The growing ubiquity of Retrieval-Augmented Generation (RAG) systems in several real-world services triggers severe concerns about their security. A RAG system improves the generative capabilities of a Large Language Model (LLM) by a retrieval mechanism that operates on a private knowledge base, whose unintended exposure could lead to severe consequences, including breaches of private and sensitive information. This paper presents a black-box attack to force a RAG system to leak its private knowledge base which, unlike existing approaches, is both adaptive and automatic. A relevance-based mechanism and an attacker-side open-source LLM favor the generation of effective queries to leak most of the (hidden) knowledge base. Extensive experimentation proves the quality of the proposed algorithm in different RAG pipelines and domains, compared to very recent related approaches, which turn out to be either not fully black-box, not adaptive, or not based on open-source models. The findings from our study highlight the urgent need for more robust privacy safeguards in the design and deployment of RAG systems. We have made the open-source code for our experimental procedure available for public use [12].

Named Entity Recognition (NER) is a critical task in natural language processing with significant implications for various downstream applications. This paper presents a comprehensive comparative study of NER performance across multiple domains, focusing on the business-oriented BUSTER dataset and the widely used CoNLL 2003 English dataset. We evaluate a diverse set of models, including fine-tuned Question Answering (QA) models such as BERT and RoBERTa, as well as state-of-the-art generative Large Language Models such as GPT series 3 and 4 [1], LLaMA 2-3 [2], [3] and Mistral [4].Our study investigates the adaptation of QA models for NER and examines the zero-shot capabilities of generative models, assessing their intrinsic ability to identify named entities without task-specific fine-tuning. Through extensive experimentation, we analyze precision, recall, and F1 scores across different entity categories, comparing performance across datasets and model families. Additionally, for QA models we explore the robustness of these models under different training setups and evaluation metrics, shedding light on their adaptability to structured and unstructured text data. Our findings provide insights into the effectiveness of both fine-tuned and zero-shot approaches, with models achieving state-of-the-art performance through fine-tuning. This contributes to a broader understanding of the NER task, particularly in domain-specific contexts.

Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared with traditional NER approaches, these models have demonstrated strong generalization capabilities. Existing LLMs primarily focus on addressing zero-shot NER on Out-of-Domain inputs, while fine-tuning on an extensive number of entity classes that often highly or completely overlap with test sets. In this work instead, we propose SLIMER, an approach designed to tackle never-seen-before entity tags by instructing the model on fewer examples, and by leveraging a prompt enriched with definition and guidelines. Experiments demonstrate that definition and guidelines yield better performance, faster and more robust learning, particularly when labelling unseen named entities. Furthermore, SLIMER performs comparably to state-of-the-art approaches in out-of-domain zero-shot NER, while being trained in a more fair, though certainly more challenging, setting.

Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for traditional crosswords, educational clue-answer pairs datasets are missing. In this article, we propose a methodology to build educational clue generation datasets that can be used to instruct Large Language Models (LLMs). By gathering from Wikipedia pages informative content associated with relevant keywords, we use Large Language Models to automatically generate pedagogical clues related to the given input keyword and its context. With such an approach, we created clue-instruct, a dataset containing 44,075 unique examples with text-keyword pairs associated with three distinct crossword clues. We used clue-instruct to instruct different LLMs to generate educational clues from a given input content and keyword. Both human and automatic evaluations confirmed the quality of the generated clues, thus validating the effectiveness of our approach.

Artificial Intelligence (AI)–generated images represent a significant threat in various fields, such as security, privacy, media forensics and content moderation. In this paper, a novel approach for the detection of StyleGAN2–generated human faces is presented, leveraging a Transfer Learning strategy to improve the Classification performance of the models. A modified version of the state– of–the–art semantic segmentation model DeepLabV3+, using either a ResNet50 or a MobileNetV3 Large as feature extraction backbones, is used to create both a face segmentation model and the synthetic image detector. To achieve this goal, the models are at first trained for face segmentation in a multi–class Classification task on a widely used semantic segmentation dataset, achieving remarkable results for both configurations. Then, the pre–trained models are retrained on a collection of real and generated images, gathered from different sources to solve a binary Classification task, namely to detect synthetic (i.e. generated) images, thus carrying out two different transfer learning strategies. The results indicate that this targeted methodology significantly improves the detection rates compared to analyzing the face as a whole, and underlines the importance of advanced image recognition technologies when tackling the challenge of detecting generated faces.

