The increasingly large amount of available biomedical literature is making it difficult to gather and synthesise all the necessary information. Moreover, this domain-specific task demands a high level of reliability in the generated text and concepts. Pre-trained large language models have recently shown promising results. Given the specific requirements of biomedical text summarisation, our evaluation focuses on extractive models, prioritising the accuracy of the generated text. In this article, we evaluate the capabilities of 18 general-domain and biomedical pre-trained language models in various configurations on the biomedical extractive summarisation task using one single and two multi-document datasets consisting of 33,000, 5,000 and 470,000 PubMed articles, respectively. We performed the comparison using several well-known metrics, namely ROUGE-1, ROUGE-2, ROUGE-L, BERTScore, BLEU and METEOR. The main contribution of this work lies in providing a detailed performance analysis, highlighting the differences between general-domain and biomedical models, and identifying key factors that influence model performance in extractive summarisation tasks within the biomedical domain. Experimental results show that biomedical models tend to result in higher recall, while general-domain models produce higher precision, while general-domain models produce higher precision. This corresponds to more expressive summaries for biomedical models and shorter summaries for general-domain models.

Studies have shown that data retrieval and visualization tools can help health professionals to improve their understanding and communication with patients, their relationship with stakeholders, and their decision-making process. However, not many efforts have been made in this direction. In this paper, we present a prototype system for the indexing, annotation, and visualization of the PubMed knowledge base to enable the search and retrieval of health-related evidence. The proposed tool builds and keeps updated an enriched graph based on PubMed articles associating them with concepts extracted from the Unified Medical Language System (UMLS) Metathesaurus. Moreover, it allows a full-text search and graph-based navigation and supports an overview of concepts and related publications. The proposed architecture enables scale-up thanks to its containerized nature and parallelization capabilities. The code is open-source under the Apache V2 license.

As a result of an increasing elderly population, the number of people with age-related diseases is increasing worldwide. Alzheimer's disease is thus becoming an emergency health and social problem. Neuropsychological evaluation and biomarker identification represent the two main approaches to identifying subjects with Alzheimer's. In this paper, we propose a web application designed to be sensitive to the cognitive changes distinctive of the early Mild Cognitive Impairment, which is a condition in which someone experiences minor cognitive problems, and the preclinical phase of Alzheimer's disease. The application is conceived to be self-administered in a comfortable and non-stressful environment. It was designed to be quick to administer, automatic to score, and able to preserve privacy because of the highly sensitive data collected. The preliminary evaluation of the application was done by enrolling 518 subjects characterised by several risk factors and the presence of a family history, which underwent standard neuropsychological screening.

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This article introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval . Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions.

In an increasingly connected world, where information is easily spread through multiple channels and platforms, digital watermarking has been broadly investigated in authorship attribution and intellectual property protection of digital content. However, text contents pose many challenges due to a low capacity to embed a watermark. In this paper, we propose a new structural watermarking method for small pieces of text that may allow to hide upwards of 15 bits of watermark per character manipulating the underlying font grayscale values. The proposed method ensures length preservation and is robust to the copy and paste activities. Moreover, the method is able to embed a password-based watermark returning visually indistinguishable watermarked text.

The World Health Organization estimates that 50 million people are currently living with dementia worldwide and this figure will almost triple by 2050. Current pharmacological treatments are only symptomatic, and drugs or other therapies are ineffective in slowing down or curing the neurodegenerative process at the basis of dementia. Therefore, early detection of cognitive decline is of the utmost importance to respond significantly and deliver preventive interventions. Recently, the researchers showed that speech alterations might be one of the earliest signs of cognitive defect, observable well in advance before other cognitive deficits become manifest. In this article, we propose a full automated method able to classify the audio file of the subjects according to the progress level of the pathology. In particular, we trained a specific type of artificial neural network, called autoencoder, using the visual representation of the audio signal of the subjects, that is, the spectrogram. Moreover, we used a data augmentation approach to overcome the problem of the large amount of annotated data usually required during the training phase, which represents one of the most major obstacles in deep learning. We evaluated the proposed method using a dataset of 288 audio files from 96 subjects: 48 healthy controls and 48 cognitively impaired participants. The proposed method obtained good classification results compared to the state-of-the-art neuropsychological screening tests and, with an accuracy of 90.57%, outperformed the methods based on manual transcription and annotation of speech.

