Reviewerly/exHarmony huggingface dataset:
Effectiveness of Knowledge Management on Customer Relationship Management in Hotel Business Performance

Knowledge Management (KM) and Customer Relationship Management (CRM) have become indispensable strategic tool for the Hotel sector to gain competitive edge in the marketing environment. Hotels sustain and survive by exploiting customer knowledge for creating relationship to improve customer satisfaction, loyalty and thus, leading to better business performance. Hotel industries heavily rely on effective relationship management and CRM essentially depends upon effective and efficient utilization of knowledge resource. Therefore, effectiveness of customer relationship management depends upon effectiveness of Integration of knowledge management process with CRM process. Hotel managers are quick to realise the importance of CRM based marketing strategy to sustain and gain edge over the competitors. The paper presents a review study of literature regarding effectiveness of KM and CRM on hotel business performance with a proposal of an integrated conceptual framework between KM and CRM process.
HistoNFC: An Innovative Tool for the Practical Teaching of Histology Using NFC Technology

Despite the development of mobile device technology over recent years, its application in the field of teaching has not yet had a parallel development. For the past two decades teaching subjects such as histology has undergone a change in the digital use of its content and didactic methods based on Web applications, e-learning portals, etc., in which the use of high definition images enables the substitution of the light microscope for a “virtual microscope”. This paper describes a mobile device solution based on the use of Near Field Communication technology for supporting teaching in medical histology. The didactic contents are managed in a database which stores information and images corresponding to the histological slides that are handed out to students in the glass slides for their analysis under a microscope. By associating a single NFC chip to each glass slide and touching this chip with a smartphone, students are given access to all the multimedia information related to the histological slides, without needing a mobile device. The system developed, called HistoNFC, enables access to this information at any moment, as well as the follow-up to the didactic activity and student evaluation. Evaluation of the system has been carried out by histology students of medical degree at the University of Córdoba, assessing aspects of the assisted-learning process as well as technologies, and the results obtained have been very favorable for both, as can be seen in detail in this study.
Linear Inequality Constraints for Neural Network Activations.

We propose a method to impose homogeneous linear inequality constraints of the form $Ax\leq 0$ on neural network activations. The proposed method allows a data-driven training approach to be combined with modeling prior knowledge about the task. One way to achieve this task is by means of a projection step at test time after unconstrained training. However, this is an expensive operation. By directly incorporating the constraints into the architecture, we can significantly speed-up inference at test time; for instance, our experiments show a speed-up of up to two orders of magnitude over a projection method. Our algorithm computes a suitable parameterization of the feasible set at initialization and uses standard variants of stochastic gradient descent to find solutions to the constrained network. Thus, the modeling constraints are always satisfied during training. Crucially, our approach avoids to solve an optimization problem at each training step or to manually trade-off data and constraint fidelity with additional hyperparameters. We consider constrained generative modeling as an important application domain and experimentally demonstrate the proposed method by constraining a variational autoencoder.
Pretending Fair Decisions via Stealthily Biased Sampling.

Fairness by decision-makers is believed to be auditable by third parties. In this study, we show that this is not always true. We consider the following scenario. Imagine a decision-maker who discloses a subset of his dataset with decisions to make his decisions auditable. If he is corrupt, and he deliberately selects a subset that looks fair even though the overall decision is unfair, can we identify this decision-maker's fraud? We answer this question negatively. We first propose a sampling method that produces a subset whose distribution is biased from the original (to pretend to be fair); however, its differentiation from uniform sampling is difficult. We call such a sampling method as stealthily biased sampling, which is formulated as a Wasserstein distance minimization problem, and is solved through a minimum-cost flow computation. We proved that the stealthily biased sampling minimizes an upper-bound of the indistinguishability. We conducted experiments to see that the stealthily biased sampling is, in fact, difficult to detect.
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset.To this end, we introduce a Convolutional Cluster Pooling layer exploiting a multi-scale clustering in order to highlight, at different resolutions, locally connected regions on the input graph. Our proposal generalises well-established neural models such as Convolutional Neural Networks (CNNs) on irregular and complex domains, by means of the exploitation of the weight sharing property in a graph-oriented architecture. In this work, such property is based on the centrality of each vertex within its soft-assigned cluster. Extensive experiments on NTU RGB+D, CIFAR-10 and 20NEWS demonstrate the effectiveness of the proposed technique in capturing both local and global patterns in graph-structured data out of different domains.
Deep HVS-IQA Net: Human Visual System Inspired Deep Image Quality Assessment Networks.

