nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear Coordinated Steering and Braking Control of Vision-Based Autonomous Vehicles in Emergency Obstacle Avoidance

This paper discusses dynamic control design for automated driving of vision-based autonomous vehicles, with a special focus on the coordinated steering and braking control in emergency obstacle avoidance. An autonomous vehicle is a complex multi-input and multi-output (MIMO) system, which possesses the features of parameter uncertainties and strong nonlinearities, and the coupled phenomena of longitudinal and lateral dynamics are evident in a combined cornering and braking maneuver. In this work, an effective coordinated control system for automated driving is proposed to deal with these coupled and nonlinear features and reject the disturbances. First, a vision algorithm is constructed to detect the reference path and provide the local location information between vehicles and reference path in real time. Then, a novel coordinated steering and braking control strategy is proposed based on the nonlinear backstepping control theory and the adaptive fuzzy sliding-mode control technique, and the asymptotic convergence of the proposed coordinated control system is proven by the Lyapunov theory. Finally, experimental tests manifest that the proposed control strategy possesses favorable tracking performance and enhances the riding comfort and stability of autonomous vehicles.
Affordance-based imitation learning in robots

In this paper we build an imitation learning algorithm for a humanoid robot on top of a general world model provided by learned object affordances. We consider that the robot has previously learned a task independent affordance-based model of its interaction with the world. This model is used to recognize the demonstration by another agent (a human) and infer the task to be learned. We discuss several important problems that arise in this combined framework, such as the influence of an inaccurate model in the recognition of the demonstration. We illustrate the ideas in the paper with some experimental results obtained with a real robot.
Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations.
 In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.
Large-Scale Liquid Simulation on Adaptive Hexahedral Grids

Regular grids are attractive for numerical fluid simulations because they give rise to efficient computational kernels. However, for simulating high resolution effects in complicated domains they are only of limited suitability due to memory constraints. In this paper we present a method for liquid simulation on an adaptive octree grid using a hexahedral finite element discretization, which reduces memory requirements by coarsening the elements in the interior of the liquid body. To impose free surface boundary conditions with second order accuracy, we incorporate a particular class of Nitsche methods enforcing the Dirichlet boundary conditions for the pressure in a variational sense. We then show how to construct a multigrid hierarchy from the adaptive octree grid, so that a time efficient geometric multigrid solver can be used. To improve solver convergence, we propose a special treatment of liquid boundaries via composite finite elements at coarser scales. We demonstrate the effectiveness of our method for liquid simulations that would require hundreds of millions of simulation elements in a non-adaptive regime.
Understanding the Demographics of Twitter Users

Every second, the thoughts and feelings of millions of peopl across the world are recorded in the form of 140-character tweets using Twitter. However, despite the enormous potential presented by this remarkable data source, we still do no t have an understanding of the Twitter population itself: Who are the Twitter users? How representative of the overall pop ulation are they? In this paper, we take the first steps toward s answering these questions by analyzing data on a set of Twitter users representing over 1% of the U.S. population. We develop techniques that allow us to compare the Twitter population to the U.S. population along three axes (geography, gender, and race/ethnicity), and find that the Twitter popul ation is a highly non-uniform sample of the population.
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on sentence classification tasks (Kim, 2014; Kalchbrenner et al., 2014). However, these models require practitioners to specify the exact model architecture and accompanying hyperparameters, e.g., the choice of filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance (Kim, 2014). We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification. One important observation borne out by our experimental results is that researchers should report performance variances, as these can be substantial due to stochastic initialization and inference.
Sharing features between objects and their attributes

Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training. Rather than treat attributes as intermediate features, we consider how learning visual properties in concert with object categories can regularize the models for both. Given a low-level visual feature space together with attribute-and object-labeled image data, we learn a shared lower-dimensional representation by optimizing a joint loss function that favors common sparsity patterns across both types of prediction tasks. We adopt a recent kernelized formulation of convex multi-task feature learning, in which one alternates between learning the common features and learning task-specific classifier parameters on top of those features. In this way, our approach discovers any structure among the image descriptors that is relevant to both tasks, and allows the top-down semantics to restrict the hypothesis space of the ultimate object classifiers. We validate the approach on datasets of animals and outdoor scenes, and show significant improvements over traditional multi-class object classifiers and direct attribute prediction models.
Supervised sequence labelling with recurrent neural networks

