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At least 235 records · Page 13Linked to original sources

arXiv2022

A survey study of success factors in data science projects

In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges. According to previous surveys on the state of data science project management, there is a significant gap between technical and organizational processes. In this article we present new empirical data from a survey to 237 data science professionals on the use of project management methodologies for data science. We provide additional profiling of the survey respondents' roles and their priorities when executing data science projects. Based on this survey study, the main findings are: (1) Agile data science lifecycle is the most widely used framework, but only 25% of the survey participants state to follow a data science project methodology. (2) The most important success factors are precisely describing stakeholders' needs, communicating the results to end-users, and team collaboration and coordination. (3) Professionals who adhere to a project methodology place greater emphasis on the project's potential risks and pitfalls, version control, the deployment pipeline to production, and data security and privacy.

cs.DB↗

arXiv2018

Navigating Diverse Data Science Learning: Critical Reflections Towards Future Practice

Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learned lessons that are of relevance to others teaching Data Science.

cs.GL↗

arXiv2023

The History of Quantum Games

In this paper, we explore the historical development of playable quantum physics related games (\textit{\textbf{quantum games}}). For the purpose of this examination, we have collected over 260 quantum games ranging from commercial games, applied and serious games, and games that have been developed at quantum themed game jams and educational courses. We provide an overview of the journey of quantum games across three dimensions: \textit{the perceivable dimension of quantum physics, the dimension of scientific purposes, and the dimension of quantum technologies}. We then further reflect on the definition of quantum games and its implications. While motivations behind developing quantum games have typically been educational or academic, themes related to quantum physics have begun to be more broadly utilised across a range of commercial games. In addition, as the availability of quantum computer hardware has grown, entirely new variants of quantum games have emerged to take advantage of these machines' inherent capabilities, \textit{quantum computer games}

cs.GL↗

arXiv2011

Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment

The CDOI outcome measure - a patient-reported outcome (PRO) instrument utilizing direct client feedback - was implemented in a large, real-world behavioral healthcare setting in order to evaluate previous findings from smaller controlled studies. PROs provide an alternative window into treatment effectiveness based on client perception and facilitate detection of problems/symptoms for which there is no discernible measure (e.g. pain). The principal focus of the study was to evaluate the utility of the CDOI for predictive modeling of outcomes in a live clinical setting. Implementation factors were also addressed within the framework of the Theory of Planned Behavior by linking adoption rates to implementation practices and clinician perceptions. The results showed that the CDOI does contain significant capacity to predict outcome delta over time based on baseline and early change scores in a large, real-world clinical setting, as suggested in previous research. The implementation analysis revealed a number of critical factors affecting successful implementation and adoption of the CDOI outcome measure, though there was a notable disconnect between clinician intentions and actual behavior. Most importantly, the predictive capacity of the CDOI underscores the utility of direct client feedback measures such as PROs and their potential use as the basis for next generation clinical decision support tools and personalized treatment approaches.

cs.AI↗

arXiv2026

Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Commercial Off-The-Shelf (COTS) components and open-source software has introduced significant cybersecurity vulnerabilities. Ensuring the cybersecurity of CubeSats is vital as they play increasingly important roles in space missions. Traditional security measures, such as intrusion detection systems (IDS), are impractical for CubeSats due to resource constraints and unique operational environments. This paper provides an in-depth review of current cybersecurity practices for CubeSats, highlighting limitations and identifying gaps in existing methods. Additionally, it explores non-cyber anomaly detection techniques that offer insights into adaptable algorithms and deployment strategies suitable for CubeSat constraints. Open research problems are identified, including the need for resource-efficient intrusion detection mechanisms, evaluation of IDS solutions under realistic mission scenarios, development of autonomous response systems, and creation of cybersecurity frameworks. The addition of TinyML into CubeSat systems is explored as a promising solution to address these challenges, offering resource-efficient, real-time intrusion detection capabilities. Future research directions are proposed, such as integrating cybersecurity with health monitoring systems, and fostering collaboration between cybersecurity researchers and space domain experts.

