nils-herrmann/clean_scirepeval huggingface dataset:
The Human Side of Enterprise

From Leadership and Motivation, Essays of Douglas McGregor, edited by W. G. Bennis and E. H. Schein (Cambridge, MA: MIT Press, 1966): 3–20. Reprinted with permission. I t has become trite to say that the most significant developments of the next quarter century will take place not in the physical but in the social sciences, that industry—the economic organ of society—has the fundamental know-how to utilize physical science and technology for the material benefit of mankind, and that we must now learn how to utilize the social sciences to make our human organizations truly effective. Many people agree in principle with such statements; but so far they represent a pious hope—and little else. Consider with me, if you will, something of what may be involved when we attempt to transform the hope into reality.
Semi-Supervised QA with Generative Domain-Adaptive Nets

We study the problem of semi-supervised question answering—-utilizing unlabeled text to boost the performance of question answering models. We propose a novel training framework, the Generative Domain-Adaptive Nets. In this framework, we train a generative model to generate questions based on the unlabeled text, and combine model-generated questions with human-generated questions for training question answering models. We develop novel domain adaptation algorithms, based on reinforcement learning, to alleviate the discrepancy between the modelgenerated data distribution and the humangenerated data distribution. Experiments show that our proposed framework obtains substantial improvement from unlabeled text.
Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms

Extracting good representations from images is essential for many computer vision tasks. In this paper, we propose hierarchical matching pursuit (HMP), which builds a feature hierarchy layer-by-layer using an efficient matching pursuit encoder. It includes three modules: batch (tree) orthogonal matching pursuit, spatial pyramid max pooling, and contrast normalization. We investigate the architecture of HMP, and show that all three components are critical for good performance. To speed up the orthogonal matching pursuit, we propose a batch tree orthogonal matching pursuit that is particularly suitable to encode a large number of observations that share the same large dictionary. HMP is scalable and can efficiently handle full-size images. In addition, HMP enables linear support vector machines (SVM) to match the performance of nonlinear SVM while being scalable to large datasets. We compare HMP with many state-of-the-art algorithms including convolutional deep belief networks, SIFT based single layer sparse coding, and kernel based feature learning. HMP consistently yields superior accuracy on three types of image classification problems: object recognition (Caltech-101), scene recognition (MIT-Scene), and static event recognition (UIUC-Sports).
3D modelling of leaves from color and ToF data for robotized plant measuring

Supervision of long-lasting extensive botanic experiments is a promising robotic application that some recent technological advances have made feasible. Plant modelling for this application has strong demands, particularly in what concerns 3D information gathering and speed. This paper shows that Time-of-Flight (ToF) cameras achieve a good compromise between both demands, providing a suitable complement to color vision. A new method is proposed to segment plant images into their composite surface patches by combining hierarchical color segmentation with quadratic surface fitting using ToF depth data. Experimentation shows that the interpolated depth maps derived from the obtained surfaces fit well the original scenes. Moreover, candidate leaves to be approached by a measuring instrument are ranked, and then robot-mounted cameras move closer to them to validate their suitability to being sampled. Some ambiguities arising from leaves overlap or occlusions are cleared up in this way. The work is a proof-of-concept that dense color data combined with sparse depth as provided by a ToF camera yields a good enough 3D approximation for automated plant measuring at the high throughput imposed by the application.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks

The effect of L regularization can be analyzed by doing a quadratic approximation of the objective function around the optimum (see, e.g. Goodfellow et al., 2017, Section 7.1.1). This analysis shows that L regularization rescales the parameters along the directions defined by the eigenvectors of the Hessian matrix. This scaling is equal to λi λi+α for the i-th eigenvector of eigenvalue λi. A similar analysis can be used for the L-SP regularization.
NaCl on 8-Bit AVR Microcontrollers

