nils-herrmann/clean_scirepeval huggingface dataset:
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.stanfordvl.github.io/ntp/.
JINS MEME algorithm for estimation and tracking of concentration of users

Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.
Transfer Learning for Traffic Speed Prediction with Effective Spatiotemporal Features

Traffic speed prediction can benefit a wide range of downstream applications of machine learning for intelligent transportation systems. Most existing approaches leverage historical time-series data, which thus makes them only able to work on the areas where large amount of historical data is available. To predict traffic speed of target areas where little historical traffic data is available, we propose a transfer learning framework that exploits historical data of some source areas and various effective spatiotemporal features. Experimental results show that classic regression models and our proposed novel model can achieve competitive performance comparing to baseline methods which utilize historical data of target areas.
Finding the WRITE Stuff: Automatic Identification of Discourse Structure in Student Essays

automated feedback that helps them revise their work and ultimately improve their writing skills. These applications also address educational researchers’ interest in individualized instruction. Specifically, feedback that refers explicitly to students’own writing is more effective than general  Our discourse analysis software, which is embedded in Criterion (www.etstechnologies.com), an online essay evaluation application, uses machine learning to identify discourse elements in student essays. The system makes decisions that exemplify how teachers perform this task. For instance, when grading student essays, teachers comment on the discourse structure. Teachers might explicitly state that the essay lacks a thesis statement or that an essay’s single main idea has insufficient support. Training the systems to model this behavior requires human judges to annotate a data sample of student essays. The annotation schema reflects the highly structured discourse of genres such as persuasive writing. Our discourse analysis system uses a voting algorithm that takes into account the discourse labeling decisions of three independent systems. The three systems employ natural language processing methods to extract essay-based features that help predict the discourse labels. They also use machine learning to classify the sentences in an essay as particular discourse elements. Our tool automatically labels discourse elements in student essays written on any topic and across writing genres.
Human motion recovery utilizing truncated schatten p-norm and kinematic constraints

Human motion capture (mocap) data, which records the movements of joints of a human body, has been widely used in many areas. However, the raw captured data inevitably contains missing data due to the limitations of capture systems. Recently, by exploiting the low-rank prior embedded in mocap data, several approaches have resorted to the low-rank matrix completion (LRMC) to fill in the missing data and obtained encouraging results. In order to solve the resulting rank minimization problem which is known as NP-hard, all existing methods use the convex nuclear norm as the surrogate of rank to pursue the convexity of the objective function. However, the nuclear norm, which ignores physical interpretations of singular values and has over-shrinking problem, obtains less accurate approximation than its nonconvex counterpart. Therefore, this paper presents a nonconvex LRMC based method wherein we exploit the state-of-art nonconvex truncated schattenp norm to approximate rank. Moreover, we add two significant constraints to the low-rank based model to preserve the spatial-temporal properties and structural characteristics embedded in human motion. We also develop a framework based on alternating direction multiplier method (ADMM) to solve the resulting nonconvex problem. Extensive experiment results demonstrate that the proposed method significantly outperforms the existing state-of-art methods in terms of both recovery error and agreement with human intuition.
Adolescents' electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age.

Adolescence is a time of increasing vulnerability for poor mental health, including depression. Sleep disturbance is an important risk factor for the development of depression during adolescence. Excessive electronic media use at night is a risk factor for both adolescents' sleep disturbance and depression. To better understand the interplay between sleep, depressive symptoms, and electronic media use at night, this study examined changes in adolescents' electronic media use at night and sleep associated with smartphone ownership. Also examined was whether sleep disturbance mediated the relationship between electronic media use at night and depressive symptoms. 362 adolescents (12-17 year olds, M = 14.8, SD = 1.3; 44.8% female) were included and completed questionnaires assessing sleep disturbance (short sleep duration and sleep difficulties) and depressive symptoms. Further, participants reported on their electronic media use in bed before sleep such as frequency of watching TV or movies, playing video games, talking or text messaging on the mobile phone, and spending time online. Smartphone ownership was related to more electronic media use in bed before sleep, particularly calling/sending messages and spending time online compared to adolescents with a conventional mobile phone. Smartphone ownership was also related to later bedtimes while it was unrelated to sleep disturbance and symptoms of depression. Sleep disturbance partially mediated the relationship between electronic media use in bed before sleep and symptoms of depression. Electronic media use was negatively related with sleep duration and positively with sleep difficulties, which in turn were related to depressive symptoms. Sleep difficulties were the more important mediator than sleep duration. The results of this study suggest that adolescents might benefit from education regarding sleep hygiene and the risks of electronic media use at night.
Online False Data Injection Attack Detection With Wavelet Transform and Deep Neural Networks

