nils-herrmann/clean_scirepeval huggingface dataset:
A Unifying Framework of Anytime Sparse Gaussian Process Regression Models with Stochastic Variational Inference for Big Data

This paper presents a novel unifying framework of anytime sparse Gaussian process regression (SGPR) models that can produce good predictive performance fast and improve their predictive performance over time. Our proposed unifying framework reverses the variational inference procedure to theoretically construct a non-trivial, concave functional that is maximized at the predictive distribution of any SGPR model of our choice. As a result, a stochastic natural gradient ascent method can be derived that involves iteratively following the stochastic natural gradient of the functional to improve its estimate of the predictive distribution of the chosen SGPR model and is guaranteed to achieve asymptotic convergence to it. Interestingly, we show that if the predictive distribution of the chosen SGPR model satisfies certain decomposability conditions, then the stochastic natural gradient is an unbiased estimator of the exact natural gradient and can be computed in constant time (i.e., independent of data size) at each iteration. We empirically evaluate the trade-off between the predictive performance vs. time efficiency of the anytime SGPR models on two real-world million-sized datasets.
Hydrogen Production from Sea Wave for Alternative Energy Vehicles for Public Transport in Trapani ( Italy )

The coupling of renewable energy and hydrogen technologies represents in the mid-term a very interesting way to match the tasks of increasing the reliable exploitation of wind and sea wave energy and introducing clean technologies in the transportation sector. This paper presents two different feasibility studies: the first proposes two plants based on wind and sea wave resource for the production, storage and distribution of hydrogen for public transportation facilities in the West Sicily; the second applies the same approach to Pantelleria (a smaller island), including also some indications about solar resource. In both cases, all buses will be equipped with fuel-cells. A first economic analysis is presented together with the assessment of the avoidable greenhouse gas emissions during the operation phase. The scenarios addressed permit to correlate the demand of urban transport to renewable resources present in the territories and to the modern technologies available for the production of hydrogen from renewable energies. The study focuses on the possibility of tapping the renewable energy potential (wind and sea wave) for the hydrogen production by electrolysis. The use of hydrogen would significantly reduce emissions of particulate matter and greenhouse gases in urban districts under analysis. The procedures applied in the present article, as well as the main equations used, are the result of previous applications made in different technical fields that show a good replicability.
Linguistic-based evaluation criteria to identify statistical machine translation errors

Machine translation evaluation methods are highly necessary in order to analyze the performance of translation systems. Up to now, the most traditional methods are the use of automatic measures such as BLEU or the quality perception performed by native human evaluations. In order to complement these traditional procedures, the current paper presents a new human evaluation based on the expert knowledge about the errors encountered at several linguistic levels: orthographic, morphological, lexical, semantic and syntactic. The results obtained in these experiments show that some linguistic errors could have more influence than other at the time of performing a perceptual evaluation.
behaviorism: a framework for dynamic data visualization

While a number of information visualization software frameworks exist, creating new visualizations, especially those that involve novel visualization metaphors, interaction techniques, data analysis strategies, and specialized rendering algorithms, is still often a difficult process. To facilitate the creation of novel visualizations we present a new software framework, behaviorism, which provides a wide range of flexibility when working with dynamic information on visual, temporal, and ontological levels, but at the same time providing appropriate abstractions which allow developers to create prototypes quickly which can then easily be turned into robust systems. The core of the framework is a set of three interconnected graphs, each with associated operators: a scene graph for high-performance 3D rendering, a data graph for different layers of semantically-linked heterogeneous data, and a timing graph for sophisticated control of scheduling, interaction, and animation. In particular, the timing graph provides a unified system to add behaviors to both data and visual elements, as well as to the behaviors themselves. To evaluate the framework we look briefly at three different projects all of which required novel visualizations in different domains, and all of which worked with dynamic data in different ways: an interactive ecological simulation, an information art installation, and an information visualization technique.
A Competitive Divide-and-Conquer Algorithm for Unconstrained Large-Scale Black-Box Optimization