Educational crossword puzzles enhance critical thinking, vocabulary development, and concept reinforcement. They encourage independent learning, improve memorization, and foster problem-solving skills. With their multisensory approach, crossword puzzles offer a valuable educational experience. With the help of AI technology, creating high-quality, diverse crosswords is now easier, promoting enjoyable and effective learning experiences. In this endeavor, we harnessed the power of multiple language models, including GPT3, GPT2-XL, and BERT, to construct a comprehensive system that generates and verifies crossword clues. Our ultimate aim is to employ this system in the creation of educational crosswords. To achieve this, we compiled an extensive dataset consisting of over seven million clue-answer pairs spanning the years 1913 to mid-2021. By leveraging this dataset, we aimed to generate original yet challenging clues that engage solvers. Our generator underwent fine-tuning using this large collection of clues and corresponding answers, covering a wide range of themes. Additionally, we implemented a few/zero-shot learning techniques, such as prompt engineering, to generate clues based on given texts. To guarantee the quality of the generated clue-answer pairs, we utilized diverse classifiers, by fine-tuning pre-existing language models on a labeled dataset and additionally, we harnessed the power of the zero-shot learning approach to validate the generated clue-answer pairs effectively. This classifier effectively filters out nonsensical or subpar pairings. The evaluation results are highly encouraging, reinforcing the efficacy of the proposed approach.

Transformers have been established as one of the most effective neural approach in performing various Natural Language Processing tasks. However, following common trend in modern deep architectures, their scale has quickly grown to an extent that reduces the concrete possibility for several enterprises to train such models from scratch. Indeed, despite their high-level performances, Transformers have the general drawback of requiring a huge amount of training data, computational resources and energy consumption to be successfully optimized. For this reason, more recent architectures like Bidirectional Encoder Representations from Transformers rely on unlabeled data to pre-train the model, which is later fine-tuned for a specific downstream task using a relatively smaller amount of training data. In a similar fashion, this paper considers a plug-and-play framework that can be used to inject multiple syntactic features, like Part-of-Speech Tagging or Dependency Parsing, into any kind of pre-trained Transformer. This novel approach allows to perform sequence-to-sequence labeling tasks by exploiting: (i) the (more abundant) available training data that is also used to learn the syntactic features, (ii) the language data that is used to pre-train the transformer model. The experimental results show that our approach improves over the baseline performances of the underlying model in different datasets, thus proving the effectiveness of employing syntactic language information for semantic regularization. In addition, we show that our architecture has a huge efficiency advantage over pure large language models. Indeed, by using a model with limited size, but whose input data are enriched with syntactic information, we show that it is possible to obtain a significant reduction of CO2 emissions without decreasing the prediction performances.

Abstract Injecting prior knowledge into the learning process of a neural architecture is one of the main challenges currently faced by the artificial intelligence community, which also motivated the emergence of neural-symbolic models. One of the main advantages of these approaches is their capacity to learn competitive solutions with a significant reduction of the amount of supervised data. In this regard, a commonly adopted solution consists of representing the prior knowledge via first-order logic formulas, then relaxing the formulas into a set of differentiable constraints by using a t-norm fuzzy logic. This paper shows that this relaxation, together with the choice of the penalty terms enforcing the constraint satisfaction, can be unambiguously determined by the selection of a t-norm generator, providing numerical simplification properties and a tighter integration between the logic knowledge and the learning objective. When restricted to supervised learning, the presented theoretical framework provides a straight derivation of the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vanishing gradient problem in very deep structures. However, the proposed learning formulation extends the advantages of the cross-entropy loss to the general knowledge that can be represented by neural-symbolic methods. In addition, the presented methodology allows the development of novel classes of loss functions, which are shown in the experimental results to lead to faster convergence rates than the approaches previously proposed in the literature.

