nils-herrmann/clean_scirepeval huggingface dataset:
Learning in Reality : A case study of Stanley , the robot that Won the DARPA Challenge

The Grand Challenge by the Defense Advances Research Projects Agency (DARPA) got a great impact on Machine Learning and Computer Vision research. Stanley was the first robot which firstly was able to drive autonomously a 175 mile course in desert terrain which was a great success in autonomous driving which may be leading to autonomous cares in urban environment in the future. Stanley faced a bunch of problems which needed to be solved to drive autonomously through the course. Some of those Problems are the terrain which has obstacles which need to be avoided and another is the driving speed which has to be maintained to win the challenge. Stanley uses therefore GPS, laser range scanners and a color camera to classify the road into drivable and non-drivable area. This classification is used to control speed and steering of the robot.
DESIGN OF BROADBAND CIRCULARLY POLARIZED SQUARE SLOT ANTENNA WITH A COMPACT SIZE

This paper proposes a newly designed compact coplanarwaveguide-fed wideband circularly polarized (CP) printed square slot antenna (PSSA), in the square slot of which are a halberd-shaped feeding signal line and a square ring patch. By placing a specially designed metal reflector behind the bidirectional CP PSSA, one can obtain unidirectional CP patterns with a associated 3-dB axial-ratio bandwidth (ARBW) almost the same as that of the bidirectional antenna. The bidirectional Land S -band PSSAs designed on FR4 substrates have 3-dB ARBWs as large as 25.3% and 29.1%, respectively. For the L-band antennas, the unidirectional design yields a 3-dB ARBW of 25.7% and a gain of about 3 dB higher than that of the bidirectional counterpart. In all these 3-dB axialratio bands, impedance matching with VSWR ≤ 2 is also achieved. Most importantly, the design concepts, procedures, and rules for the proposed antenna are presented in detail.
Diminished Auditory Responses during NREM Sleep Correlate with the Hierarchy of Language Processing

Natural sleep provides a powerful model system for studying the neuronal correlates of awareness and state changes in the human brain. To quantitatively map the nature of sleep-induced modulations in sensory responses we presented participants with auditory stimuli possessing different levels of linguistic complexity. Ten participants were scanned using functional magnetic resonance imaging (fMRI) during the waking state and after falling asleep. Sleep staging was based on heart rate measures validated independently on 20 participants using concurrent EEG and heart rate measurements and the results were confirmed using permutation analysis. Participants were exposed to three types of auditory stimuli: scrambled sounds, meaningless word sentences and comprehensible sentences. During non-rapid eye movement (NREM) sleep, we found diminishing brain activation along the hierarchy of language processing, more pronounced in higher processing regions. Specifically, the auditory thalamus showed similar activation levels during sleep and waking states, primary auditory cortex remained activated but showed a significant reduction in auditory responses during sleep, and the high order language-related representation in inferior frontal gyrus (IFG) cortex showed a complete abolishment of responses during NREM sleep. In addition to an overall activation decrease in language processing regions in superior temporal gyrus and IFG, those areas manifested a loss of semantic selectivity during NREM sleep. Our results suggest that the decreased awareness to linguistic auditory stimuli during NREM sleep is linked to diminished activity in high order processing stations.
Workflow Automation: Overview and Research Issues

Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.
RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for crosssubject and cross-view measurement, and on the NorthwesternUCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.
Dynamic Capacity Networks

We introduce the Dynamic Capacity Network (DCN), a neural network that can adaptively assign its capacity across different portions of the input data. This is achieved by combining modules of two types: low-capacity sub-networks and high-capacity sub-networks. The low-capacity sub-networks are applied across most of the input, but also provide a guide to select a few portions of the input on which to apply the high-capacity sub-networks. The selection is made using a novel gradient-based attention mechanism, that efficiently identifies the modules and input features for which the DCN’s output is most sensitive and to which we should devote more capacity. We focus our empirical evaluation on the cluttered MNIST and SVHN image datasets. Our findings indicate that DCNs are able to drastically reduce the number of computations, compared to traditional convolutional neural networks, while maintaining similar performance.
A Simple Proof of the Restricted Isometry Property for Random Matrices

