nils-herrmann/clean_scirepeval huggingface dataset:
Wireless image communication system for fire-fighting robots

Recently, it has sometimes been impossible for fire-fighting personnel to access the site of a fire, even as the fire causes tremendous property damage and loss of human life, due to high temperatures or the presence of explosive materials. In such environments, fire-fighting robots can be useful for extinguishing a fire, and they should be controlled by remote operators. In order to help a remote operator who is located far away from the fire-fighting robot, wireless image communication systems have been investigated in this study. The developed system uses a High-Speed Downlink Packet Access (HSDPA) network to transmit images, showing the view from a fire-fighting robot. The fire-fighting robots and remote controllers are equipped with industrial computers, and image-transmitting and image-receiving programs function according to various protocols to allow communication between the robots and controllers
Robust facial expression recognition using local binary patterns

A novel low-computation discriminative feature space is introduced for facial expression recognition capable of robust performance over a rang of image resolutions. Our approach is based on the simple local binary patterns (LBP) for representing salient micro-patterns of face images. Compared to Gabor wavelets, the LBP features can be extracted faster in a single scan through the raw image and lie in a lower dimensional space, whilst still retaining facial information efficiently. Template matching with weighted Chi square statistic and support vector machine are adopted to classify facial expressions. Extensive experiments on the Cohn-Kanade Database illustrate that the LBP features are effective and efficient for facial expression discrimination. Additionally, experiments on face images with different resolutions show that the LBP features are robust to low-resolution images, which is critical in real-world applications where only low-resolution video input is available.
A Hybrid Approach to Detect and Localize Texts in Natural Scene Images

Text detection and localization in natural scene images is important for content-based image analysis. This problem is challenging due to the complex background, the non-uniform illumination, the variations of text font, size and line orientation. In this paper, we present a hybrid approach to robustly detect and localize texts in natural scene images. A text region detector is designed to estimate the text existing confidence and scale information in image pyramid, which help segment candidate text components by local binarization. To efficiently filter out the non-text components, a conditional random field (CRF) model considering unary component properties and binary contextual component relationships with supervised parameter learning is proposed. Finally, text components are grouped into text lines/words with a learning-based energy minimization method. Since all the three stages are learning-based, there are very few parameters requiring manual tuning. Experimental results evaluated on the ICDAR 2005 competition dataset show that our approach yields higher precision and recall performance compared with state-of-the-art methods. We also evaluated our approach on a multilingual image dataset with promising results.
On the Latency and Energy Efficiency of Distributed Storage Systems

The increase in data storage and power consumption at data-centers has made it imperative to design energy efficient distributed storage systems (DSS). The energy efficiency of DSS is strongly influenced not only by the volume of data, frequency of data access and redundancy in data storage, but also by the heterogeneity exhibited by the DSS in these dimensions. To this end, we propose and analyze the energy efficiency of a heterogeneous distributed storage system in which <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq1-2459711.gif"/></alternatives></inline-formula> storage servers (disks) store the data of <inline-formula><tex-math notation="LaTeX">$R$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq2-2459711.gif"/></alternatives></inline-formula> distinct classes. Data of class <inline-formula><tex-math notation="LaTeX">$i$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq3-2459711.gif"/></alternatives></inline-formula> is encoded using a <inline-formula><tex-math notation="LaTeX">$(n,k_{i})$</tex-math><alternatives> <inline-graphic xlink:href="kumar-ieq4-2459711.gif"/></alternatives></inline-formula> erasure code and the (random) data retrieval requests can also vary across classes. We show that the energy efficiency of such systems is closely related to the average latency and hence motivates us to study the energy efficiency via the lens of average latency. Through this connection, we show that erasure coding serves the dual purpose of reducing latency and increasing energy efficiency. We present a queuing theoretic analysis of the proposed model and establish upper and lower bounds on the average latency for each data class under various scheduling policies. Through extensive simulations, we present qualitative insights which reveal the impact of coding rate, number of servers, service distribution and number of redundant requests on the average latency and energy efficiency of the DSS.
TADA: Trend Alignment with Dual-Attention Multi-task Recurrent Neural Networks for Sales Prediction

As a common strategy in sales-supply chains, the prediction of sales volume offers precious information for companies to achieve a healthy balance between supply and demand. In practice, the sales prediction task is formulated as a time series prediction problem which aims to predict the future sales volume for different products with the observation of various influential factors (e.g., brand, season, discount, etc.) and corresponding historical sales records. However, with the development of contemporary commercial markets, the dynamic interaction between influential factors with different semantic meanings becomes more subtle, causing challenges in fully capturing dependencies among these variables. Besides, though seeking similar trends from the history benefits the accuracy for the prediction of upcoming sales, existing methods hardly suit sales prediction tasks because the trends in sales time series are more irregular and complex. Hence, we gain insights from the encoder-decoder recurrent neural network (RNN) structure, and propose a novel framework named TADA to carry out trend alignment with dualattention, multi-task RNNs for sales prediction. In TADA, we innovatively divide the influential factors into internal feature and external feature, which are jointly modelled by a multi-task RNN encoder. In the decoding stage, TADA utilizes two attention mechanisms to compensate for the unknown states of influential factors in the future and adaptively align the upcoming trend with relevant historical trends to ensure precise sales prediction. Experimental results on two real-world datasets comprehensively show the superiority of TADA in sales prediction tasks against other state-of-the-art competitors.
Partial Key Grouping: Load-Balanced Partitioning of Distributed Streams

