Past research has shown that over 8 months may elapse between the time when a network is compromised and the time the attack is discovered. During this long gap, attackers can steal valuable intellectual property from the victim. The recent FORGE system [8] has suggested that automatically generating fake—but believable—versions of documents can delay the attacker, cost him money, and increase his uncertainty. However, in order to generate fakes, FORGE only modifies the textual component of the document in question. But in the real world, documents consist of many non-textual components such as charts, equations, formulas, diagrams, and tables. We propose the concept of a Probabilistic Logic Graph (PLG) and show that PLGs provide a single, unified framework within which the different parts of a document can be expressed. We then define the problem of generating, for a given PLG representation of a document, a set of fake yet highly believable PLGs (i.e., documents), so that an attacker looking at them (both the original and the fake ones) cannot easily identify the original document. We show that the problem of generating fake PLGs is intractable—but we propose an approximation algorithm that solves it efficiently. We evaluate the use of PLGs over a corpus of patents and show that our fakes can effectively deceive an adversary.

Today, major corporations and government organizations must face the reality that they will be hacked by malicious actors. In this paper, we consider the case of defending enterprises that have been successfully hacked by imposing additional a posteriori costs on the attacker. Our idea is simple: for every real document $d$ d , we develop methods to automatically generate a set $Fake(d)$ F a k e ( d ) of fake documents that are very similar to $d$ d . The attacker who steals documents must wade through a large number of documents in detail in order to separate the real one from the fakes. Our $\mathsf {FORGE}$ FORGE system focuses on technical documents (e.g., engineering/design documents) and involves three major innovations. First, we represent the semantic content of documents via multi-layer graphs (MLGs). Second, we propose a novel concept of “meta-centrality” for multi-layer graphs. A meta-centrality (MC) measure takes a classical centrality measure (for ordinary graphs, not MLGs) as input, and generalizes it to MLGs. The idea is to generate fake documents by replacing concepts on the basis of meta-centrality with related concepts according to an ontology. Our third innovation is to show that the problem of generating the set $Fake(d)$ F a k e ( d ) of fakes can be viewed as an optimization problem. We prove that this problem is NP-complete and then develop efficient heuristics to solve it in practice. We ran detailed experiments on two datasets: one a panel of 20 human subjects, another with a panel of 10. Our results show that $\mathsf {FORGE}$ FORGE generates highly believable fakes.

The uncontrolled growth of fake news creation and dissemination we observed in recent years causes continuous threats to democracy, justice, and public trust. This problem has significantly driven the effort of both academia and industries for developing more accurate fake news detection strategies. Early detection of fake news is crucial, however the availability of information about news propagation is limited. Moreover, it has been shown that people tend to believe more fake news due to their features [10]. In this paper, we present our framework for fake news detection and we discuss in detail an approach based on deep learning that we implemented by using Google Bert features. Our experiments conducted on two well-known and widely used real-world datasets suggest that our method can outperform the state-of-the-art approaches and allows fake news accurate detection, even in the case of limited content information.

We demonstrate GraphDBLP, a tool to allow researchers for querying the DBLP bibliography as a graph. The DBLP source data were enriched with semantic similarity relationships computed using wordembeddings. A user can interact with the system either via a Web-based GUI or using a shell-interface, both provided with three parametric and pre-defined queries. GraphDBLP would represent a first graph-database instance of the computer scientist network, that can be improved through new relationships and properties on nodes at any time, and this is the main purpose of the tool, that is freely available on Github. To date, GraphDBLP contains 5+ million nodes and 24+ million relationship.

