Reviewerly/exHarmony huggingface dataset:
Developing a New Robust Swarm-Based Algorithm for Robot Analysis

Metaheuristics are incapable of analyzing robot problems without being enhanced, modified, or hybridized. Enhanced metaheuristics reported in other works of literature are problem-specific and often not suitable for analyzing other robot configurations. The parameters of standard particle swarm optimization (SPSO) were shown to be incapable of resolving robot optimization problems. A novel algorithm for robot kinematic analysis with enhanced parameters is hereby presented. The algorithm is capable of analyzing all the known robot configurations. This was achieved by studying the convergence behavior of PSO under various robot configurations, with a view of determining new PSO parameters for robot analysis and a suitable adaptive technique for parameter identification. Most of the parameters tested stagnated in the vicinity of strong local minimizers. A few parameters escaped stagnation but were incapable of finding the global minimum solution, this is undesirable because accuracy is an important criterion for robot analysis and control. The algorithm was trained to identify stagnating solutions. The algorithm proposed herein was found to compete favorably with other algorithms reported in the literature. There is a great potential of further expanding the findings herein for dynamic parameter identification.
Blockchain-Powered Collaboration in Heterogeneous Swarms of Robots

One of the key challenges in the collaboration within heterogeneous multi-robot systems is the optimization of the amount and type of data to be shared between robots with different sensing capabilities and computational resources. In this paper, we present a novel approach to managing collaboration terms in heterogeneous multi-robot systems with blockchain technology. Leveraging the extensive research of consensus algorithms in the blockchain domain, we exploit key technologies in this field to be integrated for consensus in robotic systems. We propose the utilization of proof of work systems to have an online estimation of the available computational resources at different robots. Furthermore, we define smart contracts that integrate information about the environment from different robots in order to evaluate and rank the quality and accuracy of each of the robots' sensor data. This means that the key parameters involved in heterogeneous robotic collaboration are integrated within the Blockchain and estimated at all robots equally without explicitly sharing information about the robots' hardware or sensors. Trustability is based on the verification of data samples that are submitted to the blockchain within each data exchange transaction and validated by other robots operating in the same environment. Initial results are reported which show the viability of the concepts presented in this paper.
Control-Oriented Modelling of a Wankel Rotary Engine: A Synthesis Approach of State Space and Neural Networks

T he use of Wankel rotary engines as a range extender has been recognised as an appealing method to enhance the performance of Hybrid Electric Vehicles (HEV).They are effective alternatives to conventional reciprocating piston engines due to their considerable merits such as lightness, compactness, and higher power-to-weight ratio.However, further improvements on Wankel engines in terms of fuel economy and emissions are still needed.The objective of this work is to investigate the engine modelling methodology that is particularly suitable for the theoretical studies on Wankel engine dynamics and new control development.In this paper, control-oriented models are developed for a 225CS Wankel rotary engine produced by Advanced Innovative Engineering (AIE) UK Ltd.Through a synthesis approach that involves State Space (SS) principles and the artificial Neural Networks (NN), the Wankel engine models are derived by leveraging both first-principle knowledge and engine test data.We first re-investigate the classical physicsbased Mean Value Engine Model (MVEM).It consists of differential equations mixed with empirical static maps, which are inherently nonlinear and coupled.Therefore, we derive a SS formulation which introduces a compact control-oriented structure with low computational demand.It avoids the cumbersome structure of the MVEM and can further facilitate the advanced modern control design.On the other hand, via black-box system identification techniques, we compare the different NN architectures that are suitable for engine modelling using time-series test data: 1) the Multi-Layer Perceptron (MLP) feedforward network; 2) the Elman recurrent network; 3) the Nonlinear AutoRegressive with eXogenous inputs (NARX) recurrent network.The NN models overall tend to achieve higher accuracy than the MVEM and the SS model and do not require a priori knowledge of the underlying physics of the engine.
VocaBura

Learning a new language is difficult and time-consuming. Apart from dedicated classroom study, second language (L2) learners often lack opportunities to switch their attention to vocabulary learning over other daily routines. In this work, we propose a method that enables L2 learners to study new vocabulary items during their dead time, such as when commuting to school or work. We developed a smartphone application, VocaBura, which combines audio learning with location-relevant L1-L2 word pairs to allow users to discover new vocabulary items while walking past buildings, shops and other locations. Our evaluation results indicated that Japanese beginner level English learners were able to retain more vocabulary items with the proposed method compared to traditional audio-based study despite being less aware of L2 vocabulary acquisition having occurred. In our second study, we report on the level of English vocabulary coverage for L2 learning achievable with our proposed method. We discuss several design implications for educational technologies supporting second language learning.
Detecting Human Activities Based on a Multimodal Sensor Data Set Using a Bidirectional Long Short-Term Memory Model: A Case Study

Human falls are one of the leading causes of fatal unintentional injuries worldwide. Falls result in a direct financial cost to health systems, and indirectly, to society’s productivity. Unsurprisingly, human fall detection and prevention is a major focus of health research. In this chapter, we present and evaluate several bidirectional long short-term memory (Bi-LSTM) models using a data set provided by the Challenge UP competition. The main goal of this study is to detect 12 human daily activities (six daily human activities, five falls, and one post-fall activity) derived from multi-modal data sources - wearable sensors, ambient sensors, and vision devices. Our proposed Bi-LSTM model leverages data from accelerometer and gyroscope sensors located at the ankle, right pocket, belt, and neck of the subject. We utilize a grid search technique to evaluate variations of the Bi-LSTM model and identify a configuration that presents the best results. The best Bi-LSTM model achieved good results for precision and f1-score, 43.30 and 38.50%, respectively.
Memory-Augmented Generative Adversarial Networks for Anomaly Detection

We propose a memory-augmented deep learning model for semisupervised anomaly detection (AD). While many traditional AD methods focus on modeling the distribution of normal data, additional constraints in the modeling process are needed to distinguish between normal and abnormal data. The proposed model, named memory augmented generative adversarial networks (MEMGAN), is coupled with external memory units through attentional operations. One property of MEMGAN in the latent space is such that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoded</i> normal data are expected to reside in the convex hull of the memory units, while the abnormal ones are separated outside. This property makes the AD process of MEMGAN more robust and reliable. Experiments on AD datasets adapted from MVTec, MNIST, CIFAR10, and Arrhythmia demonstrate that MEMGAN notably improves over previous AD models. We also find that the decoded memory units in MEMGAN are more diverse and interpretable than those in previous memory-augmented models.
Privacy-preserving analytics for the securitization market: a zero-knowledge distributed ledger technology application

Abstract A zero-knowledge proof or protocol is a cryptographic technique for verifying private data without revealing it in its clear form. In this paper, we evaluate the potential for zero-knowledge distributed ledger technology to alleviate asymmetry of information in the asset-backed securitization market. To frame this inquiry, we conducted market data analyses, a review of prior literature, stakeholder interviews with investors, originators and security issuers and collaboration with blockchain engineers and researchers. We introduce a new system which could enable all market participants in the securitization lifecycle (e.g. investors, rating agencies, regulators and security issuers) to interact on a unique decentralized platform while maintaining the privacy of loan-level data, therefore providing the industry with timely analytics and performance data. Our platform is powered by zkLedger (Narula et al. 2018), a zero-knowledge protocol developed by the MIT Media Lab and the first system that enables participants of a distributed ledger to run publicly verifiable analytics on masked data.
Dynamic Texture Representation Based on Hierarchical Local Patterns

A novel effective operator, named HIerarchical LOcal Pattern (HILOP), is proposed to efficiently exploit relationships of local neighbors at a pair of adjacent hierarchical regions which are located around a center pixel of a textural image. Instead of being thresholded by the value of the central pixel as usual, the gray-scale of a local neighbor in a hierarchical area is compared to that of all neighbors in the other region. In order to capture shape and motion cues for dynamic texture (DT) representation, HILOP is taken into account investigating hierarchical relationships in plane-images of a DT sequence. The obtained histograms are then concatenated to form a robust descriptor with high performance for DT classification task. Experimental results on various benchmark datasets have validated the interest of our proposal.
Encouraging Submission of FAIR Data at <i>The Journal of Organic Chemistry</i> and <i>Organic Letters</i>

