nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Study on the Learning Outcome of Teaching Elementary Level Children using Lego Mindstorms EV3 Robotics Education Kit

Skills like computational thinking, problem solving, handling complexity, team-work and project management are essential for future careers and needs to be taught to students at the elementary level itself. Computer programming knowledge and skills, experiencing technology and conducting science and engineering experiments are also important for students at elementary level. However, teaching such skills effectively through active learning can be challenging for educators. In this paper, we present our approach and experiences in teaching such skills to several elementary level children using Lego Mindstorms EV3 robotics education kit. We describe our learning environment consisting of lessons, worksheets, hands-on activities and assessment. We taught students how to design, construct and program robots using components such as motors, sensors, wheels, axles, beams, connectors and gears. Students also gained knowledge on basic programming constructs such as control flow, loops, branches and conditions using a visual programming environment. We carefully observed how students performed various tasks and solved problems. We present experimental results which demonstrates that our teaching methodology consisting of both the course content and pedagogy was effective in imparting the desired skills and knowledge to elementary level children. The students also participated in a competitive World Robot Olympiad India event and qualified during the regional round which is an evidence of the effectiveness of the approach.
An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.
dapting the Body Appreciation Scale-2 for Children : A psychometric nalysis of the BAS-2 C mma

Positive body image among adults is related to numerous indicators of well-being. However, no research has explored body appreciation among children. To facilitate our understanding of children’s positive body image, the current study adapts and validates the Body Appreciation Scale-2 (BAS-2; Tylka & WoodBarcalow, 2015a) for use with children. Three hundred and forty-four children (54.4% girls) aged 9–11 completed the adapted Body Appreciation Scale-2 for Children (BAS-2C) alongside measures of body esteem, media influence, body surveillance, mood, and dieting. A sub-sample of 154 participants (62.3% girls) completed the questionnaire 6-weeks later to examine stability (test-retest) reliability.
Bioactivities of Compounds from Elephantopus scaber, an Ethnomedicinal Plant from Southwest China

Elephantopus scaber is an ethnomedicinal plant used by the Zhuang people in Southwest China to treat headaches, colds, diarrhea, hepatitis, and bronchitis. A new δ -truxinate derivative, ethyl, methyl 3,4,3',4'-tetrahydroxy- δ -truxinate (1), was isolated from the ethyl acetate extract of the entire plant, along with 4 known compounds. The antioxidant activity of these 5 compounds was determined by ABTS radical scavenging assay. Compound 1 was also tested for its cytotoxicity effect against HepG2 by MTT assay (IC50 = 60  μ M), and its potential anti-inflammatory, antibiotic, and antitumor bioactivities were predicted using target fishing method software.
Vision-based robust road lane detection in urban environments

Road and lane detection play an important role in autonomous driving and commercial driver-assistance systems. Vision-based road detection is an essential step towards autonomous driving, yet a challenging task due to illumination and complexity of the visual scenery. Urban scenes may present additional challenges such as intersections, multi-lane scenarios, or clutter due to heavy traffic. This paper presents an integrative approach to ego-lane detection that aims to be as simple as possible to enable real-time computation while being able to adapt to a variety of urban and rural traffic scenarios. The approach at hand combines and extends a road segmentation method in an illumination-invariant color image, lane markings detection using a ridge operator, and road geometry estimation using RANdom SAmple Consensus (RANSAC). Employing the segmented road region as a prior for lane markings extraction significantly improves the execution time and success rate of the RANSAC algorithm, and makes the detection of weakly pronounced ridge structures computationally tractable, thus enabling ego-lane detection even in the absence of lane markings. Segmentation performance is shown to increase when moving from a color-based to a histogram correlation-based model. The power and robustness of this algorithm has been demonstrated in a car simulation system as well as in the challenging KITTI data base of real-world urban traffic scenarios.
Efficiency of energy conversion for a piezoelectric power harvesting system

This paper studies the energy conversion efficiency for a rectified piezoelectric power harvester. An analytical model is proposed, and an expression of efficiency is derived under steady-state operation. In addition, the relationship among the conversion efficiency, electrically induced damping and ac–dc power output is established explicitly. It is shown that the optimization criteria are different depending on the relative strength of the coupling. For the weak electromechanical coupling system, the optimal power transfer is attained when the efficiency and induced damping achieve their maximum values. This result is consistent with that observed in the recent literature. However, a new finding shows that they are not simultaneously maximized in the strongly coupled electromechanical system.
Performance of the “if in doubt, cut it out” rule for the management of nodular melanoma

The recognition of nodular melanoma is clinically challenging, and the diagnostic accuracy of dermoscopy and confocal microscopy is lower than for superficial spreading melanoma.