State estimation is critical to the operation and control of modern power systems. However, many cyber-attacks, such as false data injection attacks, can circumvent conventional detection methods and interfere the normal operation of grids. While there exists research focusing on detecting such attacks in dc state estimation, attack detection in ac systems is also critical, since ac state estimation is more widely employed in power utilities. In this paper, we propose a new false data injection attack detection mechanism for ac state estimation. When malicious data are injected in the state vectors, their spatial and temporal data correlations may deviate from those in normal operating conditions. The proposed mechanism can effectively capture such inconsistency by analyzing temporally consecutive estimated system states using wavelet transform and deep neural network techniques. We assess the performance of the proposed mechanism with comprehensive case studies on IEEE 118- and 300-bus power systems. The results indicate that the mechanism can achieve a satisfactory attack detection accuracy. Furthermore, we conduct a preliminary sensitivity test on the control parameters of the proposed mechanism.
Documenting Software Architectures: Views and Beyond

* This author is with the Software Engineering Institute at Carnegie Mellon University.  This author is with the School of Computer Science at Carnegie Mellon University. Software architecture has emerged as a foundational concept for the successful development of large, complex systems. Signs that the field is maturing to become an engineering discipline include textbooks on the subject; the work in repeatable design, exemplified by architectural styles and patterns; robust methods for evaluating software architectures; international conferences devoted to it; and recognition of architecture as a professional practice. However, treatment of architecture to date has largely concentrated on its design, and to a lesser extent, its validation. But effectively documenting an architecture is as important as crafting it, because if the architecture is not understood (or worse, misunderstood) it cannot meet its goals as the unifying vision for system and software development. Three years ago, researchers at the Software Engineering Institute and the Carnegie Mellon School of Computer Science set out to answer the question: “How should you document an architecture so that others can successfully use it, maintain it, and build a system from it?” The result of that work is an approach we loosely call “views and beyond.”  Modern software architecture practice embraces the concept of architectural views. A view is a representation of a set of system elements and relations associated with them. Modern systems are more than complex enough to make it difficult to grasp them all at once. Instead, we restrict our attention at any one moment to one (or a small number) of the software system’s structures, which we represent as views. Some authors prescribe a fixed set of views with which to engineer and communicate an architecture. Rational’s Unified Process, for example, is based on Kruchten’s 4+1 view approach to software [4]. The Siemens Four Views model  is another example. A recent trend, however, is to recognize that architects should produce whatever views are useful for the system at hand. IEEE 1471 exemplifies this philosophy; it holds that an architecture description consists of a set of views, each of which conforms to a viewpoint, which in turn is a realization of the concerns of one or more stakeholders. This philosophy about views leads to the fundamental principle of the views-and-beyond approach:
STFCN: Spatio-Temporal FCN for Semantic Video Segmentation

This paper presents a novel method to involve both spatial and temporal features for semantic segmentation of street scenes. Current work on convolutional neural networks (CNNs) has shown that CNNs provide advanced spatial features supporting a very good performance of solutions for the semantic segmentation task. We investigate how involving temporal features also has a good effect on segmenting video data. We propose a module based on a long short-term memory (LSTM) architecture of a recurrent neural network for interpreting the temporal characteristics of video frames over time. Our system takes as input frames of a video and produces a correspondingly-sized output; for segmenting the video our method combines the use of three components: First, the regional spatial features of frames are extracted using a CNN; then, using LSTM the temporal features are added; finally, by deconvolving the spatio-temporal features we produce pixel-wise predictions. Our key insight is to build spatio-temporal convolutional networks (spatio-temporal CNNs) that have an end-to-end architecture for semantic video segmentation. We adapted fully some known convolutional network architectures (such as FCN-AlexNet and FCN-VGG16), and dilated convolution into our spatio-temporal CNNs. Our spatio-temporal CNNs achieve state-of-the-art semantic segmentation, as demonstrated for the Camvid and NYUDv2 datasets.
BTS guidelines for the insertion of a chest drain.

