nils-herrmann/clean_scirepeval huggingface dataset:
Robust tube-based predictive control for mobile robots in off-road conditions

This paper focuses on the design of a tube-based Model Predictive Control law for the control of constrained mobile robots in off-road conditions with longitudinal slip while ensuring robustness and stability. A time-varying trajectory tracking error model is used, where uncertainties are assumed to be bounded and additive. The robust tube-based MPC is compared with other motion control techniques through simulation and physical experiments. These tests show the satisfactory behavior of the presented control strategy.
A Modular High-Voltage Pulse-Generator With Sequential Charging for Water Treatment Applications

High-voltage pulse-generators can be used effectively for bacterial decontamination in water treatment applications. Applying a pulsed electric field to the infected water sample guarantees killing of harmful germs and bacteria. In this paper, a modular high-voltage pulse-generator with sequential charging is proposed for water treatment via underwater pulsed streamer corona discharge. The proposed generator consists of series-connected modules similar to an arm of a modular multilevel converter. The modules' capacitors are charged sequentially from a relatively low-voltage dc supply, then they are connected in series and discharged into the load. Two configurations are proposed in this paper, one for low repetitive pulse rate applications, and the other for high repetitive pulse rates. In the first topology, the equivalent resistance of the infected water sample is used as a charging resistance for the generator's capacitors during the charging process. While in the second topology, the water resistance is bypassed during the charging process, and an external charging resistance with proper value is used instead. In this paper, detailed designs for the proposed pulse-generators are presented and validated by simulation results using MATLAB. A scaled down experimental setup has been built to show the viability of the proposed concept.
phi-LSTM: A Phrase-based Hierarchical LSTM Model for Image Captioning

A picture is worth a thousand words. Not until recently, however, we noticed some success stories in understanding of visual scenes: a model that is capable of to detect/name objects, describe their attributes, and recognize their relationships/interactions. In this paper, we propose a phrase-based hierarchical Long Short-Term Memory (phi-LSTM) model to generate image description. The proposed model encodes sentence as a sequence of combination of phrases and words, instead of a sequence of words alone as in those conventional solutions. The two levels of this model are dedicated to i) learn to generate image relevant noun phrases, and ii) produce appropriate image description from the phrases and other words in the corpus. Adopting the convolutional neural network to learn image features and the LSTM to learn word sequence in a sentence, the proposed model has shown a better or competitive results in comparison to the state-of-the-art models on Flickr8k and Flick30k datasets.
Solidus: An Incentive-compatible Cryptocurrency Based on Permissionless Byzantine Consensus

The decentralized cryptocurrency Bitcoin has experienced great success but also encountered many challenges. One of the challenges has been the long confirmation time and low transaction throughput. Another challenge is the lack of incentives at certain steps of the protocol, raising concerns for transaction withholding, selfish mining, etc. To address these challenges, we propose Solidus, a decentralized cryptocurrency based on permissionless Byzantine consensus. A core technique in Solidus is to use proof of work for leader election to adapt the Practical Byzantine Fault Tolerance (PBFT) protocol to a permissionless setting. We also design Solidus to be incentive compatible and to mitigate selfish mining. Solidus improves on Bitcoin in confirmation time, and provides safety and liveness assuming Byzantine players and the largest coalition of rational players collectively control less than one-third of the computation power.
Computing at the Mobile Edge: Designing Elastic Android Applications for Computation Offloading

Current centralized cloud datacenters provide scalable computation- and storage resources in a virtualized infrastructure and employ a use-based "pay-as-you-go" model. But current mobile devices and their resource-hungry applications (e.g., Speech-or face recognition) demand for these resources on the spot, though a mobile device's intrinsic characteristic is its limited availability of resources (e.g., CPU, storage, bandwidth, energy). Thus, mobile cloud computing (MCC) was introduced to overcome these limitations by transparently making accessible the apparently infinite cloud resources to the mobile devices and by allowing mobile applications to (elastically) expand into the cloud. However, MCC often relies on a stable and fast connection to the mobile devices' surrogate in the cloud, which is a rare case in mobile scenarios. Moreover, the increased latency and the limited bandwidth prevent the use of real-time applications like, e.g. Cloud gaming. Instead, mobile edge computing (MEC) or fog computing tries to provide the necessary resources at the logical edge of the network by including infrastructure components to create ad-hoc mobile clouds. However, this approach requires the replication and management of the applications' business logic in an untrusted, unreliable and constantly changing environment. Consequently, this paper presents a novel approach to allow mobile app developers to easily benefit from the features of MEC. In particular, we present a programming model and framework that directly fit the common app developers' mindset to design elastic and scalable edge-based mobile applications.
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching

