KnowSe: Fostering user interaction context awareness

The CSCW area has recognized the concept of awareness as a critical issue to focus on (Schmidt et al., 2002) since “users who work together require adequate information about their environment” (Gross and Prinz, 2003). The environment of an individual encompasses her connections with other people, as well as with digital resources and actions (tasks or processes). If connections are not clear or hidden to the individual or to the group, the cost is a lack of awareness in the organization (McArthur and Bruza, 2003), which not only leads to inefficient cooperation but can even prevent it from being started. Unveiling the relations between persons, topics, tasks and processes to computer workers facilitates cooperative work by increasing the awareness of the personal social networks and the role of an individual in the organization, a project, or a group. These connections can be created and modeled manually but a better approach is to develop semi-automatic or even automatic tools to create and share them (McArthur and Bruza, 2003). Based on emails, McArthur and Bruza (2003) have computed such kind of connections, and suggest using more global corpora as well as taking into account dynamic ones

Detecting real user tasks by training on laboratory contextual attention metadata

Detecting the current task of a user is essential for providing her with contextualized and personalized support, and using Contextual Attention Metadata (CAM) can help doing so. Some recent approaches propose to perform automatic user task detection by means of task classifiers using such metadata. In this paper, we show that good results can be achieved by training such classifiers offline on CAM gathered in laboratory settings. We also isolate a combination of metadata features that present a significantly better discriminative power than classical ones

Neural Network Based Adaptation Algorithm for Online Prediction of Mechanical Properties of Steel

After production of a steel product in a steel plant, a sample of the product is tested in a laboratory for its mechanical properties like yield strength (YS), ultimate tensile strength (UTS) and percentage elongation. This paper describes a mathematical model based method which can predict the mechanical properties without testing. A neural network based adaptation algorithm was developed to reduce the prediction error. The uniqueness of this adaptation algorithm is that the model trains itself very fast when predicted and measured data are incorporated to the model. Based on the algorithm, an ASP.Net based intranet website has also been developed for calculation of the mechanical properties. In the starting Furnace Module webpage, austenite grain size is calculated using semi-empirical equations of austenite grain size during heating of slab in a reheating furnace. In the Mill Module webpage, different conditions of static, dynamic and metadynamic recrystallization are calculated. In this module, austenite grain size is calculated from the recrystallization conditions using corresponding recrystallization and grain growth equations. The last module is a cooling module. In this module, the phase transformation equations are used to predict the grain size of ferrite phase. In this module, structure-property correlation is used to predict the final mechanical properties. In the Training Module, the neural network based adapation algorithm trains the model and stores the weights and bias in a database for future predictions. Finally, the model was trained and validated with measured property data

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones

NEUROPROTECTIVE EFFECT OF METHANOLIC EXTRACT OF SARGASSUM WIGHTII ON HALOPERIDOL INDUCED CATALEPSY AND TARDIVE DYSKINESIA IN ALBINO RATS

Objective: The present study was designed to evaluate the neuroprotective effect of methanolic extract of Sargassum wightii on haloperidol-induced catalepsy and tardive dyskinesia in Wistar albino rats. Methods: In this study, thirty Wistar albino rats were randomly divided into six groups. Gr-I served as control. Haloperidol (1 mg/kg intraperitoneally) was administered to rats of Gr-II to Gr-V for twenty-one consecutive days to induce catalepsy and tardive dyskinesia. Animals of Gr-II to Gr-V were orally administered with vehicle, levodopa carbidopa combination (30 mg/kg), Sargassum extract 200 and 400 mg/kg respectively. All the drugs and vehicles were given orally one hour before haloperidol injection for twenty one consecutive days. The cataleptic scores were recorded using standard bar test. Tardive dyskinesia was assessed in terms of vacuous chewing movement (VCM) and tongue protrusion (TP) scores. After behavioural testing, all animals were sacrificed on twenty-second day and various biochemical parameters like MDA, SOD and GSH were estimated in brain tissue. Results: Chronic administration of haloperidol significantly increased cataleptic scores, VCM and TP scores. (p<0.001) Sargassum wightii extract (400 mg/kg) significantly inhibited haloperidol-induced catalepsy, VCM and TP (p<0.001) Haloperidol increased MDA and decreased SOD and GSH in brain tissue to a highly significant extent (p<0.001) Sargassum extract at 400 mg/kg also significantly reversed the haloperidol-induced alteration in brain oxidative stress markers. Conclusion: Sargassum wightii inhibits haloperidol-induced catalepsy and tardive dyskinesia. Thus it may be used as a unique therapeutic adjunct for the prevention of neuroleptic-induced extrapyramidal symptoms, however, it has to be explored more

Service Sector Industry and Sports Sponsoring Strategies of United Kingdom Market

The aim of this study is to explore the capabilities of sports sponsorship as a services issue which can be profitable for the company. It looks at how sports sponsorship can derive good return on investments for the firms. To understand the impact of sports sponsorship in generating superior brand and corporate images and thereby influence consumer and customer attitudes towards sponsoring brands. It explores and identifies factors that are creating challenges and influences the direction of sports sponsorship marketing. A case study research strategy has been adopted apart from other methods in collation and interpretation of data. It gives an insight on the organization’s key strategic service marketing issues. Keywords: Sports Sponsorship, Mega Events, Brand Tool, Ambush Marketin

Grassland in Ireland and the UK

Key points 1. Grassland is the dominant land use option in Ireland and the UK, and is characterised by a long growing season. 2. Dynamic, interactive systems of grassland management have been developed which combine high grass dry matter intakes with good sward quality. In the better grassland areas milk yields in excess of 7000 kg/cow are attainable with low levels of concentrate supplementation. 3. In the times to come, measures to protect the environment will constrain stocking rates, and fertiliser and manure use on intensive grassland enterprises. 4. A high proportion of beef and sheep farms participate in voluntary, EU-funded agrienvironmental schemes that promote less intensive production systems and high standards of environmental protection. 5. Access for the public to, and conservation by farmers of, the countryside have become increasingly important in the last 20 years. In the future, grasslands will have to meet a variety of demands and be truly multifunctional