nils-herrmann/clean_scirepeval huggingface dataset:
A modular crawler-driven robot: Mechanical design and preliminary experiments

This paper presents a tracked robot composed of the proposed crawler mechanism, in which a planetary gear reducer is employed as the transmission device and provides two outputs in different forms with only one actuator. When the crawler moves in a rough environment, collision between mechanism and environment inevitably occurs. This under-actuated crawler can absorb the impact energy that should be transmitted to the actuator. A modular concept for the crawler is proposed for enlarging its use in robot systems and mechanical design of a modular crawler is conducted. Using this crawler module, a four-crawler-driven robot is realized by easily assembling. Experiments are conducted to verify the proposed concept and mechanical design. A single crawler module can well perform the proposed three locomotion modes. The four-crawler-driven robot has good adaptability to the environment which can get over obstacles both passively and actively.
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery. In recent years, it has become an active task in the remote sensing area, and numerous algorithms have been proposed for this task, including many machine learning and data-driven approaches. However, the existing data sets for aerial scene classification, such as UC-Merced data set and WHU-RS19, contain relatively small sizes, and the results on them are already saturated. This largely limits the development of scene classification algorithms. This paper describes the Aerial Image data set (AID): a large-scale data set for aerial scene classification. The goal of AID is to advance the state of the arts in scene classification of remote sensing images. For creating AID, we collect and annotate more than 10000 aerial scene images. In addition, a comprehensive review of the existing aerial scene classification techniques as well as recent widely used deep learning methods is given. Finally, we provide a performance analysis of typical aerial scene classification and deep learning approaches on AID, which can be served as the baseline results on this benchmark.
Automatic camera and range sensor calibration using a single shot

As a core robotic and vision problem, camera and range sensor calibration have been researched intensely over the last decades. However, robotic research efforts still often get heavily delayed by the requirement of setting up a calibrated system consisting of multiple cameras and range measurement units. With regard to removing this burden, we present a toolbox with web interface for fully automatic camera-to-camera and camera-to-range calibration. Our system is easy to setup and recovers intrinsic and extrinsic camera parameters as well as the transformation between cameras and range sensors within one minute. In contrast to existing calibration approaches, which often require user intervention, the proposed method is robust to varying imaging conditions, fully automatic, and easy to use since a single image and range scan proves sufficient for most calibration scenarios. Experimentally, we demonstrate that the proposed checkerboard corner detector significantly outperforms current state-of-the-art. Furthermore, the proposed camera-to-range registration method is able to discover multiple solutions in the case of ambiguities. Experiments using a variety of sensors such as grayscale and color cameras, the Kinect 3D sensor and the Velodyne HDL-64 laser scanner show the robustness of our method in different indoor and outdoor settings and under various lighting conditions.
Topic-aware Social Influence Minimization

In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking $k$ nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.
Function-Private Functional Encryption in the Private-Key Setting

Functional encryption supports restricted decryption keys that allow users to learn specific functions of the encrypted messages. Although the vast majority of research on functional encryption has so far focused on the privacy of the encrypted messages, in many realistic scenarios it is crucial to offer privacy also for the functions for which decryption keys are provided. Whereas function privacy is inherently limited in the public-key setting, in the private-key setting it has a tremendous potential. Specifically, one can hope to construct schemes where encryptions of messages $$\mathsf{m}_1, \ldots , \mathsf{m}_T$$ m 1 , … , m T together with decryption keys corresponding to functions $$f_1, \ldots , f_T$$ f 1 , … , f T , reveal essentially no information other than the values $$\{ f_i(\mathsf{m}_j)\}_{i,j\in [T]}$$ { f i ( m j ) } i , j ∈ [ T ] . Despite its great potential, the known function-private private-key schemes either support rather limited families of functions (such as inner products) or offer somewhat weak notions of function privacy. We present a generic transformation that yields a function-private functional encryption scheme, starting with any non-function-private scheme for a sufficiently rich function class. Our transformation preserves the message privacy of the underlying scheme and can be instantiated using a variety of existing schemes. Plugging in known constructions of functional encryption schemes, we obtain function-private schemes based either on the learning with errors assumption, on obfuscation assumptions, on simple multilinear-maps assumptions, and even on the existence of any one-way function (offering various trade-offs between security and efficiency).
A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes

Received Nov 5, 2016  Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization.
Resource sharing in 5G mmWave cellular networks

