nils-herrmann/clean_scirepeval huggingface dataset:
A Broadband Circularly Polarized Crossed-Dipole Antenna

A unidirectional broadband circularly polarized (CP) crossed-dipole antenna is presented in this communication. This antenna has a simple feeding structure, which uses an unequal power network. It can simply realize 90° phase shift and four equal power divisions. The study of the antenna performance with different geometric parameters has been conducted. Working band of designed antenna covers the BeiDou Navigation Satellite System, GPS, and GLNOSS bands. An optimized antenna is designed and measured, the measured results agree well with the simulated results. The results show good performance of impendence bandwidth is approximately 66.2% (1.01-2.01 GHz), the 3-dB axial-ratio bandwidth (ARBW) of approximately 41.3% (1.21-1.97 GHz). Moreover, the measured bore-sight antenna gain is stable and varying not too much, which average value is 6.0 dBic with less than 1.5-dB variation within the 3-dB ARBW.
Direct Current Control of a Synchronous Machine in Field Coordinates

This paper presents an implementation of a modified method for direct current control (DCC) in a permanent-magnet synchronous motor (SM). The control is performed using a predictive algorithm in rotor field coordinates due to its simpler implementation. Its task is to minimize the final current error at the end of the sampling interval. Depending on the desired performance, two variants are proposed, yielding either lower switching frequency or lower current ripple. Both variants of DCC, named DCC I and DCC II, have been rearranged for applications in SMs with either surface-mounted permanent magnets or interior (buried) permanent magnets. A comparison with field-oriented control using space-vector modulation and synchronized on/off modulation shows the advantages and drawbacks of the proposed method. Simulations and measurements on a laboratory model with SM having low inductances (thus expecting high ripple) and very high number of poles (high stator supply frequency) confirm the validity of the approach. Additional considerations regarding practical problems, some of which can be found in similar predictive methods, are also presented.
Agent-based computational economics

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This chapter discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research.
Building the blocks of protocol design and analysis: challenges and lessons learned from case studies on mobile ad hoc routing and micro-mobility protocols

With the emergence of new application-specific sensor and Ad-hoc networks, increasingly complex and custom protocols will be designed and deployed. We propose a framework to systematically design and evaluate networking protocols based on a 'building block' approach. In this approach, each protocol is broken down into a set of parameterized modules called "building blocks", each having its own specific functionality. The properties of these building blocks and their interaction define the overall behavior of the protocol. In this paper, we aim to identify the major research challenges and questions in the building block approach. By addressing some of those questions, we point out potential directions to analyze and understand the behavior of networking protocols systematically. We discuss two case studies on utilizing the building block approach for analyzing Ad-hoc routing protocols and IP mobility protocols in a systematic manner.
Toward accurate dynamic time warping in linear time and space

ion speeds up the DTW algorithm by operating on a reduced representation of the data. These algorithms include IDDTW , PDTW [13], and COW  . The left side of Figure 5 shows a full-resolution cost matrix for which a minimum-distance warp path must be found. Rather than running DTW on the full resolution (1/1) cost matrix, the time series are reduced in size to make the number of cells in the cost matrix more manageable. A warp path is found for the lowerresolution time series and is mapped back to full resolution. The resulting speedup depends on how much abstraction is used. Obviously, the calculated warp path becomes increasingly inaccurate as the level of abstraction increases. Projecting the low resolution warp path to the full resolution usually creates a warp path that is far from optimal. This is because even IF an optimal warp path passes through the low-resolution cell, projecting the warp path to the higher resolution ignores local variations in the warp path that can be very significant. Indexing [9][14] uses lower-bounding functions to prune the number of times DTW is run for similarity search . Indexing speeds up applications in which DTW is used, but it does not make up the actual DTW calculation any more efficient. Our FastDTW algorithm uses ideas from both the constraints and abstraction approaches. Using a combination of both overcomes many limitations of using either method individually, and yields an accurate algorithm that is O(N) in both time and space complexity. Our multi-level approach is superficially similar to IDDTW [3] because they both evaluate several different resolutions. However, IDDTW simply executes PDTW [13] at increasingly higher resolutions until a desired “accuracy” is achieved. IDDTW does not project low resolution solutions to higher resolutions. In Section 4, we will demonstrate that these methods are more inaccurate than our method given the same amount of execution time. Projecting warp paths to higher resolutions is also done in the construction of “Match Webs” . However, their approach is still O(N) due to the simultaneous search for many warp paths (they call them “chains”). A multi-resolution approach in their application also could not continue down to the low resolutions without severely reducing the number of “chains” that could be found. Some recent research [18] asserts that there is no need to speed up the original DTW algorithm. However, this is only true under the following (common) conditions: 1) Tight Constraints A relatively strict near-linear warp path is allowable. 2) Short Time Series All time series are short enough for the DTW algorithm to execute quickly. (~3,000 points if a warp path is needed, or ~100,000 if no warp path is needed and the user has a lot of patience).
Novel switched reluctance motor drive circuit with voltage boost function without additional reactor