The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the nodes of the graph by means of an iterative diffusion procedure that, during the learning stage, must be computed at every epoch, until the fixed point of a learnable state transition function is reached, propagating the information among the neighbouring nodes. We propose a novel approach to learning in GNNs, based on constrained optimization in the Lagrangian framework. Learning both the transition function and the node states is the outcome of a joint process, in which the state convergence procedure is implicitly expressed by a constraint satisfaction mechanism, avoiding iterative epoch-wise procedures and the network unfolding. Our computational structure searches for saddle points of the Lagrangian in the adjoint space composed of weights, nodes state variables and Lagrange multipliers. This process is further enhanced by multiple layers of constraints that accelerate the diffusion process. An experimental analysis shows that the proposed approach compares favourably with popular models on several benchmarks.

Writers, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems.

In the last few years we have seen a remarkable progress from the cultivation of the idea of expressing domain knowledge by the mathematical notion of constraint. However, the progress has mostly involved the process of providing consistent solutions with a given set of constraints, whereas learning “new” constraints, that express new knowledge, is still an open challenge. In this paper we propose a novel approach to learning of constraints which is based on information theoretic principles. The basic idea consists in maximizing the transfer of information between task functions and a set of learnable constraints, implemented using neural networks subject to L1 regularization. This process leads to the unsupervised development of new constraints that are fulfilled in different sub-portions of the input domain. In addition, we define a simple procedure that can explain the behaviour of the newly devised constraints in terms of First-Order Logic formulas, thus extracting novel knowledge on the relationships between the original tasks. An experimental evaluation is provided to support the proposed approach, in which we also explore the regularization effects introduced by the proposed Information-Based Learning of Constraint (IBLC) algorithm.

Deep learning has been shown to achieve impressive results in several tasks where a large amount of training data is available. However, deep learning solely focuses on the accuracy of the predictions, neglecting the reasoning process leading to a decision, which is a major issue in life-critical applications. Probabilistic logic reasoning allows to exploit both statistical regularities and specific domain expertise to perform reasoning under uncertainty, but its scalability and brittle integration with the layers processing the sensory data have greatly limited its applications. For these reasons, combining deep architectures and probabilistic logic reasoning is a fundamental goal towards the development of intelligent agents operating in complex environments. This paper presents Relational Neural Machines, a novel framework allowing to jointly train the parameters of the learners and of a First-Order Logic based reasoner. A Relational Neural Machine is able to recover both classical learning from supervised data in case of pure sub-symbolic learning, and Markov Logic Networks in case of pure symbolic reasoning, while allowing to jointly train and perform inference in hybrid learning tasks. Proper algorithmic solutions are devised to make learning and inference tractable in large-scale problems. The experiments show promising results in different relational tasks.

In many real world applications, data are characterized by a complex structure, that can be naturally encoded as a graph.In the last years, the popularity of deep learning techniques has renewed the interest in neural models able to process complex patterns.In particular, inspired by the Graph Neural Network (GNN) model, different architectures have been proposed to extend the original GNN scheme.GNNs exploit a set of state variables, each assigned to a graph node, and a diffusion mechanism of the states among neighbor nodes, to implement an iterative procedure to compute the fixed point of the (learnable) state transition function.In this paper, we propose a novel approach to the state computation and the learning algorithm for GNNs, based on a constraint optimisation task solved in the Lagrangian framework.The state convergence procedure is implicitly expressed by the constraint satisfaction mechanism and does not require a separate iterative phase for each epoch of the learning procedure.In fact, the computational structure is based on the search for saddle points of the Lagrangian in the adjoint space composed of weights, neural outputs (node states), and Lagrange multipliers.The proposed approach is compared experimentally with other popular models for processing graphs.

Deep neural networks are usually considered black-boxes due to their complex internal architecture, that cannot straightforwardly provide human-understandable explanations on how they behave. Indeed, Deep Learning is still viewed with skepticism in those real-world domains in which incorrect predictions may produce critical effects. This is one of the reasons why in the last few years Explainable Artificial Intelligence (XAI) techniques have gained a lot of attention in the scientific community. In this paper, we focus on the case of multi-label classification, proposing a neural network that learns the relationships among the predictors associated to each class, yielding First-Order Logic (FOL)-based descriptions. Both the explanation-related network and the classification-related network are jointly learned, thus implicitly introducing a latent dependency between the development of the explanation mechanism and the development of the classifiers. Our model can integrate human-driven preferences that guide the learning-to-explain process, and it is presented in a unified framework. Different typologies of explanations are evaluated in distinct experiments, showing that the proposed approach discovers new knowledge and can improve the classifier performance.

