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At least 73 records · Page 4Linked to original sources

Naughton's Wisconsin Bibliography: A Brief Guide

Over nearly three decades at the University of Wisconsin, Jeff Naughton has left an indelible mark on computer science. He has been a global leader of the database research field, deepening its core and pushing its boundaries. Many of Naughton's ideas were translated directly into practice in commercial and open-source systems. But software comes and goes. In the end, it is the ideas themselves that have had impact, ideas written down in papers. Naughton has been a prolific scholar over the last thirty years, with over 175 publications in his bibliography, covering a wide range of topics. This document does not attempt to enumerate or even summarize the wealth of ideas that Naughton has published over the course of his academic career--the task is too daunting. Instead, the best this short note aims to do is to serve as a rough map of the territory: something to help other researchers navigate the wide spaces of Naughton's work.

cs.GL↗

Dialogue Concerning The Two Chief World Views

In 1632, Galileo Galilei wrote a book called \textit{Dialogue Concerning the Two Chief World Systems} which compared the new Copernican model of the universe with the old Ptolemaic model. His book took the form of a dialogue between three philosophers, Salviati, a proponent of the Copernican model, Simplicio, a proponent of the Ptolemaic model, and Sagredo, who was initially open-minded and neutral. In this paper, I am going to use Galileo's idea to present a dialogue between three modern philosophers, Mr. Spock, a proponent of the view that $\mathsf{P} \neq \mathsf{NP}$, Professor Simpson, a proponent of the view that $\mathsf{P} = \mathsf{NP}$, and Judge Wapner, who is initially open-minded and neutral.

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Embracing Data Science

Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially at the introductory level, should take inspiration from data science so that the discipline is not seen as somehow lesser than data science. This article provides a brief overview of data science, outlines ideas for how introductory courses could take inspiration from data science, and provides a reference to materials for developing stand-alone data science courses.

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Ethical Considerations in Artificial Intelligence Courses

The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course.

cs.AI↗

First Study on Data Readiness Level

We introduce the idea of Data Readiness Level (DRL) to measure the relative richness of data to answer specific questions often encountered by data scientists. We first approach the problem in its full generality explaining its desired mathematical properties and applications and then we propose and study two DRL metrics. Specifically, we define DRL as a function of at least four properties of data: Noisiness, Believability, Relevance, and Coherence. The information-theoretic based metrics, Cosine Similarity and Document Disparity, are proposed as indicators of Relevance and Coherence for a piece of data. The proposed metrics are validated through a text-based experiment using Twitter data.

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Research Methods in Computer Science: The Challenges and Issues

Research methods are essential parts in conducting any research project. Although they have been theorized and summarized based on best practices, every field of science requires an adaptation of the overall approaches to perform research activities. In addition, any specific research needs a particular adjustment to the generalized approach and specializing them to suit the project in hand. However, unlike most well-established science disciplines, computing research is not supported by well-defined, globally accepted methods. This is because of its infancy and ambiguity in its definition, on one hand, and its extensive coverage and overlap with other fields, on the other hand. This article discusses the research methods in science and engineering in general and in computing in particular. It shows that despite several special parameters that make research in computing rather unique, it still follows the same steps that any other scientific research would do. The article also shows the particularities that researchers need to consider when they conduct research in this field.

cs.GL↗

Kalman Filtering of Distributed Time Series

This paper aims to introduce an application to Kalman Filtering Theory, which is rather unconventional. Recent experiments have shown that many natural phenomena, especially from ecology or meteorology, could be monitored and predicted more accurately when accounting their evolution over some geographical area. Thus, the signals they provide are gathered together into a collection of distributed time series. Despite the common sense, such time series are more or less correlated each other. Instead of processing each time series independently, their collection can constitute the set of measurable states provided by some open system. Modeling and predicting the system states can take benefit from the family of Kalman filtering algorithms. The article describes an adaptation of basic Kalman filter to the context of distributed signals collections and completes with an application coming from Meteorology.

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Paths to Unconventional Computing: Causality in Complexity

I describe my path to unconventionality in my exploration of theoretical and applied aspects of computation towards revealing the algorithmic and reprogrammable properties and capabilities of the world, in particular related to applications of algorithmic complexity in reshaping molecular biology and tackling the challenges of causality in science.

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What is the next innovation after the internet of things?

