nils-herrmann/clean_scirepeval huggingface dataset:
Single image dehazing with a physical model and dark channel prior

We propose a single image dehazing method that is based on a physical model and the dark channel prior principle. The selection of an atmospheric light value is directly responsible for the color authenticity and contrast of the resulting image. Our choice of atmospheric light is based on a variogram, which slowly weakens areas in the image that do not conform to the dark channel prior. Additionally, we propose a fast transmission estimation algorithm to shorten the processing time. Along with a subjective evaluation, the image quality was also evaluated using three indicators: MSE, PSNR, and average gradient. Our experimental results show that the proposed method can obtain accurate dehazing results and improve the operational efficiency. & 2014 Elsevier B.V. All rights reserved.
Television control by hand gestures

We study how a viewer can control a television set remotely by hand gestures. We address two fundamental issues of gesture{based human{computer interaction: (1) How can one communicate a rich set of commands without extensive user training and memorization of gestures? (2) How can the computer recognize the commands in a complicated visual environment? Our solution to these problems exploits the visual feedback of the television display. The user uses only one gesture: the open hand, facing the camera. He controls the television by moving his hand. On the display, a hand icon appears which follows the user's hand. The user can then move his own hand to adjust various graphical controls with the hand icon. The open hand presents a characteristic image which the computer can detect and track. We perform a normalized correlation of a template hand to the image to analyze the user's hand. A local orientation representation is used to achieve some robustness to lighting variations. We made a prototype of this system using a computer workstation and a television. The graphical overlays appear on the computer screen, although they could be mixed with the video to appear on the television. The computer controls the television set through serial port commands to an electronically controlled remote control. We describe knowledge we gained from building the prototype.
Detecting Changes in Suicide Content Manifested in Social Media Following Celebrity Suicides

The Werther effect describes the increased rate of completed or attempted suicides following the depiction of an individual's suicide in the media, typically a celebrity. We present findings on the prevalence of this effect in an online platform: r/SuicideWatch on Reddit. We examine both the posting activity and post content after the death of ten high-profile suicides. Posting activity increases following reports of celebrity suicides, and post content exhibits considerable changes that indicate increased suicidal ideation. Specifically, we observe that post-celebrity suicide content is more likely to be inward focused, manifest decreased social concerns, and laden with greater anxiety, anger, and negative emotion. Topic model analysis further reveals content in this period to switch to a more derogatory tone that bears evidence of self-harm and suicidal tendencies. We discuss the implications of our findings in enabling better community support to psychologically vulnerable populations, and the potential of building suicide prevention interventions following high-profile suicides.
Weighted Joint Bilateral Filter with Slope Depth Compensation Filter for Depth Map Refinement

In this paper, we propose a new refinement filter for depth maps. The filter convolutes a depth map by a jointly computed kernel on a natural image with a weight map. We call the filter weighted joint bilateral filter. The filter fits an outline of an object in the depth map to the outline of the object in the natural image, and it reduces noises. An additional filter of slope depth compensation filter removes blur across object boundary. The filter set’s computational cost is low and is independent of depth ranges. Thus we can refine depth maps to generate accurate depth map with lower cost. In addition, we can apply the filters for various types of depth map, such as computed by simple block matching, Markov random field based optimization, and Depth sensors. Experimental results show that the proposed filter has the best performance of improvement of depth map accuracy, and the proposed filter can perform real-time refinement.
WSMO-Lite and hRESTS: Lightweight semantic annotations for Web services and RESTful APIs

Service-oriented computing has brought special attention to service description, especially in connection with semantic technologies. The expected proliferation of publicly accessible services can benefit greatly from tool support and automation, both ofwhich are the focus of SemanticWebService (SWS) frameworks that especially address service discovery, composition and execution. As the first SWS standard, in 2007 the World Wide Web Consortium produced a lightweight bottom-up specification called SAWSDL for adding semantic annotations to WSDL service descriptions. Building on SAWSDL, this article presents WSMO-Lite, a lightweight ontology of Web service semantics that distinguishes four semantic aspects of services: function, behavior, information model, and nonfunctional properties, which together form a basis for semantic automation. With the WSMO-Lite ontology, SAWSDL descriptions enable semantic automation beyond simple input/output matchmaking that is supported by SAWSDL itself. Further, to broaden the reach of WSMO-Lite and SAWSDL tools to the increasingly common RESTful services, the article adds hRESTS and MicroWSMO, two HTML microformats that mirror WSDL and SAWSDL in the documentation of RESTful services, enabling combiningRESTful serviceswithWSDL-based ones in a single semantic framework. To demonstrate the feasibility and versatility of this approach, the article presents common algorithms for Web service discovery and composition adapted to WSMO-Lite.
Towards unified depth and semantic prediction from a single image