This paper presents first results of the Networking and Cryptography library (NaCl) on the 8-bit AVR family of microcontrollers. We show that NaCl, which has so far been optimized mainly for different desktop and server platforms, is feasible on resource-constrained devices while being very fast and memory efficient. Our implementation shows that encryption using Salsa20 requires 268 cycles/byte, authentication using Poly1305 needs 195 cycles/byte, a Curve25519 scalar multiplication needs 22 791 579 cycles, signing of data using Ed25519 needs 23 216 241 cycles, and verification can be done within 32 634 713 cycles. All implemented primitives provide at least 128-bit security, run in constant time, do not use secret-data-dependent branch conditions, and are open to the public domain (no usage restrictions).
Ultrawideband Antennas for Multiband Satellite Communications at UHF–Ku Frequencies

In space-borne applications, reduction of size, weight, and power can be critical. Pursuant to this goal, we present an ultrawideband, tightly coupled dipole array (TCDA) capable of supporting numerous satellite communication bands, simultaneously. Such antennas enable weight reduction by replacing multiple antennas. In addition, it provides spectral efficiency by reusing intermediate frequencies for inter-satellite communication. For ease of fabrication, the array is initially designed for operation across the UHF, L, S, and lower-C bands (0.6-3.6 GHz), with emphasis on dual-linear polarization, and wide-angle scanning. The array achieves a minimum 6:1 bandwidth for VSWR less than 1.8, 2.4, and 3.1 for 0°, 45°, and 60° scans, respectively. The presented design represents the first practical realization of dual polarizations using a TCDA topology. This is accomplished through a dual-offset, split unit cell with minimized inter-feed coupling. Array simulations are verified with measured results of an 8 × 8 prototype, exhibiting very low cross polarization and near-theoretical gain across the band. Further, we present a TCDA design operating across the upper-S, C, X, and Ku bands (3-18 GHz). The array achieves this 6:1 bandwidth for VSWR <; 2 at broadside, and VSWR <; 2.6 at 45°. A discussion on design and fabrication for low-cost arrays operating at these frequencies is included.
Towards effective feature selection in machine learning-based botnet detection approaches

Botnets, as one of the most formidable cyber security threats, are becoming more sophisticated and resistant to detection. In spite of specific behaviors each botnet has, there exist adequate similarities inside each botnet that separate its behavior from benign traffic. Several botnet detection systems have been proposed based on these similarities. However, offering a solution for differentiating botnet traffic (even those using same protocol, e.g. IRC) from normal traffic is not trivial. Extraction of features in either host or network level to model a botnet has been one of the most popular methods in botnet detection. A subset of features, usually selected based on some intuitive understanding of botnets, is used by the machine learning algorithms to classify/ cluster botnet traffic. These approaches, tested against two or three botnet traces, have mostly showed satisfactory detection results. Even though, their effectiveness in detection of other botnets or real traffic remains in doubt. Additionally, effectiveness of different combination of features in terms of providing more detection coverage has not been fully studied. In this paper we revisit flow-based features employed in the existing botnet detection studies and evaluate their relative effectiveness. To ensure a proper evaluation we create a dataset containing a diverse set of botnet traces and background traffic.
SpeckleSense: fast, precise, low-cost and compact motion sensing using laser speckle

Motion sensing is of fundamental importance for user interfaces and input devices. In applications, where optical sensing is preferred, traditional camera-based approaches can be prohibitive due to limited resolution, low frame rates and the required computational power for image processing. We introduce a novel set of motion-sensing configurations based on laser speckle sensing that are particularly suitable for human-computer interaction. The underlying principles allow these configurations to be fast, precise, extremely compact and low cost. We provide an overview and design guidelines for laser speckle sensing for user interaction and introduce four general speckle projector/sensor configurations. We describe a set of prototypes and applications that demonstrate the versatility of our laser speckle sensing techniques.
Analyzing costs and accuracy of validation mechanisms for crowdsourcing platforms