cs.CR↗

arXiv2023

Mary Kenneth Keller: First US PhD in Computer Science

In June 1965, Sister Mary Kenneth Keller, BVM, received the first US PhD in Computer Science, and this paper outlines her life and accomplishments. As a scholar, she has the distinction of being an early advocate of learning-by-example in artificial intelligence. Her main scholarly contribution was in shaping computer science education in high schools and small colleges. She was an evangelist for viewing the computer as a symbol manipulator, for providing computer literacy to everyone, and for the use of computers in service to humanity. She was far ahead of her time in working to ensure a place for women in technology and in eliminating barriers preventing their participation, such as poor access to education and daycare. She was a strong and spirited woman, a visionary in seeing how computers would revolutionize our lives. A condensation of this paper appeared as, ``The Legacy of Mary Kenneth Keller, First U.S. Ph.D. in Computer Science," Jennifer Head and Dianne P. O'Leary, IEEE Annals of the History of Computing 45(1):55--63, January-March 2023.

cs.GL↗

arXiv2026

Jean-Raymond Abrial: A Scientific Biography of a Formal Methods Pioneer

Jean-Raymond Abrial is one of the central figures in the development of formal methods for software and systems engineering. Over a career spanning more than five decades, he has played a decisive role in the creation of the Z specification notation, the B-Method, and Event-B, and in demonstrating their applicability to large-scale industrial systems. This paper presents a scholarly biographical account of Abrial's life and work, tracing the evolution of his ideas from early work on real-time languages and databases, through foundational contributions to formal specification, refinement, and proof, to the development of industrial-strength tool support such as the Atelier~B and the Rodin platform. The paper situates Abrial's contributions within their historical, intellectual, and industrial contexts, and assesses their lasting impact on software engineering and formal reasoning about programs.

cs.GL↗

arXiv2024

Automation and AI Technology in Surface Mining With a Brief Introduction to Open-Pit Operations in the Pilbara

This survey article provides a synopsis on some of the engineering problems, technological innovations, robotic development and automation efforts encountered in the mining industry -- particularly in the Pilbara iron-ore region of Western Australia. The goal is to paint the technology landscape and highlight issues relevant to an engineering audience to raise awareness of AI and automation trends in mining. It assumes the reader has no prior knowledge of mining and builds context gradually through focused discussion and short summaries of common open-pit mining operations. The principal activities that take place may be categorized in terms of resource development, mine-, rail- and port operations. From mineral exploration to ore shipment, there are roughly nine steps in between. These include: geological assessment, mine planning and development, production drilling and assaying, blasting and excavation, transportation of ore and waste, crush and screen, stockpile and load-out, rail network distribution, and ore-car dumping. The objective is to describe these processes and provide insights on some of the challenges/opportunities from the perspective of a decade-long industry-university R&D partnership.

cs.CY↗

arXiv2021

Special Purpose Computers for Statistical Physics: achievements and lessons

In the late 80s and 90s, theoretical physicists of the Landau Institute for Theoretical Physics designed and developed several specialized computers for challenging computational problems in the physics of phase transitions. These computers did not have a central processing unit. They optimize algorithms to handle elementary operations on integers -- read, write, compare, and count. The approach allowed them to achieve recording run times. Computers performed calculations three orders of magnitude faster than similar calculations on the world's best supercomputers. The approach made it possible to obtain fundamentally new results, some of which have not yet been surpassed in the accuracy of calculations. The report will present the main ideas for the development of specialized computers and the scientific results obtained with their help. The lessons of planning and execution of long-term complex scientific projects will also be discussed.

cond-mat.stat-mech↗

arXiv2022

Playing catch-up in building an open research commons

On August 2, 2021 a group of concerned scientists and US funding agency and federal government officials met for an informal discussion to explore the value and need for a well-coordinated US Open Research Commons (ORC); an interoperable collection of data and compute resources within both the public and private sectors which are easy to use and accessible to all.

cs.DL↗

arXiv2020

Expression of the Holtsmark function in terms of hypergeometric $_2F_2$ and Airy $\mathrm{Bi}$ functions

The Holtsmark distribution has applications in plasma physics, for the electric-microfield distribution involved in spectral line shapes for instance, as well as in astrophysics for the distribution of gravitating bodies. It is one of the few examples of a stable distribution for which a closed-form expression of the probability density function is known. However, the latter is not expressible in terms of elementary functions. In the present work, we mention that the Holtsmark probability density function can be expressed in terms of hypergeometric function $_2F_2$ and of Airy function of the second kind $\mathrm{Bi}$ and its derivative. The new formula is simpler than the one proposed by Lee involving $_2F_3$ and $_3F_4$ hypergeometric functions.

