nils-herrmann/clean_scirepeval huggingface dataset:
Integrated multilayered triboelectric nanogenerator for harvesting biomechanical energy from human motions.

We demonstrate a new flexible multilayered triboelectric nanogenerator (TENG) with extremely low cost, simple structure, small size (3.8 cm×3.8 cm×0.95 cm) and lightweight (7 g) by innovatively integrating five layers of units on a single flexible substrate. Owing to the unique structure and nanopore-based surface modification on the metal surface, the instantaneous short-circuit current (Isc) and the open-circuit voltage (Voc) could reach 0.66 mA and 215 V with an instantaneous maximum power density of 9.8 mW/cm2 and 10.24 mW/cm3. This is the first 3D integrated TENG for enhancing the output power. Triggered by press from normal walking, the TENG attached onto a shoe pad was able to instantaneously drive multiple commercial LED bulbs. With the flexible structure, the TENG can be further integrated into clothes or even attached onto human body without introducing sensible obstruction and discomfort to human motions. The novel design of TENG demonstrated here can be applied to potentially achieve self-powered portable electronics.
Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration

Fluent and safe interactions of humans and robots require both partners to anticipate the others’ actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.
Design and implementation of a fire detection and control system for automobiles using fuzzy logic

The immense benefits of fire in road transport cannot be overemphasized. However more than two thousand vehicles are damaged by unwanted fire on a daily basis. On a global scale, incendiary losses to the automobile and insurance industries have ran into billions of dollars over the last decade. A not-so-distant contributory factor is the lack of a sophisticated fire safety system on the automobile. This has been addressed by designing and implementing fuzzy logic control system with feedback over an Arduino micro-controller system. The automatic system consisting of flame sensors, temperature sensors, smoke sensors and a re-engineered mobile carbon dioxide air-conditioning unit was tested on a medium sized physical car. Results show that the automobile fire detection and control system devoid of false alarms, detects and extinguishes fire under 20 seconds. An innovative, very promising solution module for hardware implementation in fire detection and control for automobiles has been developed by using new algorithms and fuzzy logic.
Folksonomies-Cooperative Classification and Communication Through Shared Metadata

This paper examines user-generated metadata as implemented and applied in two web services designed to share and organize digital media to better understand grassroots classification. Metadata data about data allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In libraries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated professionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without specialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described documents was heralded by SGML, the WWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community. 1 The Creation of Metadata: Professionals, Content Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. These functions are usually
Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator

Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator Hu-Chen Liu a , Long Liu b & Ping Li c d a Department of Industrial Engineering and Management, Tokyo Institute of Technology, Tokyo, Japan b College of Design and Innovation, Tongji University, Shanghai, PR China c East Hospital Affiliated to Tongji University, No.150 Jimo Road, Shanghai, PR China d Shanghai Pudong New Area Zhoupu Hospital, No.135 Guanyue Road, Shanghai, PR China Version of record first published: 21 Jan 2013.
Digital Transformation as an Enabler for Advanced Services in the Sanitation Sector

People in Base of the Pyramid markets still face difficulties when it comes to sanitation. Container-based Sanitation (CBS) services represent a promising advanced sanitation service. Despite the observed outcomes of CBS services, organizations face obstacles when providing these services. To overcome these obstacles, digital transformations of these services are being carried out. We rely on multiple case studies to understand these digital transformations. Our findings highlight (1) the challenges these case organizations faced before engaging in the digital transformation, (2) their individual digital transformation pathways, and (3) a general framework for digital transformations in BoP markets.
Sparse Coding From a Bayesian Perspective

