nils-herrmann/clean_scirepeval huggingface dataset:
Distributed Representations for Compositional Semantics

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches—meaning distributed representations that exploit co-occurrence statistics of large corpora—have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP. Part I focuses on distributed representations and their application. In particular, in Chapter 3 we explore the semantic usefulness of distributed representations by evaluating their use in the task of semantic frame identification. Part II describes the transition from semantic representations for words to compositional semantics. Chapter 4 covers the relevant literature in this field. Following this, Chapter 5 investigates the role of syntax in semantic composition. For this, we discuss a series of neural network-based models and learning mechanisms, and demonstrate how syntactic information can be incorporated into semantic composition. This study allows us to establish the effectiveness of syntactic information as a guiding parameter for semantic composition, and answer questions about the link between syntax and semantics. Following these discoveries regarding the role of syntax, Chapter 6 investigates whether it is possible to further reduce the impact of monolingual surface forms and syntax when attempting to capture semantics. Asking how machines can best approximate human signals of semantics, we propose multilingual information as one method for grounding semantics, and develop an extension to the distributional hypothesis for multilingual representations. Finally, Part III summarizes our findings and discusses future work.
Recognition of Emotional Face Expressions and Amygdala Pathology *

The amygdala is often damaged in patients with temporal lobe epilepsy, either because of the primary epileptogenic disease (e.g. sclerosis or encephalitis) or because of secondary effects of surgical interventions (e.g. lobectomy). In humans, the amygdala has been associated with a range of important emotional and social functions, in particular with deficits in emotion recognition from faces. Here we review data from recent neuropsychological research illustrating the amygdala role in processing facial expressions. We describe behavioural findings subsequent to focal lesions and possible factors that may influence the nature and severity of deficits in patients. Both bilateral and unilateral amygdala damage can impair the recog nition of facial emotions, especially fear, but such deficits are not always present and not always specific. Moreover, not all components of emotion or fear processing are impaired after amygdala damage. Dissociations have been reported between deficits in the recognition of emotions from the face of other people, and intact ability in the production of emotions in one's own facial expression [...] CRISTINZIO PERRIN, Chiara, SANDER, David, VUILLEUMIER, Patrik. Recognition of emotional face expressions and amygdala pathology. Epileptologie, 2007, vol. 3, p. 130-138
When Edge Meets Learning: Adaptive Control for Resource-Constrained Distributed Machine Learning

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
ubiGaze: ubiquitous augmented reality messaging using gaze gestures

We describe ubiGaze, a novel wearable ubiquitous method to augment any real-world object with invisible messages through gaze gestures that lock the message into the object. This enables a context and location dependent messaging service, which users can utilize discreetly and effortlessly. Further, gaze gestures can be used as an authentication method, even when the augmented object is publicly known. We developed a prototype using two wearable devices: a Pupil eye tracker equipped with a scene camera and a Sony Smartwatch 3. The eye tracker follows the users' gaze, the scene camera captures distinct features from the selected real-world object, and the smartwatch provides both input and output modalities for selecting and displaying messages. We describe the concept, design, and implementation of our real-world system. Finally, we discuss research implications and address future work.
Influence of Hippotherapy on Body Balance in the Sitting Position Among Children with Cerebral Palsy.

Cerebrally palsied children demonstrated limited independence while performing various activities of daily living, which is due to disorders of postural control. The best solution to improve postural control is the use of therapies that simultaneously focus on the sense of balance and motor skills. Such possibilities for patients with cerebral palsy are offered, for example, by hippotherapy.



To assess the influence of hippotherapy on body balance in the sitting position among children with cerebral palsy.



The study enrolled thirty-nine children aged 6-12 years with GMFCS level 1 or 2 spastic diplegia or spastic hemiplegia. The participants were divided into an intervention group (n=19) and a control group (n=20). Children from the intervention group attended 30 minutes of hippotherapy once weekly for 12 consecutive weeks. The Sitting Assessment Scale (SAS) was used to assess the patients' posture and balance.



