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The Comprehensive Antibiotic Resistance Database (CARD; card.mcmaster.ca) combines the Antibiotic Resistance Ontology (ARO) with curated AMR gene (ARG) sequences and resistance-conferring mutations to provide an informatics framework for annotation and interpretation of resistomes. As of version 3.2.4, CARD encompasses 6627 ontology terms, 5010 reference sequences, 1933 mutations, 3004 publications, and 5057 AMR detection models that can be used by the accompanying Resistance Gene Identifier (RGI) software to annotate genomic or metagenomic sequences. Focused curation enhancements since 2020 include expanded β-lactamase curation, incorporation of likelihood-based AMR mutations for Mycobacterium tuberculosis, addition of disinfectants and antiseptics plus their associated ARGs, and systematic curation of resistance-modifying agents. This expanded curation includes 180 new AMR gene families, 15 new drug classes, 1 new resistance mechanism, and two new ontological relationships: evolutionary_variant_of and is_small_molecule_inhibitor. In silico prediction of resistomes and prevalence statistics of ARGs has been expanded to 377 pathogens, 21,079 chromosomes, 2,662 genomic islands, 41,828 plasmids and 155,606 whole-genome shotgun assemblies, resulting in collation of 322,710 unique ARG allele sequences. New features include the CARD:Live collection of community submitted isolate resistome data and the introduction of standardized 15 character CARD Short Names for ARGs to support machine learning efforts.

From the Publisher: The boom in smart card technology reflects the medium's broad solutions potential. Embedded with a sophisticated microprocessor, smart cards offer unparalleled memory capacity and data encryption capability. From providing secure access to the Internet and mobile radio networks to performing security-sensitive financial transactions in credit card schemes, the Electronic Purse and Pay TV systems, smart card technology is now a multi-billion dollar industry. The Smart Card Handbook presents a state-of-the-art overview of the technology from fundamental information processing through design, manufacture and operation of smart card schemes. Written in a highly accessible style the Smart Card Handbook meets the needs of both novice and expert. This is an essential reference for computer and electronics engineers and students in microchip design and security system development. For professionals developing smart card products, this unique reference will provide an invaluable insight to all the facets of this sophisticated technology.

Financial fraud is an ever growing menace with far consequences in the financial industry. Data mining had played an imperative role in the detection of credit card fraud in online transactions. Credit card fraud detection, which is a data mining problem, becomes challenging due to two major reasons - first, the profiles of normal and fraudulent behaviours change constantly and secondly, credit card fraud data sets are highly skewed. The performance of fraud detection in credit card transactions is greatly affected by the sampling approach on dataset, selection of variables and detection technique(s) used. This paper investigates the performance of naïve bayes, k-nearest neighbor and logistic regression on highly skewed credit card fraud data. Dataset of credit card transactions is sourced from European cardholders containing 284,807 transactions. A hybrid technique of under-sampling and oversampling is carried out on the skewed data. The three techniques are applied on the raw and preprocessed data. The work is implemented in Python. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity, precision, Matthews correlation coefficient and balanced classification rate. The results shows of optimal accuracy for naïve bayes, k-nearest neighbor and logistic regression classifiers are 97.92%, 97.69% and 54.86% respectively. The comparative results show that k-nearest neighbour performs better than naïve bayes and logistic regression techniques.

The PYRIN and CARD domains are members of the six-helix bundle death domain-fold superfamily that mediates assembly of large signaling complexes in the apoptotic and inflammatory signaling pathways. Here we show that the PYRIN-CARD protein ASC functions as a caspase-1-activating adaptor. ASC interacted specifically with procaspase-1 via CARD-CARD interactions and induced its oligomerization. Consistent with these results ectopic expression of full-length ASC, but not its isolated CARD or PYRIN domain, with procaspase-1 induced activation of procaspase-1 and processing of pro-interleukin-1beta in transfected cells. Substitution of the PYRIN domain of ASC with an inducible FKBP12 oligomerization domain produced a molecule that can induce caspase-1 activation in response to stimulation with the oligomerization drug AP20187, suggesting that the PYRIN domain functions as an oligomerization domain, whereas the CARD domain functions as the effector domain in the caspase-1 activation pathway. Furthermore stable expression of an isolated CARD of ASC in THP-1 cells diminished interleukin-1beta generation in response to pro-inflammatory cytokines. These results indicate that ASC is involved in the caspase-1 signaling pathway by mediating the assembly of a caspase-1-inflammasome signaling complex in response to pro-inflammatory cytokine stimulation.

