Reviewerly/exHarmony huggingface dataset:
The Design, Productization, and Evaluation of a Serverless Workflow-Management System

The need for accessible and cost-effective IT resources has led to the near-universal adoption of cloud computing. Within cloud computing, serverless computing has emerged as a model that further abstracts away operational complexity of heterogeneous cloud resources. Central to this form of computing is Function-as-a-Service (FaaS); a cloud model that enables users to express applications as functions, further decoupling the application logic from the hardware and other operational concerns. Although FaaS has seen rapid adoption for simple use cases, there are several issues that impede its use for more complex use cases. A key issue is the lack of systems that facilitate the reuse of existing functions to create more complex, composed functions. Current approaches for serverless function composition are either proprietary, resource inefficient, unreliable, or do not scale. To address this issue, we propose an approach to orchestrate composed functions using reliably and efficiently with workflows. As a prototype, we design and implement Fission Workflows: an open-source serverless workflow system which leverages the characteristics of serverless functions to improve the (re)usability, performance, and reliability of function compositions. We evaluate our prototype using both synthetic and real-world experiments, which show that the system is comparable with or better than state-of-the-art workflow systems, while costing significantly less. Based on the experimental evaluation and the industry interest in the Fission Workflows product, we believe that serverless workflow orchestration will enable the use of serverless applications for more complex use cases.
SECURED DATA SHARING FOR CLOUD GROUP USERS USING PUBLIC AUDITING MECHANISM

Cloud storage is widely used for sharing data due to low cost maintenance. But, it is also necessary to secure data on cloud. Secure Data Sharing in Cloud focuses mainly on: a)Data recovery b) privacy and confidentiality c) key management and encryption d) secure data sharing without re-encryption and e) forward and backward access control f) Prevention of data tampering. When user wants to share data, it sends request to trusted party that generates a symmetric key which is used to encrypt the data for sharing. This key is used to compute two key shares for trusted party and user. For security purpose, once a user is revoked from the group, the blocks which were formerly signed by this revoked user need to be re-signed by an current user. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least m group managers to restore a trace key unitedly, which wipe out the misuse of single-authority power and provides nonframeability. Our system provides the public checking and efficient user revocation facility and also some nice properties, such as confidently, efficiency, countability and traceability of secure group user revocation. We also present an extension of our proposed scheme that enables the TPA to operate multiple auditing tasks at the same time.. Finally ,the security and experimental analysis show that, compared with schemes our scheme is also secure and efficient.
Exploratory Study on the Effort Perceived by In-service K-12 Teachers from Subject Areas not Specialising in Computer Science Who Are Complete CS Novices

Due to the shortage of IT teachers in Italian schools, the teaching of computational thinking is carried out by in-service K-12 teachers from scientific areas not specialised and by novices in computer science (CS). It is crucial to investigate not only the training of teachers in digital skills, but also how their beliefs, attitudes and behaviours can affect, in detail, their implementation in the classroom. From these premises, the present exploratory study investigates the self-efficacy beliefs, intrinsic motivation and perceived effort of a group of 46 teachers who, on a voluntary basis, engaged in a 20-h workshop on CS teaching. The results show a significant improvement in self-efficacy, despite their perception of strong effort to master the subject.
Path Planning and Following for an Autonomous Model Car Using an “Eye in the Sky”

Autonomous driving is a trend topic that is enabled by communication between devices. Under this scope, ROS is a useful tool for running multiple processes in a graph architecture, where each node may receive and post messages that are consumed by other nodes for their own needs. In this tutorial chapter we discuss a solution for autonomous path planning using a Randomized Random Tree (RRT) and a simple control scheme based on PIDs to follow that path. The control uses internal sensors and an external camera that works as an “eye in the sky”. This is implemented with the help of ROS version 1.12.13 using the Kinetic distribution. Results are validated using the Gazebo multi-robot simulator, version 7.0.0. The robot model used corresponds to the AutoNOMOS mini developed by PHD Raúl Rojas, while the “eye in the sky” is an artificial simple RGB camera created in Gazebo for research purposes. The Rviz package is used to monitor the simulation. The repository for this project can be found at https://github.com/Sutadasuto/AutoNOMOS_Stardust . (The original model for the AutoNOMOS mini was retrieved from https://github.com/EagleKnights/EK_AutoNOMOS_Sim ).
Motivating Students to Learn a Programming Language: Applying a Second Language Acquisition Approach in a Blended Learning Environment

Riddle, where her work focused on
Clustering Data Streams: A Complex Network Approach

