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Topic-based search systems retrieve items by contextualizing the information seeking process on a topic of interest to the user. A key issue in topic-based search of text resources is how to automatically generate multiple queries that reflect the topic of interest in such a way that precision, recall, and diversity are achieved. The problem of generating topic-based queries can be effectively addressed by Multi-Objective Evolutionary Algorithms, which have shown promising results. However, two common problems with such an approach are loss of diversity and low global recall when combining results from multiple queries. This work proposes a family of Multi-Objective Genetic Programming strategies based on objective functions that attempt to maximize precision and recall while minimizing the similarity among the retrieved results. To this end, we define three novel objective functions based on result set similarity and on the information theoretic notion of entropy. Extensive experiments allow us to conclude that while the proposed strategies significantly improve precision after a few generations, only some of them are able to maintain or improve global recall. A comparative analysis against previous strategies based on Multi-Objective Evolutionary Algorithms, indicates that the proposed approach is superior in terms of precision and global recall. Furthermore, when compared to query-term-selection methods based on existing state-of-the-art term-weighting schemes, the presented Multi-Objective Genetic Programming strategies demonstrate significantly higher levels of precision, recall, and F1-score, while maintaining competitive global recall. Finally, we identify the strengths and limitations of the strategies and conclude that the choice of objectives to be maximized or minimized should be guided by the application at hand.

The Web has become a potentially infinite information resource, turning into an essential tool for many daily activities. This resulted in an increase in the amount of information available in users' contexts that is not taken into account by current information retrieval systems. This thesis proposes a semisupervised information retrieval technique that helps users to recover context relevant information. The objective of the proposed technique is to reduce the vocabulary gap existing between the knowledge a user has about a specific topic and the relevant documents available in the Web. This thesis presents a method for learning novel terms associated with a thematic context. This is achieved by identifying those terms that are good descriptors and good discriminators of the user's current thematic context. In order to evaluate the proposed method, a theoretical framework for the evaluation of search mechanisms was developed. This served as a guide for the implementation of an evaluation framework that allowed to compare the techniques proposed in this thesis with other techniques existing in the literature. The experimental evidence indicates that the methods proposed in this thesis present significant improvements over previously published techniques. In addition, the evaluation framework was equipped with novel evaluation metrics that favor the exploration of novel material and incorporates a semantic relationship metric between documents. The algorithms developed in this thesis evolve high quality queries, which have the capability of retrieving results that are relevant to the user context. These results have a positive impact on the way users interact with available resources.

This paper proposes an incremental method that can be used by an intelligent system to learn better descriptions of a thematic context. The method starts with a small number of terms selected from a simple description of the topic under analysis and uses this description as the initial search context. Using these terms, a set of queries are built and submitted to a search engine. New documents and terms are used to refine the learned vocabulary. Evaluations performed on a large number of topics indicate that the learned vocabulary is much more effective than the original one at the time of constructing queries to retrieve relevant material.

The email is used daily by millions of people to communicate around the globe and it is a mission-critical application for many businesses. Over the last decade, unsolicited bulk email has become a major problem for email users. An overwhelming amount of spam is flowing into users' mailboxes daily. In 2004, an estimated 62% of all email was attributed to spam. Spam is not only frustrating for most email users, it strains the IT infrastructure of organizations and costs businesses billions of dollars in lost productivity. In recent years, spam has evolved from an annoyance into a serious security threat, and is now a prime medium for phishing of sensitive information, as well the spread of malicious software. This work presents a first approach to attack the spam problem. We propose an algorithm that will improve a classifier's results by adjusting its training set data. It improves the document's vocabulary representation by detecting good topic descriptors and discriminators.

ABSTRACTBackgroundThere is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families. MethodsThis is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community. ResultsThe societal burden of AD, composed of public, patient, and informal care costs, was about €20,000/yr. Out of this, the cost borne by the public sector was €4,534/yr. The main driver of public cost was the national cash-for-care allowance (€2,324/yr), followed by drug prescriptions (€1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at €13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure. ConclusionThe burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556).

Background The epidemic of Alzheimer's disease (AD) represents a significant challenge for the health care and social service systems of many developed countries. AD affects both patients and family caregivers, on whom the main burden of care falls, putting them at higher risk of stress, anxiety, mortality and lower quality of life. Evidence remains controversial concerning the effectiveness of providing support to caregivers of AD patients, through case management, counseling, training, technological devices and the integration of existing care services. The main objectives of the UP-TECH project are: 1) to reduce the care burden of family caregivers of AD patients; and 2) to maintain AD patients at home. Methods/design A total of 450 dyads comprising AD patients and their caregivers in five health districts of the Marche region, Italy, will be randomized into three study arms. Participants in the first study arm will receive comprehensive care and support from a case manager (an ad hoc trained social worker) (UP group). Subjects in the second study arm will be similarly supported by a case manager, but in addition will receive a technological toolkit (UP-TECH group). Participants in the control arm will only receive brochures regarding available services. All subjects will be visited at home by a trained nurse who will assess them using a standardized questionnaire at enrollment (M0), 6 months (M6) and 12 months (M12). Follow-up telephone interviews are scheduled at 24 months (M24). The primary outcomes are: 1) caregiver burden, measured using the Caregiver Burden Inventory (CBI); and 2) the actual number of days spent at home during the study period, defined as the number of days free from institutionalizations, hospitalizations and stays in an observation unit of an emergency room. Discussion The UP-TECH project protocol integrates previous evidence on the effectiveness of strategies in dementia care, that is, the use of case management, new technologies, nurse home visits and efforts toward the integration of existing services in an ambitious holistic design. The analysis of different interventions is expected to provide sound evidence of the effectiveness and cost of programs supporting AD patients in the community. Trial registration ClinicalTrials.gov: NCT01700556