In this paper, we discuss resource sharing, a key dimension in mmWave network design in which spectrum, access and/or network infrastructure resources can be shared by multiple operators. It is argued that this sharing paradigm will be essential to fully exploit the tremendous amounts of bandwidth and the large number of antenna degrees of freedom available in these bands, and to provide statistical multiplexing to accommodate the highly variable nature of the traffic. In this paper, we investigate and compare various sharing configurations in order to capture the enhanced potential of mmWave communications. Our results reflect both the technical and the economical aspects of the various sharing paradigms. We deliver a number of key insights, corroborated by detailed simulations, which include an analysis of the effects of the distinctive propagation characteristics of the mmWave channel, along with a rigorous multi-antenna characterization. Key findings of this study include (i) the strong dependence of the comparative results on channel propagation and antenna characteristics, and therefore the need to accurately model them, and (ii) the desirability of a full spectrum and infrastructure sharing configuration, which may result in increased user rate as well as in economical advantages for both service provider.
A Low power and area efficient CLA adder design using Full swing GDI technique

The low power VLSI design has an important role in designing of many electronic systems. While designing any combinational or sequential circuits, the important parameters like power consumption, implementation area, voltage leakage and performance of the circuit are to be considered. Design of area, high speed and powerefficient data path logic systems forms the largest areas of research in VLSI system design. This paper presents a low power Carry look ahead adder design using Full swing Gate diffusion (FS-GDI) technique. The proposed CLA implementation utilizes improved full-swing GDI F1 and F2 gates, which are the counterparts of standard CMOS NAND and NOR gates. The basic Gate Diffusion Input (GDI) logic style suffers from some practical limitations like swing degradation, fabrication complexity in standard CMOS process and bulk connections. These limitations can be overcome by Full swing GDI technique. The proposed technique utilizes a single swing restoration (SR) transistor to improve the output swing of F1 and F2 GDI gates. A 16-bit CLA is designed and Simulations are performed by Mentor graphics 130nm CMOS technology ELDO simulator. Simulation results have shown a greater reduction in delay, power dissipation and area.
Feature vector selection and projection using kernels

This paper provides new insight into kernel methods by using data selection. The kernel trick is used to select from the data a relevant subset forming a basis in a feature space F . Thus the selected vectors de,ne a subspace in F . Then, the data is projected onto this subspace where classical algorithms are applied. We show that kernel methods like generalized discriminant analysis (Neural Comput. 12 (2000) 2385) or kernel principal component analysis (Neural Comput. 10 (1998) 1299) can be expressed more easily. Moreover, it will turn out that the size of the basis is related to the complexity of the model. Therefore, the data selection leads to a complexity control and thus to a better generalization. The approach covers a wide range of algorithms. We investigate the function approximation on real classi,cation problems and on a regression problem.
N-gram Counts and Language Models from the Common Crawl

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a collection over 9 billion web pages. This release improves upon the Google n-gram counts in two key ways: the inclusion of low-count entries and deduplication to reduce boilerplate. By preserving singletons, we were able to use Kneser-Ney smoothing to build large language models. This paper describes how the corpus was processed with emphasis on the problems that arise in working with data at this scale. Our unpruned Kneser-Ney English 5-gram language model, built on 975 billion deduplicated tokens, contains over 500 billion unique n-grams. We show gains of 0.5–1.4 BLEU by using large language models to translate into various languages.
Wideband Circularly-Polarized Patch Antenna

A novel wideband circularly-polarized patch antenna is presented. First, a new broadband 90deg hybrid feed network is proposed to have a bandwidth of 75% for the 10-dB return loss, good two output ports amplitude balance and a consistent 90deg (plusmn3deg) phase difference between two output ports. Then, a circular patch antenna fed by four sequential-rotation proximity-coupled L-probes orientated to have phase of 0deg, 90deg, 180deg and 270deg is presented. The four L-probes are connected to the respective ports of a feed network comprising a pair of the proposed broadband 90deg hybrid. It is found that the antenna can deliver a measured impedance bandwidth of 79.4% from 1.04 to 2.41 GHz for SWR<2, measured 2-dB and 3-dB axial-ratio bandwidths of 57% from 1.3 to 2.35 GHz and 82% from 1 to 2.4 GHz, respectively, and a measured gain bandwidth of 56.1% from 1.26 to 2.32 GHz for gain>3 dBi. The simulated impedance bandwidth is 94% from 0.9 to 2.5 GHz for SWR<2. The simulated 3-dB axial-ratio bandwidth is 63.8% from 1.25 to 2.42 GHz. The simulated gain bandwidth is 59.3% from 1.33 to 2.45 GHz for the gain>3 dBi. Good agreement is observed between simulation and measurement
A low voltage low 1/f noise CMOS bandgap reference

