Law and Digital Society

In this article we argue the need for more socio-legal scrutiny in a digitally mediated and data-driven development. We focus and briefly outline socio-legally relevant aspects of the “sharing” economy, that poses a number of conceptual issues on how we understand and regulate innovative platform based ventures. This also arguably underscores a number of issues relating to the role of consumer and user data and the implications of this “datafication”, not least in terms of questions of accountability and balancing of both powers and privacy in a data-driven world that often is described as a “black box” (cf. Pasquale, 2015) in the sense that much of the automated processes – such as the workings of algorithms and third party trade of consumer data – is withheld from insight and transparency

Intellectual Property Law Compliance in Europe: Illegal File Sharing and the Role of Social Norms

The current study empirically demonstrates the widely discussed gap between copyright law and social norms. Theoretically founded in the sociology of law, the study uses a well-defined concept of norms to quantitatively measure changes in the strength of social norms before and after the implementation of legislation. The ‘IPRED law’ was implemented in Sweden on 1 April 2009, as a result of the EU IPR Enforcement Directive 2004/48/EC. It aims at enforcing copyright, as well as other IP rights, when they are violated, especially online. A survey was conducted three months before the IPRED law came into force, and it was repeated six months later. The approximately one thousand respondents between fifteen and twenty-five years-of-age showed, among other things, that although actual file-sharing behaviour had to some extent decreased in frequency, social norms remained unaffected by the law

A Projected Gradient Descent Method for CRF Inference allowing End-To-End Training of Arbitrary Pairwise Potentials

Are we using the right potential functions in the Conditional Random Field models that are popular in the Vision community? Semantic segmentation and other pixel-level labelling tasks have made significant progress recently due to the deep learning paradigm. However, most state-of-the-art structured prediction methods also include a random field model with a hand-crafted Gaussian potential to model spatial priors, label consistencies and feature-based image conditioning. In this paper, we challenge this view by developing a new inference and learning framework which can learn pairwise CRF potentials restricted only by their dependence on the image pixel values and the size of the support. Both standard spatial and high-dimensional bilateral kernels are considered. Our framework is based on the observation that CRF inference can be achieved via projected gradient descent and consequently, can easily be integrated in deep neural networks to allow for end-to-end training. It is empirically demonstrated that such learned potentials can improve segmentation accuracy and that certain label class interactions are indeed better modelled by a non-Gaussian potential. In addition, we compare our inference method to the commonly used mean-field algorithm. Our framework is evaluated on several public benchmarks for semantic segmentation with improved performance compared to previous state-of-the-art CNN+CRF models.Comment: Presented at EMMCVPR 2017 conferenc

Geofysiska undersökningsmetoder för geoenergisystem

I Sverige finns det idag mellan 400 000 – 500 000 geoenergianläggningar och det uppskattas att nettotillskottet energi från geoenergianläggningar är ungefär 18 – 20 TWh per år. Geoenergianläggningar använder markens översta 300 – 500 metrar för att utvinna värme eller kyla. Vid anläggning av geoenergisystem kan man använda olika borrtekniker, mer eller mindre lämpliga beroende på olika geologiska och hydrogeologiska förutsättningar. Inför anläggning av ett geoenergisystem är det således viktigt att utreda geologiska och hydrogeologiska parametrar som underlättar anläggning, val av borrteknik och senare dimensionering av systemet. Många av dessa parametrar påverkar bland annat markens termiska egenskaper. De metoder som beskrivs i detta arbete, undersökningsborrning, undersökningar i borrhål och geofysiska undersökningsmetoder, kan användas för att utreda dessa parametrar. De termiska egenskaperna i marken är avgörande vid dimensioneringen av slutna geoenergisystem och termisk responstest, som utreder borrhålets termiska egenskaper under naturliga förhållande, är en undersökningsmetod som rekommenderas inför dimensionering av samtliga större slutna geoenergisystem. Vidare diskuteras och jämförs undersökningsmetoderna utifrån svagheter, styrkor och kostnad. Information från undersökningsborrning och undersökningar i borrhål ger information endast i ett borrhåls omedelbara närhet, medan de geofysiska undersökningarna ger data för att upprätta en modell över ett större område. Den stora skillnaden i de olika undersökningsmetoderna gör att de geofysiska undersökningsmetoderna huvudsakligen är lämpade i områden med varierande geologi. Dock bör alltid de modeller som produceras utifrån geofysisk data bekräftas av en eller flera undersökningsborrningar.There are about 400 000 – 500 000 shallow geothermal energy systems in Sweden and every year the net additions of energy from geothermal energy systems are about 18 – 20 TWh. Usually it is the upper 300 – 500 meters of the ground that are used for extracting heat or cold from cored strata. When constructing a shallow geothermal energy system, there are different drilling techniques available for use that are more or less suitable under different geological and hydrogeological circumstances. Different geological and hydrogeological circumstances also affect the thermal properties of the ground are thus also important for optimizing the size and type of the geothermal energy system. To investigate all the parameters of importance, both for drilling purposes and sizing of a closed looped geothermal energy system, different investigation methods are explained, evaluated and compared. Geophysics, investigation drilling and down the whole measurements can be used to determine different geological and hydrogeological parameters that are important for closed loop systems. To investigate the ground thermal properties in situ, a thermal response test should always be conducted for more accurate sizing purposes for larger closed loop systems. Investigation drilling and down the whole measurements give only information in close vicinity of the drilling site while geophysical investigation methods can be used to produce a geological model over a larger part of an area; geophysical methods are thus very useful in areas of a varying geology. However, investigation drilling should always be performed to confirm the geological model produced by geophysical investigations