In this paper we study a constraint-based representation of neural network architectures. We cast the learning problem in the Lagrangian framework and we investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints, learning from the available supervisions. The computational structure of the proposed Local Propagation (LP) algorithm is based on the search for saddle points in the adjoint space composed of weights, neural outputs, and Lagrange multipliers. All the updates of the model variables are locally performed, so that LP is fully parallelizable over the neural units, circumventing the classic problem of gradient vanishing in deep networks. The implementation of popular neural models is described in the context of LP, together with those conditions that trace a natural connection with Backpropagation. We also investigate the setting in which we tolerate bounded violations of the architectural constraints, and we provide experimental evidence that LP is a feasible approach to train shallow and deep networks, opening the road to further investigations on more complex architectures, easily describable by constraints.

Unsupervised learning from continuous visual streams is a challenging problem that cannot be naturally and efficiently managed in the classic batch-mode setting of computation. The information stream must be carefully processed accordingly to an appropriate spatio-temporal distribution of the visual data, while most approaches of learning commonly assume uniform probability density. In this paper we focus on unsupervised learning for transferring visual information in a truly online setting by using a computational model that is inspired to the principle of least action in physics. The maximization of the mutual information is carried out by a temporal process which yields online estimation of the entropy terms. The model, which is based on second-order differential equations, maximizes the information transfer from the input to a discrete space of symbols related to the visual features of the input, whose computation is supported by hidden neurons. In order to better structure the input probability distribution, we use a human-like focus of attention model that, coherently with the information maximization model, is also based on second-order differential equations. We provide experimental results to support the theory by showing that the spatio-temporal filtering induced by the focus of attention allows the system to globally transfer more information from the input stream over the focused areas and, in some contexts, over the whole frames with respect to the unfiltered case that yields uniform probability distributions.

We consider a scenario where an artificial agent is reading a stream of text composed of a set of narrations, and it is informed about the identity of some of the individuals that are mentioned in the text portion that is currently being read. The agent is expected to learn to follow the narrations, thus disambiguating mentions and discovering new individuals. We focus on the case in which individuals are entities and relations and propose an end-to-end trainable memory network that learns to discover and disambiguate them in an online manner, performing one-shot learning and dealing with a small number of sparse supervisions. Our system builds a not-given-in-advance knowledge base, and it improves its skills while reading the unsupervised text. The model deals with abrupt changes in the narration, considering their effects when resolving coreferences. We showcase the strong disambiguation and discovery skills of our model on a corpus of Wikipedia documents and on a newly introduced data set that we make publicly available.

In this paper, we introduce the convex fragment of Łukasiewicz logic and discuss its possible applications in different learning schemes. The provided theoretical results are highly general because they can be exploited in any learning framework involving logical constraints. The method is of particular interest since the fragment guarantees to deal with convex constraints, which are shown to be equivalent to a set of linear constraints. Within this framework, we are able to formulate learning with kernel machines as well as collective classification as a quadratic programming problem.

Relevance ranking consists in sorting a set of objects with respect to a given criterion. However, in personalized retrieval systems, the relevance criteria may usually vary among different users and may not be predefined. In this case, ranking algorithms that adapt their behavior from users' feedbacks must be devised. Two main approaches are proposed in the literature for learning to rank: the use of a scoring function, learned by examples, that evaluates a feature-based representation of each object yielding an absolute relevance score, a pairwise approach, where a preference function is learned to determine the object that has to be ranked first in a given pair. In this paper, we present a preference learning method for learning to rank. A neural network, the comparative neural network (CmpNN), is trained from examples to approximate the comparison function for a pair of objects. The CmpNN adopts a particular architecture designed to implement the symmetries naturally present in a preference function. The learned preference function can be embedded as the comparator into a classical sorting algorithm to provide a global ranking of a set of objects. To improve the ranking performances, an active-learning procedure is devised, that aims at selecting the most informative patterns in the training set. The proposed algorithm is evaluated on the LETOR dataset showing promising performances in comparison with other state-of-the-art algorithms.

