Miracle-dz/UniFAR-FTUs huggingface dataset:
SERVICE INNOVATIONS IN SOCIAL MEDIA & BLOGGING WEBSITES: ENHANCING CUSTOMER’S PSYCHOLOGICAL ENGAGEMENT TOWARDS ONLINE ENVIRONMENT FRIENDLY PRODUCTS

The purpose of this paper is to determine the impact of three dimensions of Blog service innovation (Service Privacy, Webpage Content & Diversification) on customer’s psychological engagement with environment-friendly products available online. They are mediated by two dimensions of online relationship quality (Commitment & Satisfaction). A sample of 251 social media and blogging sites users responded to a structured questionnaire adapted from existing literature. First, confirmatory factor analysis was carried out, and then data were analyzed through structural equation modelling using the bootstrap methodology in AMOS 24. This paper explains the loyalty and psychological engagement pattern of social media users in accordance with the innovations in services provided by organizations in their websites, which provide or discuss information about eco-friendly products. They are explicitly mediated through online relationship quality factor so the customers could be engaged batter after determining their psychological intent, which is in this case, is explicitly related to the loyalty of customers. From a managerial perspective, this research not only provides the vital information on customer’s behaviour in relationship with the innovations in services introduced by organizations in their websites but also could help them to minimize their cost and maximize their profits according to the target audience behavioral patterns.
Applying Human-Robot Interaction Technology in Retail and Service Businesses

: - This review of human–robot interaction technology (HRI) suggests how retailers can enhance customer service and improve their operations through the use of service robots. We have reviewed earlier studies and identified current and emerging robotic technologies that exhibit potential for use in retail businesses. The review of HRI technologies presents actionable information with which businesses can improve service and efficiency. This study suggests how robotic technology can elevate customer service and transform retailing. Although extensive research investigates the psychological, neurological, and engineering issues of human–robot interaction, few studies establish how robot technology can elevate customer service and transform retailing. This study provides practical technology information in retail service robots that have noteworthy potential for assisting elderly and physically challenged consumers.
Comparison of incidence of three major pathogens causing bovine subclinical mastitis in relation to SCC and enzymatic activities in large and small dairy herds in Egypt

The study was designed to investigate the current incidence of Staphylococcus aureus, Streptococcus agalactiae and Escherichia coli in bovine subclinical mastitis (SCM) in two groups of herds, large dairy herd: group 1 (≥100 head/ farm) and small and medium dairy herds: group 2 (≤50 head / farm + small owner’s herds) in Egypt. S. aureus and S. agalactiae are contagious major pathogens of mastitis, while E. coli is an environmental
The Multi-Level Marketing Pandemic

Among the many societal effects of the COVID-19 pandemic has been a sharp rise in the activities of multi-level marketing companies (MLMs). MLMs are business enterprises in which participants seek not only to sell products to friends, family, and social media contacts, but also to recruit them as MLM participants, with the promise of “building their own business from home. ” False promises often pervade MLM sales pitches. Evidence shows that few participants see even a dollar of profit from their MLM work;the vast majority of recruits quickly abandon their MLM dreams and lose their investments. Yet the pitch has become all the more appealing in the course of the COVID-19 pandemic. Many people are desperate—unemployed and in need of immediate earnings and also in need of flexible, at-home work due to health concerns and family responsibilities. MLMs have been particularly appealing to the working mothers who, evidence shows, have borne the brunt of the impact of COVID-19 both on employment and on childcare and other household-related responsibilities.Regulators have long scrutinized and fought the worst abuses of MLMs. They have sought to find and shut down the unscrupulous MLMs that are in fact nothing more than fraudulent pyramid schemes, and also to curtail the misrepresentations and exaggerations that are all too often the stock-in-trade of participants in more legitimate MLMs. But regulators, including the Federal Trade Commission, face significant legal and practical limitations in their ability to promulgate and enforce MLM regulations. This Article examines how regulators have addressed MLM activity and proposes means of stemming the pandemic-driven expansion of unlawful MLM activities. It assesses efforts by regulators, by social media companies, and by self-regulatory organizations set up by MLMs themselves. Comprehensive, long-term success at curbing the abuses of MLMs will require more significant regulatory action that is currently permitted by law. But immediate steps outlined here can provide some much-needed relief for consumers harmed by the unlawful MLM activities that have been fostered by the COVID-19 pandemic.
Amenity Value of Green Space: Evidence from Philadelphia

