nils-herrmann/clean_scirepeval huggingface dataset:
Artificial neural networks in business: Two decades of research

In recent two decades, artificial neural networks have been extensively used in many business applications. Despite the growing number of research papers, only few studies have been presented focusing on the overview of published findings in this important and popular area. Moreover, the majority of these reviews was introduced more than 15 years ago. The aim of this work is to expand the range of earlier surveys and provide a systematic overview of neural network applications in business between 1994 and 2015. We have covered a total of 412 articles and classified them according to the year of publication, application area, type of neural network, learning algorithm, benchmark method, citations and journal. Our investigation revealed that most of the research has aimed at financial di tress and bankruptcy problems, stock price forecasting, and decision support, with special attention to classification tasks. Besides conventional multilayer feedforward network with gradient descent backpropagation, various hybrid networks have been developed in order to improve the performance of standard models. Even though neural networks have been established as wellknown method in business, there is enormous space for additional research in order to improve their functioning and increase our understanding of this influential area.
Foreign Subtitles Help but Native-Language Subtitles Harm Foreign Speech Perception

Understanding foreign speech is difficult, in part because of unusual mappings between sounds and words. It is known that listeners in their native language can use lexical knowledge (about how words ought to sound) to learn how to interpret unusual speech-sounds. We therefore investigated whether subtitles, which provide lexical information, support perceptual learning about foreign speech. Dutch participants, unfamiliar with Scottish and Australian regional accents of English, watched Scottish or Australian English videos with Dutch, English or no subtitles, and then repeated audio fragments of both accents. Repetition of novel fragments was worse after Dutch-subtitle exposure but better after English-subtitle exposure. Native-language subtitles appear to create lexical interference, but foreign-language subtitles assist speech learning by indicating which words (and hence sounds) are being spoken.
Multi-candidate reduction: Sentence compression as a tool for document summarization tasks

This article examines the application of two single-document sentence compression techniques to the problem of multidocument summarization—a ‘‘parse-and-trim’’ approach and a statistical noisy-channel approach. We introduce the multi-candidate reduction (MCR) framework for multi-document summarization, in which many compressed candidates are generated for each source sentence. These candidates are then selected for inclusion in the final summary based on a combination of static and dynamic features. Evaluations reveal that sentence compression is a valuable component of a larger multi-document summarization framework.
A Machine Learning Approach for Feature-Sensitive Motion Planning

Although there are many motion planning techniques, there is no single one that performs optimally in every environment for every movable object. Rather, each technique has different strengths and weaknesses which makes it best-suited for particular types of situations. Also, since a given environment can consist of vastly different regions, there may not even be a single planner that is well suited for the problem. Ideally, one would use a suite of planners in concert to solve the problem by applying the best-suited planner in each region. In this paper, we propose an automated framework for feature-sensitive motion planning. We use a machine learning approach to characterize and partition C-space into (possibly overlapping) regions that are well suited to one of the planners in our library of roadmap-based motion planning methods. After the best-suited method is applied in each region, their resulting roadmaps are combined to form a roadmap of the entire planning space. We demonstrate on a range of problems that our proposed feature-sensitive approach achieves results superior to those obtainable by any of the individual planners on their own. “A Machine Learning Approach for ...”, Morales et al. TR04-001, Parasol Lab, Texas A&M, February 2004 1
Accelerating Convolutional Neural Networks for Mobile Applications

Convolutional neural networks (CNNs) have achieved remarkable performance in a wide range of computer vision tasks, typically at the cost of massive computational complexity. The low speed of these networks may hinder real-time applications especially when computational resources are limited. In this paper, an efficient and effective approach is proposed to accelerate the test-phase computation of CNNs based on low-rank and group sparse tensor decomposition. Specifically, for each convolutional layer, the kernel tensor is decomposed into the sum of a small number of low multilinear rank tensors. Then we replace the original kernel tensors in all layers with the approximate tensors and fine-tune the whole net with respect to the final classification task using standard backpropagation. \\ Comprehensive experiments on ILSVRC-12 demonstrate significant reduction in computational complexity, at the cost of negligible loss in accuracy. For the widely used VGG-16 model, our approach obtains a 6.6$\times$ speed-up on PC and 5.91$\times$ speed-up on mobile device of the whole network with less than 1\% increase on top-5 error.
Sambot: A self-assembly modular robot for swarm robot