In image quality enhancement processing, it is the most important to predict how humans perceive processed images since human observers are the ultimate receivers of the images. Thus, objective image quality assessment (IQA) methods based on human visual sensitivity from psychophysical experiments have been extensively studied. Thanks to the powerfulness of deep convolutional neural networks (CNN), many CNN based IQA models have been studied. However, previous CNN-based IQA models have not fully utilized the characteristics of human visual systems (HVS) for IQA problems by simply entrusting everything to CNN where the CNN-based models are often trained as a regressor to predict the scores of subjective quality assessment obtained from IQA datasets. In this paper, we propose a novel HVS-inspired deep IQA network, called Deep HVS-IQA Net, where the human psychophysical characteristics such as visual saliency and just noticeable difference (JND) are incorporated at the front-end of the Deep HVS-IQA Net. To our best knowledge, our work is the first HVS-inspired trainable IQA network that considers both the visual saliency and JND characteristics of HVS. Furthermore, we propose a rank loss to train our Deep HVS-IQA Net effectively so that perceptually important features can be extracted for image quality prediction. The rank loss can penalize the Deep HVS-IQA Net when the order of its predicted quality scores is different from that of the ground truth scores. We evaluate the proposed Deep HVS-IQA Net on large IQA datasets where it outperforms all the recent state-of-the-art IQA methods.
A Quantum IP Predictor-Corrector Algorithm for Linear Programming.

We introduce a new quantum optimization algorithm for dense Linear Programming problems, which can be seen as the quantization of the Interior Point Predictor-Corrector algorithm \cite{Predictor-Corrector} using a Quantum Linear System Algorithm \cite{DenseHHL}. The (worst case) work complexity of our method is, up to polylogarithmic factors, $O(L\sqrt{n}(n+m)\overline{||M||_F}\bar{\kappa}^2\epsilon^{-2})$ for $n$ the number of variables in the cost function, $m$ the number of constraints, $\epsilon^{-1}$ the target precision, $L$ the bit length of the input data, $\overline{||M||_F}$ an upper bound to the Frobenius norm of the linear systems of equations that appear, $||M||_F$, and $\bar{\kappa}$ an upper bound to the condition number $\kappa$ of those systems of equations. This represents a quantum speed-up in the number $n$ of variables in the cost function with respect to the comparable classical Interior Point algorithms when the initial matrix of the problem $A$ is dense: if we substitute the quantum part of the algorithm by classical algorithms such as Conjugate Gradient Descent, that would mean the whole algorithm has complexity $O(L\sqrt{n}(n+m)^2\bar{\kappa} \log(\epsilon^{-1}))$, or with exact methods, at least $O(L\sqrt{n}(n+m)^{2.373})$. Also, in contrast with any Quantum Linear System Algorithm, the algorithm described in this article outputs a classical description of the solution vector, and the value of the optimal solution.
On the Sensitivity of Adversarial Robustness to Input Data Distributions

Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution. Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier. Empirical investigations further confirm our finding. We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies. This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves. Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.
PERILAKU PENGGUNA TERHADAP SISTEM GURU PEMBELAJAR ONLINE DI SEKOLAH MENENGAH PERTAMA NEGERI KOTA PALEMBANG

GPO (Online Learning Teacher) is a program developed by Kemdikbud post-UKG (Teacher Competency Exam) 2015 which aims to improve teacher competence. Learning Teachers Online mode consists of 3 (three) models: GP Full Dedicated Mode, GP Front Facing Mode and GP Combination Mode Mode. In the application of the GP program does not necessarily run as planned, while the conditions that occur in the field to meet the problems in the application, due to factors that affect users GP program itself, such as the use of information technology used is sometimes considered by some teachers as a difficulty due to lack of knowledge will be computer technology. Unified Theory of Acceptance and Use of Technology (UTAUT) states that one's acceptance of information technology (user intention). two variables of research that variables have significant effect to behavioral intention, effort expectancy have significant effect to behavioral intention and facilitating condition correlate positively to user behavior and three other variables that is social influence, no significant influence on behavioral intention and behavioral intention unrelated posistif with user interest User Behavioral) Online Teacher Learning System (GPO) system system of Junior High School teachers
Reducing training time by efficient localized kernel regression