We give a simple technique for verifying the Restricted Isometry Property (as introduced by Candès and Tao) for random matrices that underlies Compressed Sensing. Our approach has two main ingredients: (i) concentration inequalities for random inner products that have recently provided algorithmically simple proofs of the Johnson–Lindenstrauss lemma; and (ii) covering numbers for finite-dimensional balls in Euclidean space. This leads to an elementary proof of the Restricted Isometry Property and brings out connections between Compressed Sensing and the Johnson– Lindenstrauss lemma. As a result, we obtain simple and direct proofs of Kashin’s theorems on widths of finite balls in Euclidean space (and their improvements due to Gluskin) and proofs of the existence of optimal Compressed Sensing measurement matrices. In the process, we also prove that these measurements have a certain universality with respect to the sparsity-inducing basis. Communicated by Emmanuel J. Candès. R. Baraniuk · M. Davenport Department of Electrical and Computer Engineering, Rice University, MS-380, 6100 Main Street, Houston, TX 77005, USA R. Baraniuk e-mail: richb@rice.edu M. Davenport e-mail: md@rice.edu R. DeVore (!) Industrial Mathematics Institute, Department of Mathematics and Statistics, University of South Carolina, Columbia, SC 29208, USA e-mail: devore@math.sc.edu M. Wakin Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA e-mail: wakin@umich.edu 254 Constr Approx (2008) 28: 253–263
Publicity Trends in Arrests of " Online Predators " How the National Juvenile Online Victimization (n‐jov) Study Was Conducted

about " online predators " * – sex of‐ fenders who use the Internet to meet juvenile victims – has raised considerable alarm about the extent to which Internet use may be put‐ ting children and adolescents at risk for sexual abuse and exploitation. Media stories and Internet safety messages have raised fears by describing violent offenders who use the Inter‐ net to prey on naïve children by tricking them into face‐to‐face meetings or tracking them down through information posted online. Law enforcement has mobilized on a number of fronts, setting up task forces to identify and prosecute online predators, developing under‐ cover operations, and urging social networking sites to protect young users. Unfortunately, however, reliable information on the scope and nature of the online predator problem remains scarce. Established criminal justice data collection systems do not gather detailed data on such crimes that could help inform public policy and education. To remedy this information vacuum, the Crimes against Children Research Center at the University of New Hampshire conducted two waves of a The N‐JOV Study collected information from a national sample of law en‐ forcement agencies about the prevalence of arrests for and characteristics of online sex crimes against minors during two 12 month periods: July 1, 2000 through June 30, 2001 (Wave 1) and calendar year 2006 (Wave 2). For both Waves, we used a two‐phase process of mail surveys followed by telephone interviews to collect data from a national sample of the same lo‐ cal, county, state, and federal law enforcement agencies. First, we sent the mail surveys to a national sample of more than 2,500 agencies. These sur‐ veys asked if agencies had made arrests for online sex crimes against minors during the respective one‐year timeframes. Then we conducted detailed telephone interviews with law enforcement investigators about a random sample of arrest cases reported in the mail surveys. For the telephone interviews, we designed a sampling procedure that took into account the number of arrests reported by an agency, so that we would not unduly burden respondents in agencies with many cases. If an agency reported between one and three arrests for online sex crimes, we conducted follow‐up interviews for every case. For agencies that reported more than three arrests, we conducted interviews for all cases that involved youth vic‐ tims (victims who were located and contacted during the investigation), and sampled other arrest cases (i.e., …
Electrically Small Resonators for Planar Metamaterial , Microwave Circuit and Antenna Design : A Comparative Analysis