A Cross-Season Correspondence Dataset for Robust Semantic Segmentation

In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions.Comment: In Proc. CVPR 201

Professionalizzazione, Gender, e Animato nelle Comunità di File Sharing Globale

Abstract in Italian Nell’aprile 2011, al famoso logo della community di file sharing globale The Pirate Bay fu aggiunta l’immagine di una lente di ingrandimento e il nome del sito cambiò in The Research Bay. Gli utenti che cliccarono sul nuovo logo furono reindirizzati su un sondaggio online; durante le 72 ore di svolgimento dello studio, 75.000 file-sharer compilarono il questionario preparato dal gruppo di ricerca Cybernorms. Il sondaggio, in lingua inglese, conteneva domande a risposta multipla e domande aperte: lo scopo era migliorare la comprensione dei comportamenti, delle motivazione e delle dinamiche alla base del fenomeno del file sharing. Per tale motivo, le norme sociali interne alla comunità di file sharing, in netto contrasto con la legge, sono state il focus principale dello studio. Il proposito alla base di questo studio è stato il tentativo di descrivere dall’interno una community di file sharing e fare luce sui profili demografici e sulle strutture sociali sottese al fenomeno diventato una delle più grandi sfide per la Proprietà Intellettuale. Analizzando i dati del sondaggio abbiamo riscontrato due temi vitali al fine di comprender le community di file sharing come The Pirate Bay: 1) Il gender: una comunità composta da giovani uomini; 2) La “professionalizzazione” o specializzazione: la suddivisione del lavoro tra gli utenti. Si potrebbe parlare di professionalizzazione o di specializzazione dei ruoli all’interno dell’“ecosistema” del file sharing. Gli utenti che hanno risposto ai nostri quesiti rappresentano un legame con una catena più ampia, una componente vitale di un ecosistema di condivisione più esteso. Tale professionalizzazione suggerisce la presenza di una organizzazione strutturata all’interno della comunità di cui BitTorrent gioca un ruolo importante ma non omnicomprensivo. Non si tratta di una struttura costruita ad hoc piuttosto di un’organizzazione per la disseminazione di contenuti dove il gender svolge un ruolo significativo. Un gruppo piuttosto ristretto e specializzato di giovani uomini, con buone competenze tecniche e legali, scarica contenuti attraverso il protocollo BitTorrent e a sua volta lo passa su network locali dove viene distribuito attraverso differenti strumenti come il passa mano di supporti digitali. Ciò fa si che l’intera catena di scambi sia protetta da eventuali forme di controllo. Gli scambi offline, infatti, sono decisamente più complessi da monitorare e controllare

Online Piracy, Anonymity and Social Change – Deviance Through Innovation

This article analyses current trends in the use of anonymity services among younger Swedes (15-25) and focuses on individuals engaging in illegal file sharing in order to better understand the rationale behind both file sharing as well as online anonymity, especially in relation to enforcement of copyright. By comparing the findings of a survey conducted on three different occasions (early 2009, late 2009 and early 2012), we measure the fluctuations in the use of anonymity services among approximately 1,000 15-25-year olds in Sweden, compare them to file sharing frequencies and, to some extent, trends within legal enforcement. The article also suggests that the key to understanding any relationship between copyright enforcement and fluctuations in online anonymity can be found in the law’s relationship to social norms in terms of legitimacy by showing a correlation between file sharing frequency and the use of anonymity services. The findings indicate that larger proportions of frequent file sharers (downloaders) also use anonymity services more often than those who file share less. However, in comparison to the earlier surveys, the strongest increase in the use of anonymity services is found in the groups where file sharing is less frequent, suggesting that reasons for actively making oneself less traceable online other than avoiding copyright enforcement have emerged since the initial two surveys in 2009. Further, the overall increase (from 8.6% to 14.9%) in using anonymity services found for the whole group of respondents suggests both that high file sharing frequency is a driver for less traceability as well as a larger trend for online anonymity relating to other factors than mere file sharing of copyright infringing content – for example, increased governmental identification, data retention and surveillance in the online environment. The results are analysed in Merton’s terminology as file sharers and protocol architects adapting in terms of both innovation and rebellion in the sense that institutional means for achieving specific cultural goals are rejected. This means, to some extent, participating in or contributing to the construction of other means for reaching cultural goals

Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation (e.g. the general data protection regulation (GDPR)). Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing as GDPR should not apply for medical images which do not belong to a specific person. However, in order to share synthetic images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation. Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80\% - 90\% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Furthermore, we demonstrate that common metrics for evaluating synthetic images, Fr\'echet inception distance (FID) and inception score (IS), do not correlate well with the obtained performance when using the synthetic images for training segmentation networks.Comment: 20 Pages. arXiv admin note: text overlap with arXiv:2211.0408

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?

Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 10 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7 % to 14.0 % on specific classes.Comment: 5 pages, submitted to ISBI 202