This paper seeks to discuss whether and how digital collaborative sharing platforms can foster citizen engagement in urban neighbourhoods by addressing various challenges experienced by local initiatives. To that aim, we conducted a case study in the Saupstad neighbourhood of Trondheim in Norway. Our case study includes qualitative interviews conducted with volunteers and general managers of a selection of central volunteering initiatives in Saupstad where we aimed to map the current state of neighbourhood volunteerism, and identify the challenges experienced both at the individual and organizational level. We also involved stakeholders in a co-creative dialogue meeting to discuss and develop scenarios to overcome the identified challenges and foster citizen engagement in volunteering activities. Based on the empirical data we collected, we have identified challenges centralized around the interrelated themes such as 'volunteer motivation', 'volunteer recruitment', 'effective dissemination of information' and 'collaboration and communication with local actors'. In this paper we discuss to what extent collaborative platforms can address these challenges and be utilized to foster citizen participation.
Blockchains and the GDPR pursue similar objectives where they seek to grant users greater control over their personal data. While the latter pursues this goal by imposing duties of care to centralized controllers and collectors of data, blockchains go a step beyond by trying to eliminate these stakeholders and the need to trust them. Nevertheless, the rules set out by the GDPR apply whenever personal data are at stake, and various actors of the blockchain ecosystem risk liability for controlling of processing data in violation of privacy requirements. A possible solution is to re-contextualize the concepts of data controlling and responsibility, as framed by the GDPR, in light of blockchains’ enhanced individual autonomy. In this paper, we set the framework for a further inquiry on the role of users as both data subjects and data controllers of distributed ledgers.
Neural network-based Open-ended conversational agents automatically generate responses based on predictive models learned from a large number of pairs of utterances. The generated responses are typically acceptable as a sentence but are often dull, generic, and certainly devoid of any emotion. In this paper we present neural models that learn to express a given emotion in the generated response. We propose four models and evaluate them against 3 baselines. An encoder-decoder framework-based model with multiple attention layers provides the best overall performance in terms of expressing the required emotion. While it does not outperform other models on all emotions, it presents promising results in most cases.
It is a fact that technologies do not have fixed effects on people. Some researches show how phenomena such as social influence and conformity appear increasingly multiform and complex, in particular, because people engage more and more in web interactions. The study of conformity in the online environment has highlighted how, to model these dynamics, it is necessary to consider the peculiarities of such environment, since it presents characteristics that differentiate its interactions from the face-to-face ones. Therefore, this research aims at investigating whether and how the type of environment influences the level of conformity to different types of local norms shown to the experimental subjects during a bargaining web-based game. The evidence of the research, conducted on 484 participants, have suggested that there are different psychological processes involved in the conformity phenomenon depending on these factors. The results are discussed in the light of Self-categorization theory, as well as the SIDE model, and illustrate the relevance of considering these processes and their characteristics to promote the implementation of more efficient (and effective) online environments.
This paper seeks to discuss whether and how digital collaborative sharing platforms can foster citizen engagement in urban neighbourhoods by addressing various challenges experienced by local initiatives. To that aim, we conducted a case study in the Saupstad neighbourhood of Trondheim in Norway. Our case study includes qualitative interviews conducted with volunteers and general managers of a selection of central volunteering initiatives in Saupstad where we aimed to map the current state of neighbourhood volunteerism, and identify the challenges experienced both at the individual and organizational level. We also involved stakeholders in a co-creative dialogue meeting to discuss and develop scenarios to overcome the identified challenges and foster citizen engagement in volunteering activities. Based on the empirical data we collected, we have identified challenges centralized around the interrelated themes such as ‘volunteer motivation’, ‘volunteer recruitment’, ‘effective dissemination of information’ and ‘collaboration and communication with local actors’. In this paper we discuss to what extent collaborative platforms can address these challenges and be utilized to foster citizen participation.