math-ph↗

arXiv2020

Artificial Intelligence, Chaos, Prediction and Understanding in Science

Machine learning and deep learning techniques are contributing much to the advancement of science. Their powerful predictive capabilities appear in numerous disciplines, including chaotic dynamics, but they miss understanding. The main thesis here is that prediction and understanding are two very different and important ideas that should guide us about the progress of science. Furthermore, it is emphasized the important role played by that nonlinear dynamical systems for the process of understanding. The path of the future of science will be marked by a constructive dialogue between big data and big theory, without which we cannot understand.

cs.GL↗

arXiv2010

Modeling Time in Computing: A Taxonomy and a Comparative Survey

The increasing relevance of areas such as real-time and embedded systems, pervasive computing, hybrid systems control, and biological and social systems modeling is bringing a growing attention to the temporal aspects of computing, not only in the computer science domain, but also in more traditional fields of engineering. This article surveys various approaches to the formal modeling and analysis of the temporal features of computer-based systems, with a level of detail that is suitable also for non-specialists. In doing so, it provides a unifying framework, rather than just a comprehensive list of formalisms. The paper first lays out some key dimensions along which the various formalisms can be evaluated and compared. Then, a significant sample of formalisms for time modeling in computing are presented and discussed according to these dimensions. The adopted perspective is, to some extent, historical, going from "traditional" models and formalisms to more modern ones.

cs.GL↗

arXiv2023

Charles Babbage, Ada Lovelace, and the Bernoulli Numbers

This chapter makes needed corrections to an unduly negative scholarly view of Ada Lovelace. Credit between Lovelace and Babbage is not a zero-sum game, where any credit added to Lovelace somehow detracts from Babbage. Ample evidence indicates Babbage and Lovelace each had important contributions to the famous 1843 Sketch of Babbage's Analytical Engine and the accompanying Notes. Further, Lovelace's correspondence with two highly accomplished figures in 19th century mathematics, Charles Babbage and Augustus De Morgan, establish her mathematical background and sophistication. Babbage and Lovelace's treatment of the Bernoulli numbers in Note 'G' spotlights this aspect of their collaboration. Finally, while acknowledging significant definitional problems in calling Lovelace the world's "first computer programmer," I affirm that Lovelace created an elemental sequence of instructions -- that is, an algorithm -- for computing the series of Bernoulli numbers.

math.HO↗

arXiv2017

Effectiveness of Anonymization in Double-Blind Review

Double-blind review relies on the authors' ability and willingness to effectively anonymize their submissions. We explore anonymization effectiveness at ASE 2016, OOPSLA 2016, and PLDI 2016 by asking reviewers if they can guess author identities. We find that 74%-90% of reviews contain no correct guess and that reviewers who self-identify as experts on a paper's topic are more likely to attempt to guess, but no more likely to guess correctly. We present our findings, summarize the PC chairs' comments about administering double-blind review, discuss the advantages and disadvantages of revealing author identities part of the way through the process, and conclude by advocating for the continued use of double-blind review.

cs.DL↗

arXiv2021

The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge'', an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020''. We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

cs.NI↗

arXiv2020

An Environment for Sustainable Research Software in Germany and Beyond: Current State, Open Challenges, and Call for Action

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin.

cs.GL↗

arXiv2021

Integrated Random Projection and Dimensionality Reduction by Propagating Light in Photonic Lattices

It is proposed that the propagation of light in disordered photonic lattices can be harnessed as a random projection that preserves distances between a set of projected vectors. This mapping is enabled by the complex evolution matrix of a photonic lattice with diagonal disorder, which turns out to be a random complex Gaussian matrix. Thus, by collecting the output light from a subset of the waveguide channels, one can perform an embedding from a higher-dimension to a lower-dimension space that respects the Johnson-Lindenstrauss lemma and nearly preserves the Euclidean distances. It is discussed that distance-preserving random projection through photonic lattices requires intermediate disorder levels that allow diffusive spreading of light from a single channel excitation, as opposed to strong disorder which initiates the localization regime. The proposed scheme can be utilized as a simple and powerful integrated dimension reduction stage that can greatly reduce the burden of a subsequent neural computing stage.

physics.optics↗