nils-herrmann/clean_scirepeval huggingface dataset:
TR 15-016 Mining Electronic Health Records ( EHR ) : A Survey

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient healthcare management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research. We conclude with a case study of patients diagnosed with Type 2 diabetes mellitus (T2DM).
Flexible Spatio-Temporal Networks for Video Prediction

We describe a modular framework for video frame prediction. We refer to it as a Flexible Spatio-Temporal Network (FSTN) as it allows the extrapolation of a video sequence as well as the estimation of synthetic frames lying in between observed frames and thus the generation of slow-motion videos. By devising a customized objective function comprising decoding, encoding, and adversarial losses, we are able to mitigate the common problem of blurry predictions, managing to retain high frequency information even for relatively distant future predictions. We propose and analyse different training strategies to optimize our model. Extensive experiments on several challenging public datasets demonstrate both the versatility and validity of our model.
Systems of Global Governance in the Era of Human-Machine Convergence

Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical
A Survey of Modern Questions and Challenges in Feature Extraction

The problem of extracting features from given input data is of critical importance for the successful application of machine learning. Feature extraction, as usually understood, seeks an optimal transformation from input data into a (typically real-valued) feature vector that can be used as an input for a learning algorithm. Over time, this problem has been attacked using a growing number of diverse techniques that originated in separate research communities, including feature selection, dimensionality reduction, manifold learning, distance metric learning and representation learning. The goal of this paper is to contrast and compare feature extraction techniques coming from different machine learning areas, discuss the modern challenges and open problems in feature extraction and suggest novel solutions to some of them.
A Neural Probabilistic Structured-Prediction Method for Transition-Based Natural Language Processing

We propose a neural probabilistic structured-prediction method for transition-based natural language processing, which integrates beam search and contrastive learning. The method uses a global optimization model, which can leverage arbitrary features over nonlocal context. Beam search is used for efficient heuristic decoding, and contrastive learning is performed for adjusting the model according to search errors. When evaluated on both chunking and dependency parsing tasks, the proposed method achieves significant accuracy improvements over the locally normalized greedy baseline on the two tasks, respectively.
Parsing to Programs: A Framework for Situated QA

This paper introduces Parsing to Programs, a framework that combines ideas from parsing and probabilistic programming for situated question answering. As a case study, we build a system that solves pre-university level Newtonian physics questions. Our approach represents domain knowledge of Newtonian physics as programs. When presented with a novel question, the system learns a formal representation of the question by combining interpretations from the question text and any associated diagram. Finally, the system uses this formal representation to solve the questions using the domain knowledge. We collect a new dataset of Newtonian physics questions from a number of textbooks and use it to train our system. The system achieves near human performance on held-out textbook questions and section 1 of AP Physics C mechanics - both on practice questions as well as on freely available actual exams held in 1998 and 2012.
Lexicalization in natural language generation: A survey

In natural language generation, a meaning representation of some kind is successively transformed into a sentence or a text. Naturally, a central subtask of this problem is the choice of words, orlexicalization. In this paper, we propose four major issues that determine how a generator tackles lexicalization, and survey the contributions that researchers have made to them. Open problems are identified, and a possible direction for future research is sketched.
Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder : An Engineering Perspective

The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?
Drowsy driver detection system using eye blink patterns

This paper presents an automatic drowsy driver monitoring and accident prevention system that is based on monitoring the changes in the eye blink duration. Our proposed method detects visual changes in eye locations using the proposed horizontal symmetry feature of the eyes. Our new method detects eye blinks via a standard webcam in real-time at 110fps for a 320×240 resolution. Experimental results in the JZU  eye-blink database showed that the proposed system detects eye blinks with a 94% accuracy with a 1% false positive rate.
Clinical Spectrum and Epidemiological Profile of Patients Admitted to Pediatric Intensive Care Unit at a Tertiary Care Centre in South India

