Time Aware Knowledge Extraction for Microblog Summarization on Twitter

Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.Comment: 33 pages, 10 figure

Multisignal 1D-compression by F-transform for wireless sensor networks applications

In wireless sensor networks a large amount of data is collected for each node. The challenge of trans-ferring these data to a sink, because of energy constraints, requires suitable techniques such as datacompression. Transform-based compression, e.g. Discrete Wavelet Transform (DWT), are very popularin this field. These methods behave well enough if there is a correlation in data. However, especiallyfor environmental measurements, data may not be correlated. In this work, we propose two approachesbased on F-transform, a recent fuzzy approximation technique. We evaluate our approaches with Dis-crete Wavelet Transform on publicly available real-world data sets. The comparative study shows thecapabilities of our approaches, which allow a higher data compression rate with a lower distortion, evenif data are not correlated

adaptive goal selection for improving situation awareness the fleet management case study

Abstract: Lack of Situation Awareness (SA) when dealing with complex dynamic environments is recognized as one of the main causes of human errors, leading to serious and critical incidents. One of the main issues is the attentional tunneling manifested, for instance, by human operators (in Decision Support Systems) focusing their attention on a single goal and loosing the awareness of the global picture of the monitored environments. A further issue is represented by stimuli, coming from such environments, which may divert the attention of the operators from the most important aspects and cause erroneous decisions. Thus, the need to define systems helping human operators to improve SA with respect to the two aforementioned drawbacks emerges. These systems should help operators in focusing their attention on active goals and, when really needed, switching it on new goals, in a sort of continuous adaptation. In this work an adaptive goal selection approach exploiting both goal-driven and data-driven information processing is proposed. The approach has been defined and injected in an existing multi-agent framework for Situation Awareness and applied in a Fleet Management System. The approach has been evaluated by means of the SAGAT methodology

Sustainable Decommissioning of Offshore Platforms: a Proposal of Life-Cycle Cost-Benefit Analysis in ItalianOil and Gas Industry

The decommissioning of offshore Oil & Gas platforms, at the end of their life cycle, has been a very controversial topic in recent years. Moreover, the decommissioning complexity increases if we consider a shift towards a linear economy to a circular one. The latter pushes to innovate business models and re-configure the value chain activities in a sustainable way. Starting from these considerations, this work aims to identify a cost-benefit model suitable for evaluating sustainable business models of offshore platforms. After a literature review of different models for analysing maintenance and decommissioning Real Options (ROs), the Life-Cycle Cost-Benefit (LCCB) analysis has been selected as the most adequate managerial tool for evaluating and comparing the Net Present Value (NPV) of platforms compared the maintenance and decommissioning costs. The LCCB tool could aid the managers in the oil and gas industry to quantify the decommissioning and maintenance costs including capital expenditure (CapEx) and risk expenditure (RiskEx). In the future steps, to test the LCCB model, an empirical analysis could be carried out on a sample of organizations interested in the sustainable decommissioning of offshore platforms

The Role of Oil and Gas Offshore Platform Reconversion in Creating Artificial Reefs

In recent times, decommissioning of offshore platforms has become an even more discussed topic, for its relevant environmental, social, and economic repercussions. In particular, by carrying out economic considerations, all the divestiture possibilities applicable to an offshore platform and the relative sustainable business models (SBMs) will be analyzed in a wide framework of the circular economy and sustainable principles. In this scenario, sustainable decommissioning (SD) of offshore platforms process refers to multidimensional and interdisciplinary challenges, which requires a deep understanding of technical, legal, economic, financial, social, and environmental variables. The decommissioning of these structures is an issue that has gained a great deal of international attention and will require in the next years an open dialogue and exchange between institutions, oil and gas companies, enterprises, and the environment

An ecosystems perspective on the reconversion of offshore platforms: Towards a multi‐level governance