Sparse coding is a promising theme in computer vision. Most of the existing sparse coding methods are based on either <i>l</i><sub>0</sub> or <i>l</i><sub>1</sub> penalty, which often leads to unstable solution or biased estimation. This is because of the nonconvexity and discontinuity of the <i>l</i><sub>0</sub> penalty and the over-penalization on the true large coefficients of the <i>l</i><sub>1</sub> penalty. In this paper, sparse coding is interpreted from a novel Bayesian perspective, which results in a new objective function through maximum a posteriori estimation. The obtained solution of the objective function can generate more stable results than the <i>l</i><sub>0</sub> penalty and smaller reconstruction errors than the <i>l</i><sub>1</sub> penalty. In addition, the convergence property of the proposed algorithm for sparse coding is also established. The experiments on applications in single image super-resolution and visual tracking demonstrate that the proposed method is more effective than other state-of-the-art methods.
Microfibrillated cellulose and new nanocomposite materials: a review - DTU Orbit (15/10/2018)

(15/10/2018)  Microfibrillated cellulose and new nanocomposite materials: a review Due to their abundance, high strength and stiffness, low weight and biodegradability, nano-scale cellulose fiber materials (e.g., microfibrillated cellulose and bacterial cellulose) serve as promising candidates for bio-nanocomposite production. Such new high-value materials are the subject of continuing research and are commercially interesting in terms of new products from the pulp and paper industry and the agricultural sector. Cellulose nanofibers can be extracted from various plant sources and, although the mechanical separation of plant fibers into smaller elementary constituents has typically required high energy input, chemical and/or enzymatic fiber pre-treatments have been developed to overcome this problem. A challenge associated with using nanocellulose in composites is the lack of compatibility with hydrophobic polymers and various chemical modification methods have been explored in order to address this hurdle. This review summarizes progress in nanocellulose preparation with a particular focus on microfibrillated cellulose and also discusses recent developments in bio-nanocomposite fabrication based on nanocellulose.
A dual-band half-width microstrip leaky-wave antenna for beam scanning in the forward and backward directions

A microstrip leaky-wave antenna (LWA) loaded periodically with C-shaped slots is described. Uniform half-width (HW) microstrip LWAs (HW-MLWAs) operate in a single band and radiate a main beam that scans only in the forward direction. The proposed antenna operates in two frequency bands and the main beam can be steered in both the forward and backward directions by sweeping the frequency. The -10 dB reflection bandwidths of the antenna are 4.2 to 4.9 GHz and 8.23 to 8.8 GHz. The beam scanning ranges of the antenna are 22° to 59° and -76° to -18° when the frequency is swept from 4.2 to 5 GHz and 7.85 to 8.9 GHz, respectively.
Decentralizing Digital Identity: Open Challenges for Distributed Ledgers

Distributed Ledger Technology (DLT) has been proposed as a new way to incorporate decentralization into a wide range of digital infrastructures. Applications of DLT to digital identity are increasing in prevalence, with a recent survey reporting that 55% of DLT technologies in development track digital identity. However, while proofs of concept, open source software, and new ideas are readily available, it is still unclear the extent to which DLT can play a role to underpin new forms of digital identity. In this position paper, we situate this fast-moving application domain into the broader challenges faced in digital identity, with the aim to highlight the socio-technical nature of the challenge at hand, and to propose directions for future research.
Learning Local Feature Descriptors Using Convex Optimisation

The objective of this work is to learn descriptors suitable for the sparse feature detectors used in viewpoint invariant matching. We make a number of novel contributions towards this goal. First, it is shown that learning the pooling regions for the descriptor can be formulated as a convex optimisation problem selecting the regions using sparsity. Second, it is shown that descriptor dimensionality reduction can also be formulated as a convex optimisation problem, using Mahalanobis matrix nuclear norm regularisation. Both formulations are based on discriminative large margin learning constraints. As the third contribution, we evaluate the performance of the compressed descriptors, obtained from the learnt real-valued descriptors by binarisation. Finally, we propose an extension of our learning formulations to a weakly supervised case, which allows us to learn the descriptors from unannotated image collections. It is demonstrated that the new learning methods improve over the state of the art in descriptor learning on the annotated local patches data set of Brown et al. and unannotated photo collections of Philbin et al. .
Organ-on-a-chip platforms for studying drug delivery systems.

