Reviewerly/exHarmony huggingface dataset:
IMPACT OF CUSTOMER RELATIONSHIP MANAGEMENT ON FOOD AND BEVERAGE SERVICES QUALITY: THE MEDIATING ROLE OF EMPLOYEES SATISFACTION

Purpose of the study: This study is carried out to investigate the mediating role of employee satisfaction in the relationship between customer relationship management and food and beverage services quality in the 5-stars hotels in Jordan.&#x0D; Methodology: The survey method was used, which includes the use of the field method for collecting data from 5-stars hotels in Jordan, which employees numbered 9,657 and the number of 5-stars hotels is 33in Amman, 352 were effective for analysis and after analyzing the data using the statistical program AMOS.&#x0D; Main Findings: The most important findings were as follows: There is a significant impact of employee satisfaction on the relationship between customer relationship management and food and beverage services quality.&#x0D; Applications of this study: This study comes out to help hotel managers understand the impact of their actions on the ES in their hotels to raise the efficiency of the services provided in the field of FBSQ and to recommended researchers to do more studies in the field of food and beverage and link them in the behavior of employees and customer, which is a result of hotels profit, and also entertains them to the return on the local economy.&#x0D; Novelty/Originality of this study: Food and beverages services are one of the essential services which business managers should consider if they have to retain their customers and improve the image of their business so this study came out to investigate the mediate role of employee’s satisfaction in the relationship between customer relationship management and food and beverage services quality in the 5-stars hotels.
Fuzzy Contour Trees: Alignment and Joint Layout of Multiple Contour Trees

Abstract We describe a novel technique for the simultaneous visualization of multiple scalar fields, e.g. representing the members of an ensemble, based on their contour trees. Using tree alignments, a graph‐theoretic concept similar to edit distance mappings, we identify commonalities across multiple contour trees and leverage these to obtain a layout that can represent all trees simultaneously in an easy‐to‐interpret, minimally‐cluttered manner. We describe a heuristic algorithm to compute tree alignments for a given similarity metric, and give an algorithm to compute a joint layout of the resulting aligned contour trees. We apply our approach to the visualization of scalar field ensembles, discuss basic visualization and interaction possibilities, and demonstrate results on several analytic and real‐world examples.
Program Synthesis Using Deduction-Guided Reinforcement Learning

In this paper, we present a new program synthesis algorithm based on reinforcement learning. Given an initial policy (i.e. statistical model) trained off-line, our method uses this policy to guide its search and gradually improves it by leveraging feedback obtained from a deductive reasoning engine. Specifically, we formulate program synthesis as a reinforcement learning problem and propose a new variant of the policy gradient algorithm that can incorporate feedback from a deduction engine into the underlying statistical model. The benefit of this approach is two-fold: First, it combines the power of deductive and statistical reasoning in a unified framework. Second, it leverages deduction not only to prune the search space but also to guide search. We have implemented the proposed approach in a tool called Concord and experimentally evaluate it on synthesis tasks studied in prior work. Our comparison against several baselines and two existing synthesis tools shows the advantages of our proposed approach. In particular, Concord solves 15% more benchmarks compared to Neo, a state-of-the-art synthesis tool, while improving synthesis time by 8.71 $$\times $$ on benchmarks that can be solved by both tools.
A novel authentication scheme based on edge computing for blockchain-based distributed energy trading system

Abstract Distributed energy trading system is a new business model of energy industry. Applying blockchain technology, energy supply contracts can be communicated directly between producers and consumers. However, blockchain is without any identity authentication and legitimate user identities are possibly forged if there is not any user identity authentication procedure in a trading system. In this paper, we design an authentication scheme for blockchain-based energy trading systems by using edge computing, including journalizing processes, registration, and identity authentication. In our scheme, each node has computing power, which can ensure the meter data can be processed locally or on the edge. In addition, the correctness and security analysis of the scheme are also given in this paper. The analysis shows that our scheme is unforgeability and can protect users’ privacy.
Range-based target localization and pursuit with autonomous vehicles: An approach using posterior CRLB and model predictive control