This paper presents a general framework to integrate prior knowledge in the form of logic constraints among a set of task functions into kernel machines. The logic propositions provide a partial representation of the environment, in which the learner operates, that is exploited by the learning algorithm together with the information available in the supervised examples. In particular, we consider a multi-task learning scheme, where multiple unary predicates on the feature space are to be learned by kernel machines and a higher level abstract representation consists of logic clauses on these predicates, known to hold for any input. A general approach is presented to convert the logic clauses into a continuous implementation, that processes the outputs computed by the kernel-based predicates. The learning task is formulated as a primal optimization problem of a loss function that combines a term measuring the fitting of the supervised examples, a regularization term, and a penalty term that enforces the constraints on both supervised and unsupervised examples. The proposed semi-supervised learning framework is particularly suited for learning in high dimensionality feature spaces, where the supervised training examples tend to be sparse and generalization difficult. Unlike for standard kernel machines, the cost function to optimize is not generally guaranteed to be convex. However, the experimental results show that it is still possible to find good solutions using a two stage learning schema, in which first the supervised examples are learned until convergence and then the logic constraints are forced. Some promising experimental results on artificial multi-task learning tasks are reported, showing how the classification accuracy can be effectively improved by exploiting the a priori rules and the unsupervised examples.

Similarity neural networks (SNNs) are a novel neural network model designed to learn similarity measures for pairs of patterns, exploiting binary supervision. SNNs guarantee to compute non negative and symmetric measures, and show good generalization capabilities even if a small set of supervised pairs is used for training. The application of the new model to K-Means like semi-supervised clustering is investigated, introducing a technique that allows the algorithm to compute cluster centroids by means of Backpropagation on the input layer of the SNN, biased by a regularization function. The experiments carried out on some datasets from the UCI repository show that SNN based clustering almost always outperforms other methods proposed in the literature.

The collective feedback of the users of an Information Retrieval system has been proved to be useful in many tasks. A popular approach in the literature is to process the logs stored by Internet Service Providers (ISP), Intranet proxies or Web search engines to extract a query-document bi-partite graph. In this paper, we propose to use a richer data structure which is able to preserve most of the information available in the logs including query refinements, page visits and search activity. In particular, we represent the query refinements as separate transitions between the corresponding query nodes in the graph and we augment the graph by associating one node to each single user. Users are linked to the queries which they have issued and to the documents they have visited. The resulting data structure is a complete representation of the collective search activity performed by the users of a search engine or of an Intranet. The experimental results show that this more powerful representation can be successfully used to improve the quality of query clustering and to discover query suggestions.

This paper presents an improved active shape model algorithm, that exploits auto associative neural networks (AANNs) to estimate the local feature models. The proposed technique aims at solving face feature localization tasks, nevertheless it can be used also in the more general case of object detection. Three main contributions are presented. The first one consists in the estimation of elliptic search areas by means of the training data. The second one is the use of AANNs as local feature detectors, since this network model is particularly suited to solve classification tasks with unbalanced classes. Finally, an optimized technique to set up the learning environment, needed to train the AANNs, is described. The performances of the proposed algorithm compare favorably with original ASMs and with two recent improved versions.

This paper presents a software system that is able to generate crosswords with no human intervention including definition generation and crossword compilation. In particular, the proposed system crawls relevant sources of the Web, extracts definitions from the downloaded pages using state-of-the-art natural language processing (NLP) techniques and, finally, attempts at compiling a crossword schema with the extracted definitions using a constrain satisfaction programming (CSP) solver. The crossword generator has relevant applications in entertainment, educational and rehabilitation contexts.

Recursive neural networks (RNNs) and graph neural networks (GNNs) are two connectionist models that can directly process graphs. RNNs and GNNs exploit a similar processing framework, but they can be applied to different input domains. RNNs require the input graphs to be directed and acyclic, whereas GNNs can process any kind of graphs. The aim of this paper consists in understanding whether such a difference affects the behaviour of the models on a real application. An experimental comparison on an image classification problem is presented, showing that GNNs outperforms RNNs. Moreover the main differences between the models are also discussed w.r.t. their input domains, their approximation capabilities and their learning algorithms.