Matching elements of two data schemas or two data instances plays a key role in data warehousing, e-business, or even biochemical applications. In this paper we present a matching algorithm based on a fixpoint computation that is usable across different scenarios. The algorithm takes two graphs (schemas, catalogs, or other data structures) as input, and produces as output a mapping between corresponding nodes of the graphs. Depending on the matching goal, a subset of the mapping is chosen using filters. After our algorithm runs, we expect a human to check and if necessary adjust the results. As a matter of fact, we evaluate the ‘accuracy’ of the algorithm by counting the number of needed adjustments. We conducted a user study, in which our accuracy metric was used to estimate the labor savings that the users could obtain by utilizing our algorithm to obtain an initial matching. Finally, we illustrate how our matching algorithm is deployed as one of several high-level operators in an implemented testbed for managing information models and mappings.
Adapting the Sample Size in Particle Filters Through KLD-Sampling

Over the last years, particle filters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particle filters by adapting the size of sample sets during the estimation process. The key idea of the KLD-sampling method is to bound the approximation error introduced by the sample-based representation of the particle filter. The name KLD-sampling is due to the fact that we measure the approximation error by the Kullback-Leibler distance. Our adaptation approach chooses a small number of samples if the density is focused on a small part of the state space, and it chooses a large number of samples if the state uncertainty is high. Both the implementation and computation overhead of this approach are small. Extensive experiments using mobile robot localization as a test application show that our approach yields drastic improvements over particle filters with fixed sample set sizes and over a previously introduced adaptation technique.
Citizens as sensors : the world of volunteered geography

In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation.
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems

The following techniques for uncertainty and sensitivity analysis are briefly summarized: Monte Carlo analysis, differential analysis, response surface methodology, Fourier amplitude sensitivity test, Sobol’ variance decomposition, and fast probability integration. Desirable features of Monte Carlo analysis in conjunction with Latin hypercube sampling are described in discussions of the following topics: (i) properties of random, stratified and Latin hypercube sampling, (ii) comparisons of random and Latin hypercube sampling, (iii) operations involving Latin hypercube sampling (i.e., correlation control, reweighting of samples to incorporate changed distributions, replicated sampling to test reproducibility of results), (iv) uncertainty analysis (i.e,, cumulative distribution functions, complementary cumulative distribution functions, box plots), (v) sensitivity analysis (i.e., scatterplots, regression analysis, correlation analysis, rank transformations, searches for nonrandom patterns), and (vi) analyses involving stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty.
Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma

We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous sounds. However, the ideal property of a set of acoustic features that maximizes ASD performance has not been clarified. By considering outlier-detection-based ASD as a statistical hypothesis test, we defined optimality as an objective function that adopts Neyman-Pearson lemma; the acoustic feature extractor is optimized to extract a set of acoustic features which maximize the true positive rate under an arbitrary false positive rate. The variational auto-encoder is applied as an acoustic feature extractor and optimized to maximize the objective function. We confirmed that the proposed method improved the F-measure score from 0.02 to 0.06 points compared to those of conventional methods, and ASD results of a stereolithography 3D-printer in a real-environment show that the proposed method is effective in identifying anomalous sounds.
SurfNet: Generating 3D Shape Surfaces Using Deep Residual Networks

