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This document considers the counteracting requirements of privacy and accountability applied to identity management. Based on the requirements of GDPR applied to identity attributes, two forms of identity, with differing balances between privacy and accountability, are suggested, termed "publicly-recognised identity" and "domain-specific identity". These forms of identity can be further refined using "pseudonymisation" and as described in GDPR. This leads to the different forms of identity on the spectrum of accountability vs privacy. It is recommended that the privacy and accountability requirements, and hence the appropriate form of identity, are considered in designing an identification scheme and in the adoption of a scheme by data processing systems. Also, users should be aware of the implications of the form of identity requested by a system, so that they can decide whether this is acceptable.

The term Assistive Technology has evolved over the years and identifies equipment or product systems, whether acquired, modified, or customized, that are used to increase, maintain, or improve functional capabilities of individuals with disabilities. Considering the advances that have been made, what trends can be identified to provide evidence of the evolution of AT as devices that foster accessibility and empower users with different abilities? Through a systematic literature review we identify research items that offer evidence of the evolution of the meaning, purpose, and applications of AT throughout the history. This paper provides evidence that AT evolved from products to improve functional capabilities of individuals with disabilities toward enabling technologies that facilitate tasks for people with different needs, abilities, gender, age, and culture. This evolution will lead to a positive demystification of the meaning and applications of AT toward broad usage acceptance among mainstream users.

This document considers the counteracting requirements of privacy and accountability applied to identity management. Based on the requirements of GDPR 1 applied to identity attributes two forms of identity, with differing balances between privacy and accountability, are suggested termed "publicly-recognised identity" and "domain-specific identity". These forms of identity can be further refined using "pseudonymisation" and as described in GDPR. This leads to the different forms of identity on the spectrum of accountability vs privacy. It is recommended that the privacy and accountability requirements, and hence the appropriate form of identity, is considered in designing an identification scheme, and in the adoption of a scheme by data processing systems. Also, users should be aware of the implications of the form of identity requested by a system so that they can decide whether this is acceptable.

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.

Data and Science has stood out in the generation of results, whether in the projects of the scientific domain or business domain. CERN Project, Scientific Institutes, companies like Walmart, Google, Apple, among others, need data to present their results and make predictions in the competitive data world. Data and Science are words that together culminated in a globally recognized term called Data Science. Data Science is in its initial phase, possibly being part of formal sciences and also being presented as part of applied sciences, capable of generating value and supporting decision making. Data Science considers science and, consequently, the scientific method to promote decision making through data intelligence. In many cases, the application of the method (or part of it) is considered in Data Science projects in scientific domain (social sciences, bioinformatics, geospatial projects) or business domain (finance, logistic, retail), among others. In this sense, this article addresses the perspectives of Data Science as a multidisciplinary area, considering science and the scientific method, and its formal structure which integrate Statistics, Computer Science, and Business Science, also taking into account Artificial Intelligence, emphasizing Machine Learning, among others. The article also deals with the perspective of applied Data Science, since Data Science is used for generating value through scientific and business projects. Data Science persona is also discussed in the article, concerning the education of Data Science professionals and its corresponding profiles, since its projection changes the field of data in the world.

Bitcoin is a peer-to-peer electronic payment system that has rapidly grown in popularity in recent years. Usually, the complete history of Bitcoin blockchain data must be queried to acquire variables with economic meaning. This task has recently become increasingly difficult, as there are over 1.6 billion historical transactions on the Bitcoin blockchain. It is thus important to query Bitcoin transaction data in a way that is more efficient and provides economic insights. We apply cohort analysis that interprets Bitcoin blockchain data using methods developed for population data in the social sciences. Specifically, we query and process the Bitcoin transaction input and output data within each daily cohort. This enables us to create datasets and visualizations for some key Bitcoin transaction indicators, including the daily lifespan distributions of spent transaction output (STXO) and the daily age distributions of the cumulative unspent transaction output (UTXO). We provide a computationally feasible approach for characterizing Bitcoin transactions that paves the way for future economic studies of Bitcoin.

Third-party software, or skills, are essential components in Smart Personal Assistants (SPA). The number of skills has grown rapidly, dominated by a changing environment that has no clear business model. Skills can access personal information and this may pose a risk to users. However, there is little information about how this ecosystem works, let alone the tools that can facilitate its study. In this paper, we present the largest systematic measurement of the Amazon Alexa skill ecosystem to date. We study developers' practices in this ecosystem, including how they collect and justify the need for sensitive information, by designing a methodology to identify over-privileged skills with broken privacy policies. We collect 199,295 Alexa skills and uncover that around 43% of the skills (and 50% of the developers) that request these permissions follow bad privacy practices, including (partially) broken data permissions traceability. In order to perform this kind of analysis at scale, we present SkillVet that leverages machine learning and natural language processing techniques, and generates high-accuracy prediction sets. We report a number of concerning practices including how developers can bypass Alexa's permission system through account linking and conversational skills, and offer recommendations on how to improve transparency, privacy and security. Resulting from the responsible disclosure we have conducted, 13% of the reported issues no longer pose a threat at submission time.

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and specialized knowledge in deep learning. In contrast, sketching is possibly the most universally accessible way to convey a visual concept. In this work, we present a method, GAN Sketching, for rewriting GANs with one or more sketches, to make GANs training easier for novice users. In particular, we change the weights of an original GAN model according to user sketches. We encourage the model's output to match the user sketches through a cross-domain adversarial loss. Furthermore, we explore different regularization methods to preserve the original model's diversity and image quality. Experiments have shown that our method can mold GANs to match shapes and poses specified by sketches while maintaining realism and diversity. Finally, we demonstrate a few applications of the resulting GAN, including latent space interpolation and image editing.

Keeping the dialogue state in dialogue systems is a notoriously difficult task. We introduce an ontology-based dialogue manage(OntoDM), a dialogue manager that keeps the state of the conversation, provides a basis for anaphora resolution and drives the conversation via domain ontologies. The banking and finance area promises great potential for disambiguating the context via a rich set of products and specificity of proper nouns, named entities and verbs. We used ontologies both as a knowledge base and a basis for the dialogue manager; the knowledge base component and dialogue manager components coalesce in a sense. Domain knowledge is used to track Entities of Interest, i.e. nodes (classes) of the ontology which happen to be products and services. In this way we also introduced conversation memory and attention in a sense. We finely blended linguistic methods, domain-driven keyword ranking and domain ontologies to create ways of domain-driven conversation. Proposed framework is used in our in-house German language banking and finance chatbots. General challenges of German language processing and finance-banking domain chatbot language models and lexicons are also introduced. This work is still in progress, hence no success metrics have been introduced yet.