Refusal to give access to ‘confidential’ information about politicians violated NGO’s Article 10 rights

On 26 March 2020, the European Court of Human Rights unanimously found that a refusal by the Ukrainian authorities to give a non-governmental organisation (NGO) access to information about the education and work history of top politicians as contained in their official CVs, filed as candidates for Parliament, violated the NGO’s right of access to public documents under Article 10 ECHR. The Court in Centre for Democracy and the Rule of Law v. Ukraine, highlighted that it was the first case from Ukraine on access to information since the Grand Chamber’s seminal 2016 Magyar Helsinki Bizottság v. Hungary judgment, and that it raised ‘novel’ issues for Ukraine’s authorities and courts. This judgment, delivered during the Covid-19 pandemic, clearly illustrates how important it is, more than ever, that the Court applies strict scrutiny under Article 10 in cases on access to public documents, recognising the importance of transparency on matters of public interest

Studio Monitori and Others v. Georgia : access to public documents must be ‘instrumental’ for the right to freedom of expression

In the case of Studio Monitori and Others v. Georgia the European Court of Human Rights (ECtHR) in its judgment of 30 January 2020 has confirmed that the right to freedom of expression and information as guaranteed by Article 10 of the European Convention on Human Rights (ECHR) can only be invoked in order to obtain access to public documents when a set of conditions are fulfilled. It is one of the cases following the judgment of the Grand Chamber in Magyar Helsinki Bizottság v. Hungary to test the scope and limits of the right of access to information and the applicability of Article 10 ECHR. The most important consequence of the judgment in Studio Monitori and Others is that NGOs, journalists or other public watchdogs requesting access to public documents have to motivate and clarify in their request that access to the documents they are applying for is instrumental, and even necessary, for their journalistic reporting and that the requested documents contain information of public interest

From Image-level to Pixel-level Labeling with Convolutional Networks

We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and naturally fits the Multiple Instance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class). We propose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pixels which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object segmentation task by a large margin. We also compare the performance of our model with state of the art fully-supervised segmentation approaches.Comment: CVPR201

Recurrent Convolutional Neural Networks for Scene Parsing

Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accuracy, it is essential for a scene parser to capture image long range dependencies. In a feed-forward architecture, this can be simply achieved by considering a sufficiently large input context patch, around each pixel to be labeled. We propose an approach consisting of a recurrent convolutional neural network which allows us to consider a large input context, while limiting the capacity of the model. Contrary to most standard approaches, our method does not rely on any segmentation methods, nor any task-specific features. The system is trained in an end-to-end manner over raw pixels, and models complex spatial dependencies with low inference cost. As the context size increases with the built-in recurrence, the system identifies and corrects its own errors. Our approach yields state-of-the-art performance on both the Stanford Background Dataset and the SIFT Flow Dataset, while remaining very fast at test time

Phrase-based Image Captioning

Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO

Mobile phone and e-government in Turkey: practices and technological choices at the cross-road

Enhanced data services through mobile phones are expected to be soon fully transactional and embedded within future mobile consumption practices. While private services will surely continue to take the lead, others such as government and NGOs will become more prominent m-players. It is not yet sure which form of technological standards will take the lead including enhance SMS based operations or Internet based specifically developed mobile phone applications. With the introduction of interactive transactions via mobile phones, currently untapped segment of the populations (without computers) have the potential to be accessed. Our research, as a reflection of the current market situation in an emerging country context, in the case of mobile phones analyzes the current needs or emergence of dependencies regarding the use of m/e-government services from the perspective of municipality officers. We contend that more research is needed to understand current preparatory bottlenecks and front loading activities to be able to encourage future intention to use e-government services through mobile phone technologies. This study highlights and interprets the current emerging practices and praxis for consuming m-government services within government

An actor-network theory (ANT) approach to Turkish e-government gateway initiative

There are various models proposed in the literature to analyze trajectories of e-Government projects in terms of success and failure. Yet, only the Actor-Network Theory (ANT) perspective (Heeks and Stanforth, 2007) considers the interaction factors among network actors and actants. This paper proposes the ANT for approaching to the Turkish e-Government Gateway initiative as a case study. In doing so, it provides valuable insight in terms of both local and global actor-networks which surround the initiative