Today on almost every desk in every office sits a computer. Eighty years ago, desktops were equipped with a nonelectronic data processing machine: a card file. In Paper Machines, Markus Krajewski ...traces the evolution of this proto-computer of rearrangeable parts (file cards) that became ubiquitous in offices between the world wars. The story begins with Konrad Gessner, a sixteenth-century Swiss polymath who described a new method of processing data: to cut up a sheet of handwritten notes into slips of paper, with one fact or topic per slip, and arrange as desired. In the late eighteenth century, the card catalog became the librarian's answer to the threat of information overload. Then, at the turn of the twentieth century, business adopted the technology of the card catalog as a bookkeeping tool. Krajewski explores this conceptual development and casts the card file as a "universal paper machine" that accomplishes the basic operations of Turing's universal discrete machine: storing, processing, and transferring data. In telling his story, Krajewski takes the reader on a number of illuminating detours, telling us, for example, that the card catalog and the numbered street address emerged at the same time in the same city (Vienna), and that Harvard University's home-grown cataloging system grew out of a librarian's laziness; and that Melvil Dewey (originator of the Dewey Decimal System) helped bring about the technology transfer of card files to business.
Power analysis attacks allow the extraction of secret information from smart cards. Smart cards are used in many applications including banking, mobile communications, pay TV, and electronic ...signatures. In all these applications, the security of the smart cards is of crucial importance. Power Analysis Attacks: Revealing the Secrets of Smart Cardsis the first comprehensive treatment of power analysis attacks and countermeasures. Based on the principle that the only way to defend against power analysis attacks is to understand them, this book explains how power analysis attacks work. Using many examples, it discusses simple and differential power analysis as well as advanced techniques like template attacks. Furthermore, the authors provide an extensive discussion of countermeasures like shuffling, masking, and DPA-resistant logic styles. By analyzing the pros and cons of the different countermeasures, this volume allows practitioners to decide how to protect smart cards.
Postcards are usually associated with banal holiday pleasantries, but they are made possible by sophisticated industries and institutions, from printers to postal services. When they were invented, ...postcards established what is now taken for granted in modern times: the ability to send and receive messages around the world easily and inexpensively. Fundamentally they are about creating personal connections - links between people, places, and beliefs. Lydia Pyne examines postcards on a global scale, to understand them as artifacts that are at the intersection of history, science, technology, art, and culture. In doing so, she shows how postcards were the first global social network and also, here in the twenty-first century, how postcards are not yet extinct.
This book provides a broad overview of the many card systems and solutions in practical use today. It combines a cross-discipline overview of smart cards, tokens and related security and ...applications, plus a technical reference to support further research and study.
•Smart card passengers are assigned to trains recorded in automated data.•Passenger-to-train assignment leads to a train-level pattern of crowding.•Crowding costs can be estimated in a revealed ...preference route choice setting.•Standing costs 26.5% of travel time, the crowding multiplier does not exceed 1.981.
Crowding discomfort is an external cost of public transport trips imposed on fellow passengers that has to be measured in order to derive optimal supply-side decisions. This paper presents a comprehensive method to estimate the user cost of crowding in terms of the equivalent travel time loss, in a revealed preference route choice framework. Using automated demand and train location data we control for fluctuations in crowding conditions on the entire length of a metro journey, including variations in the density of standing passengers and the probability of finding a seat. The estimated standing penalty is 26.5% of the uncrowded value of in-vehicle travel time. An additional passenger per square metre on average adds 11.9% to the travel time multiplier. These results are in line with earlier revealed preference values, and suggest that stated choice methods may overestimate the user cost of crowding. As a side-product, and an important input of the route choice analysis, we derive a novel passenger-to-train assignment method to recover the daily crowding and standing probability pattern in the metro network.
Everyone is affected by credit card fraud, if they are aware of it or not. Every day there are a variety of ways that scams and fraudsters can get your card and personal information. Today so much ...business occurs over the Internet or via the phone where no card is present. What can start as a seemingly legitimate purchase can easily turn into fraudulent charges -- or worse, sometimes a physical confrontation, when a criminal steals a credit card from a consumer who meets to pick up a product or receive a service. In Preventing Credit Card Fraud, Jen Grondahl Lee and Gini Graham Scott provide a helpful guide to protecting yourself against the threat of credit card fraud. While it may not be possible to protect yourself against all fraudsters, who have turned scamming Internet businesses into an art, these tips and techniques will help you avoid many frauds. As a growing concern in today's world, there is a need to be better informed of what you can do to keep your personal information secure and avoid becoming a victim of credit card fraud. Preventing Credit Card Fraud is an important resource for both merchants and consumers engaged in online purchases and sales to defend themselves against fraud.
In recent years, contactless transactions have risen rapidly. It includes NFC, MST, contactless cards, and many other payment methods. These payment methods have certain security issues, and ...attackers are in a regular search for the exploits to break its security. These security issues require proper analysis to secure user data from attackers. This article will discuss the contactless smart cards and payment systems in detail including the techniques used for securing user data and different possible attacks on the technology used for communication. The article also presents some countermeasures to prevent the attack and issues with those countermeasures. In addition, the article includes some future research issues and suggestions to overcome the security issues in contactless payment system.
Focusing on a private collection of 60 postcards of modern architecture in Mumbai, New Delhi, Kolkata, Chennai and Agra, the contributors to this volume explore the many dimensions of modern ...architecture in India from the 1890s to the 1970s and share their own perspective on these objects. Experts on architectural history and visual studies, as well as postcard collectors provide new insights into a territory and its architectural heritage which is still largely unknown in Europe, and reflect on the postcard as a medium for historical research.
Commuting reflects the long-term travel behavior of people and significantly impacts urban traffic congestion and emission. Recent advances in data availability provide new opportunities to ...understand commuting patterns efficiently and effectively. This study develops a series of data mining methods to identify the spatiotemporal commuting patterns of Beijing public transit riders. Using one-month transit smart card data, we measure spatiotemporal regularity of individual commuters, including residence, workplace, and departure time. This data could be used to identify transit commuters by leveraging spatial clustering and multi-criteria decision analysis approaches. A disaggregated-level survey is performed to demonstrate the effectiveness of the proposed methods with a commuter identification accuracy that reaches as high as 94.1%. By visualizing the spatial distribution of the homes and workplaces of transit commuters, we determine a clear disparity between commuters and noncommuters and confirm the existence of job–house imbalance in Beijing. The findings provide useful insights for policymakers to shape a more balanced job–housing relationship by adjusting the monocentric urban structure of Beijing. In addition, the extracted individual-level commuting patterns can be used as valuable information for public transit network design and optimization. These strategies are expected to reduce car dependency, shorten excess commute, and alleviate traffic congestion.
•Smart card and manual survey data are combined for bus arrival time calculation.•The ridership versus the seating capacity affects the riders’ card swiping behaviors.•The trend of time gap between ...the card swiping and bus arrival time is portrayed.•Model validation shows only 0.6% and 3.8% of mean and standard error rate deviations.
Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.
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