nils-herrmann/clean_scirepeval huggingface dataset:
Cloudpath: a multi-tier cloud computing framework

Path computing is a new paradigm that generalizes the edge computing vision into a multi-tier cloud architecture deployed over the geographic span of the network. Path computing supports scalable and localized processing by providing storage and computation along a succession of datacenters of increasing sizes, positioned between the client device and the traditional wide-area cloud data-center. CloudPath is a platform that implements the path computing paradigm. CloudPath consists of an execution environment that enables the dynamic installation of light-weight stateless event handlers, and a distributed eventual consistent storage system that replicates application data on-demand. CloudPath handlers are small, allowing them to be rapidly instantiated on demand on any server that runs the CloudPath execution framework. In turn, CloudPath automatically migrates application data across the multiple datacenter tiers to optimize access latency and reduce bandwidth consumption.
GPflow: A Gaussian Process Library using TensorFlow

GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware. 1. GPflow and TensorFlow are available as open source software under the Apache 2.0 license.
Role-dynamics: fast mining of large dynamic networks

To understand the structural dynamics of a large-scale social, biological or technological network, it may be useful to discover behavioral roles representing the main connectivity patterns present over time. In this paper, we propose a scalable non-parametric approach to automatically learn the structural dynamics of the network and individual nodes. Roles may represent structural or behavioral patterns such as the center of a star, peripheral nodes, or bridge nodes that connect different communities. Our novel approach learns the appropriate structural role dynamics for any arbitrary network and tracks the changes over time. In particular, we uncover the specific global network dynamics and the local node dynamics of a technological, communication, and social network. We identify interesting node and network patterns such as stationary and non-stationary roles, spikes/steps in role-memberships (perhaps indicating anomalies), increasing/decreasing role trends, among many others. Our results indicate that the nodes in each of these networks have distinct connectivity patterns that are non-stationary and evolve considerably over time. Overall, the experiments demonstrate the effectiveness of our approach for fast mining and tracking of the dynamics in large networks. Furthermore, the dynamic structural representation provides a basis for building more sophisticated models and tools that are fast for exploring large dynamic networks.
The Natural Language Decathlon: Multitask Learning as Question Answering

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new multitask question answering network (MQAN) that jointly learns all tasks in decaNLP without any task-specific modules or parameters more effectively than sequence-to-sequence and reading comprehension baselines. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN’s multi-pointer-generator decoder is key to this success and that performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.
An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings

Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.
Filtering failure logs for a BlueGene/L prototype

The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L, which can accommodate as many as 128K processors. In this paper, we present our experiences in collecting and filtering error event logs from a 8192 processor BlueGene/L prototype at IBM Rochester, which is currently ranked #8 in the Top-500 list. We analyze the logs collected from this machine over a period of 84 days starting from August 26, 2004. We perform a three-step filtering algorithm on these logs: extracting and categorizing failure events; temporal filtering to remove duplicate reports from the same location; and finally coalescing failure reports of the same error across different locations. Using this approach, we can substantially compress these logs, removing over 99.96% of the 828,387 original entries, and more accurately portray the failure occurrences on this system.
Numerical Solution of Optimal Control Problems by Direct Collocation

