Reviewerly/exHarmony huggingface dataset:
Making Sense of Robots: Consumer Discourse on Robots in Tourism and Hospitality Service Settings

Originality/value: The paper introduces technology sensemaking and technology ideology as important theoretical frameworks to understand consumer perceptions, attitudes, uses and relationships in regard to service robots in hospitality and tourism contexts.
Image Mosaic Based on Improved Logarithmic Polar Coordinate Transformation and Ransac Algorithm

Complex factors with the electronic noise, X-ray scattering and uneven illumination often disturb the image registration. A new algorithm was proposed in this paper. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the Harris corner matching points were extracted in overlapping positions and purified by the improved Ransac algorithm. Finally, images were processed by NSCT transform algorithm to make the image joint seemed smooth and natural. Experiments confirmed that, the new algorithm is accurate and efficient, and has high robustness to complex environment.
Struktur Teks Eksposisi Pada Rubrik “Humaniora” Solopos dan Pemanfaatannya dalam Pembelajaran Bahasa Indonesia Kelas X SMA

This study aims to: (1) find the exposition text structure in the rubric of Humanities Solopos, (2) explain the thesis in the structure of the exposition text in the rubric Humanities Solopos, (3) describe the utilization of the structure of the exposition text in the rubric of Humanities Solopos in learning Indonesian X grade high school. This type of research uses descriptive qualitative methods. The data in this study are in the form of exposition text in the rubric Humanities of the daily newspaper Solopos. The data source in this study is in the form of news in the Solopos newspaper. Data collection techniques using refer and note techniques, because the source of the data is text. In this study the validity used is theory triangulation. The data analysis technique used is the Agih method. The results of the research of 30 exposition text data in the rubric Humanities in the January 2019 edition of the Solopos newspaper, First, the exposition text has a complete constituent structure which includes: statement of opinion (thesis), argumentation, and reaffirmation. Second, the thesis section in the exposition text structure based on the paragraph development mindset in the argument section in the Humanities rubric Solopos has three mindsets, namely general-specific order mindset, cause-effect mindset, and point of view mindset. Third, the structure of the exposition text in the Solopos Humanities rubric is used as Indonesian language learning material related to the exposition text material in class X SMA 1 semester in Basic Competence 3.4 Analyzing the structure and language of the exposition text.
An item recommendation model with content semantic

Current recommender service providers are offering interesting items for user-based user behaviour and ignoring the content semantic of items. The item semantic is should be taken into account as an accurate reflection of items. We present a recommender model that leverages content semantic and user rating. In this model, the item similarity is firstly calculated with content semantic by best Word2vec method, an item recommendation list is built by the similarities. Next, the user rating is used to model the user preference and build the other item list recommended by traditional recommendation method. Then, the two item lists is mixed together as final list for user. Comparing the above algorithm to traditional recommendation algorithms on MovieLens, FilmTrust and Online Retail datasets, we run experiments that show the presented algorithm has is greatly improved on accuracy and increase by an average of 25.32% to 31.41%, and present good scalability.
Quer durch Durch

Right Through Durch: Insights from a Saxophone Quartet by Fabien Levy With Les deux ampoules d’un sablier peu a peu se comprennent (1996) for solo harp, Fabien Levy (* 1968) set the degre zero for the compositional concepts he would subsequently pursue. These approaches were further developed in Durch, in memoriam Gerard Grisey (1998) for saxophone quartet. As the main focus of the article, this piece serves not only to enrich a perception-based engagement with Levy’s work, but also to trace certain compositional tendencies he inherited and adapted, which also appear in his later works. Via the tension between »analytical« structure and sensually perceptible form, Durch is permeable to a variety of musical traditions; among other things, these are used to produce virtual voices.
Simultaneously Learning Adaptive Neighbors and Clustering Label via Semi-Supervised NMF

Graph-based Nonnegative matrix factorization (NMF) methods usually construct an affinity graph in the original high-dimensional space, and then this graph is directly applied to the low-dimensional space, which has effectively improved the clustering performance. However, because of the curse of dimensionality, the graph may not precisely encode the geometric structure of samples in the low-dimensional space. In this paper, we propose a novel semi-supervised NMF method which can simultaneously learn adaptive neighbors and clustering label in the low-dimensional space. Specifically, the high-dimensional data are first projected onto the low-dimensional feature space, where the adaptive neighborhood weights of each feature are exploited to learn a flexible weight graph. Simultaneously, by incorporating the label information, the discriminative feature representations are learned, which assists the weight graph to better capture the potential semantic information. In addition, an efficient alternating minimization algorithm is introduced to solve the proposed model, and extensive experiments are constructed to validate the superiority of the proposed model compared with state-of-the-art methods on five real-word datasets.
Two Case Studies of Topic-Related Multidimensional Reading Services in China