The decommissioning of offshore platforms has been increasingly discussed due to its economic, social, and environmental impacts. The high complexity of this multilevel context pushes for the adoption of a service ecosystem view to explore the value propositions and actors' relations involved in resource exchanges. This study follows a mixed-method approach based on semistructured interviews conducted with oil and gas stakeholders and content analysis of the secondary data collected. The results highlight the ecosystem elements and identify the main drivers for sustainable growth in the process of the reconversion of oil and gas assets. A “meta” level is theorized to investigate how the actors' purposes can be harmonized with an ecosystem's goal to encourage the diffusion of a sustainable-oriented culture in the context of offshore decommissioning. In this sense, the study provides several insights for researchers and professionals in both the local and national governance field and the oil and gas industry

An AmI-Based Software Architecture Enabling Evolutionary Computation in Blended Commerce: The Shopping Plan Application

This work describes an approach to synergistically exploit ambient intelligence technologies, mobile devices, and evolutionary computation in order to support blended commerce or ubiquitous commerce scenarios. The work proposes a software architecture consisting of three main components: linked data for e-commerce, cloud-based services, and mobile apps. The three components implement a scenario where a shopping mall is presented as an intelligent environment in which customers use NFC capabilities of their smartphones in order to handle e-coupons produced, suggested, and consumed by the abovesaid environment. The main function of the intelligent environment is to help customers define shopping plans, which minimize the overall shopping cost by looking for best prices, discounts, and coupons. The paper proposes a genetic algorithm to find suboptimal solutions for the shopping plan problem in a highly dynamic context, where the final cost of a product for an individual customer is dependent on his previous purchases. In particular, the work provides details on the Shopping Plan software prototype and some experimentation results showing the overall performance of the genetic algorithm

Towards an Ontology Design Pattern for UAV Video Content Analysis

Video scene understanding is leading to an increased research investment in developing artificial intelligence technologies, pattern recognition, and computer vision, especially with the advance in sensor technologies. Developing autonomous unmanned vehicles, able to recognize not just targets appearing in a scene but a complete scene the targets are involved in (describing events, actions, situations, etc.) is becoming crucial in the recent advanced intelligent surveillance systems. At the same time, besides these consolidated technologies, the Semantic Web Technologies are also emerging, yielding seamless support to the high-level understanding of the scenes. To this purpose, the paper proposes a systematic ontology modeling to support and improve video content analysis, by generating a comprehensive high-level scene description, achieved by semantic reasoning and querying. The ontology schema comes from as an integration of new and existing ontologies and provides some design pattern guideline to get a high-level description of a whole scenario. It starts from the description of basic targets in the video scenario, thanks to the support of video tracking algorithms and target classification; then provides a higher level interpretation, compounding event-driven target interactions (for local activity comprehension), to reach gradually an abstraction high level that enables a concise and complete scenario description

Fuzzy Group Decision Making for Influence-Aware Recommendations

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works

Drift-Aware Methodology for Anomaly Detection in Smart Grid

Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, smart metering and nonintrusive load monitoring play a crucial role in fighting energy thefts and for optimizing the energy consumption of the home, building, city, and so forth. The estimated number of smart meters will exceed 800 million by 2020. By providing near real-time data about power consumption, smart meters can be used to analyze electricity usage trends and to point out anomalies guaranteeing companies' safety and avoiding energy wastes. In literature, there are many proposals approaching the problem of anomaly detection. Most of them are limited because they lack context and time awareness and the false positive rate is affected by the change in consumer habits. This research work focuses on the need to define anomaly detection method capable of facing the concept drift, for instance, family structure changes; a house becomes a second residence, and so forth. The proposed methodology adopts long short term memory network in order to profile and forecast the consumers' behavior based on their recent past consumptions. The continuous monitoring of the consumption prediction errors allows us to distinguish between possible anomalies and changes (drifts) in normal behavior that correspond to different error motifs. The experimental results demonstrate the suitability of the proposed framework by pointing out an anomaly in a near real-time after a training period of one week