nils-herrmann/clean_scirepeval huggingface dataset:
Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings

Automatic mapping of buildings from remote sensing imagery is currently limited by insufficient detection accuracy. A recent technique that can largely promote the building detection accuracy is convolutional neural network, which is an important branch of deep learning and has substantially improved the state-of-the-art performance of image segmentation. A benchmark dataset bears significance in fair comparisons for further accelerating the development of building detection toward automatic mapping. However, several problems still remain in the current public datasets that address this task. First, although building detection is generally considered equivalent to extracting roof outlines, most datasets directly provide building footprints as ground truths for testing and evaluation; the challenges of these benchmarks are more complicated than roof segmentation, as relief displacement leads to varying degrees of misalignment between roof outlines and footprints. On the other hand, an image dataset should feature a large quantity and high spatial resolution to effectively train a high-performance deep learning model for accurate mapping of buildings. Unfortunately, the remote sensing community still lacks proper benchmark datasets that can simultaneously satisfy these requirements. In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS). This dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation. We implement several state-of-the-art deep learning methods of semantic segmentation for performance evaluation and analysis of the proposed dataset. The results can serve as the baseline for future work.
A comparison of the effects of problem-based learning and lecturing on the development of students' critical thinking.

Educational approaches are thought to have facilitative or hindering effects on students' critical thinking development. The aim of this study was to compare the effects of problem-based learning (PBL) and lecturing approaches on the development of students' critical thinking.



All 79 Year 1 undergraduate nursing students at a university in Hong Kong were randomly assigned to 1 of 2 parallel courses delivered by either PBL (n = 40) or lecturing (n = 39) over 1 academic year. The primary outcome measure was students' critical thinking disposition as measured by the California Critical Thinking Disposition Inventory (CCTDI). Individual interviews were also conducted to elicit the students' perceptions of their learning experience. Data were collected at 4 timepoints spanning 3 years.



The overall CCTDI and subscale scores for the PBL group were not significantly different from those of the lecture group at the first timepoint (pretest). Compared with lecture students, PBL students showed significantly greater improvement in overall CCTDI (P = 0.0048), Truthseeking (P = 0.0008), Analyticity (P = 0.0368) and Critical Thinking Self-confidence (P = 0.0342) subscale scores from the first to the second timepoints; in overall CCTDI (P = 0.0083), Truthseeking (P = 0.0090) and Analyticity (P = 0.0354) subscale scores from the first to the third timepoints; and in Truthseeking (P = 0.0173) and Systematicity (P = 0.0440) subscale scores from the first to the fourth timepoints.



There were significant differences in the development of students' critical thinking dispositions between those who undertook the PBL and lecture courses, respectively.
Skeletal muscle lipid content and insulin resistance: evidence for a paradox in endurance-trained athletes.

We examined the hypothesis that an excess accumulation of intramuscular lipid (IMCL) is associated with insulin resistance and that this may be mediated by the oxidative capacity of muscle. Nine sedentary lean (L) and 11 obese (O) subjects, 8 obese subjects with type 2 diabetes mellitus (D), and 9 lean, exercise-trained (T) subjects volunteered for this study. Insulin sensitivity (M) determined during a hyperinsulinemic (40 mU x m(-2)min(-1)) euglycemic clamp was greater (P < 0.01) in L and T, compared with O and D (9.45 +/- 0.59 and 10.26 +/- 0.78 vs. 5.51 +/- 0.61 and 1.15 +/- 0.83 mg x min(-1)kg fat free mass(-1), respectively). IMCL in percutaneous vastus lateralis biopsy specimens by quantitative image analysis of Oil Red O staining was approximately 2-fold higher in D than in L (3.04 +/- 0.39 vs. 1.40 +/- 0.28% area as lipid; P < 0.01). IMCL was also higher in T (2.36 +/- 0.37), compared with L (P < 0.01). The oxidative capacity of muscle determined with succinate dehydrogenase staining of muscle fibers was higher in T, compared with L, O, and D (50.0 +/- 4.4, 36.1 +/- 4.4, 29.7 +/- 3.8, and 33.4 +/- 4.7 optical density units, respectively; P < 0.01). IMCL was negatively associated with M (r = -0.57, P < 0.05) when endurance-trained subjects were excluded from the analysis, and this association was independent of body mass index. However, the relationship between IMCL and M was not significant when trained individuals were included. There was a positive association between the oxidative capacity and M among nondiabetics (r = 0.37, P < 0.05). In summary, skeletal muscle of trained endurance athletes is markedly insulin sensitive and has a high oxidative capacity, despite having an elevated lipid content. In conclusion, the capacity for lipid oxidation may be an important mediator of the association between excess muscle lipid accumulation and insulin resistance.
The cross section of expected stock returns

This paper studies the cross-sectional properties of return forecasts derived from Fama-MacBeth regressions. These forecasts mimic how an investor could, in real time, combine many firm characteristics to obtain a composite estimate of a stock’s expected return. Empirically, the forecasts vary substantially across stocks and have strong predictive power for actual returns. For example, using ten-year rolling estimates of FamaMacBeth slopes and a cross-sectional model with 15 firm characteristics (all based on low-frequency data), the expected-return estimates have a cross-sectional standard deviation of 0.87% monthly and a predictive slope for future monthly returns of 0.74, with a standard error of 0.07.
Occupational therapy using a sensory integrative approach for children with developmental disabilities.