To test a management strategy consisting in the excision of any nodular lesion that cannot be confidently and precisely classified as a benign tumor after clinical and dermoscopic examination.



Clinical and dermoscopic images of excised nodular lesions were retrospectively collected and evaluated. The evaluators were asked to record the level of diagnostic confidence for each lesion, by declaring if they were confident or doubtful about the given diagnosis. The NNE (number needed to excise) value was used to evaluate the efficacy of the proposed method.



A total of 1,319 excised nodular lesions formed the study set. The NNE for any malignancy was 3.9 (634/164), while the NNE for melanoma was 13.2 (634/48). NNE for hypo and amelanotic melanoma was 27.3 (327/12).



Excising doubtful nodular lesions seems to be an effective management strategy not to miss nodular melanoma, resulting in an acceptable rate of unnecessary excision of benign lesions.
SLOWBot (chatbot) Lifestyle Assistant

SLOWbot is a research project conducted via a collaboration between iaso health and FBK (Fondazione Bruno Kessler). There are now thousands of available healthy aging apps, but most don't deliver on their promise to support a healthy aging process in people that need it the most. The neediest include the over-fifties age group, particularly those wanting to prevent the diseases of aging or whom already have a chronic disease. Even the motivated "quantified selfers" discard their health apps after only a few months. Our research aims to identify new ways to ensure adherence to a healthy lifestyle program tailored for an over fifties audience which is delivered by a chatbot. The research covers the participant onboarding process and examines barriers and issues with gathering predictive data that might inform future improved uptake and adherence as well as an increase in health literacy by the participants. The healthy lifestyle program will ultimately be delivered by our "SLOWbot" which guides the participant to make informed and enhanced health decision making, specifically around food choices (a "longevity eating plan").
Are You up for the Challenge? Towards the Development of a Big Data Capability Assessment Model

Utilizing Big Data scenarios that are generated from increasing digitization and data availability is a core topic in IS research. There are prospective advantages in generating business value from those scenarios through improved decision support and new business models. In order to harvest those potential advantages Big Data capabilities are required, including not only technological aspects of data management and analysis but also strategic and organisational aspects. To assess these capabilities, one can use capability assessment models. Employing a qualitative meta-analysis on existing capability assessment models, it can be revealed that the existing approaches greatly differ in their fundamental structure due to heterogeneous model elements. The heterogeneous elements are therefore synthesized and transformed into consistent assessment dimensions to fulfil the requirements of exhaustive and mutually exclusive aspects of a capability assessment model. As part of a broader research project to develop a consistent and harmonized Big Data Capability Assessment Model (BDCAM) a new design for a capability matrix is proposed including not only capability dimensions but also Big Data life cycle tasks in order to measure specific weaknesses along the process of data-driven value creation.
Cloud-Based Fine-Grained Health Information Access Control Framework for LightweightIoT Devices with Dynamic Auditing andAttribute Revocation

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptography is required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.
How Kids Code and How We Know: An Exploratory Study on the Scratch Repository

Block-based programming languages like Scratch, Alice and Blockly are becoming increasingly common as introductory languages in programming education. There is substantial research showing that these visual programming environments are suitable for teaching programming concepts. But, what do people do when they use Scratch? In this paper we explore the characteristics of Scratch programs. To this end we have scraped the Scratch public repository and retrieved 250,000 projects. We present an analysis of these projects in three different dimensions. Initially, we look at the types of blocks used and the size of the projects. We then investigate complexity, used abstractions and programming concepts. Finally we detect code smells such as large scripts, dead code and duplicated code blocks. Our results show that 1) most Scratch programs are small, however Scratch programs consisting of over 100 sprites exist, 2) programming abstraction concepts like procedures are not commonly used and 3) Scratch programs do suffer from code smells including large scripts and unmatched broadcast signals.
Pump scheduling optimization model for water supply system using AWGA

