Search

The dysregulation of the β-cell functional mass, which is a reduction in the number of β-cells and their ability to secure adequate insulin secretion, represents a key mechanistic factor leading to the onset of type 2 diabetes (T2D). Obesity is recognised as a leading cause of β-cell loss and dysfunction and a risk factor for T2D. The natural history of β-cell failure in obesity-induced T2D can be divided into three steps: (1) β-cell compensatory hyperplasia and insulin hypersecretion, (2) insulin secretory dysfunction, and (3) loss of β-cell mass. Adipose tissue (AT) secretes many hormones/cytokines (adipokines) and fatty acids that can directly influence β-cell function and viability. As this secretory pattern is altered in obese and diabetic patients, it is expected that the cross-talk between AT and pancreatic β-cells could drive the maintenance of the β-cell integrity under physiological conditions and contribute to the reduction in the β-cell functional mass in a dysmetabolic state. In the current review, we summarise the evidence of the ability of the AT secretome to influence each step of β-cell failure, and attempt to draw a timeline of the alterations in the adipokine secretion pattern in the transition from obesity to T2D that reflects the progressive deterioration of the β-cell functional mass. [ABSTRACT FROM AUTHOR]

Question Answering (QA) over Knowledge Graphs (KG) aims to develop a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata, and so on. Question Answering systems need to translate the user's question, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern. It becomes even more troublesome when trying to cope with questions that require modifiers in the final query, i.e., aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to further step in this direction. This work aims to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art. [ABSTRACT FROM AUTHOR]

In this article, we present MyrrorBot, a personal digital assistant implementing a natural language interface that allows the users to: (i) access online services, such as music, video, news, and food recommendations, in a personalized way, by exploiting a strategy for implicit user modeling called holistic user profiling; (ii) query their own user models, to inspect the features encoded in their profiles and to increase their awareness of the personalization process. Basically, the system allows the users to formulate natural language requests related to their information needs. Such needs are roughly classified in two groups: quantified self-related needs (e.g., Did I sleep enough? Am I extrovert?) and personalized access to online services (e.g., Play a song I like). The intent recognition strategy implemented in the platform automatically identifies the intent expressed by the user and forwards the request to specific services and modules that generate an appropriate answer that fulfills the query. In the experimental evaluation, we evaluated both qualitative (users' acceptance of the system, usability) as well as quantitative (time required to complete basic tasks, effectiveness of the personalization strategy) aspects of the system, and the results showed that MyrrorBot can improve the way people access online services and applications. This leads to a more effective interaction and paves theway for further development of our system. [ABSTRACT FROM AUTHOR]

In this article, we present a platform that allows the creation of a comprehensive representation of the user that we call a holistic user model (HUM). Such a representation is based on the intuition that users' personal data take different forms and come from several heterogeneous sources. Accordingly, we designed a pipeline that: (1) extracts personal data from three examples of important classes of such sources, namely social networks, wearable devices and smartphones; (2) processes these data through natural language processing and machine learning techniques; (3) stores the output of such processing in a user model that encodes different aspects of people's life, such as demographic data, interests, affect values, social relations, activities and physical states. The resulting representation is made available to the user and to external developers. In the first case, a web interface allows the user to browse through her own personal data and to consult different facets of her HUM, in order to improve her self-awareness. In the latter, holistic user profiles are exposed through a REST interface and can be exploited by third-party applications to provide personalized services based on HUMs. In the experimental session, we evaluated usability and acceptability of the HUM in a user study which investigated how people were willing to use it. The results confirmed the effectiveness of our design choices and built the foundations for future usage of these profiles in personalized applications. [ABSTRACT FROM AUTHOR]

“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players. [ABSTRACT FROM PUBLISHER]

Wealth Management is a business model operated by banks and brokers, that offers a broad range of investment services to individual clients, in order to help them reach their investment objectives. Wealth management services include investment advisory, subscription of mandates, sales of financial products, collection of investment orders by clients. Due to the complexity of the task, which largely requires a deep knowledge of the financial domain, a recend trend in the area is to exploit recommendation technologies to support financial advisors and to improve the effectiveness of the process. This paper proposes a framework to support financial advisors in the task of providing clients with personalized investment strategies. Our methodology is based on the exploitation of case-based reasoning. A prototype version of the platform has been adopted to generate personalized portfolios, and the performance of the framework shows that the yield obtained by recommended portfolios overcomes that of portfolios proposed by human advisors in most experimental settings. [ABSTRACT FROM AUTHOR]

