A Comparative Analysis Using Silhouette Extraction Methods for Dynamic Objects in Monocular Vision

Moving or dynamic object analysis continues to be an increasingly active research field in computer vision with many types of research investigating different methods for motion tracking, object recognition, pose estimation, or motion evaluation (e.g. in sports sciences). Many techniques are available to measure the forces and motion of the people, such as force plates to measure ground reaction forces for a jump or running sports. In training and commercial solution, the detailed motion of athlete's available motion capture devices based on optical markers on the athlete's body and multiple calibrated fixed cameras around the sides of the capture volume can be used. In some situations, it is not practical to attach any kind of marker or transducer to the athletes or the existing machinery are being used, while it is required by a pure vision-based approach to use the natural appearance of the person or object. When a sporting event is taking place, there are opportunities for computer vision to help the referee and other personnel involved in the sports to keep track of incidents occurring, which may provide full coverage and analysis in details of the event for sports viewers. The research aims at using computer vision methods, specially designed for monocular recording, for measuring sports activities, such as high jump, wide jump, or running. Just for indicating the complexity of the project: a single camera needs to understand the height at a particular distance using silhouette extraction. Moving object analysis benefits from silhouette extraction and this has been applied to many domains including sports activities. This paper comparatively discusses two significant techniques to extract silhouettes of a moving object (a jumping person) in monocular video data in different scenarios. The results show that the performance of silhouette extraction varies in dependency on the quality of used video data.

Map Whiteboard Cloud Solution for Collaborative Editing of Geographic Information

For the purpose of exploiting the potential of cloud connectivity in geographical information systems, the Map Whiteboard technology introduced in this article does for web mapping what Google Docs does for word processing; create a shared user interface where multiple parties collaboratively can develop maps and map data while seeing each other work in realtime. To develop the Map Whiteboard concept, we have applied a methodology whereby we have collected technical and functional requirements through a series of hackathons, implemented a prototype in several stages, and subjected this to rigorous testing in a lab environment and with selected users from relevant environments at intermediate scale. The work has resulted in a fully functional prototype that exploits WebSockets via a cloud service to reflect map and data changes between multiple connected clients. The technology has a demonstrated potential for use in a wide range of web GIS applications, something that is facilitated by the interfaces already implemented towards mainstream mapping frameworks like OpenLayers and QGIS-two of the most popular frameworks for Web GIS solutions. Further development and testing are required before operationalization in mission-critical environments. In conclusion, the Map Whiteboard concept offers a starting point for exploiting cloud connectivity within GIS to facilitate the digitalization of common processes within the government and private sector. The technology is ready for early adopters and welcomes the contribution of interested parties.

A Risk Analysis on Blockchain Technology Usage for Electronic Health Records

Health data is one of the most valuable data and highly sensitive. Its careful handling is essential in today' s digitalized world and cloud technology use for sharing. Health Information Systems facilitate the storage and accessibility of health data for better care along the patient path. As the integration of all historical patient data, the Electronic Health Record is at the heart of health data management. The centralization of stored health data represents a single point of failure and trust, making data exchange across institutions difficult and insecure. Blockchain technology builds on consensus mechanisms and immutable chains of blocks for validating and securing data transactions as a modern decentralized approach. The application of blockchain technology for Electronic Health Records is promising but still a young concept. Due to the wide range of discussion, this paper aims at identifying risks by using blockchain technology in the eHealth sector. Based on a systematic literature review, various authors' argumentations and findings are examined and concluded to set up the empirical study. The semistructured qualitative interview study aims to find out the threats of blockchain. The paper concludes with an overall discussion and some implementation recommendations.

Data-Driven Analysis Based on Graphical and Statistical Modelling of the Water Quality of Niger Delta Region of Nigeria

Data-driven models derived from data science tools have been used to investigate water quality in some parts of the Niger Delta Region (NDR) of Nigeria. 11 communities were affected in this study. 11 water samples obtained from 25 available sources collected from January 2019 to December 2019 include rainwater, surface water and groundwater. These samples were analysed for their physicochemical and bacteriological parameters. The physical characteristics of the water points range from pH of 6.61-7.2, electrical conductivity (EC) of 450-1742 unit, the turbidity of 0.72-13.65 unit, and total dissolved solids (TDS) of 225-794. The chemical scientific dataset generated were subjected to several scientific data models such as principal component analysis (PCA), piper, Pie, Collins, and Schoeller interpretation. There is a piece of evidence that the water resources are potable in sections where Escherichia coli and total coliforms do not exceed the international and regional recommended limits of 0 per 100 ml of the sample. Also, the community water points are good for livestock and excellent for both recreation and irrigation purposes. Possible water contamination sources include faecal pollution from shallow wells and unconfined aquifers. Land use planning, enactment and implementation of environmental laws are necessary for this region to have effective surface water and groundwater resource management.