The world had witnessed several generations of the Internet. Starting with the Fixed Internet, then the Mobile Internet, scientists now focus on many types of research related to the "Thing" Internet (or Internet of Things). The question is "what is the next Internet generation after the Thing Internet?" This paper envisions about the Tactile Internet which could be the next Internet generation in the near future. The paper will introduce what is the tactile internet, why it could be the next future Internet, as well as the impact and its application in the future society. Furthermore, some challenges and the requirements are presented to guide further research in this near future field.

cs.NI↗

|{Math, Philosophy, Programming, Writing}| = 1

Philosophical thinking has a side effect: by aiming to find the essence of a diverse set of phenomena, it often makes it difficult to see the differences between them. This can be the case with Mathematics, Programming, Writing and Philosophy itself. Their unified essence is having a shared understanding of the world helped by off-loading our cognitive efforts to suitable languages.

cs.CY↗

How to Read a Research Compendium

Researchers spend a great deal of time reading research papers. Keshav (2012) provides a three-pass method to researchers to improve their reading skills. This article extends Keshav's method for reading a research compendium. Research compendia are an increasingly used form of publication, which packages not only the research paper's text and figures, but also all data and software for better reproducibility. We introduce the existing conventions for research compendia and suggest how to utilise their shared properties in a structured reading process. Unlike the original, this article is not build upon a long history but intends to provide guidance at the outset of an emerging practice.

cs.GL↗

Technology, Propaganda, and the Limits of Human Intellect

"Fake news" is a recent phenomenon, but misinformation and propaganda are not. Our new communication technologies make it easy for us to be exposed to high volumes of true, false, irrelevant, and unprovable information. Future AI is expected to amplify the problem even more. At the same time, our brains are reaching their limits in handling information. How should we respond to propaganda? Technology can help, but relying on it alone will not suffice in the long term. We also need ethical policies, laws, regulations, and trusted authorities, including fact-checkers. However, we will not solve the problem without the active engagement of the educated citizen. Epistemological education, recognition of self biases and protection of our channels of communication and trusted networks are all needed to overcome the problem and continue our progress as democratic societies.

cs.GL↗

GOTO Rankings Considered Helpful

Rankings are a fact of life. Whether or not one likes them, they exist and are influential. Within academia, and in computer science in particular, rankings not only capture our attention but also widely influence people who have a limited understanding of computing science research, including prospective students, university administrators, and policy-makers. In short, rankings matter. This position paper advocates for the adoption of "GOTO rankings": rankings that use Good data, are Open, Transparent, and Objective, and the rejection of rankings that do not meet these criteria.

cs.GL↗

A man with a computer face (to the 80th anniversary of Ivan Edward Sutherland)

The article presents the main milestones of the science and technology biography of Ivan Edward Sutherland. The influence of the family and the school on the development of its research competencies is shown, and little-known biographical facts explaining the evolution of his scientific interests is presented: from dynamic object-oriented graphic systems through systems of virtual reality to asynchronous circuits.

cs.GL↗

Human Indignity: From Legal AI Personhood to Selfish Memes

It is possible to rely on current corporate law to grant legal personhood to Artificially Intelligent (AI) agents. In this paper, after introducing pathways to AI personhood, we analyze consequences of such AI empowerment on human dignity, human safety and AI rights. We emphasize possibility of creating selfish memes and legal system hacking in the context of artificial entities. Finally, we consider some potential solutions for addressing described problems.

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100+ Metrics for Software Startups - A Multi-Vocal Literature Review

Metrics can be used by businesses to make more objective decisions based on data. Software startups in particular are characterized by the uncertain or even chaotic nature of the contexts in which they operate. Using data in the form of metrics can help software startups to make the right decisions amidst uncertainty and limited resources. However, whereas conventional business metrics and software metrics have been studied in the past, metrics in the spe-cific context of software startup are not widely covered within academic literature. To promote research in this area and to create a starting point for it, we have conducted a multi-vocal literature review focusing on practitioner literature in order to compile a list of metrics used by software startups. Said list is intended to serve as a basis for further research in the area, as the metrics in it are based on suggestions made by practitioners and not empirically verified.

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Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. The Explainable Artificial Intelligence research program aims to develop analytic techniques with which to render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques' explanatory success. The aim of the present discussion is to develop such a framework, while paying particular attention to different stakeholders' distinct explanatory requirements. Building on an analysis of 'opacity' from philosophy of science, this framework is modeled after David Marr's influential account of explanation in cognitive science. Thus, the framework distinguishes between the different questions that might be asked about an opaque computing system, and specifies the general way in which these questions should be answered. By applying this normative framework to current techniques such as input heatmapping, feature-detector identification, and diagnostic classification, it will be possible to determine whether and to what extent the Black Box Problem can be solved.

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Retracing and assessing the CEP project

The last decade witnessed a renewed interest in the development of the Italian computer industry and in the role of the Fifties pioneers in Rome, Milan, Ivrea, and Pisa. The aim of the paper is to retrace some steps of the CEP project, carried out by the University of Pisa in collaboration with Olivetti, by reassessing the documents preserved in the University archives. The project was a seminal enterprise for Italy, and among its accomplishments it delivered in 1957 the first Italian computer. The mix of public sector funding and industrial foretelling witnessed by the project is one of the leading examples in Italy of best practices, and its success paved the way for the birth of Computer Science in the country as an industry as well as a scientific discipline.

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