D Laws, E Neville, J Duffy, on behalf of the British Thoracic Society Pleural Disease Group, a subgroup of the British Thoracic Society Standards of Care Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Voxel-based morphometry of the human brain: Methods and applications

In recent years, a whole-brain unbiased objective technique, known as voxel-based morphometry (VBM), has been developed to characterise brain differences in vivo using structural magnetic resonance images. The present review provides a brief description of VBM and then focuses on exemplar applications in healthy and diseased subjects. The procedure involves normalising high-resolution structural magnetic resonance images to a standard template in stereotactic space. Normalised images are then segmented into gray and white matter and smoothed using an isotropic Gaussian kernel. Finally, a series of voxel-wise comparisons of gray and white matter in different groups of subjects are performed, using Random Field theory to correct for multiple comparisons. VBM has been useful in characterizing subtle changes in brain structure in a variety of diseases associated with neurological and psychiatric dysfunction. These include schizophrenia, developmental and congenital disorders, temporal lobe epilepsy and even cluster headache. In addition, VBM has been successful in identifying gross structural abnormalities, such as those observed in herpes simplex encephalitis, multiple sclerosis and Alzheimer’s disease. Studies of normal subjects, on the other hand, have focussed on the impact of learning and practice on brain structure. These studies have led to the finding that environmental demands may be associated with changes in gray and white matter. For instance, it has been reported that the structure of the brain alters when human beings learn to navigate, read music, speak a second language and even perform a complex motor task such as juggling. We conclude the present review by discussing the potential limitations of the technique.
Unsupervised local deep feature for image recognition

Unsupervised feature learning is an important problem in computer vision and machine learning. Various unsupervised learning methods, e.g., PCA, KMeans, autoencoder, have been applied for visual recognition tasks. Especially, autoencoder has superior image recognition performance, which is usually used as a global feature extractor. To make better use of autoencoder for image recognition, we propose unsupervised local deep feature (ULDF). We collect patches from training images and use autoencoder to transform vectorized patches into short codes. Then these codes are encoded using Fisher Vector with spatial pyramid. Image recognition with ULDF only requires an efficient linear SVM classifier. Finally, we perform multi-scale voting to achieve more accurate classification performance. In the experiments, we evaluate ULDF on the handwritten digit recognition task and the shape classification task, and show that it achieves highly competitive performance comparing to the state-of-the-art image recognition methods. The results suggest that the proposed local deep feature is superior than traditional global autoencoder.
Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults.

Time-series, cross-sectional, and prospective cohort studies have observed associations between mortality and particulate air pollution but have been limited by ecologic design or small number of subjects or study areas. The present study evaluates effects of particulate air pollution on mortality using data from a large cohort drawn from many study areas. We linked ambient air pollution data from 151 U.S. metropolitan areas in 1980 with individual risk factor on 552,138 adults who resided in these areas when enrolled in a prospective study in 1982. Deaths were ascertained through December, 1989. Exposure to sulfate and fine particulate air pollution, which is primarily from fossil fuel combustion, was estimated from national data bases. The relationships of air pollution to all-cause, lung cancer, and cardiopulmonary mortality was examined using multivariate analysis which controlled for smoking, education, and other risk factors. Although small compared with cigarette smoking, an association between mortality and particulate air pollution was observed. Adjusted relative risk ratios (and 95% confidence intervals) of all-cause mortality for the most polluted areas compared with the least polluted equaled 1.15 (1.09 to 1.22) and 1.17 (1.09 to 1.26) when using sulfate and fine particulate measures respectively. Particulate air pollution was associated with cardiopulmonary and lung cancer mortality but not with mortality due to other causes. Increased mortality is associated with sulfate and fine particulate air pollution at levels commonly found in U.S. cities. The increase in risk is not attributable to tobacco smoking, although other unmeasured correlates of pollution cannot be excluded with certainty.
New research perspectives on Ambient Intelligence