We study generalization properties of kernel regularized least squares regression based on a partitioning approach. We show that optimal rates of convergence are preserved if the number of local sets grows sufficiently slowly with the sample size. Moreover, the partitioning approach can be efficiently combined with local Nystrom subsampling, improving computational cost twofold.
Development of a Teaching Aid for Teaching Dynamic Motion Using the Tango Platform

We developed a teaching aid for teaching dynamic motion using a marker-less AR device based on the Tango platform. The mass point motion along a real potential is rendered in an augmented reality space. This teaching aid enables learners to observe a mass point motion in any potential. The accuracy of the motion measurements was evaluated based on the results of uniformly accelerated motion along the slopes and free-fall motion.
ANOTHER PROOF OF WIENER'S SHORT SECRET EXPONENT

Wiener’s short secret exponent attack is a well-known crypt-analytical result upon the RSA cryptosystem using a Diophantine’s method called continued fractions. We recall that Wiener’s attack works efficiently on RSA with the condition that the secret exponent&nbsp;&nbsp; . Later, the upper bound was improved satisfying&nbsp;&nbsp;. In this work, we present another proof to Wiener’s short secret exponent satisfying&nbsp;. We remark that our result is slightly better than the previously mentioned attacks.
Photogrammetric determination of the refractive properties of liquid crystal displays

Abstract In phase-measuring deflectometry for measuring reflecting and transparent surfaces, a high-contrast pattern displayed on a liquid crystal screen is usually optically detected. One of the most important non-idealities of liquid crystal displays with regard to the achievable measurement uncertainty is the refraction in the glass substrate, which leads to an angle-dependent offset between the actual location and the observed location of a feature displayed on the screen. Owing to the typical thicknesses and refractive indices of the glass substrates used, this offset reaches several tenths of a millimeter even at moderate viewing angles. In the approach presented here, the refraction-related offset of features displayed on the screen is determined by simultaneous observation of displayed features and features mounted on the screen. The model used so far was improved, so that apart from a distance of the features to be determined before the evaluation no further previous knowledge is necessary. Within the scope of the investigations carried out, statements on the measurement uncertainty of the method could also be made and verified for the first time.
The Finger-Based Interactive Projection using a Monocular Camera

Ordinary projection screen is not sensitive to interaction, it cannot meet the demands of teaching, virtual reality, and other applications. Due to the fact that people always use hands to complete a variety of human–computer interaction, the finger-based interactive projection technology is worth being researched. In this paper, an ordinary monocular camera is used to acquire video frame on projection screen, and the touch signal of finger in frame is used as the input of interactive projection system. Because the differences between spatial frequency of common digital camera and the projection screen is small, the frame obtained from camera will contain moire fringe, which needs to be filtered in image frequency domain. Then the difference between current frame edge and previous frame edge is calculated to obtain moving object edge clues. According to these clues, the most possible contour curve is searched in current frame edge, and the curve is fitted by polynomial approximation method. Its curvature integration is used to match with the curvature integration of finger template curve. After that the fingers in the curve are recognized. Because color information is not needed, this method can be used to recognize gloved fingers. Finally, finger shadow is used to judge whether the finger touches projection screen to complete interactive process. The experiments of writing and collaboratively rotating picture on projector screen show that this method can effectively complete interactive operation with the projection screen and can realize the multi-user operation.
Compile-Time Query Optimization for Big Data Analytics