Clustering data streams is an interesting and challenging problem. Although several solutions have been proposed in the literature, some drawbacks remain. For instance, how to deal effectively with the offline process for partitioning the micro-clusters into macro-clusters is still an open problem. Typically, the k-means algorithm is considered in this phase, which despite precise results, require a mandatory user-defined parameter k, that defines the number of expected clusters. In this paper, we propose a new clustering method for data stream, named Prototype Networks. This method takes the complex network structure to represent the set of micro-clusters. This approach has proven to be advantageous mainly because these networks have an inherent community structure. As a consequence, the offline phase might be easily handled by a community detection algorithm, such as Infomap. The communities detected represents the cluster structure of the data assuming that the network construction was designed for this purpose. Computer experiments demonstrated the feasibility of the proposed approach. Moreover, the proposed method can detect automatically the number of clusters in evolving scenarios, which is a useful feature when dealing with data streams with concept drift.
Identifying the Source of Records in Discovery and Troubleshooting Using Alma and Primo

Issues with access to resources and the display of metadata are discovered in Primo. But what is the actual source of the problem? Is it the bibliographic record, inventory (physical and/or electronic), Alma settings or Primo Back Office configuration?This presentation seeks to bridge the functional areas of resource management and discovery.The presenter will share best practices to identify the source of Primo records: Marc-based (including the Institution, Network, and Community Zones) and Primo Central as well as explaining how data from Alma displays in Primo. Topics will include how Primo transforms Alma data into PNX (Primo Normalized XML), Primo's dedupe and merge of Alma records, as well as real-life, troubleshooting scenarios and their resolution.Note: The presentation is based on an Alma configuration in a consortial setting with a Network Zone and using Primo Back Office (i.e. not Primo VE). But the content should be broadly applicable
Speedy Desktop Apps With GTK and Rust

A step-by-step walkthrough of the lessons and techniques I learned from building a GTK application in Rust.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KEPUASAN PELANGGAN TERHADAP KINERJA MERCHANDISER UNILEVER TEAM BERBASIS WEB PADA PT ROXY PRAMESWARI CIBITUNG

PT Roxy Prameswari Cibitung is engaged in the distributor of ice cream walls.With the presence of unilever team merchandiser employees as a promotional team who are tasked with displaying products in a shop properly and correctly, a more effective information system is needed so that a system that is still manual can be developed to be computerized.Decision support system for customer satisfaction assessment on the performance of webbased unilever team merchandisers using the prototype method.This design is used for media liaison between customers and PT Roxy Prameswari Cibitung accessed through the web.The purpose of designing a decision support system is to facilitate customers in evaluating the performance of unilever team merchandisers.The results show that the decision support system for customer satisfaction is more effective than the previous manual system.
Pitch Perfect? En kvalitativ studie i hur ett urval av barbershopdirigenter arbetar med intonation i barbershopkörer

As an active barbershop director and vocal coach I have a great interest in broadening my knowledge about working with groups and intonation and to find out how other directors reason around creating an understanding for the chorus work among the chorus members. This study examins a few barbershop directors' views on intonation and how they work with intonation with their choruses. The study also examins how intonation and overtone reinforcement are connected. This is a qualitative interview study, where I interviewed three directors, active both in Sweden and internationally. The results of the study indicates that there are different views on which tuning systems one should use when working with barbershop. The three directors believe that our ears are our main instrument for intonation and that theoretical knowledge never can replace that. They however believe that a certain understanding of the theoretical connection between tuning and reinforcing overtones is vital for progressing as a barbershop ensemble. All three informants put a lot of focus on exercises in matching sound and listening and they try to work in an educational way with the ambition that the chorus members will improve their listening and their ability to adjust their own pitch when needed.
Machine learning to detect anomalies in datacenter

This thesis investigates the possibility of using anomaly detection on performance data of virtual servers in a datacenter to detect malfunctioning servers. Using anomaly detection can potentially ...
О применении кривых Безье для улучшения CSS анимации