Recurrent neural networks are powerful sequence learners. They are able to incorporate context information in a flexible way, and are robust to localised distortions of the input data. These properties make them well suited to sequence labelling, where input sequences are transcribed with streams of labels. Long short-term memory is an especially promising recurrent architecture, able to bridge long time delays between relevant input and output events, and thereby access long range context. The aim of this thesis is to advance the state-of-the-art in supervised sequence labelling with recurrent networks in general, and long short-term memory in particular. Its two main contributions are (1) a new type of output layer that allows recurrent networks to be trained directly for sequence labelling tasks where the alignment between the inputs and the labels is unknown, and (2) an extension of long short-term memory to multidimensional data, such as images and video sequences. Experimental results are presented on speech recognition, online and offline handwriting recognition, keyword spotting, image segmentation and image classification, demonstrating the advantages of advanced recurrent networks over other sequential algorithms, such as hidden Markov Models.
Cache-oblivious streaming B-trees

A <b><i>streaming B-tree</i></b> is a dictionary that efficiently implements insertions and range queries. We present two cache-oblivious streaming B-trees, the <b><i>shuttle tree</i></b>, and the <b><i>cache-oblivious lookahead array (COLA)</i></b>.
 For block-transfer size <i>B</i> and on <i>N</i> elements, the shuttle tree implements searches in optimal <i>O</i>(log <sub><i>B</i>+1</sub><i>N</i>) transfers, range queries of <i>L</i> successive elements in optimal <i>O</i>(log <sub><i>B</i>+1</sub><i>N</i> +<i>L/B</i>) transfers, and insertions in <i>O</i>((log <sub><i>B</i>+1</sub><i>N</i>)/<i>B</i><sup>Θ(1/(log log <i>B</i>)<sup>2</sup>)</sup>+(log<sup>2</sup><i>N</i>)/<i>B</i>) transfers, which is an asymptotic speedup over traditional B-trees if <i>B</i> ≥ (log <i>N</i>)<sup>1+<i>c</i> log log log<sup>2</sup> <i>N</i></sup> for any constant <i>c</i> >1.
 A COLA implements searches in <i>O</i>(log <i>N</i>) transfers, range queries in O(log <i>N</i> + <i>L/B</i>) transfers, and insertions in amortized <i>O</i>((log <i>N</i>)/<i>B</i>) transfers, matching the bounds for a (cache-aware) buffered repository tree. A partially deamortized COLA matches these bounds but reduces the worst-case insertion cost to <i>O</i>(log <i>N</i>) if memory size <i>M</i> = Ω(log <i>N</i>). We also present a cache-aware version of the COLA, the <b><i>lookahead array</i></b>, which achieves the same bounds as Brodal and Fagerberg's (cache-aware) B<sup>ε</sup>-tree.
 We compare our COLA implementation to a traditional B-tree. Our COLA implementation runs 790 times faster for random inser-tions, 3.1 times slower for insertions of sorted data, and 3.5 times slower for searches.
A Comparison of 10 Sampling Algorithms for Configurable Systems

Almost every software system provides configuration options to tailor the system to the target platform and application scenario. Often, this configurability renders the analysis of every individual system configuration infeasible. To address this problem, researchers have proposed a diverse set of sampling algorithms. We present a comparative study of 10 state-of-the-art sampling algorithms regarding their fault-detection capability and size of sample sets. The former is important to improve software quality and the latter to reduce the time of analysis. In a nutshell, we found that sampling algorithms with larger sample sets are able to detect higher numbers of faults, but simple algorithms with small sample sets, such as most-enabled-disabled, are the most efficient in most contexts. Furthermore, we observed that the limiting assumptions made in previous work influence the number of detected faults, the size of sample sets, and the ranking of algorithms. Finally, we have identified a number of technical challenges when trying to avoid the limiting assumptions, which questions the practicality of certain sampling algorithms.
The Design of High-Level Features for Photo Quality Assessment