ADVERTISEMENT RETURN TO ISSUEPREVEditorialNEXTEncouraging Submission of FAIR Data at The Journal of Organic Chemistry and Organic LettersAngela M. HunterAngela M. HunterMore by Angela M. Hunterhttp://orcid.org/0000-0002-3474-525X, Erick M. CarreiraErick M. CarreiraMore by Erick M. Carreirahttp://orcid.org/0000-0003-1472-490X, and Scott J. MillerScott J. MillerMore by Scott J. Millerhttp://orcid.org/0000-0001-7817-1318Cite this: J. Org. Chem. 2020, 85, 4, 1773–1774Publication Date (Web):February 12, 2020Publication History Received30 January 2020Published online12 February 2020Published inissue 21 February 2020https://doi.org/10.1021/acs.joc.0c00248Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views4185Altmetric-Citations5LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (375 KB) Get e-AlertsSUBJECTS:Computational chemistry,Inorganic compounds,Mathematical methods,Nuclear magnetic resonance spectroscopy,Organic chemistry Get e-Alerts
Strategy for Crack Width Measurement of Multiple Crack Patterns in Civil Engineering Material Testing Using a Monocular Image Sequence Analysis

Abstract An image sequence analysis procedure is developed to quantitatively analyze complex multiple crack patterns in tension tests of fiber-reinforced composite specimens. Planar textured surfaces of such specimens can be observed with a monocular image sequence using a camera of suitable spatial and temporal resolution. Due to the narrow crack paths, a dense high-precision displacement vector field is computed applying least-squares image matching techniques. Some uniformly distributed matching points are triangulated into a mesh. To measure deformations, principal strains and crack widths are computed for each face. Stretched triangles presumably containing one or multiple cracks are subdivided into three new triangles to densify the mesh in critical regions. The subdivision is repeated for some iterations. The crack width computation of the triangles requires at least three vertices and its displacements. Due to the dense displacement vector field, there are more points available. In this paper, an algorithm for the crack width computation in a least-squares fit is presented.
Improving the resolution time performance of an application support process using Six Sigma methodology

Purpose This paper is a case study on the successful application of Six Sigma methodology in the information technology industry. The purpose of this paper is to improve the resolution time performance of an application support process. Design/methodology/approach Through brainstorming, the potential factors influencing the resolution time are identified. From the potential factors, the important factors, namely, day-wise ticket volume, team’s software engineering skill and domain expertise are shortlisted using test of hypothesis, correlation, etc. Then a model is developed using principal component regression, linking the critical to quality characteristic with the root causes or important factors. Finally, a solution methodology is developed using the model to obtain the team composition and size with optimum software skill and domain expertise to resolve the tickets within the required time. Findings The implementation of the solution resulted in improving the process performance significantly. The process performance index increased from 0.00 to 1.2 and parts per million reduced from 501366.31 to 153. 33. Practical implications The software engineers can use the similar approach to improve the performance of core software activities such as coding, testing and bug fixing. The approach can also be used for improving the performance of other skill-based operations such as error reduction in medical diagnostics. Originality/value This is one of the rare Six Sigma case studies on improving skill-based processes such as software development. The study also demonstrates the usefulness of the Six Sigma methodology for solving dynamic problems whose solution needs to be continuously adjusted with the changes in the input or process conditions.
Cine como herramienta de aprendizaje creativo en Educación Primaria

The research aims to analyze if short fi lms are an appropriate resource to stimulate divergent thinking in children watch images that enhance the imagination and inventiveness. The study was carried out with Grade 1 subjects using a quantitative and qualitative paradigm, through an experimental design of group comparison of the creativity variable, before and after the implementation of a program based on short fi lms for children. After the implementation of the program, favorable changes were found in the creative function of children in experimental groups and not in the control group.
Digital Reference – A Semantic Web for Semiconductor Manufacturing and Supply Chains Containing Semiconductors

Within the public-private ECSEL Joint Undertaking, the project Productive4.0 aims for improvements with regard to digitalization for electronic components like semiconductors and systems as key enabling technologies. The semiconductor industry has a high growth potential within the frame of digitalization as manufactured products are intensively used in production and B2B environment. Thus, Supply chains employing and fabricating semiconductors become the core driver and beneficent of digitalization. In order to exploit digitalization potentials, Semantic Web technologies are used to create a digital twin for semiconductor supply chains and supply chains containing semiconductors. Exemplary benefits of this lingua franca which can be understood by humans and machines alike for various applications like simulation, deep learning, security, trust, and automation are shown in this paper.
Generalization of port-based teleportation and controlled teleportation capability

