Reviewerly/exHarmony huggingface dataset:
PENINGKATAN HASIL BELAJAR SISWA MATERI LIMBAH LUNAK MELALUI MEDIA PEMBELAJARAN CARTA

Learning requires extensive knowledge in this matter of soft waste material. The purpose of this study is to improve student learning outcomes through learning media. The research method used in this study is a classroom action research method with 2 cycles. The study was conducted at Junior High School 1 Tambun Selatan 1, Bekasi in January - March 2019. The results showed that there was an increase in student learning outcomes seen from the gain score. Learning to use charts is now making students more enthusiastic in learning. This causes an increase in learning outcomes. The conclusion of this study is that media chart has succeeded in improving learning outcomes.
Jhsmit/ColiCoords: ColiCoords 0.1.4

Another 100 commits!Features:Updated examples and docsAdded perimeter transformationAdded slider to IterCellPlotAdded anglular distributions at cell's polesBugfixes:Various storm rendering fixesBinary image annotation bugfixesMany more small bugfixesOther storm_weight for rendering storm now False by default
timothydmorton/isochrones: v2.0.1

Fixed a bug with building the samples dataframes for BinaryStarModel and TripleStarModel .Add some functionality to write a large number of star.ini files at once.
MAPmAKER: Performing Multi-Robot LTL Planning under Uncertainty

Robot applications are being increasingly used in real life to help humans performing dangerous, heavy, and/or monotonous tasks. They usually rely on planners that given a robot or a team of robots compute plans that specify how the robot(s) can fulfill their missions. Current robot applications ask for planners that make automated planning possible even when only partial knowledge about the environment in which the robots are deployed is available. To tackle such challenges we developed MAPmAKER, which provides a decentralized planning solution and is able to work in partially known environments. Decentralization is realized by decomposing the robotic team into subteams based on their missions, and then by running a classical planning algorithm. Partial knowledge is handled by calling several times a classical planning algorithm. Demo video available at: https://youtu.be/TJzC_u2yfzQ
Reducing Symbol Search Overhead on Stream-Based Lossless Data Compression

Lossless data compression is emerged to utilize in the BigData applications in the recent days. The conventional algorithms mainly generate a symbol lookup table to replace a frequent data pattern in the inputted data to a symbol, and then compresses the information. This kind of the dictionary-based compression mechanism potentially has an overhead problem regarding the number of symbol matchings in the table. This paper focuses on a novel method to reduce the number of searches in the table using a bank separation technique. This paper reports design and implementation of the bank select method on the LCT-DLT, and shows the performance evaluations to validate the effects of the method.
Identifying Developers by Their Application Usage

In this paper we propose and evaluate a method based on recurrent neural networks to identify users by their application usage. The method was tested on the data collected by non-invasive metrics collection system developed at Innopolis University. The first results achieved on initial dataset show a high user identification accuracy and a potential for further research.
CNN Implementation for Semantic Heads Segmentation Using Top-View Depth Data in Crowded Environment

The paper “Convolutional Networks for semantic Heads Segmentation using Top-View Depth Data in Crowded Environment” [1] introduces an approach to track and detect people in cases of heavy occlusions based on CNNs for semantic segmentation using top-view RGB-D visual data. The purpose is the design of a novel U-Net architecture, U-Net 3, that has been modified compared to the previous ones at the end of each layer. In order to evaluate this new architecture a comparison has been made with other networks in the literature used for semantic segmentation. The implementation is in Python code using Keras API with Tensorflow library. The input data consist of depth frames, from Asus Xtion Pro Live OpenNI recordings (.oni). The dataset used for training and testing of the networks has been manually labeled and it is freely available as well as the source code. The aforementioned networks have their stand-alone Python script implementation for training and testing. A Python script for the on-line prediction in OpenNI recordings (.oni) is also provided. Evaluation of the networks has been made with different metrics implementations (precision, recall, F1 Score, Sørensen-Dice coefficient), included in the networks scripts.
Hypergraph restructuration for the supervision of system of systems