Depth estimation and semantic segmentation are two fundamental problems in image understanding. While the two tasks are strongly correlated and mutually beneficial, they are usually solved separately or sequentially. Motivated by the complementary properties of the two tasks, we propose a unified framework for joint depth and semantic prediction. Given an image, we first use a trained Convolutional Neural Network (CNN) to jointly predict a global layout composed of pixel-wise depth values and semantic labels. By allowing for interactions between the depth and semantic information, the joint network provides more accurate depth prediction than a state-of-the-art CNN trained solely for depth prediction . To further obtain fine-level details, the image is decomposed into local segments for region-level depth and semantic prediction under the guidance of global layout. Utilizing the pixel-wise global prediction and region-wise local prediction, we formulate the inference problem in a two-layer Hierarchical Conditional Random Field (HCRF) to produce the final depth and semantic map. As demonstrated in the experiments, our approach effectively leverages the advantages of both tasks and provides the state-of-the-art results.
Control Strategy for Flexible Microgrid Based on Parallel Line-Interactive UPS Systems

In this paper, the control strategy for a flexible microgrid is presented. The microgrid presented here consists of several line-interactive uninterruptible power supply (UPS) systems connected in parallel. The control technique is based on the droop method to avoid critical communications among UPS units. Thus, a flexible microgrid is obtained to operate in either grid-connected or islanded mode. A small-signal analysis is presented in order to analyze the system stability, which gives rules to design the main control parameters. Simulation and experimental results are presented, showing the feasibility of the proposed controller.
Can we make operating systems reliable and secure?

Microkernels long discarded as unacceptable because of their lower performance compared with monolithic kernels might be making a comeback in operating systems due to their potentially higher reliability, which many researchers now regard as more important than performance. Each of the four different attempts to improve operating system reliability focuses on preventing buggy device drivers from crashing the system. In the Nooks approach, each driver is individually hand wrapped in a software jacket to carefully control its interactions with the rest of the operating system, but it leaves all the drivers in the kernel. The paravirtual machine approach takes this one step further and moves the drivers to one or more machines distinct from the main one, taking away even more power from the drivers. Both of these approaches are intended to improve the reliability of existing (legacy) operating systems. In contrast, two other approaches replace legacy operating systems with more reliable and secure ones. The multiserver approach runs each driver and operating system component in a separate user process and allows them to communicate using the microkernel's IPC mechanism. Finally, Singularity, the most radical approach, uses a type-safe language, a single address space, and formal contracts to carefully limit what each module can do.
Discriminative Reranking for Grammatical Error Correction with Statistical Machine Translation

Research on grammatical error correction has received considerable attention. For dealing with all types of errors, grammatical error correction methods that employ statistical machine translation (SMT) have been proposed in recent years. An SMT system generates candidates with scores for all candidates and selects the sentence with the highest score as the correction result. However, the 1-best result of an SMT system is not always the best result. Thus, we propose a reranking approach for grammatical error correction. The reranking approach is used to re-score N-best results of the SMT and reorder the results. Our experiments show that our reranking system using parts of speech and syntactic features improves performance and achieves state-of-theart quality, with an F0.5 score of 40.0.
A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online

This paper proposes a temporal tracking algorithm based on Random Forest that uses depth images to estimate and track the 3D pose of a rigid object in real-time. Compared to the state of the art aimed at the same goal, our algorithm holds important attributes such as high robustness against holes and occlusion, low computational cost of both learning and tracking stages, and low memory consumption. These are obtained (a) by a novel formulation of the learning strategy, based on a dense sampling of the camera viewpoints and learning independent trees from a single image for each camera view, as well as, (b) by an insightful occlusion handling strategy that enforces the forest to recognize the object's local and global structures. Due to these attributes, we report state-of-the-art tracking accuracy on benchmark datasets, and accomplish remarkable scalability with the number of targets, being able to simultaneously track the pose of over a hundred objects at 30~fps with an off-the-shelf CPU. In addition, the fast learning time enables us to extend our algorithm as a robust online tracker for model-free 3D objects under different viewpoints and appearance changes as demonstrated by the experiments.
The Signal Transmission Mechanism on the Surface of Human Body for Body Channel Communication

