technology, the antenna is suitable for integration into T/X circuits and antenna arrays.
New DC–DC Converter for Energy Storage System Interfacing in Fuel Cell Hybrid Electric Vehicles

The use of energy storage devices such as batteries or supercapacitors is almost mandatory in fuel cell hybrid electric vehicles, in order to guarantee load leveling, assuring braking energy recovery and good performances in transient operations. To this end, converters with bidirectional power flows are needed to connect the accumulators to the dc-link of the motor drive system. In this paper, a new conversion structure for the bidirectional interfacing of two dc voltage sources with a higher voltage dc-link is presented, where only three controllable power electronic switches are needed and where the only limit is that the sum of the two dc voltages must be lower or equal than the motor drive dc-link voltage. The analysis of the converter structure, the control and modulation systems that have been developed, and simulations and experimental results are reported
Efficient simplification of point-sampled surfaces

In this paper we introduce, analyze and quantitatively compare a number of surface simplification methods for point-sampled geometry. We have implemented incremental and hierarchical clustering, iterative simplification, and particle simulation algorithms to create approximations of point-based models with lower sampling density. All these methods work directly on the point cloud, requiring no intermediate tesselation. We show how local variation estimation and quadric error metrics can be employed to diminish the approximation error and concentrate more samples in regions of high curvature. To compare the quality of the simplified surfaces, we have designed a new method for computing numerical and visual error estimates for point-sampled surfaces. Our algorithms are fast, easy to implement, and create high-quality surface approximations, clearly demonstrating the effectiveness of point-based surface simplification.
Copy-move forgery detection based on convolutional kernel network

Conventional copy-move forgery detection methods mostly make use of hand-crafted features to conduct feature extraction and patch matching. However, the discriminative capability and the invariance to particular transformations of hand-crafted features are not good enough, which imposes restrictions on the performance of copy-move forgery detection. To solve this problem, we propose to utilize Convolutional Kernel Network to conduct copy-move forgery detection. Convolutional Kernel Network is a kind of data-driven local descriptor with the deep convolutional architecture. It can achieve competitive performance for its excellent discriminative capability. To well adapt to the condition of copy-move forgery detection, three significant improvements are made: First of all, our Convolutional Kernel Network is reconstructed for GPU. The GPU-based reconstruction results in high efficiency and makes it possible to apply to thousands of patches matching in copy-move forgery detection. Second, a segmentation-based keypoint distribution strategy is proposed to generate homogeneous distributed keypoints. Last but not least, an adaptive oversegmentation method is adopted. Experiments on the publicly available datasets are conducted to testify the state-of-the-art performance of the proposed method.
Detecting Emotions in Social Media: A Constrained Optimization Approach

Emotion detection can considerably enhance our understanding of users’ emotional states. Understanding users’ emotions especially in a real-time setting can be pivotal in improving user interactions and understanding their preferences. In this paper, we propose a constraint optimization framework to discover emotions from social media content of the users. Our framework employs several novel constraints such as emotion bindings, topic correlations, along with specialized features proposed by prior work and well-established emotion lexicons. We propose an efficient inference algorithm and report promising empirical results on three diverse datasets.
Guest Editors' Introduction: TDD--The Art of Fearless Programming

Test-driven development is a discipline of design and programming where every line of new code is written in response to a test the programmer writes just before coding. This special issue of IEEE Software includes seven feature articles on various aspects of TDD and a Point/Counterpoint debate on the use of mock objects in applying it. The articles demonstrate the ways TDD is being used in nontrivial situations (database development, embedded software development, GUI development, performance tuning), signifying an adoption level for the practice beyond the visionary phase and into the early mainstream. In this introduction to the special issue on TDD, the guest editors also summarize selected TDD empirical studies from industry and academia.
Virtual machine time travel using continuous data protection and checkpointing

Virtual machine (VM) time travel enables reverting a virtual machine's state, both transient and persistent, to past points in time. This capability can be used to improve virtual machine availability, to enable forensics on past VM states, and to recover from operator errors. We present an approach to virtual machine time travel which combines Continuous Data Protection (CDP) storage support with live-migration-based virtual machine checkpointing. In particular, we present a novel approach for CDP which enables efficient reverts of the storage state to past points in time and makes it possible to undo a revert, and this is achieved using a simple branched-temporal data structure. We also present a design and implementation of a simple live-migration-based checkpointing mechanism in Xen.
Exploiting drug-disease relationships for computational drug repositioning

Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.
Accurate, scalable in-network identification of p2p traffic using application signatures

The ability to accurately identify the network traffic associated with different P2P applications is important to a broad range of network operations including application-specific traffic engineering, capacity planning, provisioning, service differentiation,etc. However, traditional traffic to higher-level application mapping techniques such as default server TCP or UDP network-port baseddisambiguation is highly inaccurate for some P2P applications.In this paper, we provide an efficient approach for identifying the P2P application traffic through application level signatures. We firstidentify the application level signatures by examining some available documentations, and packet-level traces. We then utilize the identified signatures to develop online filters that can efficiently and accurately track the P2P traffic even on high-speed network links.We examine the performance of our application-level identification approach using five popular P2P protocols. Our measurements show thatour technique achieves less than 5% false positive and false negative ratios in most cases. We also show that our approach only requires the examination of the very first few packets (less than 10packets) to identify a P2P connection, which makes our approach highly scalable. Our technique can significantly improve the P2P traffic volume estimates over what pure network port based approaches provide. For instance, we were able to identify 3 times as much traffic for the popular Kazaa P2P protocol, compared to the traditional port-based approach.
Robust Subspace Segmentation by Simultaneously Learning Data Representations and Their Affinity Matrix

The goal of subspace segmentation is to partition a set of data drawn from a union of subspace into their underlying subspaces. The performance of spectral clustering based approaches heavily depends on learned data affinity matrices, which are usually constructed either directly from the raw data or from their computed representations. In this paper, we propose a novel method to simultaneously learn the representations of data and the affinity matrix of representation in a unified optimization framework. A novel Augmented Lagrangian Multiplier based algorithm is designed to effectively and efficiently seek the optimal solution of the problem. The experimental results on both synthetic and real data demonstrate the efficacy of the proposed method and its superior performance over the state-of-the-art alternatives.
Minimum Bayes Risk decoding and system combination based on a recursion for edit distance

In this paper we describe a method that can be used for Minimum Bayes Risk (MBR) decoding for speech recognition. Our algorithm can take as input either a single lattice, or multiple lattices for system combination. It has similar functionality to the widely used Consensus method, but has a clearer theoretical basis and appears to give better results both for MBR decoding and system combination. Many different approximations have been described to solve the MBR decoding problem, which is very difficult from an optimization point of view. Our proposed method solves the problem through a novel forward–backward recursion on the lattice, not requiring time markings. We prove that our algorithm iteratively improves a bound on the Bayes risk.
Occurrence and distribution of microplastics in marine sediments along the Belgian coast.

Plastic debris is known to undergo fragmentation at sea, which leads to the formation of microscopic particles of plastic; the so called 'microplastics'. Due to their buoyant and persistent properties, these microplastics have the potential to become widely dispersed in the marine environment through hydrodynamic processes and ocean currents. In this study, the occurrence and distribution of microplastics was investigated in Belgian marine sediments from different locations (coastal harbours, beaches and sublittoral areas). Particles were found in large numbers in all samples, showing the wide distribution of microplastics in Belgian coastal waters. The highest concentrations were found in the harbours where total microplastic concentrations of up to 390 particles kg(-1) dry sediment were observed, which is 15-50 times higher than reported maximum concentrations of other, similar study areas. The depth profile of sediment cores suggested that microplastic concentrations on the beaches reflect the global plastic production increase.
Towards smart traffic management systems: Vacant on-street parking spot detection based on video analytics