This article proposes a competitive divide-and-conquer algorithm for solving large-scale black-box optimization problems for which there are thousands of decision variables and the algebraic models of the problems are unavailable. We focus on problems that are partially additively separable, since this type of problem can be further decomposed into a number of smaller independent subproblems. The proposed algorithm addresses two important issues in solving large-scale black-box optimization: (1) the identification of the independent subproblems without explicitly knowing the formula of the objective function and (2) the optimization of the identified black-box subproblems. First, a Global Differential Grouping (GDG) method is proposed to identify the independent subproblems. Then, a variant of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is adopted to solve the subproblems resulting from its rotation invariance property. GDG and CMA-ES work together under the cooperative co-evolution framework. The resultant algorithm, named CC-GDG-CMAES, is then evaluated on the CEC’2010 large-scale global optimization (LSGO) benchmark functions, which have a thousand decision variables and black-box objective functions. The experimental results show that, on most test functions evaluated in this study, GDG manages to obtain an ideal partition of the index set of the decision variables, and CC-GDG-CMAES outperforms the state-of-the-art results. Moreover, the competitive performance of the well-known CMA-ES is extended from low-dimensional to high-dimensional black-box problems.
Vision-based rain sensing with an in-vehicle camera

An intelligent wiper speed adjustment system can be found in most middle and upper class cars. A core piece of this gadget is the rain sensor on the windshield. With the upcoming number of cars being equipped with an in-vehicle camera for vision-based applications the call for integrating all sensors in the area of the rearview mirror into one device rises to reduce the number of parts and variants. In this paper, functionality of standard rain sensors and different vision-based approaches are explained and a novel rain sensing concept based on an automotive in-vehicle camera for Driver Assistance Systems (DAS) is developed to enhance applicability. Hereby, the region at the bottom of the field of view (FOV) of the imager is used to detect raindrops, while the upper part of the image is still usable for other vision-based applications. A simple algorithm is set up to keep the additional processing time low and to quantitatively gather the rain intensity. Mechanisms to avoid false activations of the wipers are introduced. First experimental experiences based on real scenarios show promising results.
Predicting User Satisfaction with Intelligent Assistants

There is a rapid growth in the use of voice-controlled intelligent personal assistants on mobile devices, such as Microsoft's Cortana, Google Now, and Apple's Siri. They significantly change the way users interact with search systems, not only because of the voice control use and touch gestures, but also due to the dialogue-style nature of the interactions and their ability to preserve context across different queries. Predicting success and failure of such search dialogues is a new problem, and an important one for evaluating and further improving intelligent assistants. While clicks in web search have been extensively used to infer user satisfaction, their significance in search dialogues is lower due to the partial replacement of clicks with voice control, direct and voice answers, and touch gestures.
 In this paper, we propose an automatic method to predict user satisfaction with intelligent assistants that exploits all the interaction signals, including voice commands and physical touch gestures on the device. First, we conduct an extensive user study to measure user satisfaction with intelligent assistants, and simultaneously record all user interactions. Second, we show that the dialogue style of interaction makes it necessary to evaluate the user experience at the overall task level as opposed to the query level. Third, we train a model to predict user satisfaction, and find that interaction signals that capture the user reading patterns have a high impact: when including all available interaction signals, we are able to improve the prediction accuracy of user satisfaction from 71% to 81% over a baseline that utilizes only click and query features.
A Novel Scalable DBSCAN Algorithm with Spark