Planar metamaterials and many microwave circuits and antennas are designed by means of resonators with dimensions much smaller than the wavelength at their resonance frequency. There are many types of such electrically small resonators, and the main purpose of this paper is to compare them as building blocks for the implementation of microwave components. Aspects such as resonator size, bandwidth, their circuit models when they are coupled to transmission lines (as is usually required), as well as key applications, will be considered.
A Game Theoretic Framework for Incentives in P2P Systems

Peer-To-Peer (P2P) networks are self-organizing, distributed systems, with no centralized authority or infrastructure. Because of the voluntary participation, the availability of resources in a P2P system can be highly variable and unpredictable. In this paper, we use ideas from Game Theory to study the interaction of strategic and rational peers, and propose a differential service-based incentive scheme to improve the system’s performance.
Bank distress in the news: Describing events through deep learning

While many models are purposed for detecting the occurrence of significant events in financial systems, the task of providing qualitative detail on the developments is not usually as well automated. We present a deep learning approach for detecting relevant discussion in text and extracting natural language descriptions of events. Supervised by only a small set of event information, comprising entity names and dates, the model is leveraged by unsupervised learning of semantic vector representations on extensive text data. We demonstrate applicability to the study of financial risk based on news (6.6M articles), particularly bank distress and government interventions (243 events), where indices can signal the level of bank-stress-related reporting at the entity level, or aggregated at national or European level, while being coupled with explanations. Thus, we exemplify how text, as timely, widely available and descriptive data, can serve as a useful complementary source of information for financial and systemic risk analytics.
Boost-Flyback Single-Stage PFC Converter with Large DC Bus Voltage Ripple

The life time of lighting emitting diode (LED) can reach 100,000 hours. Compared to other power supplies, the power supply for LED requires long life time in addition to high efficiency and high power factor. Electrolytic capacitor has the shortest life in switching power supplies. In order to prolong the life time of power supply, it is necessary to reduce the storage capacitance and use other kind of capacitor instead of electrolytic capacitor. In this paper the relationship between storage capacitor voltage ripple and capacitance is analyzed. The PF and DC bus voltage of the Boost-Flyback single-stage PFC converter is analyzed. When Flyback works in DCM, the DC bus voltage is independent of the load. Then the capacitance is reduced and the voltage ripple is increased, in this way, the high voltage ceramic capacitor can be used instead of electrolytic capacitor. The analysis is verified by the experiment.
Comparative study of encryption algorithm over big data in cloud systems

Big data refers to data that is too large and complex to be processed. Big data handles voluminous amount of structured, semi structured and unstructured data with standard tools. Big Data also refers to the data where the volume, velocity or variety of data. It combines the historic data with the present data to predict the outcomes. In this regard, providing security for these data is a challenging task. Apache Hadoop was one of the tool designed to handle big data. Apache Hadoop along with other software products were used to process and interpret the results of big data. Hadoop includes various main components like Map reduce and HDFS for handling big data. Cloud computing is the technology that provides the online data storage. But here providing security is the key issue. In this paper, an integrated approach is introduced to encrypt and decrypt the data before sending on cloud. To achieve better performance and security performance analysis on different techniques can be applied based on different parameters.
A Component Model for Augmented / Mixed Reality Applications with Reconfigurable Data-flow

In this paper, we will introduce a new componentbased framework for mixed and augmented reality applications. We will see the developed framework intends to meet several requirements such as portability, variable component granularity, scalability, and a high level of abstraction for end-users. The provided framework, at runtime, is composed of a set of several libraries where components are stored, an XML file in which configuration and communications between components are described and a runtime program that deals with the two previous parts of the system. Our component model can also deal with reconfigurable data-flows by the use of a finite state-machine, using several component configurations within an application.
Statistical Methods for Speech Recognition

As known, to finish this book, you may not need to get it at once in a day. Doing the activities along the day may make you feel so bored. If you try to force reading, you may prefer to do other entertaining activities. But, one of concepts we want you to have this book is that it will not make you feel bored. Feeling bored when reading will be only unless you don't like the book. statistical methods for speech recognition really offers what everybody wants.
The Web Never Forgets: Persistent Tracking Mechanisms in the Wild