This workshop was dedicated to discussing the latest development in detecting a broad range of social issues in online content, from inequalities expressed in texts (hate speech, prejudice, divisive/uncivil messages, political bias and racism etc.), to social polarization based on user views and sentiment. With proliferation of social media, such content is increasingly impactful, while its detection at scale presents a huge methodological challenge. The workshop participants shared their research experience by reflecting both on advantages and limitations of new automated and mixed research methods of text analysis they had used for processing and analysing such content. The workshop was held for the first time at INSCI'2018.
In the past few years, Internet of Things has become of common use in various areas, and especially in agriculture. There are many ways to increase the production using technology on agriculture. One of the most used is monitoring systems to follow characteristic properties and, in some cases, to control them by triggering a process that would correct an unwanted situation. Therefore, knowing the usefulness of this type of system, the present work proposes the monitoring of an environment using Internet of Things (IoT): Wireless Sensor Networks (WSN) are placed in strategic location in order to collect and send data to a web service. Currently, two topologies were tested to monitor the temperature and soil moisture of a soccer field. The samples were stored in a database and the next step is to integrate the system within the web application that we have already developed.
In the last years, a rapid increase of life expectancy has been observed and thus ageing is becoming a hot topic in different research fields. Ensuring the wellbeing of elderly people has a positive impact on healthcare, economy and social equality and thus developing a system that takes into account and integrates physiological parameters, cognitive and physical training, elderly-user experience and perception of the system itself, may represent an innovative multi-modal mean through which ameliorate older people wellbeing.
Many approaches have been proposed to allow customers' segmentation in retail sector. However, very few contributions exploit the existing semantics links that may exist between objects and resulting groups. The aim of this paper is to overcome this drawback by using semantic similarity measures (SSM) in customers' segmentation to provide clusters based on product' topology instead of numerical indicators usually used (i.e. monetary indicators). More precisely, we intend to show the main advantage of SSM with a product taxonomy in the retail field. Usually, traditional approaches consider as similar three customers buying respectively apple, orange and beer. However, human intuition tends to group customers who buy orange and apple because both are fruits. Our approach is defined to identify this kind of grouping through SSM and abstract concepts belonging to product taxonomy. Experiments are conducted on real data from a French Retailer store and show the relevance of the proposed approach.
Authors are the researchers working on the project "Collective intelligence on the Internet: applications in the public sphere, research methods and models of civic participation". This paper contains the first remarks of this research project. First, the authors show selected social problems of Central and Eastern Europe, particularly the Polish-Ukrainian relations. The phenomenon of the Ukrainians who are working, studying and settling in Poland is a context. Next, a present state of the research on collective intelligence (CI) on the internet and its compatibility with deliberative democracy theory is briefly presented. The main part of the paper is a critical review of the CI research methods in terms of their adequacy to the study of the public sphere initiatives concerning the selected social problems. The two separate problems come together under a single goal of enabling observation of Polish-Ukrainian interactions as manifestations of collective intelligence, and furthermore, the future use of collected results to stimulate the growth of bridge capital between Poles and Ukrainians. We focus on the possibilities of application of the selected methods in the social field we are interested in, as well as necessary changes and modifications. The selected methods are online deliberation and analytics with the use of IBIS model, using the indicators measuring social phenomena - CI Potential Index and UPVoCI scale, and finally a qualitative analysis with the use of CI Genome framework.
We illustrate a method for enhancing the participation of citizens in University research and sustainability activities through a synergy between science cafés and science shops.
Mind Cognitive Impairment is one of the most common clinical manifestations affecting the elderly. In this paper, we report the work in progress (in the frame of our SENIOR project) to provide elderly with new Nudge theory driven advices for influencing their interest to a conscious and functional participation to "targeted" social communities where suggestions on the overall wellness can be shared, recognized as usefull by users and supported by health care providers.
