Reviewerly/exHarmony huggingface dataset:
Going Vegan: The Role(s) of ICT in Vegan Practice Transformation

With the debate on climate change, topics of diet change and the reduction of animal products have become increasingly important in both public and academic discourses. However, sustainable ICT studies have so far focused on individual aspects, in particular investigating the criticized persuasive design approach. We argue for a broader perspective on the role(s) of ICT, one that helps in identifying opportunities to support consumer practice transformation, beyond motivational aspects. Based on retrospective interviews with 16 vegans, we argue to understand practice transformation as co-evolution of practices and ICT artefacts, as this perspective helps to understand how tensions arising from complex entanglements of practices, socio-material contexts, and communities can be resolved. Rather than a motivational process, we observe various roles of ICT artefacts co-evolving with practices: Ranging from initial irritation, to access to information about vegan practices, to the learning of vegan food literacy, to the negotiation of a vegan identity, and vegan norms at the intersection of the ‘odd’ and the ‘norm’.
The Role of Project Manager in Agile Software Teams: A Systematic Literature Review

The use of Agile methodologies in software development has grown steadily over recent years.One of the main emphases of these methods is employing cross-functional and self-organized teams and highly skilled developers in software projects.In such a condition, project management would be a serious concern.Indeed, it would be confusing whether Agile teams are really in need of the role of the project manager.While Agile methodologies do not explicitly define the role of the project manager, many reports mention the existence of this role in Agile projects in real environments.So, it seems that the existence of this role is debated.Conducting a Systematic Literature Review, this study tried to find out answers to the ambiguities and questions regarding the role of Agile project management, the role of the project manager, and related issues.Focusing on the primary studies, the results show that there is no independent job called project manager in Agile methodologies.However, there is a need for it.Moreover, in the absence of this role in Agile methodologies and the need for it, it seems that this role would be structurally different from the traditional role of the project manager in terms of responsibilities and duties.Finally, the results show that pre-defined roles in Agile methodologies are often responsible for the project manager duties in software teams with no project manager.
Anomaly detection in milling tools using acoustic signals and generative adversarial networks

Acoustic monitoring presents itself as a flexible but under-reported method of tool condition monitoring in milling operations. This paper demonstrates the power of the monitoring paradigm by presenting a method of characterizing milling tool conditions by detecting anomalies in the time-frequency domain of the tools' acoustic spectrum during cutting operations. This is done by training a generative adversarial neural network on only a single, readily obtained class of acoustic data and then inverting the generator to perform anomaly detection. Anomalous and non-anomalous data are shown to be nearly linearly separable using the proposed method, resulting in 90.56% tool condition classification accuracy and a 24.49% improvement over classification without the method.
The Scope of an Integrated Analysis of Trust Switching Barriers, Customer Satisfaction and Loyalty

This study investigated the impact of service quality, intimacy, trust, and playfulness on customer loyalty.The integrated variables were investigated alongside their effect on customer loyalty.Variance-based structural equation modeling with PLS (PLS-SEM) was selected to assess the proposed hypotheses, with the analysis sample of the study including 627 questionnaires distributed to the sampled subjects.The positive effect of service quality, playfulness, intimacy, trust, and satisfaction was found, and a statistically significant impact was shown of switching barriers and trust on customer loyalty.Contrastingly, the results found no significant effect of customer satisfaction on customer loyalty.Our study offers a deeper understanding of some of the most important factors concerning customer satisfaction and loyalty and the effect of these factors on increasing market share and competitive advantage.The research goal of this paper is to examine the impact of service quality on business competitiveness.Enterprises need to understand what service quality is and how it will help provide a great competitive advantage, e.g.how service quality models could allow managers to identify where problems with quality lie.This will help managers determine where to improve the efficiency and profitability of the overall performance of the enterprise.For service industries, service quality increases competitive advantage and long-term profitability.Service quality will help organizations compete efficiently and differentiate themselves from other enterprises striving to achieve the same goals.For this and other reasons, service quality should be seen as critically important for businesses.
Recursive estimation for sparse Gaussian process regression

Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs to larger datasets, several sparse approximations based on so-called inducing points have been proposed in the literature. In this work we investigate the connection between a general class of sparse inducing point GP regression methods and Bayesian recursive estimation which enables Kalman Filter like updating for online learning. The majority of previous work has focused on the batch setting, in particular for learning the model parameters and the position of the inducing points, here instead we focus on training with mini-batches. By exploiting the Kalman filter formulation, we propose a novel approach that estimates such parameters by recursively propagating the analytical gradients of the posterior over mini-batches of the data. Compared to state of the art methods, our method keeps analytic updates for the mean and covariance of the posterior, thus reducing drastically the size of the optimization problem. We show that our method achieves faster convergence and superior performance compared to state of the art sequential Gaussian Process regression on synthetic GP as well as real-world data with up to a million of data samples.
An Edge Computing-Based Photo Crowdsourcing Framework for Real-Time 3D Reconstruction

Image-based three-dimensional (3D) reconstruction utilizes a set of photos to build 3D model and can be widely used in many emerging applications such as augmented reality (AR) and disaster recovery. Most of existing 3D reconstruction methods require a mobile user to walk around the target area and reconstruct objectives with a hand-held camera, which is inefficient and time-consuming. To meet the requirements of delay intensive and resource hungry applications in 5G, we propose an edge computing-based photo crowdsourcing (EC-PCS) framework in this paper. The main objective is to collect a set of representative photos from ubiquitous mobile and Internet of Things (IoT) devices at the network edge for real-time 3D model reconstruction, with network resource and monetary cost considerations. Specifically, we first propose a photo pricing mechanism by jointly considering their freshness, resolution and data size. Then, we design a novel photo selection scheme to dynamically select a set of photos with the required target coverage and the minimum monetary cost. We prove the NP-hardness of such problem, and develop an efficient greedy-based approximation algorithm to obtain a near-optimal solution. Moreover, an optimal network resource allocation scheme is presented, in order to minimize the maximum uploading delay of the selected photos to the edge server. Finally, a 3D reconstruction algorithm and a 3D model caching scheme are performed by the edge server in real time. Extensive experimental results based on real-world datasets demonstrate the superior performance of our EC-PCS system over the existing mechanisms.
A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.
CNN Model for Image Classification on MNIST and Fashion-MNIST Dataset

Paper presents application of convolutional neural network for image classification problem.MNIST and Fashion-MNIST datasets used to test the performance of CNN model.Paper presents five different architectures with varying convolutional layers, filter size and fully connected layers.Experiments conducted with varying hyper-parameters namely activation function, optimizer, learning rate, dropout rate and batch size.Results show that selection of activation function, optimizer and dropout rate has impact on accuracy of results.All architectures give accuracy more than 99% for MNIST dataset.Fashion-MNIST dataset is complex than MNIST.For Fashion-MNIST dataset architecture 3 gives better results.Review of obtained results and from literature shows that CNN is suitable for image classification for MNIST and Fashion-MNIST dataset.
An End-to-End Deep Learning Network for 3D Object Detection From RGB-D Data Based on Hough Voting

Existing outdoor three-dimensional (3D) object detection algorithms mainly use a single type of sensor, for example, only using a monocular camera or radar point cloud. However, camera sensors are affected by light and lose depth information. When scanning a distant object or an occluded object, the data collected by the short-range radar point cloud sensor are very sparse, which affects the detection algorithm. To address the above challenges, we design a deep learning network that can combine the texture information of two-dimensional (2D) data and the geometric information of 3D data for object detection. To solve the problem of a single sensor, we use a reverse mapping layer and an aggregation layer to combine the texture information of RGB data with the geometric information of point cloud data and design a maximum pooling layer to deal with the input of multi-view cameras. In addition, to solve the defects of the 3D object detection algorithm based on the region proposal network (RPN) method, we use the Hough voting algorithm implemented by a deep neural network to suggest objects. Experimental results show that our algorithm has a 1.06% decrease in average precision (AP) compared to PointRCNN in easy car object detection, but our algorithm requires 37.7% less time to calculate than PointRCNN under the same hardware environment. Moreover, our algorithm improves the AP by 1.14% compared to PointRCNN in hard car object detection.
Geometry of Faithful Entanglement

