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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhihong Wang ◽  
Yongbiao Li ◽  
Dingcheng Li ◽  
Ming Li ◽  
Bincheng Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quality Evaluation ◽  
Rapid Development ◽  
Single Point ◽  
Privacy Preserving ◽  
Learning Technologies ◽  
Quality Of Data ◽  
Task Processing ◽  
Worker Quality

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.

Quality Monitoring System for Seismic While Drilling

2013 ◽  
Vol 318 ◽  
pp. 572-575
Author(s):  
Li Li Yu ◽  
Yu Hong Li ◽  
Ai Feng Wang
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Signal Analysis ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Quality Of Data ◽  
Whole Process ◽  
Engineering Parameters ◽  
Network Status

In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

scholarly journals Machine Learning for the Educational Sciences

2021 ◽  
Author(s):  
Sven Hilbert ◽  
Stefan Coors ◽  
Elisabeth Barbara Kraus ◽  
Bernd Bischl ◽  
Mario Frei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Decisive Role ◽  
Quality Of Data ◽  
Practical Applications ◽  
Educational Sciences ◽  
Complex Relationships ◽  
The Impact ◽  
Analytical Approaches

Classical statistical methods are limited in the analysis of highdimensional datasets. Machine learning (ML) provides a powerful framework for prediction by using complex relationships, often encountered in modern data with a large number of variables, cases and potentially non-linear effects. ML has turned into one of the most influential analytical approaches of this millennium and has recently become popular in the behavioral and social sciences. The impact of ML methods on research and practical applications in the educational sciences is still limited, but continuously grows as larger and more complex datasets become available through massive open online courses (MOOCs) and large scale investigations.The educational sciences are at a crucial pivot point, because of the anticipated impact ML methods hold for the field. Here, we review the opportunities and challenges of ML for the educational sciences, show how a look at related disciplines can help learning from their experiences, and argue for a philosophical shift in model evaluation. We demonstrate how the overall quality of data analysis in educational research can benefit from these methods and show how ML can play a decisive role in the validation of empirical models. In this review, we (1) provide an overview of the types of data suitable for ML, (2) give practical advice for the application of ML methods, and (3) show how ML-based tools and applications can be used to enhance the quality of education. Additionally we provide practical R code with exemplary analyses, available at https: //osf.io/ntre9/?view only=d29ae7cf59d34e8293f4c6bbde3e4ab2.

scholarly journals Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4651
Author(s):  
Yuanbo Cui ◽  
Fei Gao ◽  
Wenmin Li ◽  
Yijie Shi ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Location Privacy ◽  
Personal Information ◽  
Single Point ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Content Protection ◽  
Query Privacy ◽  
Service Data

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.

A Blood Bank Management System-Based Internet of Things and Machine Learning Technologies

2020 ◽  
pp. 184-222
Author(s):  
Ahmed Mousa ◽  
Ahmed El-Sayed ◽  
Ali Khalifa ◽  
Marwa El-Nashar ◽  
Yousra Mancy Mancy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Undergraduate Students ◽  
Management System ◽  
Main Idea ◽  
Learning Technologies ◽  
Bank Management ◽  
Academic Researchers ◽  
Blood Banks

Nearly all of the Egyptian hospitals are currently suffering from shortage in rare blood types (e.g., -AB, -B, +AB), which are needed to perform vital surgeries. This leads them (hospitals or doctors) to ask patients' relatives to donate the amount of the required blood. The alternative is that they are forced to pay for the blood if the required type and amount is already available in these hospitals or the blood banks. The main idea of this work is solving problems related to the blood banks from collecting blood from donators to distributing blood bags for interested hospitals. This system is developed in order to enhance the management, performance, and the quality of services for the management of blood banks, which will be positively reflected on many patients in hospitals. This chapter targets undergraduate students, academic researchers, development engineers, and course designers and instructors.