Current hierarchical embeddings are inaccurate in both reconstructing the original taxonomy and answering reachability queries over Direct Acyclic Graph. In this paper, we propose a new hierarchical embedding, the Euclidean Embedding (EE), that is correct by design due to its mathematical formulation and associated lemmas. Such embedding can be constructed during the visit of a taxonomy, thus making it faster to generate if compared to other learning-based embeddings. After proposing a novel set of metrics for determining the embedding accuracy with respect to the reachability queries, we compare our proposed embedding with state-of-the-art approaches using full trees from 3 to 1555 nodes and over a real-world Direct Acyclic Graph of 1170 nodes. The benchmark shows that EE outperforms our competitors in both accuracy and efficiency.

A fundamental problem in Social Network Analysis is how to move from single-layer to multi-layer, which provide a holistic view. User profiles resolution has received considerable attention since it allows to match users on different online social networks (OSNs). However, to the best of our knowledge, no study has focused on nesting operation for merging OSNs graphs. This work is a first step in the direction of defining the data model and the algorithm to perform approximate nesting of multiple OSNs graphs, based on user features. We provide initial experimental evidence based on synthetic data.

The current online digital world, consisting of thousands of newspapers, blogs, social media, and cloud file sharing services, is providing easy and unlimited access to a large treasure of text contents. Making copies of these text contents is simple and virtually costless. As a result, producers and owners of text content are interested in the protection of their intellectual property (IP) rights. Digital watermarking has become crucially important in the protection of digital contents. Out of all, text watermarking poses many challenges, since text is characterized by a low capacity to embed a watermark and allows only a restricted number of alternative syntactic and semantic permutations. This becomes even harder when authors want to protect not just a whole book or article, but each single sentence or paragraph, a problem well known to copyright law. In this paper, we present a fine-grain text watermarking method that protects even small portions of the digital content. The core method is based on homoglyph characters substitution for latin symbols and whitespaces. It allows to produce a watermarked version of the original text, preserving the anonymity of the users according to the right to privacy. In particular, the embedding and extraction algorithms allow to continuously protect the watermark through the whole document in a fine-grain fashion. It ensures visual indistinguishability and length preservation, meaning that it does not cause overhead to the original document, and it is robust to the copy and past of small excerpts of the text. We use a real dataset of 1.8 million New York articles to evaluate our method. We evaluate and compare the robustness against common attacks, and we propose a new measure for partial copy and paste robustness. The results show the effectiveness of our approach providing an average length of 101 characters needed to embed the watermark and allowing to protect paragraph-long excerpt or smaller the 94.5% of the times.

In the last decades, Social Networks (SNs) have deeply changed interactions and habits of the users that are also prone to create more than one profile on the same SN. On the flip side, fake profiles (i.e., impersonating profiles), have become a considerable problem in digital investigations. In this paper, we propose a method for user profiles resolution through a cluster-based approach of the smartphone fingerprints extracted from the images being posted on SNs. The proposed method is thus able to detect fake profiles. To evaluate our approach, we use a real dataset of 1,500 images from 10 different smartphone devices and Facebook and WhatsApp platforms. The results show that the average of sensitivity and specificity for user profiles resolution is about 98%.

Smart cities face the challenge of combining sustainable national welfare with high living standards. In the last decades, life expectancy increased globally, leading to various age-related issues in almost all developed countries. Frailty affects elderly who are experiencing daily life limitations due to cognitive and functional impairments and represents a remarkable burden for national health systems. In this paper, we proposed two different predictive models for frailty by exploiting 12 socioclinical databases. Emergency hospitalization or all-cause mortality within a year were used as surrogates of frailty. The first model was able to assign a frailty risk score to each subject older than 65 years old, identifying five different classes for tailor made interventions. The second prediction model assigned a worsening risk score to each subject in the first nonfrail class, namely the probability to move in a higher frailty class within the year. We conducted a retrospective cohort study based on the whole elderly population of the Municipality of Bologna, Italy. We created a baseline cohort of 95 368 subjects for the frailty risk model and a baseline cohort of 58 789 subjects for the worsening risk model, respectively. To evaluate the predictive ability of our models through calibration and discrimination estimates, we used, respectively, a six-year and a four-year observation period. Good discriminatory power and calibration were obtained, demonstrating a good predictive ability of the models.

One of the most shared content in Social Media (SM) is text, making it vulnerable to copy and authorship misappropriation. Due to the low data noise, watermark embedding is very hard. This problem is exacerbated in the context of SM, where the amount of data in a single message can be extremely small, like in Twitter. Firstly, in this paper we investigate whether SM do applies watermarks on the texts. Then, we propose a text watermarking method able to work on all the SM platforms considered, while ensuring visual indistinguishability and length preservation of the original text and robustness to copy and paste. We conduct an extended evaluation on eighteen different SM platforms by using 6,000 posts from six public figures' profiles.