By an appropriate discretization of control and state variables, a constrained optimal control problem is transformed into a finite dimensional nonlinear program which can be solved by standard SQP-methods . Convergence properties of the discretization are derived. From a solution of this method known as direct collocation, these properties are used to obtain reliable estimates of adjoint variables. In the presence of active state constraints, these estimates can be significantly improved by including the switching structure of the state constraint into the optimization procedure. Two numerical examples are presented. 1 Statement of problems Systems governed by ordinary differential equations arise in many applications as, e. g., in astronautics, aeronautics, robotics, and economics. The task of optimizing these systems leads to the optimal control problems investigated in this paper. The aim is to find a control vector u(t) and the final time tf that minimize the functional J [u, tf ] = Φ(x(tf ), tf) (1) subject to a system of n nonlinear differential equations ẋi(t) = fi(x(t), u(t), t), i = 1, . . . , n, 0 ≤ t ≤ tf , (2) boundary conditions ri(x(0), x(tf ), tf) = 0, i = 1, . . . , k ≤ 2n, (3) and m inequality constraints gi(x(t), u(t), t) ≥ 0, i = 1, . . . , m, 0 ≤ t ≤ tf . (4) Here, the l vector of control variables is denoted by u(t) = (u1(t), . . . , ul(t)) T and the n vector of state variables is denoted by x(t) = (x1(t), . . . , xn(t)) T . The functions Φ : IR → IR, f : IR → IR, r : IR → IR, and g : IR → IR are assumed to be continuously differentiable. The controls ui : [0, tf ] → IR, i = 1, . . . , l, are assumed to be bounded and measureable and tf may be fixed or free. 1 2 Discretization This section briefly recalls the discretization scheme as described in more detail in . Some of the basic ideas of this discretization scheme have been formerly outlined by Kraft  and Hargraves and Paris [11]. A discretization of the time interval 0 = t1 < t2 < . . . < tN = tf (5) is chosen. The parameters Y of the nonlinear program are the values of control and state variables at the grid points tj, j = 1, . . . , N, and the final time tf Y = (u(t1), . . . , u(tN), x(t1), . . . , x(tN ), tN) ∈ IR. (6) The controls are chosen as piecewise linear interpolating functions between u(tj) and u(tj+1) for tj ≤ t < tj+1 uapp(t) = u(tj) + t− tj tj+1 − tj (u(tj+1)− u(tj)). (7) The states are chosen as continuously differentiable functions and piecewise defined as cubic polynomials between x(tj) and x(tj+1) with ẋapp(s) := f(x(s), u(s), s) at s = tj, tj+1,
Structure and Network in the YouTube Core

In this paper, we present results of an empirical investigation into the social structure of YouTube, addressing friend relations and their correlation with tags applied to uploaded videos. Results indicate that YouTube producers are strongly linked to others producing similar content. Furthermore, there is a socially cohesive core of producers of mixed content, with smaller cohesive groups around Korean music video and anime music videos. Thus, social interaction on YouTube appears to be structured in ways similar to other social networking sites, but with greater semantic coherence around content. These results are explained in terms of the relationship of video producers to the tagging of uploaded content on the site.
License Plate Recognition using Multi-cluster and Multilayer Neural Networks

Vehicle license plate recognition has been a much studied research area in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is rather different for each country. In this paper, an automatic license plate recognition system is proposed for Malaysian vehicles with standard license plates based on image processing, feature extraction and neural networks. The image-processing library is developed in-house which we referred to as Vision System Development Platform (VSDP). Multi-cluster approach is applied to locate the license plate at the right position while Kirsch Edge feature extraction technique is used to extract features from the license plates characters which are then used as inputs to the neural network classifier. The neural network model is the standard multilayered perceptron trained using the back-propagation algorithm. The prototyped system has an accuracy of more than 91% however, suggestions to further improve the system are discussed in this paper based on the analysis of the error
A Graphene Field-Effect Device

In this letter, a top-gated field-effect device (FED) manufactured from monolayer graphene is investigated. Except for graphene deposition, a conventional top-down CMOS-compatible process flow is applied. Carrier mobilities in graphene pseudo-MOS structures are compared to those obtained from the top-gated Graphene-FEDs. The extracted values exceed the universal mobility of silicon and silicon-on-insulator MOSFETs
TagATune: A Game for Music and Sound Annotation

Annotations of audio files can be used to search and index music and sound databases, provide data for system evaluation, and generate training data for machine learning. Unfortunately, the cost of obtaining a comprehensive set of annotations manually is high. One way to lower the cost of labeling is to create games with a purpose that people will voluntarily play, producing useful metadata as a by-product. TagATune is an audio-based online game that aims to extract descriptions of sounds and music from human players. This paper presents the rationale, design and preliminary results from a pilot study using a prototype of TagATune to label a subset of the FreeSound database.
AN HMM-BASED SPEECH SYNTHESIS SYSTEM APPLIED TO ENGLISH