Assessing the amenity value of green space is important to urban revitalization. This paper examines the impact of vacant lots greened by the Pennsylvania Horticultural Society in Philadelphia for the period of 2007-2017 on housing prices. The paper uses a quasi-experimental design to estimate the effect of greening vacant land on the sale prices of nearby houses. We find a positive treatment effect of greening vacant space, with sale prices increasing by 4.3% (which varies by neighborhood income) and show that greening vacant land can eliminate the negative impact of vacant land on housing prices. We also implement and demonstrate the importance of sample selection bias adjustment for the identification of the effect of greening on housing prices.
The Types of Human Resource Practices and Cultural Effects

Prior studies support that culture as well as institution influences the way a multinational corporation (MNC) subsidiary implements human resource (HR) practices. However, they tend to ignore variations in HR practices. Through an extensive review of the literature, the current paper asserts that the cultural effect on the implementation of HR practices varies depending on the type of HR practices. To support this view, the paper discusses how MNC subsidiaries implement distinct types of HR practices transferred from the MNC parent. The HR practices discussed are: (1) recruitment, (2) employee training and development, (3) performance appraisal, (4) compensation, benefits, and pensions, (5) teamwork, and (6) quality control (QC) circles. The differences in the implementation of HR practices caused by culture seem to be salient in HR practices where personal relationships are essential. Namely, (3) performance appraisal seems to be the most culturally sensitive. (1) Recruitment is also culturally sensitive as culture determines how and whom subsidiaries recruit. However, it is also subject to institutional effects because an MNC parent controls it by setting internal regulations. Cultural effects on (4) compensation, benefits, and pensions cannot be ignored either. Yet, institutional effects seem to be stronger than culture because minimum wages and pensions are regulated by companies and governments. Although culture can influence the time and the types of (2) employee training and development, its impact on this practice seems smaller than that on the previous three practices. Finally, it is hard to grasp the real cultural effects on (5) teamwork and (6) QC circles. When these ‘popular’ HR practices are explicitly codified, they become highly transferable. Thanks to the codification, subsidiaries can ceremoniously implement them by simply copying forms, avoiding cultural effects.
Cyclical Labor Market Policy Adjustments Under Wage Bargaining

This paper analyzes the optimal cyclical behavior of labor market policies in an economy with asset and labor market frictions. The policies of interest include unemployment insurance (UI) and employment protection (EP). In addition to their supply-side effects, labor market policies affect the aggregate demand via earning risk and redistribution channels. Under bilateral wage bargaining, I find that procyclical UI and countercyclical EP deliver superior welfare outcomes through stabilization via both supply and demand channels.
LEGO, but with Servers: Creating the Building Blocks to Design and Simulate Datacenters

Tracking the rapidly growing consumption of cloud services, datacenters will likely increase in both number and capacity, and also improve their operational eﬃciency. Both of these developments will require numerous new datacenter designs. Because we are faced with a shortage of datacenter designers, it is important to make datacenter design accessible to a wider range of people, so that this growth may continue. To achieve this, we propose in this work to create datacenter designs with the use of pre-designed building blocks, or prefabs . We propose a hierarchical design for a data structure that allows for the representation of such building blocks. Additionally, we design the accompanying methods that enable us to perform operations on prefabs, when included in publicly available datacenter simulators such as OpenDC. We design a prototype to test the suitability of our designs, allowing us to carry out an implementation of prefabs into the codebase of OpenDC. We conduct small-scale but representative experiments based on the KLM method to assess the time taken to construct common datacenter layouts, and ﬁnd that the prefab-based approach reduces the required time by a factor of 3.4, over the conventional approach not based on prefabs. We conclude that the addition of prefabs allows for a signiﬁcant reduction in the number of interactions required to build datacenters in OpenDC. This reduction in interactions also thus leads to a reduction in the time taken to build datacenters in OpenDC. Our work enables a faster means of iteratively improving datacenter designs, while also enabling new users to get started with datacenter design much faster than they were previously able to.
Transmission of global wheat prices to domestic markets in Kenya: A cointegration approach

This paper evaluates the extent to which changes in international wheat prices are transmitted to domestic markets in Kenya using an error correction model (ECM) that employs monthly producer price data for the period 2002 to 2020. Domestic wheat markets in Kenya were found to be strongly integrated while, international wheat markets were cointegrated with domestic prices at the port of Mombasa. The long-run elasticity of price transmission was estimated at 0.91, which implies that 91% of the changes in international wheat prices are transmitted to domestic markets in Kenya. The speed of adjustment was estimated at -0.069, which implies that it takes about 14 months for the changes in the international wheat price to be fully transmitted to the Kenyan domestic market. Wheat farmers in Kenya seem to be insulated from international price shocks given the long period of time it takes for domestic markets to adjust to international price changes. Even though not explicitly analysed, government border policies, market and infrastructure impediments seem to be underlying causes of the incomplete price pass-through, along with the low speeds of adjustments. Our analysis suggests that the main constraint to a complete pass-through is the existence of price-setting power at the producer level of the wheat market in Kenya. Investments in infrastructure development and the promotion of liberal trade policies can improve the transmission of international wheat price signals to domestic markets in Kenya.
Longitudinal network models and permutation‐uniform Markov chains