Bezier curves usage possibilities for improvement of CSS animations taking physical properties of real-world objects into account have been studied. Methods for creation of web animation based on Bezier curves with CSS3, SVG and Canvas have been analyzed in this paper. The main application area of Bezier curves in cascading stylesheets are transitions and animation where interim conditions calculation are required. Several kinds of cubical curves were considered for solving different animation tasks. Standard gain and attenuation curve considered optimum in most of cases. Usually it is used for popup menus and for modal window opening. Besides objects different properties change function time interval of such changes is also defined during animation. Application areas during interface designing are diverse - from navigation implementation and special effects to the events sequence animation or feedback visualization. This paper contains conducted experiment, which has animation implemented with Bezier curves and without them. The main purpose of the experiment lies in evaluation of possibility to improve CSS animation by Bezier curves usage. Online store cased has been analyzed. Created animation in case of specific product image is pressed by user opens modal window with complete information regarding the good. The same animation closes modal window. It is determined that animation without cubical curve leads to immediate image enlargement which falls out of human sight perception. Frequent change of object states without smooth animation will have negative impact as on users attention so on the sight. Therefore, Bezier curve usage for implementation of CSS animation allows to improve the UI and to make the user experience more comfortable and convenient. As a result of conducted research expedience of Bezier curves usage for CSS animation improvement has been proven. Experiment conducted according to formulated recommendations. Analysis of obtained data showed that smoothness of movements significantly improves concentration of users attention, has positive effect on perception and distinguishes the web resource among analogues.
Navigation and Grasping with a Mobile Manipulator: from Simulation to Experimental Results

Cobot is the name for collaborative robots. This kind of robot is intended to work in close contact with the human being and to collaborate, by increasing the production rate and by reducing the human onerous tasks, in terms of repetitiveness and precision. At the stateof the art, Cobots are often fixed on a support platform, static in their workstation. The aim of this thesis is, hence, to explore, test and validate navigation algorithms for a holonomic mobile robot and in a second moment, to study its behavior with a Cobot mounted on it, in a pick-move-place application. To this purpose, the first part of the thesis addresses the mobile navigation, while the second part the mobile manipulation. Concerning mobile robotics, in the first place, a theoretical background is given and the kinematic model of a holonomic robot is derived. Then, the problem of simultaneous localization and mapping (SLAM) is addressed, i.e. how the robot is able to build a map while localizing itself. Finally, a dedicated chapter will explain the algorithms responsible for exploration and navigation: planners, exploration of frontiers and Monte Carlo localization. Once the necessary theoretical background has been given, these algorithms will be tested both in simulation and in practice on a real robot. In the second part, some theoretical knowledge about manipulators is given and also the kinematic model of the Cobot is derived, together with the algorithm used for a collision free trajectory planning. To conclude, the results of the complete task are shown, first of all in simulation and then on the real robotic system.
Prediction of Dollar Exchange Rate Based on Wavelet Neural Network

In order to predict the trend of the exchange rate between USD and RMB in the financial world, a method for predicting the exchange rate between USD and RMB based on wavelet neural network and grey relational analysis was proposed. The grey relational analysis was used to calculate the correlation degree between the influencing factors and the exchange rate of USD, and several parameters with high correlation degree were optimized. The wavelet neural network was used to find the mapping relationship between the influencing factors and the exchange rate of USD. The method was tested by selecting the data related to the exchange rate of USD in the third quarter of 2018. The experimental results showed that the average relative error of the calculated exchange rate of USD was 1.43%. After the gray correlation analysis method was used, the experimental convergence speed and calculation accuracy were also improved. Therefore, the wavelet neural network method can use the known data of dollar exchange rate to predict the change trend of USD exchange rate, which has a high reliability, practicality and application prospect in the field of financial research.
PENGEMBANGAN MEDIA PEMBELAJARAN MONOPOLI PADA MATERI SEL (MONOSEL)

Learning media in the form of games is able to provide a pleasant learning atmosphere for students. Therefore, a game-based learning media is needed. This study aimed to develop learning media on cell material. The type of research was research & development (R & D) using Borg and Gall design.. The trial was carried out for class X students of SMA 1 Bekasi. The research data was obtained from the product assessment questionnaire. The results of the media expert's research were 83%; material experts were 76.25%; small group trials were 83.5%; and large group trials were 83.5%. The overall assessment obtained an average percentage of 81.56% with a very decent interpretation.
David R. Olson, (2016). The Mind on Paper: Reading, Consciousness and Rationality. Cambridge: Cambridge University Press. doi:10.1017/CBO9781316678466, xiii + 270, $51.59 USD/ £36.95

David R. Olson, (2016). The Mind on Paper: Reading, Consciousness and Rationality. Cambridge: Cambridge University Press. doi:10.1017/CBO9781316678466, xiii + 270, $51.59 USD/ £36.95 - Volume 94 Issue 3
Learning Based Square Root Unscented Kalman Filter for Indoor Vehicle Positioning

