nils-herrmann/clean_scirepeval huggingface dataset:
Path-space manipulation of physically-based light transport

Industry-quality content creation relies on tools for lighting artists to quickly prototype, iterate, and refine final renders. As industry-leading studios quickly adopt physically-based rendering (PBR) across their art generation pipelines, many existing tools have become unsuitable as they address only simple effects without considering underlying PBR concepts and constraints. We present a novel light transport manipulation technique that operates directly on path-space solutions of the rendering equation. We expose intuitive direct and indirect manipulation approaches to edit complex effects such as (multi-refracted) caustics, diffuse and glossy indirect bounces, and direct/indirect shadows. With our sketch- and object-space selection, all built atop a parameterized regular expression engine, artists can search and isolate shading effects to inspect and edit. We classify and filter paths on the fly and visualize the selected transport phenomena. We survey artists who used our tool to manipulate complex phenomena on both static and animated scenes.
The Evolution of Sink Mobility Management in Wireless Sensor Networks: A Survey

Sink mobility has long been recognized as an efficient method of improving system performance in wireless sensor networks (WSNs), e.g. relieving traffic burden from a specific set of nodes. Though tremendous research efforts have been devoted to this topic during the last decades, yet little attention has been paid for the research summarization and guidance. This paper aims to fill in the blank and presents an up-to-date survey on the sink mobility issue. Its main contribution is to review mobility management schemes from an evolutionary point of view. The related schemes have been divided into four categories: uncontrollable mobility (UMM), path-restricted mobility (PRM), location-restricted mobility (LRM) and unrestricted mobility (URM). Several representative solutions are described following the proposed taxonomy. To help readers comprehend the development flow within the category, the relationship among different solutions is outlined, with detailed descriptions as well as in-depth analysis. In this way, besides some potential extensions based on current research, we are able to identify several open issues that receive little attention or remain unexplored so far.
Sentiment classification of Hinglish text

In order to determine the sentiment polarity of Hinglish text written in Roman script, we experimented with different combinations of feature selection methods and a host of classifiers using term frequency-inverse document frequency feature representation. We carried out in total 840 experiments in order to determine the best classifiers for sentiment expressed in the news and Facebook comments written in Hinglish. We concluded that a triumvirate of term frequency-inverse document frequency-based feature representation, gain ratio based feature selection, and Radial Basis Function Neural Network as the best combination to classify sentiment expressed in the Hinglish text.
A Survey of Algorithms for Keyword Search on Graph Data

In this chapter, we survey methods that perform keyword search on graph data. Keyword search provides a simple but user-friendly interface to retrieve information from complicated data structures. Since many real life datasets are represented by trees and graphs, keyword search has become an attractive mechanism for data of a variety of types. In this survey, we discuss methods of keyword search on schema graphs, which are abstract representation for XML data and relational data, and methods of keyword search on schema-free graphs. In our discussion, we focus on three major challenges of keyword search on graphs. First, what is the semantics of keyword search on graphs, or, what qualifies as an answer to a keyword search; second, what constitutes a good answer, or, how to rank the answers; third, how to perform keyword search efficiently. We also discuss some unresolved challenges and propose some new research directions
A Laplacian Framework for Option Discovery in Reinforcement Learning

Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents’ intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.
IoT based monitoring and control of appliances for smart home

The recent technology in home automation provides security, safety and comfortable life at home. That is why in the competitive environment and fast world, home automation technology is required for every person. This purposed home automation technology provides smart monitoring and control of the home appliances as well as door permission system for interaction between the visitor and home/office owner. The control and monitoring the status (ON/OFF of the appliances) have been implemented using multiple ways such as The Internet, electrical switch, and Graphical User Interface (GUI) interface. The system has low-cost design, user-friendly interface, and easy installation in home or multi-purpose building. Using this technology, the consumer can reduce the wastage of electrical power by regular monitoring of home appliances or the proper ON/OFF scheduling of the devices.
Design of new intelligent street light control system

In China, the methods of time-control, optical-control and time-optical-control are in common used to control street lamp, particularly in small and medium-sized cities. But due to the backward lighting control and administrative method, the precision is bad, and the result of work is also poor. Through many kinds of sensor combination sense environment's change, the multi-sensor exhibition can combinatory logically control the new intelligent street light controller system. And based on the degree of illumination control fixed time, in the automatic foundation fixed time, according to the multi-sensing exhibition survey data's special combination change, to control the street light nimbly; the system can also realize the automatic timing control, by pre-installing time to control street light switch and ultimately to control the street light time. Simultaneously the system can also realize the automatic sunshine control, which may act according to the actual determination of the sunlight degree of illumination and the degree of illumination control criterion and automatically control street light. On the basis of the merits of the regular control and the optical control, a new street smart controller is designed, with dual functions including timing control and automatic photoelectric control. It allows street lamps automatically lit in the evening, lighting the road for a few hours (adjustable time). After 0:00, when few vehicles or pedestrians go past, it turn off automatically. And terminal controller has wake-up function. After the street light turn out automatically, when the vehicles or pedestrians are going through, street light will be waken up by terminal controller. When the vehicles or pedestrians go past, the lights are off automatically. Design of new intelligent street light control system does not only achieve energy-saving power but also extend the service life of lighting equipment. Moreover, it is controllable, ease of maintenance. At the same time, it is helpful to highlight the festive and other characteristics, and ultimately make street light network, intelligence, humanity and art. An optimal configuration is reported. Finally, the results of experiment are obtained that: when a motor is closed to 15 meters, LED lights are on automatically. While people passed, the distance is proximity 10 meters.
A survey on content-centric technologies for the current Internet: CDN and P2P solutions