This article provides a brief summary of the development of topic-related multidimensional reading services in academic libraries in China. The authors mainly introduce the methods and practices that Tongji University Library has developed for this type of service. The authors also include a briefing about a multidimensional reading service event called “Reading the city Jinling - the Red Mansions” which was hosted by Nanjing Agriculture University Library in 2017. Theoretical and practical explorations suggest that academic libraries could integrate more resources from different areas to promote a deeper understanding of reading, and to make more contributions to knowledge transformation and innovation in China.
An Active Noise Correction Graph Embedding Method Based on Active Learning for Graph Noisy Data

In various scenarios of the real world, there are various graph data. Most graph structures are confronted with the problems of complex structure and large consumption of memory space. Graph embedding is an effective method to overcome such challenges, which converts graph structure into a low-dimensional dense vector space. In the real world, label acquisition is expensive, and there may be noise in the data. Therefore, it is important to find valuable noise nodes as much as possible to improve the performance of downstream task. In this paper, we propose a novel active sampling strategy for graph noisy data named Active Noise Correction Graph Embedding method (ANCGE). Given the label budget, the proposed method aims to use semi-supervised graph embedding algorithm to find valuable mislabeled nodes. ANCGE measures the value of noise nodes according to their representativeness and influence on the graph. The experimental results on three open datasets demonstrate the effectiveness of our method and its stability under different noise rates.
A service composition approach based on overall QoS and modified graphplan

Increasing emphasis on users' preferences and the growth of services on the web make service composition a time consuming and complicated work. In this paper, an approach for web service composition that combines overall QoS and modified graphplan is proposed. Fuzzy logic is applied to handle uncertainty and support decision making. Specifically, values of overall QoS of services are generated by using fuzzy analytical hierarchy process, entropy and user's preferences. The graphplan is modified by pruning services according to the values of overall QoS of services in forward and backward expansions, while producing solutions of compositions. Furthermore, case studies and experiments are performed and show that our approach has better solutions compared with original graphplan and some other approaches.
P581Electronic health records (EHRs) data validation in atherosclerotic/cardiovascular clinical phenotypes

Abstract Background Research efforts to develop strategies to effectively identify patients and reduce the burden of cardiovascular diseases is essential for the future of the health system. Most research studies have used only coded parts of electronic health records (EHRs) for case-detection obtaining missed data cases, reducing study quality and in some case bias findings. Incorporating information from free-text into case-detection through Big Data and Artificial Intelligence techniques improves research quality. Savana has developed EHRead, a powerful technology that applies Natural Language Processing, Machine Learning and Deep Learning, to analyse and automatically extracts highly valuable medical information from unstructured free text contained in the EHR to support research and practice. Purpose We aimed to validate the linguistic accuracy performance of Savana, in terms of Precision (P), Recall (R) and overall performance (F-Score) in the cardiovascular domain since this is one of the most prevalent disease in the general population. This means validating the extent to which the Savana system identifies mentions to atherosclerotic/cardiovascular clinical phenotypes in EHRs. Methods The project was conducted in 3 Spanish sites and the system was validated using a corpus that consisted of 739 EHRs, including the emergency, medical and discharge records, written in free text. These EHRs were randomly selected from the total number of clinical documents generated during the period of 2012–2017 and were fully anonymized to comply with legal and ethical requirements. Two physicians per site reviewed records (randomly selected) and annotated all direct references to atherosclerotic/cardiovascular clinical phenotypes, following the annotation guidelines previously developed. A third physician adjudicated discordant annotations. Savana's performance was automatically calculated using as validation resource the gold standard created by the experts. Results We found good levels of performance achieved by Savana in the identification of mentions to atherosclerotic/cardiovascular clinical phenotypes, yielding an overall P, R, and F-score of 0.97, 0.92, and 0.94, respectively. We also found that going through all the EHRs and identifying the mentions to atherosclerotic/cardiovascular clinical phenotypes, the expert spent ∼ 60h while Savana ∼ 36 min. Conclusion(s) Innovative techniques to identify atherosclerotic/cardiovascular clinical phenotypes could be used to support real world data research and clinical practice. Overall Savana showed a high performance, comparable with those obtained by an expert physician annotator doing the same task. Additionally, a significant reduction of time in using automatic information extraction system was achieved.
LibGuides: Test Preparation Materials: Home