Some children improved their posture and balance during the study. Generally, control of trunk and head position and function of arms were getting better, while footwork was the weakest.



Hippotherapy has positive effects on the position and function of individual parts of the body, thus making it possible for cerebrally palsied children to improve posture and the ability to maintain balance in the sitting position.
Generalized Pythagoras Trees for visualizing hierarchies

Pythagoras Trees are fractals that can be used to depict binary hierarchies. But this binary encoding is an obstacle for visualizing hierarchical data such as file systems or phylogenetic trees, which branch into n sub-hierarchies. Although any hierarchy can be modeled as a binary one by subsequently dividing n-ary branches into a sequence of n − 1 binary branches, we follow a different strategy. In our approach extending Pythagoras Trees to arbitrarily branching trees, we only need a single visual element for an n-ary branch instead of spreading the binary branches along a strand. Each vertex in the hierarchy is visualized as a rectangle sized according to a metric. We analyze several visual parameters such as length, width, order, and color of the nodes against the use of different metrics. The usefulness of our technique is illustrated by two case studies visualizing directory structures and a large phylogenetic tree. We compare our approach with existing tree diagrams and discuss questions of geometry, perception, readability, and aesthetics.
English Resource Semantics

Recent years have seen a dramatic increase in interest in semantically­informed natural language processing, including parsing into semantic representations, grounded language processing that connects linguistic structures to world representations, proposals to integrate compositional and distributional approaches to semantics, and approaches to semantically­sensitive tasks including sentiment analysis, summarization, generation, machine translation, and information extraction which take into account linguistic structure beyond n­grams. The semantic inputs to this work include a wide range of representations, from word embeddings, to syntactic dependencies used as a proxy for semantic dependencies, to sentence­level semantic representations either partial (e.g. semantic role labels) or fully articulated.
A pilot study of the efficacy of heart rate variability (HRV) biofeedback in patients with fibromyalgia.

Fibromyalgia (FM) is a non-inflammatory rheumatologic disorder characterized by musculoskeletal pain, fatigue, depression, cognitive dysfunction and sleep disturbance. Research suggests that autonomic dysfunction may account for some of the symptomatology of FM. An open label trial of biofeedback training was conducted to manipulate suboptimal heart rate variability (HRV), a key marker of autonomic dysfunction.



Twelve women ages 18-60 with FM completed 10 weekly sessions of HRV biofeedback. They were taught to breathe at their resonant frequency (RF) and asked to practice twice daily. At sessions 1, 10 and 3-month follow-up, physiological and questionnaire data were collected.



There were clinically significant decreases in depression and pain and improvement in functioning from Session 1 to a 3-month follow-up. For depression, the improvement occurred by Session 10. HRV and blood pressure variability (BPV) increased during biofeedback tasks. HRV increased from Sessions 1-10, while BPV decreased from Session 1 to the 3 month follow-up.



These data suggest that HRV biofeedback may be a useful treatment for FM, perhaps mediated by autonomic changes. While HRV effects were immediate, blood pressure, baroreflex, and therapeutic effects were delayed. This is consistent with data on the relationship among stress, HPA axis activity, and brain function.
Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

Convolutional neural network (CNN) is well known for its capability of feature learning and has made revolutionary achievements in many applications, such as scene recognition and target detection. In this paper, its capability of feature learning in hyperspectral images is explored by constructing a five-layer CNN for classification (C-CNN). The proposed C-CNN is constructed by including recent advances in deep learning area, such as batch normalization, dropout, and parametric rectified linear unit (PReLU) activation function. In addition, both spatial context and spectral information are elegantly integrated into the C-CNN such that spatial-spectral features are learned for hyperspectral images. A companion feature-learning CNN (FL-CNN) is constructed by extracting fully connected feature layers in this C-CNN. Both supervised and unsupervised modes are designed for the proposed FL-CNN to learn sensor-specific spatial-spectral features. Extensive experimental results on four benchmark data sets from two well-known hyperspectral sensors, namely airborne visible/infrared imaging spectrometer (AVIRIS) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed C-CNN outperforms the state-of-the-art CNN-based classification methods, and its corresponding FL-CNN is very effective to extract sensor-specific spatial-spectral features for hyperspectral applications under both supervised and unsupervised modes.
DYNAMIC DAYLIGHT GLARE EVALUATION