The signal transmission mechanism on the surface of the human body is studied for the application to body channel communication (BCC). From Maxwell's equations, the complete equation of electrical field on the human body is developed to obtain a general BCC model. The mechanism of BCC consists of three parts according to the operating frequencies and channel distances: the quasi-static near-field coupling part, the reactive induction-field radiation part, and the surface wave far-field propagation part. The general BCC model by means of the near-field and far-field approximation is developed to be valid in the frequency range from 100 kHz to 100 MHz and distance up to 1.3 m based on the measurements of the body channel characteristics. Finally, path loss characteristics of BCC are formulated for the design of BCC systems and many potential applications.
Concepts for Modelling Enterprise Architectures

A coherent description of enterprise architecture provides insight, enables communication among stakeholders and guides complicated change processes. Unfortunately, so far no enterprise architecture description language exists that fully enables integrated enterprise modelling, because for each architectural domain, architects use their own modelling techniques and concepts, tool support, visualisation techniques, etc. In this paper we outline such an integrated language and we identify and study concepts that relate architectural domains. In our language concepts for describing the relationships between architecture descriptions at the business, application, and technology levels play a central role, related to the ubiquitous problem of business–ICT alignment, whereas for each architectural domain we conform to existing languages or standards such as UML. In particular, usage of services offered by one layer to another plays an important role in relating the behaviour aspects of the layers. The structural aspects of the layers are linked through the interface concept, and the information aspects through realisation relations.
Improved Distributed Principal Component Analysis

We study the distributed computing setting in which there are multiple servers, each holding a set of points, who wish to compute functions on the union of their point sets. A key task in this setting is Principal Component Analysis (PCA), in which the servers would like to compute a low dimensional subspace capturing as much of the variance of the union of their point sets as possible. Given a procedure for approximate PCA, one can use it to approximately solve problems such as k-means clustering and low rank approximation. The essential properties of an approximate distributed PCA algorithm are its communication cost and computational efficiency for a given desired accuracy in downstream applications. We give new algorithms and analyses for distributed PCA which lead to improved communication and computational costs for k-means clustering and related problems. Our empirical study on real world data shows a speedup of orders of magnitude, preserving communication with only a negligible degradation in solution quality. Some of these techniques we develop, such as a general transformation from a constant success probability subspace embedding to a high success probability subspace embedding with a dimension and sparsity independent of the success probability, may be of independent interest.
Exact Calculation of the Hessian Matrix for the Multilayer Perceptron

The elements of the Hessian matrix consist of the second derivatives of the error measure with respect to the weights and thresholds in the network. They are needed in Bayesian estimation of network regularization parameters, for estimation of error bars on the network outputs, for network pruning algorithms, and for fast retraining of the network following a small change in the training data. In this paper we present an extended backpropagation algorithm that allows all elements of the Hessian matrix to be evaluated exactly for a feedforward network of arbitrary topology. Software implementation of the algorithm is straightforward.
Graph Cut Based Continuous Stereo Matching Using Locally Shared Labels

We present an accurate and efficient stereo matching method using locally shared labels, a new labeling scheme that enables spatial propagation in MRF inference using graph cuts. They give each pixel and region a set of candidate disparity labels, which are randomly initialized, spatially propagated, and refined for continuous disparity estimation. We cast the selection and propagation of locally-defined disparity labels as fusion-based energy minimization. The joint use of graph cuts and locally shared labels has advantages over previous approaches based on fusion moves or belief propagation, it produces submodular moves deriving a subproblem optimality, enables powerful randomized search, helps to find good smooth, locally planar disparity maps, which are reasonable for natural scenes, allows parallel computation of both unary and pairwise costs. Our method is evaluated using the Middlebury stereo benchmark and achieves first place in sub-pixel accuracy.
Development of anthropomorphic robot hand with dual-mode twisting actuation and electromagnetic joint locking mechanism

In this paper, the anthropomorphic robot hand is newly proposed by adopting dual-mode twisting actuation and EM joint locking mechanism. The proposed robot hand consists of five finger modules. Each finger has four links and three joints, and Joint 2 and 3 are coupled by the four-bar linkage mechanism. The dual-mode twisting actuation allows that the robot finger can move fast (up to 356.7 deg/sec) in Mode I and generate a large grasping force(up to 36.5 N) in Mode II. In addition, the workspace of the robot finger module is enlarged by EM joint locking mechanism depending on the locking states. In order to verify the effectiveness of the mechanisms adopted in the robot hand, we theoretically and numerically analyze the performances of the robot finger module such as bending speed, fingertip force, and workspace. Finally, through the developed robot hand, we perform the grasping test for various objects and the grasping performance is experimentally demonstrated.
Algorithm 755: ADOL-C: A Package for the Automatic Differentiation of Algorithms Written in C/C++