Switched reluctance motor (SRM) is attractive for the driving motor of electric vehicles (EVs) and hybrid electric vehicles (HEVs) from the view point which does not use rare-earth materials. However, special drive circuit, which is different from general three-phase inverter used for permanent magnet synchronous motor and induction motor, is required in order to drive SRM. By the way, in some of commercial HEVs, boost chopper combined with three-phase inverter is used in order to apply high voltage to motor windings at high-speed range. In this system, the size of the additional reactor, which is required in boost chopper, is a problem. Then, this paper proposes a novel drive circuit with voltage boost function without additional reactor for SRM. In addition, control scheme and controller configuration for the proposed circuit are described. Moreover, the effectiveness of the proposed circuit is verified by simulation and experimental results.
Robust Loss Functions under Label Noise for Deep Neural Networks

In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and on algorithms for estimating true labels from noisy labels. An alternate approach would be to look for loss functions that are inherently noise-tolerant. For binary classification there exist theoretical results on loss functions that are robust to label noise. In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems. These results generalize the existing results on noise-tolerant loss functions for binary classification. We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we illustrate the robustness of risk minimization with such loss functions for learning neural networks.
On Incident Handling and Response: A state-of-the-art approach

Incident Response has always been an important aspect of Information Security but it is often overlooked by security administrators. Responding to an incident is not solely a technical issue but has many management, legal, technical and social aspects that are presented in this paper. We propose a detailed management framework along with a complete structured methodology that contains best practices and recommendations for appropriately handling a security incident. We also present the state-of-the art technology in computer, network and software forensics as well as automated trace-back artifacts, schemas and protocols. Finally, we propose a generic Incident Response process within a corporate environment.
A Data Mining Approach to the Diagnosis of Tuberculosis by Cascading Clustering and Classification

In this paper, a methodology for the automated detection and classification of Tuberculosis(TB) is presented. Tuberculosis is a disease caused by mycobacterium which spreads through the air and attacks low immune bodies easily. Our methodology is based on clustering and classification that classifies TB into two categories, Pulmonary Tuberculosis(PTB) and retroviral PTB(RPTB) that is those with Human Immunodeficiency Virus (HIV) infection. Initially K-means clustering is used to group the TB data into two clusters and assigns classes to clusters. Subsequently multiple different classification algorithms are trained on the result set to build the final classifier model based on K-fold cross validation method. This methodology is evaluated using 700 raw TB data obtained from a city hospital. The best obtained accuracy was 98.7% from support vector machine (SVM) compared to other classifiers. The proposed approach helps doctors in their diagnosis decisions and also in their treatment planning procedures for different categories.
A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.
Reduction Techniques for Model Checking Markov Decision Processes

The quantitative analysis of a randomized system, modeled by a Markov decision process, against an LTL formula can be performed by a combination of graph algorithms, automata-theoretic concepts and numerical methods to compute maximal or minimal reachability probabilities. In this paper, we present various reduction techniques that serve to improve the performance of the quantitative analysis, and report on their implementation on the top of the probabilistic model checker \LiQuor. Although our techniques are purely heuristic and cannot improve the worst-case time complexity of standard algorithms for the quantitative analysis, a series of examples illustrates that the proposed methods can yield a major speed-up.
ERMS: An Elastic Replication Management System for HDFS

The Hadoop Distributed File System (HDFS) is a distributed storage system that stores large-scale data sets reliably and streams those data sets to applications at high bandwidth. HDFS provides high performance, reliability and availability by replicating data, typically three copies of every data. The data in HDFS changes in popularity over time. To get better performance and higher disk utilization, the replication policy of HDFS should be elastic and adapt to data popularity. In this paper, we describe ERMS, an elastic replication management system for HDFS. ERMS provides an active/standby storage model for HDFS. It utilizes a complex event processing engine to distinguish real-time data types, and then dynamically increases extra replicas for hot data, cleans up these extra replicas when the data cool down, and uses erasure codes for cold data. ERMS also introduces a replica placement strategy for the extra replicas of hot data and erasure coding parities. The experiments show that ERMS effectively improves the reliability and performance of HDFS and reduce storage overhead.
Jump to better conclusions: SCAN both left and right

Lake and Baroni (2018) recently introduced the SCAN data set, which consists of simple commands paired with action sequences and is intended to test the strong generalization abilities of recurrent sequence-to-sequence models. Their initial experiments suggested that such models may fail because they lack the ability to extract systematic rules. Here, we take a closer look at SCAN and show that it does not always capture the kind of generalization that it was designed for. To mitigate this we propose a complementary dataset, which requires mapping actions back to the original commands, called NACS. We show that models that do well on SCAN do not necessarily do well on NACS, and that NACS exhibits properties more closely aligned with realistic usecases for sequence-to-sequence models.
Feature Representation in Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are powerful models that achieve impressive results for image classification. In addition, pre-trained CNNs are also useful for other computer vision tasks as generic feature extractors . This paper aims to gain insight into the feature aspect of CNN and demonstrate other uses of CNN features. Our results show that CNN feature maps can be used with Random Forests and SVM to yield classification results that outperforms the original CNN. A CNN that is less than optimal (e.g. not fully trained or overfitting) can also extract features for Random Forest/SVM that yield competitive classification accuracy. In contrast to the literature which uses the top-layer activations as feature representation of images for other tasks , using lower-layer features can yield better results for classification.
Analytics in Motion: High Performance Event-Processing AND Real-Time Analytics in the Same Database

