Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Social Entrepreneurship That Truly Benefits the Poor: An Integrative Justice Approach

The phenomenal growth of social entrepreneurship over thelast decade has ably demonstrated how technology, innovation, and anentrepreneurial spirit can afford better solutions to the vexing social andenvironmental problems of our time than can traditional aid and charitybasedefforts. In most cases, but not always, the poor and disadvantagedhave benefited from the growth of social entrepreneurship. In order toensure that social entrepreneurship does indeed benefit the poor, it isimperative that there be normative guidelines for fair and just engagementwith impoverished populations. A model that has been presented in themarketing and public policy literature is the integrative justice model (IJM)for impoverished populations. While the IJM was developed primarily in thecontext of multinational corporations (MNCs) operating in emerging markets,its applicability extends beyond MNCs. This article attempts to apply theIJM principles in the context of social entrepreneurship in order to providesocial entrepreneurial organizations (SEOs) with a normative frameworkaimed at ensuring that the poor truly benefit from their activities. Basedon this framework, the article suggests certain areas to which SEOs oughtto be particularly attentive in their practice. The article also makes somesuggestions for further research

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Department of Management EngineeringFirms participating in printer industries have invested their constrained resources into technology development in order to sustain their competitiveness in the industry. Considering the fast-changing market circumstances, each firm???s own investment decisions on technology portfolio may directly affect their performance. In this study, we analyzed patent data, namely number of forward citations and technological classification data (CPC). Using this data, the technological portfolio of a specific firm can be identified, which can further help our understanding on firms??? R&D investment strategies. Number of studies mainly focused on patent class combinations of individual technology level, but portfolios of patent class at a firm level have been understudied. In this study, we tracked the change of class composition within each firms??? technological patents??? portfolio and attempted to identify practical and theoretical implications to portfolio management. We utilized Entropy Index, Co-occurrence and cosine similarities measurements for each indicating diversification, patent scope and portfolio similarities within each patents??? classification subclasses. Additionally, performance evaluation of each portfolio is conducted using forward citation data. This paper shows that in-depth patent data analysis can allow us to explore deeper insights at various levels, individual technology, products and product lines, and firms sufficing different stories.ope

Innovation Indicators: for a critical reflection on their use in Low- and Middle-Income Countries (LMICs)

It has been widely recognized that innovation is an important driver of economic growth. Many Low- and Middle-Income Countries (LMICs) have adopted innovation indicators to monitor innovation performance and to evaluate the impact of innovation policies. This paper argues that innovation indicators should be customized to the different socio-economic structures of LMICs. For this, the definition of innovation needs to be relevant to the multitude of innovation actors and processes in LMICs. LMICs also need to build competences not only in the construction of innovation indicators within their statistical systems, but also in the use of these indicators by among others policy makers. Especially as the fourth edition of the Oslo Manual (OM 2018) has broadened the scope of “innovation”, opening up policy space for LMICs to accommodate the diversity in their national systems of innovation and to develop accompanying innovation indicators.JEL Classification Codes: O38, O32, O29, P47http://www.grips.ac.jp/list/jp/facultyinfo/iizuka-michiko

Using the ISO/IEC 9126 product quality model to classify defects : a Controlled Experiment

Background: Existing software defect classification schemes support multiple tasks, such as root cause analysis and process improvement guidance. However, existing schemes do not assist in assigning defects to a broad range of high level software goals, such as software quality characteristics like functionality, maintainability, and usability. Aim: We investigate whether a classification based on the ISO/IEC 9126 software product quality model is reliable and useful to link defects to quality aspects impacted. Method: Six different subjects, divided in two groups with respect to their expertise, classified 78 defects from an industrial web application using the ISO/IEC 9126 quality main characteristics and sub-characteristics, and a set of proposed extended guidelines. Results: The ISO/IEC 9126 model is reasonably reliable when used to classify defects, even using incomplete defect reports. Reliability and variability is better for the six high level main characteristics of the model than for the 22 sub- characteristics. Conclusions: The ISO/IEC 9126 software quality model provides a solid foundation for defect classification. We also recommend, based on the follow up qualitative analysis performed, to use more complete defect reports and tailor the quality model to the context of us

Virtual and rapid prototyping of an underactuated space end effector

A fast and reliable verification of an initial concept is an important need in the field of mechatronics. Usually, the steps for a successful design require multiple iterations involving a sequence of design phases-the initial one and several improvements-and the tests of the resulting prototypes, in a trial and error scheme. Now a day’s software and hardware tools allow for a faster approach, in which the iterations between design and prototyping are by far reduced, even to just one in favorable situation. This work presents the design, manufacturing and testing of a robotic end effector for space applications, realized through virtual prototyping, followed by rapid prototyping realization. The first process allows realizing a mathematical model of the robotic system that, once all the simulations confirm the effectiveness of the design, can be directly used for the rapid prototyping by means of 3D printing. The workflow and the results of the process are described in detail in this paper, showing the qualitative and quantitative evaluation of the performance of both the virtual end effector and the actual physical robotic hand