Graph Neural Network for Service Recommender System in Digital Service Marketplace

The emergence of the platform economy has resulted in the decline of many traditional forms of doing business. Freelance work makes use of a platform to connect businesses or people with other businesses or persons in order to solve particular issues or deliver specific services in return for payment. The pairing process involves a buyer that needs work done, a platform that handles the algorithm, and a worker who is willing to do the job via the platform. This research argues that by efficiently pairing the talents of workers to the requirements of buyers, the platforms have the ability to expedite business operations for buyers, empower platform workers, and significantly improve the overall customer experience. Graph Convolutional Networks (GCNs) are inspired by CNNs and aim to expand the convolution operation from grid records to graph records, which in turn facilitates advances in the graph domain. In order to develop reliable and accurate embeddings for digital service recommendation, we employed a graph-based technique on a freelance platform dataset using the graph linkages of services and buyer data. We employed an aggregation-based inductive graph convolution network, namely, Graph SAmple and aggreGatE (GraphSAGE). It is a generalized inductive architecture that learns to construct embeddings for previously unknown data by sampling and combining attributes from a node's immediate neighborhood. We also applied PinSage, a stochastic Graph Convolutional Network (GCN) that can learn node embeddings in platform networks with many digital services. When a robust recommender system is used in digital service marketplace, it can offer promising results that may increase users' satisfaction with the service and boost the platform's ability to increase revenue

Personalized Employee Training Based on Learning Styles Using Unsupervised Machine Learning

Advancement in technology, artificial intelligence, and machine learning have resulted in an explosion in the creation of tech-enabled training solutions over the past decade, contributing to the popularity of personalized learning. This research advocates for a transition away from archaic, rote learning paradigms and toward individualized employee learning experiences in which instructional styles and training tactics are tailored to the requirements of each individual employee rather than standard lesson preparation that exist today. This technique can also foster enjoyable and engaging training environments that benefit both employees and the organizations. We applied unsupervised machine learning algorithm, namely, K-means, and Hierarchical clustering algorithms to classify 1000 employees into different clusters based on the Felder-Silverman Learning Styles Model (FSLSM). As expected, no one of the employees could be precisely classified into a single category, and they demonstrated a variety of learning methods and tactics. The experiments showed 3 significant clusters across the different pairs of Processing, Input, Understanding, and Perception dimensions of the FSLSM. The findings suggest that employees can be grouped into at least 3 clusters to create personalized training materials and approaches for each group. We also discussed suitable instruction techniques, contents, and paths for each cluster. The proposed model and the findings would work in both digital and offline settings. &nbsp

What Does Universal Access Mean?

Universal access (UA) to the Internet and the associated information infrastructure has become an important economic and societal goal. Yet, a comprehensive and systematicunderstanding of the UA concept is still lacking. In this paper, we apply naturalistic techniques of inquiry to analyze the Philadelphia Wireless initiative, and develop a series of propositions that constitute a proposed new model of UA. The analysis reveals that UA is a multi-dimensional construct that is influenced by different stakeholders with varied and conflicting interests. UA, in the modern era, represents a human-technology alliance that exhibits great diversity across individuals, technologies, and associated social contexts. This departs from the traditional top-down notion of universal access that focused mainly on physical connectivity. The human and technological elements aredeeply embedded within institutional dependencies that are essential, yet also alternatively enable or constrain meaningful underlying use of the information infrastructure. The implications of this complexity for achieving universal access and policy making are discussed

The response of surface ozone to climate change over the Eastern United States

International audienceWe investigate the response of surface ozone (O3) to future climate change in the eastern United States by performing simulations corresponding to present (1990s) and future (2050s) climates using an integrated model of global climate, tropospheric gas-phase chemistry, and aerosols. A future climate has been imposed using ocean boundary conditions corresponding to the IPCC SRES A2 scenario for the 2050s decade. Present-day anthropogenic emissions and CO2/CH4 mixing ratios have been used in both simulations while climate-sensitive emissions were allowed to vary with the simulated climate. The severity and frequency of O3 episodes in the eastern U.S. increased due to future climate change, primarily as a result of increased O3 chemical production. The 95th percentile O3 mixing ratio increased by 5 ppbv and the largest frequency increase occured in the 80?90 ppbv range; the US EPA's current 8-h ozone primary standard is 80 ppbv. The increased O3 chemical production is due to increases in: 1) natural isoprene emissions; 2) hydroperoxy radical concentrations resulting from increased water vapor concentrations; and, 3) NOx concentrations resulting from reduced PAN. The most substantial and statistically significant (p3 season over the eastern U.S. in a future climate to include late spring and early fall months. Increased chemical production and shorter average lifetime are two consistent features of the seasonal response of surface O3, with increased dry deposition loss rates contributing most to the reduced lifetime in all seasons except summer. Significant interannual variability is observed in the frequency of O3 episodes and we find that it is necessary to utilize 5 years or more of simulation data in order to separate the effects of interannual variability and climate change on O3 episodes in the eastern United States

Cultural and review characteristics in the formation of trust in online product reviews: A multinational investigation

Recent changes in web technologies have given a voice to consumers in online discussion of products and services. While the web has long been a source of information about products and services, web content was controlled by those who knew how to develop for the web, or those who could hire web developers. The trend toward web software that permits novice users to contribute to conversations about products has been embraced by online retailers, who facilitate and encourage online user reviews of products. Researchers are just starting to understand the relationship between online user reviews and purchase intention, however have determined that trust is central to the development of purchase intention. In this study, we report the results of a simulation based web purchase experiment that included subjects in Colombia, the People’s Republic of China and the United States. The experiment included manipulations for both information quality and a social component of the review, and espoused culture scores of subjects where measured. We find that information quality, the social component and espoused uncertainty avoidance influence trust in the review. We were not able to support an interaction effect between information quality and uncertainty avoidance and trust, nor an interaction effect between the social component and collectivism

ENHANCING MODEL SECURITY: LEVERAGING USER-GENERATED IDS AS EMBEDDED WATERMARKS IN MACHINE LEARNING MODELS

The potential theft or unauthorized use of machine learning models developed by a company can lead to significant financial losses and damage to the company\u27s intellectual property. While existing methods of protecting machine learning models such as encryption or access controls can be circumvented by skilled attacker, techniques presented herein involve the integration of embedded watermarks into machine learning models. Such techniques involving the integration of embedded watermarks may not only uniquely identify a model but may also include a unique user identification/identity that can make it possible to track usage of the model and detect any unauthorized use of the model. Thus, if a model is leaked, redistributed, or misused, the watermark for the model makes it possible to identify a source of the leak/misuse, allowing for better traceability and accountability