Recommender systems are increasingly playing an important role in our life, enabling users to find “what they need” within large data collections and supporting a variety of applications, from e-commerce to e-tourism. In this paper, we present a Big Data architecture supporting typical cultural heritage applications. On the top of querying, browsing, and analyzing cultural contents coming from distributed and heterogeneous repositories, we propose a novel user-centered recommendation strategy for cultural items suggestion. Despite centralizing the processing operations within the cloud, the vision of edge intelligence has been exploited by having a mobile app (Smart Search Museum) to perform semantic searches and machine-learning-based inference so as to be capable of suggesting museums, together with other items of interest, to users when they are visiting a city, exploiting jointly recommendation techniques and edge artificial intelligence facilities. Experimental results on accuracy and user satisfaction show the goodness of the proposed application.

The main objective of this work is the proposal of a decentralized data structure storing a large amount of data under the assumption that it is not possible or convenient to use a single workstation to host all data.The index is distributed over a computer network and the performance of the search, insert, delete operations are close to the traditional indices that use a single workstation.It is based on k-d trees and it is distributed across a network of "peers", where each one hosts a part of the tree and uses message passing for communication between peers.In particular, we propose a novel version of the k-nearest neighbour algorithm that starts the query in a randomly chosen peer and terminates the query as soon as possible.Preliminary experiments have demonstrated that in about 65% of cases it starts a query in a random peer that does not involve the peer containing the root of the tree and in the 98% of cases it terminates the query in a peer that does not contain the root of the tree.

Summary Nowadays, social networks provide users an interactive platform to create and share heterogeneous content for a lot of different purposes (eg, to comment events and facts, and express and share personal opinions on specific topics), allowing millions of individuals to create online profiles and share personal information with vast networks known and sometimes also unknown people. Knowledge about users, content, and relationships in a social network may be used for an adversary attack of some victims easily. Although a number of works have been done for data privacy preservation on relational data, they cannot be applied in social networks and in general for big data analytics. In this paper, we first propose a novel data model that integrates and combines information on users belonging to 1 or more heterogeneous online social networks, together with the content that is generated, shared, and used within the related environments, using an hypergraph data structure; then we implemented the most diffused centrality measures and also introduced a new centrality measure—based on the concept of “neighborhood” among users—that may be efficiently applied for a number of data privacy issues, such as lurkers and neighborhood attack prevention, especially in “interest‐based” social networks. Some experiments using the Yelp dataset are discussed.

Despite the great amount of research done in the Online Social Networks (OSNs) field, only few works have investigated the use of multimedia data in such realm. Instead, it is the authors' opinion that a novel data model that takes into account the intrinsic characteristics of multimedia may be of great help in managing Multimedia OSNs for providing more effective algorithms. In this paper, we describe a novel OSN data model that supports easy management of multimedia content in a unique framework, providing a more effective and efficient mechanism for data and information management in a variety of applications, especially for Influence Analysis aims.

Nowadays, the need for well-structured ontologies in the medical domain is rising, especially due to the significant support these ontologies bring to a number of groundbreaking applications, such as intelligent medical diagnosis system and decision-support systems. Indeed, the considerable production of clinical data belonging to restricted sub domains has stressed the need for efficient methodologies to automatically process enormous amounts of un-structured, domain specific information in order to make use of the knowledge these data provide. In this work, we propose a lexicon-grammar based methodology for efficient information extraction and retrieval on unstructured medical records in order to enrich a simple ontology descriptive of such a kind of documents. We describe the NLP methodology for extracting RDF triples from unstructured medical records, and show how an existing ontology built by a domain expert can be populated with the set of triples and then enriched through its linking to external resources.

In this paper, we present a framework supporting information retrieval over corpora of documents using an automatic sematic query expansion approach. The main idea is to expand the set of words used as query terms exploiting the notion of semantic similarity between the concepts related to the search terms. We leverage existing lexical resources and similarity metrics computed among terms to generate - by a proper mapping into a vectorial space - an index for the fast retrieval of a set of terms "semantically correlated" to a given query term. The vector of expanded terms is then exploited in the query stage to retrieve documents that are significantly related to specific combinations of the query terms. Preliminary experimental results concerning efficiency and effectiveness of the proposed approach are reported and discussed.

