Data science, data crime and the law

2018 ◽  
pp. 214-244 ◽  
Author(s):  
Maria Grazia Porcedda
Keyword(s):  
Data Science ◽  
Science Data ◽  
The Law

scholarly journals COVID-19 Evidence Accelerator: A parallel analysis to describe the use of Hydroxychloroquine with or without Azithromycin among hospitalized COVID-19 patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248128
Author(s):  
Mark Stewart ◽  
Carla Rodriguez-Watson ◽  
Adem Albayrak ◽  
Julius Asubonteng ◽  
Andrew Belli ◽  
...  
Keyword(s):  
Best Practices ◽  
Adverse Events ◽  
Data Science ◽  
Proportional Hazards ◽  
Cox Proportional Hazards ◽  
Parallel Analysis ◽  
Science Data ◽  
Systems Research ◽  
Cox Proportional Hazards Models ◽  
Global Threat

Background The COVID-19 pandemic remains a significant global threat. However, despite urgent need, there remains uncertainty surrounding best practices for pharmaceutical interventions to treat COVID-19. In particular, conflicting evidence has emerged surrounding the use of hydroxychloroquine and azithromycin, alone or in combination, for COVID-19. The COVID-19 Evidence Accelerator convened by the Reagan-Udall Foundation for the FDA, in collaboration with Friends of Cancer Research, assembled experts from the health systems research, regulatory science, data science, and epidemiology to participate in a large parallel analysis of different data sets to further explore the effectiveness of these treatments. Methods Electronic health record (EHR) and claims data were extracted from seven separate databases. Parallel analyses were undertaken on data extracted from each source. Each analysis examined time to mortality in hospitalized patients treated with hydroxychloroquine, azithromycin, and the two in combination as compared to patients not treated with either drug. Cox proportional hazards models were used, and propensity score methods were undertaken to adjust for confounding. Frequencies of adverse events in each treatment group were also examined. Results Neither hydroxychloroquine nor azithromycin, alone or in combination, were significantly associated with time to mortality among hospitalized COVID-19 patients. No treatment groups appeared to have an elevated risk of adverse events. Conclusion Administration of hydroxychloroquine, azithromycin, and their combination appeared to have no effect on time to mortality in hospitalized COVID-19 patients. Continued research is needed to clarify best practices surrounding treatment of COVID-19.

scholarly journals Occam’s Razor for Big Data? On Detecting Quality in Large Unstructured Datasets

2019 ◽  
Vol 9 (15) ◽  
pp. 3065 ◽  
Author(s):  
Dresp-Langley ◽  
Ekseth ◽  
Fesl ◽  
Gohshi ◽  
Kurz ◽  
...  
Keyword(s):  
Big Data ◽  
Data Science ◽  
Computation Time ◽  
Video Data ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Science Data ◽  
Occam’S Razor ◽  
Principle Of Parsimony ◽  
Occam's Razor

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

scholarly journals Integration and Evaluation of QUIC and TCP-BBR in longhaul Science Data Transfers

2019 ◽  
Vol 214 ◽  
pp. 08026 ◽  
Author(s):  
Raul H. C. Lopes ◽  
Virginia N. L. Franqueira ◽  
Duncan Rand
Keyword(s):  
Data Science ◽  
Perfect Match ◽  
The Other ◽  
Fine Tuning ◽  
The Internet ◽  
Science Data ◽  
Internet Protocols ◽  
Real Possibility ◽  
Data Transfers ◽  
And Performance

Two recent and promising additions to the internet protocols are TCP-BBR and QUIC. BBR defines a congestion policy that promises a better control in TCP bottlenecks on long haul transfers and can also be used in the QUIC protocol. TCP-BBR is implemented in the Linux kernels above 4.9. It has been shown, however, to demand careful fine tuning in the interaction, for example, with the Linux Fair Queue. QUIC, on the other hand, replaces HTTP and TLS with a protocol on the top of UDP and thin layer to serve HTTP. It has been reported to account today for 7% of Google’s traffic. It has not been used in server-to-server transfers even if its creators see that as a real possibility. Our work evaluates the applicability and tuning of TCP-BBR and QUIC for data science transfers. We describe the deployment and performance evaluation of TCP-BBR and comparison with CUBIC and H-TCP in transfers through the TEIN link to Singaren (Singapore). Also described is the deployment and initial evaluation of a QUIC server. We argue that QUIC might be a perfect match in security and connectivity to base services that are today performed by the Xroot redirectors.

scholarly journals Verification, Analytical Validation, and Clinical Validation (V3): The Foundation of Determining Fit-for-Purpose for Biometric Monitoring Technologies (BioMeTs) (Preprint)

2019 ◽  
Author(s):  
Jennifer Goldsack ◽  
Andrea Coravos ◽  
Jessie Bakker ◽  
Brinnae Bent ◽  
Ariel V. Dowling ◽  
...  
Keyword(s):  
Best Practices ◽  
Data Science ◽  
Evidence Base ◽  
Evaluation Framework ◽  
Clinical Validation ◽  
Analytical Validation ◽  
Science Data ◽  
Interdisciplinary Field ◽  
Digital Medicine ◽  
Monitoring Technologies