For a low voltage bandgap reference design, there is a tradeoff between the supply voltage and the performance of the bandgap reference in terms of untrimmed accuracy and 1/f noise performance. In this paper, the application of chopper stabilizing technique is proposed to address this tradeoff. Over five times of improvement in untrimmed accuracy and 1/f noise performance were demonstrated through a low voltage bandgap reference fabricated in a 0.8 /spl mu/m CMOS process.
Science and Human Behavior at fifty.

The golden anniversary of Science and Human Behavior is cause for celebration. Toward that end, the present paper is largely an historical consideration of the book, its inception and reception, both at the time of its publication and in subsequent years. The range and intensity of reactions to S&HB mark its impact and show it to be among Skinner's most important works, if not the most important. S&HB was written as an introductory psychology text--a vigorous use of the book in our teaching could do much to benefit the dissemination of behavior analysis.
5G 3GPP-Like Channel Models for Outdoor Urban Microcellular and Macrocellular Environments

For the development of new 5G systems to operate in bands up to 100 GHz, there is a need for accurate radio propagation models at these bands that currently are not addressed by existing channel models developed for bands below 6 GHz. This document presents a preliminary overview of 5G channel models for bands up to 100 GHz. These have been derived based on extensive measurement and ray tracing results across a multitude of frequencies from 6 GHz to 100 GHz, and this document describes an initial 3D channel model which includes: 1) typical deployment scenarios for urban microcells (UMi) and urban macrocells (UMa), and 2) a baseline model for incorporating path loss, shadow fading, line of sight probability, penetration and blockage models for the typical scenarios. Various processing methodologies such as clustering and antenna decoupling algorithms are also presented.
Zone Divisional Hierarchical Routing Protocol for Wireless Sensor Network

Clustering prolongs energy resources, improves scalability and preserves communication bandwidth. Clustering is either classified as static and dynamic or as equal and unequal. In cluster based routing protocols that employ multi-hop communication, imbalanced energy consumption among the nodes results in hot-spots. Unequal clustering overcomes hot-spots but requires a high overhead and is prone to connectivity issues. To offer guaranteed connectivity and alleviate the hot-spot problem, a zone divisional hierarchical routing protocol has been proposed in this paper. The network is divided into equal sized static rectangular clusters which are assigned to two zones namely near zone and far zone. The zone facing the base station is known as the near zone and rest of the network space makes up the far zone which is further divided into sub-zones. Dual cluster heads approach for sharing the reception, aggregation and forwarding tasks is proposed. The performance evaluation of the proposed protocol against the existing protocols reveals that the method offers energy-efficient multi-hop communication support, uses negligible overhead, prevents creation of hot-spots, avoids early death of nodes, uses balanced energy consumption across the network and maximizes the network lifetime.
An integrated cloud-based platform for labor monitoring and data analysis in precision agriculture

Harvest labor has became a prevailing cost in cherry and other Specialty Crops industry. We developed an integrated solution that provided real-time labor monitoring, payroll accrual, and labor-data-based analysis. At the core of our solution is a cloud-based information system that collects labor data from purposely designed labor monitoring devices, and visualizes real-time labor productivity data through a mobile-friendly user interface. Our solution used a proprietary process  to accurately associate labor data with its related worker and position under a many-to-many employment relation. We also describe our communication API and protocol, which are specifically designed to improve the reliability of data communication within an orchard. Besides its immediate benefits in improving the efficiency and accuracy of labor monitoring, our solution also enables data analysis and visualization based on harvest labor data. As an example, we discuss our approach of yield mapping based on harvest labor data. We implemented the platform and deployed the system on a cloud-based computing platform for better scalability. An early version of the system has been tested during the 2012 harvest season in cherry orchards in the U.S. Pacific Northwest Region.
Hat-Delta --- One Right Does Make a Wrong