Novel microfluidic tools allow new ways to manufacture and test drug delivery systems. Organ-on-a-chip systems - microscale recapitulations of complex organ functions - promise to improve the drug development pipeline. This review highlights the importance of integrating microfluidic networks with 3D tissue engineered models to create organ-on-a-chip platforms, able to meet the demand of creating robust preclinical screening models. Specific examples are cited to demonstrate the use of these systems for studying the performance of drug delivery vectors and thereby reduce the discrepancies between their performance at preclinical and clinical trials. We also highlight the future directions that need to be pursued by the research community for these proof-of-concept studies to achieve the goal of accelerating clinical translation of drug delivery nanoparticles.
Wave-front Transformation with Gradient Metasurfaces

Relying on abrupt phase discontinuities, metasurfaces characterized by a transversely inhomogeneous surface impedance profile have been recently explored as an ultrathin platform to generate arbitrary wave fronts over subwavelength thicknesses. Here, we outline fundamental limitations of passive gradient metasurfaces in molding the impinging wave and show that local phase compensation is essentially insufficient to realize arbitrary wave manipulation, but full-wave designs should be considered. These findings represent a critical step towards realistic and highly efficient conformal wave manipulation beyond the scope of ray optics, enabling unprecedented nanoscale light molding.
The Social Media Index: Measuring the Impact of Emergency Medicine and Critical Care Websites

The number of educational resources created for emergency medicine and critical care (EMCC) that incorporate social media has increased dramatically. With no way to assess their impact or quality, it is challenging for educators to receive scholarly credit and for learners to identify respected resources. The Social Media index (SMi) was developed to help address this.



We used data from social media platforms (Google PageRanks, Alexa Ranks, Facebook Likes, Twitter Followers, and Google+ Followers) for EMCC blogs and podcasts to derive three normalized (ordinal, logarithmic, and raw) formulas. The most statistically robust formula was assessed for 1) temporal stability using repeated measures and website age, and 2) correlation with impact by applying it to EMCC journals and measuring the correlation with known journal impact metrics.



The logarithmic version of the SMi containing four metrics was the most statistically robust. It correlated significantly with website age (Spearman r=0.372; p<0.001) and repeated measures through seven months (r=0.929; p<0.001). When applied to EMCC journals, it correlated significantly with all impact metrics except number of articles published. The strongest correlations were seen with the Immediacy Index (r=0.609; p<0.001) and Article Influence Score (r=0.608; p<0.001).



The SMi's temporal stability and correlation with journal impact factors suggests that it may be a stable indicator of impact for medical education websites. Further study is needed to determine whether impact correlates with quality and how learners and educators can best utilize this tool.
QUESTION CLASSIFICATION USING STATISTICAL APPROACH : A COMPLETE

In recent days, question classification is growing in popularity as it has an important role in question answering systems, information retrieval and it can be used in a wide range of other domains. The main aim of question classification is to accurately assign labels to questions based on expected answer type. Past research works have relied on matching questions against hand-crafted rules. However, rules require enormous effort to create and often suffer from being too specific. A great deal of current research works on question classification is based on statistical approach to overcome these issues by employing machine learning techniques such as Support Vector Machine and Artificial Neural Network. This paper presents an updated literature survey of current methods or approaches for question classification in the areas of question answering systems, information retrieval and educational environment. Question classification involving other languages besides English has also been examined.
Knowledge of extraction from trained neural network by using decision tree

Inside the sets of data, hidden knowledge can be acquired by using neural network. These knowledge are described within topology, using activation function and connection weight at hidden neurons and output neurons. Is hardly to be understanding since neural networks act as a black box. The black box problem can be solved by extracting knowledge (rule) from trained neural network. Thus, the aim of this paper is to extract valuable information from trained neural networks using decision. Further, the Levenberg Marquardt algorithm was applied to training 30 networks for each datasets, using learning parameters and basis weights differences. As the number of hidden neurons increase, mean squared error and mean absolute percentage error decrease, and more time they need to deal with the dataset, that is result of investigation from neural network architectures. Decision tree induction generally performs better in knowledge extraction result with accuracy and precision level from 84.07 to 93.17 percent. The extracted rule can be used to explaining the process of the neural network systems and also can be applied in other systems like expert systems.
Markov Chains for Exploring Posterior Distributions