Consider longitudinal networks whose edges turn on and off according to a discrete‐time Markov chain with exponential‐family transition probabilities. We characterize when their joint distributions are also exponential families with the same parameter, improving data reduction. Further we show that the permutation‐uniform subclass of these chains permit interpretation as an independent, identically distributed sequence on the same state space. We then apply these ideas to temporal exponential random graph models, for which permutation uniformity is well suited, and discuss mean‐parameter convergence, dyadic independence, and exchangeability. Our framework facilitates our introducing a new network model; simplifies analysis of some network and autoregressive models from the literature, including by permitting closed‐form expressions for maximum likelihood estimates for some models; and facilitates applying standard tools to longitudinal‐network Markov chains from either asymptotics or single‐observation exponential random graph models.
A metamaterial with applications in broad band antennas used in radio astronomy and satellite communications

Electromagnetic metamaterials at microwave frequencies are well established in industry and research. Recent work has shown how a specific kind of metallic metamaterial can contribute towards improving the performance of the feedhorn antennas used in radio astronomy and satellite telecommunications. In this article, we discuss an innovative type of meta-ring of remarkable manufacturability, able to improve the state of the art in these fields. A pioneering meta-horn antenna formed of meta-rings is then fabricated and characterized in the laboratory. It shows an excellent feature on an octave bandwidth, especially in terms of cross-polarization, a key figure of merit in both radio astronomy and telecommunications, and also side-lobe level, return-loss and gain.
Friddy multiagent price stabilization model

In a multiagent network model consisting of nodes, each network node has an agent and priced Friddy coins, and the agent can buy or sell Friddy coins in the marketplace. Though every node may not effectively have an equal price during the transaction time, the prices has to reach an equilibrium by iterating buy and sell transaction on a macro level. First, we present a protocol model in which each buying agent in the network (buyer or seller) makes a bid to the lowest priced coins in the part of the network they are at, and that could be an exchange for instance; and each selling agent selects the highest bid, if any. Second, using our model we derive a condition to stabilize price. We also show the equilibrium price can be derived from the total circulating fund and the total Friddy coins for any network. This is a special case of the Fisher’s quantity equation1, and that is the measure we will use to gauge the accuracy of our model. In this paper we will test the best bidding strategy is available to our coin protocol. Third, you will see that we have analyzes stabilization time for what we call path and cycle networks. Finally, simulate market experiments to estimate the tame it should take to stabilize the price of Friddy coin, it’s important to note the larger number of market bidders of our coin the more there is an impact on the spreading of funds and the more the model should be tested to understand its ability to stabilize the coin in the ecosystem. This model has shown strong to calibrate the network topologies impact on price stabilization.
Existence, Stability and Scalability of Orthogonal Convolutional Neural Networks

Imposing orthogonality on the layers of neural networks is known to facilitate the learning by limiting the exploding/vanishing of the gradient; decorrelate the features; improve the robustness. This paper studies the theoretical properties of orthogonal convolutional layers.We establish necessary and sufficient conditions on the layer architecture guaranteeing the existence of an orthogonal convolutional transform. The conditions prove that orthogonal convolutional transforms exist for almost all architectures used in practice for 'circular' padding.We also exhibit limitations with 'valid' boundary conditions and 'same' boundary conditions with zero-padding.Recently, a regularization term imposing the orthogonality of convolutional layers has been proposed, and impressive empirical results have been obtained in different applications (Wang et al. 2020). The second motivation of the present paper is to specify the theory behind this.We make the link between this regularization term and orthogonality measures. In doing so, we show that this regularization strategy is stable with respect to numerical and optimization errors and that, in the presence of small errors and when the size of the signal/image is large, the convolutional layers remain close to isometric.The theoretical results are confirmed with experiments and the landscape of the regularization term is studied. Experiments on real data sets show that when orthogonality is used to enforce robustness, the parameter multiplying the regularization termcan be used to tune a tradeoff between accuracy and orthogonality, for the benefit of both accuracy and robustness.Altogether, the study guarantees that the regularization proposed in Wang et al. (2020) is an efficient, flexible and stable numerical strategy to learn orthogonal convolutional layers.
Deep Neural Network Voice Activity Detector for Downsampled Audio Data: An Experiment Report

