Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Security and Privacy Issues of Big Data

This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more and more personal data and content through their devices and computers to social networks and public clouds. So, a secure framework to social networks is a very hot topic research. This last topic is addressed in one of the two sections of the current chapter with case studies. In addition, the traditional mechanisms to support security such as firewalls and demilitarized zones are not suitable to be applied in computing systems to support Big Data. SDN is an emergent management solution that could become a convenient mechanism to implement security in Big Data systems, as we show through a second case study at the end of the chapter. This also discusses current relevant work and identifies open issues.Comment: In book Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence, IGI Global, 201

Two Wrongs Don\u27t Negate a Copyright: Don\u27t Make Students Turnitin if You Won\u27t Give it Back

The story goes something like this: There was a particularly difficult college professor notorious for a low grading scale. After years of low grade following low grade, one paper finally earned a B minus, the highest grade ever awarded by this professor. Word spread about the paper, and the student author sold it to the highest bidder, who later turned in the same paper to the same professor and received a B. The next year, after being recycled yet again, the paper received a B plus. When the paper was recycled and submitted a fourth time, it finally received an A and a comment from the professor, “I’ve read this paper four times now, and I like it better each time.” The story may be true, but it is more likely a college legend. Either way, this story and others like it help reduce the strain of college life and keep alive the eternal hope of one day outwitting a professor. But the stories also foster an improper sense of ease and comfort with plagiarism. Recycling another’s paper and resubmitting it does not gradually lead down the path to higher grades; rather, this is likely an academic violation that could lead to failing grades or worse. In a “cut-and-paste” Internet environment where plagiarism is easier than ever, academic institutions face the daunting challenges of promoting honesty and respect for the work of others and of ensuring the integrity of the learning and grading processes. Many academic institutions have accordingly turned to commercial plagiarism prevention and detection services, such as those provided by a company called Turnitin. Yet those institutions that use the Turnitin system may be fostering infringement of the intellectual property rights of their students. When Virginia’s McLean High School recently announced plans to use Turnitin, students balked and collected 1,190 student signatures on a petition that opposed mandatory use of the system, because that use of Turnitin would infringe their intellectual property rights by automatically adding their essays to the company’s massive database. In response, school officials backed off a plan to require students in all grades to submit essays to Turnitin, deciding instead to phase in use of Turnitin by making it mandatory only for ninthand tenth-grade students in specified classes. As one student stated, “‘It seems they pretty much changed the policy so they don’t have to deal with the people who are protesting it.’” But as the plagiarism problem grows and questions about the legality and effectiveness of the Turnitin system linger and extend far beyond McLean High School, the academic world will be able to dodge the problem only for so long. Part II of this Note examines the plagiarism problem facing the academic world; Part III examines the origins and workings of the Turnitin system; Part IV analyzes the copyright issues raised by Turnitin’s service and examines the broader question whether plagiarism prevention justifies deferential treatment in a fair use inquiry; Part V examines further implications of the copyright analysis; Part VI examines Turnitin’s effectiveness; and Part VII provides alternative solutions to the plagiarism problem

International gaming:laws and regulations around games in the digital era: conference proceedings

Games have a complicated relationship with the law and with the globalization of the gaming market in the digital era, legality is something that must be considered in the development, publication, playing and moderation of games. Games have been and continue to be regulated around the globe, and issues arise at different times in a games life cycle: intellectual property (IP), including patent and copyright protection, must be considered from the initial development of the game; International legality, age ratings, and infringement must be considered in the publication of the game; and players must consider the legality of their manner of obtaining the game and whether or not that game is even legal in the country they are playing in. In addition to this, there are legal issues surrounding abuse and harassment in games, leading several developers to implement moderation and other safety regulations internally in order to pass risk assessments from international regulations. In this way, the laws and regulations around games and gaming in the digital era need to be scrutinized.

International gaming:laws and regulations around games in the digital era: conference proceedings

Games have a complicated relationship with the law and with the globalization of the gaming market in the digital era, legality is something that must be considered in the development, publication, playing and moderation of games. Games have been and continue to be regulated around the globe, and issues arise at different times in a games life cycle: intellectual property (IP), including patent and copyright protection, must be considered from the initial development of the game; International legality, age ratings, and infringement must be considered in the publication of the game; and players must consider the legality of their manner of obtaining the game and whether or not that game is even legal in the country they are playing in. In addition to this, there are legal issues surrounding abuse and harassment in games, leading several developers to implement moderation and other safety regulations internally in order to pass risk assessments from international regulations. In this way, the laws and regulations around games and gaming in the digital era need to be scrutinized.

An Automatic Helmet Detection System using Convolution Neural Network

Applications for automatic licence plate identification and helmet detection are most useful on busy highways where accidents are more often. Although the government has adopted a number of restrictions, the motorcycle riders haven't been following them very well, necessitating the use of several cunning strategies. Today, it is difficult to distinguish between helmeted and non-helmeted motorcyclists, thus new technology is required to do so as well as to read the rider's licence plate. It aids in accident prevention and increases people's mental alertness. In this study, CNN machine learning set of rules are used to create automatic helmet identification and automatic licence plate recognition applications. faster CNN algorithm is utilised to find the helmet