UNSTRUCTURED Digital medicine is an interdisciplinary field, drawing together stakeholders with expertise in engineering, manufacturing, clinical science, data science, biostatistics, regulatory considerations, ethics, patient advocacy, and healthcare policy, to name a few. While this diversity is undoubtedly valuable, it can lead to confusion regarding terminology and best practices. There are many instances, as we detail in this paper, where a single term is used by different groups to mean different things, as well as cases where multiple terms are used to describe essentially the same concept. Our intent is to clarify core terminology and best practices for the evaluation of Biometric Monitoring Technologies (BioMeTs), without unnecessarily introducing new terms. We propose and describe a three-component framework intended to provide a foundational evaluation framework for BioMeTs. This framework includes 1) verification, 2) analytical validation, and 3) clinical validation. We aim for this common vocabulary to enable more effective communication and collaboration, generate a common and meaningful evidence base for BioMeTs, and improve the accessibility of the digital medicine field.

scholarly journals Remote work and human capital: new challenges and threats (international experience)

2021 ◽  
pp. 107-119
Author(s):  
Oleksandr Burov
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Data Science ◽  
Well Being ◽  
Business Environment ◽  
Remote Work ◽  
Science Data ◽  
Creative Abilities ◽  
The Impact

Keywords: human capital, remote work, cybersecurity, workforce, digital economics The article considers the role of human capital in the transitionto the remote work. The analysis of world changes in the field of safe and effectiveuse of digital business environment and qualification of workforce in the conditions ofgrowth of remote work is carried out. The analysis was conducted in the following areas:general features of the digitalizing in crisis and innovation, a new paradigm of business«Data is the new gold», the organization of the workforce in the transition to teleworking,the priorities of today's professions, the problems of cybersecurity in teleworking. It has been articulated that the main requirements for the today’s workforce are intellectualand creative abilities, competence in the field of creation and use of ICT, bigdata (data science, data mining, data analytics) and artificial intelligence, the role ofwhich has grown even more due to the COVID-19 pandemic. The human component ofintellectual capital (in the form of knowledge, skills and competencies, as well as intellectualand creative abilities) is gaining new importance in the digital economy.The analysis of relationship of the crisis and innovation made on the basis of the ClarivateDerwent report has demonstrated the impact of the pandemic on the global lifecycle of research and innovation projects in the first half of 2020, namely that COVID-19violated innovation strategy of the innovative leaders worldwide. The analysis hasdemonstrated: in the new conditions of accelerated digitalization, ingenuity and speed ofdecision-making and innovation are needed more than ever. These priorities will affectthe world economy in the coming year.Special attention in analysis has been paid to the new business paradigm related touse and role of data. It was highlighted that digitization generates vast amounts of datathat offer many opportunities for business, human well-being, and the environment. As aresult, new capabilities and opportunities arise for business with the ecosystem of cooperationand partnership, as well as collaboration of stakeholders.The core of changes in digitalization is reskilling and upskilling of the workforce accountingnew workplaces and new requirements for them. It is recognized that talentmanagement and creative people selection can be the main engine in future transformationof economics, and workforce becomes an effective pole for investments. At the sametime, it is argued that remote worker is outside the scope of corporate protection, and virtuallyany production information, like human capital, becomes much more vulnerablein such conditions and requires appropriate cybersecurity methods.As a conclusion, it is articulated that the ability of companies to use big data is beginningto play a significant role in the economy, which in turn requires the involvementand training of data processing and analysis specialists. The direction of professions thatis being actively formed recently — data science — is one of the most priority in the labormarket. At the same time, the labor market needs skills and abilities in the field of interpersonalcommunication (soft skills), which are able to ensure the effective operation ofpeople and systems of hybrid intelligence «human-artificial intelligence».For the further research it has been recommended a comprehensive study of protectionof objects and subjects of intellectual property in open networks.

scholarly journals A view from data science

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110401
Author(s):  
Anna Sapienza ◽  
Sune Lehmann
Keyword(s):  
Big Data ◽  
Data Science ◽  
Common Ground ◽  
Science Data ◽  
Social Scientists

For better and worse, our world has been transformed by Big Data. To understand digital traces generated by individuals, we need to design multidisciplinary approaches that combine social and data science. Data and social scientists face the challenge of effectively building upon each other’s approaches to overcome the limitations inherent in each side. Here, we offer a “data science perspective” on the challenges that arise when working to establish this interdisciplinary environment. We discuss how we perceive the differences and commonalities of the questions we ask to understand digital behaviors (including how we answer them), and how our methods may complement each other. Finally, we describe what a path toward common ground between these fields looks like when viewed from data science.

scholarly journals Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits

2020 ◽  
Vol 61 (8) ◽  
pp. 1408-1418 ◽  
Author(s):  
Keiichi Mochida ◽  
Ryuei Nishii ◽  
Takashi Hirayama
Keyword(s):  
Health Status ◽  
Plant Growth ◽  
Data Science ◽  
Environmental Changes ◽  
Agronomic Traits ◽  
Plant Root ◽  
Crop Productivity ◽  
Growth Stages ◽  
Science Data ◽  
Plant Environment

Abstract To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants’ later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant–environment interactions by elucidating plants’ temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant–environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

Algorithms for Convex Optimization

2021 ◽  
Author(s):  
Nisheeth K. Vishnoi
Keyword(s):  
Convex Optimization ◽  
Data Science ◽  
Optimization Problems ◽  
Algorithm Design ◽  
Continuous Optimization ◽  
Maximum Flow ◽  
Maximum Matching ◽  
Science Data ◽  
Mirror Descent ◽  
Continuous Optimization Problems

In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself.

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