As well known, Semantic Web technologies make available a set of facilities that allow data to be shared and reused across applications. The last generation of Content Management System (CMS) can leverage such technologies to improve the content management task incorporating semantic annotations of the produced resources. Here, we present the benefits deriving from the application of semantic technologies in a CMS environment. To this goal, we collect preliminary results about the effectiveness of the integration of a semantic annotation engine within the Intrage Web Portal for content management purposes. The obtained results show that the approach is quite promising and encourage the current research.

In the last years, the large availability of information and knowledge models formalized by ontologies has demanded effective and efficient methodologies for reusing and integrating such models in global conceptualizations of a specific knowledge or application domain. The ability to effectively and efficiently perform knowledge reuse is a crucial factor in the development of ontologies, which are a potential solution to the problem of information standardization and a viaticum towards the realization of knowledge-based digital ecosystem. In this paper, an approach to ontology reuse based on heterogeneous matching techniques will be presented; in particular, we will show how the process of ontology building will be improved and simplified, by automating the selection and the reuse of existing data models to support the creation of digital ecosystems. The proposed approach has been applied to the food domain, specifically to food production.

Transaction management in different application contexts is still a challenging task. In this paper we propose a novel method in order to improve concurrency of a particular kind of transaction, known as long running transactions. Differently from other techniques presented in the literature, we design a sort of hybrid approach between optimistic and pessimistic concurrency models. From one hand, our basic idea consists in taking into account frequent disconnections or inactivity periods of a generic transaction during its life-cycle and, from the other one, we consider the semantics related to operations produced by transactions. Our solution avoid an indefinite or long resource locking due to disconnecting (or idle) transactions or a high rate of preventive aborts; eventually, a transaction semantic compatibility is exploited in order to increase the concurrency of reconcilable operations on the same resources. To these purposes, we have implemented a middleware with the aims of emulating a transactional scheduling, and several experiments have been carried out.

In this paper we present our development experience of a Semantic Content Management System able to handle and manage uniformly heterogeneous contents of different kinds (texts, video and images) considering the related semantics. To this aim, we exploit several Semantic Web technologies: RDF/OWL for data modeling and representation, SPARQL as querying language, Multimedia Information Extraction techniques, W3C standard models for creating taxonomies and resource annotation, vocabularies and microformats. We also follow the Best Practices and Issues for the Web of Data by reusing LOD information for the Entity annotation of the managed content, thus providing CMS with advanced capabilities such as contents’ authoring and tagging.

There are numerous applications where we wish to discover unexpected activities in a sequence of time-stamped observation data--for instance, we may want to detect inexplicable events in transactions at a website or in video of an airport tarmac. In this paper, we start with a known set $({\cal A})$ of activities (both innocuous and dangerous) that we wish to monitor. However, in addition, we wish to identify "unexplained" subsequences in an observation sequence that are poorly explained (e.g., because they may contain occurrences of activities that have never been seen or anticipated before, i.e., they are not in $({\cal A})$). We formally define the probability that a sequence of observations is unexplained (totally or partially) w.r.t. $({\cal A})$. We develop efficient algorithms to identify the top-$(k)$ Totally and partially unexplained sequences w.r.t. $({\cal A})$. These algorithms leverage theorems that enable us to speed up the search for totally/partially unexplained sequences. We describe experiments using real-world video and cyber-security data sets showing that our approach works well in practice in terms of both running time and accuracy.

