Information encountering re-encountered: A conceptual re-examination of serendipity in the context of information acquisition

Purpose In order to understand the totality, diversity and richness of human information behavior, increasing research attention has been paid to examining serendipity in the context of information acquisition. However, several issues have arisen as this research subfield has tried to find its feet; we have used different, inconsistent terminology to define this phenomenon (e.g. information encountering, accidental information discovery, incidental information acquisition), the scope of the phenomenon has not been clearly defined and its nature was not fully understood or fleshed-out. Design/methodology/approach In this paper, information encountering (IE) was proposed as the preferred term for serendipity in the context of information acquisition. Findings A reconceptualized definition and scope of IE was presented, a temporal model of IE and a refined model of IE that integrates the IE process with contextual factors and extends previous models of IE to include additional information acquisition activities pre- and postencounter. Originality/value By providing a more precise definition, clearer scope and richer theoretical description of the nature of IE, there was hope to make the phenomenon of serendipity in the context of information acquisition more accessible, encouraging future research consistency and thereby promoting deeper, more unified theoretical development

Information Acquisition and Adoption of Organic Farming Practices: Evidence from Farm Operations in Crete, Greece

The objective of the paper is to model the degree of organic farming adoption as well as the importance of technical information acquisition in the adoption decision process. In doing so, a trivariate ordered probit model is specified and implemented in the case of organic farming adoption in Crete, Greece. The results suggest that the decisions of information acquisition and adoption are indeed correlated and different farming information sources play a complementary role. Policies required to encourage organic farming adoption should be primarily structural while the provision of technical information is more crucial than conversion subsidies if total organic adoption is to be pursued.Technology adoption, information acquisition, organic farming, Crete, Greece

Ecological Theory of Language Acquisition

This poster outlines an Ecological Theory of Language Acquisition (ETLA). The theory views the early phases of the language acquisition process as an emergent consequence of the interaction between the infant and its linguistic environment. The newborn infant is considered to be linguistically and phonetically naïve but endowed with the ability to register a wide range of multi-sensory inputs along with the ability to detect similarity between the multi-sensory stimuli it is exposed to. The initial steps of the language acquisition process are explained as unintended and inevitable consequences of the infant’s multisensory interaction with the adult. The theoretical model deriving from ETLA is tested using the experimental data presented in the two additional contributions from our research team (Gustavsson et al, “Integration of audiovisual information in 8-months-old infants”; Lacerda, Marklund et al. “On the linguistic implications of context-bound adult-infant interactions”). The generality of the ETLA’s concept is likely to be of significance for a wide range of scientific areas, like robotics, where a central issue concerns addressing general problems of how organisms or systems might develop the ability to tap on the structure of the information embedded in their operating environments

Computer supported estimation of input data for transportation models

Control and management of transportation systems frequently rely on optimization or simulation methods based on a suitable model. Such a model uses optimization or simulation procedures and correct input data. The input data define transportation infrastructure and transportation flows. Data acquisition is a costly process and so an efficient approach is highly desirable. The infrastructure can be recognized from drawn maps using segmentation, thinning and vectorization. The accurate definition of network topology and nodes position is the crucial part of the process. Transportation flows can be analyzed as vehicle’s behavior based on video sequences of typical traffic situations. Resulting information consists of vehicle position, actual speed and acceleration along the road section. Data for individual vehicles are statistically processed and standard vehicle characteristics can be recommended for vehicle generator in simulation models

Predicting global usages of resources endowed with local policies

The effective usages of computational resources are a primary concern of up-to-date distributed applications. In this paper, we present a methodology to reason about resource usages (acquisition, release, revision, ...), and therefore the proposed approach enables to predict bad usages of resources. Keeping in mind the interplay between local and global information occurring in the application-resource interactions, we model resources as entities with local policies and global properties governing the overall interactions. Formally, our model takes the shape of an extension of pi-calculus with primitives to manage resources. We develop a Control Flow Analysis computing a static approximation of process behaviour and therefore of the resource usages.Comment: In Proceedings FOCLASA 2011, arXiv:1107.584

Acquisitions as a Response to Deregulation: Evidence from the Cable Television Industry

This paper studies the dynamics of an industry that is subject to exclusive geographical licensing. I develop a model of license ownership that predicts the evolution of profit-maximizing entry and acquisition decisions by firms over time, starting from an initial allocation of licenses. The entry and acquisition process is modeled as a one-sided coalition-formation game as in Farrell and Scotchmer (1988), where acquisition payoffs depend on economies of scale and agglomeration (economies of density). I estimate the model for the cable television industry in Canada using a panel that I have constructed from 1990 to 1996. The dataset builds up from the national regulator's license ownership decision files, and contains license-level information on acquisition decisions, subscribership, and subscription profits. The model is estimated in two steps. I first estimate firms' license-level profit functions, and then estimate the parameters of the fixed, merger and entry cost functions by Simulated Maximum Likelihood. Through counterfactual simulations, I use the estimated model to quantify the extent to which economies of scale and density drive acquisition behaviour, and to evaluate how merger activity reacts to a partial deregulation that occurs in 1994. Counterfactual experiments are also used to evaluate policies that stimulate entry or reduce acquisitions in the early years of the sample. The main finding is that these policies can lead to more productive dominant firms in the long-run as the industry consolidates.Acquisition, Entry, Coalition Formation, Economies of Density, Economies of Scale, Simulated Maximum Likelihood, Cable Television

Imputation of missing data in photovoltaic panel monitoring system

In scientific research, data acquisition and processing play a fundamental role. In photovoltaic systems, given their nature, this process presents deficiencies due to various factors such as the dispersion of the installed modules, climatic conditions or the amount of information that must be obtained, so the processes of data acquisition, storage and processing are very important. The present research developed a data acquisition, storage and processing system for photovoltaic systems, following the European standards IEC 60904 and IEC 61724 for data acquisition, Fog Computing for information storage and finally Machine Learning was used for processing. The results showed that the KNN-based model obtained a SCORE of 99.08%, MAE of 25.3 and MSE of 93.16. Concluding that the KNN-based model is the most robust model for data imputation in PV system monitoring

Improving SIEM for critical SCADA water infrastructures using machine learning

Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset