A COLLABORATIVE MODEL FOR VIRTUAL ENTERPRISE

Collaborative process characteristics have three dimensions: actors, activities and action’s logic. The aim of this paper is to present a virtual portal’s model that helps managing consortiums. Our model based on dynamic e-collaboration and it has a modular structure, multilayer approach. System’s functionality of virtual enterprise is collaborative model is concern on users’ login, based on role and access control, searching and providing distributed resources, accessibility, metadata management and improved information’s management. Our proposal for developing solution offers a functional architecture of a virtual enterprise using dynamic e-collaboration and shared space.dynamic e-collaboration, multilayer solution, modular approach

A comparison of neural and non-neural machine learning models for food safety risk prediction with European Union RASFF data.

European Union launched the RASFF portal in 1977 to ensure cross-border monitoring and a quick reaction when public health risks are detected in the food chain. There are not enough resources available to guarantee a comprehensive inspection policy, but RASFF data has enormous potential as a preventive tool. However, there are few studies of food and feed risk issues prediction and none with RASFF data. Although deep learning models are good prediction systems, it must be confirmed whether in this field they behave better than other machine learning techniques. The importance of categorical variables encoding as input for numerical models should be specially studied. Results in this paper show that deep learning with entity embedding is the best combination, with accuracies of 86.81%, 82.31%, and 88.94% in each of the three stages of the simplified RASFF process in which the tests were carried out. However, the random forest models with one hot encoding offer only slightly worse results, so it seems that in the quality of the results the coding has more weight than the prediction technique. Our work also demonstrates that the use of probabilistic predictions (an advantage of neural models) can also be used to optimize the number of inspections that can be carried out.pre-print301 K

Review of recent research towards power cable life cycle management

Power cables are integral to modern urban power transmission and distribution systems. For power cable asset managers worldwide, a major challenge is how to manage effectively the expensive and vast network of cables, many of which are approaching, or have past, their design life. This study provides an in-depth review of recent research and development in cable failure analysis, condition monitoring and diagnosis, life assessment methods, fault location, and optimisation of maintenance and replacement strategies. These topics are essential to cable life cycle management (LCM), which aims to maximise the operational value of cable assets and is now being implemented in many power utility companies. The review expands on material presented at the 2015 JiCable conference and incorporates other recent publications. The review concludes that the full potential of cable condition monitoring, condition and life assessment has not fully realised. It is proposed that a combination of physics-based life modelling and statistical approaches, giving consideration to practical condition monitoring results and insulation response to in-service stress factors and short term stresses, such as water ingress, mechanical damage and imperfections left from manufacturing and installation processes, will be key to success in improved LCM of the vast amount of cable assets around the world

Conductive Textiles and their use in Combat Wound Detection, Sensing, and Localization Applications

Conductive textiles, originally used for electromagnetic shielding purposes, have recently been utilized in body area network applications as fabric antennas and distributed sensors used to document and analyze kinematic movement, health vital signs, or haptic interactions. This thesis investigates the potential for using conductive textiles as a distributed sensor and integrated communication system component for use in combat wound detection, sensing, and localization applications. The goal of these proof-of-concept experiments is to provide a basis for robust system development which can expedite and direct the medical response team in the field. The combat wound detection system would have the capability of predicting the presence and location of cuts or tears within the conductive fabric as a realization of bullet or shrapnel penetration. Collected data, along with health vitals gathered from additional sensors, will then be wirelessly transmitted via integrated communication system components, to the appropriate medical response team. A distributed sensing method is developed to accurately predict the location and presence of textile penetrations. This method employs a Wheatstone bridge and multiplexing circuitry to probe a resistor network. Localized changes in resistance illustrate the presence and approximate location of cuts within the conductive textile. Additionally, this thesis builds upon manually defined textile antennas presented in literature by employing a laser cutting system to accurately define antenna dimensions. With this technique, a variety of antennas are developed for various purposes including large data transmission as would be expected from multi-sensor system integration. The fabrication technique also illustrates multilayer antenna development. To confirm simulation results, electrical parameters are extracted using a single-frequency resonance method. These parameters are used in the simulation and design of single-element and two-element wideband slot antennas as well as the design of a wideband monopole antenna. The monopole antenna is introduced to an indoor ultra-wideband (UWB) localization system to illustrate the capability of pinpointing the wearer of textile antennas for localization applications. A cavity-backed dog-bone slot antenna is developed to establish the ability to incorporate conductive vias by sewing conductive thread. This technique can be easily extrapolated to the development of textile substrate integrated waveguide (SIW) technologies

Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Predicting burglaries and other incidents

Predicting Burglaries and Other Incidents (PBOI) is a project to estimate the possibilities of burglary at a given location at a specified time duration. Crime prediction using fixed-location observation points are being used by police. Prediction using location-independent methods, which use a variety of environmental and other additional sources (data), is currently being explored by researchers.PBOI is a pilot-project to study the feasibility of estimating burglaries using machine learning methods, which uses data from Interpolis/Achmea, Dutch police, TNO and a number of open data sources.PBOI uses open-source tools to import, analyze, generate a model (using data), and generate predictions. The model is generated using a machine learning algorithm. The algorithm is used to model systems from historic data, which can be mapped to non-parametric functions with independent variables.Based on the findings and a comparative study, the special algorithm performs better than others on the crime prediction domain. The predictions are populated on a map, which can help police and insurance professionals, to make informed decisions and to avoid burglaries and inform clients respectively