3D shape models are naturally parameterized using vertices and faces, i.e., composed of polygons forming a surface. However, current 3D learning paradigms for predictive and generative tasks using convolutional neural networks focus on a voxelized representation of the object. Lifting convolution operators from the traditional 2D to 3D results in high computational overhead with little additional benefit as most of the geometry information is contained on the surface boundary. Here we study the problem of directly generating the 3D shape surface of rigid and non-rigid shapes using deep convolutional neural networks. We develop a procedure to create consistent &#x2018;geometry images&#x2019; representing the shape surface of a category of 3D objects. We then use this consistent representation for category-specific shape surface generation from a parametric representation or an image by developing novel extensions of deep residual networks for the task of geometry image generation. Our experiments indicate that our network learns a meaningful representation of shape surfaces allowing it to interpolate between shape orientations and poses, invent new shape surfaces and reconstruct 3D shape surfaces from previously unseen images. Our code is available at https://github.com/sinhayan/surfnet.
Neural style transfer: a paradigm shift for image-based artistic rendering?

In this meta paper we discuss image-based artistic rendering (IB-AR) based on neural style transfer (NST) and argue, while NST may represent a paradigm shift for IB-AR, that it also has to evolve as an interactive tool that considers the design aspects and mechanisms of artwork production. IB-AR received significant attention in the past decades for visual communication, covering a plethora of techniques to mimic the appeal of artistic media. Example-based rendering represents one the most promising paradigms in IB-AR to (semi-)automatically simulate artistic media with high fidelity, but so far has been limited because it relies on pre-defined image pairs for training or informs only low-level image features for texture transfers. Advancements in deep learning showed to alleviate these limitations by matching content and style statistics via activations of neural network layers, thus making a generalized style transfer practicable. We categorize style transfers within the taxonomy of IB-AR, then propose a semiotic structure to derive a technical research agenda for NSTs with respect to the grand challenges of NPAR. We finally discuss the potentials of NSTs, thereby identifying applications such as casual creativity and art production.
3D Object Pose Refinement in Range Images

Estimating the pose of objects from range data is a problem of considerable practical importance for many vision applications. This paper presents an approach for accurate and efficient 3D pose estimation from 2.5D range images. Initialized with an approximate pose estimate, the proposed approach refines it so that it accurately accounts for an acquired range image. This is achieved by using a hypothesize-and-test scheme that combines Particle Swarm Optimization (PSO) and graphicsbased rendering to minimize a cost function of object pose that quantifies the misalignment between the acquired and a hypothesized, rendered range image. Extensive experimental results demonstrate the superior performance of the approach compared to the Iterative Closest Point (ICP) algorithm that is commonly used for pose refinement.
Peer Influence on Attrition in Massively Open Online Courses

In this work, we investigate the role of relational bonds in keeping students engaged in online courses. Specifically, we quantify the manner in which students who demonstrate similar behavior patterns influence each other’s commitment to the course through their interaction with them either explicitly or implicitly. To this end, we design five alternative operationalizations of relationship bonds, which together allow us to infer a scaled measure of relationship between pairs of students. Using this, we construct three variables, namely number of significant bonds, number of significant bonds with people who have dropped out in the previous week, and number of such bonds with people who have dropped in the current week. Using a survival analysis, we are able to measure the prediction strength of these variables with respect to dropout at each time point. Results indicate that higher numbers of significant bonds predicts lower rates of dropout; while loss of significant bonds is associated with higher rates of dropout.
Malware Classification Using Euclidean Distance and Artificial Neural Networks

Most of the samples discovered are variations of known malicious programs and thus have similar structures, however, there is no method of malware classification that is completely effective. To address this issue, the approach proposed in this paper represents a malware in terms of a vector, in which each feature consists of the amount of APIs called from a Dynamic Link Library (DLL). To determine if this approach is useful to classify malware variants into the correct families, we employ Euclidean Distance and a Multilayer Perceptron with several learning algorithms. The experimental results are analyzed to determine which method works best with the approach. The experiments were conducted with a database that contains real samples of worms and trojans and show that is possible to classify malware variants using the number of functions imported per library. However, the accuracy varies depending on the method used for the classification.
Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

