Inverse psoriasis represents a clinical variant of psoriasis that is sometimes difficult to diagnose due to its clinical similarity with other skin disorders involving the folds, mainly including mechanical intertrigo, fungal and bacterial infections, contact dermatitis, seborrheic dermatitis, and lichen planus. Dermoscopy represents a useful tool for an enhanced non-invasive diagnosis. The treatment of inverse psoriasis may be challenging and include topical corticosteroids, topical calcineurin inhibitors, vitamin D analogs, traditional oral systemic therapies such as cyclosporine and methotrexate, and biologic therapies.

The automatic document metadata extraction process is an important task in a world where thousands of documents are just one Wick" away. Thus, powerful indices are necessary to support effective retrieval. The upcoming XML standard represents an important step in this direction as its semistructured representation conveys document mctadata together with the text of the document. For example, retrieval of scientific papers by authors or affiliations would be a straightforward tasks if papers were stored in XML. Unfortunately, today, the largest majority of documents on the web are available in forms that do not carry additional semantics. Converting existing documents to a semistructured representation is time consuming and no automatic process can be easily applied. In this paper we discuss a system, based on a novel spatial/visual knowledge principle, for extracting metadata from scientific papers stored as PostScript files. Our system embeds the general knowledge about the graphical layout of a scientific paper to guide the metadata extraction process. Our system can effectively assist the automatic index creation for digital libraries.

This article develops and illustrates a new knowledge discovery algorithm tailored to the action requirements of management science applications. The challenge is to develop tactical planning forecasts at the SKU level. We use a traditional market-response model to extract information from continuous variables and use datamining techniques on the residuals to extract information from the many-valued nominal variables, such as the manufacturer or merchandise category. This combination means that a more complete array of information can be used to develop tactical planning forecasts. The method is illustrated using records of the aggregate sales during promotion events conducted by a 95-store retail chain in a single trading area. In a longitudinal cross validation, the statistical forecast (PromoCast\trademark ) predicted the exact number of cases of merchandise needed in 49% of the promotion events and was within ± one case in 82% of the events. The dataminer developed rules from an independent sample of 1.6 million observations and applied these rules to almost 460,000 promotion events in the validation process. The dataminer had sufficient confidence to make recommendations on 46% of these forecasts. In 66% of those recommendations, the dataminer indicated that the forecast should not be changed. In 96% of those promotion events where "no change" was recommended, this was the correct "action" to take. Even including these "no change" recommendations, the dataminer decreased the case error by 9% across all promotion events in which rules applied.datamining, rule generators, residual analysis, promotion event forecasting

The rapid increase of technological innovations in the mobile phone industry induces the research community to develop new and advanced systems to optimize services offered by mobile phones operators (telcos) to maximize their effectiveness and improve their business. Data mining algorithms can run over data produced by mobile phones usage (e.g. image, video, text and logs files) to discover user's preferences and predict the most likely (to be purchased) offer for each individual customer. One of the main challenges is the reduction of the learning time and cost of these automatic tasks. In this paper we discuss an experiment where a commercial offer is composed by a small picture augmented with a short text describing the offer itself. Each customer's purchase is properly logged with all relevant information. Upon arrival of new items we need to learn who the best customers (prospects) for each item are, that is, the ones most likely to be interested in purchasing that specific item. Such learning activity is time consuming and, in our specific case, is not applicable given the large number of new items arriving every day. Basically, given the current customer base we are not able to learn on all new items. Thus, we need somehow to select among those new items to identify the best candidates. We do so by using a joint analysis between visual features and text to estimate how good each new item could be, that is, whether or not is worth to learn on it. Preliminary results show the effectiveness of the proposed approach to improve classical data mining techniques.

The issue of antimicrobial resistance (AMR) is a focus of the World Health Organization, which proposes educational interventions targeting the public and healthcare professionals. Here, we present the first attempt at a regionwide multicomponent campaign in Sicily (Italy), called “Obiettivo Antibiotico”, which aims to raise the awareness of prudent use of antibiotics in the public and in healthcare professionals. The campaign was designed by an interdisciplinary academic team, and an interactive website was populated with different materials, including key messages, letters, slogans, posters, factsheets, leaflets, and videos. The campaign was launched in November 2018 and, as of 21 December 2018, the website had a total of 1159 unique visitors, of which 190 became champions by pledging to take simple actions to support the fight against AMR. Data from social media showed that the audience was between 18 and 54 years of age, with a high proportion of female participants (64%). Interestingly, the LinkedIn page received more than 1200 followers, and Facebook 685 followers. The number of actions taken (pledges) by the audience was 458, evenly divided between experts (53%) and the general public (47%). Additional efforts are needed to reach more people, thus future efforts should focus on further promotion within the Sicilian region to sustain the engagement with the campaign.