This article focuses on the ability of system of systems (SoS) to cope with failures of one of its constituents. The aim is to avoid the collapse of the whole system. To find a new state of the operation, we introduce a hypergraphic model-based supervision framework dedicated to SoS. It involves two main steps named the modelling and the reconfiguration. Our main objective is to reduce computational complexity of the supervision procedure. For the modelling part, given the high density of connections of the hypergraph representing SoS, we propose to decompose this model by means of the hypergraph tree-based acyclicity property. The latter yields a resolution of the problem in a linear time. For the reconfiguration part, we introduce a new subgraph isomorphism procedure enhanced by a filtering function. The lens is to deal with the hypergraphs constraints of complexity induced by such method. Finally, constraint programming method, and according to solver approaches, some models are implemented.
A HYBRID PSO OPTIMIZED VIRTUAL MACHINE SCHEDULING ALGORITHM IN CLOUD COMPUTING

The service to the end user in cloud computing is offered as virtual machine instances as demanded for a specified duration of time and billed on pay per use basis. A major problem faced in cloud computing is the virtual machine scheduling problem. The existing algorithms efficiently cannot satisfy the requirements with respect to resource utilisation, bandwidth utilisation, and cost. Also, most of them are of the fixed type which leads to wastage of resources. To overcome these problems, the hybrid particle swarm optimisation (HPSO) algorithm is proposed by efficiently allocating the resources to the users. This algorithm combines the genetic algorithm (GA) and the variable neighbourhood search (VNS) algorithm with the particle swarm optimisation (PSO) technique to increase the utilisation rate of the virtual machine as well as to minimise the total completion time. The proposed system is evaluated by performing simulations. The experimental results show that the proposed algorithm minimises the total completion time and increase the resource utilisation than PSO, GA and VNS algorithm.
Independent Work As An Important Form Of Formation Of Self-Educational Competence Of Students Of Humanitarian Specialties

Under the influence of humanistic psychology, teachers are increasingly inclined to draw conclusions regarding the priority of individual, personal and humanistic approaches to the organization of independent work at higher education institutions. The relevance of new teaching, didactic and methodological developments on the organization of students' independent work indicates the need to optimize the learning process to improve the quality of education, the intellectual level, as well as the creative potential of an individual. The article presents a theoretical analysis of independent work as the leading form of self-educational activities of humanitarian students and proposes a practical solution to the problem of the formation of self-educational competence of humanitarian students at a university, which is reflected in the experimental verification of the elaborated pedagogical conditions of its formation. Special attention is paid to the analysis of the foreign experience of students' independent work.
Querying APIs With SPARQL.

Abstract Although the amount of RDF data has been steadily increasing over the years, the majority of information on the Web is still residing in other formats, and is often not accessible to Semantic Web services. A lot of this data is available through APIs serving JSON documents. In this work we propose a way of extending SPARQL with the option to consume JSON APIs and integrate this information into SPARQL query answers, obtaining a language that combines data from the “traditional” Web to the Semantic Web. Our proposal is based on an extension of the SERVICE operator with the ability to connect to JSON APIs. With the aim of evaluating these queries as efficiently as possible, we show that the main bottleneck is the amount of API requests, and present an algorithm that produces “worst-case optimal” query plans that reduce the number of requests as much as possible. We note that the analysis of this algorithm is studied in terms of an algorithm for evaluating relational queries with access methods with the minimal number of access queries, which is of independent interest. We show the superiority of the worst-case optimal approach in a series of experiments that take existing SPARQL benchmarks, and augment them with the ability to connect to JSON APIs in order to obtain additional information.
RETRACTED: A novel nonlinear VSG integrating ELM with noise injection for enhancing energy modelling and analysis on small data

Abstract Building a robust and accurate energy analysis model is considered as an important issue in the field of petrochemical industries. Under the circumstance of small samples, the accuracy of the energy analysis model is unacceptable. In order to solve this problem, a novel noise injection integrated with extreme learning machine based nonlinear virtual sample generation method is proposed. Through injecting noise in the output matrix of the hidden layer of Extreme learning machine (ELM), a virtual information matrix that is different from the original one generated using the original small dataset can be obtained. Then the newly generated information matrix is adopted to produce good-quality virtual samples for supplement knowledge for small samples. To authenticate the effectiveness of the proposed method, the proposed method is developed as an energy analysis model for an ethylene production process. Simulation results indicate that good virtual samples can be generated using the proposed method, and the accuracy of the energy analysis model is much improved with the aid of the newly generated virtual samples. The proposed method will effectively help production departments of petrochemical industries set more suitable targets of energy consumption and make better use of available resources.
Modeling of Parity Status of The Mother and Basic Immunization Giving to Infants with Semiparametric Bivariate Probit (Case Study: North Kalimantan Province in 2017)