The temporal dimension of a text document defines the temporal scope of its narrated event. This temporal dimension acquires more importance in corpora created along several years of production, such as digital libraries. Temporal aspects of text have been the subject of many researches with specific tasks, notably information retrieval and event detection, while no studies have been conducted to quantify and analyze the richness of the temporal dimension of different text collections. Analysing thirteen text collections we show how the extent and characteristics of the time presence in text varies among collections that have different scopes, although time intervals are mentioned in almost all the text units analyzed. We found that unique intervals follow the same distribution, given by the Zipf's law, that holds for single words.

Digital watermarking has become crucially important in authentication and copyright protection of the digital contents, since more and more data are daily generated and shared online through digital archives, blogs and social networks. Out of all, text watermarking is a more difficult task in comparison to other media watermarking. Text cannot be always converted into image, it accounts for a far smaller amount of data (eg. social network posts) and the changes in short texts would strongly affect the meaning or the overall visual form. In this paper we propose a text watermarking technique based on homoglyph characters substitution for latin symbols1. The proposed method is able to efficiently embed a password based watermark in short texts by strictly preserving the content. In particular, it uses alternative Unicode symbols to ensure visual indistinguishability and length preservation, namely content-preservation. To evaluate our method, we use a real dataset of 1.8 million New York articles. The results show the effectiveness of our approach providing an average length of 101 characters needed to embed a 64bit password based watermark.

A common problem in social network analysis is the presence of missing data. This problem has been extensively investigated in single layer networks, that is, considering one network at a time. However, in multilayer networks, in which a holistic view of multiple networks is taken, the problem has not been specifically studied, and results for single layer networks are reused with no adaptation. In this work, we take an exhaustive and systematic approach to understand the effect of missing data in multilayer networks. Differently from the single layer networks, depending on layer interdependencies, the common network properties can increase or decrease with respect to the properties of the complete network. Another important aspect we observed through our experiments on real datasets is that multilayer network properties like layer correlation and relevance can be used to understand the impact of missing data compared to measuring traditional network measures.

There is an inherent presence of communities in online social networks. These communities can be defined based on i) link structure or ii) the attributes of individuals. Attributes can indicate as interests in specific topics, like science-fiction books or romantic movies, or more in general their explicit affiliation to a group inside the network. In this paper, we analyze community structures as defined by how people are associated to third concepts like attributes. To understand the community patterns we analyze three large and one small social network datasets. Our analysis shows that, irrespective of the number of nodes for any particular interest in the network, at least 50% of the nodes are part of the same connected component in the graph induced by each interest. Another interesting result of our analysis is that the majority of sub-communities (50% or above) for any interest are separated by small hops (two to three) from each other.

In the last decade, various social platforms have been introduced on the web. Due to their specific orientation (friendship, professional connections, image sharing, etc.) users often join multiple networks. An important problem across these networks is the resolution of users profiles. That is, to identify if set of user profiles from different networks with different user ids or nicknames belong to the same user. The problem is more meaningful for resolving different profiles in digital forensic and criminal investigations. In this paper, we propose a method for profile resolution with the help of pictures being posted on different social platforms. We use the smartphone cameras which have become the source of instant image capturing and uploading process. In particular, we exploit the characteristic noise present in the images due to the manufacturing defects, to match user profiles across social platforms. To test our approach we select five different smartphones with two pairs of identical models, and three social platforms, namely Facebook, Google+ and WhatsApp. We evaluate our approach using real dataset of 1000 high-resolution pictures. The results indicate that even in the worst case our approach can provide profile matching upto 89.83%.

The management of legal domains is gaining great importance in the context of data management. In fact, the geographical distribution of data as implied -- for example -- by cloud-based services requires that the legal restrictions and obligations are to be taken into account whenever data circulates across different legal domains. In this paper, we start to investigate an approach for coping with the complex issues that arise when dealing with data spanning different legal domains. Our approach consists of a conceptual model that takes into account the notion of legal domain (to be paired with the corresponding data) and a reference architecture for implementing our approach in an actual relational DBMS.

One of the most interesting and still not completely understood phenomena happening in Social Network Sites is their ability to spread (or not) units of information which may aggregate to form large distributed conversations. In this paper we present the result of an empirical study on a Large Social Database (LSD) aimed at measuring the factors enabling information spreading in Social Network Sites.

In this paper we describe a demo concerning the management of uncertain schemata. Many works have studied the problem of representing uncertainty on attribute values or tuples, like the fact that a value is 10 with probability .3 or 20 with probability .7, leading to the implementation of probabilistic database management systems. In our demo we deal with the representation of uncertainty about the meta-data, i.e., about the meaning of these values. Using our system it is possible to create alternative probabilistic schemata on a database, execute queries over uncertain schemata and verify how this additional information is stored in an underlying relational database and how queries are executed.

