A new influence based network for opinion propagation in social network based scenarios

Thanks to the expansive development of the Internet based technologies the on-line communities in which millions of users interact in real time is living and apogee. Leverage these networks as tools to carry out massive decision making processes such as the ones involved in e-democracy and e-health communities constitutes not only an extraordinary opportunity but an important research challenge. In this context issues such us influence propagation, agents interaction, and malicious users identification and isolation are key to provide successful solutions to this challenge. In this contribution we aim to address these issues by presenting a new opinion propagation network in which the influence that each agent exert with respect to their neighbours is assessed by means of a combination of the following three aspects: (i)agents’ self-confidence, (ii)inter-agents opinions similarity, (iii) the quality of the information provided by each agent, that is, the lack of contradiction also called as consistency. The proposed network allows to allocate more influence to those agents providing higher quality information and to isolate those who may present a malicious behaviour.This contribution has been carried out thanks to the financial support of the EU project H2020-MSCA-IF-2016-DeciTrustNET-746398 and the National Spanish project TIN2016-75850-P

Ergonomic risk factors associated with muscuslokeletal disorders in computer workstation

Ergonomics Risk Factors (ERFs) at computer works are commonly related to Musculoskeletal Disorders (MSDs) such as repetitive movements, doing work in awkward postures and static postures while prolonged seating at works. The main objective of this study was to investigate the ergonomic risk factors associated with MSDs among employees in computer workstation. In this study, the data were obtained by structured interview using self-reported questionnaire and direct observation. The results show that there is significant association between neck and stress score with musculoskeletal symptoms and among office workers. As a conclusion, by assessing ERFs at workplace, the effectiveness of workplace interventions can be evaluated without waiting for changes in the prevalence of MSDs

A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.KK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I0

Optimized Scheduling of Repetitive Construction Projects under Uncertainty

Uncertainty is an inherent characteristic of construction projects. Neglecting uncertainties associated with different input parameters in the planning stage could well lead to misleading and/or unachievable project schedules. Many attempts have been made in the past to account for uncertainty during planning for construction projects and many tools and techniques were presented to facilitate modelling of such uncertainty. Some of the presented techniques are widely accepted and used frequently like Project Evaluation and Review Technique (PERT) and Monte Carlo Simulation, while others are more complicated and less popular, such as fuzzy set-based scheduling. Although accounting for uncertainty has been a topic of interest for more than four decades, it was rarely attempted to account for uncertainty when scheduling repetitive construction projects. Repetitive projects impose an additional challenge to the already complicated construction scheduling process that accounts for the need to maintain crew work continuity throughout project execution. This special characteristic necessitates producing scheduling techniques specifically suited to resource driven scheduling. Therefore, the main objective of this research is to produce a comprehensive scheduling, monitoring and control methodology for repetitive construction projects that is capable of accounting for uncertainties in various input parameters, while allowing for optimized acceleration and time-cost trade-off analysis. The proposed methodology encompasses three integrated models; Optimized Scheduling and Buffering Model, Monitoring and Dynamic Rescheduling Model and Acceleration Model. The first model presents an optimization technique that accounts for uncertainty in input parameters. It employs a modified dynamic programming technique that utilizes fuzzy set theory to model uncertainties. This model includes a schedule defuzzification tool and a buffering tool. The defuzzification tool converts the optimized fuzzy schedule into a deterministic one, and the buffering tool utilizes user’s required level of confidence in the produced schedule to build and insert time buffers, thus providing protection against anticipated delays affecting the project. The Monitoring and Dynamic Rescheduling Model capitalizes on the repetitive nature of these projects, by using actual progress on site to reduce uncertainty in the remaining part of the schedule. This model also tracks project progress through comparing the actual buffer consumption to the planned buffer consumption. The Acceleration Model presents an iterative unit based optimized acceleration procedure. It comprises a modified algorithm for identifying critical units of the project to accelerate. This model presents queuing criteria that accounts for uncertainty in additional cost of acceleration and for contractor’s judgment in relation to prioritizing critical units for acceleration. Moreover, this model offers six strategies for schedule acceleration and maintains crew work continuity. Together, the three developed models offer an integrated system that is capable of accounting for uncertainty in different variables through different project stages, aiming at helping managers keep repetitive construction projects on track. The presented optimization technique is automated in an Object Oriented program; coded in C# programming language. A number of case studies are analyzed and presented to demonstrate and validate the capabilities and features of the presented methodology

Fuzzy Set-based Risk Management for Construction Projects

Efficient and comprehensive risk management is critical for successful delivery of engineering, procurement, and construction management (EPCM) projects. Complexity of construction projects is on the rise, which makes it necessary to model uncertainties and to manage risk items related to this class of projects. For decades, researchers and construction practitioners worked together to introduce methods for risk identification and assessment. Considerably less effort was directed towards the development of methods for mitigation, monitoring, and control. The respective individual limitations of these methods prevent the development of comprehensive model which satisfies the needs of practitioners. In this research a comprehensive risk management model “CRMM” is developed to address the limitations of existing methods and to fill the gap between research and practice. The developed model implements a micro system approach to introduce a novel risk identification methodology that provides a systematic procedure to identify risk associated with construction projects. The identification procedure implements root cause analysis and brainstorming technique to identify risk items, consequences, and root causes. The developed CRMM also introduces new method for determination of risk ownership utilizing fuzzy set theory and “One Risk – One Owner” concept. The ownership determination method allocates risk to the owner with highest ability, effectiveness, and capacity to deal with that risk. It also introduces a new qualitative and quantitative evaluation process that utilizes fuzzy set theory and fuzzy probability theory, as well as a new risk mapping procedure which allows for the determination of risk level associated with any project component (e.g., category). The quantitative assessment methodology allows for the use of linguistic and numeric fuzzy evaluations. Fuzzy Linguistic-Numeric Conversion Scheme (FLNCS) is introduced to convert the linguistic evaluations into numeric. The quantitative assessment methodology also introduces the pre-mitigation contingency that represents the contingency fund required for a risk in case no mitigation strategy is implemented. In this respect a novel risk mitigation framework is developed to generate and evaluate possible mitigation strategies for each risk being considered. It also provides a selection procedure which allows users to select the most effective mitigation strategy; making use of fuzzy set theory. The mitigation methodology introduces the post-mitigation contingency that quantifies the contingency required for the selected mitigation strategy. Performance of selected mitigation strategy is monitored using a newly developed risk monitoring method that compares the actually depleted contingency to the post mitigation contingency. The developed monitoring method provides an early warning that alerts users of detected possible failure of selected mitigation strategy. It also determines the correct time for initiation of control process based on a set of qualitative factors. Once risk control process is initiated, the developed control method identifies, evaluates, and selects the most effective control action(s) to support the selected mitigation strategy. In cases where the selected control action fails, the developed control method notifies the user to revise the risk management plan. These notifications allows user to avoid potential failures of similar risk items which are expected to occur in the future. The developed CRMM was coded using VB.Net under Microsoft® windows and .NET framework environment to facilitate its application. A set of case studies are collected from literature and analysed to validate the developed methods within CRMM and to illustrate their essential features. Also, a numerical example elucidates the complete computational processes of the developed comprehensive model

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field