nils-herrmann/clean_scirepeval huggingface dataset:
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy

Identifying the hypernym relations that hold between words is a fundamental task in NLP. Word embedding methods have recently shown some capability to encode hypernymy. However, such methods tend not to explicitly encode the hypernym hierarchy that exists between words. In this paper, we propose a method to learn a hierarchical word embedding in a specific order to capture the hypernymy. To learn the word embeddings, the proposed method considers not only the hypernym relations that exists between words on a taxonomy, but also their contextual information in a large text corpus. The experimental results on a supervised hypernymy detection and a newlyproposed hierarchical path completion tasks show the ability of the proposed method to encode the hierarchy. Moreover, the proposed method outperforms previously proposed methods for learning word and hypernym-specific word embeddings on multiple benchmarks.
Action-Reaction: Forecasting the Dynamics of Human Interaction

Forecasting human activities from visual evidence is an emerging area of research which aims to allow computational systems to make predictions about unseen human actions. We explore the task of activity forecasting in the context of dual-agent interactions to understand how the actions of one person can be used to predict the actions of another. We model dual-agent interactions as an optimal control problem, where the actions of the initiating agent induce a cost topology over the space of reactive poses – a space in which the reactive agent plans an optimal pose trajectory. The technique developed in this work employs a kernel-based reinforcement learning approximation of the soft maximum value function to deal with the high-dimensional nature of human motion and applies a mean-shift procedure over a continuous cost function to infer a smooth reaction sequence. Experimental results show that our proposed method is able to properly model human interactions in a high dimensional space of human poses. When compared to several baseline models, results show that our method is able to generate highly plausible simulations of human interaction.
OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring

A wearable, multi-modal sensor is presented that can non-invasively monitor a patient's activity level and heart function concurrently for more than a week. The 4in2 sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin . Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity for long-term observation into a patient's physical activity and subtle longitudinal changes.
Exploring compassion: a meta-analysis of the association between self-compassion and psychopathology.

Compassion has emerged as an important construct in studies of mental health and psychological therapy. Although an increasing number of studies have explored relationships between compassion and different facets of psychopathology there has as yet been no systematic review or synthesis of the empirical literature. We conducted a systematic search of the literature on compassion and mental health. We identified 20 samples from 14 eligible studies. All studies used the Neff Self Compassion Scale (Neff, 2003b). We employed meta-analysis to explore associations between self-compassion and psychopathology using random effects analyses of Fisher's Z correcting for attenuation arising from scale reliability. We found a large effect size for the relationship between compassion and psychopathology of r=-0.54 (95% CI=-0.57 to -0.51; Z=-34.02; p<.0001). Heterogeneity was significant in the analysis. There was no evidence of significant publication bias. Compassion is an important explanatory variable in understanding mental health and resilience. Future work is needed to develop the evidence base for compassion in psychopathology, and explore correlates of compassion and psychopathology.
Suede: a Wizard of Oz prototyping tool for speech user interfaces

Speech-based user interfaces are growing in popularity. Unfortunately, the technology expertise required to build speech UIs precludes many individuals from participating in the speech interface design process. Furthermore, the time and knowledge costs of building even simple speech systems make it difficult for designers to iteratively design speech UIs. SUEDE, the speech interface prototyping tool we describe in this paper, allows designers to rapidly create prompt/response speech interfaces. It offers an electronically supported Wizard of Oz (WOz) technique that captures test data, allowing designers to analyze the interface after testing. This informal tool enables speech user interface designers, even non-experts, to quickly create, test, and analyze speech user interface prototypes.
Combining qualitative and quantitative sampling, data collection, and analysis techniques in mixed-method studies.