Many emerging programming environments for large-scale data analysis, such as Map-Reduce, Spark, and Flink, provide Scala-based APIs that consist of powerful higher-order operations that ease the development of complex data analysis applications. However, despite the simplicity of these APIs, many programmers prefer to use declarative languages, such as Hive and Spark SQL, to code their distributed applications. Unfortunately, most current data analysis query languages are based on the relational model and cannot effectively capture the rich data types and computations required for complex data analysis applications. Furthermore, these query languages are not well-integrated with the host programming language, as they are based on an incompatible data model. To address these shortcomings, we introduce a new query language for data-intensive scalable computing that is deeply embedded in Scala, called DIQL, and a query optimization framework that optimizes and translates DIQL queries to byte code at compile-time. In contrast to other query languages, our query embedding eliminates impedance mismatch as any Scala code can be seamlessly mixed with SQL-like syntax, without having to add any special declaration. DIQL supports nested collections and hierarchical data and allows query nesting at any place in a query. With DIQL, programmers can express complex data analysis tasks, such as PageRank and matrix factorization, using SQL-like syntax exclusively. The DIQL query optimizer uses algebraic transformations to derive all possible joins in a query, including those hidden across deeply nested queries, thus unnesting nested queries of any form and any number of nesting levels. The optimizer also uses general transformations to push down predicates before joins and to prune unneeded data across operations. DIQL has been implemented on three Big Data platforms, Apache Spark, Apache Flink, and Twitter's Cascading/Scalding, and has been shown to have competitive performance relative to Spark DataFrames and Spark SQL for some complex queries. This paper extends our previous work on embedded data-intensive query languages by describing the complete details of the formal framework and the query translation and optimization processes, and by providing more experimental results that give further evidence of the performance of our system.
Attribute Grammars for Incremental Scene Graph Rendering

Scene graphs, as found in many visualization systems are a well-established concept for modeling virtual scenes in computer graphics. State-of-the-art approaches typically issue appropriate draw commands while traversing the graph. Equipped with a functional programming mindset we take a different approach and utilize attribute grammars as a central concept for modeling the problem domain declaratively. Instead of issuing draw commands imperatively, we synthesize first class objects describing appropriate draw commands. In order to make this approach practical in the face of dynamic changes to the scene graph, we utilize incremental evaluation, and thereby avoid repeated evaluation of unchanged parts. Our application prototypically demonstrates how complex systems benefit from domain-specific languages, declarative problem solving and the implications thereof. Besides from being concise and expressive, our solution demonstrates a real-world use case of self-adjusting computation which elegantly extends scene graphs with well-defined reactive semantics and efficient, incremental execution.
Vetting Anti-patterns in Java to Kotlin Translation

With Kotlin becoming a viable language replacement for Java, there is a need for translators and data flow analysis libraries to create maintainable and readable source code. Instagram, Uber, and Gradle are only a few of the large corporations that have either switched from Java to Kotlin completely or started to use it in internal tools in order to reduce code base size. Developers have claimed that Kotlin is fun to use in comparison to Java and much of the boilerplate code is reduced. With Java being the main language for the open source organization, PhenoApps, there is a need to support both Java and Kotlin to increase the maintainability of the code. Fortunately, JetBrains has an open-source IDE plugin for translating Java to Kotlin; however, the translation has some fundamental issues which shall be discussed further in this paper. Introducing, j2k, a CLI translation tool which includes various anti-pattern detection for syntactical formatting, performance, and other Android requirements. The new tool introduced within this paper, j2kCLI allows users to directly translate strings of Java code to Kotlin, or entire directories. This facilitates the maintainability of a large open source code base.
EMAN: The Human Visual Feature Based No-Reference Subjective Quality Metric

As the human vision is a definitive assessor of video quality, the expanded interest for no-reference subjective quality assessment (SQA) is focusing on a definitive goal of coordinating with human observation. However, the widely used subjective estimator-mean opinion score (MOS) is often biased by the testing environment, viewers mode, expertise, domain knowledge, and other factors which may influence on actual assessment. In this paper, a no-reference SQA metric is devised by simply exploiting the nature of human eye browsing on videos and analyzing the associated quality correlation features. The high efficiency video coding (HEVC) reference test model is first employed to produce different forms of coded video quality which then displayed to a number of partakers. Their eye-tracker recorded spatiotemporal gaze-data indicate more concentrated eye-traversing approach for relatively better quality. Thus, we calculate the quality assessment related to assorted features such as length pursuit, angle deflection, pupil deviation, and gaze interlude from recorded gaze trajectory. The content and resolution invariant operations are carried out prior to synthesizing them using an adaptive weighted function to develop a new quality metric-eye maneuver (EMAN). Tested results reveal that the quality evaluation carried out by the EMAN is comparatively better than MOS and structural similarity (SSIM) in terms of assessing different aspects of coded video quality for a wide range of single view video contents. For the free viewpoint video (FVV), where the reference frame is not available, the EMAN could also better distinguish different qualities compared to the MOS and SSIM.