Modern data-centric flows in the telecommunications industry require real time analytical processing over a rapidly changing and large dataset. The traditional approach of separating OLTP and OLAP workloads cannot satisfy this requirement. Instead, a new class of integrated solutions for handling hybrid workloads is needed. This paper presents an industrial use case and a novel architecture that integrates key-value-based event processing and SQL-based analytical processing on the same distributed store while minimizing the total cost of ownership. Our approach combines several well-known techniques such as shared scans, delta processing, a PAX-fashioned storage layout, and an interleaving of scanning and delta merging in a completely new way. Performance experiments show that our system scales out linearly with the number of servers. For instance, our system sustains event streams of 100,000 events per second while simultaneously processing 100 ad-hoc analytical queries per second, using a cluster of 12 commodity servers. In doing so, our system meets all response time goals of our telecommunication customers; that is, 10 milliseconds per event and 100 milliseconds for an ad-hoc analytical query. Moreover, our system beats commercial competitors by a factor of 2.5 in analytical and two orders of magnitude in update performance.
Quad Tilt Wing VTOL UAV : Aerodynamic Characteristics and Prototype Flight Test

A QTW VTOL UAV, featured tandem tilt wings and propellers mounted at the mid-span of each wing, is one of the most promising UAV configurations having both a VTOL capability and high cruise performance. A small prototype QTW UAV has been constructed to prove the concept and full transition between vertical and horizontal flight has been successfully demonstrated under remote manual control. The essential aerodynamic characteristics of the QTW derived from wind tunnel data are summarized in this paper, and a tandem wing concept which achieves both hovering and cruising stability has been applied to design the prototype UAV. A flight control system allowing continuous control through all flight phases enabled a pilot to perform vertical take-off, accelerating transition, cruise, decelerating transition and hover landing, with sufficient flying qualities.
Enabling warping on stereoscopic images

Warping is one of the basic image processing techniques. Directly applying existing monocular image warping techniques to stereoscopic images is problematic as it often introduces vertical disparities and damages the original disparity distribution. In this paper, we show that these problems can be solved by appropriately warping both the disparity map and the two images of a stereoscopic image. We accordingly develop a technique for extending existing image warping algorithms to stereoscopic images. This technique divides stereoscopic image warping into three steps. Our method first applies the user-specified warping to one of the two images. Our method then computes the target disparity map according to the user specified warping. The target disparity map is optimized to preserve the perceived 3D shape of image content after image warping. Our method finally warps the other image using a spatially-varying warping method guided by the target disparity map. Our experiments show that our technique enables existing warping methods to be effectively applied to stereoscopic images, ranging from parametric global warping to non-parametric spatially-varying warping.
Video Annotation and Tracking with Active Learning

We introduce a novel active learning framework for video annotation. By judiciously choosing which frames a user should annotate, we can obtain highly accurate tracks with minimal user effort. We cast this problem as one of active learning, and show that we can obtain excellent performance by querying frames that, if annotated, would produce a large expected change in the estimated object track. We implement a constrained tracker and compute the expected change for putative annotations with efficient dynamic programming algorithms. We demonstrate our framework on four datasets, including two benchmark datasets constructed with key frame annotations obtained by Amazon Mechanical Turk. Our results indicate that we could obtain equivalent labels for a small fraction of the original cost.
Towards Deep and Representation Learning for Talent Search at LinkedIn

Talent search and recommendation systems at LinkedIn strive to match the potential candidates to the hiring needs of a recruiter or a hiring manager expressed in terms of a search query or a job posting. Recent work in this domain has mainly focused on linear models, which do not take complex relationships between features into account, as well as ensemble tree models, which introduce non-linearity but are still insufficient for exploring all the potential feature interactions, and strictly separate feature generation from modeling. In this paper, we present the results of our application of deep and representation learning models on LinkedIn Recruiter. Our key contributions include: (i) Learning semantic representations of sparse entities within the talent search domain, such as recruiter ids, candidate ids, and skill entity ids, for which we utilize neural network models that take advantage of LinkedIn Economic Graph, and (ii) Deep models for learning recruiter engagement and candidate response in talent search applications. We also explore learning to rank approaches applied to deep models, and show the benefits for the talent search use case. Finally, we present offline and online evaluation results for LinkedIn talent search and recommendation systems, and discuss potential challenges along the path to a fully deep model architecture. The challenges and approaches discussed generalize to any multi-faceted search engine.
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning-based techniques. However, in most recent and promising techniques such as CNN-based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac data sets and public benchmarks. In addition, we demonstrate how the learnt deep models of 3-D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.