We address the general problem of multiple target localization and pursuit using measurements of the ranges from the targets to a set of autonomous pursuing vehicles, referred to as trackers. We develop a general framework for targets with models exhibiting uncertainty in the initial state, process, and measurement noise. The main objective is to compute optimal motions for the trackers that maximize the range-based information available for target localization and at the same time yield good target pursuit performance. The solution proposed is rooted in an estimation-theoretical setting that involves the computation of an appropriately defined Bayesian Fisher Information Matrix (FIM). The inverse of the latter yields a posterior Cramér–Rao Lower Bound (CRLB) on the covariance of the targets’ state estimation errors that can be possibly achieved with any estimator. Using the FIM, sufficient conditions on the trackers’ motions are derived for the ideal relative geometry between the trackers and the targets for which the range information acquired is maximal. This allows for an intuitive understanding of the types of ideal tracker trajectories. To deal with realistic constraints on the trackers’ motions and the requirement that the trackers pursue the targets, we then propose a model predictive control (MPC) framework for optimal tracker motion generation with a view to maximizing the predicted range information for target localization while taking explicitly into account the trackers’ dynamics, strict constraints on the trackers’ states and inputs, and prior knowledge about the targets’ states. The efficacy of the MPC is assessed in simulation through the help of representative examples motivated by operational scenarios involving single and multiple targets and trackers.
La-MAML: Look-ahead Meta Learning for Continual Learning

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks. While meta-learning shows great potential for reducing interference between old and new tasks, the current training procedures tend to be either slow or offline, and sensitive to many hyper-parameters. In this work, we propose Look-ahead MAML (La-MAML), a fast optimisation-based meta-learning algorithm for online-continual learning, aided by a small episodic memory. Our proposed modulation of per-parameter learning rates in our meta-learning update allows us to draw connections to prior work on hypergradients and meta-descent. This provides a more flexible and efficient way to mitigate catastrophic forgetting compared to conventional prior-based methods. La-MAML achieves performance superior to other replay-based, prior-based and meta-learning based approaches for continual learning on real-world visual classification benchmarks. Source code can be found here: https://github.com/montrealrobotics/La-MAML
Direct measurement of the recovery time of superconducting nanowire single-photon detectors

One of the key properties of single-photon detectors is their recovery time, i.e., the time required for the detector to recover its nominal efficiency. In the case of superconducting nanowire single-photon detectors (SNSPDs), which can feature extremely short recovery times in free-running mode, a precise characterization of this recovery time and its time dynamics is essential for many quantum optics or quantum communication experiments. We introduce a fast and simple method to characterize precisely the recovery time of SNSPDs. It provides full information about the recovery of the efficiency in time for a single or several consecutive detections. We also show how the method can be used to gain insight into the behavior of the bias current inside the nanowire after a detection, which allows predicting the behavior of the detector and its efficiency in any practical experiment using these detectors.
Innovative use of health informatics to augment contact tracing during the COVID-19 pandemic in an acute hospital

Abstract This case report describes the innovative design and build of an algorithm that integrates available data from separate hospital-based informatics systems, which perform different daily functions to augment the contact-tracing process of COVID-19 patients by identifying exposed neighboring patients and healthcare workers and assessing their risk. Prior to the establishment of the algorithm, contact-tracing teams comprising 6 members would spend up to 10 hours each to complete contact tracing for 5 new COVID-19 patients. With the augmentation by the algorithm, we observed ≥ 60% savings in overall man-hours needed for contact tracing when there were 5 or more daily new cases through a time–motion study and Monte Carlo simulation. This improvement to the hospital’s contact-tracing process supported more expeditious and comprehensive downstream contact-tracing activities as well as improved manpower utilization in contact tracing.
Smile, you’re being traced! Some thoughts about the ethical issues of digital contact tracing applications

In response to the current Coronavirus SARS-CoV-2 pandemic, many countries are developing digital strategies for the identification of individuals who have been in contact with infected persons. Those strategies are mainly based on Contact Tracing applications. While the effectiveness of these technologies has not yet been demonstrated, and that their deployment conditions remain socially complex, they raise serious ethical questions. This paper presents an overview of the development of Contact Tracing and, suggests a reflection and possible solutions for their ethical and sustainable deployment through a more active and transparent citizen engagement.
AllenAct: A Framework for Embodied AI Research

