Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.Comment: 21 pages, 6 figures, 13 table

Towards the Usage of MBT at ETSI

In 2012 the Specialists Task Force (STF) 442 appointed by the European Telcommunication Standards Institute (ETSI) explored the possibilities of using Model Based Testing (MBT) for test development in standardization. STF 442 performed two case studies and developed an MBT-methodology for ETSI. The case studies were based on the ETSI-standards GeoNetworking protocol (ETSI TS 102 636) and the Diameter-based Rx protocol (ETSI TS 129 214). Models have been developed for parts of both standards and four different MBT-tools have been employed for generating test cases from the models. The case studies were successful in the sense that all the tools were able to produce the test suites having the same test adequacy as the corresponding manually developed conformance test suites. The MBT-methodology developed by STF 442 is based on the experiences with the case studies. It focusses on integrating MBT into the sophisticated standardization process at ETSI. This paper summarizes the results of the STF 442 work.Comment: In Proceedings MBT 2013, arXiv:1303.037

Some resonances between Eastern thought and Integral Biomathics in the framework of the WLIMES formalism for modelling living systems

Forty-two years ago, Capra published “The Tao of Physics” (Capra, 1975). In this book (page 17) he writes: “The exploration of the atomic and subatomic world in the twentieth century has …. necessitated a radical revision of many of our basic concepts” and that, unlike ‘classical’ physics, the sub-atomic and quantum “modern physics” shows resonances with Eastern thoughts and “leads us to a view of the world which is very similar to the views held by mystics of all ages and traditions.“ This article stresses an analogous situation in biology with respect to a new theoretical approach for studying living systems, Integral Biomathics (IB), which also exhibits some resonances with Eastern thought. Stepping on earlier research in cybernetics1 and theoretical biology,2 IB has been developed since 2011 by over 100 scientists from a number of disciplines who have been exploring a substantial set of theoretical frameworks. From that effort, the need for a robust core model utilizing advanced mathematics and computation adequate for understanding the behavior of organisms as dynamic wholes was identified. At this end, the authors of this article have proposed WLIMES (Ehresmann and Simeonov, 2012), a formal theory for modeling living systems integrating both the Memory Evolutive Systems (Ehresmann and Vanbremeersch, 2007) and the Wandering Logic Intelligence (Simeonov, 2002b). Its principles will be recalled here with respect to their resonances to Eastern thought

Assistance in Model Driven Development: Toward an Automated Transformation Design Process

Model driven engineering aims to shorten the development cycle by focusing on abstractions and partially automating code generation. We long lived in the myth of automatic Model Driven Development (MDD) with promising approaches, techniques, and tools. Describing models should be a main concern in software development as well as model verification and model transformation to get running applications from high level models. We revisit the subject of MDD through the prism of experimentation and open mindness. In this article, we explore assistance for the stepwise transition from the model to the code to reduce the time between the analysis model and implementation. The current state of practice requires methods and tools. We provide a general process and detailed transformation specifications where reverse-engineering may play its part. We advocate a model transformation approach in which transformations remain simple, the complexity lies in the process of transformation that is adaptable and configurable. We demonstrate the usefulness, and scalability of our proposed MDD process by conducting experiments. We conduct experiments within a simple case study in software automation systems. It is both representative and scalable. The models are written in UML; the transformations are implemented mainly using ATL, and the programs are deployed on Android and Lego EV3. Last we report the lessons learned from experimentation for future community work

How did the discussion go: Discourse act classification in social media conversations

We propose a novel attention based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question-answers, stance detection or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71\% and 66\% respectively to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information etc. play their roles in charaterizing online discussions on Facebook