Machine Learning by Data Mining REPTree and M5P for Predicating Novel Information for PM10

We examined data mining as a technique to extract knowledge from database to predicate PM10 concentration related to meteorological parameters. The purpose of this paper is to compare between the two types of machine learning by data mining decision tree algorithms Reduced Error Pruning Tree (REPTree) and divide and conquer M5P to predicate Particular Matter 10 (PM10) concentration depending on meteorological parameters. The results of the analysis showed M5P tree gave higher correlation compared with REPTree, moreover lower errors, and higher number of rules, the elapsed time for processing REPTree is less than the time processing of M5P. Both of these trees proved that humidity absorbed PM10. The paper recommends REPTree and M5P for predicting PM10 and other pollution gases.

Evaluating the Effect of Studying Computer Ethics and Computer Ethics Rules and Regulations on Computer Ethics at Work

With the increasing reliance on computer and Internet, the danger of cybercrime attacks was increased. This research paper aims to evaluate the effect of studying computer ethics and computer ethics rules and regulations on computer ethics at work. A self-administrated questionnaire was designed for data collection. The data was collected from 374 responds working in private and public sectors. The results showed a strong correlation between studying computer ethics and computer ethics rules and regulations, a strong correlation between studying computer ethics and computer ethics at work, and a moderate correlation between computer ethics rules and regulations and computer ethics at work. The gender and age have statistically significant effect on computer ethics at work, while education level and job position have no statistically significant effect on computer ethics at work. According to the respond’s opinions, this paper recommends giving intensively interest in educating the computer ethics modules, to all levels of education as well as organizing continuous learning workshops to employees for all job positions, to increase awareness against computer crimes.

Applying Latest Data Science Technology in Cancer Screening Programs

Cancer screening programs have been implemented in many different countries for many years to collect information of the fatal diseases, to provide early diagnosis, to support medical research, and to help governments making policies. However, few of those programs have utilized latest data science technologies, therefore not be able to generate the maximum benefits from those programs. To overcome this problem and improve the quality of cancer screening programs, this paper firstly (i) reviews the typical architecture and IT technologies used in current screening programs and recognizes their limitations; then (ii) introduces recent developments in data science that could be implemented in screening programs; finally (iii) proposes the structure of General Medical Screening Framework (GMSF), which could be developed to host future cancer screening programs that will advance data coverage, data accuracy, data usage and lower in the costs. The structure of GMSF and its key elements are described in this paper and some practical approaches to build GMSF are discussed. This work might initialize a series or research to bring the latest IT technologies, particularly data science technologies, into cancer screening programs, and significantly increase the efficiency and reduce the cost of future cancer screening programs.

Security Challenges over Cloud Environment from Service Provider Prospective

Cloud computing becomes very popular and growing rapidly since the last few years, various information technology giants such as Amazon, Google, Microsoft and others speed up their growth in development of cloud computing systems and enhance services for consumers all over the world. Cloud computing provides virtual storage for the clients to avail storage, application, platform and services from their deployed servers over the internet. Various security issues such as; data insecure, data leakage, data availability, attacks; etc. could arise due to poor security policies. This paper discussed in detail security issues in terms of the CIA (Confidentiality, Integrity, and Availability) triangle from a service provider perspective over the services they provide to the end users. By these measurements high security can be achieve in cloud computing. Additionally, to protect confidential data from users on cloud the privacy actions are not updated accordingly. Severally, data backups on cloud cause high security risks. This survey paper analyzes the main security issues which are currently present in the cloud computing and offers best practices to service providers and enterprises hoping to control cloud service.

Improving Decision Quality for Business Users Based on Cloud-based Self-Service Business Intelligence Tools

The ever-growing volume of data promotes data-driven decision-making in more cases and more areas than before. The development of user-friendly self-service BI (SSBI) tools enable business users to autonomously execute tasks in the area of Business Intelligence (BI), statistical analysis, or data science. Cloud computing offers the opportunity to provide SSBI as services as well. This paper focusses on cloud-based SSBI tools and their support for data-driven decision-making by business users. This paper aims to identify the influence of a deeper understanding of business informatics on (a) the handling of the cloud-based SSBI tools and (b) the data-driven decision making performance. An experimental setting was used to collect empirical data. Two groups with equal knowledge in business administration, but different backgrounds in business informatics have been defined. Based on different backgrounds in business informatics, the results show no significant difference in handling the cloud-based SSBI tool but reveal significant differences in decision-making performance.