0140-3664/$ see front matter 2011  A doi:10.1016/j.comcom.2011.10.005 q This work was partially funded by the European AWARE RECOGNITION (257756), FIRE-SCAMPI (25841 ⇑ Tel.: +39 0503153269. E-mail address: a.passarella@iit.cnr.it One of the most striking properties of the Internet is its flexibility to accommodate features it was not conceived for. Among the most significant examples, in this survey we consider the transition of the Internet from a reliable fault-tolerant network for host-to-host communication to a content-centric network, i.e. a network mostly devoted to support efficient generation, sharing and access to content. We survey this research area according to a top-down approach. We present a conceptual framework that encompasses the key building blocks required to support content-centric networking in the Internet. Then we describe in detail the two most important types of content-centric Internet technologies, i.e., Content-Delivery Networks (CDNs) and P2P systems. For each of them, we show how they cover the key building blocks. We then identify the functional components of CDN and P2P content management solutions, and discuss the main solutions proposed in the literature for each of them. We consider different types of content (both real time and non real time), and different networking environments (fixed, mobile, . . .). Finally, we also discuss the main recent research trends focused on how to design the Future Internet as a native content-centric network.
Twisted GFSR Generators

The generalized feed back shift register (GFSR) algorithm suggested by Lewis and Payne is a widely used pseudorandom number generator, but has the following serious drawbacks: (1) an initialization scheme to assure higher order equidistribution is involved and is time consuming; (2) each bit of the generated words constitutes an m-sequence based on a primitive trinomials, which shows poor randomness with respect to weight distribution; (3) a large working area is necessary; (4) the period of sequence is far shorter than the theoretical upper bound. This paper presents the twisted GFSR (TGFSR) algorithm, a slightly but essentially modified version of the GFSR, which solves all the above problems without loss of merit. Some practical TGFSR generators were implemented and passed strict empirical tests. These new generators are most suitable for simulation of a large distributive system, which requires a number of mutually independent pseudorandom number generators with compact size.
Predicting Outcome in Subarachnoid Hemorrhage (SAH) Utilizing the Full Outline of UnResponsiveness (FOUR) Score.

Existing scoring systems for aneurysmal subarachnoid hemorrhage (SAH) patients fail to accurately predict patient outcome. Our goal was to prospectively study the Full Outline of UnResponsiveness (FOUR) score as applied to newly admitted aneurysmal SAH patients.



All adult patients presenting to Health Sciences Center in Winnipeg from January 2013 to July 2015 (2.5 year period) with aneurysmal SAH were prospectively enrolled in this study. All patients were followed up to 6 months. FOUR score was calculated upon admission, with repeat calculation at 7 and 14 days. The primary outcomes were: mortality, as well as dichotomized 1- and 6-month Glasgow Outcome Scale (GOS) and modified Rankin Scale (mRS) values.



Sixty-four patients were included, with a mean age of 54.2 years (range 26-85 years). The mean FOUR score upon admission pre- and post-external ventricular drain (EVD) was 10.3 (range 0-16) and 11.1 (range 3-16), respectively. There was a statistically significant association between pre-EVD FOUR score (total, eye, respiratory and motor sub-scores) with mortality, 1-month GOS, and 6-month GOS/mRS (p < 0.05 in all). The day 7 total, eye, respiratory, and motor FOUR scores were associated with mortality, 1-month GOS/mRS, and 6-month GOS/mRS (p < 0.05 in all). The day 14 total, eye, respiratory, and motor FOUR scores were associated with 6-month GOS (p < 0.05 in all). The day 7 cumulative FOUR score was associated with the development of clinical vasospasm (p < 0.05).



The FOUR score at admission and day 7 post-SAH is associated with mortality, 1-month GOS/mRS, and 6-month GOS/mRS. The FOUR score at day 14 post-SAH is associated with 6-month GOS. The brainstem sub-score was not associated with 1- or 6-month primary outcomes.
Recovering Low-Rank Matrices From Few Coefficients in Any Basis

We present novel techniques for analyzing the problem of low-rank matrix recovery. The methods are both considerably simpler and more general than previous approaches. It is shown that an unknown matrix of rank can be efficiently reconstructed from only randomly sampled expansion coefficients with respect to any given matrix basis. The number quantifies the “degree of incoherence” between the unknown matrix and the basis. Existing work concentrated mostly on the problem of “matrix completion” where one aims to recover a low-rank matrix from randomly selected matrix elements. Our result covers this situation as a special case. The proof consists of a series of relatively elementary steps, which stands in contrast to the highly involved methods previously employed to obtain comparable results. In cases where bounds had been known before, our estimates are slightly tighter. We discuss operator bases which are incoherent to all low-rank matrices simultaneously. For these bases, we show that randomly sampled expansion coefficients suffice to recover any low-rank matrix with high probability. The latter bound is tight up to multiplicative constants.
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery

Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features Remote Sens. 2015, 7 14681 from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the lowand mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.
Ultra-wideband antipodal vivaldi antenna array with Wilkinson power divider feeding network

In this paper, a two element antipodal Vivaldi antenna array in combination with an ultra-wideband (UWB) Wilkinson power divider feeding network is presented. The antennas are placed in a stacked configuration to provide more gain and to limit cross talk between transmit and receive antennas in radar applications. Each part can be realized with standard planar technology. For a single antenna element, a comparison in performance is done between standard FR4 and a Rogers® RO3003 substrate. The antenna spacing is obtained using the parametric optimizer of CST MWS®. The performance of the power divider and the antenna array is analyzed by means of simulations and measurements.
An empirical analysis of semantic network in online crowdfunding: evidence from kickstarter

Crowdfunding, which helps getting financial sources for niche market entrepreneurs, lowers the entry barrier to start a social and for-profit project. However, it is widely believed that overwhelmingly successful projects, called "blockbuster" projects, would have a significant impact on the overall crowdfunding projects. In theoretical perspectives, there have been two countervailing effects of blockbuster, cannibalization effects and spill-over effects. Therefore, this motivates our research to investigate the competitions among projects within same categories. We expect to fill in the gaps of prior literatures which concluded that the blockbuster project has the same effects on all the other projects within same categories. As a major contribution of this paper, we obtain market structure insights from the creator generated contents based on a text-mining approach and combine it with semantic network analysis to assess the degree to which the latent competition causes cannibalize or spill-over effects inside the same category. Our study uses unique data set, compiled by Python based self-developed crawler. The dataset includes 148,398 projects and 2,915,821 records of daily pledged amount. Our analysis of the concurrent impact of blockbuster project shows the much larger spillover effects to the inside cluster relative to the outside clusters. However, this spillover effect to the outside cluster significantly increase as the magnitude to the inside cluster in the long run perspective. The results of this paper suggest practical marketing implications to entrepreneurs and platform.
Energy efficient wearable sensor node for IoT-based fall detection systems

Falls can cause serious traumas such as brain injuries and bone fractures, especially among elderly people. Fear of falling might reduce physical activities resulting in declining social interactions and eventually causing depression. To lessen the effects of a fall, timely delivery of medical treatment can play a vital role. In a similar scenario, an IoT-based wearable system can pave the most promising way to mitigate serious consequences of a fall while providing the convenience of usage. However, to deliver sufficient degree of monitoring and reliability, wearable devices working at the core of fall detection systems are required to work for a prolonged period of time. In this work, we focus on energy efficiency of a wearable sensor node in an Internet-of-Things (IoT) based fall detection system. We propose the design of a tiny, lightweight, flexible and energy efficient wearable device. We investigate different parameters (e.g. sampling rate, communication bus interface, transmission protocol, and transmission rate) impacting on energy consumption of the wearable device. In addition, we provide a comprehensive analysis of energy consumption of the wearable in different configurations and operating conditions. Furthermore, we provide hints (hardware and software) for system designers implementing the optimal wearable device for IoT-based fall detection systems in terms of energy efficiency and high quality of service. The results clearly indicate that the proposed sensor node is novel and energy efficient. In a critical condition, the wearable device can be used continuously for 76 h with a 1000 mAh li-ion battery.
Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.
Profile hidden Markov models and metamorphic virus detection

Metamorphic computer viruses “mutate” by changing their internal structure and, consequently, different instances of the same virus may not exhibit a common signature. With the advent of construction kits, it is easy to generate metamorphic strains of a given virus. In contrast to standard hidden Markov models (HMMs), profile hidden Markov models (PHMMs) explicitly account for positional information. In principle, this positional information could yield stronger models for virus detection. However, there are many practical difficulties that arise when using PHMMs, as compared to standard HMMs. PHMMs are widely used in bioinformatics. For example, PHMMs are the most effective tool yet developed for finding family related DNA sequences. In this paper, we consider the utility of PHMMs for detecting metamorphic virus variants generated from virus construction kits. PHMMs are generated for each construction kit under consideration and the resulting models are used to score virus and non-virus files. Our results are encouraging, but several problems must be resolved for the technique to be truly practical.
System-level performance evaluation of downlink non-orthogonal multiple access (NOMA)

As a promising downlink multiple access scheme for further LTE enhancement and future radio access (FRA), this paper investigates the system-level performance of non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) on the receiver side. The goal is to clarify the potential gains of NOMA over orthogonal multiple access (OMA) such as OFDMA, taking into account key link adaptation functionalities of the LTE radio interface such as adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ), time/frequency-domain scheduling, and outer loop link adaptation (OLLA), in addition to NOMA specific functionalities such as dynamic multi-user power allocation. Based on computer simulations, we show under multiple configurations that the system-level performance achieved by NOMA is superior to that for OMA.