Quality Evaluation Model for Highway General Mechanical and Electrical Product

2013 ◽  
Vol 706-708 ◽  
pp. 2095-2098
Author(s):  
Cheng Zan Chu ◽  
Li Wei Zhu ◽  
Ran Na
Keyword(s):  
Product Quality ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Rapid Development ◽  
Mechanical Equipment ◽  
The Past ◽  
Generic Quality ◽  
Quality Evaluation Model

Highway general mechanical and electrical product quality is one of the important factors for guaranteeing highway efficient and safe operation. Over the past decade, with the rapid development of highway construction and mechanical equipment manufacturing technology, more and more mechanical and electrical products applied in highway. For the objective and scientific evaluation of quality of mechanical and electrical products, highway mechanical and electrical product quality index system and quality evaluation model were researched based on product generic quality evaluation method, and then verified by actual product case.

Quality Evaluation for Model Based Definition of Aerospace Products

2014 ◽  
Vol 945-949 ◽  
pp. 30-34 ◽  
Author(s):  
Kai Mi ◽  
Yun Hu ◽  
Chao Yin
Keyword(s):  
Data Exchange ◽  
Quality Evaluation ◽  
Rapid Development ◽  
Quality Data ◽  
Quality Model ◽  
Digital Products ◽  
Model Based ◽  
Definition Of ◽  
Downstream Processes

With the rapid development of computer aided technology, the application of model based definition (MBD) in the research and development (R&D) processes of aerospace products become more and more widespread. The quality of MBD refers to its ability to meet the requirements of the downstream processes like subsequent design, manufacture, simulation, and inspection etc. Based on MBD’s quality characteristics, such as modeling and simulation quality, design process quality, geometry and topology quality, data quality and data exchange quality, this paper constructs a quality model for aerospace digital products and proposes both qualitative and quantitative quality evaluation methods for MBD of aerospace products.

scholarly journals A Survey on Unsupervised K-Means Algorithm in Big Data Environment

2021 ◽  
pp. 1-8
Author(s):  
Fatama Sharf Al-deen ◽  
Fadl Mutaher Ba-Alwi
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Literature Review ◽  
Relational Databases ◽  
Rapid Development ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Prominent Feature ◽  
Data Environment

Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

Teachers’ Professional Training for the Implementation of Learning Technologies

2017 ◽  
Vol 9 (1) ◽  
pp. 68
Author(s):  
Venera Këndusi
Keyword(s):  
Teaching And Learning ◽  
Professional Training ◽  
Rapid Development ◽  
Learning Technologies ◽  
Primary School Teachers ◽  
Learning Facilitation ◽  
Quality Of Teaching ◽  
Frequent Use ◽  
Technological Developments

Regarding to the rapid development of technique and technology in all areas of life, also in the education field such developments have a great impact. The frequent use of technologies and their advancement influence on students for the main purpose to achieve faster the knowledge. For this reason teachers should be in trend with new technological developments and they should be familiar with the technology usage in schools. In order that teaching process to be attractive and at the same time to achieve the highest quality of education there is a need that teachers should know the usage and application of technology during the process of teaching. The effect and the progress of teaching labor are gained with usage improvement of teaching technology. The purpose of this research is related to the professional training of primary school teachers in Gjakova municipality for the use of learning technologies as computers, projectors, laptop, etc, and their application in teaching practices in order to improve the quality of teaching and learning facilitation.

Simulation of English teaching quality evaluation model based on gaussian process machine learning

2020 ◽  
pp. 1-11
Author(s):  
Huang Wenming
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Teaching Quality ◽  
Distribution Characteristics ◽  
English Teaching ◽  
Traditional Evaluation

The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system.

Data Stream Quality Evaluation for the Generation of Alarms in the Health Domain

2015 ◽  
Vol 24 (3) ◽  
pp. 361-369
Author(s):  
Saúl Fagúndez ◽  
Joaquín Fleitas ◽  
Adriana Marotta
Keyword(s):  
Data Quality ◽  
Data Streams ◽  
Data Stream ◽  
Quality Evaluation ◽  
False Alarms ◽  
Health It ◽  
Quality Model ◽  
Quality Of Data ◽  
Stream Quality

AbstractThe use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems.

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