A guide to the test preparation materials available in the OHS Library, Frederick County Public Libraries, and online.
Understanding and Supporting Knowledge Management in Agile Software Development

Background. Agile Software Development (ASD) promises agility and flexibility in dealing with uncertainty by prioritizing interaction between people supported by informal communication and knowled ...
Measurement-device-independent Quantum Key Distribution with Inaccurate Coherent Sources

Measurement-device-independent quantum key distribution is proposed to fight against all possible side channel detector attacks. The security of the original proposal relies on the assumption that the legitimate users can character their sources exactly, which might not be satisfied in practice. Here we propose a measurement-device-independent quantum key distribution protocol with inaccurate coherent sources. The assumption is that legitimate users only have the knowledge that sources, for encoding, are coherent sources, while the accuracy of the encoding operation can not be controlled exactly. We give the security proofs for it under collective attacks, and simulation results of the security bound are also presented by employing parameters of current quantum key distribution experiments.
A Survey on different Filters for Noise Reduction in Digital Images

Image processing still faces the challenge of reducing noise from images. Noise is always present in digital images during image acquisition, coding, transmission and processing steps. Noise is very difficult to remove from digital images without the prior knowledge. Image processing proves to be very successful in permitting and extended range of algorithms to be applied to input data set in the form of image and regain decisive results. These algorithms lean to avoid problems such as noise and signal distortion. So, in this paper, we express a brief overview of various noise models and the survey on filters which are used to remove the noise from the images. By using these filters we can achieve an image after noise removal which having higher clarity in terms of both interpretation and analysis for study in different fields and this paper is to be analyzed the filters those are Mean, median ,adaptive median and wiener filter used to reduce noises such as Gaussian, Salt and pepper and Speckle noise.
PENERAPAN METODE PICTURE AND PICTURE DALAM PEMBELAJARAN MENULIS TEKS EKSPOSISI PADA SISWA KELAS X SMK

This research is motivated by the existence of observational data that shows the value of student learning outcomes is low, so students experience difficulties during learning. Based on this, the study of exposition text writing is carried out using the picture and picture method. The objectives to be obtained are to find out the application of the picture and picture method in improving students' writing skills and to find out the differences in the learning outcomes of writing exposition text using picture and picture. The research method used is Quasi-Experimental Design. The sample used in the study was the X class of Vocational School which numbered 25 students. Data collection techniques with initial test data collection and final test data as a comparison. Based on the results of these tests, then there are results of the average pretest value obtained value of 63.20 and the results of the average posttest value obtained value of 81.80 so that the difference in value with a difference of 18.6. This shows that the application of the picture and picture method is effectively used so that it can improve the ability to write exposition text in class X of Vocational School.
Integration of Teaching-Modes of LBL-PBL-CBL-RBL Education of Molecular Biology Course

Molecular biology is the frontier of life science and an important basic theoretical course.It is abstract and complex, and develops rapidly.In order to improve the teaching quality of molecular biology, the project team reformed the traditional single teaching mode, integrated and applied four teaching modes ie.LBL, PBL, CBL and RBL in the teaching of molecular biology, gave full play to the advantages of various teaching modes, and constructed a teacher-led-student-oriented teaching system.In this paper, its implementation process is introduced in detail to provide reference for the teaching of other biology courses.
SEO med inlänkar genom marknadsföringswebbplats : Hur bra är Gutenberg-editorn i jämförelse

More and more people use the internet, and by extension, search engines, to find information, products and services. A company’s search engine ranking for certain keywords can be crucial to attract ...
Application of Recommender System in Intelligent Community under Big Data Scenario

Nowadays, intelligent community is one of indispensable parts in social construction. The construction of intelligent community promotes the construction and development of intelligent city, which can improve residents' living quality. Eating is an important part inhuman lives. In this paper, we develop a food recommendation system. This system is based on big data, Association Rule-based Recommendation and Collaborative Filtering Recommendation. By analyzing a large number of historical user behaviors, this system recommends restaurants, supermarkets, recipes and meal delivery service.
When Is a Semi-honest Secure Multiparty Computation Valuable?

