nils-herrmann/clean_scirepeval huggingface dataset:
Rule Based Machine Translation Combined with Statistical Post Editor for Japanese to English Patent Translation

Since sentences in patent texts are long, they are difficult to translate by a machine. Although statistical machine translation is one of the major streams of the field, long patent sentences are difficult to translate not using syntactic analysis. We propose the combination of a rule based method and a statistical method. It is a rule based machine translation (RMT) with a statistical based post editor (SPE). The evaluation by the NIST score shows RMT+SPE is more accurate than RMT only. Manual checks, however, show the outputs of RMT+SPE often have strange expressions in the target language. So we propose a new evaluation measure NMG (normalized mean grams). Although NMG is based on n-gram, it counts the number of words in the longest word sequence matches between the test sentence and the target language reference corpus. We use two reference corpora. One is the reference translation only the other is a large scaled target language corpus. In the former case, RMT+SPE wins in the later case, RMT wins.
Continuous Requirements Engineering and Human-Centered Agile Software Development

The idea of Continuous Requirements Engineering in relation to a Human-Centered Agile Development Process is discussed. First, it is argued that Continuous Requirements Engineering has to cover design-time and runtime aspects. In this way maintenance is covered as well. Second, arguments are provided for integrating aspects of usability and user experience into requirements specifications. This has to be done continuously. Therefore, the term Continuous Human-Centered Development is introduced and discussed. Based on a process model for SCRUM some aspects of integrating HCD into the development process are discussed.
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

Recently there has been a lot of interest in learning common representations for multiple views of data. Typically, such common representations are learned using a parallel corpus between the two views (say, 1M images and their English captions). In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, V1 and V2) but parallel data is available between each of these views and a pivot view (V3). We propose a model for learning a common representation for V1, V2 and V3 using only the parallel data available between V1V3 and V2V3. The proposed model is generic and even works when there are n views of interest and only one pivot view which acts as a bridge between them. There are two specific downstream applications that we focus on (i) transfer learning between languages L1,L2,...,Ln using a pivot language L and (ii) cross modal access between images and a language L1 using a pivot language L2. Our model achieves state-of-the-art performance in multilingual document classification on the publicly available multilingual TED corpus and promising results in multilingual multimodal retrieval on a new dataset created and released as a part of this work.
The brain in your pocket: Evidence that Smartphones are used to supplant thinking

q Funding for this study was provided by the Natural Sciences and Engineering Research Council of Canada. ⇑ Corresponding author at: Department of Psychology, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. E-mail address: gpennyco@uwaterloo.ca (G. Pennycook). 1 These authors contributed equally to this work. Nathaniel Barr , Gordon Pennycook ⇑,1, Jennifer A. Stolz, Jonathan A. Fugelsang
Lower and upper bounds for the Bin Packing Problem with Fragile Objects

We are given a set of items, each characterized by a weight and a fragility, and a large number of uncapacitated bins. Our aim is to find the minimum number of bins needed to pack all items, in such a way that in each bin the sum of the item weights is less than or equal to the smallest fragility of the items in the bin. The problem is known in the literature as the Bin Packing Problem with Fragile Objects, and appears in the telecommunication field, when one has to assign cellular calls to available channels by ensuring that the total noise in a channel does not exceed the noise acceptance limit of a call. We propose several techniques to compute lower and upper bounds for such problem. For what concerns lower bounds, we develop combinatorial techniques and a column generation algorithm. For upper bounds, we present a large set of heuristics followed by a Variable Neighborhood Search algorithm. We also show a few techniques to compute in a fast and heuristic way dual information that is then used to strengthen the valid lower bounds. One of this attempts improves the convergence of the column generation approach. Extensive computational tests show that the use of these heuristics techniques, both to compute upper bounds and to improve the lower bounds, is particularly effective.
Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling

A fuzzy rule-based decision support system (DSS) is presented for the diagnosis of coronary artery disease (CAD). The system is automatically generated from an initial annotated dataset, using a four stage methodology: 1) induction of a decision tree from the data; 2) extraction of a set of rules from the decision tree, in disjunctive normal form and formulation of a crisp model; 3) transformation of the crisp set of rules into a fuzzy model; and 4) optimization of the parameters of the fuzzy model. The dataset used for the DSS generation and evaluation consists of 199 subjects, each one characterized by 19 features, including demographic and history data, as well as laboratory examinations. Tenfold cross validation is employed, and the average sensitivity and specificity obtained is 62% and 54%, respectively, using the set of rules extracted from the decision tree (first and second stages), while the average sensitivity and specificity increase to 80% and 65%, respectively, when the fuzzification and optimization stages are used. The system offers several advantages since it is automatically generated, it provides CAD diagnosis based on easily and noninvasively acquired features, and is able to provide interpretation for the decisions made.
Improved CCG Parsing with Semi-supervised Supertagging

Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.
Clustering event logs using iterative partitioning

The importance of event logs, as a source of information in systems and network management cannot be overemphasized. With the ever increasing size and complexity of today's event logs, the task of analyzing event logs has become cumbersome to carry out manually. For this reason recent research has focused on the automatic analysis of these log files. In this paper we present IPLoM (Iterative Partitioning Log Mining), a novel algorithm for the mining of clusters from event logs. Through a 3-Step hierarchical partitioning process IPLoM partitions log data into its respective clusters. In its 4th and final stage IPLoM produces cluster descriptions or line formats for each of the clusters produced. Unlike other similar algorithms IPLoM is not based on the Apriori algorithm and it is able to find clusters in data whether or not its instances appear frequently. Evaluations show that IPLoM outperforms the other algorithms statistically significantly, and it is also able to achieve an average F-Measure performance 78% when the closest other algorithm achieves an F-Measure performance of 10%.
Self-stabilizing Systems in Spite of Distributed Control

The synchronization task between loosely coupled cyclic sequential processes (as can be distinguished in, for instance, operating systems) can be viewed as keeping the relation “the system is in a legitimate state” invariant. As a result, each individual process step that could possibly cause violation of that relation has to be preceded by a test deciding whether the process in question is allowed to proceed or has to be delayed. The resulting design is readily—and quite systematically—implemented if the different processes can be granted mutually exclusive access to a common store in which “the current system state” is recorded.
Feature Selection with Linked Data in Social Media

Feature selection is widely used in preparing highdimensional data for effective data mining. Increasingly popular social media data presents new challenges to feature selection. Social media data consists of (1) traditional high-dimensional, attribute-value data such as posts, tweets, comments, and images, and (2) linked data that describes the relationships between social media users as well as who post the posts, etc. The nature of social media also determines that its data is massive, noisy, and incomplete, which exacerbates the already challenging problem of feature selection. In this paper, we illustrate the differences between attributevalue data and social media data, investigate if linked data can be exploited in a new feature selection framework by taking advantage of social science theories, extensively evaluate the effects of user-user and user-post relationships manifested in linked data on feature selection, and discuss some research issues for future work.
Colour spaces: perceptual, historical and applicational background

Absnacr-In this paper we present an overview of colour spaces used In electrical engineering and image processing. We Streis the imparfance of the perceptual, historical and sppllcananal~hackgraund that led to a colour spare. The colour spaces presented are : RGB;opponentsolours rpsces, phenomenal colaur spscer, CMY, CMYK, TV colour s~aces (UW and YIQ), PhotoYCC, CIE XYZ, Lab and Luv colour spaces. Keywordr-colaur spaces, RGB, HSV, CIE
Prosopagnosia and structural encoding of faces: evidence from event-related potentials.

Event-related brain potentials (ERPs) were recorded in response to unfamiliar faces and to houses from a severely prosopagnosic patient (PHD) and 24 control subjects. For all control subjects, faces elicited an enhanced negativity at lateral temporal electrodes (N170). This component was absent for PHD. Comparable results were obtained in response to inverted faces and houses. A selective deficit in face recognition is therefore reflected by abnormalities in ERP components specific to faces. As PHD was shown to have substantial deficits on tasks requiring the structural analysis of faces, these findings are consistent with the view that the N170 reflects processes involved in the structural encoding of faces, and may be a measure of selective impairments in the analysis of face components.
Towards a Neural Statistician

