Machine Learning Models Under the Copyright Microscope: Is EU Copyright Fit for Purpose?

This paper is based on a presentation given in December 2019 at the Lund’s University conference ‘Artificial Intelligence (AI), Data Protection and Intellectual Property in a European context’. The purpose of this article is to analyse the suitability of the copyright system over Machine Learning (ML) models, the so-called ‘core components’ of ML systems. Computer programs protection has always caused certain difficulties for intellectual property law. Internationally, agreement was reached in the 1970s to protect computer programs as literary works of copyright. ML models have been called ‘learning algorithms’, ‘AI computer programs’, and ‘software 2.0’. Yet there is no unanimity about what they are technically. This is relevant from a copyright perspective, because the regime of protection granted by copyright will be different depending on whether the ML model qualifies as a computer program, as a mathematical method, or as another type of work. Additionally, all proprietary and open source software licensing relies on copyright protection. In most open licenses, the license is not triggered if it is applied to something that is not protected by copyright (or related rights). Thus, it seems pertinent to question whether EU copyright law provides adequate protection for the core components of machine learning systems, the ML models.

The Tyranny of Openness: What Happened to Peer Production?

This paper examines a “culture war” underway among software peer-production communities through relevant blog posts, legal documents, forum discussions, and other sources. Software licensing has been a defining strategy for peer producers, and much of the conflict at hand revolves around whether licensing should more fully incorporate ethics and economics, respectively. Feminist analysis can aid in tracing the contours of discontent through its emphasis on social processes that enable and infuse productive activity—processes that peer producers have trained themselves to ignore. The emerging critiques, and the experiments they have inspired, gesture toward fuller understandings of what “free” and “open” might mean.

An efficient and secure data storage in cloud computing using modified RSA public key cryptosystem

<span lang="EN-US">Cloud Computing is the ability to improve the utility or train new human resources without investing in new infrastructure, or add capabilities to existence without the latest software licensing. It expanded the capabilities of Information Technology (IT). From the past few years, cloud computing has developed from a good business concept in the best rising sectors of the IT industry. But more information on individuals and companies was put in the cloud, and concerns began to think about how secure the cloud environment was. Despite cloud surrounding structures, enterprise users still do not want to expand their business in the cloud. Security reduces the growth of cloud computing and continues to spread the market with complexity with data privacy and data protection. The security of cloud computing has constantly been an significant aspect of improved quality of service from cloud service providers. Data storage in the cloud has a problem related to data security. However, cloud computing construct many new security challenges which have not been well examine. In order to ensure that the user's data in the cloud is secure, we have proposed an effective mechanism with a distinctive feature of data integrity and privacy. This paper focusing on problems relating to the cloud data storage techniques and security in virtual environment. We recommend a method for providing data storage and security in cloud using public key Cryptosystem, which uses the concept of the modified RSA algorithm to provide better security for the data stored in the cloud. </span>

staRdom: Versatile Software for Analyzing Spectroscopic Data of Dissolved Organic Matter in R

The roles of dissolved organic matter (DOM) in microbial processes and nutrient cycles depend on its composition, which requires detailed measurements and analyses. We introduce a package for R, called staRdom (“spectroscopic analysis of DOM in R”), to analyze DOM spectroscopic data (absorbance and fluorescence), which is key to deliver fast insight into DOM composition of many samples. staRdom provides functions that standardize data preparation and analysis of spectroscopic data and are inspired by practical work. The user can perform blank subtraction, dilution correction, Raman normalization, scatter removal and interpolation, and fluorescence normalization. The software performs parallel factor analysis (PARAFAC) of excitation–emission matrices (EEMs), including peak picking of EEMs, and calculates fluorescence indices, absorbance indices, and absorbance slope indices from EEMs and absorbance spectra. A comparison between PARAFAC solutions by staRdom in R compared with drEEM in MATLAB showed nearly identical solutions for most datasets, although different convergence criteria are needed to obtain similar results and interpolation of missing data is important when working with staRdom. In conclusion, staRdom offers the opportunity for standardized multivariate decomposition of spectroscopic data without requiring software licensing fees and presuming only basic R knowledge.