Document clustering is a very hard task in automatic text processing since it requires extracting regular patterns from a document collection without a priori knowledge on the category structure. This task can be difficult also for humans because many different but valid partitions may exist for the same collection. Moreover, the lack of information about categories makes it difficult to apply effective feature selection techniques to reduce the noise in the representation of texts. Despite these intrinsic difficulties, text clustering is an important task for Web search applications in which huge collections or quite long query result lists must be automatically organized. Semi-supervised clustering lies in between automatic categorization and auto-organization. It is assumed that the supervisor is not required to specify a set of classes, but only to provide a set of texts grouped by the criteria to be used, to organize the collection. In this paper, we present a novel algorithm for clustering text documents which exploits the EM algorithm together with a feature selection technique based on information gain. The experimental results show that only very few documents are needed to initialize the clusters and that the algorithm is able to properly extract the regularities hidden in a huge unlabeled collection.

Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. Cross-language text categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper, we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular, in the proposed approach, we assume that a predefined category set and a collection of labeled training data is available for a given language L/sub 1/. A classifier for a different language L/sub 2/ is trained by translating the available labeled training set for L/sub 1/ to L/sub 2/ and by using an additional set of unlabeled documents from L/sub 2/. This technique allows us to extract correct statistical properties of the language L/sub 2/ which are not completely available in automatically translated examples, because of the different characteristics of language L/sub 1/ and of the approximation of the translation process. Our experimental results show that the performance of the proposed method is very promising when applied on a test document set extracted from newsgroups in English and Italian.

An artificial neural network model, capable of processing general types of graph structured data, has recently been proposed. This paper applies the new model to the computation of customised page ranks problem in the World Wide Web. The class of customised page ranks that can be implemented in this way is very general and easy because the neural network model is learned by examples. Some preliminary experimental findings show that the model generalizes well over unseen Web pages, and hence, may be suitable for the task of page rank computation on a large Web graph.

In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection.

In this paper, a new recursive neural network model, able to process directed acyclic graphs with labeled edges, is introduced, in order to address the problem of object detection in images. In fact, the detection is a preliminary step in any object recognition system. The proposed method assumes a graph-based representation of images, that combines both spatial and visual features. In particular, after segmentation, an edge between two nodes stands for the adjacency relationship of two homogeneous regions, the edge label collects information on their relative positions, whereas node labels contain visual and geometric information on each region (area, color, texture, etc.). Such graphs are then processed by the recursive model in order to determine the eventual presence and the position of objects inside the image. Some experiments on face detection, carried out on scenes acquired by an indoor camera, are reported, showing very promising results. The proposed technique is general and can be applied in different object detection systems, since it does not include any a priori knowledge on the particular problem.

Although search engines are playing a crucial role for the retrieval of information from the Web, they cannot guarantee the quality required for most relevant business activities as well as for many top-level research projects. In this paper we present MumbleSearch, a Web Content Monitor which is especially conceived to extract and organize topic-based information with emphasis on quality requirements. We present the architecture of the software platform and its deployment for a real-world application, involving Italian Small and Medium Enterprises (SME).

Effective solutions for Web search engines can take advantage of algorithms for the automatic organization of documents into homogeneous clusters. Unfortunately, document clustering is not an easy task especially when the documents share a common set of topics, like in vertical search engines. In this paper we propose two clustering algorithms which can be tuned by the feedback of an expert. The feedback is used to choose an appropriate basis for the representation of documents, while the clustering is performed in the projected space. The algorithms are evaluated on a dataset containing papers from computer science conferences. The results show that an appropriate choice of the representation basis can yield better performance with respect to the original vector space model.

The definition of efficient page ranking algorithms is becoming an important issue in the design of the query interface of Web search engines. Information flooding is a common experience especially when broad topic queries are issued. Queries containing only one or two keywords usually match a huge number of documents, while users can only afford to visit the first positions of the returned list, which do not necessarily refer to the most appropriate answers. Some successful approaches to page ranking in a hyperlinked environment, like the Web, are based on link analysis. We propose a general probabilistic framework for Web page scoring systems (WPSS), which incorporates and extends many of the relevant models proposed in the literature. In particular, we introduce scoring systems for both generic (horizontal) and focused (vertical) search engines. Whereas horizontal scoring algorithms are only based on the topology of the Web graph, vertical ranking also takes the page contents into account and are the base for focused and user adapted search interfaces. Experimental results are reported to show the properties of some of the proposed scoring systems with special emphasis on vertical search.