Abstract The bivariate probit regression model is a probit regression model consisting of two response variables with errors between the two variables correlate each other. The correlation between the two response variables can occur as a result of the presence of endogeneity, a condition in which a response variable becomes an exogenous variable in another response variable. Besides, the important issue that cannot be underestimated is undetectable nonlinear relationships between response variables and predictors, especially discrete or continuous predictor variables. The bivariate probit regression that does not ignore endogeneity cannot detect the nonlinear relationships between response variables and predictors, so one of the regression models that can overcome the problem is bivariate probit regression model with a semiparametric approach. The first step in semiparametric bivariate probit modeling is testing the hypothesis of exogeneity to determine whether there is a case of endogeneity or not. The exogenous test used in this study is the Lagrange Multiplier (LM) and Likelihood Ratio (LR) test. The data used in this study consisted of two binary categorical response variables, they are parity status of the mother and basic immunization giving to infants in North Kalimantan Province in 2017. The results of the exogenous test using the LM test and LR test stated that there was a significant correlation between response variables. The AIC value of the semiparametric bivariate probit model is 1301.602, while the bivariate probit model produces AIC of 1316.789, so it can be concluded that the semiparametric bivariate probit model provides better modeling results than the bivariate probit model.
INFLECTION OF CADA AND NUMBER FEATURE VALUATION IN BP

&lt;p&gt;This research investigates nominal concord in structures of non-standard Brazilian Portuguese (BP) where &lt;em&gt;cada&lt;/em&gt; is inflected with the plural morpheme &lt;em&gt;-s&lt;/em&gt; while the phrases following it may not bear any plural marking. In order to account for this, I will consider that cardinals and silent nouns work as a boundary dividing the DP into two domains such that phrases to their left are marked with the plural morpheme while phrases to their right are unmarked, a pattern found across languages. Additionally, I will argue that DPs with &lt;em&gt;cadas&lt;/em&gt; have a silent noun SET and that this silent noun conveys a set reading as well as valued plural features. In this case, &lt;em&gt;cada&lt;/em&gt; is interpreted as either ‘such’ or ‘every’ rather than ‘each’ and is followed by a noun or a cardinal ≥ 2 (plus a noun). Accordingly, because &lt;em&gt;cadas&lt;/em&gt; precedes SET, it is marked with &lt;em&gt;-s&lt;/em&gt;. This plural silent noun is followed by a preposition, which allows its embedded NP to be singular.&lt;/p&gt;
Authorization Framework for Medical Data

In this paper, the authors describe an approach for sharing sensitive medical data with the consent of the data owner.The framework builds on the advantages of the Semantic Web technologies and makes it secure and robust for sharing sensitive information in a controlled environment.The framework uses a combination of Role-Based and Rule-Based Access Policies to provide security to a medical data repository as per the FAIR guidelines.A lightweight ontologywas developed, to collect consent from the users indicating which part of their data they want to share with another user having a particular role.Here, the authors have considered the scenario of sharing the medical data by the owner of data, say the patient, with relevant persons such as physicians, researchers, pharmacist, etc.To prove this concept, the authors developed a prototype and validated using the Sesame OpenRDF Workbench with 202,908 triples and a consent graph stating consents per patient.
A Improved Adaptive Cuckoo Search Algorithm Based the Population Feature and Iteration Information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Lévy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.
Cohering power and decohering power of Gaussian unitary operations

Having a suitable measure to quantify the coherence of quantum states, a natural task is to evaluate the power of quantum channels for creating or destroying the coherence of input quantum states. In the present paper, by introducing the maximal coherent Gaussian states based on the relative entropy measure of coherence, we propose the (generalized) cohering power and the (generalized) decohering power of Gaussian unitary operations for continuous-variable systems. Some basic properties are obtained and the cohering power and decohering power of two special kinds of Gaussian unitary operations are calculated.
Aptiv Becoming a More Sustainable Business