This paper presents a novel self-assembly modular robot (Sambot) that also shares characteristics with self-reconfigurable and self-assembly and swarm robots. Each Sambot can move autonomously and connect with the others. Multiple Sambot can be self-assembled to form a robotic structure, which can be reconfigured into different configurable robots and can locomote. A novel mechanical design is described to realize function of autonomous motion and docking. Introducing embedded mechatronics integrated technology, whole actuators, sensors, microprocessors, power and communication unit are embedded in the module. The Sambot is compact and flexble, the overall size is 80×80×102mm. The preliminary self-assembly and self-reconfiguration of Sambot is discussed, and several possible configurations consisting of multiple Sambot are designed in simulation environment. At last, the experiment of self-assembly and self-reconfiguration and locomotion of multiple Sambot has been implemented.
Iterating between Tools to Create and Edit Visualizations

A common workflow for visualization designers begins with a generative tool, like D3 or Processing, to create the initial visualization; and proceeds to a drawing tool, like Adobe Illustrator or Inkscape, for editing and cleaning. Unfortunately, this is typically a one-way process: once a visualization is exported from the generative tool into a drawing tool, it is difficult to make further, data-driven changes. In this paper, we propose a bridge model to allow designers to bring their work back from the drawing tool to re-edit in the generative tool. Our key insight is to recast this iteration challenge as a merge problem - similar to when two people are editing a document and changes between them need to reconciled. We also present a specific instantiation of this model, a tool called Hanpuku, which bridges between D3 scripts and Illustrator. We show several examples of visualizations that are iteratively created using Hanpuku in order to illustrate the flexibility of the approach. We further describe several hypothetical tools that bridge between other visualization tools to emphasize the generality of the model.
Stock market prediction with multiple classifiers

Stock market prediction is attractive and challenging. According to the efficient market hypothesis, stock prices should follow a random walk pattern and thus should not be predictable with more than about 50 percent accuracy. In this paper, we investigated the predictability of the Dow Jones Industrial Average index to show that not all periods are equally random. We used the Hurst exponent to select a period with great predictability. Parameters for generating training patterns were determined heuristically by auto-mutual information and false nearest neighbor methods. Some inductive machine-learning classifiers—artificial neural network, decision tree, and k-nearest neighbor were then trained with these generated patterns. Through appropriate collaboration of these models, we achieved prediction accuracy up to 65 percent.
Complete Triaxis Magnetometer Calibration in the Magnetic Domain

This paper presents an algorithm for calibrating erroneous tri-axis magnetometers in the magnetic field domain. Unlike existing algorithms, no simplification is made on the nature of errors to ease the estimation. A complete error model, including instrumentation errors (scale factors, nonorthogonality, and offsets) and magnetic deviations (soft and hard iron) on the host platform, is elaborated. An adaptive least squares estimator provides a consistent solution to the ellipsoid fitting problem and the magnetometer’s calibration parameters are derived. The calibration is experimentally assessed with two artificial magnetic perturbations introduced close to the sensor on the host platform and without additional perturbation. In all configurations, the algorithm successfully converges to a good estimate of the said errors. Comparing the magnetically derived headings with a GNSS/INS reference, the results show a major improvement in terms of heading accuracy after the calibration.
6D Relocalisation for RGBD Cameras Using Synthetic View Regression

With the advent of real-time dense scene reconstruction from handheld RGBD cameras , one key aspect to enable robust operation is the ability to relocalise in a previously mapped environment or after loss of measurement. Tasks such as operating on a workspace, where moving objects and occlusions are likely, require a recovery competence in order to be useful. For RGBD cameras, this must also include the ability to relocalise in areas with reduced visual texture.
Parameterized and Scripted Gaits for Modular Snake Robots

Snake robots, sometimes called hyper-redundant mechanisms, can use their many degrees of freedom to achieve a variety of locomotive capabilities. These capabilities are ideally suited for disaster response because the snake robot can thread through tightly packed volumes, accessing locations that people and conventional machinery otherwise cannot. Snake robots also have the advantage of possessing a variety of locomotion capabilities that conventional robots do not. Just like their biological counterparts, snake robots achieve these locomotion capabilities using cyclic motions called gaits. These cyclic motions directly control the snake robot’s internal degrees of freedom which, in turn, causes a net motion, say forward, lateral and rotational, for the snake robot. The gaits described in this paper fall into two categories: parameterized and scripted. The parameterized gaits, as their name suggests, can be described by a relative simple parameterized function, whereas the scripted cannot. This paper describes the functions we prescribed for gait generation and our experiences in making these robots operate in real experiments.
Facial expression recognition under illumination variation