In non-residential buildings, comfort and energy demand for heating, cooling and lighting are significantly influenced by the façade. Up to now, only non-weighted luminance-based methods for calculating and evaluating annual daylight glare exist (Lee et al., 2005; Mardaljevic and Lomas., 1998). Within this paper, different methods based on the daylight glare probability DGP (Wienold and Christoffersen, 2006) for a dynamic calculation of glare are discussed and evaluated: 1. Timestep by timestep calculation – RADIANCE reference method. 2. Simplified daylight glare probability DGPs – DGP only based on vertical eye illuminance. Results of this method are similar to average luminance based evaluations. 3. Enhanced simplified DGP calculation DGP based on vertical eye illuminance and simplified images. The enhanced simplified DGP method is validated against two hour-by-hour full year calculations, using a fabric and a Venetian blinds shading system. For the yearly evaluation of dynamic glare results, a histogram analysis and a glare rating classification is proposed.
SyntaxNet Models for the CoNLL 2017 Shared Task

We describe a baseline dependency parsing system for the CoNLL2017 Shared Task. This system, which we call “ParseySaurus,” uses the DRAGNN framework [Kong et al., 2017] to combine transitionbased recurrent parsing and tagging with character-based word representations. On the v1.3 Universal Dependencies Treebanks, the new system outpeforms the publicly available, state-of-the-art “Parsey’s Cousins” models by 3.47% absolute Labeled Accuracy Score (LAS) across 52 treebanks.
Inducing Features of Random Fields

We present a technique for constructing random elds from a set of training samples. The learning paradigm builds increasingly complex elds by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the Kullback-Leibler divergence between the model and the empirical distribution of the training data. A greedy algorithm determines how features are incrementally added to the eld and an iterative scaling algorithm is used to estimate the optimal values of the weights. The random eld models and techniques introduced in this paper di er from those common to much of the computer vision literature in that the underlying random elds are nonMarkovian and have a large number of parameters that must be estimated. Relations to other learning approaches including decision trees and Boltzmann machines are given. As a demonstration of the method, we describe its application to the problem of automatic word classi cation in natural language processing.
Can we beat Hadamard multiplexing? Data driven design and analysis for computational imaging systems

Computational Imaging (CI) systems that exploit optical multiplexing and algorithmic demultiplexing have been shown to improve imaging performance in tasks such as motion deblurring, extended depth of field, light field and hyper-spectral imaging. Design and performance analysis of many of these approaches tend to ignore the role of image priors. It is well known that utilizing statistical image priors significantly improves demultiplexing performance. In this paper, we extend the Gaussian Mixture Model as a data-driven image prior (proposed by Mitra et. al ) to under-determined linear systems and study compressive CI methods such as light-field and hyper-spectral imaging. Further, we derive a novel algorithm for optimizing multiplexing matrices that simultaneously accounts for (a) sensor noise (b) image priors and (c) CI design constraints. We use our algorithm to design data-optimal multiplexing matrices for a variety of existing CI designs, and we use these matrices to analyze the performance of CI systems as a function of noise level. Our analysis gives new insight into the optimal performance of CI systems, and how this relates to the performance of classical multiplexing designs such as Hadamard matrices.
Smartphone addiction, daily interruptions and self-reported productivity