As a variant of the original quantum teleportation, port-based teleportation has been proposed, and its various kinds of useful applications in quantum information processing have been explored. Two users in the port-based teleportation initially share an arbitrary pure state, which can be represented by applying one user's local operation to a bipartite maximally entangled state. If the maximally entangled state is a $2M$-qudit state, then it can be expressed as $M$ copies of a two-qudit maximally entangled state, where $M$ is the number of ports. We here consider a generalization of the original port-based teleportation obtained from employing copies of an arbitrary bipartite (mixed) resource instead of copies of a pure maximally entangled one. By means of the generalization, we construct a concept of controlled port-based teleportation by combining controlled teleportation with port-based teleportation, and analyze its performance in terms of several meaningful quantities such as the teleportation fidelity, the entanglement fidelity, and the fully entangled fraction. In addition, we present quantities called the control power and the minimal control power for the controlled version on a given tripartite quantum state.
Gravity as a Reference for Estimating a Person’s Height From Video

Estimating the metric height of a person from monocular imagery without additional assumptions is ill-posed. Existing solutions either require manual calibration of ground plane and camera geometry, special cameras, or reference objects of known size. We focus on motion cues and exploit gravity on earth as an omnipresent reference 'object' to translate acceleration, and subsequently height, measured in image-pixels to values in meters. We require videos of motion as input, where gravity is the only external force. This limitation is different to those of existing solutions that recover a person's height and, therefore, our method opens up new application fields. We show theoretically and empirically that a simple motion trajectory analysis suffices to translate from pixel measurements to the person's metric height, reaching a MAE of up to 3.9 cm on jumping motions, and that this works without camera and ground plane calibration.
Skeleton‐based attention‐aware spatial–temporal model for action detection and recognition

Action detection and recognition are popular subjects of research in the field of computer vision. The task of action detection can be regarded as the sum of action location and recognition. Action features described by using information concerning the human skeleton have the advantages of robustness against external factors and requiring a small amount of calculation. This study proposes a skeleton‐based action analysis model based on a recurrent neural network framework. The model learns action features by modelling static and dynamic features of skeleton joints and the importance of different video frames by introducing an attention module. For action location, conditional random field loss function is introduced to establish the context dependency of output labels. In the aspect of action recognition, the hierarchical training mechanism with triple loss models action features at coarse‐grained and fine‐grained levels. The authors’ proposed method delivers state‐of‐the‐art results on action location and recognition tasks.
A Smart Home System based on Internet of Things

The Internet of Things (IoT) describes a network infrastructure of identifiable things that share data through the Internet. A smart home is one of the applications for the Internet of Things. In a smart home, household appliances could be monitored and controlled remotely. This raises a demand for reliable security solutions for IoT systems. Authorization and authentication are challenging IoT security operations that need to be considered. For instance, unauthorized access, such as cyber-attacks, to a smart home system could cause danger by controlling sensors and actuators, opening the doors for a thief. This paper applies an extra layer of security of multi-factor authentication to act as a prevention method for mitigating unauthorized access. One of those factors is face recognition, as it has recently become popular due to its non-invasive biometric techniques, which is easy to use with cameras attached to most trending computers and smartphones. In this paper, the gaps in existing IoT smart home systems have been analyzed, and we have suggested improvements for overcoming them by including necessary system modules and enhancing user registration and log-in authentication. We propose software architecture for implementing such a system. To the best of our knowledge, the existing IoT smart home management research does not support face recognition and liveness detection within the authentication operation of their suggested software architectures.
On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Implementing supervised machine learning on the Hindi corpus for classification and prediction of verses is an untouched and useful area.Classifying and predictions benefits many applications like organizing a large corpus, information retrieval and so on.The metalinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today.Text classification algorithms along with Natural Language Processing (NLP) facilitates fast, cost-effective, and scalable solution.Performance evaluation of these predictors is a challenging task.To reduce manual efforts and time spent for reading the document, classification of text data is important.In this paper, 697 Hindi poems are classified based on four topics using four eager machine-learning algorithms.In the absence of any other technique, which achieves prediction on Hindi corpus, misclassification error is used and compared to prove the betterment of the technique.Support vector machine performs best amongst all.
EvoAAA: An evolutionary methodology for automated neural autoencoder architecture search

Machine learning models work better when curated features are provided to them. Feature engineering methods have been usually used as a preprocessing step to obtain or build a proper feature set. In late years, autoencoders (a specific type of symmetrical neural network) have been widely used to pe rform representation learning, proving their competitiveness against classical feature engineering algorithms. The main obstacle in the use of autoencoders is finding a good architecture, a process that most experts confront manually. An automated autoencoder symmetrical architecture search procedure, based on evolutionary methods, is proposed in this paper. The methodology is tested against nine heterogeneous data sets. The obtained results show the ability of this approach to find better architectures, able to concentrate most of the useful information in a minimized encoding, in a reduced time.