Sociometric badges are an emerging technology for study how teams interact in physical places. Audio data recorded by sociometric badges is often downsampled to not record discussions of the sociometric badges holders. To gain more information about interactions inside teams with sociometric badges a Voice Activity Detector (VAD) is deployed to measure verbal activity of the interaction. Detecting voice activity from downsampled audio data is challenging because down-sampling destroys information from the data. We developed a VAD using deep learning techniques that achieves only moderate accuracy in a low noise meeting setting and in across variable noise levels despite excellent validation performance. Experiences and lessons learned while developing the VAD are discussed.
Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language

This technical report introduces an adapted version of the LOME frame semantic parsing model (Xia et al., EACL 2021) which is capable of automatically annotating texts according to the "Kicktionary" domain-specific framenet resource. Several methods for training a model even with limited available training data are proposed. While there are some challenges for evaluation related to the nature of the available annotations, preliminary results are very promising, with the best model reaching F1-scores of 0.83 (frame prediction) and 0.81 (semantic role prediction).
Prediction of dynamical systems using geometric constraints imposed by observations

Solution of Ordinary Differential Equation (ODE) model of dynamical system may not agree with its observed values. Often this discrepancy can be attributed to unmodeled forcings in the evolution rule of the dynamical system. In this article, an approach for data-based model improvement is described which exploits the geometric constraints imposed by the system observations to estimate these unmodeled terms. The nominal model is augmented using these extra forcing terms to make predictions. This approach is applied to navigational satellite orbit prediction to bring down the error to approximately 12% of the error when using the nominal force model for a 2-hour prediction. In another example improved temperature predictions over the nominal heat equation are obtained for one-dimensional conduction.
Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision

The abundance and richness of Internet photos of landmarks and cities has led to significant progress in 3D vision over the past two decades, including automated 3D reconstructions of the world’s landmarks from tourist photos. However, a major source of information available for these 3D-augmented collections—namely language, e.g., from image captions—has been virtually untapped. In this work, we present WikiScenes, a new, large-scale dataset of landmark photo collections that contains descriptive text in the form of captions and hierarchical category names. WikiScenes forms a new testbed for multimodal reasoning involving images, text, and 3D geometry. We demonstrate the utility of WikiScenes for learning semantic concepts over images and 3D models. Our weakly-supervised framework connects images, 3D structure, and semantics—utilizing the strong constraints provided by 3D geometry—to associate semantic concepts to image pixels and 3D points.1
Mean Field Analysis of Hypergraph Contagion Models

We typically interact in groups, not just in pairs. For this reason, it has recently been proposed that the spread of information, opinion or disease should be modelled over a hypergraph rather than a standard graph. The use of hyperedges naturally allows for a nonlinear rate of transmission, in terms of both the group size and the number of infected group members, as is the case, for example, when social distancing is encouraged. We consider a general class of individual-level, stochastic, susceptible-infected-susceptible models on a hypergraph, and focus on a mean field approximation proposed in [Arruda et al., Phys. Rev. Res., 2020]. We derive spectral conditions under which the mean field model predicts local or global stability of the infection-free state. We also compare these results with (a) a new condition that we derive for decay to zero in mean for the exact process, (b) conditions for a different mean field approximation in [Higham and de Kergorlay, Proc. Roy. Soc. A, 2021], and (c) numerical simulations of the microscale model.
Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes

The aerodynamic optimization process of cars requires multiple iterations between aerodynamicists and stylists. Response Surface Modeling and Reduced-Order Modeling are commonly used to eliminate the overhead due to Computational Fluid Dynamics, leading to faster iterations. However, a primary drawback of these models is that they can work only on the parametrized geometric features they were trained with. This study evaluates if deep learning models can predict the drag coefficient for an arbitrary input geometry without explicit parameterization. We use two similar data sets based on the publicly available DrivAer geometry for training. We use a modified U-Net architecture that uses Signed Distance Fields to represent the input geometries. Our models outperform the existing models by at least 11% in prediction accuracy for the drag coefficient. We achieved this improvement by combining multiple data sets that were created using different parameterizations, which is not possible with the methods currently used. We have also shown that it is possible to predict velocity fields and drag coefficient concurrently and that simple data augmentation methods can improve the results. In addition, we have performed an occlusion sensitivity study on our models to understand what information is used to make predictions. From the occlusion sensitivity study, we showed that the models were able to identify the geometric features and have discovered that the model has learned to exploit the global information present in the SDF. In contrast to the currently operational method, where design changes are restricted to the initially defined parameters, this study brings surrogate models one step closer to the long-term goal of having a model that can be used for approximate aerodynamic evaluation of unseen, arbitrary vehicle shapes, thereby providing complete design freedom to the vehicle stylists.