A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good. Graphical abstrac

A planetary nervous system for social mining and collective awareness

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering questions about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.Seventh Framework Programme (European Commission) (grant agreement No. 284709

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately

The Complex Construction of Multilingual Identity: A Comparative Study in Three European Contexts of L3+ Learning

The commonly-cited phrase that bi- and multilingualism are no longer exceptions but rather the rule in most parts of the world is often-cited. This plurilingual reality has profound effects on the daily lives of many, and thus also represents a common experience of a considerable proportion of today’s students in compulsory education. Despite this wide recognition, there remains still a paucity of studies that examine the impact that the acquisition of multiple languages has upon a learner’s sense of self, and moreover, studies that consider methods though which we might evaluate and model these constructions of complex self-concept. A greater understanding of such processes of identification has potential social and educational implications. This PhD study has explored the phenomenon of adolescent multilingual language learner identity development via a mixed-methods, comparative context approach at three school sites based in Finland, France & England. Individual and group identity (re)constructions are considered across the system boundaries and the emergent isolated and cross-context variables of impact are identified and analysed. Defining the context-linked specificities of multilingual identity construction, as well as seeking to identify themes applicable inter-context, responds to Henry’s (2012), among others, call for a greater understanding of the nature of these variable interactions within the complex negation of self in language learning. In so doing, this thesis also demonstrates how the conceptualisation of learner self within a complexity framework can be operationalised in practical terms.UK Arts and Humanities Research Counci

Cyber-security Risk Assessment

Cyber-security domain is inherently dynamic. Not only does system configuration changes frequently (with new releases and patches), but also new attacks and vulnerabilities are regularly discovered. The threat in cyber-security is human, and hence intelligent in nature. The attacker adapts to the situation, target environment, and countermeasures. Attack actions are also driven by attacker's exploratory nature, thought process, motivation, strategy, and preferences. Current security risk assessment is driven by cyber-security expert's theories about this attacker behavior. The goal of this dissertation is to automatically generate the cyber-security risk scenarios by: * Capturing diverse and dispersed cyber-security knowledge * Assuming that there are unknowns in the cyber-security domain, and new knowledge is available frequently * Emulating the attacker's exploratory nature, thought process, motivation, strategy, preferences and his/her interaction with the target environment * Using the cyber-security expert's theories about attacker behavior The proposed framework is designed by using the unique cyber-security domain requirements identified in this dissertation and by overcoming the limitations of current risk scenario generation frameworks. The proposed framework automates the risk scenario generation by using the knowledge as it becomes available (or changes). It supports observing, encoding, validating, and calibrating cyber-security expert's theories. It can also be used for assisting the red-teaming process. The proposed framework generates ranked attack trees and encodes the attacker behavior theories. These can be used for prioritizing vulnerability remediation. The proposed framework is currently being extended for developing an automated threat response framework that can be used to analyze and recommend countermeasures. This framework contains behavior driven countermeasures that uses the attacker behavior theories to lead the attacker away from the system to be protected

Static and dynamic metaphoricity in U.S.-China trade discourse:A transdisciplinary perspective

Metaphor scholars have widely explored metaphor use in political discourse. Nevertheless, the current research does not account for the ‘gradable metaphoricity’ in political discourse analysis. This dissertation fills this gap by addressing this specific issue in two frameworks: (1) viewing political metaphor from a static and gradient perspective (Source-Target mapping; Conventional vs. Novel vs. Dead), and (2) viewing political metaphor from a gradable and dynamic perspective (a matter of salience and awareness of metaphoricity). A systematic literature review in chapter 2 points out that the static and dynamic perspectives differ significantly in underlying assumptions and organizing principles, although both are indistinctly referred to by metaphor scholars as constituting a ‘gradable’ view. The former takes metaphor as a static conceptual unit or lexical unit, but the latter tends to accord a central role of activation of metaphoricity to metaphorical expressions. To launch a theoretical advancement about the dynamic view in political discourse, chapter 3 offers a usage-based model of gradable and dynamic metaphors—the YinYang Dynamics of Metaphoricity (YYDM). In addition, this thesis investigates political metaphors from an interdisciplinary angle, incorporating theory from the field of International Relations. An empirical evaluation of political (discourse) studies in chapter 4 shows the large absence of transdisciplinary perspectives. Addressing the abovementioned gaps, this dissertation reports on two empirical analyses of trade metaphors in a big corpus that represents the official trade positions of the United States and China during the presidencies of Bill Clinton and Jiang Zemin (1993-1997) as well as Donald Trump and Xi Jinping (2017-2021). Based on a codebook of a cross-linguistic metaphor identification procedure in chapter 5, the first empirical part contributes to the static and gradient perspective and includes two corpus-based studies of metaphorical framing about trade (chapters 6-7). The diachronic and cross-linguistic use of source domains from a socio-cognitive approach in chapter 6 reveals that source domains are semantic fields that vary with trade discourse contexts (interests, power, and power relations). Chapter 7 shows that the use of trade metaphors (source domains of Conventional and Novel metaphors) to construct and legitimize political ideologies correlates with differences between political genres. The second part contributes to the gradable and dynamic view by applying the transdisciplinary model of YinYang Dynamics of Metaphoricity in chapters 8-10. In chapter 8, an evaluation of the new model in the Clinton-Jiang trade discourse shows that the dynamic cognitive process (transformation of metaphoricity) and rhetorical process (argumentation and persuasion) mutually develop with the evolution of the socio-political process (trade perspectives and trade events). Chapter 9 investigates the transformation of metaphoricity in the Trump-Xi trade discourse and finds that cognitive processes (patterns of metaphoricity activation) and affective processes (emotions or sentiments) mutually develop with the evolution of socio-political processes (trade perspectives and trade events). Based on the findings in chapters 8-9, chapter 10 further shows several phenomena in the Clinton-Jiang and Trump-Xi trade discourses: the movement of metaphors on the metaphoricity spectrum, the bodily motivation of gradable and dynamic metaphoricity, and the interconnected political discourse systems. Drawing on all the theoretical and empirical insights revealed in the dissertation, the final section of the thesis outlines a future direction, i.e., moving towards a transdisciplinary and dynamic approach to metaphor in political discourse analysis