Proceedings of the AAAI Conference on Artificial Intelligence
While all kinds of mixed data---from personal data, over panel and scientific data, to public and... more While all kinds of mixed data---from personal data, over panel and scientific data, to public and commercial data---are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leaf distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-Renyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any distribution and permits efficient learning and inference.Our e...
Proceedings of the AAAI Conference on Artificial Intelligence
Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been success... more Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been successfully employed for tractable inference. Here, we extend their scope towards unsupervised representation learning: we encode samples into continuous and categorical embeddings and show that they can also be decoded back into the original input space by leveraging MPE inference. We characterize when this Sum-Product Autoencoding (SPAE) leads to equivalent reconstructions and extend it towards dealing with missing embedding information. Our experimental results on several multi-label classification problems demonstrate that SPAE is competitive with state-of-the-art autoencoder architectures, even if the SPNs were never trained to reconstruct their inputs.
Recent studies suggest that robots play an important role to cope Autistic Spectrum Disorder (ASD... more Recent studies suggest that robots play an important role to cope Autistic Spectrum Disorder (ASD). This paper presents a multimodal interface based on a multilevel treatment protocol customized to improve eye contact, joint attention, and imitation. An evaluation of the system has been performed involving 6 high functioning children with autism spectrum disorders. The experiments carried out make it possible to evaluate the behavioral response of the children in the eye contact exercise. ASD children had achieved better results than traditional therapy thanks to the multimodal interface.
Discussions on social Web platforms carry a lot of information which is more and more difficult t... more Discussions on social Web platforms carry a lot of information which is more and more difficult to analyze. Given a virtual community of users that discuss a particular topic of interest, an important task is to extract a model of the whole debate in order to automatically evaluate what are the most reliable claims. This paper proposes to approach this task using abstract argumentation, and define a new argument system, called Bipolar Weighted Argumentation Framework. It is able to capture all the useful information from a discussion thread, including the strength of positive (i.e., supports) and negative (i.e., attacks) relations between arguments. It also provides a way to assess an acceptability degree for each argument by means of the strength propagation of indirect relations ending to it, and a strategy to build such a framework from an online debate with a hierarchical structure. A model obtained from a real life discussion (a Reddit thread) is discussed and qualitatively eva...
The possibility for people to leave comments in blogs and forums on the Internet allows to study ... more The possibility for people to leave comments in blogs and forums on the Internet allows to study their attitude (in terms of valence or even of specific feelings) on various topics. For some digital libraries this may be a precious opportunity to understand how their content is perceived by their users and, as a consequence, to suitably direct their future strategic choices. So, libraries might want to enrich their sites with the possibility, for their users, to provide feedback on the items they have consulted. Of course, manually analyzing all the available comments would be infeasible. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at automatically analyzing and classifying text documents based on the underlying opinions expressed by their authors.
Sum-Product networks (SPNs) are expressive deep architectures for representing probability distri... more Sum-Product networks (SPNs) are expressive deep architectures for representing probability distributions, yet allowing exact and efficient inference. SPNs have been successfully applied in several domains, however always as black-box distribution estimators. In this paper, we argue that due to their recursive definition, SPNs can also be naturally employed as hierarchical feature extractors and thus for unsupervised representation learning. Moreover, when converted into Max-Product Networks (MPNs), it is possible to decode such representations back into the original input space. In this way, MPNs can be interpreted as a kind of generative autoencoder, even if they were never trained to reconstruct the input data. We show how these learned representations, if visualized, indeed correspond to "meaningful parts" of the training data. They also yield a large improvement when used in structured prediction tasks. As shown in extensive experiments, SPN and MPN encoding and decodi...
In this work, we tackle the problem of predicting unknown values of numeric features expressed as... more In this work, we tackle the problem of predicting unknown values of numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. However, solving such problems singularly does not allow to exploit likely correlations existing among the target values. To this purpose, we propose a method for learning predictive clustering trees for solving multi-target regression problems. Such trees are characterized by leaf nodes containing prototype vectors for local regression while inner nodes (test nodes) contain concept descriptions to be used for traversing the tree and select specific regions of the ideal instance-space. The feasibility of the approach has been experimentally evaluated on Linked Data Cloud datasets, showing interesting results.