We propose a principled method for designing high level features forphoto quality assessment. Our resulting system can classify between high quality professional photos and low quality snapshots. Instead of using the bag of low-level features approach, we first determine the perceptual factors that distinguish between professional photos and snapshots. Then, we design high level semantic features to measure the perceptual differences. We test our features on a large and diverse dataset and our system is able to achieve a classification rate of 72% on this difficult task. Since our system is able to achieve a precision of over 90% in low recall scenarios, we show excellent results in a web image search application.
Mobile exergames for preventing diseases related to childhood obesity

Childhood obesity and physical inactivity are well-known problems which are typically related with videogames. The goal of this study is to develop digital games which motivate children and adolescents to exercise more, instead. The games are realized in such way that no special game consoles are required. The games can be played with a regular computer while mobile phones and heart rate monitors are used as the game controllers. Thus the games can also be played from a public screen in schools, for example, thus offering new exercising possibilities among youngsters. In this study, two exercising games (exergames) are presented and their ability to motivate youngsters is examined by group studies. The results indicate that the developed exergames are suitable in school environment. It is concluded that the games were found engaging and they motivated players to exercise. This indicates a positive effect of exergames in prevention of diseases which are related to childhood obesity and physical inactivity.
Character-based Neural Machine Translation

Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages. In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations. The resulting unlimited-vocabulary and affixaware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German-English WMT task.
A tutorial on synthetic aperture radar

Synthetic Aperture Radar (SAR) has been widely used for Earth remote sensing for more than 30 years. It provides high-resolution, day-and-night and weather-independent images for a multitude of applications ranging from geoscience and climate change research, environmental and Earth system monitoring, 2-D and 3-D mapping, change detection, 4-D mapping (space and time), security-related applications up to planetary exploration. With the advances in radar technology and geo/bio-physical parameter inversion modeling in the 90s, using data from several airborne and spaceborne systems, a paradigm shift occurred from the development driven by the technology push to the user demand pull. Today, more than 15 spaceborne SAR systems are being operated for innumerous applications. This paper provides first a tutorial about the SAR principles and theory, followed by an overview of established techniques like polarimetry, interferometry and differential interferometry as well as of emerging techniques (e.g., polarimetric SAR interferometry, tomography and holographic tomography). Several application examples including the associated parameter inversion modeling are provided for each case. The paper also describes innovative technologies and concepts like digital beamforming, Multiple-Input Multiple-Output (MIMO) and bi- and multi-static configurations which are suitable means to fulfill the increasing user requirements. The paper concludes with a vision for SAR remote sensing.
IMPLICIT SLICING METHOD FOR ADDITIVE MANUFACTURING PROCESSES

All additive manufacturing processes involve a distinct preprocessing stage in which a set of instructions, or GCode, that control the process specific manufacturing tool are generated, otherwise known as slicing. In regards to fused deposition modeling, the GCode defines many crucial parameters which are needed to produce a part, including tool path, infill density, layer height, feed rate, tool head and plate temperature. The majority of current commercial slicing programs generate tool paths explicitly, and do not consider particular geometric properties of parts, such as thin walls, small corners and round profiles that can result in critical voids leading to part failure. This work replicates an implicit slicing algorithm in which functionally derived infill patterns are overlaid onto each layer of a part, reducing the possibility of undesired voids and flaws. This work also further investigates the effects that varying implicitly derived infill patterns have on a part’s mechanical properties through tensile testing dog bone specimens with three different infill patterns and comparing ultimate stress and elastic modulus properties.
Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network