Crowdsourcing is becoming more and more important for commercial purposes. With the growth of crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, a huge work force and a large knowledge base can be easily accessed and utilized. But due to the anonymity of the workers, they are encouraged to cheat the employers in order to maximize their income. Thus, this paper we analyze two widely used crowd-based approaches to validate the submitted work. Both approaches are evaluated with regard to their detection quality, their costs and their applicability to different types of typical crowdsourcing tasks.
Ground truth evaluation of large urban 6D SLAM

In the past many solutions for simultaneous localization and mapping (SLAM) have been presented. Recently these solutions have been extended to map large environments with six degrees of freedom (DoF) poses. To demonstrate the capabilities of these SLAM algorithms it is common practice to present the generated maps and successful loop closing. Unfortunately there is often no objective performance metric that allows to compare different approaches. This fact is attributed to the lack of ground truth data. For this reason we present a novel method that is able to generate this ground truth data based on reference maps. Further on, the resulting reference path is used to measure the absolute performance of different 6D SLAM algorithms building a large urban outdoor map.
Xception: Deep Learning with Depthwise Separable Convolutions

We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation leads us to propose a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions. We show that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset (which Inception V3 was designed for), and significantly outperforms Inception V3 on a larger image classification dataset comprising 350 million images and 17,000 classes. Since the Xception architecture has the same number of parameters as Inception V3, the performance gains are not due to increased capacity but rather to a more efficient use of model parameters.
DUAL-BAND EQUAL / UNEQUAL WILKINSON POWER DIVIDERS BASED ON COUPLED-LINE SECTION WITH SHORT-CIRCUITED

This paper presents dual-band equal/unequal Wilkinson power dividers based on a coupled-line section with short-circuited stub (called as the “coupled-line section” for short), which consists of a pair of parallel coupled lines and a short-circuited stub. With the analyses of the phase shift and equivalent characteristic impedance, the coupled-line section is used to replace the quarter-wavelength branch line in the conventional equal/unequal Wilkinson power divider to obtain excellent dual-band operation. The closed-form equations and design procedures of dual-band Wilkinson power divider are given, where one degree of design freedom is obtained and design flexibility is shown. As two examples, a dual-band equal Wilkinson power divider with the frequency ratio of 1.8 : 1 and an unequal one with the high power dividing ratio of 7 : 1 and frequency ratio of 1.8 : 1 are designed, fabricated and measured. The measurements are in good agreement with the simulations. It is shown that the proposed power dividers have simple topologies, and can be easily fabricated with small frequency ratios and high power dividing ratios. Received 1 November 2010,  Bo Li (libo0204220226@yahoo.com.cn). † Also with Department of Information and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to a standard implementation of LSTMs on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem.
Implementation of inverse perspective mapping algorithm for the development of an automatic lane tracking system

Vision based automatic lane tracking system requires information such as lane markings, road curvature and leading vehicle be detected before capturing the next image frame. Placing a camera on the vehicle dashboard and capturing the forward view results in a perspective view of the road image. The perspective view of the captured image somehow distorts the actual shape of the road, which involves the width, height, and depth. Respectively, these parameters represent the x, y and z components. As such, the image needs to go through a pre-processing stage to remedy the distortion using a transformation technique known as an inverse perspective mapping (IPM). This paper outlines the procedures involved.
Compact Multifrequency Slot Antenna Design Incorporating Embedded Arc-Strip

A compact coplanar waveguide (CPW)-fed multifrequency circular slot antenna design loaded with arc-shaped metallic strips is investigated and verified in this letter. The proposed design implemented on a 0.8-mm FR-4 substrate shows that the arc-shaped metallic strips yield the multiple resonant modes for multiband vision. Unlike other multifrequency antennas whose sizes are determined by the lower resonant frequency, the compact size of the proposed slot is determined by the upper resonant frequency. Other operating frequencies are obtained by inserting arc-shaped metallic strips without increasing the overall antenna area. The prototype for different applications examined by modifying the angles of the arc-shaped metallic strips reveals that the input impedance bandwidths of the operating frequencies range from 5.0% to 64.7%. Moreover, these operating resonant bands exhibit the broadside radiation patterns with efficiency better than 45%.
GraphGAN: Graph Representation Learning With Generative Adversarial Nets

