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Abstract-In this paper we propose a communication protocol for Radio Frequency Identification (RFID) systems that is based on the tags responding only to authenticated readers, otherwise tags always maintain RF silence. The protocol is practical from a deployment point of view and it not only meets the formal definitions of strong privacy and untraceability, but also addresses most, of the concerns raised by privacy advocates on behalf of consumers. Both passive and active RFID systems can use this protocol, and with slight modifications it can also be used on wireless-sensor networks. The protocol is expected to more efficiently utilize the RF spectrum by minimizing tag and reader collisions and as a result it should be possible to accommodate more readers and tags in a given area.

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In sports, including Test cricket, athletes from years past serve as performance role models and set benchmarks for subsequent generations of players. Sports fans often wonder: are players of today as good as greats from the past? Alternatively, how do today’s athletes compare with greats from yesteryears? This paper attempts to answer that question for Test match cricket. We applied data mining to batting performance of eighty, now retired, Test Cricket Greats (TCG from hereon) from eight major Test cricket countries. Batting performance attributes included batting average, strike rate, numbers of fifties and hundreds scored, among others. Using k-Means cluster analysis, TCG performance records were classified into three clusters which was our Training Model. Two clusters were populated by established batsmen and the third cluster included bowlers, all-rounders with significant bowling, and some batsmen. The Learning Model was applied to predict classifications of thirty two Test Cricket Active (TCA from hereon) players. Statistical tests were performed, cluster wise, to highlight similarities and dis-similarities between TCA and TCG players. Results show that several active players, while still mid-career, have already achieved batting performance records which are at par with the best of TCG.

The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors' discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models are used to try and predict the stock price movements of two meme stocks: GameStop ($GME) and AMC Entertainment ($AMC). Two sentiment metrics of the daily social media discussions about these stocks on Reddit are generated and used together with 85 other fundamental and technical indicators as the feature set for the machine learning models. It is demonstrated that through the use of a carefully chosen mix of a meme stock's fundamental indicators, technical indicators, and social media sentiment scores, it is possible to predict the stocks' next-day closing prices. Also, using an anomaly detection model, and the daily Reddit discussions about a meme stock, it was possible to identify potential market manipulators.