We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce PARTIAL KEY GROUPING (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation. In so doing, it achieves better load balancing than key grouping while being more scalable than shuffle grouping. We test PKG on several large datasets, both real-world and synthetic. Compared to standard hashing, PKG reduces the load imbalance by up to several orders of magnitude, and often achieves nearly-perfect load balance. This result translates into an improvement of up to 175% in throughput and up to 45% in latency when deployed on a real Storm cluster. PARTIAL KEY GROUPING has been integrated in Apache Storm v0.10.
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces

Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.



In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).



We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.



Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.
Compact Broadband Microstrip Crossover With Isolation Improvement and Phase Compensation

In this letter, we present a broadband microstrip crossover structure in two different configurations. Both structures have compact size, i.e., less than 10 mm × 10 mm. Across the measured band from dc to 10 GHz, the structure 1 has less than 1 dB insertion loss, more than 20 dB return loss and at least 23.3 dB isolation. Within the same band, the structure 2 has less than 1 dB insertion loss, more than 22 dB return loss and at least 18.9 dB isolation. Phase difference between two paths in both structures is kept in less than 5 °.
Classification of Defect Clusters on Semiconductor Wafers Via the Hough Transformation

The Hough transformation employing a normal line-to-point parameterization is widely applied in digital image processing for feature detection. In this paper, we demonstrate how this same transformation can be adapted to classify defect signatures on semiconductor wafers as an aid to visual defect metrology. Given a rectilinear grid of die centers on a wafer, we demonstrate an efficient and effective procedure for classifying defect clusters composed of lines at angles of 0deg, 45deg, 90deg, and 135deg from the horizontal, as well as adjacent compositions of such lines. Included are defect clusters representing stripes, scratches at arbitrary angles, and center and edge defects. The principle advantage of the procedure over current industrial practice is that it can be fully automated to screen wafers for further engineering analysis.
Metal-oxide-semiconductor field-effect transistor with a vacuum channel.

High-speed electronic devices rely on short carrier transport times, which are usually achieved by decreasing the channel length and/or increasing the carrier velocity. Ideally, the carriers enter into a ballistic transport regime in which they are not scattered. However, it is difficult to achieve ballistic transport in a solid-state medium because the high electric fields used to increase the carrier velocity also increase scattering. Vacuum is an ideal medium for ballistic transport, but vacuum electronic devices commonly suffer from low emission currents and high operating voltages. Here, we report the fabrication of a low-voltage field-effect transistor with a vertical vacuum channel (channel length of ~20 nm) etched into a metal-oxide-semiconductor substrate. We measure a transconductance of 20 nS µm(-1), an on/off ratio of 500 and a turn-on gate voltage of 0.5 V under ambient conditions. Coulombic repulsion in the two-dimensional electron system at the interface between the oxide and the metal or the semiconductor reduces the energy barrier to electron emission, leading to a high emission current density (~1 × 10(5) A cm(-2)) under a bias of only 1 V. The emission of two-dimensional electron systems into vacuum channels could enable a new class of low-power, high-speed transistors.
Zero-Shot Learning — The Good, the Bad and the Ugly

Due to the importance of zero-shot learning, the number of proposed approaches has increased steadily recently. We argue that it is time to take a step back and to analyze the status quo of the area. The purpose of this paper is three-fold. First, given the fact that there is no agreed upon zero-shot learning benchmark, we first define a new benchmark by unifying both the evaluation protocols and data splits. This is an important contribution as published results are often not comparable and sometimes even flawed due to, e.g. pre-training on zero-shot test classes. Second, we compare and analyze a significant number of the state-of-the-art methods in depth, both in the classic zero-shot setting but also in the more realistic generalized zero-shot setting. Finally, we discuss limitations of the current status of the area which can be taken as a basis for advancing it.
Biotea: semantics for Pubmed Central

A significant portion of biomedical literature is represented in a manner that makes it difficult for consumers to find or aggregate content through a computational query. One approach to facilitate reuse of the scientific literature is to structure this information as linked data using standardized web technologies. In this paper we present the second version of Biotea, a semantic, linked data version of the open-access subset of PubMed Central that has been enhanced with specialized annotation pipelines that uses existing infrastructure from the National Center for Biomedical Ontology. We expose our models, services, software and datasets. Our infrastructure enables manual and semi-automatic annotation, resulting data are represented as RDF-based linked data and can be readily queried using the SPARQL query language. We illustrate the utility of our system with several use cases. Our datasets, methods and techniques are available at http://biotea.github.io.
Forensics examination of volatile system data using virtual introspection