Extracting facial feature is a key step in facial expression recognition (FER). Inaccurate feature extraction very often results in erroneous categorizing of facial expressions. Especially in robotic application, environmental factors such as illumination variation may cause FER system to extract feature inaccurately. In this paper, we propose a robust facial feature point extraction method to recognize facial expression in various lighting conditions. Before extracting facial features, a face is localized and segmented from a digitized image frame. Face preprocessing stage consists of face normalization and feature region localization steps to extract facial features efficiently. As regions of interest corresponding to relevant features are determined, Gabor jets are applied based on Gabor wavelet transformation to extract the facial points. Gabor jets are more invariable and reliable than gray-level values, which suffer from ambiguity as well as illumination variation while representing local features. Each feature point can be matched by a phase-sensitivity similarity function in the relevant regions of interest. Finally, the feature values are evaluated from the geometric displacement of facial points. After tested using the AR face database and the database built in our lab, average facial expression recognition rates of 84.1% and 81.3% are obtained respectively.
Answer-Type Prediction for Visual Question Answering

Recently, algorithms for object recognition and related tasks have become sufficiently proficient that new vision tasks can now be pursued. In this paper, we build a system capable of answering open-ended text-based questions about images, which is known as Visual Question Answering (VQA). Our approach's key insight is that we can predict the form of the answer from the question. We formulate our solution in a Bayesian framework. When our approach is combined with a discriminative model, the combined model achieves state-of-the-art results on four benchmark datasets for open-ended VQA: DAQUAR, COCO-QA, The VQA Dataset, and Visual7W.
The Unified Logging Infrastructure for Data Analytics at Twitter

In recent years, there has been a substantial amount of work on large-scale data analytics using Hadoop-based platforms running on large clusters of commodity machines. A lessexplored topic is how those data, dominated by application logs, are collected and structured to begin with. In this paper, we present Twitter’s production logging infrastructure and its evolution from application-specific logging to a unified “client events” log format, where messages are captured in common, well-formatted, flexible Thrift messages. Since most analytics tasks consider the user session as the basic unit of analysis, we pre-materialize “session sequences”, which are compact summaries that can answer a large class of common queries quickly. The development of this infrastructure has streamlined log collection and data analysis, thereby improving our ability to rapidly experiment and iterate on various aspects of the service.
Traffic classification through simple statistical fingerprinting

The classification of IP ows according to the application that generated them is at the basis of any modern network management platform. However, classical techniques such as the ones based on the analysis of transport layer or application layer information are rapidly becoming ineffective. In this paper we present a ow classification mechanism based on three simple properties of the captured IP packets: their size, inter-arrival time and arrival order. Even though these quantities have already been used in the past to define classification techniques, our contribution is based on new structures called protocol fingerprints, which express such quantities in a compact and efficient way, and on a simple classification algorithm based on normalized thresholds. Although at a very early stage of development, the proposed technique is showing promising preliminary results from the classification of a reduced set of protocols.
Adaptive Large Margin Training for Multilabel Classification

Multilabel classification is a central problem in many areas of data analysis, including text and multimedia categorization, where individual data objects need to be assigned multiple labels. A key challenge in these tasks is to learn a classifier that can properly exploit label correlations without requiring exponential enumeration of label subsets during training or testing. We investigate novel loss functions for multilabel training within a large margin framework—identifying a simple alternative that yields improved generalization while still allowing efficient training. We furthermore show how covariances between the label models can be learned simultaneously with the classification model itself, in a jointly convex formulation, without compromising scalability. The resulting combination yields state of the art accuracy in multilabel webpage classification.
Listening to Action-related Sentences Activates Fronto-parietal Motor Circuits

Observing actions made by others activates the cortical circuits responsible for the planning and execution of those same actions. This observationexecution matching system (mirror-neuron system) is thought to play an important role in the understanding of actions made by others. In an fMRI experiment, we tested whether this system also becomes active during the processing of action-related sentences. Participants listened to sentences describing actions performed with the mouth, the hand, or the leg. Abstract sentences of comparable syntactic structure were used as control stimuli. The results showed that listening to action-related sentences activates a left fronto-parieto-temporal network that includes the pars opercularis of the inferior frontal gyrus (Broca's area), those sectors of the premotor cortex where the actions described are motorically coded, as well as the inferior parietal lobule, the intraparietal sulcus, and the posterior middle temporal gyrus. These data provide the first direct evidence that listening to sentences that describe actions engages the visuomotor circuits which subserve action execution and observation.
Using artificial neural networks to predict first-year traditional students second year retention rates