We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.
 Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.
Designing the whyline: a debugging interface for asking questions about program behavior

Debugging is still among the most common and costly of programming activities. One reason is that current debugging tools do not directly support the inquisitive nature of the activity. Interrogative Debugging is a new debugging paradigm in which programmers can ask why did and even why didn't questions directly about their program's runtime failures. The Whyline is a prototype Interrogative Debugging interface for the Alice programming environment that visualizes answers in terms of runtime events directly relevant to a programmer's question. Comparisons of identical debugging scenarios from user tests with and without the Whyline showed that the Whyline reduced debugging time by nearly a factor of 8, and helped programmers complete 40% more tasks.
A Lightweight Semantic Web-based Approach for Data Annotation on IoT Gateways

Internet of Things (IoT) applications rely on networks composed of set of heterogeneous sensors and smart devices, which have the capability to constantly, observe the surroundings and gather data. This heterogeneity is reflected on raw data gathered by such type of systems. Consequently, the task of high-level IoT applications to interpret such data and detect events in the real world is more complex. Moreover, data heterogeneity leads to the lack of interoperability between IoT applications. Semantic Web (SW) technologies have been widely adopted to model and integrate data from different sources on the web; extending them to the IoT domain can be used to mitigate the aforementioned challenges. Semantically annotating IoT data is a fundamental step toward developing smarter and interoperable IoT applications. However, this type of process requires a large amount of computing resources, especially in scenarios where a large number of sensors is expected to be involved such as smart city. To address these challenges, we propose a lightweight semantic annotation approach that can be implemented on resource-constrained IoT gateways connected to a limited number of sensors. To evaluate the feasibility of the proposed approach, we have carried out a set of experimentations using a middleware prototype implementation. Several benchmarks are considered such as: Data size, response time, and resource utilization.
Large Margin DAGs for Multiclass Classification

We present a new learning architecture: the Decision Direct ed Acyclic Graph (DDAG), which is used to combine many two-class classi fier into a multi-class classifier. For an N -class problem, the DDAG containsN(N 1)=2 classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperpla n s; the resulting bound on the test error depends on N and on the margin achieved at the nodes, but not on the dimension of the space. This motiv ates an algorithm, DAGSVM, which operates in a kernel-induced feat ure space and uses two-class maximal margin hyperplanes at each decis ion-node of the DDAG. The DAGSVM is substantially faster to train and e valuate than either the standard algorithm or Max Wins, while mai ntaining comparable accuracy to both of these algorithms.
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval

Most topic models, such as latent Dirichlet allocation, rely on the bag-of-words assumption. However, word order and phrases are often critical to capturing the meaning of text in many text mining tasks. This paper presents topical n-grams, a topic model that discovers topics as well as topical phrases. The probabilistic model generates words in their textual order by, for each word, first sampling a topic, then sampling its status as a unigram or bigram, and then sampling the word from a topic-specific unigram or bigram distribution. Thus our model can model "white house" as a special meaning phrase in the 'politics' topic, but not in the 'real estate' topic. Successive bigrams form longer phrases. We present experiments showing meaningful phrases and more interpretable topics from the NIPS data and improved information retrieval performance on a TREC collection.
Autonomous Battery Exchange of UAVs with a Mobile Ground Base

This paper presents the autonomous battery exchange operation for small scale UAVs, using a mobile ground base that carries a robotic arm and a service station containing the battery exchange mechanism. The goal of this work is to demonstrate the means to increase the autonomy and persistence of robotic systems without requiring human intervention. The design and control of the system and its components are presented in detail, as well as the collaborative software framework used to plan and execute complex missions. Finally, the results of autonomous outdoor experiments are presented, in which the ground rover successfully localizes, retrieves, services, and deploys the landed UAV, proving its capacity to extend and enhance autonomous operations.
Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction . But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework , the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning  or reinforcement learning . Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.