The water supply system has a high operational cost associated with its operations. This is characteristically due to the operations of the pumps that consume significantly high amount of electric energy. In order to minimize the electric energy consumption and reduce the maintenance cost of the system, this paper proposes the use of an Adaptive Weighted sum Genetic Algorithm (AWGA) in creating an optimal pump schedule, which can minimize the cost of electricity and satisfy the constraint of the maximum and minimum levels of water in the reservoir as well. The Adaptive weighted sum GA is based on popular weighted sum approach GA for multi-objective optimization problem wherein the weights multipliers of the individual fitness functions are adaptively selected. The algorithm has been tested using a hypothetical case study and promising results have been obtained and presented.
Infection and preterm birth.

As many as 50% of spontaneous preterm births are infection-associated. Intrauterine infection leads to a maternal and fetal inflammatory cascade, which produces uterine contractions and may also result in long-term adverse outcomes, such as cerebral palsy. This article addresses the prevalence, microbiology, and management of intrauterine infection in the setting of preterm labor with intact membranes. It also outlines antepartum treatment of infections for the purpose of preventing preterm birth.
Information Propagation in Interaction Networks

We study the potential flow of information in interaction networks, that is, networks in which the interactions between the nodes are being recorded. The central notion in our study is that of an information channel. An information channel is a sequence of interactions between nodes forming a path in the network which respects the time order. As such, an information channel represents a potential way information could have flown in the interaction network. We propose algorithms to estimate information channels of limited time span from every node to other nodes in the network. We present one exact and one more efficient approximate algorithm. Both algorithms are onepass algorithms. The approximation algorithm is based on an adaptation of the HyperLogLog sketch, which allows easily combining the sketches of individual nodes in order to get estimates of how many unique nodes can be reached from groups of nodes as well. We show how the results of our algorithm can be used to build efficient influence oracles for solving the Influence maximization problem which deals with finding top k seed nodes such that the information spread from these nodes is maximized. Experiments show that the use of information channels is an interesting data-driven and model-independent way to find top k influential nodes in interaction networks.
A Linear-Time Kernel Goodness-of-Fit Test

We propose a novel adaptive test of goodness-of-fit, with computational cost linear in the number of samples. We learn the test features that best indicate the differences between observed samples and a reference model, by minimizing the false negative rate. These features are constructed via Stein’s method, meaning that it is not necessary to compute the normalising constant of the model. We analyse the asymptotic Bahadur efficiency of the new test, and prove that under a mean-shift alternative, our test always has greater relative efficiency than a previous linear-time kernel test, regardless of the choice of parameters for that test. In experiments, the performance of our method exceeds that of the earlier linear-time test, and matches or exceeds the power of a quadratic-time kernel test. In high dimensions and where model structure may be exploited, our goodness of fit test performs far better than a quadratic-time two-sample test based on the Maximum Mean Discrepancy, with samples drawn from the model.
SparseMAP: Differentiable Sparse Structured Inference

Structured prediction requires searching over a combinatorial number of structures. To tackle it, we introduce SparseMAP: a new method for sparse structured inference, and its natural loss function. SparseMAP automatically selects only a few global structures: it is situated between MAP inference, which picks a single structure, and marginal inference, which assigns nonzero probability to all structures, including implausible ones. SparseMAP can be computed using only calls to a MAP oracle, making it applicable to problems with intractable marginal inference, e.g., linear assignment. Sparsity makes gradient backpropagation efficient regardless of the structure, enabling us to augment deep neural networks with generic and sparse structured hidden layers. Experiments in dependency parsing and natural language inference reveal competitive accuracy, improved interpretability, and the ability to capture natural language ambiguities, which is attractive for pipeline systems.
Mesocrystals--ordered nanoparticle superstructures.