Researchers have increasingly turned to mixed-method techniques to expand the scope and improve the analytic power of their studies. Yet there is still relatively little direction on and much confusion about how to combine qualitative and quantitative techniques. These techniques are neither paradigm- nor method-linked; researchers' orientations to inquiry and their methodological commitments will influence how they use them. Examples of sampling combinations include criterion sampling from instrument scores, random purposeful sampling, and stratified purposeful sampling. Examples of data collection combinations include the use of instruments for fuller qualitative description, for validation, as guides for purposeful sampling, and as elicitation devices in interviews. Examples of data analysis combinations include interpretively linking qualitative and quantitative data sets and the transformation processes of qualitizing and quantitizing.
Comparing memory systems for chip multiprocessors

There are two basic models for the on-chip memory in CMP systems:hardware-managed coherent caches and software-managed streaming memory. This paper performs a direct comparison of the two modelsunder the same set of assumptions about technology, area, and computational capabilities. The goal is to quantify how and when they differ in terms of performance, energy consumption, bandwidth requirements, and latency tolerance for general-purpose CMPs. We demonstrate that for data-parallel applications, the cache-based and streaming models perform and scale equally well. For certain applications with little data reuse, streaming scales better due to better bandwidth use and macroscopic software prefetching. However, the introduction of techniques such as hardware prefetching and non-allocating stores to the cache-based model eliminates the streaming advantage. Overall, our results indicate that there is not sufficient advantage in building streaming memory systems where all on-chip memory structures are explicitly managed. On the other hand, we show that streaming at the programming model level is particularly beneficial, even with the cache-based model, as it enhances locality and creates opportunities for bandwidth optimizations. Moreover, we observe that stream programming is actually easier with the cache-based model because the hardware guarantees correct, best-effort execution even when the programmer cannot fully regularize an application's code.
Automatic parking identification and vehicle guidance with road awareness

Advanced driver assistance systems (ADAS) are becoming more common in safety and convenience applications. The computer vision based ADAS described in this paper, is an add-on system, suitable to variety of cars. It detects vacant legal parking spots, and safely guides the vehicle into the selected parking. Detection can be performed in both indoor parking lots and along roadsides. The system is composed of three standard computer-connected webcams, which are attached to the vehicle. Upon slowing down, the system starts searching automatically for a right hand-side vacant parking spot, while being aware to parking color signs. Once detected, the parking orientation is determined, and the driver is notified. Once a parking is selected by the driver, the relative position between the vehicle and the parking spot is monitored. Vocal and visual parking guidance instructions are presented to the driver. In addition, if during parking, an object is moving on the road towards the car, a safety alert is given. The system is universal in the sense that, as an add-on system, it can be installed on any private 4-wheeled vehicle, and is suited to urban driving environment.
LoRa indoor coverage and performance in an industrial environment: Case study

LoRa is a long range, low power, low bit rate, single hop wireless communication technology. It is intended to be used for Internet of Things (IoT) networks, where devices are battery powered and limited bandwidth is needed. In combination with its scalability and the low end device price, LoRa is a candidate technology for low bandwidth industrial applications with a high number of communication devices spread across large areas. The use case for this paper is taken from the flower industry, where a large number of trolleys need to communicate with a server during their movement across the auction floor area. Once trolleys are outside of the auction floor they can use the public LoRaWAN network to communicate with the server, without switching communication technology. The LoRaWAN network consists of multiple end nodes and a single gateway per cell, acting as a transparent bridge between the end nodes and the network server. The measurements show that with a single LoRa gateway we can cover an indoor area of around 34000m2 only with spreading factor 7, while for spreading factor 12 the total covered area will be even higher. Also, the area outside the factory is covered when switching to spreading factor 12. We also show that the number of nodes (trolleys) that can be served by a gateway in such a case can be as high as 6000.
Paper-based versus computer-based assessment: key factors associated with the test mode effect