Secure Multiparty Computation protocols secure under the malicious model provide a strong guarantee of privacy and correctness. The semi-honest model provides what appears to be a much weaker guarantee, requiring parties to follow the protocol correctly. We show that for all but a small class of problems, those in the non-cooperatively computable class, the correctness guarantee of the malicious protocol effectively requires semi-honest parties as well. This suggests a wider utility than previously thought for semi-honest protocols.
Probabilistic Neural Network for Reasons of the Low Quality LTE Networks Classification

Normally, network optimization engineers classify the causes of low quality cells according to their own experience. However, artificial classification method is impractical for large amounts of data and is also non-reproducible. Engineers cannot classify the causes of a large number of low network quality cells, and there are 47 classes of causes which can lead to serious inaccuracies classification. The aim of this work is to develop an automated approach based on Probabilistic Neural Network for classifying the causes of the low quality LTE networks. The results show that probabilistic neural network can quickly and accurately classify the causes of low quality cells. It provides effective data support for the improvement of LTE networks quality.
Texture Classification using a Hybrid Deep and Handcrafted Features

In this paper, we have proposed a hybrid descriptor for the texture classification task. The feature variables are extracted from the approximation coefficients of the image, through a combination of deep neural network and handcrafted feature. The AlexNet along with completed joint scale local binary pattern (CJLBP) is used for illumination, scaling, and orientation invariance description. The wavelet decomposition layer provides robustness against additive white Gaussian noise. The feature dimensionality is reduced by using Principal Component Analysis. We have evaluated our proposed descriptor on the images of Outex texture databases. The experimental results presented in the paper in term of classification accuracy show that our proposed descriptor outperforms state-of-the-art feature extraction scheme.
An analysis of continuous consistency models in real time peer-to-peer fighting games

This study analyses different methods of maintaining a consistent state between two peers in a real time fighting game played over a network. Current methods of state management are explored in a c ...
The Raspberry Pi Desktop Apps

We’ve set up our equipment, and we’ve played with the basic desktop a bit; now it’s time to get some actual work done. In this chapter, we’ll go through an overview of all the best GUI desktop apps that you can use to get your work done. There’s an office suite, numerous writing tools and email clients, choices of web browsers, notes apps, audio and video editing tools, illustration and graphic apps, and even some online file management tools.
Resource scheduling for quality-critical applications on cloud infrastructure

Cloud computing can provide virtualized, elastic, and on-demand computer system resources for supporting complex distributed applications, which has become the ubiquitous and primary computing paradigm for today's business. Meanwhile, the performance of cloud applications is getting more and more attention, particularly along with quality-critical applications which have critical requirements for Quality of Service (QoS) or Quality of Experience (QoE). In this thesis, we investigate how to efficiently schedule resources to satisfy quality-critical requirements of diverse applications on cloud infrastructure. First, we propose a Deadline-aware Deployment System (DDS) for time critical applications in clouds. DDS enables users to automatically deploy time critical applications and provides scheduling mechanisms to guarantee deployment deadlines. Second, we propose an Enhanced Container Scheduler (ECSched) for efficiently scheduling concurrent container requests with multi-resource constraints on heterogeneous clusters. ECSched formulates the container scheduling problem as a minimum-cost flow problem (MCFP) and performs a well-designed algorithm to schedule the concurrent requests in an online manner. Third, we propose new algorithms to optimize the placement of service-based applications in clouds. The objective of the proposed algorithms is to minimize inter-machine traffic while satisfying the multi-resource demands of service-based applications. Finally, we present GoTask, an approach that can learn to well schedule DAG jobs with multi-resource constraints on heterogeneous clusters. GoTask directly learns DAG scheduling policies from experience through deep reinforcement learning.
The Influence of Cooperative Learning Model Type Teams Tournament Games to Students ' Emotional Intelligencea

Improved student learning outcomes is the main objective in the process of learning. Many factors influence the improvement of student learning outcomes either intrinsically or extrinsic. One of the intrinsic factors that plays an important role, namely students ' ability in managing emotional (emotional intelligence). Increased emotional intelligence rated important because it is an asset that is long term. Many ways that can be done to improve emotional intelligence students, ranging from personal approach among teachers and students, guidance counseling, until by the time the learning process takes place. This research aims to know the influence of the application of the learning Teams model Games Tournament (TGT) against increased Emotional Intelligence (EI) students of Class XI IPS Senior High School in Sukoharjo Regency. Cooperative model type TGT was chosen because it has the stages corresponding to the existing indicators on EI. The method of data collection using the instrument the test results now and learn EI of students. This research uses the associative method with linear regression data analysis techniques is simple and Independent sample test (t-test). The results showed that cooperative learning model type TGT has an impact on EI on students.