The World Wide Web has been at the center of a revolution in how algorithms are designed with massive amounts of data in mind. The essence of this revo- lution is conceptually very simple: real-world massive data sets are, more often than not, highly structured and regular. Regularities can be used in two com- plementary ways. First, systematic regularities within massive data sets can be used to craft algorithms that are potentially suboptimal in the worst-case, but highly effective for expected cases. Second, nonsystematic regularities—those that are too subtle to be encoded within an algorithm—can be discovered by automated methods so that the solutions are actually determined by the un- derlying data. In both cases, the existence of enormous problem instances that arise from a highly regular source is key to building more effective methods.

The Internet and the Web are continuously evolving giving rise to a rich and extremely dynamic environment where an increasing number of users require and expect new and more sophisticated services. Because of this, a field called “ Web Intelligence” is starting to receive interest from the Artificial Intelligence community. The Web and Internet pose new challenges to AI algorithms, which have been successfully applied in many other fields, at the same time stimu- lating the development of new techniques. In particular, as pointed out in the introduction to the first part of this special issue (Vol. 4, no. 2, May 2004), ma- chine learning methods have been extensively studied and have been applied to create intelligent systems that are actively involved with the Internet and Web.

The recursive paradigm extends the neural network processing and learning algorithms to deal with structured inputs. In particular, recursive neural network (RNN) models have been proposed to process information coded as directed positional acyclic graphs (DPAGs) whose maximum node outdegree is known a priori. Unfortunately, the hypothesis of processing DPAGs having a given maximum node outdegree is sometimes too restrictive, being the nature of some real-world problems intrinsically disordered. In many applications the node outdegrees can vary considerably among the nodes in the graph, it may be unnatural to define a position for each child of a given node, and it may be necessary to prune some edges to reduce the number of the network parameters, which is proportional to the maximum node outdegree. In this paper, we proposed a new recursive neural network model which allows us to process directed acyclic graphs (DAGs) with labeled edges, relaxing the positional constraint and the correlated maximum outdegree limit. The effectiveness of the new scheme is experimentally tested on an image classification task. The results show that the new RNN model outperforms the standard RNN architecture, also allowing us to use a smaller number of free parameters.

The presence of near-replicas of documents is very common on the Web. Documents may be replicated completely or partially for different reasons (versions, mirrors, etc.), or the same resource can be associated to different URLs (dynamically generated pages, etc.). Whilst replication can improve information accessibility by the users, the presence of near-replicated documents can hinder the effectiveness of search engines (for example, decreasing the coverage). We propose a method to detect similar pages, in particular replicas and near-replicas, which is based on a pair of signatures. The first signature is obtained by a random projection of the bag-of-words vector representing the page contents. The second signature is computed by a recursive equation which exploits the connectivity among the Web pages to code the context of each page. The accuracy of the proposed approach is analyzed and validated by experimental results which show that on the given dataset near-replicas can be detected with a precision-recall of 93%.

In this paper, we consider the possibility of altering the PageRank of web pages, from an administrator's point of view, through the modification of the PageRank equation. It is shown that this problem can be solved using the traditional quadratic programming techniques. In addition, it is shown that the number of parameters can be reduced by clustering web pages together through simple clustering techniques. This problem can be formulated and solved using quadratic programming techniques. It is demonstrated experimentally on a relatively large web data set, viz., the WT10G, that it is possible to modify the PageRanks of the web pages through the proposed method using a set of linear constraints. It is also shown that the PageRank of other pages may be affected; and that the quality of the result depends on the clustering technique used. It is shown that our results compared well with those obtained by a HITS based method.

The prediction of the linguistic origin of surnames is a basic functionality required in the design of high-quality multilanguage speech synthesizers. The assignment of a given string representing a surname to a specific language is typically based on a set of rules which can hardly be written in an explicit form. The approach we propose faces this problem combining a rule-based system with a module based on evidential reasoning and a module based on neural networks. The resulting hybrid system combines the different sources of information, merging both knowledge from experts on linguistics and knowledge automatically acquired using learning from examples. The system has been validated on a large database containing surnames belonging to four different languages, showing its effectiveness for real-world applications.