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

The Comprehensive Antibiotic Resistance Database (CARD; http://arpcard.mcmaster.ca) is a manually curated resource containing high quality reference data on the molecular basis of antimicrobial resistance (AMR), with an emphasis on the genes, proteins and mutations involved in AMR. CARD is ontologically structured, model centric, and spans the breadth of AMR drug classes and resistance mechanisms, including intrinsic, mutation-driven and acquired resistance. It is built upon the Antibiotic Resistance Ontology (ARO), a custom built, interconnected and hierarchical controlled vocabulary allowing advanced data sharing and organization. Its design allows the development of novel genome analysis tools, such as the Resistance Gene Identifier (RGI) for resistome prediction from raw genome sequence. Recent improvements include extensive curation of additional reference sequences and mutations, development of a unique Model Ontology and accompanying AMR detection models to power sequence analysis, new visualization tools, and expansion of the RGI for detection of emergent AMR threats. CARD curation is updated monthly based on an interplay of manual literature curation, computational text mining, and genome analysis.

The Internet has taken its place beside the telephone and the television as an important part of people's lives. Consumers rely on the Internet to shop, bank and invest online. Most online shoppers use credit cards to pay for their purchases. As credit card becomes the most popular mode of payment, cases of fraud associated with it are also increasing. In this paper, we model the sequence of operations in credit card transaction processing using a Hidden Markov Model (HMM) and show how it can be used for the detection of frauds. An HMM is trained with normal behavior of cardholder. If an incoming credit card transaction is not accepted by the HMM with sufficiently high probability, it is considered to be fraudulent. We present detailed experimental results to show the effectiveness of our approach.

Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud and NRI (non-received issue) fraud. The network detected significantly more fraud accounts (an order of magnitude more) with significantly fewer false positives (reduced by a factor of 20) over rule-based fraud detection procedures. We discuss the performance of the network on this data set in terms of detection accuracy and earliness of fraud detection. The system has been installed on an IBM 3090 at Mellon Bank and is currently in use for fraud detection on that bank's credit card portfolio.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions. The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns. Where cardholders are clustered into different groups based on their transaction amount. Then using sliding window strategy [1], to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers [3],[5],[6],[8] are trained over the groups separately. And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds. Thus, followed by a feedback mechanism to solve the problem of concept drift [1]. In this paper, we worked with European credit card fraud dataset.

In this paper we start from the position that sources of inspiration play an important role in the design process albeit in a frequently intangible way. We present the Inspiration Card Workshop as a collaborative method for combining findings from domain studies, represented in Domain Cards, with sources of inspiration from applications of technology, represented in Technology Cards, to create new concepts for design. We report our findings from three projects in which we have used the method and argue that the use of Inspiration Cards can successfully frame and guide workshops with disparate participants and bring various sources of inspiration into the design process. We furthermore compare the method to four related methods in the design process, namely Future Workshops, Metaphorical Design, Interaction Relabelling and Lateral Thinking.

This paper examines how monitoring power consumption signals might breach smart-card security. Both simple power analysis and differential power analysis attacks are investigated. The theory behind these attacks is reviewed. Then, we concentrate on showing how power analysis theory can be applied to attack an actual smart card. We examine the noise characteristics of the power signals and develop an approach to model the signal-to-noise ratio (SNR). We show how this SNR can be significantly improved using a multiple-bit attack. Experimental results against a smart-card implementation of the Data Encryption Standard demonstrate the effectiveness of our multiple-bit attack. Potential countermeasures to these attacks are also discussed.

This paper utilizes a unique data set of credit card accounts to analyze how people respond to credit supply. Increases in credit limits generate an immediate and significant rise in debt, counter to the Permanent-Income Hypothesis. The “MPC out of liquidity” is largest for people starting near their limit, consistent with binding liquidity constraints. However, the MPC is significant even for people starting well below their limit, consistent with precautionary models. Nonetheless, there are other results that conventional models cannot easily explain, for example, why so many people are borrowing on their credit cards, and simultaneously holding low yielding assets. The long-run elasticity of debt to the interest rate is approximately -1.3, less than half of which represents balance-shifting across cards.