DBSCAN is a well-known clustering algorithm which is based on density and is able to identify arbitrary shaped clusters and eliminate noise data. However, parallelization of DBSCAN is a challenging work because based on MPI or OpenMP environments, there exist the issues of lack of fault-tolerance and there is no guarantee that workload is balanced. Moreover, programming with MPI requires data scientists to have an advanced experience to handle communication between nodes which is a big challenge. We present a new parallel DBSCAN algorithm using the new big data framework Spark. In order to reduce search time, we apply kd-tree in our algorithm. More specifically, we propose a novel approach to avoid communication between executors so that we can locally obtain partial clusters more efficiently. Based on Java API, we select appropriate data structures carefully: Using Queue to contain neighbors of the data point, and using Hashtable when checking the status of and processing the data points. In addition, we use other advanced features from Spark to make our implementation more effective. We implement the algorithm in Java and evaluate its scalability by using different number of processing cores. Our experiments demonstrate that the algorithm we propose scales up very well. Using data sets containing up to 1 million high-dimensional points, we show that our proposed algorithm achieves speedups up to 6 using 8 cores (10k), 10 using 32 cores (100k), and 137 using 512 cores (1m). Another experiment using 10k data points is conducted and the result shows that the algorithm with MapReduce achieves speedups to 1.3 using 2 cores, 2.0 using 4 cores, and 3.2 using 8 cores.
Classification of breast tissue by texture analysis

The identification of glandular tissue in breast X-rays (mammograms) is important both in assessing asymmetry between left and right breasts, and in estimating the radiation risk associated with mammographic screening. The appearance of glandular tissue in mammograms is highly variable, ranging from sparse streaks to dense blobs. Fatty regions are generally smooth and dark. Texture analysis provides a flexible approach to discriminating between glandular and fatty regions. We have performed a series of experiments investigating the use of granulometry and texture energy to classify breast tissue. Results of automatic classifications have been compared with a consensus annotation provided by two expert breast radiologists. On a set of 40 mammograms, a correct classification rate of 80% has been achieved using texture energy analysis.
Deformable Shape Completion with Graph Convolutional Autoencoders

The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learning-based method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality.
Temporal segmentation and activity classification from first-person sensing

Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and periodicity of human actions, the complexity of representing articulated motion, and the exponential nature of all possible movement combinations. We provide initial results from investigating two distinct problems -classification of the overall task being performed, and the more difficult problem of classifying individual frames over time into specific actions. We explore first-person sensing through a wearable camera and inertial measurement units (IMUs) for temporally segmenting human motion into actions and performing activity classification in the context of cooking and recipe preparation in a natural environment. We present baseline results for supervised and unsupervised temporal segmentation, and recipe recognition in the CMU-multimodal activity database (CMU-MMAC).
Applying data mining techniques to predict annual yield of major crops and recommend planting different crops in different districts in Bangladesh

Agricultural crop production depends on various factors such as biology, climate, economy and geography. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decisions and policies which lead to increased production. In this paper, our focus is on application of data mining techniques to extract knowledge from the agricultural data to estimate crop yield for major cereal crops in major districts of Bangladesh.
Critical System Properties : Survey and Taxonomy 1

Computer systems are increasingly employed in circumstances where their failure (or even their correct operation, if they are built to flawed requirements) can have serious consequences. There is a surprising diversity of opinion concerning the properties that such “critical systems” should possess, and the best methods to develop them. The dependability approach grew out of the tradition of ultra-reliable and fault-tolerant systems, while the safety approach grew out of the tradition of hazard analysis and system safety engineering. Yet another tradition is found in the security community, and there are further specialized approaches in the tradition of real-time systems. In this report, I examine the critical properties considered in each approach, and the techniques that have been developed to specify them and to ensure their satisfaction. Since systems are now being constructed that must satisfy several of these critical system properties simultaneously, there is particular interest in the extent to which techniques from one tradition support or conflict with those of another, and in whether certain critical system properties are fundamentally compatible or incompatible with each other. As a step toward improved understanding of these issues, I suggest a taxonomy, based on Perrow’s analysis, that considers the complexity of component interactions and tightness of coupling as primary factors. C. Perrow. Normal Accidents: Living with High Risk Technologies. Basic Books, New York, NY, 1984.
2011 Compendium of Physical Activities: a second update of codes and MET values.

The Compendium of Physical Activities was developed to enhance the comparability of results across studies using self-report physical activity (PA) and is used to quantify the energy cost of a wide variety of PA. We provide the second update of the Compendium, called the 2011 Compendium.