The knowledge of clinical spectrum and epidemiological profile of critically ill children plays a significant role in the planning of health policies that would mitigate various factors related to the evolution of diseases prevalent in these sectors. The data collected enable prospective comparisons to be made with benchmark standards including regional and international units for the continuous pursuit of providing essential health care and improving the quality of patient care.  To study the clinical spectrum and epidemiological profile of the critically ill children admitted to the pediatric intensive care unit at a tertiary care center in South India.  Descriptive data were collected retrospectively from the Hospital medical records between 2013 and 2016.  A total of 1833 patients were analyzed during the 3-year period, of which 1166 (63.6%) were males and 667 (36.4%) were females. A mean duration of stay in pediatric intensive care unit (PICU) was 2.21 ± 1.90 days. Respiratory system was the most common system affected in our study 738 (40.2 %). Acute poisoning in children constituted 99 patients (5.4%). We observed a mortality rate of 1.96%, with no association with age or sex. The mortality rate was highest in infants below 1-year of age (50%). In our study, the leading systemic cause for both admission and death was the respiratory system.  This study analyses the epidemiological pattern of patients admitted to PICU in South India. We would also like to emphasize on public health prevention strategies and community health education which needs to be reinforced, especially in remote places and in rural India. This, in turn, would help in decreasing the cases of unknown bites, scorpion sting, poisoning and arthropod-borne illnesses, which are more prevalent in this part of the country.
How Far are We from Solving the 2D & 3D Face Alignment Problem? (and a Dataset of 230,000 3D Facial Landmarks)

This paper investigates how far a very deep neural network is from attaining close to saturating performance on existing 2D and 3D face alignment datasets. To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets. (b)We create a guided by 2D landmarks network which converts 2D landmark annotations to 3D and unifies all existing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network.  We show that both 2D and 3D face alignment networks achieve performance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from https://www.adrianbulat.com/face-alignment/
Reservoir Computing in Forecasting Financial Markets

The ability of the echo state network to learn chaotic time series makes it an interesting tool for financial forecasting where data is very nonlinear and complex. In this study I initially examine the Mackey-Glass system to determine how different global parameters can optimize training in an echo state network. In order to simultaneously optimize multiple parameters I conduct a grid search to explore the mean squared error surface plot. In the the grid search I find that error is relatively stable over certain ranges of the leaking rate and spectral radius. However, the ranges over which the Mackey-Glass system minimizes error do not correspond with an error surface plot minimum for financial data, as a result of intrinsic qualities such as step size and timescale of dynamics in the data. The study of chaos in financial time series data leads me to find alternate understandings of the distribution of the relative stock price over time. I find the Lorentzian distribution and the Voigt profile are good models for explaining the thick tails that characterize large returns and losses, which are not explained in the common Gaussian model. These distributions act as an untrained random model to benchmark the predictions of the echo state network trained on the historical price changes in the S&P 500. The global reservoir parameters, optimized in a grid search given financial input data, does not lead to significant predictive abilities. Studies of the committees of multiple reservoirs are shown to give similar forecasts to single reservoirs. Compared to a benchmark random sample from the defined distribution of previous input, the echo state network is not able to make significantly better forecasts, suggesting the necessity of more sophisticated statistical techniques and the need to better understand chaotic dynamics in finance.
Integrating Bridge Structural Health Monitoring and Condition-Based Maintenance Management