The advent of the smartphone has dramatically altered how we communicate, navigate, work and entertain ourselves. While the advantages of this new technology are clear, constant use may also bring negative consequences, such as a loss of productivity due to interruptions in work life. A link between smartphone overuse and loss of productivity has often been hypothesized, but empirical evidence on this question is scarce. The present study addressed this question by collecting self-report data from N = 262 participants, assessing private and work-related smartphone use, smartphone addiction and self-rated productivity. Our results indicate a moderate relationship between smartphone addiction and a self-reported decrease in productivity due to spending time on the smartphone during work, as well as with the number of work hours lost to smartphone use. Smartphone addiction was also related to a greater amount of leisure time spent on the smartphone and was strongly related to a negative impact of smartphone use on daily non-work related activities. These data support the idea that tendencies towards smartphone addiction and overt checking of the smartphone could result in less productivity both in the workplace and at home. Results are discussed in relation to productivity and technostress.
1 Basic model for Speech-to-Speech Translation

This paper reviews the technology used in Speech-to-Speech Translation that is the phrases spoken in one language are immediately spoken in another language by the device. Speech-to-Speech Translation is a three step software process which includes Automatic speech Recognition, Machine Translation and voice synthesis. This paper includes the major speech translation projects using different approaches for speech recognition, translation and text to speech synthesis highlighting the major pros and cons for the approach being used.
EPIC: A Differential Privacy Framework to Defend Smart Homes Against Internet Traffic Analysis

The Internet of Things (IoT) becomes a novel paradigm as more and more devices are connected to the Internet, enabling several innovative applications such as smart home, industrial automation, and connected health. However, the cyber-attack to these applications is a big issue and countermeasures are in dire need to provide system security and user privacy. In this paper, we address the traffic analysis attack to smart homes, where adversaries intercept the Internet traffic from/to the smart home gateway and profile residents’ behaviors through digital traces. Traditional cryptographic tools may not work well due to the effectiveness of adversaries’ machine learning algorithms in classifying encrypted traffic, so here we propose a privacy-preserving traffic obfuscation framework to achieve the goal. To be specific, we leverage the smart community network of wirelessly connected smart homes and intentionally direct each smart home’s traffic to another home gateway before entering the Internet. The design jointly considers the network energy consumption and the resource constraints in IoT devices, while achieving strong differential privacy guarantee so that adversaries cannot link any traffic flow to a specific smart home. Besides, we consider a hostile smart community network and develop secure multihop routing protocols to guarantee the source/destination unlinkability and satisfy each user’s personalized privacy requirement. To evaluate the effectiveness of our framework in protecting privacy and reducing network energy consumption, extensive simulations are conducted and the results demonstrate that our design outperforms other differential privacy mechanism in preserving privacy and minimizing network utility cost.
Digital Transformation in the Automotive Industry: towards a Generic Value Network

The emergence of digital innovations is accelerating and intervening existing business models by delivering opportunities for new services. Drawing on the automotive industry, leading trends like self-driving cars, connectivity and car sharing are creating new business models. These are simultaneously giving rise for innovative market entrants, which begin to transform the automotive industry. However, literature does not provide a generic value network of the automotive industry, including new market players. The paper aims to visualize the current automotive ecosystem, by evolving a generic value network using the e3-value method. We define different roles, which are operating in the automotive industry by analyzing 650 companies reported in the Crunchbase database and present the value streams within the ecosystem. To validate the proposed generic value network we conducted five preliminary interviews with experts from the automotive industry. Our results show the central role of mobility service platforms, emerging disruptive technology providers and the dissemination of industries, e.g., as OEMs collaborate with mobile payment providers. Scholars in this field can apply the developed generic value network for further research, while car manufacturers may apply the model to position themselves in their market and to identify possible disruptive actors or potential business opportunities.
Towards End-To-End Speech Recognition with Recurrent Neural Networks

This paper presents a speech recognition system that directly transcribes audio data with text, without requiring an intermediate phonetic representation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the Connectionist Temporal Classification objective function. A modification to the objective function is introduced that trains the network to minimise the expectation of an arbitrary transcription loss function. This allows a direct optimisation of the word error rate, even in the absence of a lexicon or language model. The system achieves a word error rate of 27.3% on the Wall Street Journal corpus with no prior linguistic information, 21.9% with only a lexicon of allowed words, and 8.2% with a trigram language model. Combining the network with a baseline system further reduces the error rate to 6.7%.