Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

IEEE Transactions on Pattern Analysis and Machine Intelligence

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

Lecture Notes in Economics and Mathematical Systems

Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.

Genome-scale phylogeny and the detection of systematic biases

During the last two decades many researchers have analyzed the reliability growth of software during the testing and operational phases and proposed the mathematical models to estimate and predict the reliability measures. During the software testing on the detection of a failure the fault that has caused the failure is isolated and removed. Most of the existing research in this area considers that similar testing efforts and strategy are required on each debugging effort. However this may not be true in practice. Different faults may require different amount of testing efforts and testing strategy for their removal. In software reliability modeling in order to incorporate this phenomenon faults are classified into different categories as simple, hard and/or complex faults. This categorization is also extended to n-types of faults. Some of the existing research incorporates this phenomenon considering that the fault removal rate is different for different types of faults and remains constant during the overall period of testing. However this assumption may not apply in general testing environment in practice. It is a common observation that as the testing progresses the fault detection and/or removal rate changes. This change can be due to a number of reasons. The changing testing environment, testing strategy, skill, motivation and constitution of the testing and debugging personnel etc. are some of the major reasons behind this change. In this paper we have formulated the model for the software system developed for safety critical application under a specific testing environment. The model is validated on real life data sets.

Software reliability growth modelling for errors of different severity using change point

Hospital readmission is an important contributor to total medical expenditure and is an emerging indicator of quality of care. The goal of this study is to analyze key factors using machine learning methods and patients’ medical records of a reputed Indian hospital which impact the all-purpose readmission of a patient with diabetes and compare different classification models that predict readmission and evaluate the best model. This study classified the patients into two different risk groups of readmission (Yes or No) within 30 days of discharge based on patients’ characteristics using 2-year clinical and administrative data. It proposed an architecture of this prediction model and identified various risk factors using text mining techniques. Also, groups of consistently occurring factors that inference readmission rates were revealed by associative rule mining. It then evaluated the classification accuracy using five different data mining classifiers and conducted cost analysis. Out of total 9381 records, 1211 (12.9 %) encounters were found as readmissions. This study found that risk factors like hospital department where readmission happens, history of recent prior hospitalization and length of stay are strong predictors of readmission. Random forest was found to be the optimal classifier for this task using the evaluation metric area under precision-recall curve (0.296). From the cost analysis, it is observed that a cost of INR 15.92 million can be saved for 9381 instances of diabetic patient encounters. This work, the first such study done from Indian Healthcare perspective, built a model to predict the risk of readmission within 30 days of discharge for diabetes. This study concludes that the model could be incorporated in healthcare institutions to witness its effectiveness. Cost analysis shows huge savings which is significant for any healthcare system especially in developing countries like India.

Predictive risk modelling for early hospital readmission of patients with diabetes in India

Big Data is data of high volume and high variety being produced or generated at high velocity which cannot be stored, managed, processed or analyzed using the existing traditional software tools, techniques and architectures. With big data many challenges such as scale, heterogeneity, speed and privacy are associated but there are opportunities as well. Potential information is locked in big data which if properly leveraged will make a huge difference to business. With the help of big data analytics, meaningful insights can be extracted from big data which is heterogeneous in nature comprising of structured, unstructured and semi-structured content. One prime challenge in big data analytics is that nearly 95% data is unstructured. This paper describes what big data and big data analytics is. A review of different techniques and approaches to analyze unstructured data is given. This paper emphasizes the importance of analysis of unstructured data along with structured data in business to extract holistic insights. The need for appropriate and efficient analytical methods for knowledge discovery from huge volumes of heterogeneous data in unstructured formats has been highlighted.

Unravelling unstructured data: A wealth of information in big data

Enterprise Resource Planning, ERP software has come a long way since its inception as Inventory Management and Control Systems of 1960s. The value of ERP Implementation Strategy has been stressed over the years and it has been included as an important Critical Success Factor, CSF, as recorded by previous researchers. Traditional ERP implementation followed more or less a sequential approach akin to the Waterfall Model. Researchers over the years have categorized ERP Implementation methodology and developed frameworks. These are based on varied ERP Implementation observations. Given the variety of methodologies and frameworks available, the real-world ERP implementation demands the development and adoption of a strategy as a guiding principle for underlying methods. This paper suggests a new classification approach based on the ERP implementation strategy that can be categorized as custom-made, vendor-specific or consultant-specific. This research paper also conducts a comparative study of leading vendor-specific ERP implementation methodologies along-with their example cases. It then discusses how the principles of Agile Methodology as laid down in the Agile Manifesto [1] are being incorporated in ERP implementations.

Comparative study of ERP implementation strategies

Enterprise resource planning (ERP) systems present themselves as a practical answer to link all enterprise-wide operations. In spite of robust ERP software packages available world-wide with organizations delving in heavy investments, many ERP implementations fail or do not attain the desired objectives, thus leaving an indelible mark on the organizational budgets. Past and current researchers have exhaustively studied and listed several critical success factors (CSF) that are critical to the success of ERP implementations. In this research paper, we present a framework of CSFs based utility tool that can be used to assess the ERP Health at various stages of the ERP implementation. This tool provides the implementers to effectively monitor the CSF through various stages of ERP Implementation, such that any dip in the CSF would send a signal to implementers to work on the particular CSF showing trouble and thus bring back the ongoing ERP implementation back on the success track.

Sunil Kumar Khatri

Papers

Software reliability growth modelling for errors of different severity using change point

Predictive risk modelling for early hospital readmission of patients with diabetes in India

Unravelling unstructured data: A wealth of information in big data

Comparative study of ERP implementation strategies

Critical success factor utility based tool for ERP health assessment: a general framework