We outline two methods for locating bugs in a program. This is done by comparing computations of the same program with different input. At least one of these computations must produce a correct result, while exactly one must exhibit some erroneous behaviour. Firstly reductions that are thought highly likely to be correct are eliminated from the search for the bug. Secondly, a program slicing technique is used to identify areas of code that are likely to be correct. Both methods have been implemented. In combination with algorithmic debugging they provide a system that quickly and accurately identifies bugs.
60-GHz Circularly Polarized Antenna Array Made in Low-Cost Fabrication Process

Circularly polarized (CP) antenna operating in the unlicensed 60 GHz frequency range is useful for millimeter-wave radio and radar applications. In this letter, a 2 × 2 right-handed circularly polarized (RHCP) planar array is demonstrated by using four CP antenna elements. A single element has a gain of 7.5 dBiC, while the resulting 2 × 2 array yields a CP gain of 12.5 dBiC. Substrate integrated waveguide (SIW) cavity is used to back each element in order to reduce the mutual coupling between elements and also improve the overall gain. An experimental antenna prototype made through a conventional low-cost printed circuit board (PCB) technique operates in RHCP mode covering the frequency range of interest from 58 to 62 GHz. The antenna is compact, showing a good radiation performance.
Interval Type-2 Fuzzy Logic Systems Made Simple

To date, because of the computational complexity of using a general type-2 fuzzy set (T2 FS) in a T2 fuzzy logic system (FLS), most people only use an interval T2 FS, the result being an interval T2 FLS (IT2 FLS). Unfortunately, there is a heavy educational burden even to using an IT2 FLS. This burden has to do with first having to learn general T2 FS mathematics, and then specializing it to an IT2 FSs. In retrospect, we believe that requiring a person to use T2 FS mathematics represents a barrier to the use of an IT2 FLS. In this paper, we demonstrate that it is unnecessary to take the route from general T2 FS to IT2 FS, and that all of the results that are needed to implement an IT2 FLS can be obtained using T1 FS mathematics. As such, this paper is a novel tutorial that makes an IT2 FLS much more accessible to all readers of this journal. We can now develop an IT2 FLS in a much more straightforward way
Parameter Space Noise for Exploration

Deep reinforcement learning (RL) methods generally engage in exploratory behavior through noise injection in the action space. An alternative is to add noise directly to the agent’s parameters, which can lead to more consistent exploration and a richer set of behaviors. Methods such as evolutionary strategies use parameter perturbations, but discard all temporal structure in the process and require significantly more samples. Combining parameter noise with traditional RL methods allows to combine the best of both worlds. We demonstrate that both offand on-policy methods benefit from this approach through experimental comparison of DQN, DDPG, and TRPO on high-dimensional discrete action environments as well as continuous control tasks. Our results show that RL with parameter noise learns more efficiently than traditional RL with action space noise and evolutionary strategies individually.
Compact two-layer slot array antenna with SIW for 60GHz wireless applications

In a variety of microwave and millimeter-wave applications where high performance antennas are required, waveguide slot arrays have received considerable attention due to their high aperture efficiency, low side lobe levels, and low cross polarization. Resonant slot arrays usually suffer from narrow bandwidth and high cost due to high precision required in manufacturing. Furthermore, because of using standard rectangular waveguides, the antenna array is thick and heavy and is not suitable for monolithic integration with high frequency printed circuits.
Semi-supervised On-Line Boosting for Robust Tracking

Recently, on-line adaptation of binary classifiers for tracking has been investigated. On-line learning allows for simple classifiers since only the current view of the object from its surrounding background needs to be discriminated. However, on-line adaptation faces one key problem: Each update of the tracker may introduce an error which, finally, can lead to tracking failure (drifting). The contribution of this paper is a novel on-line semi-supervised boosting method which significantly alleviates the drifting problem in tracking applications. This allows to limit the drifting problem while still staying adaptive to appearance changes. The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier. This comes without any parameter tuning. In the experiments, we demonstrate real-time tracking of our SemiBoost tracker on several challenging test sequences where our tracker outperforms other on-line tracking methods. Helmut Grabner1,2 Christian Leistner1 Horst Bischof1 1 Institute for Computer Graphics and Vision, Graz University of Technology, Austria 2 Computer Vision Laboratory, ETH-Zurich, Switzerland Motivation Robust Tracking Loop search Region actual object position create confidence map update classifier (tracker) evaluate classifier