The development of structural health monitoring (SHM) technology has evolved for over fifteen years in Hong Kong since the implementation of the “Wind And Structural Health Monitoring System (WASHMS)” on the suspension Tsing Ma Bridge in 1997. Five cable-supported bridges in Hong Kong, namely the Tsing Ma (suspension) Bridge, the Kap Shui Mun (cable-stayed) Bridge, the Ting Kau (cable-stayed) Bridge, the Western Corridor (cable-stayed) Bridge, and the Stonecutters (cable-stayed) Bridge, have been instrumented with sophisticated long-term SHM systems. These SHM systems mainly focus on the tracing of structural behavior and condition of the long-span bridges over their lifetime. Recently, a structural health monitoring and maintenance management system (SHM&MMS) has been designed and will be implemented on twenty-one sea-crossing viaduct bridges with a total length of 9,283 km in the Hong Kong Link Road (HKLR) of the Hong Kong – Zhuhai – Macao Bridge of which the construction commenced in mid-2012. The SHM&MMS gives more emphasis on durability monitoring of the reinforced concrete viaduct bridges in marine environment and integration of the SHM system and bridge maintenance management system. It is targeted to realize the transition from traditional corrective and preventive maintenance to condition-based maintenance (CBM) of in-service bridges. The CBM uses real-time and continuous monitoring data and monitoring-derived information on the condition of bridges (including structural performance and deterioration mechanisms) to identify when the actual maintenance is necessary and how cost-effective maintenance can be conducted. This paper outlines how to incorporate SHM technology into bridge maintenance strategy to realize CBM management of bridges.
GUIDE FOR CONSTRUCTING SELF-EFFICACY SCALES

Perceived self-efficacy is concerned with people's beliefs in their capabilities to produce given attainments (Bandura, 1997). One cannot be all things, which would require mastery of every realm of human life. People differ in the areas in which they cultivate their efficacy and in the levels to which they develop it even within their given pursuits. For example, a business executive may have a high sense of organizational efficacy but low parenting efficacy. Thus, the efficacy belief system is not a global trait but a differentiated set of self-beliefs linked to distinct realms of functioning. Multidomain measures reveal the patterning and degree of generality of people's sense of personal efficacy. There is no all-purpose measure of perceived self-efficacy. The " one measure fits all " approach usually has limited explanatory and predictive value because most of the items in an all-purpose test may have little or no relevance to the domain of functioning. Moreover, in an effort to serve all purposes, items in such a measure are usually cast in general terms divorced from the situational demands and circumstances. This leaves much ambiguity about exactly what is being measured or the level of task and situational demands that must be managed. Scales of perceived self-CHAPTER 14 Note that this date is incorrect. The book was published in 2006. 308 A. BANDURA efficacy must be tailored to the particular domain of functioning that is the object of interest. Although efficacy beliefs are multifaceted, social cognitive theory identifies several conditions under which they may co-vary even across distinct domains of functioning (Bandura, 1997). When different spheres of activity are governed by similar sub-skills there is some inter-domain relation in perceived efficacy. Proficient performance is partly guided by higher-order self-regulatory skills. These include generic skills for diagnosing task demands, constructing and evaluating alternative courses of action, setting proximal goals to guide one's efforts, and creating self-incentives to sustain engagement in taxing activities and to manage stress and debilitating intrusive thoughts. Generic self-management strategies developed in one realm of activity are serviceable in other activity domains with resulting co-variation in perceived efficacy among them. Co-development is still another correlative process. Even if different activity domains are not sub-served by common sub-skills, the same perceived efficacy can occur if development of competencies is socially structured so that skills in dissimilar domains are developed together. For example, students are likely to develop similarly high perceived self-efficacy …
The influence of system characteristics on e-learning use

The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested alternative models that seek to explain student intention to use an e-learning system when the system is used as a supplementary learning tool within a traditional class or a stand-alone distance education method. The models integrated determinants from the well-established technology acceptance model as well as system and participant characteristics cited in the research literature. Following a demonstration and use phase of the e-learning system, data were collected from 259 college students. Structural equation modeling provided better support for a model that hypothesized stronger effects of system characteristics on e-learning system use. Implications for both researchers and practitioners are discussed.
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation

Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation. We study the more challenging problem of learning DCNNs for semantic image segmentation from either (1) weakly annotated training data such as bounding boxes or image-level labels or (2) a combination of few strongly labeled and many weakly labeled images, sourced from one or multiple datasets. We develop Expectation-Maximization (EM) methods for semantic image segmentation model training under these weakly supervised and semi-supervised settings. Extensive experimental evaluation shows that the proposed techniques can learn models delivering competitive results on the challenging PASCAL VOC 2012 image segmentation benchmark, while requiring significantly less annotation effort. We share source code implementing the proposed system at https: //bitbucket.org/deeplab/deeplab-public.