Mesocrystals are 3D ordered nanoparticle superstructures, often with internal porosity, which receive much recent research interest. While more and more mesocrystal systems are found in biomineralization or synthesized, their potential as material still needs to be explored. It needs to be revealed, which new chemical and physical properties arise from the mesocrystal structure, or how they change by the ordered aggregation of nanoparticles to fully exploit the promising potential of mesocrystals. Also, the mechanisms for mesocrystal synthesis need to be explored to adapt it to a wide class of materials. The last three years have seen remarkable progress, which is summarized here. Also potential future directions of this reaserch field are discussed. This shows the importance of mesocrystals not only for the field of materials research and allows the appliction of mesocrystals in advanced materials synthesis or property improvement of existing materials. It also outlines attractive research directions in this field.
Generating High Quality Proposition Banks for Multilingual Semantic Role Labeling

Semantic role labeling (SRL) is crucial to natural language understanding as it identifies the predicate-argument structure in text with semantic labels. Unfortunately, resources required to construct SRL models are expensive to obtain and simply do not exist for most languages. In this paper, we present a two-stage method to enable the construction of SRL models for resourcepoor languages by exploiting monolingual SRL and multilingual parallel data. Experimental results show that our method outperforms existing methods. We use our method to generate Proposition Banks with high to reasonable quality for 7 languages in three language families and release these resources to the research community.
A solution approach to valuation with unhedgeable risks

We study a class of stochastic optimization models of expected utility in markets with stochastically changing investment opportunities. The prices of the primitive assets are modelled as diffusion processes whose coefficients evolve according to correlated diffusion factors. Under certain assumptions on the individual preferences, we are able to produce reduced form solutions. Employing a power transformation, we express the value function in terms of the solution of a linear parabolic equation, with the power exponent depending only on the coefficients of correlation and risk aversion. This reduction facilitates considerably the study of the value function and the characterization of the optimal hedging demand. The new results demonstrate an interesting connection with valuation techniques using stochastic differential utilities and also, with distorted measures in a dynamic setting.
Pleated Pneumatic Artificial Muscle-Based Actuator System as a Torque Source for Compliant Lower Limb Exoskeletons

Powered lower limb exoskeletons require high-performance actuator systems, capable of producing zero to high-assistive torque and at the same time yielding to human interaction torques. Such variable impedance can be achieved by the means of compliant actuators. Because of their intrinsic compliance and high force-to-weight ratio pneumatic muscles are a viable option. However, previous pneumatic muscle powered exoskeleton designs either used them as a position source or failed to meet the high-dynamic torque requirements when using them as a torque source. This paper contributes to the improvement of pneumatic muscle-based actuator systems as a torque source for exoskeleton-type robots. The use of pleated pneumatic artificial muscles in a novel actuator system design allows for a higher torque range in a larger range of motion. Performance evaluation results are given for a 1 DOF test setup and a powered knee exoskeleton. The proposed torque controller achieves the dynamic torques required for zero to full assistance at moderate walking speeds.
Recent advances in planar optics : from plasmonic to dielectric metasurfaces

PATRICE GENEVET, FEDERICO CAPASSO,* FRANCESCO AIETA, MOHAMMADREZA KHORASANINEJAD, AND ROBERT DEVLIN Université Côte d’Azur, CNRS, CRHEA, rue Bernard Gregory, Sophia Antipolis 06560 Valbonne, France John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA Hewlett-Packard Laboratories, Palo Alto, California 94304, USA e-mail: Patrice.genevet@crhea.cnrs.fr *
Expanding the solar spectrum used by photosynthesis.

A limiting factor for photosynthetic organisms is their light-harvesting efficiency, that is the efficiency of their conversion of light energy to chemical energy. Small modifications or variations of chlorophylls allow photosynthetic organisms to harvest sunlight at different wavelengths. Oxygenic photosynthetic organisms usually utilize only the visible portion of the solar spectrum. The cyanobacterium Acaryochloris marina carries out oxygenic photosynthesis but contains mostly chlorophyll d and only traces of chlorophyll a. Chlorophyll d provides a potential selective advantage because it enables Acaryochloris to use infrared light (700-750 nm) that is not absorbed by chlorophyll a. Recently, an even more red-shifted chlorophyll termed chlorophyll f has been reported. Here, we discuss using modified chlorophylls to extend the spectral region of light that drives photosynthetic organisms.