While static examination of computer systems is an important part of many digital forensics investigations, there are often important system properties present only in volatile memory that cannot be effectively recovered using static analysis techniques, such as offline hard disk acquisition and analysis. An alternative approach, involving the live analysis of target systems to uncover this volatile data, presents significant risks and challenges to forensic investigators as observation techniques are generally intrusive and can affect the system being observed. This paper provides a discussion of live digital forensics analysis through virtual introspection and presents a suite of virtual introspection tools developed for Xen (VIX tools). The VIX tools suite can be used for unobtrusive digital forensic examination of volatile system data in virtual machines, and addresses a key research area identified in the virtualization in digital forensics research agenda [22].
Merging Markov and DCT features for multi-class JPEG steganalysis

Blind steganalysis based on classifying feature vectors derived from images is becoming increasingly more powerful. For steganalysis of JPEG images, features derived directly in the embedding domain from DCT coefficients appear to achieve the best performance (e.g., the DCT features and Markov features). The goal of this paper is to construct a new multi-class JPEG steganalyzer with markedly improved performance. We do so first by extending the 23 DCT feature set, then applying calibration to the Markov features described in and reducing their dimension. The resulting feature sets are merged, producing a 274-dimensional feature vector. The new feature set is then used to construct a Support Vector Machine multi-classifier capable of assigning stego images to six popular steganographic algorithms—F5, OutGuess, Model Based Steganography without , and with deblocking, JP Hide&Seek, and Steghide. Comparing to our previous work on multi-classification, 12 the new feature set provides significantly more reliable results.
A new test set compression scheme for circular scan

A new test data compression scheme for circular scan is proposed in this paper. For circular scan, the response of the previous test vector is used as the next test vector’s template, and only the conflicting bits between the previous response and the next vector are required to be updated. To reduce the test data volume and test application time, the problem addressed here is minimizing the number of conflicting bits by optimally reordering test vectors. Each vector represents a city, and the number of conflicting bits between two test vectors is regarded as the distance between them. Thus, the problem corresponds to the travelling salesman problem (TSP), which is NP-complete. The genetic algorithm is used to solve this problem. The experimental results show that the proposed scheme could reduce the test data volume efficiently without any additional hardware cost.
Where to Add Actions in Human-in-the-Loop Reinforcement Learning

In order for reinforcement learning systems to learn quickly in vast action spaces such as the space of all possible pieces of text or the space of all images, leveraging human intuition and creativity is key. However, a human-designed action space is likely to be initially imperfect and limited; furthermore, humans may improve at creating useful actions with practice or new information. Therefore, we propose a framework in which a human adds actions to a reinforcement learning system over time to boost performance. In this setting, however, it is key that we use human effort as efficiently as possible, and one significant danger is that humans waste effort adding actions at places (states) that aren’t very important. Therefore, we propose Expected Local Improvement (ELI), an automated method which selects states at which to query humans for a new action. We evaluate ELI on a variety of simulated domains adapted from the literature, including domains with over a million actions and domains where the simulated experts change over time. We find ELI demonstrates excellent empirical performance, even in settings where the synthetic “experts” are quite poor.
LIGHTNETs: Smart LIGHTing and Mobile Optical Wireless NETworks — A Survey

Recently, rapid increase of mobile devices pushed the radio frequency (RF)-based wireless technologies to their limits. Free-space-optical (FSO), a.k.a. optical wireless, communication has been considered as one of the viable solutions to respond to the ever-increasing wireless capacity demand. Particularly, Visible Light Communication (VLC) which uses light emitting diode (LED) based smart lighting technology provides an opportunity and infrastructure for the high-speed low-cost wireless communication. Though stemming from the same core technology, the smart lighting and FSO communication have inherent tradeoffs amongst each other. In this paper, we present a tutorial and survey of advances in these two technologies and explore the potential for integration of the two as a single field of study: LIGHTNETs. We focus our survey to the context of mobile communications given the recent pressing needs in mobile wireless networking. We deliberate on key challenges involved in designing technologies jointly performing the two functions simultaneously: LIGHTing and NETworking.
A Thin Shell Volume for Modeling Human Hair

Hair-to-hair interaction is often ignored in human hair modeling, due to its computational and algorithmic complexity. In this paper, we present our experimental approach to simulate the complex behavior of long human hair, taking into account hairto-hair interactions. We describe a thin shell volume (TSV) model for enhancing hair realism by simulating complex hair-hair interaction. The TSV is a thin bounding volume that encloses a given hair surface. The TSV method enables virtual hair combing for surface-model hairstyles. Combing produces the effects of hair-hair interaction that occur in real human hair. Any hair model based on a surface representation can benefit from this approach. The TSV is presented mainly as a tool to modeling human hair, but this model also gives rise to global hair animation control.
AR-Drone as a Platform for Robotic Research and Education

This paper presents the AR-Drone quadrotor helicopter as a robotic platform usable for research and education. Apart from the description of hardware and software, we discuss several issues regarding drone equipment, abilities and performance. We show, how to perform basic tasks of position stabilization, object following and autonomous navigation. Moreover, we demonstrate the drone ability to act as an external navigation system for a formation of mobile robots. To further demonstrate the drone utility for robotic research, we describe experiments in which the drone has been used. We also introduce a freely available software package, which allows researches and students to quickly overcome the initial problems and focus on more advanced issues.