Enabling Fairness-Aware and Privacy-Preserving for Quality Evaluation in Vehicular Crowdsensing: A Decentralized Approach

With the rapid development of vehicular crowdsensing, it becomes easier and more efficient for mobile devices to sense, compute, and measure various data. However, how to address the fair quality evaluation between the platform and participants while preserving the privacy of solutions is still a challenge. In the work, we present a fairness-aware and privacy-preserving scheme for worker quality evaluation by leveraging the blockchain, trusted execution environment (TEE), and machine learning technologies. Specifically, we build our framework atop the decentralized blockchain which can resist a single point of failure/compromise. The smart contracts paradigm in blockchain enforces correct and automatic program execution for task processing. In addition, machine learning and TEE are utilized to evaluate the quality of data collected by the sensors in a privacy-preserving and fair way, eliminating human subject judgement of the sensing solutions. Finally, a prototype of the proposed scheme is implemented to verify the feasibility and efficiency with a benchmark dataset.

Predicting Employee Attrition Using Machine Learning Techniques

There are several areas in which organisations can adopt technologies that will support decision-making: artificial intelligence is one of the most innovative technologies that is widely used to assist organisations in business strategies, organisational aspects and people management. In recent years, attention has increasingly been paid to human resources (HR), since worker quality and skills represent a growth factor and a real competitive advantage for companies. After having been introduced to sales and marketing departments, artificial intelligence is also starting to guide employee-related decisions within HR management. The purpose is to support decisions that are based not on subjective aspects but on objective data analysis. The goal of this work is to analyse how objective factors influence employee attrition, in order to identify the main causes that contribute to a worker’s decision to leave a company, and to be able to predict whether a particular employee will leave the company. After the training, the obtained model for the prediction of employees’ attrition is tested on a real dataset provided by IBM analytics, which includes 35 features and about 1500 samples. Results are expressed in terms of classical metrics and the algorithm that produced the best results for the available dataset is the Gaussian Naïve Bayes classifier. It reveals the best recall rate (0.54), since it measures the ability of a classifier to find all the positive instances and achieves an overall false negative rate equal to 4.5% of the total observations.

The Commodities in Post Capitalist Society: Domination of the Invention of Technology in Society Activities

The aim of this article is to describe the domination of the invention of technology in society activities in the form of commodities in post capitalist society. The products produced by the capitalist corporations have made the society very consumptive; they have become highly dependent on communication technology products such as gadgets, mobile phones, and computers. Changes in conventional business transactions into electronic transactions, media activities that have made the community as a spectacle for others, as well as changes in worker quality from skill worker to knowledge worker. Nevertheless, it is important to observe why people become dependent on these kinds of commodities. What kind of commodities will provide to the society in post capitalist era and how it is provide? This article is devoted to answer these questions.

Training for Manufactured Construction (TRAMCON) – Benefits and Challenges for Workforce Development at Manufactured Housing Industry

Manufactured Housing (MH) is the process of producing building units or entire buildings in an offsite factory and transporting them to the site for installation and assembly. The application of advanced manufacturing technologies into the housing process not only will increase productivity, but also can provide a safer work environment, stable work location, long-term growth opportunities, and career progression for employees. Today, the MH workforce is facing problems with worker quality and retention. The rising demand for MH indicates the need for training a multi-skilled labor force for this industry. This paper evaluates the essence of an educational program for MH industry and discusses the rationale for training the MH workforce in comparison to conventional training programs. In response to the stated problem of Inadequate training programs, the curriculum for Training Manufactured Construction (TRAMCON) was developed by the University of Florida and delivered throughout Florida by the TRAMCON Consortium. While the quantitative results in labor performance improvement in the factory plants have not yet been established, the major strengths and challenges of the program are discussed.