The 2011 Compendium retains the previous coding scheme to identify the major category headings and specific PA by their rate of energy expenditure in MET. Modifications in the 2011 Compendium include cataloging measured MET values and their source references, when available; addition of new codes and specific activities; an update of the Compendium tracking guide that links information in the 1993, 2000, and 2011 compendia versions; and the creation of a Web site to facilitate easy access and downloading of Compendium documents. Measured MET values were obtained from a systematic search of databases using defined key words.



The 2011 Compendium contains 821 codes for specific activities. Two hundred seventeen new codes were added, 68% (561/821) of which have measured MET values. Approximately half (317/604) of the codes from the 2000 Compendium were modified to improve the definitions and/or to consolidate specific activities and to update estimated MET values where measured values did not exist. Updated MET values accounted for 73% of all code changes.



The Compendium is used globally to quantify the energy cost of PA in adults for surveillance activities, research studies, and, in clinical settings, to write PA recommendations and to assess energy expenditure in individuals. The 2011 Compendium is an update of a system for quantifying the energy cost of adult human PA and is a living document that is moving in the direction of being 100% evidence based.
Scripting a refactoring with Rascal and Eclipse

To facilitate experimentation with creating new, complex refactorings, we want to reuse existing transformation and analysis code as orchestrated parts of a larger refactoring: i.e., to script refactorings. The language we use to perform this scripting must be able to deal with the diversity of languages, tools, analyses, and transformations that arise in practice. To illustrate one solution to this problem, in this paper we describe, in detail, a specific refactoring script for switching from the Visitor design pattern to the Interpreter design pattern. This script, written in the meta-programming language Rascal, and targeting an interpreter written in Java, extracts facts from the interpreter code using the Eclipse JDT, performs the needed analysis in Rascal, and then transforms the interpreter code using a combination of Rascal code and existing JDT refactorings. Using this script we illustrate how a new, real and complex refactoring can be scripted in a few hundred lines of code and within a short timeframe. We believe the key to successfully building such refactorings is the ability to pair existing tools, focused on specific languages, with general-purpose meta-programming languages.
Stochastic models underlying Croston ' s method for intermittent demand forecasting

Croston’s method is a widely used to predict inventory demand when it is inter­ mittent. However, it is an ad hoc method with no properly formulated underlying stochastic model. In this paper, we explore possible models underlying Croston’s method and three related methods, and we show that any underlying model will be inconsistent with the prop­ erties of intermittent demand data. However, we find that the point forecasts and prediction intervals based on such underlying models may still be useful.
An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

Which ZSL method is more robust to GZSL? An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild Wei-Lun Chao*1, Soravit Changpinyo*1, Boqing Gong2, and Fei Sha1,3 1U. of Southern California, 2U. of Central Florida, 3U. of California, Los Angeles NSF IIS-1566511, 1065243, 1451412, 1513966, 1208500, CCF-1139148, USC Graduate Fellowship, a Google Research Award, an Alfred P. Sloan Research Fellowship and ARO# W911NF-12-1-0241 and W911NF-15-1-0484.
Influential Neighbours Selection for Information Diffusion in Online Social Networks

The problem of maximizing information diffusion through a network is a topic of considerable recent interest. A conventional problem is to select a set of any arbitrary k nodes as the initial influenced nodes so that they can effectively disseminate the information to the rest of the network. However, this model is usually unrealistic in online social networks since we cannot typically choose arbitrary nodes in the network as the initial influenced nodes. From the point of view of an individual user who wants to spread information as much as possible, a more reasonable model is to try to initially share the information with only some of its neighbours rather than a set of any arbitrary nodes; but how can these neighbours be effectively chosen? We empirically study how to design more effective neighbours selection strategies to maximize information diffusion. Our experimental results through intensive simulation on several real- world network topologies show that an effective neighbours selection strategy is to use node degree information for short-term propagation while a naive random selection is also adequate for long-term propagation to cover more than half of a network. We also discuss the effects of the number of initial activated neighbours. If we particularly select the highest degree nodes as initial activated neighbours, the number of initial activated neighbours is not an important factor at least for long-term propagation of information.
Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and “what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.