The President and CEO of Aptiv presents and then discusses his progress in carrying out his ‘long‐term plan’ to transform an automotive parts supplier that was once part of General Motors into a ‘global technology company.’ The company's mission is to maintain and strengthen its current position as ‘a partner of choice’ of the world's largest automakers in designing and manufacturing ‘the brain and the nervous system of the vehicle’—and in so doing, to make vehicle transportation ‘safer, greener, and more connected.’ With 15 major tech centers and 126 manufacturing sites spread across 44 countries, and with over 160,000 employees, including 18,600 engineers and scientists, the company has the scale and resources to carry out that mission. And attesting to the resilience of its business model, in 2018, a year in which the global number of cars sold actually went down, the company increased its revenue by 10% and its operating cash flow by 50%. In the area of safety, the company is helping its customers build vehicles that move the industry closer to its goal of zero traffic fatalities and accidents by delivering the building blocks of active safety systems—which include perception and vision systems, and the high‐speed and high‐reliability networks that connect them. (And promising even greater gains in safety, the company's advances in fully automated vehicles are helping make self‐driving cars a reality.) In terms of green initiatives, the company is focused on minimizing the vehicles' ‘total lifecycle impact on the environment’ by providing the high‐voltage distribution and connection systems for electric cars, while also using ‘smarter’ vehicle architectures to achieve significant reductions in weight and mass. Finally, the company is helping the industry incorporate the increased connectivity that aims to provide a seamless integration between the passenger, the vehicle, and the Internet of Things. The company's commitment to corporate social responsibility goes well beyond its products and solutions. As an important part of its disciplined approach to creating sustainable long‐term value for its shareholders, the company tracks a range of key performance indicators designed to ensure that ‘we devote the right amount of time and attention to each of our key stakeholders.’ Along with its stakeholder programs and ongoing investment in human capital, which have helped the company win the designation ‘one of the world's most ethical companies’ in seven consecutive years, the CEO also cites the important roles played by its highly collaborative and engaged board of directors, and by a long‐term incentive pay plan for its senior management team that, along with standard financial measures, uses a ‘strategic results modifier’ that reflects the company's success in meeting ‘non‐financial goals that are related to talent, culture, and product quality.’
Increasing Students Awareness of Mobile Privacy and Security Using Modules

Mobile devices are fast becoming the dominant computing platform for an increasing number of people. Indeed millions of people are using their mobile phones as the main way to access the internet and social and entertainment media. This surge in mobile devices usage has been accompanied by an increase in malware specifically designed to infect mobile devices. From an educational standpoint, it is then becoming imperative to inform students about the risks and threats of mobile devices, not only as users but also as developers of mobile software.  However, the Computer Science and other IT related disciplines are suffering from an overcrowding of their respective curricula with an ever increasing number of topics and courses that need to be covered within a limited amount of time. A possible solution to this issue is the use of teaching modules. Modular teaching provides a framework in which new skills can be introduced with little time commitment on the part of the student and the instructor alike. It is also ideal for introducing subjects that are important to know and could fit within different subjects. This paper presents an experience in modular teaching of mobile security and privacy. Two modules have been designed and presented to two cohorts of students (n=14, n=10) and learning assessed through tests and surveys. Results show that the modular approach is indeed beneficial in filling students’ knowledge gaps in mobile security and that interleaving hands-on activities with instructional material can yield better retention and understanding of the topic.
Bridging the Gap between Abstractions and Critical-Path Heuristics via Hypergraphs

Abstractions and critical-path heuristics are among the most important families of admissible heuristics in classical planning. In this paper, we present a new family of heuristics, which we name hyperabstractions, given by the combination of the principal ideas underlying abstractions and critical-path heuristics. Hyperabstractions approximate goal distances through a mapping from states to sets of abstract states. The abstract transition behavior forms a relation between abstract states and sets of abstract states, and is formally represented via the notion of hypergraphs. We show that both abstractions and critical-path heuristics can naturally be expressed as members of this family. Moreover, we devise a method to construct hyperabstractions, using either a set of abstractions or a critical-path heuristic as a seed, in a way that guarantees that the resulting distance estimations dominate those of the input heuristics, sometimes even strictly. By finding suitable cost partitionings for hyperabstraction heuristics, this dominance result is preserved even in comparison to the additive combination of the input heuristics. Our experiments indicate the potential of this new class of heuristics, opening a wide range of future research topics.
Everything Is a Learning Moment in Year One

The Department ChairVolume 30, Issue 1 p. 25-26 Special Topics: New Chair Development Everything Is a Learning Moment in Year One Jamie Borchardt, Jamie BorchardtSearch for more papers by this author Jamie Borchardt, Jamie BorchardtSearch for more papers by this author First published: 02 July 2019 https://doi.org/10.1002/dch.30274Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume30, Issue1Summer 2019Pages 25-26 RelatedInformation