The C++ package ADOL-C described here facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++. The resulting derivative evaluation routines may be called from C/C++, Fortran, or any other language that can be linked with C. The numerical values of derivative vectors are obtained free of truncation errors at a small multiple of the run-time and randomly accessed memory of the given function evaluation program. Derivative matrices are obtained by columns or rows. For solution curves defined by ordinary differential equations, special routines are provided that evaluate the Taylor coefficient vectors and their Jacobians with respect to the current state vector. The derivative calculations involve a possibly substantial (but always predictable) amount of data that are accessed strictly sequentially and are therefore automatically paged out to external files.
Song From PI: A Musically Plausible Network for Pop Music Generation

We present a novel framework for generating pop music. Our model is a hierarchical Recurrent Neural Network, where the layers and the structure of the hierarchy encode our prior knowledge about how pop music is composed. In particular, the bottom layers generate the melody, while the higher levels produce the drums and chords. We conduct several human studies that show strong preference of our generated music over that produced by the recent method by Google. We additionally show two applications of our framework: neural dancing and karaoke, as well as neural story singing.
Reconstruction of Phase Space of Dynamical Systems Using Method of Time Delay

Selected elements of dynamical system (DS) theory approach to nonlinear time series analysis are introduced. Key role in this concept plays a method of time delay. The method enables us reconstruct phase space trajectory of DS without knowledge of its governing equations. Our variant is tested and compared with wellknown TISEAN package for Lorenz and Hénon systems.  There are number of methods of nonlinear time series analysis (e.g. nonlinear prediction or noise reduction) that work in a phase space (PS) of dynamical systems. We assume that a given time series of some variable is generated by a dynamical system. A specific state of the system can be represented by a point in the phase space and time evolution of the system creates a trajectory in the phase space. From this point of view we consider our time series to be a projection of trajectory of DS to one (or more – when we have more simultaneously measured variables) coordinates of phase space. This view was enabled due to formulation of embedding theorem ,  at the beginning of the 1980s. It says that it is possible to reconstruct the phase space from the time series. One of the most frequently used methods of phase space reconstruction is the method of time delay. The main task while using this method is to determine values of time delay τ and embedding dimension m. We tested individual steps of this method on simulated data generated by Lorenz and Hénon systems. We compared results computed by our own programs with outputs of program package TISEAN created by R. Hegger, H. Kantz, and T. Schreiber . Method of time delay The most frequently used method of PS reconstruction is the method of time delay. If we have a time series of a scalar variable we construct a vector ( ) , ,..., 1 , N i t x i = in phase space in time ti as following: ( ) ( ) ( ) ( ) ( ) ( ) [ ], 1 ,..., 2 , , τ τ τ − + + + = m t x t x t x t x t i i i i i X where i goes from 1 to N – (m – 1)τ, τ is time delay, m is a dimension of reconstructed space (embedding dimension) and M = N – (m – 1)τ is number of points (states) in the phase space. According to embedding theorem, when this is done in a proper way, dynamics reconstructed using this formula is equivalent to the dynamics on an attractor in the origin phase space in the sense that characteristic invariants of the system are conserved. The time delay method and related aspects are described in literature, e.g. . We estimated the two parameters—time delay and embedding dimension—using algorithms below. Choosing a time delay To determine a suitable time delay we used average mutual information (AMI), a certain generalization of autocorrelation function. Average mutual information between sets of measurements A and B is defined :
Creating corpora for speech-to-speech translation

This paper presents three approaches to creating corpora that we are working on for speech-to-speech translation in the travel conversation task. The first approach is to collect sentences that bilingual travel experts consider useful for people going-to/coming-from another country. The resulting English-Japanese aligned corpora are collectively called the basic travel expression corpus (BTEC), which is now being translated into several other languages. The second approach tries to expand this corpus by generating many "synonymous" expressions for each sentence. Although we can create large corpora by the above two approaches relatively cheaply, they may be different from utterances in actual conversation. Thus, as the third approach, we are collecting dialogue corpora by letting two people talk, each in his/her native language, through a speech-to-speech translation system. To concentrate on translation modules, we have replaced speech recognition modules with human typists. We will report some of the characteristics of these corpora as well.