There are numerous applications (e.g., video surveillance, fraud detection, cybersecurity) in which we wish to identify unexplained sets of events. Most related past work has been domain-dependent (e.g., video surveillance, cybersecurity) and has focused on the valuable class of statistical anomalies in which statistically unusual events are considered. In contrast, suppose there is a set A of known activity models (both harmless and harmful) and a log L of time-stamped observations. We define a part L '⊆ L of the log to represent an unexplained situation when none of the known activity models can explain L ' with a score exceeding a user-specified threshold. We represent activities via probabilistic penalty graphs (PPGs) and show how a set of PPGs can be combined into one Super-PPG for which we define an index structure. Given a compute cluster of ( K + 1) nodes (one of which is a master node), we show how to split a Super-PPG into K subgraphs, each of which can be independently processed by a compute node. We provide algorithms for the individual compute nodes to ensure seamless handoffs that maximally leverage parallelism. PADUA is domain-independent and can be applied to many domains (perhaps with some specialization). We conducted detailed experiments with PADUA on two real-world datasets—the ITEA CANDELA video surveillance dataset and a network traffic dataset appropriate for cybersecurity applications. PADUA scales extremely well with the number of processors and significantly outperforms past work both in accuracy and time. Thus, PADUA represents the first parallel architecture and algorithm for identifying unexplained situations in observation data, offering both scalability and accuracy.

The extraordinary technological progress we have witnessed in recent years has made it possible to generate and exchange multimedia content at an unprecedented rate. As a consequence, massive collections of multimedia objects are now widely available to a large population of users. As the task of browsing such large collections could be daunting, Recommender Systems are being developed to assist users in finding items that match their needs and preferences. In this article, we present a novel approach to recommendation in multimedia browsing systems, based on modeling recommendation as a social choice problem. In social choice theory, a set of voters is called to rank a set of alternatives, and individual rankings are aggregated into a global ranking. In our formulation, the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We first define what constitutes a choice in the browsing domain and then define a mechanism to aggregate individual choices into a global ranking. The result is a framework for computing customized recommendations by originally combining intrinsic features of multimedia objects, past behavior of individual users, and overall behavior of the entire community of users. Recommendations are ranked using an importance ranking algorithm that resembles the well-known PageRank strategy. Experiments conducted on a prototype of the proposed system confirm the effectiveness and efficiency of our approach.

Nowadays Augmented Reality (AR) is an emerging technology that promises significant results in a variety of applications. Despite the great advances in computer graphics and multimedia technologies, there is still a lack in the definition and building of a complete 3D object management system, in order to efficiently store, manage and retrieve such objects. In this paper a novel data model and a building and querying language for 3D objects are presented. This preliminary environment has been properly designed to be sufficiently powerful for including the functionalities and characteristics of modern 3D description languages, such as X3D and Collada, and at same time provides a formalism for optimization aims. Several examples from real Italian Cultural Heritage sites are provided and preliminary results are commented and discussed, showing the main advantages of the proposed system for both creation and information retrieval goals.

The definition of ontologies within the multimedia domain still remains a challenging task, due to the complexity of multimedia data and the related knowledge. In this paper, we present a novel framework (MOWIS) that aims at realizing a system for building Multimedia Ontologies from Web Information Sources. In particular, we propose: i) a multimedia ontology model that combines both low level descriptors and high level semantic concepts; ii) automatic construction of ontologies using the Flickr web services that provide images, tags, keywords and sometimes useful annotation describing both the image content and personal interesting information. Eventually, we describe an example of automatic ontology generation in a specific domain and present some preliminary experimental results.

The exchange of information between heterogeneous and distributed health information systems preserving the semantics is an important open issue for the health care sector. In this paper, we propose an approach that, exploiting the Semantic Web technologies, has the objective of allowing semantic interoperability among software agents for e-Health applications that preserves, not only the semantic of transmitted messages, but also the subjectivity of agent's world vision in the communication The proposed approach exploits the Semantic Triangle Model to differentiate the roles of referents (real world objects) and concepts (mental image or impression about a real object) in the communication process and ensures an effective semantic interoperability without the need of a unique shared conceptualization.