Ten years of AmI research have led to many new insights and understandings about the way highly interactive environments should be designed to meet the requirement of being truly unobtrusive and supportive from an end-user perspective. Probably the most revealing finding is the fact that, in addition to cognitive intelligence and computing, also elements from social intelligence and design play a dominant role in the realization of the vision. In this paper we discuss these novel insights and their resulting impact on the AmI research landscape. We introduce a number of new AmI research perspectives that are related to social intelligence and in addition we argue that new ways of working are required applying the concept of Experience Research resulting in a true user-centered approach to Ambient Intelligence.
Culture care theory, research, and practice.

Today nurses are facing a world in which they are almost forced to use transculturally-based nursing theories and practices in order to care for people of diverse cultures. The author, who in the mid-50s pioneered the development of the first transcultural nursing theory with a care focus, discusses the relevance, assumptions, and predictions of the culture care theory along with the ethnonursing research method. The author contends that transcultural nursing findings are gradually transforming nursing practice and are providing a new paradigm shift from traditional medical and unicultural practice to multiculturally congruent and specific care modalities. A few research findings are presented to show the importance of being attentive to cultural care diversities and universalities as the major tenets of the theory. In addition, some major contributions of the theory are cited along with major challenges for the immediate future.
Lane-level route planning based on a multi-layer map model

The demand for high precision navigation services for human drivers and even intelligent vehicles cannot be satisfied by traditional road-level maps, and on the other hand, current enhanced digital maps with abundant details for vehicle localization and navigation simultaneously bring about practical difficulties for lane-level route planning. In this paper, aiming at achieving lane-level route planning, a three-layer map model combining the traditional road-level data with high precision lane-level data is created, in which an intermediate layer serves as a bridge between road and lane level. Then, based on the lane-level map model, a hierarchical route planning framework is established, including road-level route planning, node contraction according to the intermediate layer, and lane-level route planning with the cost model in lane level which takes the lane change and turn into account. A simulation result is implemented to reveal the validation of this hierarchical routing algorithm based on this novel lane-level map model.
Roman-txt: forms and functions of roman urdu texting

In this paper, we present a user study conducted on students of a local university in Pakistan and collected a corpus of Roman Urdu text messages. We were interested in forms and functions of Roman Urdu text messages. To this end, we collected a mobile phone usage dataset. The data consists of 116 users and 346, 455 text messages. Roman Urdu text, is the most widely adopted style of writing text messages in Pakistan. Our user study leads to interesting results, for instance, we were able to quantitatively show that a number of words are written using more than one spelling; most participants of our study were not comfortable in English and hence they write their text messages in Roman Urdu; and the choice of language adopted by the participants sometimes varies according to who the message is being sent. Moreover we found that many young students send text messages(SMS) of intimate nature.
On-line planning of time-optimal, jerk-limited trajectories

Service robots which directly interact with humans in highly unstructured, unpredictable and dynamic environments must be able to flexibly adapt their motion in reaction to unforeseen events or obstacles and they must provide a new feasible trajectory in real-time. Hence, algorithms come into focus which replan the motion path and its time evolution from arbitrary initial conditions within milliseconds. We present a real-time algorithm to generate synchronised and time-optimal third-order manipulator trajectories complying maximal motion limits on velocity, acceleration and jerk. Experimental results carried out on a Mitsubishi PA10-7C arm are presented.
Baseline prediction of point of sales data for trade promotion optimization

Baseline prediction is an important to devise marketing strategy for a consumer goods product. Simulation techniques, time series algorithms are often used to generate baseline for the future. However the algorithm that fits a particular point of sales (POS) data varies according to the datasets. Sample set of point of sales data were simulated under different conditions and constraints incorporating seasonal and non seasonal trends. This study has compared the performance of two time series models namely Winters model and linear exponential smoothening on the simulated datasets. Winters model was found to be a better fit for the point of sales data that were used for testing.