This investigation seeks to confirm several key factors in computer-based versus paper-based assessment. Based on earlier research, the factors considered here include content familiarity, computer familiarity, competitiveness, and gender. Following classroom instruction, freshman business undergraduates (N = 105) were randomly assigned to either a computer-based or identical paper-based test. ANOVA of test data showed that the computer-based test group outperformed the paper-based test group. Gender, competitiveness, and computer familiarity were NOT related to this performance difference, though content familiarity was. Higher-attaining students benefited most from computer-based assessment relative to higher-attaining students under paper-based testing. With the current increase in computer-based assessment, instructors and institutions must be aware of and plan for possible test mode effects. As pointed out by other authors in this special issue, for many reasons, the use of computer-based assessment is increasing. Examples include state drivers’ license exams, military training exams, job application exams in the private sector, entrance exams in postsecondary education, and certification exams by professional groups (Russo, 2002; Trotter, 2001). However, in the literature, there is mounting empirical evidence that identical paper-based and computer-based tests will not obtain the same results. Such findings are referred to as the “test mode effect.” For example, paper-based test scores were greater than computer-based test scores for both mathematics and English CLEP tests (Mazzeo, Druesne, Raffeld, Checketts and Muhlstein, 1991) and for recognizing fighter plane silhouettes (Federico, 1989); while computer-based test scores were greater than paper-based test scores for a dental hygiene course unit midterm examination (DeAngelis, 2000); though other studies 08_Clar 15/10/02 12:41 pm Page 593
Never-Ending Multiword Expressions Learning

This paper introduces NEMWEL, a system that performs Never-Ending MultiWord Expressions Learning. Instead of using a static corpus and classifier, NEMWEL applies supervised learning on automatically crawled news texts. Moreover, it uses its own results to periodically retrain the classifier, bootstrapping on its own results. In addition to a detailed description of the system’s architecture and its modules, we report the results of a manual evaluation. It shows that NEMWEL is capable of learning new expressions over time with improved precision.
Self-Adaptive Systems in Organic Computing: Strategies for Self-Improvement

With the intensified use of intelligent things, the demands on the technological systems are increasing permanently. A possible approach to meet the continuously changing challenges is to shift the system integration from design to run-time by using adaptive systems. Diverse adaptivity properties – so-called self* properties – form the basis of these systems and one of the properties is self-improvement. It describes the ability of a system not only to adapt to a changing environment according to a predefined model, but also the capability to adapt the adaptation logic of the whole system. In this paper, a closer look is taken at the structure of self-adaptive systems. Additionally, the systems’ ability to improve themselves during run-time is described from the perspective of Organic Computing. Furthermore, four different strategies for self-improvement are presented, following the taxonomy of self-adaptation suggested by Christian Krupitzer et al.
Probabilistic Community and Role Model for Social Networks

Numerous models have been proposed for modeling social networks to explore their structure or to address application problems, such as community detection and behavior prediction. However, the results are still far from satisfactory. One of the biggest challenges is how to capture all the information of a social network in a unified manner, such as links, communities, user attributes, roles and behaviors.
 In this paper, we propose a unified probabilistic framework, the Community Role Model (CRM), to model a social network. CRM incorporates all the information of nodes and edges that form a social network. We propose methods based on Gibbs sampling and an EM algorithm to estimate the model's parameters and fit our model to real social networks. Real data experiments show that CRM can be used not only to represent a social network, but also to handle various application problems with better performance than a baseline model, without any modification to the model, showing its great advantages.
Social semantic query expansion

Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.
An analysis of training and generalization errors in shallow and deep networks

An open problem around deep networks is the apparent absence of over-fitting despite large overparametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case of regression problems when each unit evaluates a trigonometric polynomial. It is well understood that a trigonometric monomial can be synthesized with a good degree of approximation by neural networks with fixed weights and thresholds. Approximation by trigonometric polynomials serves as a “role model” for every other approximation process, including that by neural and RBF networks. In this paper, we argue that the maximum loss functional is necessary to measure the generalization error. We give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error. We prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data.
Image Classification with the Fisher Vector: Theory and Practice

A standard approach to describe an image for classification and retrieval purposes is to extract a set of local patch descriptors, encode them into a high dimensional vector and pool them into an image-level signature. The most common patch encoding strategy consists in quantizing the local descriptors into a finite set of prototypical elements. This leads to the popular Bag-of-Visual words representation. In this work, we propose to use the Fisher Kernel framework as an alternative patch encoding strategy: we describe patches by their deviation from an “universal” generative Gaussian mixture model. This representation, which we call Fisher vector has many advantages: it is efficient to compute, it leads to excellent results even with efficient linear classifiers, and it can be compressed with a minimal loss of accuracy using product quantization. We report experimental results on five standard datasets—PASCAL VOC 2007, Caltech 256, SUN 397, ILSVRC 2010 and ImageNet10K—with up to 9M images and 10K classes, showing that the FV framework is a state-of-the-art patch encoding technique.
Understanding the usage of mobile payment systems- The impact of personality on the continuance usage