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces “fake” samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.
Spatial, Temporal and Spatio-Temporal Databases - Hot Issues and Directions for PhD Research

Spatial and temporal database systems, both in theory and in practice, have developed dramatically over the past two decades to the point where usable commercial systems, underpinned by a robust theoretical foundation, are now starting to appear. While much remains to be done, topics for research must be chosen carefully to avoid embarking on impractical or unprofitable areas. This is particularly true for doctoral research where the candidate must build a tangible contribution in a relatively short time.The panel session at the Eighth International Symposium on Spatial and Temporal Databases (SSTD 2003) held on Santorini Island, Greece [7] in July 2003 thus took as its focus the question What to focus on (and what to avoid) in Spatial and Temporal Databases: recommendations for doctoral research. This short paper, authored by the panel members, summarizes these discussions.
Closures and Cavities in the Human Connectome

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans process and perform complex behaviors. Because cognitive processes involve large and distributed networks of brain areas, principled examinations of multi-node routes within larger connection patterns can offer fundamental insights into the complexities of brain function. Here, we investigate both densely connected groups of nodes that could perform local computations as well as larger patterns of interactions that would allow for parallel processing. Finding such structures necessitates that we move from considering exclusively pairwise interactions to capturing higher order relations, considerations naturally expressed in the language of algebraic topology. In this parlance, clusters of all-to-all connected sets of brain regions that may readily share information to perform a coordinated function are called cliques. We detect cliques in the average structural connectomes of 8 healthy adults scanned in triplicate and discover the presence of more highdimensional cliques than expected in null networks constructed via wiring minimization. This provides architecture through which brain network can perform rapid, local processing. Complementary to this study of locally dense structures, we employ a tool called persistent homology to locate cycles, topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cycles exist consistently across subjects, differ from those observed in null model networks, and – importantly – link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain’s structural architecture.
Benefits, Adoption Barriers and Myths of Open Data and Open Government

Although a significant number of public organizations have embraced the idea of open data, many are still reluctant to do this. One root cause is that the publicizing of data represents a shift from a closed to an open system of governance, which has a significant impact upon the relationships between public agencies and the users of open data. Yet no systematic research is available which compares the benefits of an open data with the barriers to its adoption. Based on interviews and a workshop, the benefits and adoption barriers for open data have been derived. The findings show that a gap exists between the promised benefits and barriers. They furthermore suggest that a conceptually simplistic view is often adopted with regard to open data, one which automatically correlates the publicizing of data with use and benefits. Five ‘myths’ are formulated promoting the use of open data and placing the expectations within a realistic perspective. Further, the recommendation is given to take a user’s view and to actively govern the relationship between government and its users.
Challenges and opportunities in electronic textiles modeling and optimization

This paper addresses an emerging new field of research that combines the strengths and capabilities of electronics and textiles in one: electronic textiles, or e-textiles. E-textiles, also called Smart Fabrics, have not only "wearable" capabilities like any other garment, but also local monitoring and computation, as well as wireless communication capabilities. Sensors and simple computational elements are embedded in e-textiles, as well as built into yarns, with the goal of gathering sensitive information, monitoring vital statistics and sending them remotely (possibly over a wireless channel) for further processing. Possible applications include medical (infant or patient) monitoring, personal information processing systems, or remote monitoring of deployed personnel in military or space applications. We illustrate the challenges imposed by the dual textile/electronics technology on their modeling and optimization methodology.
Feature Selection via Dependence Maximization

We introduce a framework for feature selection based on depe ndence maximization between the selected features and the labels of an estimation problem, u sing the Hilbert-Schmidt Independence Criterion. The key idea is that good features should be highl y dependent on the labels. Our approach leads to a greedy procedure for feature selection. We show that a number of existing feature selectors are special cases of this framework. Experiments on both artificial and real-world data show that our feature selector works well in practice.