In this paper we describe an activity of information integration performed on databases with patent data and company indicators. In particular, we present a detailed case study on company name matching. We show how to choose and tune existing methods to work on the domain object of this paper, and describe an efficient implementation to process large volumes of data. The integration activity involves the application of approximate string matching techniques. Then, we show the experimental results obtained on real data sets, highlighting the pros and cons of approximate string matching in this specific domain, and analyze the impact of domain knowledge on the results of the matching activity.

Abstract In the context of the progressive incarnation of Grid systems in terms of a Service Oriented Architecture, it is essential to improve the automatic evaluation and selection of Grid services. Current mechanisms for service evaluation lack expressiveness as regards the representation of service requesters' expectations. In this paper, we present a model for the quantitative expression of service attributes, for the association of the possible values of these attributes with the requester satisfaction and for the aggregation of a set of satisfactions in an overall score by means of different aggregation logics. The proposed approach enriches the expressiveness of the mechanisms for the evaluation of Grid services. Copyright © 2005 John Wiley & Sons, Ltd.

In this paper we present a model for the management of relational, XML, and mixed data. The main high-level approaches to manipulate XML, i.e., SQL/XML, XQuery, and object/relational XML columns, can all be based on our common model and algebra. Our query algebra, yet very simple, can represent queries not expressible by other proposals and by the current implementation of TAX. Moreover, we show that relational-like logical query rewriting can be extended to our algebraic expressions.

Schema integration is the process by which schemata from heterogeneous databases are conceptually integrated into a single cohesive schema. In this work we propose a modeling framework for schema integration, capturing the inherent uncertainty accompanying the integration process. The model utilizes a fuzzy framework to express a confidence measure, associated with the outcome of a schema integration process. In this paper we provide a systematic analysis of the process properties and establish a criterion for evaluating the quality of matching algorithms, which map attributes among heterogeneous schemata.

We introduce a new method for rule termination analysis within active databases. This method analyzes the interaction between transactions and triggers, by means of evolution graphs. In this paper trigger information and transaction updates are considered in order to study rule termination and simulate execution. First we present the algorithm for testing rule termination and then show that several termination analysis methods are captured by our method. The proposed approach turns out to be practical and general with respect to various rule languages and thus may be applied to many database systems.

Web and multimedia data are becoming very important. A fundamental characteristic of these data is imprecision. Query languages for web and multimedia data must express imprecision in features matching, similarity queries and user preferences. In addition specific operators need to be introduced to organize the answers in a user friendly style. The aim of this work is to provide a formal framework in which to formulate very powerful queries and presentations of the answers. To this end, a fuzzy algebra and a presentation algebra are introduced. The fuzzy algebra extends the classical relational algebra over fuzzy relations. Both algebras allow user preferences in the form of weights to be attached to predicates and operators. The effect of this weights is to alter the classic behaviour of query expressions to better suite user requirements. In addition, optimization issues are presented in the form of algebraic manipulation of expressions thus leading to a set of equivalence and containment rules.

Introduces a method for rule termination analysis within active databases which relies on evolution graphs simulating rule processing statically, and considering both rule activation and deactivation. The evolution graph provides a more detailed analysis than traditional graph-based approaches. We show that several termination analysis methods are captured by evolution graphs. The algorithm for testing rule termination is presented and its correctness shown. The proposed approach turns out to be practical and general with respect to the various rule languages, and thus it may be applied to several database systems.

In this paper, we develop a new approach that provides a smooth integration of extensional updates and declarative query languages for deductive databases. The approach is based on a declarative specification of updates in rule bodies. Updates are not executed as soon as evaluated. Instead, they are collected and then applied to the database when the query evaluation is completed. We call this approach nonimmediate update semantics. We provide a top-down and equivalent bottom-up semantics which reflect the corresponding computation models. We also package set of updates into transactions and we provide a formal semantics for transactions. Then, in order to handle complex transactions, we extend the transaction language with control constructors still preserving formal semantics and semantics equivalence.

Describes operational aspects of a novel approach to active rule processing based on a transaction transformation technique. A user-defined transaction, which is viewed as a sequence of atomic database updates forming a semantic unit, is translated by means of active rules into a new transaction that explicitly includes the additional updates due to active rule processing. It follows that the execution of the new transaction in a passive environment corresponds to the execution of the original transaction within the active environment defined by the given rules. Both immediate and deferred execution models are considered. The approach presents two main features. First, it relies on a well known formal basis that allow us to derive solid results on equivalence, confluence and optimization issues. Second, it is easy to implement as it does not require any specific run-time support.>

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