Unscented Kalman ﬁlter (UKF) depends on system model in indoor vehicle positioning. However, the constructed model does not always agree with the actual model because of the inherent error due to lacking complete cognition for the target system. Gaussian process regression based square root UKF (GPSRUKF) can lower the error and improve estimation precision. The proposed method is based on the fact that Gaussian process regression learns the deviation between the model and the actual sampling data. Additionally, square root UKF (SRUKF) overcomes the defect that UKF requires more numerical stability for state covariance updates which need to be re-factorized to obtain the sigma points set in every iteration. Experiments on indoor vehicle positioning shows GP-SRUKF has a better performance by compensating deviation comparing with SRUKF and UKF only in nonlinear systems when we build the target model by sampling data in advance.
Improved Link Prediction Using PCA

Link Prediction is known as a challenging problem in the area of online social media. Earlier, learning model for link prediction task has been proposed by many researchers. But the classification of imbalanced and high dimensional data is an interesting and challenging problem in machine learning due to presence of unbalanced and redundant or correlated data which break down the classification performance. In this paper, we have balanced the data and used Principle Component Analysis (PCA) to reduce the correlated data and improved the performance of link prediction model. Experiment is carried out on social network data set and the use of PCA method has improved the performance in classification of links.
Techniques for safe and highly available cloud applications

Building trustworthy and highly available cloud applications is inherently complex and error-prone. Current industrial solutions are either ad-hoc solutions, relying heavily on the developerâĂŹs expertise, or use protocols that blindly restrict concurrency reducing performance and availability. We discusses sound techniques that leverage recent theoretical advances that either reduce or avoid coordinating the execution of operations, while enforcing the correctness of the application. To minimise coordination based on application-specific invariants, an analysis technique was developed that identifies which operations would be unsafe under concurrent execution. Given the set of problematic operations, the developer can then devise and formally check an optimized coordination protocol that guarantees the application invariants. Alternatively, coordination can be eschew altogether by extending operations with repair actions that ensure application invariants are always maintained. The modified operations preserve the original semantics when no conflicting updates occur. If a concurrent conflict occurs, we leverage conflict resolution policies to ensure a deterministic result that preserves the application invariants.
SR Policies Extensions for Path Segment and Bidirectional Path in BGP-LS

This document specifies the way of collecting configuration and statesof SR policies carrying Path Segment and bidirectional pathinformation by using BPG-LS. Such information can be used by externalconponents for many use cases such as performance measurement, pathre-optimization and end-to-end protection.
An Improved Biometric Multi-Server Authentication Scheme for Chang et al.'s Protocol

The remote authentication has been advancing with the growth of online services being offered on remotely basis. This calls for an optimal authentication framework other than single-server authentication. In this connection, the multi-server authentication architecture has been introduced in the literature that enables the users to avail variety of services of various servers, using a single pair of identity and password. Lately, we have witnessed a few multi-server authentication schemes in the literature, although security with loopholes. One of those multi-server authentication schemes has been presented by Chang et al. recently. Our analysis shows that the Chang et al. is vulnerable to impersonation attack, stolen smart card attack. In this study, we have reviewed the protocol thoroughly, and proposed an improved model, that is resistant to all known and identified attacks. The formal and informal security analysis for proposed model is also presented in this study, besides performance and its evaluation analysis.
Predictive Modeling of Hospital Readmissions with Sparse Bayesian Extreme Learning Machine

Patients at high risk of readmissions are a strain to healthcare system and contribute to bed shortage. A tool that accurately identifies patients at high risk of hospital readmission is essential. In this study, we aimed to build a predictive model using machine learning to assess the risk of 30-day readmissions. We retrospectively collected a cohort of admitted patients from Singapore General Hospital. We extracted both clinical and administrative data from hospital electronic health records. The primary outcome was 30-day hospital readmissions. We used sparse Bayesian extreme learning machine (SBELM) to build a predictive model where patient demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute and chronic illness burden were included as predictors. We compared our model with the established LACE index in terms of predictive performance using receiver operating characteristic (ROC) analysis. Our SBELM model achieved better performance (AUC = 0.762 [95% confidence interval: 0.747–0.778], 70.0% sensitivity, 68.8% specificity, 29.8% positive predictive value (PPV) and 92.4% negative predictive value (NPV) at cutoff of 0.137) compared with the LACE index (AUC = 0.716 [95% confidence interval: 0.699–0.732], 65.9% sensitivity, 66.8% specificity, 27.3% PPV and 91.2% NPV at cutoff score of 9). Our model shows promising discriminatory ability and may be useful in further developing multidisciplinary interventions to prevent repeated hospital admissions.