An Internet of Things and Fuzzy Markup Language Based Approach to Prevent the Risk of Falling Object Accidents in the Execution Phase of Construction Projects.

The Internet of Things (IoT) paradigm is establishing itself as a technology to improve data acquisition and information management in the construction field. It is consolidating as an emerging technology in all phases of the life cycle of projects and specifically in the execution phase of a construction project. One of the fundamental tasks in this phase is related to Health and Safety Management since the accident rate in this sector is very high compared to other phases or even sectors. For example, one of the most critical risks is falling objects due to the peculiarities of the construction process. Therefore, the integration of both technology and safety expert knowledge in this task is a key issue including ubiquitous computing, real-time decision capacity and expert knowledge management from risks with imprecise data. Starting from this vision, the goal of this paper is to introduce an IoT infrastructure integrated with JFML, an open-source library for Fuzzy Logic Systems according to the IEEE Std 1855-2016, to support imprecise experts' decision making in facing the risk of falling objects. The system advises the worker of the risk level of accidents in real-time employing a smart wristband. The proposed IoT infrastructure has been tested in three different scenarios involving habitual working situations and characterized by different levels of falling objects risk. As assessed by an expert panel, the proposed system shows suitable results.

A dataset of EEG signals from a single-channel SSVEP-based brain computer interface.

The paper presents a collection of electroencephalography (EEG) data from a portable Steady State Visual Evoked Potentials (SSVEP)-based Brain Computer Interface (BCI). The collection of data was acquired by means of experiments based on repetitive visual stimuli with four different flickering frequencies. The main novelty of the proposed data set is related to the usage of a single-channel dry-sensor acquisition device. Different from conventional BCI helmets, this kind of device strongly improves the users' comfort and, therefore, there is a strong interest in using it to pave the way towards the future generation of Internet of Things (IoT) applications. Consequently, the dataset proposed in this paper aims to act as a key tool to support the research activities in this emerging topic of human-computer interaction.

Bloodstain pattern analysis as optimisation problem.

Bloodstain pattern analysis (BPA) is an approach to support forensic investigators in reconstructing the dynamics of bloody crimes. This forensic technique has been successfully applied in solving heinous and complex murder cases around the world and, recently, computer-based BPA approaches have been designed to better support investigators both in terms of speed and quality of analysis. However, despite its widespread use, current automatic techniques for BPA try to define some algorithmic steps to replicate a sequence of subjective investigators' tasks without relying on any mathematical formalism to compute an objective reconstruction of the crime. The lack of an objective mathematical foundation is a critical issue in a scenario where the quality of evidences can strongly affect a court trial and the life of people involved in that trial. This paper introduces the very first formal representation of BPA by means of an optimisation problem, on which to base the next generation of crime reconstruction techniques. As an example of the benefits provided by the proposed formal representation of BPA, a case study based on a genetic algorithm shows how the BPA optimisation problem can support investigators in performing a fast, precise, automatic and objective analysis.