This paper describes an HMM-based speech synthesis system (HTS), in which speech waveform is generated from HMMs themselves, and applies it to English speech synthesis using the general speech synthesis architecture of Festival. Similarly to other datadriven speech synthesis approaches, HTS has a compact language dependent module: a list of contextual factors. Thus, it could easily be extended to other languages, though the first version of HTS was implemented for Japanese. The resulting run-time engine of HTS has the advantage of being small: less than 1 M bytes, excluding text analysis part. Furthermore, HTS can easily change voice characteristics of synthesized speech by using a speaker adaptation technique developed for speech recognition. The relation between the HMM-based approach and other unit selection approaches is also discussed.
Diagonal scaling in Douglas-Rachford splitting and ADMM

The performance of Douglas-Rachford splitting and the alternating direction method of multipliers (ADMM) (i.e. Douglas-Rachford splitting on the dual problem) are sensitive to conditioning of the problem data. For a restricted class of problems that enjoy a linear rate of convergence, we show in this paper how to precondition the optimization data to optimize a bound on that rate. We also generalize the preconditioning methods to problems that do not satisfy all assumptions needed to guarantee a linear convergence. The efficiency of the proposed preconditioning is confirmed in a numerical example, where improvements of more than one order of magnitude are observed compared to when no preconditioning is used.
Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts’ predictions of the future development.
Sliding-mode control of the DC-DC flyback converter in discontinuous conduction mode

In this paper, a novel approach for two-loop control of the DC-DC flyback converter in discontinuous conduction mode is presented by using sliding mode controller. The proposed controller can regulate output of the converter in wide range of input voltage and load resistance. In order to verify accuracy and efficiency of the developed sliding mode controller, proposed method is simulated in MATLAB/Simulink. It is shown that the developed controller has faster dynamic response compared with standard integrated circuit (MIC38C42-5) based regulators.
A constrained matching pursuit approach to audio declipping

We present a novel sparse representation based approach for the restoration of clipped audio signals. In the proposed approach, the clipped signal is decomposed into overlapping frames and the declipping problem is formulated as an inverse problem, per audio frame. This problem is further solved by a constrained matching pursuit algorithm, that exploits the sign pattern of the clipped samples and their maximal absolute value. Performance evaluation with a collection of music and speech signals demonstrate superior results compared to existing algorithms, over a wide range of clipping levels.
A systematic literature review to identify and classify software requirement errors

Most software quality research has focused on identifying faults (i.e., information is incorrectly recorded in an artifact). Because software still exhibits incorrect behavior, a different approach is needed. This paper presents a systematic literature review to develop taxonomy of errors (i.e., the sources of faults) that may occur during the requirements phase of software lifecycle. This taxonomy is designed to aid developers during the requirement inspection process and to improve overall software quality. The review identified 149 papers from the software engineering, psychology and human cognition literature that provide information about the sources of requirements faults. A major result of this paper is a categorization of the sources of faults into a formal taxonomy that provides a starting point for future research into error-based approaches to improving software quality.
Parallel particle swarm optimization clustering algorithm based on MapReduce methodology

Large scale data sets are difficult to manage. Difficulties include capture, storage, search, analysis, and visualization of large data. In particular, clustering of large scale data has received considerable attention in the last few years and many application areas such as bioinformatics and social networking are in urgent need of scalable approaches. The new techniques need to make use of parallel computing concepts in order to be able to scale with increasing data set sizes. In this paper, we propose a parallel particle swarm optimization clustering (MR-CPSO) algorithm that is based on MapReduce. The experimental results reveal that MR-CPSO scales very well with increasing data set sizes and achieves a very close to the linear speedup while maintaining the clustering quality. The results also demonstrate that the proposed MR-CPSO algorithm can efficiently process large data sets on commodity hardware.
Design and Development of an Optimal-Control-Based Framework for Trajectory Planning, Threat Assessment, and Semi-autonomous Control of Passenger Vehicles in Hazard Avoidance Scenarios