Payment convenience has benefited from the revolution in mobile technologies. M-payment users, however, seem inconsistent in their payment activity, resisting change from traditional payment methods. Ensuring consumer continuance of m-payment technology usage is critical to ensuring the ubiquity of m-payment solutions. Although research has examined the influence of individual difference on the acceptance of m-payment, most studies fail to consider whether ongoing acceptance is maintained by the user, or whether a change in perception occurs as a result of use. Moreover, current studies consider user demographic profiles to segment mobile users, yet this dismisses the impact of individual difference, e.g. personality or cognitive style. This paper proposes a model that can be used to investigate the impact of individual difference on user perception of m- payment systems. The Expectation Confirmation Model (ECM) and Unified Theory of Acceptance and Use of Technology (UTAUT2) model factors (i.e. effort expectancy, performance expectancy, social influence, facilitating condition, habit, hedonic motivation, price value, trust and perceived risk) allow capture of data relating to two use perceptions; pre- and post­usage perception. The proposed model allows capture and comparison of pre-usage expectation and post-usage beliefs, allowing consideration of perception variance as a result of technology use. This model will be applied to gain a deeper insight into how to address users' satisfaction, acceptance, and continuance usage of Near Field Communication m-payment technologies.
LEARNING PHYSICAL DYNAMICS

We present the Neural Physics Engine (NPE), a framework for learning simulators of intuitive physics that naturally generalize across variable object count and different scene configurations. We propose a factorization of a physical scene into composable object-based representations and a neural network architecture whose compositional structure factorizes object dynamics into pairwise interactions. Like a symbolic physics engine, the NPE is endowed with generic notions of objects and their interactions; realized as a neural network, it can be trained via stochastic gradient descent to adapt to specific object properties and dynamics of different worlds. We evaluate the efficacy of our approach on simple rigid body dynamics in two-dimensional worlds. By comparing to less structured architectures, we show that the NPE’s compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.
CALIBRATION OF A PMD-CAMERA USING A PLANAR CALIBRATION PATTERN TOGETHER WITH A MULTI-CAMERA SETUP

We discuss the joint calibration of novel 3D range cameras based on the time-of-flight principle with the Photonic Mixing Device (PMD) and standard 2D CCD cameras. Due to the small field-of-view (fov) and low pixel resolution, PMD-cameras are difficult to calibrate with traditional calibration methods. In addition, the 3D range data contains systematic errors that need to be compensated. Therefore, a calibration method is developed that can estimate full intrinsic calibration of the PMD-camera including optical lens distortions and systematic range errors, and is able to calibrate the external orientation together with multiple 2D cameras that are rigidly coupled to the PMD-camera. The calibration approach is based on a planar checkerboard pattern as calibration reference, viewed from multiple angles and distances. By combining the PMD-camera with standard CCD-cameras the internal camera parameters can be estimated more precisely and the limitations of the small fov can be overcome. Furthermore we use the additional cameras to calibrate the systematic depth measurement error of the PMD-camera. We show that the correlation between rotation and translation estimation is significantly reduced with our method.
Global-Locally Self-Attentive Dialogue State Tracker

Dialogue state tracking, which estimates user goals and requests given the dialogue context, is an essential part of taskoriented dialogue systems. In this paper, we propose the Global-Locally SelfAttentive Dialogue State Tracker (GLAD), which learns representations of the user utterance and previous system actions with global-local modules. Our model uses global modules to share parameters between estimators for different types (called slots) of dialogue states, and uses local modules to learn slot-specific features. We show that this significantly improves tracking of rare states and achieves stateof-the-art performance on the WoZ and DSTC2 state tracking tasks. GLAD obtains 88.1% joint goal accuracy and 97.1% request accuracy on WoZ, outperforming prior work by 3.7% and 5.5%. On DSTC2, our model obtains 74.5% joint goal accuracy and 97.5% request accuracy, outperforming prior work by 1.1% and 1.0%.