Smart Cities rely on the use of ICTs for a more efficient and intelligent use of resources, whilst improving citizens' quality of life and reducing the environmental footprint. As far as the livability of cities is concerned, traffic is one of the most frequent and complex factors directly affecting citizens. Particularly, drivers in search of a vacant parking spot are a non-negligible source of atmospheric and acoustic pollution. Although some cities have installed sensor-based vacant parking spot detectors in some neighbourhoods, the cost of this approach makes it unfeasible at large scale. As an approach to implement a sustainable solution to the vacant parking spot detection problem in urban environments, this work advocates fusing the information from small-scale sensor-based detectors with that obtained from exploiting the widely-deployed video surveillance camera networks. In particular, this paper focuses on how video analytics can be exploited as a prior step towards Smart City solutions based on data fusion. Through a set of experiments carefully planned to replicate a real-world scenario, the vacant parking spot detection success rate of the proposed system is evaluated through a critical comparison of local and global visual features (either alone or fused at feature level) and different classifier systems applied to the task. Furthermore, the system is tested under setup scenarios of different complexities, and experimental results show that while local features are best when training with small amounts of highly accurate on-site data, they are outperformed by their global counterparts when training with more samples from an external vehicle database.
Privacy-Preserving Keyword Search Over Encrypted Data in Cloud Computing

Search over encrypted data is a technique of great interest in the cloud computing era, because many believe that sensitive data has to be encrypted before outsourcing to the cloud servers in order to ensure user data privacy. Devising an efficient and secure search scheme over encrypted data involves techniques from multiple domains – information retrieval for index representation, algorithms for search efficiency, and proper design of cryptographic protocols to ensure the security and privacy of the overall system. This chapter provides a basic introduction to the problem definition, system model, and reviews the state-of-the-art mechanisms for implementing privacy-preserving keyword search over encrypted data. We also present one integrated solution, which hopefully offer more insights into this important problem.
Digitally controlled active clamp interleaved flyback converters for improving efficiency in photovoltaic grid-connected micro-inverter

Flyback converters show the characteristics of current source when operating in discontinuous conduction mode (DCM) and boundary conduction mode (BCM), which makes it widely used in photovoltaic grid-connected micro-inverter. In this paper, an active clamp interleaved flyback converter operating with combination of DCM and BCM is proposed in micro-inverter to achieve zero voltage switching (ZVS) for both of primary switches and fully recycle the energy in the leakage inductance. The proposed control method makes active-clamping part include only one clamp capacitor. In DCM area, only one flyback converter operates and turn-off of its auxiliary switch is suggested here to reduce resonant conduction losses, which improve the efficiency at light loads. Performance of the proposed circuit is validated by the simulation results and experimental results.
Pseudo random number generator using Elman neural network

This paper proposes a pseudo random number generator using Elman neural network. The proposed neural network is a recurrent neural network able to generate pseudo-random numbers from the weight matrices obtained from the layer weights of the Elman network. The proposed method is not computationally demanding and is easy to implement for varying bit sequences. The random numbers generated using our method have been subjected to frequency test and ENT test program. The results show that recurrent neural networks can be used as a pseudo random number generator(prng).
Narrowband Wireless Access for Low-Power Massive Internet of Things: A Bandwidth Perspective

LPWAN is a type of wireless telecommunication network designed to allow long range communications with relaxed requirements on data rate and latency between the core network and a high-volume of battery-operated devices. This article first reviews the leading LPWAN technologies on both unlicensed spectrum (SIGFOX, and LoRa) and licensed spectrum (LTE-M and NB-IoT). Although these technologies differ in many aspects, they do have one thing in common: they all utilize the narrow-band transmission mechanism as a leverage to achieve three fundamental goals, that is, high system capacity, long battery life, and wide coverage. This article introduces an effective bandwidth concept that ties these goals together with the transmission bandwidth, such that these contradicting goals are balanced for best overall system performance.
Importance sampling for unbiased on-demand evaluation of knowledge base population

Knowledge base population (KBP) systems take in a large document corpus and extract entities and their relations. Thus far, KBP evaluation has relied on judgements on the pooled predictions of existing systems. We show that this evaluation is problematic: when a new system predicts a previously unseen relation, it is penalized even if it is correct. This leads to significant bias against new systems, which counterproductively discourages innovation in the field. Our first contribution is a new importance-sampling based evaluation which corrects for this bias by annotating a new system’s predictions ondemand via crowdsourcing. We show this eliminates bias and reduces variance using data from the 2015 TAC KBP task. Our second contribution is an implementation of our method made publicly available as an online KBP evaluation service. We pilot the service by testing diverse state-ofthe-art systems on the TAC KBP 2016 corpus and obtain accurate scores in a cost effective manner.
Evaluation of LBP and HOG Descriptors for Clothing Attribute Description

In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75% in most cases, reaching 80% for the necktie or sleeve length attributes.