This research investigates the use of Artificial Neural Networks (ANNs) to predict first year student retention rates. Based on a significant body of previous research, this work expands on previous attempts to predict student outcomes using machine-learning techniques. Using a large data set provided by Columbus State University's Information Technology department, ANNs were used to analyze incoming first-year traditional freshmen students' data over a period from 2005--2011. Using several different network designs, the students' data was analyzed, and a basic predictive network was devised. While the overall accuracy was high when including the first and second semesters worth of data, once the data set was reduced to a single semester, the overall accuracy dropped significantly. Using different network designs, more complex learning algorithms, and better training strategies, the prediction accuracy rate for a student's return to the second year approached 75% overall. Since the rate is still low, there is room for improvements, and several techniques that might increase the reliability of these networks are discussed.
Maximum-Likelihood Detection for Energy-Efficient Timing Acquisition in NB-IoT

Initial timing acquisition in narrow-band IoT (NB- IoT) devices is done by detecting a periodically transmitted known sequence. The detection has to be done at lowest possible latency, because the RF- transceiver, which dominates downlink power consumption of an NB-IoT modem, has to be turned on throughout this time. Auto-correlation detectors show low computational complexity from a signal processing point of view at the price of a higher detection latency. In contrast a maximum likelihood cross-correlation detector achieves low latency at a higher complexity as shown in this paper. We present a hardware implementation of the maximum likelihood cross-correlation detection. The detector achieves an average detection latency which is a factor of two below that of an auto- correlation method and is able to reduce the required energy per timing acquisition by up to 34%.
Observation of Gravitational Waves from a Binary Black Hole Merger.

On September 14, 2015 at 09:50:45 UTC the two detectors of the Laser Interferometer Gravitational-Wave Observatory simultaneously observed a transient gravitational-wave signal. The signal sweeps upwards in frequency from 35 to 250 Hz with a peak gravitational-wave strain of 1.0×10(-21). It matches the waveform predicted by general relativity for the inspiral and merger of a pair of black holes and the ringdown of the resulting single black hole. The signal was observed with a matched-filter signal-to-noise ratio of 24 and a false alarm rate estimated to be less than 1 event per 203,000 years, equivalent to a significance greater than 5.1σ. The source lies at a luminosity distance of 410(-180)(+160)  Mpc corresponding to a redshift z=0.09(-0.04)(+0.03). In the source frame, the initial black hole masses are 36(-4)(+5)M⊙ and 29(-4)(+4)M⊙, and the final black hole mass is 62(-4)(+4)M⊙, with 3.0(-0.5)(+0.5)M⊙c(2) radiated in gravitational waves. All uncertainties define 90% credible intervals. These observations demonstrate the existence of binary stellar-mass black hole systems. This is the first direct detection of gravitational waves and the first observation of a binary black hole merger.
Wideband Flush-Mounted Surface Wave Antenna of Very Low Profile

This paper presents the design of a compact and wideband surface wave antenna that exhibits very low profile and can be flush-mounted on a conformal platform. The proposed antenna comprises a tapered grounded ceramic slab, a unidirectional surface wave launcher, and a tapered impedance transition. The ceramic slab is smoothly tapered to transform the guided surface wave into radiated space wave over a wide frequency range. The surface wave launcher employs a probe-fed parallel plate waveguide with a modified parabolic reflecting wall and a metallic post, which are critical for effectively transforming the cylindrical waves to a unidirectional plane wave within a broad frequency range. Simulated results show that the proposed surface wave antenna achieves a wide operation bandwidth from 6.1 to 18 GHz with a thickness of only ${\bf 0}.{\bf 12}{\lambda _0}$ at the center frequency. Acceptable gain values with stable and quasi end-fire radiation beams are obtained over the entire frequency band. A prototype of the proposed antenna is fabricated and tested. Measured results are in good agreement with simulated ones.
3D FEM simulation of small stripline for EMC testing

This paper overviews the measurement and simulation of the electric parameters of a small stripline. The stripline was designed and built for electromagnetic compatibility (EMC) testing of small electronic devices with the accent on electromagnetic immunity (EMI) testing. The paper describes the measurement of electric field inside the stripline in an anechoic chamber and the simulation using parametric 3D finite element method (FEM) model that confirms the measurement results. The simulation results demonstrate also the usable area inside the stripline for EMI testing.