This article provides an introduction and overview of sensory integration theory as it is used in occupational therapy practice for children with developmental disabilities. This review of the theoretical tenets of the theory, its historical foundations, and early research provides the reader with a basis for exploring current uses and applications. The key principles of the sensory integrative approach, including concepts such as "the just right challenge" and "the adaptive response" as conceptualized by A. Jean Ayres, the theory's founder, are presented to familiarize the reader with the approach. The state of research in this area is presented, including studies underway to further delineate the subtypes of sensory integrative dysfunction, the neurobiological mechanisms of poor sensory processing, advances in theory development, and the development of a fidelity measure for use in intervention studies. Finally, this article reviews the current state of the evidence to support this approach and suggests that consensual knowledge and empirical research are needed to further elucidate the theory and its utility for a variety of children with developmental disabilities. This is especially critical given the public pressure by parents of children with autism and other developmental disabilities to obtain services and who have anecdotally noted the utility of sensory integration therapy for helping their children function more independently. Key limiting factors to research include lack of funding, paucity of doctorate trained clinicians and researchers in occupational therapy, and the inherent heterogeneity of the population of children affected by sensory integrative dysfunction. A call to action for occupational therapy researchers, funding agencies, and other professions is made to support ongoing efforts and to develop initiatives that will lead to better diagnoses and effective intervention for sensory integrative dysfunction, which will improve the lives of children and their families.
A User Simulator for Task-Completion Dialogues

Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Third, collecting and annotating human-machine or human-human conversations for taskoriented dialogues requires extensive domain knowledge. Because building an appropriate dataset can be both financially costly and time-consuming, one popular approach is to build a user simulator based upon a corpus of example dialogues. Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Dialogue agents trained on these simulators can serve as an effective starting point. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online. To ease empirical algorithmic comparisons in dialogues, this paper introduces a new, publicly available simulation framework, where our simulator, designed for the movie-booking domain, leverages both rules and collected data. The simulator supports two tasks: movie ticket booking and movie seeking. Finally, we demonstrate several agents and detail the procedure to add and test your own agent in the proposed framework.
Coconditional Autoencoding Adversarial Networks for Chinese Font Feature Learning

In this work, we propose a novel framework named Coconditional Autoencoding Adversarial Networks (CocoAAN) for Chinese font learning, which jointly learns a generation network and two encoding networks of different feature domains using an adversarial process. The encoding networks map the glyph images into style and content features respectively via the pairwise substitution optimization strategy, and the generation network maps these two kinds of features to glyph samples. Together with a discriminative network conditioned on the extracted features, our framework succeeds in producing realistic-looking Chinese glyph images flexibly. Unlike previous models relying on the complex segmentation of Chinese components or strokes, our model can “parse” structures in an unsupervised way, through which the content feature representation of each character is captured. Experiments demonstrate our framework has a powerful generalization capacity to other unseen fonts and characters.
Pervasive Computing: A Paradigm for the 21st Century

P u b l i s h e d b y t h e I E E E C o m p u t e r S o c i e t y Pervasive Computing: A Paradigm for the 21st Century In 1991, Mark Weiser, then chief technology officer for Xerox’s Palo Alto Research Center, described a vision for 21st century computing that countered the ubiquity of personal computers. “The most profound technologies are those that disappear,” he wrote. “They weave themselves into the fabric of everyday life until they are indistinguishable from it.” Computing has since mobilized itself beyond the desktop PC. Significant hardware developments—as well as advances in location sensors, wireless communications, and global networking—have advanced Weiser’s vision toward technical and economic viability. Moreover, the Web has diffused some of the psychological barriers that he also thought would have to disappear. However, the integration of information technology into our lives still falls short of Weiser’s concluding vision:
Accelerated Profile HMM Searches

Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the "multiple segment Viterbi" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call "sparse rescaling". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches.
Mobile Edge Computing: A Survey

Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.
Planning Formula One race strategies using discrete-event simulation

A discrete-event simulation model that imitates most on-track events, including car failures, passing manoeuvres and pit stops during a Formula One race, is presented. The model is intended for use by a specific team. It will enable decision-makers to plan and evaluate their race strategy, consequently providing them with a possible competitive advantage. The simulation modelling approach presented in this paper captures the mechanical complexities and physical interactions of a race car with its environment through a time-based approach. Model verification and validation are demonstrated using three races from the 2005 season. The application of the model is illustrated by evaluating the race strategies employed by a specific team during these three races.  (2009) 60, 952–961. doi:10.1057/palgrave.jors.2602626 Published online 9 July 2008
Implementation of STEAM Education to Improve Mastery Concept