An efficient learner is one who reuses what they already know to tackle a new problem. For a machine learner, this means understanding the similarities amongst datasets. In order to do this, one must take seriously the idea of working with datasets, rather than datapoints, as the key objects to model. Towards this goal, we demonstrate an extension of a variational autoencoder that can learn a method for computing representations, or statistics, of datasets in an unsupervised fashion. The network is trained to produce statistics that encapsulate a generative model for each dataset. Hence the network enables efficient learning from new datasets for both unsupervised and supervised tasks. We show that we are able to learn statistics that can be used for: clustering datasets, transferring generative models to new datasets, selecting representative samples of datasets and classifying previously unseen classes. We refer to our model as a neural statistician, and by this we mean a neural network that can learn to compute summary statistics of datasets without supervision.
Brain Activity during Simulated Deception: An Event-Related Functional Magnetic Resonance Study

TheGuilty Knowledge Test (GKT) has been used extensively to model deception. An association between the brain evoked response potentials and lying on the GKT suggests that deception may be associated with changes in other measures of brain activity such as regional blood flow that could be anatomically localized with event-related functional magnetic resonance imaging (fMRI). Blood oxygenation level-dependent fMRI contrasts between deceptive and truthful responses were measured with a 4 Tesla scanner in 18 participants performing the GKT and analyzed using statistical parametric mapping. Increased activity in the anterior cingulate cortex (ACC), the superior frontal gyrus (SFG), and the left premotor, motor, and anterior parietal cortex was specifically associated with deceptive responses. The results indicate that: (a) cognitive differences between deception and truth have neural correlates detectable by fMRI, (b) inhibition of the truthful response may be a basic component of intentional deception, and (c) ACC and SFG are components of the basic neural circuitry for deception.
Synchronous reluctance motor multi-static MEC model

High efficiency, low cost and high reliable electrical machines are designed to meet very demanding specifications for a wide range of applications. The SynchRM (Synchronous Reluctance Motor) has very interesting properties and features. One of the main advantages is that the motor can be designed without rare earth materials permanent magnets. In this article, a modelling method of such machines using the MEC (Magnetic Equivalent Circuit) is proposed. The main challenges are to obtain a very accurate parametric model in order to calculate torque variations and to make possible geometric optimizations in future work.
Experimental implementation of underactuated potential energy shaping on a powered ankle-foot orthosis

Traditional control methodologies of rehabilitation orthoses/exoskeletons aim at replicating normal kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. An alternative control category, kinetic control, enforces kinetic goals (e.g., torques or energy) instead of kinematic trajectories, which could provide a flexible learning environment for the user while freeing up therapists to make corrections. We propose that the theory of underactuated potential energy shaping, which falls into the category of kinetic control, could be used to generate virtual body-weight support for stroke gait rehabilitation. After deriving the nonlinear control law and simulating it on a human-like biped model, we implemented this controller on a powered ankle-foot orthosis that was designed specifically for testing torque control strategies. Experimental results with an able-bodied human subject demonstrate the feasibility of the control approach for both positive and negative virtual body-weight augmentation.
Creating health typologies with random forest clustering

In this paper, we describe the creation of a health-specific geodemographic classification system for the whole city of Birmingham UK. Compared to some existing open source and commercial systems, the proposed work has a couple of distinct advantages: (i) It is particularly designed for the public health domain by combining most reliable health data and other sources accounting for the main determinants of health. (ii) A novel random forest clustering algorithm is used for generating clusters and it has several obvious advantages over the commonly used k-means algorithm in practice. These resultant health typologies will help local authorities to understand and design customized health interventions for the population. A Birmingham map illustrating the distribution of all health typologies is produced.
Always-ON visual node with a hardware-software event-based binarized neural network inference engine

This work introduces an ultra-low-power visual sensor node coupling event-based binary acquisition with Binarized Neural Networks (BNNs) to deal with the stringent power requirements of always-on vision systems for IoT applications. By exploiting in-sensor mixed-signal processing, an ultra-low-power imager generates a sparse visual signal of binary spatial-gradient features. The sensor output, packed as a stream of events corresponding to the asserted gradient binary values, is transferred to a 4-core processor when the amount of data detected after frame difference surpasses a given threshold. Then, a BNN trained with binary gradients as input runs on the parallel processor if a meaningful activity is detected in a pre-processing stage. During the BNN computation, the proposed Event-based Binarized Neural Network model achieves a system energy saving of 17.8% with respect to a baseline system including a low-power RGB imager and a Binarized Neural Network, while paying a classification performance drop of only 3% for a real-life 3-classes classification scenario. The energy reduction increases up to 8x when considering a long-term always-on monitoring scenario, thanks to the event-driven behavior of the processing sub-system.
Prevalence of chronic pain with neuropathic characteristics in the general population