Page ranking is a fundamental step towards the construction of effective search engines for both generic (horizontal) and focused (vertical) search. Ranking schemes for horizontal search like the PageRank algorithm used by Google operate on the topology of the graph, regardless of the page content. On the other hand, the recent development of vertical portals (vortals) makes it useful to adopt scoring systems focussed on the topic and taking the page content into account.In this paper, we propose a general framework for Web Page Scoring Systems (WPSS) which incorporates and extends many of the relevant models proposed in the literature. Finally, experimental results are given to assess the features of the proposed scoring systems with special emphasis on vertical search.

Content-based search and organization of Web documents poses new issues in information retrieval. We propose a novel approach for the classification of HTML documents based on a structured representation of their contents which are split into logical contexts (paragraphs, sections, anchors, etc.). The classification is performed using Hidden Tree-Markov Models (HTMMs), an extension of Hidden Markov Models for processing structured objects. We report some promising experimental results showing that the use of the structured representation improves the classification accuracy in most of the cases.

Given a user's visual query, most visual information retrieval (VIR) systems rank the images in the database according to a predefined measure of similarity and return the most similar ones. We propose an adaptive VIR system that uses a retrieval process based on relevance feedback in order to learn the similarity criterion from the user. Our system is based on a structured representation of the image which is then processed by a recursive neural network. The search algorithm refines its response trying to minimize the number of steps required to find the target image.

A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable of inferring deterministic finite automata from sets of example and counterexample strings; however, discrete algorithmic methods are much better at this task and clearly outperform DTRNN in terms of space and time complexity. We show how DTRNN may be used to learn not the exact language that explains the whole learning set but an approximate and much simpler language that explains a great majority of the examples by using simpler rules. This is accomplished by gradually varying the error function in such a way that the DTRNN is eventually allowed to classify clearly but incorrectly those strings that it has found to be difficult to learn, which are treated as exceptions. The results show that in this way, the DTRNN usually manages to learn a simplified approximate language.

In the case of static data of high dimension it is often useful to reduce the dimensionality before performing pattern recognition and learning tasks. One of the main reasons for this is that models for lower-dimensional data usually have fewer parameters to be determined. The problem of finding fixed-length vector representations for labelled directed ordered acyclic graphs (DOAGs) can be regarded as a feature extraction problem in which the dimensionality of the input space is infinite. We address the fundamental problem of finding fixed-length vector representations for DOAGs in an unsupervised way using a maximum entropy approach. Some preliminary experiments on image retrieval are reported.

Recurrent neural networks processing symbolic strings can be regarded as adaptive neural parsers. Given a set of positive and negative examples, picked up from a given language, adaptive neural parsers can effectively be trained to infer the language grammar. In this paper we use adaptive neural parsers to face the problem of inferring grammars from examples that are corrupted by a kind of noise that simply changes their membership.We propose a training algorithm, referred to as hybrid finite state filter (HFF), which is based on a parsimony principle that penalizes the development of complex rules.We report very promising experimental results showing that the proposed inductive inference scheme is indeed capable of capturing rules, while removing noise.

Many researchers are quite skeptical about the actual behavior of neural network learning algorithms like backpropagation. One of the major problems is with the lack of clear theoretical results on optimal convergence, particularly for pattern mode algorithms. In this paper, we prove the companion of Rosenblatt's PC (perceptron convergence) theorem for feedforward networks (1960), stating that pattern mode backpropagation converges to an optimal solution for linearly separable patterns.

Proposes a novel unified approach for integrating explicit knowledge and learning by example in recurrent networks. The explicit knowledge is represented by automaton rules, which are directly injected into the connections of a network. This can be accomplished by using a technique based on linear programming, instead of learning from random initial weights. Learning is conceived as a refinement process and is mainly responsible for uncertain information management. We present preliminary results for problems of automatic speech recognition.>

Many researchers have recently focused their efforts on devising efficient algorithms, mainly based on optimization schemes, for learning the weights of recurrent neural networks. As in the case of feedforward networks, however, these learning algorithms may get stuck in local minima during gradient descent, thus discovering sub-optimal solutions. This paper analyses the problem of optimal learning in recurrent networks by proposing conditions that guarantee local minima free error surfaces. An example is given that also shows the constructive role of the proposed theory in designing networks suitable for solving a given task. Moreover, a formal relationship between recurrent and static feedforward networks is established such that the examples of local minima for feedforward networks already known in the literature can be associated with analogous ones in recurrent networks.