Science Technology Engineering, Art, Mathematics (STEAM) is an integration of art into Science Technology Engineering, Mathematics (STEM). Connecting art to science makes learning more effective and innovative. This study aims to determine the increase in mastery of the concept of high school students after the application of STEAM education in learning with the theme of Water and Us. The research method used is one group Pretestposttest design with students of class VII (n = 37) junior high school. The instrument used in the form of question of mastery of concepts in the form of multiple choices amounted to 20 questions and observation sheet of learning implementation. The results of the study show that there is an increase in conceptualization on the theme of Water and Us which is categorized as medium (<g>=0, 46) after the application of the STEAM approach. The conclusion obtained that by applying STEAM approach in learning can improve the mastery of concept
Model-Agnostic Private Learning

We design differentially private learning algorithms that are agnostic to the learning model assuming access to a limited amount of unlabeled public data. First, we provide a new differentially private algorithm for answering a sequence of m online classification queries (given by a sequence of m unlabeled public feature vectors) based on a private training set. Our algorithm follows the paradigm of subsample-and-aggregate, in which any generic non-private learner is trained on disjoint subsets of the private training set, and then for each classification query, the votes of the resulting classifiers ensemble are aggregated in a differentially private fashion. Our private aggregation is based on a novel combination of the distance-to-instability framework [26], and the sparse-vector technique [15, 18]. We show that our algorithm makes a conservative use of the privacy budget. In particular, if the underlying non-private learner yields a classification error of at most α ∈ (0, 1), then our construction answers more queries, by at least a factor of 1/α in some cases, than what is implied by a straightforward application of the advanced composition theorem for differential privacy. Next, we apply the knowledge transfer technique to construct a private learner that outputs a classifier, which can be used to answer an unlimited number of queries. In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases. Similar to non-private sample complexity, our bounds are completely characterized by the VC dimension of the concept class.
Attributed Network Embedding for Learning in a Dynamic Environment

Network embedding leverages the node proximity manifested to learn a low-dimensional node vector representation for each node in the network. The learned embeddings could advance various learning tasks such as node classification, network clustering, and link prediction. Most, if not all, of the existing works, are overwhelmingly performed in the context of plain and static networks. Nonetheless, in reality, network structure often evolves over time with addition/deletion of links and nodes. Also, a vast majority of real-world networks are associated with a rich set of node attributes, and their attribute values are also naturally changing, with the emerging of new content patterns and the fading of old content patterns. These changing characteristics motivate us to seek an effective embedding representation to capture network and attribute evolving patterns, which is of fundamental importance for learning in a dynamic environment. To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly. In this paper, we tackle this problem by proposing a novel dynamic attributed network embedding framework - DANE. In particular, DANE first provides an offline method for a consensus embedding and then leverages matrix perturbation theory to maintain the freshness of the end embedding results in an online manner. We perform extensive experiments on both synthetic and real attributed networks to corroborate the effectiveness and efficiency of the proposed framework.
Phase change materials for smart textiles – An overview

Phase change materials (PCM) take advantage of latent heat that can be stored or released from a material over a narrow temperature range. PCM possesses the ability to change their state with a certain temperature range. These materials absorb energy during the heating process as phase change takes place and release energy to the environment in the phase change range during a reverse cooling process. Insulation effect reached by the PCM depends on temperature and time. Recently, the incorporation of PCM in textiles by coating or encapsulation to make thermo-regulated smart textiles has grown interest to the researcher. Therefore, an attempt has been taken to review the working principle of PCM and their applications for smart temperature regulated textiles. Different types of phase change materials are introduced. This is followed by an account of incorporation of PCM in the textile structure are summarized. Concept of thermal comfort, clothing for cold environment, phase change materials and clothing comfort are discussed in this review paper. Some recent applications of PCM incorporated textiles are stated. Finally, the market of PCM in textiles field and some challenges are mentioned in this review paper.
Background subtraction and human detection in outdoor videos using fuzzy logic

This paper presents a scale and view invariant approach for human detection in the presence of various other objects like animals, vehicles, etc. Human detection is one of the essential steps in applications like activity recognition, gait recognition, human centric surveillance etc. Inaccurate detection of humans in such applications may increase the number of false alarms. In the proposed work, fuzzy logic has been used to model a robust background for object detection. Three different features are extracted from the contours of the detected objects. These features are aggregated using fuzzy inference system. Then human contour is identified using template matching. The proposed method consists of four main steps; Moving Object Detection, Feature Extraction, Feature Aggregation, and Human Contour Detection.
FADES: Fine-Grained Edge Offloading with Unikernels

FADES is an edge offloading architecture that empowers us to run compact, single purpose tasks at the edge of the network to support a variety of IoT and cloud services. The design principle behind FADES is to efficiently exploit the resources of constrained edge devices through fine-grained computation offloading. FADES takes advantage of MirageOS unikernels to isolate and embed application logic in concise Xen-bootable images. We have implemented FADES and evaluated the system performance under various hardware and network conditions. Our results show that FADES can effectively strike a balance between running complex applications in the cloud and simple operations at the edge. As a solid step to enable fine-grained edge offloading, our experiments also reveal the limitation of existing IoT hardware and virtualization platforms, which shed light on future research to bring unikernel into IoT domain.