In this paper, we propose a multidimensional mapping approach for heterogeneous textual data that exploits firstly the spatial dimension and secondly the thematic dimension. Based on the Spatial Textual Representation (STR) as well as the Geodict geographic database, the contribution presented in this paper integrates the thematic dimension of documents. To support our proposal on mapping textual documents, we evaluate the different aspects of the process using two real corpora, including one corpus that is highly heterogeneous.
Internet service disruption is at best an inconvenience and at worst devastating for many businesses, governments and individuals. Small Island Developing States (SIDS), which are particularly vulnerable to natural disasters, often enjoy the least resilient network architectures and suffer the longest periods of disruption when service is most needed. As authoritative Domain Name System (DNS) servers generally lie off-island, the failure of international access links cripples local service availability, even when domestic links are physically operational. This paper proposes a simple monitoring strategy to reduce the impact of infrastructure losses in such circumstances. It examines the probability that local hosts are unreachable due to unavailable DNS mapping data gathered over a monitoring period and reports on a complementary strategy, and associated platform, to manually capture missing mappings. Together these mechanisms generate a set of local DNS mappings that enable SIDS’ Internet Exchange Points (IXPs) to perform intra-island routing independent of external DNS servers.
The International Register of the Ideas and Innovations (IRI) is a "visionary" and at present an experimental project that wants to protect all over the world the intellectual work leveraging on the blockchain paradigm (BC). IRI protects all the intellectual work not only the traditional ones (patents, trademarks, copyright, industrial design, geographical indication) but also those not protected by traditional offices such as ideas, projects, models, services, artefacts. The starting point is how to generate trust between many and different subjects engaged in the innovation value chain in order to create, build and collaborate in the developing of innovation. For this reason BC is also used to certify the actions, activities and relationships between researchers, inventors, students, innovators, funders, industries, user communities during the creation, construction and marketing of innovation. Detaching from the main BC platforms that need the consensus of the transaction based on a mechanism of proofing (especially regarding the financial area like bitcoin or Ethereum) the core of the project is the Proof-of-Originality algorithm (PoOr) that detects and compares the originality of the proposal. The challenge is to verify how the publication on IRI of an idea, project,., assumes a "de-facto" protection. If an innovator can protect the idea and if the actions and the relationships that allowed the realization of the innovation are certified, he can share it and this allows the co-construction, the development and at the same time this generates trust for the selling or the search for funding.
Data crossing seeks the extraction of novel knowledge through correlations and dependencies among heterogeneous data, and is considered a key process in sustainable science to push back the current frontiers of knowledge, especially to address challenges such as the socio-economic impacts of climate change. To tackle such complex challenges, interdisciplinary approaches and data sharing methodologies are ubiquitous, with a strong focus on data openness and ensuring that the fair principles hold. Data lakes are data repositories, recently developed to store such big heterogeneous data that are then available for crossing and be exploited without a priori objectives regarding their usage (unlike data warehouses). Such data lakes can then be used to populate Open and Linked Open Data in a central location regardless of its source or format. In this context of no prior knowledge regarding its usage, it may be tempting to store and share all the available data. However, this comes with two main disadvantages: (1) overwhelming amount of data that could prevent end users from exploiting the data, (2) and environmental reasons (energy consumption of data storage). Moreover, data of poor quality may deserve the lake usability and be deleted. We thus claim in this position paper that a data life cycle must be designed so as to integrate data death for some of the data. The choice of the data to be stored regarding the ones to forget is then of crucial importance in data lakes. We propose here some first positions for this aspect of data governance.
We study the problem of color-avoiding percolation in a network, i.e., the problem of finding a path that avoids a certain number of colors, associated to vulnerabilities of nodes or links. We show that this problem can be formulated as a self-organized critical problem, in which the asymptotic phase space can be obtained in one simulation. By using the fragment method, we are able to obtain the phase diagram for many problems related to color-avoiding percolation, showing in particular that results obtained for scale-free networks can be recovered using the dilution of the rule on regular lattices.