A typical concept in quantum state analysis is based on the idea that states in the vicinity of some pure entangled state share the same properties, implying that states with a high fidelity must be entangled. States whose entanglement can be detected in this way are also called faithful. We prove a structural result on the corresponding fidelity-based entanglement witnesses, resulting in a simple condition for faithfulness of a two-party state. For the simplest case of two qubits faithfulness can directly be decided and for higher dimensions accurate analytical criteria are given. Finally, our results show that faithful entanglement is, in a certain sense, useful entanglement; moreover, they establish connections to computational complexity and simplify several results in entanglement theory.
An Electronic Data Capture Framework (ConnEDCt) for Global and Public Health Research: Design and Implementation

Background When we were unable to identify an electronic data capture (EDC) package that supported our requirements for clinical research in resource-limited regions, we set out to build our own reusable EDC framework. We needed to capture data when offline, synchronize data on demand, and enforce strict eligibility requirements and complex longitudinal protocols. Based on previous experience, the geographical areas in which we conduct our research often have unreliable, slow internet access that would make web-based EDC platforms impractical. We were unwilling to fall back on paper-based data capture as we wanted other benefits of EDC. Therefore, we decided to build our own reusable software platform. In this paper, we describe our customizable EDC framework and highlight how we have used it in our ongoing surveillance programs, clinic-based cross-sectional studies, and randomized controlled trials (RCTs) in various settings in India and Ecuador. Objective This paper describes the creation of a mobile framework to support complex clinical research protocols in a variety of settings including clinical, surveillance, and RCTs. Methods We developed ConnEDCt, a mobile EDC framework for iOS devices and personal computers, using Claris FileMaker software for electronic data capture and data storage. Results ConnEDCt was tested in the field in our clinical, surveillance, and clinical trial research contexts in India and Ecuador and continuously refined for ease of use and optimization, including specific user roles; simultaneous synchronization across multiple locations; complex randomization schemes and informed consent processes; and collecting diverse types of data (laboratory, growth measurements, sociodemographic, health history, dietary recall and feeding practices, environmental exposures, and biological specimen collection). Conclusions ConnEDCt is customizable, with regulatory-compliant security, data synchronization, and other useful features for data collection in a variety of settings and study designs. Furthermore, ConnEDCt is user friendly and lowers the risks for errors in data entry because of real time error checking and protocol enforcement.
Analysis of the relationship between financial development and economic growth in the EU countries

To what extent does financial development determine economic growth?Despite the obvious relationship between the level of financial development and economic growth rates, there is still no consensus on the significance and focus of this relation.Is there a directed impact of the level of financial development on economic growth, or does the development of a financial sector follow economic growth?Or is the relation between financial development and economic growth bidirectional?The aim of the research is to analyze the causal relationship between quantitative and dynamic differences in financial development and economic growth in the EU countries in the period 1995 -2017.The period of the research from 1995 to 2017 is determined by the availability of financial development indicators for the EU countries.In order to prove the directed impact of the level of financial development on economic growth in the EU countries in the period 1995-2017, the average values of growth in the financial development index with the lag forwarding by one year, with the lag falling behind by one year, without the lag, and average values of the GDP growth per capita were analyzed.
Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps

Point of interest auto-completion (POI-AC) is a featured function in the search engine of many Web mapping services. This function keeps suggesting a dynamic list of POIs as a user types each character, and it can dramatically save the effort of typing, which is quite useful on mobile devices. Existing approaches on POI-AC for industrial use mainly adopt various learning to rank (LTR) models with handcrafted features and even historically clicked POIs are taken into account for personalization. However, these prior arts tend to reach performance bottlenecks as both heuristic features and search history of users cannot directly model personal input habits. In this paper, we present an end-to-end neural-based framework for POI-AC, which has been recently deployed in the search engine of Baidu Maps, one of the largest Web mapping applications with hundreds of millions monthly active users worldwide. In order to establish connections among users, their personal input habits, and correspondingly interested POIs, the proposed framework (abbr. P3AC) is composed of three components, i.e., a multi-layer Bi-LSTM network to adapt to personalized prefixes, a CNN-based network to model multi-sourced information on POIs, and a triplet ranking loss function to optimize both personalized prefix embeddings and distributed representations of POIs. We first use large-scale real-world search logs of Baidu Maps to assess the performance of P3AC offline measured by multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR), and normalized Discounted Cumulative Gain (nDCG). Extensive experimental results demonstrate that it can achieve substantial improvements. Then we decide to launch it online and observe that some other critical indicators on user satisfaction, such as the average number of keystrokes and the average typing speed at keystrokes in a POI-AC session, which significantly decrease as well. In addition, we have released both the source codes of P3AC and the experimental data to the public for reproducibility tests.
Smart Contracts with Blockchain in the Public Sector

society that is ultimately based on State authority.Thus, providing the same services offered by the state and the corresponding public authorities (maintaining their validity), in a
A Comparative Analysis of NSGA-II and NSGA-III for Autoscaling Parameter Sweep Experiments in the Cloud

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.
The design of scalar AES Instruction Set Extensions for RISC-V

Secure, efficient execution of AES is an essential requirement on most computing platforms. Dedicated Instruction Set Extensions (ISEs) are often included for this purpose. RISC-V is a (relatively) new ISA that lacks such a standardized ISE. We survey the state-of-the-art industrial and academic ISEs for AES, implement and evaluate five different ISEs, one of which is novel. We recommend separate ISEs for 32 and 64-bit base architectures, with measured performance improvements for an AES-128 block encryption of 4x and 10x with a hardware cost of 1.1K and 8.2K gates respectively, when compared to a software-only implementation based on use of T-tables. We also explore how the proposed standard bit-manipulation extension to RISC-V can be harnessed for efficient implementation of AES-GCM. Our work supports the ongoing RISC-V cryptography extension standardisation process.
Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Object detection methods based on Convolutional Neural Networks (CNNs) require a large number of images with annotation information to train.In aircraft detection from remote sensing images (RSIs), aircraft targets are usually small and the cost of manual annotation is very high.In this article, we tackle the problem of weakly supervised aircraft detection from RSIs, which aims to learn detectors with only image-level annotations, i.e., without bounding-box labeled data during the training stage.Based on the fact that the feature maps learned from the CNN network are localizable, we propose a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL).In AlexNet-WSL, we utilize the AlexNet CNN as backbone network, but replace the last two fully connected layers with a Global Average Pooling (GAP) and two convolutional layers.Based on the class activation maps, we generate heat maps via reverse weighting for locating the target object.Unlike object detection methods that require object location data for training, our proposal only needs image-level labelled data.We furthermore build a set of remote sensing aircraft images, the Weakly Supervised Aircraft Detection Dataset (WSADD) for algorithm benchmarking.The experimental results on the WSADD show that AlexNet-WSL effectively detects the aircraft and achieves a detection effect equivalent to the Faster R-CNN method and the YOLOv3 method, which both require bounding-box labelled data for training, with a lower false alarm rate and a shorter training time.
Pupils’ Motivation and Perceptions on ESL Lessons through Online Quiz-Games

Some scholars believe that the English proficiency and literacy level among the pupils in this country is deteriorating alarmingly and to overcome this issue, many measures have been taken by the government. Research showed that this issue could be caused by lack of motivation among learners to use English in their lives. This study examined pupils’ motivation and perceptions of ESL lessons through online quiz-games namely Kahoot! and Quizizz. There were 60 primary school learners involved in this study. Data were collected using a survey questionnaire, and were analyzed and discussed quantitatively in descriptive analysis. The findings revealed that these learners had an elevated motivation and positive perceptions on the inculcation of online quiz-games in English lessons. The positive responses shown by the pupils have reflected their positive motivation, perceptions and acceptance towards learning English with the integration of online quiz-games like Kahoot! and Quizizz. This is because the elements of fun, enjoyment and competition in games can make students more motivated and interested in learning English.