We conducted a large nationwide postal survey to estimate the prevalence of chronic pain with or without neuropathic characteristics in the French general population. A questionnaire aimed at identifying chronic pain (defined as daily pain for at least 3 months), evaluating its intensity, duration and body locations, was sent to a representative sample of 30,155 subjects. The DN4 questionnaire was used to identify neuropathic characteristics. Of the questionnaires, 24,497 (81.2%) were returned and 23,712 (96.8%) could be assessed. Seven thousand five hundred and twenty-two respondents reported chronic pain (prevalence=31.7%; [95%CI: 31.1-32.3]) and 4709 said the pain intensity was moderate to severe (prevalence=19.9%; [95%CI: 19.5-20.4]). Neuropathic characteristics were reported by 1631 respondents with chronic pain (prevalence=6.9%; [95%CI: 6.6-7.2]), which was moderate to severe in 1209 (prevalence=5.1% [95%CI: 4.8-5.4]). A higher prevalence of chronic pain with neuropathic characteristics was associated with middle age (50-64 years), manual professions and those living in rural areas. It was more frequently located in the lower limbs and its intensity and duration were higher in comparison with chronic pain without neuropathic characteristics. This large national population-based study indicates that a significant proportion of chronic pain patients report neuropathic characteristics. We identified distinctive socio-demographic profile and clinical features indicating that chronic pain with neuropathic characteristics is a specific health problem.
Anisotropic material properties of fused deposition modeling ABS

Rapid Prototyping (RP) technologies provide the ability to fabricate initial prototypes from various model materials. Stratasys Fused Deposition Modeling (FDM) is a typical RP process that can fabricate prototypes out of ABS plastic. To predict the mechanical behavior of FDM parts, it is critical to understand the material properties of the raw FDM process material, and the effect that FDM build parameters have on anisotropic material properties. This paper characterizes the properties of ABS parts fabricated by the FDM 1650. Using a Design of Experiment (DOE) approach, the process parameters of FDM, such as raster orientation, air gap, bead width, color, and model temperature were examined. Tensile strengths and compressive strengths of directionally fabricated specimens were measured and compared with injection molded FDM ABS P400 material. For the FDM parts made with a 0.003 inch overlap between roads, the typical tensile strength ranged between 65 and 72 percent of the strength of injection molded ABS P400. The compressive strength ranged from 80 to 90 percent of the injection molded FDM ABS. Several build rules for designing FDM parts were formulated based on experimental results.
AMW : A Generic Model Weaver

In the MDA approach proposed by the OMG (Object Management Group) as well as in other MDE (Model Driven Engineering) approaches such as Microsoft “Software Factories”, model transformation is a central operation. However it is often necessary to create links between models, where distinct operations will be executed based on the link semantics. These links are captured by weaving models. One of the possible application domains is data mapping. We have been engaged in building a model weaver prototype as part of the construction of AMMA, a MDE platform. The basic assumption in such platforms is to consider models as first class entities. In the present work we go one step beyond to consider also model weaving as models, and thus as manageable entities. This paper suggests that several situations in software engineering could be captured by model weaving techniques. Present achievements (mainly output generation of a weaving model, extensible weaving metamodels, partial GUI generation, etc.) and future potential extensions are discussed.
Keyframe Reduction Techniques for Motion Capture Data

Two methods for keyframe reduction of motion capture data are presented. Keyframe reduction of motion capture data enables animators to easily edit motion data with smaller number of keyframes. One of the approaches achieves keyframe reduction and noise removal simultaneously by fitting a curve to the motion information using dynamic programming. The other approach uses curve simplification algorithms on the motion capture data until a predefined threshold of number of keyframes is reached. Although the error rate varies with different motions, the results show that curve fitting with dynamic programming performs as good as curve simplification methods.
Recognizing creative needs in user interface design

The creative process requires experimentation, the exploration of variations, and the continual evaluation of one's progress. While these processes are frequently non-linear and iterative, modern user interfaces do not explicitly support these practices, and instead impose a linear progression through tasks that is a poor fit for creative pursuits. In this paper we use data from three case studies, and draw upon Sch$#246;n's theory of reflection-in-action to illustrate specific deficiencies in current user interfaces when used in creative endeavors. We then develop a set of guidelines for user interface design and demonstrate their application in three designs intended to support tasks in the domain of image manipulation.