Feature extraction plays an essential role in hand written character recognition because of its effect on the capability of classifiers. This paper presents a framework for investigating and comparing the recognition ability of two classifiers: Deep-Learning Feedforward-Backpropagation Neural Network (DFBNN) and Extreme Learning Machine (ELM). Three data sets: Thai handwritten characters, Bangla handwritten numerals, and Devanagari handwritten numerals were studied. Each data set was divided into two categories: non-extracted and extracted features by Histograms of Oriented Gradients (HOG). The experimental results showed that using HOG to extract features can improve recognition rates of both of DFBNN and ELM. Furthermore, DFBNN provides higher slightly recognition rates than those of ELM.
RASCAL: Calculation of Graph Similarity using Maximum Common Edge Subgraphs

A new graph similarity calculation procedure is introduced for comparing labeled graphs. Given a minimum similarity threshold, the procedure consists of an initial screening process to determine whether it is possible for the measure of similarity between the two graphs to exceed the minimum threshold, followed by a rigorous maximum common edge subgraph (MCES) detection algorithm to compute the exact degree and composition of similarity. The proposed MCES algorithm is based on a maximum clique formulation of the problem and is a significant improvement over other published algorithms. It presents new approaches to both lower and upper bounding as well as vertex selection.  It is often desired in many applications to compare objects represented as graphs and to determine the degree of similarity between the objects. This is sometimes referred to as inexact graph matching. Inexact graph matching is of importance in many applications such as protein ligand docking , video indexing , and computer vision [3, 4]. One algorithmic approach to this problem is to use the maximum common subgraph (MCS) between the graphs being compared. In this paper, a novel and efficient algorithm based on the edge induced formulation of the MCS problem is presented. The algorithm has been developed for use in chemical similarity searching applications where molecular structures are represented as graphs such that
Design of a Wheelchair with Legs for People with Motor Disabilities

Low cost. For example, golf carts, outdoor chairs and special purpose sand buggies. wheelchair. AbstractA proof-of-concept prototype wheelchair with legs for people with motor disabilities is proposed, with the objective of demonstrating the feasibility of a completely new approach to mobility. Our prototype system consists of a chair equipped with wheels and legs, and is capable of traversing uneven terrain and circumventing obstacles. The important design considerations, the system design and analysis, and an experimental prototype of a chair are discussed. The results from the analysis and experimentation show the feasibility of the proposed concept and its advantages.
Bro: A System for Detecting Network Intruders in Real-Time

We describe Bro, a stand-alone system for detecting network intruders in real-time by passively monitoring a network link over which the intruder's traffic transits. We give an overview of the system's design, which emphasizes highspeed (FDDI-rate) monitoring, real-time notification, clear separation between mechanism and policy, and extensibility. To achieve these ends, Bro is divided into an “event engine” that reduces a kernel-filtered network traffic stream into a series of higher-level events, and a “policy script interpreter” that interprets event handlers written in a specialized language used to express a site's security policy. Event handlers can update state information, synthesize new events, record information to disk, and generate real-time notifications via syslog. We also discuss a number of attacks that attempt to subvert passive monitoring systems and defenses against these, and give particulars of how Bro analyzes the six applications integrated into it so far: Finger, FTP, Portmapper, Ident, Telnet and Rlogin. The system is publicly available in source code form.
Supply Chain Risk Management: Literature Review and Future Research

Supply Chain Risk Management has increasingly becoming a more popular research area recently. Various papers, with different focus and approaches, have been published. This paper aims to survey supply chain risk management (SCRM) literature. Paper published in relevant journals from 2000 to 2006 will be analyzed and classified into five categories: conceptual, descriptive, empirical, exploratory cross-sectional, and exploratory longitudinal. The literature review will provide the basis for outlining future research opportunities in this field.
Image Steganography using LSB and LSB+Huffman Code

Steganography is an important area of research in recent years involving a various number of applications. It is the science of embedding information into cover of the media such as text, image, audio, and video. This paper uses two techniques for Steganography (text into image): Least Significant Bit (LSB) and Least Significant Bit with Huffman code (LSB+HUFF). It uses the zigzag scanning for the two methods to increase the security, and compares the results using Peak Signal to Noise Ratio (PSNR). All images used here is a gray scale images to implement the study; what implemented on gray scale image can be applied on colored image.