Consider a video surveillance application that monitors some location. The application knows a set of activity models (that are either normal or abnormal or both), but in addition, the application wants to find video segments that are unexplained by any of the known activity models - these unexplained video segments may correspond to activities for which no previous activity model existed. In this paper, we formally define what it means for a given video segment to be unexplained (totally or partially) w.r.t. a given set of activity models and a probability threshold. We develop two algorithms - FindTUA and FindPUA - to identify Totally and Partially Unexplained Activities respectively, and show that both algorithms use important pruning methods. We report on experiments with a prototype implementation showing that the algorithms both run efficiently and are accurate.

Recommender systems in e-commerce applications help consumers with information useful to decide which products to purchase, suggesting products, services, and information items to potential consumers. Nowadays recommender systems interfaces are more oriented to technical people than to normal consumer, that are really not necessarily expert of statistic, scores and so on. In this paper we propose to join the capabilities of relevance feedback with recommendation strategies in a more useful architecture based on 3D navigation systems. A general framework is described, together with novel techniques oriented to an effective human-computer interaction. An example of the proposed system is also discussed.

E-government processes are dedicated to the improvement of the efficiency, inexpensiveness and accessibility of public administration services: dematerialization activities, introduced to manage bureaucratic digital documents in a proper way, are among the main tasks of the e-government works. In this paper we present a novel RDF model of digital documents for improving the dematerialization effectiveness, that constitutes the starting point of an information system able to manage documental streams in the most efficient way. Such model takes into account the important need that is required in several e-government applications which, depending on authorities or final users or time, provides different representations of the same multimedia contents.

In this paper, we present a new vision of multimedia recommender systems based on an a novel paradigm that combines both analysis of user behavior and semantic descriptors of multimedia objects. In particular, we model recommendation as a two step process: First we propose a model of user behavior, based on a semantic network that captures the necessary knowledge of the domain of interest; second we propose a recommendation strategy based on both this kind of a-priori knowledge and on the current usage patterns, considering the actual and complex structure of multimedia data. We have implemented a prototype that supports our proposed recommendation strategy; eventually a real use of our system based on a 3D interface is presented and discussed.

In the classical theory of social choice, a set of voters is called to rank a set of alternatives and a social ranking of the alternatives is generated. In this paper, we model recommendation in the context of browsing systems as a social choice problem, where the set of voters and the set of alternatives both coincide with the set of objects in the data collection. We then propose an importance ranking method that strongly resembles the well known PageRank ranking system, and takes into account both the browsing behavior of the users and the intrinsic features of the objects in the collection. We apply the proposed approach in the context of multimedia browsing systems and show that it can generate effective recommendations and can scale well for large data collections.

There is now a growing need to identify various kinds of activities that occur in videos. In this paper, we first present a logical language called Probabilistic Activity Description Language (PADL) in which users can specify activities of interest. We then develop a probabilistic framework which assigns to any subvideo of a given video sequence a probability that the subvideo contains the given activity, and we finally develop two fast algorithms to detect activities within this framework. OffPad finds all minimal segments of a video that contain a given activity with a probability exceeding a given threshold. In contrast, the OnPad algorithm examines a video during playout (rather than afterwards as OffPad does) and computes the probability that a given activity is occurring (even if the activity is only partially complete). Our prototype Probabilistic Activity Detection System (PADS) implements the framework and the two algorithms, building on top of existing image processing algorithms. We have conducted detailed experiments and compared our approach to four different approaches presented in the literature. We show that-for complex activity definitions-our approach outperforms all the other approaches.

E-government activities have the aim to optimize the work of the governmental offices and offer, to citizens and businesses, effective, accessible and efficient services. Dematerialization, which is among the main activities of e-government, is devoted to optimize information communication, in terms of consumed time and resources, as well as processing documents. In this work we propose a new model for multimedia document, suitable for e-government activities, and an information system able to integrate and process different multimedia data type (as images, text, graphic objects, audio, video, composite multimedia, etc.) providing facilities for indexing, storage, retrieval, control of multimedia data, together with long term preservation strategies.