The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from the computer vision, NLP, and robotics communities. This growth has been facilitated by the creation of a large number of simulated environments (such as AI2-THOR, Habitat and CARLA), tasks (like point navigation, instruction following, and embodied question answering), and associated leaderboards. While this diversity has been beneficial and organic, it has also fragmented the community: a huge amount of effort is required to do something as simple as taking a model trained in one environment and testing it in another. This discourages good science. We introduce AllenAct, a modular and flexible learning framework designed with a focus on the unique requirements of Embodied AI research. AllenAct provides first-class support for a growing collection of embodied environments, tasks and algorithms, provides reproductions of state-of-the-art models and includes extensive documentation, tutorials, start-up code, and pre-trained models. We hope that our framework makes Embodied AI more accessible and encourages new researchers to join this exciting area. The framework can be accessed at: https://allenact.org/
HyperTendril: Visual Analytics for User-Driven Hyperparameter Optimization of Deep Neural Networks

To mitigate the pain of manually tuning hyperparameters of deep neural networks, automated machine learning (AutoML) methods have been developed to search for an optimal set of hyperparameters in large combinatorial search spaces. However, the search results of AutoML methods significantly depend on initial configurations, making it a non-trivial task to find a proper configuration. Therefore, human intervention via a visual analytic approach bears huge potential in this task. In response, we propose HyperTendril, a web-based visual analytics system that supports user-driven hyperparameter tuning processes in a model-agnostic environment. HyperTendril takes a novel approach to effectively steering hyperparameter optimization through an iterative, interactive tuning procedure that allows users to refine the search spaces and the configuration of the AutoML method based on their own insights from given results. Using HyperTendril, users can obtain insights into the complex behaviors of various hyperparameter search algorithms and diagnose their configurations. In addition, HyperTendril supports variable importance analysis to help the users refine their search spaces based on the analysis of relative importance of different hyperparameters and their interaction effects. We present the evaluation demonstrating how HyperTendril helps users steer their tuning processes via a longitudinal user study based on the analysis of interaction logs and in-depth interviews while we deploy our system in a professional industrial environment.
Quantum Behavior-Based Enhanced Fruit Fly Optimization Algorithm with Application to UAV Path Planning

potential well Quantum behavior Path planning A B S T R A C TAs a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields.To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper.When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA.In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics.Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum.Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs.Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms.In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs.
Dampak Pandemi Covid-19 Terhadap Kegiatan Belajar Mengajar Di MI/SD (Studi KBM Berbasis Daring Bagi Guru dan Siswa)

Education is very important to improve the quality of life of every person. In education we are taught many things ranging from the value of life, general knowledge, morals and a variety of other sciences. In education there is the term KBM (Teaching and Learning Activities) where there are students and teachers who are connected in the delivery of knowledge or information as we know it happens in schools. But lately a virus outbreak has arisen that is troubling the world's population. Covid-19 virus or coronavirus is a disease originating from China and experts state that strong indications that the cause of SARS, this virus causes respiratory infections that spread through respiratory secretions, then the nose in the walls of the upper respiratory tract, some facts mention that this coronavirus raises many deaths. In this case, the government rules out to stop any activities and switch to stay and do activities at home. All institutions are closed as well as the world of education. So it switches to studying at home using an online system. For this reason, this study was conducted with the aim of knowing the effect of the co-19 pandemic in teaching and learning activities, especially the MI/SD level. This research was conducted at the home of each writer precisely in Bojonegoro, Lamongan, Sidoarjo and Surabaya by observing younger siblings who were still in elementary/MI school on March 17 to 18 April 2020. This study used a qualitative methodology. The data collection techniques using interviews, observation and documentation. Based on the results of the study note that social media is a science in learning activities during the co-19 pandemic, but in addition to being a bridge of knowledge, social media also has a negative influence on elementary school age children. Therefore the role of parents is very important in supervising children during the online learning process because elementary school age children/MIs are still at an unstable level and very much need guidance in carrying out online-based learning so that nothing unexpected happens.
Federated Model Distillation with Noise-Free Differential Privacy

Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead of weight removes this obstacle and eliminates the risk of white-box inference attacks in conventional federated learning. However, the predictions from local models are sensitive and would leak training data privacy to the public. To address this issue, one naive approach is adding the differentially private random noise to the predictions, which however brings a substantial trade-off between privacy budget and model performance. In this paper, we propose a novel framework called FEDMD-NFDP, which applies a Noise-Free Differential Privacy (NFDP) mechanism into a federated model distillation framework. Our extensive experimental results on various datasets validate that FEDMD-NFDP can deliver not only comparable utility and communication efficiency but also provide a noise-free differential privacy guarantee. We also demonstrate the feasibility of our FEDMD-NFDP by considering both IID and non-IID setting, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
Towards a Virtual Keyboard Scheme Based on Wearing One Motion Sensor Ring on Each Hand

In this paper, we present an improved ring-type virtual keyboard scheme that can achieve impressive performance with only one smart ring on a finger of each hand. The smart ring integrates a 6-DoF Inertial Measurement Unit (IMU) and a 3-DoF magnetometer sensor for collecting motion data during typing. First, a new keyboard layout is designed, by changing the previous rectangular layout to an arc structure, this method increases the difference in attitude angle between adjacent keys, which greatly improved the keystroke recognition accuracy. Secondly, other than the attitude angle feature, we also adopt acceleration data, gyroscope data and magnetometer data to describe the subtle differences between different keystrokes motion. Then, feature importance evaluation and feature correlation analysis were used to select features with high contribution rate and low similarity to describe keystrokes. Finally, nine effective features were selected from the attitude angle and magnetometer data for the final keystroke recognition. By weighing the number of selected features, recognition speed and recognition accuracy of training models, the keystroke recognition speed can increase by nearly 4 times while ensuring 98.53% of the keystroke recognition accuracy. This new ring-type virtual keyboard input scheme has the advantages in portability, small volume, and lower cost over many existing human-computer interface methods.
Local Path Planning of Mobile Robot Based on Artificial Potential Field

When a mobile robot plans its local path under the environment with obstacles using the artificial potential field strategy, it may fall into the dead zone and fails to reach the target point because of the problem of local minimum point exited in the traditional method. In order to solve the problem, the left turning potential field method and virtual target point method are proposed based on artificial potential field. The left turning potential field method forces the robot to turn at the local minimum point and jump out of here; the virtual target point method is to set the virtual target point at an appropriate position when the robot is at the local minimum point. Meanwhile the robot should ignore the impact of the target point and obstacles on it, so that it can turn around and jump out of the local minimum point. Through simulation and test, it is proved that the mobile robot can bypass the local minimum point and reach the target point successfully by the designed methods.
Data-driven prediction and analysis of chaotic origami dynamics

Abstract Advances in machine learning have revolutionized capabilities in applications ranging from natural language processing to marketing to health care. Recently, machine learning techniques have also been employed to learn physics, but one of the formidable challenges is to predict complex dynamics, particularly chaos. Here, we demonstrate the efficacy of quasi-recurrent neural networks in predicting extremely chaotic behavior in multistable origami structures. While machine learning is often viewed as a “black box”, we conduct hidden layer analysis to understand how the neural network can process not only periodic, but also chaotic data in an accurate manner. Our approach shows its effectiveness in characterizing and predicting chaotic dynamics in a noisy environment of vibrations without relying on a mathematical model of origami systems. Therefore, our method is fully data-driven and has the potential to be used for complex scenarios, such as the nonlinear dynamics of thin-walled structures and biological membrane systems.
State of Data Privacy During COVID-19

Welcome to the first edition of the “Data” column. Our goal is to bring the latest trends and developments on data in light of developments from around the world. In this first column, we focus on the state of the practice of data privacy during the COVID-19 pandemic. We explore what data are collected during this time and how private companies, health agencies, and governments are planning to use these data to control the outbreak and spread of the coronavirus as well as taking some measures for safe use of the highly personal data from users around the world.
Adversarial Patch Attacks on Monocular Depth Estimation Networks

Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years. However, depth estimation from a monocular image alone is essentially an ill-posed problem, and thus, it seems that this approach would have inherent vulnerabilities. To reveal this limitation, we propose a method of adversarial patch attack on monocular depth estimation. More specifically, we generate artificial patterns (adversarial patches) that can fool the target methods into estimating an incorrect depth for the regions where the patterns are placed. Our method can be implemented in the real world by physically placing the printed patterns in real scenes. We also analyze the behavior of monocular depth estimation under attacks by visualizing the activation levels of the intermediate layers and the regions potentially affected by the adversarial attack.