Iris recognition is a promising method by which to accurately identify a person. During the iris recognition stage, the features of the iris are extracted, including the unique, individual texture of the iris. The ability to extract the texture of the iris in non-cooperative environments from eye images captured at different distances, containing reflections, and under visible wavelength illumination will lead to increased iris recognition performance. A method that combined multiscale sparse representation of local Radon transform was proposed to down sample a normalized iris into different lengths of scales and different orientations of angles to form an iris feature vector. This research was tested using 1000 eye images from the UBIRIS.v2 database. The results showed that the proposed method performed better than existing methods when dealing with iris images captured at different distances. & 2013 Elsevier Ltd. All rights reserved.
XiOS: Extended Application Sandboxing on iOS

Until very recently it was widely believed that iOS malware is effectively blocked by Apple's vetting process and application sandboxing. However, the newly presented severe malicious app attacks (e.g., Jekyll) succeeded to undermine these protection measures and steal private data, post Twitter messages, send SMS, and make phone calls. Currently, no effective defenses against these attacks are known for iOS.
 The main goal of this paper is to systematically analyze the recent attacks against iOS sandboxing and provide a practical security framework for iOS app hardening which is fully independent of the Apple's vetting process and particularly benefits enterprises to protect employees' iOS devices. The contribution of this paper is twofold: First, we show a new and generalized attack that significantly reduces the complexity of the recent attacks against iOS sandboxing. Second, we present the design and implementation of a novel and efficient iOS app hardening service, XiOS, that enables fine-grained application sandboxing, and mitigates the existing as well as our new attacks. In contrast to previous work in this domain (on iOS security), our approach does not require to jailbreak the device. We demonstrate the efficiency and effectiveness of XiOS by conducting several benchmarks as well as fine-grained policy enforcement on real-world iOS applications.
A fully automatic algorithm for contour detection of bones in hand radiographs using active contours

This paper 1 presents an algorithm for automatically detecting bone contours from hand radiographs using active contours. Prior knowledge is first used to locate initial contours for the snakes inside each bone of interest. Next, an adaptive snake algorithm is applied so that parameters are properly adjusted for each bone specifically. We introduce a novel truncation technique to prevent the external forces of the snake from pulling the contour outside the bones boundaries, yielding excelent results.
Text-Attentional Convolutional Neural Network for Scene Text Detection

Recent deep learning models have demonstrated strong capabilities for classifying text and non-text components in natural images. They extract a high-level feature globally computed from a whole image component (patch), where the cluttered background information may dominate true text features in the deep representation. This leads to less discriminative power and poorer robustness. In this paper, we present a new system for scene text detection by proposing a novel text-attentional convolutional neural network (Text-CNN) that particularly focuses on extracting text-related regions and features from the image components. We develop a new learning mechanism to train the Text-CNN with multi-level and rich supervised information, including text region mask, character label, and binary text/non-text information. The rich supervision information enables the Text-CNN with a strong capability for discriminating ambiguous texts, and also increases its robustness against complicated background components. The training process is formulated as a multi-task learning problem, where low-level supervised information greatly facilitates the main task of text/non-text classification. In addition, a powerful low-level detector called contrast-enhancement maximally stable extremal regions (MSERs) is developed, which extends the widely used MSERs by enhancing intensity contrast between text patterns and background. This allows it to detect highly challenging text patterns, resulting in a higher recall. Our approach achieved promising results on the ICDAR 2013 data set, with an F-measure of 0.82, substantially improving the state-of-the-art results.
A robust RGB-D SLAM system for 3D environment with planar surfaces

With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this paper, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces.
A Statistical Analysis of Cryptocurrencies

Stephen Chan 1, Jeffrey Chu 1, Saralees Nadarajah 1,* and Joerg Osterrieder 2 1 School of Mathematics, University of Manchester, Manchester M13 9PL, UK; stephen.chan@manchester.ac.uk (S.C.); jeffrey.chu@manchester.ac.uk (J.C.) 2 School of Engineering, Zurich University of Applied Sciences, 8401 Winterthur, Switzerland;