The increasing interest in the context of e-government requires intelligent techniques for legal information and knowledge management. In this paper we describe a system that, given a number of legal paper documents, automatically transforms them into suitable RDF statements, using several ontological and linguistic knowledge levels. Although we describe a general methodology for a number of application domains, our system is particularly suitable for the notary realm.

Traditional image classification techniques are based on the analysis of low-level visual features or on textual information. In this paper, we describe a novel solution which tries to improve image analysis and processing algorithms by incorporating keywords and textual annotation produced by humans in a folksonomy environment, namely Flickr. The obtained experimental results demonstrate that the proposed categorization process achieves quite good performances in terms of efficiency and effectiveness.

Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension-Probabilistic Petri Nets (PPN)-and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.

Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension—Probabilistic Petri Nets (PPN)—and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.

We view scenes in the real world by moving our eyes three to four times each second and integrating information across subsequent fixations (foveation points). By taking advantage of this fact, in this paper we propose an original approach to partitioning of a video into shots based on a foveated representation of the video. More precisely, the shot-change detection method is related to the computation, at each time instant, of a consistency measure of the fixation sequences generated by an ideal observer looking at the video. The proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. Results on videos of various content types are reported and validate the proposed approach.

We present a method for the detection of microcalcifications in digital mammographic images. Our approach is based on the wavelet transform, but differently from other techniques proposed in the literature, the detection is directly accomplished in the wavelet domain and no inverse transform is required. After a preliminary denoising pass, microcalcifications are separated from background tissue. This is performed by exploiting information gained through evaluation of Renyi's entropy at the different decomposition levels of the wavelet space. Experimental results achieved on the standard Nimegen data set are shown and discussed.

Presents a method for the detection of small objects embedded in a noisy background. The detection is performed on the wavelet transformed image. After a preliminary de-noising pass, the objects are separated from background by exploiting the evaluation of Renyi's information at the different decomposition levels of the wavelet transform. We apply the proposed technique to detect microcalcifications in digital mammographic images.

We present results obtained by different contrast enhancement methods applied to medical images. We take into account classical histogram specification, local and wavelet-based techniques and a novel approach for multiscale contrast enhancement. The latter, whose rationale grounds in theories of visual perception, exploits a local definition of the Fechner-Weber's contrast within the context of a non-linear scale-space representation generated by anisotropic diffusion. Our experimental fields concerns a difficult kind of medical images, namely digital mammographic images.

For the purpose of enhancing mammographic images, the authors introduce filters based on anisotropic diffusion, and they investigate their relations with classical methods like median, weighted median and tree-structured filters. The performances of the different techniques are quantitatively evaluated and discussed.

Filters for mammography based on anisotropic diffusion are introduced and evaluated with respect to well known filters adopted within this field. Results reported concern with images including microcalcifications. The performances of anisotropic filters are assessed both on the basis of figures of merit, allowing a preliminary quantitative enhancement evaluation, and according to goal-directed evaluation relying upon a simple segmentation module. Results indicate that proposed filters exhibit an effective and appealing behavior but they are subject to a more complex tuning, if compared to traditional filters.

In todays hospitals the medical workstation is a basic component of any image management and communication system. The design of such component can be very complex, because of the challenging engineering requirements. In this work we present an architectural model of a flexible and portable software platform upon which medical workstations can be realized. The model is developed within the overall framework provided by the object oriented paradigm.>

An object-oriented approach to the development of an environment for document processing and analysis is presented. The use of object-oriented techniques may play an important role in the domain we are considering. Document processing systems built today are different from what they were a few years ago: they are larger and more complex. The need of achieving effective and stable systems has gained higher priority. Furthermore, recent systems are expected to devote a deeper attention to the user's perspective. A general model that can be suitably adopted for building such systems is outlined, and it is shown how the object-oriented paradigm can provide a unifying framework for developing the experimental environment. Methodological steps involved are illustrated and a preliminary version of the environment is described.>