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Interpretable Basis Decomposition for Visual Explanation

Explanations of the decisions made by a deep neural network are important for human end-users to be able to understand and diagnose the trustworthiness of the system. Current neural networks used for visual recognition are generally used as black boxes that do not provide any human interpretable justification for a prediction. In this work we propose a new framework called Interpretable Basis Decomposition for providing visual explanations for classification networks. By decomposing the neural activations of the input image into semantically interpretable components pre-trained from a large concept corpus, the proposed framework is able to disentangle the evidence encoded in the activation feature vector, and quantify the contribution of each piece of evidence to the final prediction. We apply our framework for providing explanations to several popular networks for visual recognition, and show it is able to explain the predictions given by the networks in a humaninterpretable way. The human interpretability of the visual explanations provided by our framework and other recent explanation methods is evaluated through Amazon Mechanical Turk, showing that our framework generates more faithful and interpretable explanations.
Analysis and Design of an Input-Series Two-Transistor Forward Converter For High-Input Voltage Multiple-Output Applications

In this paper, an input-series two-transistor forward converter is proposed and investigated, which is aimed at high-input voltage multiple-output applications. In this converter, all of the switches are operating synchronously, and the input voltage sharing (IVS) of each series-module is achieved automatically by the coupling of primary windings of the common forward integrated transformer. The active IVS processes are analyzed based on the model of the forward integrated transformer. Through the influence analysis when the mismatches in various series-modules are considered, design principles of the key parameters in each series-module are discussed to suppress the input voltage difference. Finally, a 96-W laboratory-made prototype composed of two forward series-modules is built, and the feasibility of the proposed method and the theoretical analysis are verified by the experimental results.
Automatic question generation for intelligent tutoring systems

In this paper, we present the system of automatic MCQs (Multiple Choice Questions) generation for any given input text along with a set of distractors. The system is trained on a Wikipedia-based dataset consisting of URLs of Wikipedia articles. The important words (keywords) which consist of both bigrams and unigrams are extracted and stored in a dictionary along with many other components of the knowledge base. We have used Inverse Document Frequency (IDF) measure for ranking the extracted keywords and Context-Based Similarity approach using Paradigmatic Relation discovery techniques for generation of distractors. In addition, the question generation phase includes eliminating sentences starting with Discourse Connectives to avoid a question with incomplete information. We have obtained significant accuracy compared to many similar approaches. The results are quite promising considering that there is no human intervention. The task of automatic question generation can be of quite an importance in MOOCs (Massive Open Online Courses), Intelligent Tutoring Systems and for self-assessment while learning a new concept. Though we have developed our system for the field of physics, it can be extended to any field.
Affective Design of E-Commerce User Interfaces : How to Maximise Perceived Trustworthiness

Successful e-commerce user experience design depends on a large number of factors. This paper focuses on consumers’ acceptance of and trust in an e-commerce system, based on the transaction’s value and perceived risk. The model of trust for ecommerce (MoTEC) by Egger (2000) provides a framework making explicit factors likely to affect customer trust. For each model component, design principles are provided, along with more concrete guidelines. It will be shown that the user interface is only one element of the customer experience. Designing for trust therefore requires user experience strategists to look beyond the mere design of the web site and pay attention to more general management and marketing issues.
GNSS/Cellular Hybrid Positioning System for Mobile Users in Urban Scenarios

This paper proposes a hybrid scheme for user positioning in an urban scenario using both a Global Navigation Satellite System (GNSS) and a mobile cellular network. To maintain receiver complexity (and costs) at a minimum, the location scheme combines the time-difference-of-arrival (TDOA) technique measurements obtained from the cellular network with GNNS pseudorange measurements. The extended Kalman filter (EKF) algorithm is used as a data integration system over the time axis. Simulated results, which are obtained starting from real measurements, demonstrate that the use of cellular network data may provide increased location accuracy when the number of visible satellites is not adequate. In every case, the obtained accuracy is within the limits required by emergency location services, e.g., Enhanced 911 (E911).