This paper describes the design of an optimal-control-based active safety framework that performs trajectory planning, threat assessment, and semiautonomous control of passenger vehicles in hazard avoidance scenarios. This framework allows for multiple actuation modes, diverse trajectory-planning objectives, and varying levels of autonomy. A model predictive controller iteratively plans a best-case vehicle trajectory through a navigable corridor as a constrained optimal control problem. The framework then uses this trajectory to assess the threat posed to the vehicle and intervenes in proportion to this threat. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor of travel. Simulation and experimental results are presented here to demonstrate the framework’s ability to incorporate configurable intervention laws while sharing control with a human driver.
Data for Development: the D4D Challenge on Mobile Phone Data

The Orange “Data for Development” (D4D) challenge is an open data challenge on anonymous call patterns of Orange’s mobile phone users in Ivory Coast. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Ivory Coast population. Participants to the challenge are given access to four mobile phone datasets and the purpose of this paper is to describe the four datasets. The website http://www.d4d.orange.com contains more information about the participation rules. The datasets are based on anonymized Call Detail Records (CDR) of phone calls and SMS exchanges between five million of Orange’s customers in Ivory Coast between December 1, 2011 and April 28, 2012. The datasets are: (a) antenna-to-antenna traffic on an hourly basis, (b) individual trajectories for 50,000 customers for two week time windows with antenna location information, (3) individual trajectories for 500,000 customers over the entire observation period with sub-prefecture location information, and (4) a sample of communication graphs for 5,000 customers. The geofast web interface www.geofast.net for the visualisation of mobile phone communications (countries available: France, Belgium, Ivory Coast). ∗University of Louvain, B-1348 Louvain-la-Neuve, Belgium. vincent.blondel@uclouvain.be †Orange Labs, France 1 ar X iv :1 21 0. 01 37 v2 [ cs .C Y ] 2 8 Ja n 20 13
Corpus Based Urdu Lexicon Development

The paper discusses various phases in Urdu lexicon development from corpus. First the issues related with Urdu orthography such as optional vocalic content, Unicode variations, name recognition, spelling variation etc. have been described, then corpus acquisition, corpus cleaning, tokenization etc has been discussed and finally Urdu lexicon development i.e. POS tags, features, lemmas, phonemic transcription and the format of the lexicon has been discussed .
Adaptive Distance Metric Learning for Clustering

A good distance metric is crucial for unsupervised learning from high-dimensional data. To learn a metric without any constraint or class label information, most unsupervised metric learning algorithms appeal to projecting observed data onto a low-dimensional manifold, where geometric relationships such as local or global pairwise distances are preserved. However, the projection may not necessarily improve the separability of the data, which is the desirable outcome of clustering. In this paper, we propose a novel unsupervised adaptive metric learning algorithm, called AML, which performs clustering and distance metric learning simultaneously. AML projects the data onto a low-dimensional manifold, where the separability of the data is maximized. We show that the joint clustering and distance metric learning can be formulated as a trace maximization problem, which can be solved via an iterative procedure in the EM framework. Experimental results on a collection of benchmark data sets demonstrated the effectiveness of the proposed algorithm.
GANerated Hands for Real-Time 3 D Hand Tracking from Monocular RGB – Supplementary Document –

The architecture of GeoConGAN is based on the CycleGAN [13], i.e. we train two conditional generator and two discriminator networks for synthetic and real images, respectively. Recently, also methods using only one generator and discriminator for enrichment of synthetic images from unpaired data have been proposed. Shrivastava et al.  and Liu et al.  both employ an L1 loss between the conditional synthetic input and the generated output (in addition to the common discriminator loss) due to the lack of image pairs. This loss forces the generated image to be similar to the synthetic image in all aspects, i.e. it might hinder the generator in producing realistic outputs if the synthetic data is not already close. Instead, we decided to use the combination of cycle-consistency and geometric consistency loss to enable the generator networks to move farther from the synthetic data thus approaching the distribution of real world data more closely while preserving the pose of the hand. Our GeoConGAN contains ResNet generator and Least Squares PatchGAN discriminator networks.  We train GeoConGAN in Tensorflow  for 20,000 iterations with a batch size of 8. We initialize the Adam optimizer  with a learning rate of 0.0002, β1 = 0.5, and β2 = 0.999.