The Social Web is now a successful reality with its quickly growing number of users and applications. Also the Semantic Web, which started with the objective of describing Web resources in a machine-processable way, is now outgrowing the research labs and is being massively exploited in many websites, incorporating high-quality user-generated content and semantic annotations. The primary goal of this special section is to showcase some recent research at the intersection of the Social Web and the Semantic Web that explores the benefits that adaptation and personalization have to offer in the Web of the future, the so-called Social Adaptive Semantic Web. We have selected two articles out of fourteen submissions based on the quality of the articles and we present the main lessons learned from the overall analysis of these submissions. Categories and Subject Descriptors: H.3.3 [Information Search and Retrieval] General Terms: Algorithms, Experimentation [ABSTRACT FROM AUTHOR]

With the increasing amount of information in electronic form the fields of Machine Learning and Data Mining continue to grow by providing new advances in theory, applications and systems. The aim of this paper is to consider some recent theoretical aspects and approaches to ML and DM with an emphasis on the Italian research. [ABSTRACT FROM AUTHOR]

Artificial Intelligence technologies are growingly used within several software systems ranging from Web services to mobile applications. It is by no doubt true that the more AI algorithms and methods are used the more they tend to depart from a pure 'AI' spirit and end to refer to the sphere of standard software. In a sense, AI seems strongly connected with ideas, methods and tools that are not (yet) used by the general public. On the contrary, a more realistic view of it would be a rich and pervading set of successful paradigms and approaches. Industry is currently perceiving semantic technologies as a key contribution of AI to innovation. In this paper a survey of current industrial experiences is used to discuss different semantic technologies at work in heterogeneous areas, ranging from Web services to semantic search and recommender systems. The resulting picture confirms the vitality of the area and allows to sketch a general taxonomy of approaches, that is the main contribution of this paper. [ABSTRACT FROM AUTHOR]

A knowledge infusion process gives a system the linguistic and cultural knowledge—normally the prerogative of human beings—required to play a complex language game. [ABSTRACT FROM PUBLISHER]

The article presents the approach and framework on the use of web-service processes for real-time service provisioning (RTSP) to enhance the quality-of-service (QoS) for content delivery. It reveals the possibility of combined QoS parameters to render differentiated services, as well as to allocate existing resources among clients. It presents the seven parameters of the web-service QoS model and the two classes of parameters in RSTP layering. It uses support framework and prediction model to determine the faults and present the input instances for the classifier. It also discusses how monitoring is performed under web-service QoS model and RSTP layering model. Moreover, it presents the optimization problem when the framework is implemented in Quadrantis Project.

The rapid evolution of Internet services has led to a constantly increasing number of Web sites and to an increase in the available information. The main challenge is to support Web users in order to facilitate navigation through Web sites and to improve searching among the extremely large Web repository, such as digital libraries, online product catalogues, or other generic information sources. The complexity of today's services could be lowered by means of proactive support or advice from the system. The proactiveness could be achieved using dialoguing agents that exploit user profiles to provide personal recommendations. In this paper, we will present a general methodology to cover the entire process of designing advanced solutions for online services. The methodology has been adopted to elicit user requirements for the system developed in the COGITO project, and to evaluate the performance of the final prototype. [ABSTRACT FROM AUTHOR]

Conversational recommender systems produce personalized recommendations of potentially useful items by utilizing natural language dialogues for detecting user preferences, as well as for providing recommendations. In this work we investigate the role of affective factors such as attitudes, emotions, likes and dislikes in conversational recommender systems and how they can be used as implicit feedback to improve the information filtering process. We thus developed a multimodal framework for recognizing the attitude of the user during their conversation with DIVA, a Dress-shopping InteractiVe Assistant aimed at recommending fashion apparel. Wee took into account speech prosody, body poses and facial expressions for providing implicit feedback to the system and for refining the recommendation accordingly. The shopping assistant has been embodied in the Social Robot NAO and has been tested in the dress shopping scenario. Our experimental results show that the proposed method is a promising way to implicitly profile the user and improve the performance of recommendations when explicit feedback is not available, thus demonstrating its effectiveness and viability. [ABSTRACT FROM AUTHOR]