In this paper, we tackle the problem of clustering individual resources in the context of the Web... more In this paper, we tackle the problem of clustering individual resources in the context of the Web of Data, that is characterized by a huge amount of data published in a standard data model with a well-defined semantics based on Web ontologies. In fact, clustering methods offer an effective solution to support a lot of complex related activities, such as ontology construction, debugging and evolution, taking into account the inherent incompleteness underlying the representation. Web ontologies already encode a hierarchical organization of the resources by means of the subsumption hierarchy of the classes, which may be expressed explicitly, with proper subsumption axioms, or it must be detected indirectly, by reasoning on the available axioms that define the classes (classification). However it frequently happens that such classes are sparsely populated as the hierarchy often reflect a view of the knowledge engineer prior to the actual introduction of assertions involving the individu...
Designing robot-based treatments for children with Autistic Spectrum Disorder (ASD) is a growing ... more Designing robot-based treatments for children with Autistic Spectrum Disorder (ASD) is a growing research field. This paper presents an artificial intelligence system based on a robot-assisted treatment of autism. The robot acts as a social mediator, trying to elicit specific behaviors in autistic children. A first preliminary evaluation of the system has been performed involving 3 high functioning children with autism spectrum disorders. The experiments carried out make it possible to evaluate the behavioral response of the children in the eye contact exercise.
Abstract. In the phase of evaluation of accepted arguments, one may find that not all the argumen... more Abstract. In the phase of evaluation of accepted arguments, one may find that not all the arguments of discussion are essential when drawing conclusions. Especially when the cardinality of the set of arguments is high, the task of identifying the most relevant arguments of the whole discussion in huge Argument Systems through the analysis of its synthesis may favor better interpretability and may allow us to extract semantics that include the the strongest arguments. We propose a new matrix interpretation of argumentation graphs and exploit a matrix decomposition technique, i.e. the Singular Value Decomposition, in order to yield a synthetized argument system with only the most prominent arguments.
Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamic... more Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamics. It aims at reducing risk in market capital investments. Grouping stocks by similar latent trend can be cast into a clustering problem. The classical K-Means clustering algorithm does not fit the task of financial data analysis. Hence, we investigate Non-negative Matrix Factorization (NMF) techniques which, contrary to K-Means, turn out to be very effective when applied to stock data. In particular, recently developed NMF techniques, which incorporate convexity constraints, generate more disjoint latent trend groupings than the traditional sector-based groupings. In this paper, the NMF technique and its variants are applied to NASDAQ stock data (i.e., daily closing prices). Experimental results confirm that (convex ) NMF techniques are highly recommended to produce trend based assets and build a good diversified portfolio.
Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We fo... more Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of predicting missing links in large knowledge graphs, so to discover new facts about the world. Recently, representation learning models that embed entities and predicates in continuous vector spaces achieved new state-of-the-art results on this problem. A major limitation in these models is that the training process, which consists in learning the optimal entity and predicate embeddings for a given knowledge graph, can be very computationally expensive: it may even require days of computations for large knowledge graphs. In this work, by leveraging adaptive learning rates, we propose a principled method for reducing the training time by an order of magnitude, while learning more accurate link prediction models. Furthermore, we employ the proposed training method for evaluating a set of novel and scalable models. Our evaluations show significant improvements over stat...
Computational models of argument aim at engaging argumentation-related activities with human user... more Computational models of argument aim at engaging argumentation-related activities with human users. In the present work we propose a new generalized version of abstract argument system, called Trust-affected Bipolar Weighted Argumentation Framework (T-BWAF). In this framework, two mainly interacting components are exploited to reason about the acceptability of arguments. The former is the BWAF, which combines and extends the theoretical models and properties of bipolar and weighted Argumentation Frameworks. The latter is the Trust Users Graph, which allow us to quantify gradual pieces of information regarding the source (who is the origin) of an argument. The synergy between them allow us to consider further gradual information which lead to a definition of intrinsic strength of an argument. For this reason, the evaluation of arguments for T-BWAF is defined under a ranking-based semantics, i.e. by assigning a numerical acceptability degree to each argument.
Proceedings of the International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents, 2016
Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorde... more Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorder (ASD). This paper presents a humanoid social robot-assisted behavioral system based on a therapeutic multilevel treatment protocol customized to improve eye contact, joint attention, symbolic play, and basic emotion recognition. In the system, the robot acts as a social mediator, trying to elicit specific behaviors in child, taking into account his/her multimodal signals. Statistical differences in eye contact and facial expression imitation behaviors after the use of the system are reported as preliminary results.
In the context of the Web of Data, plenty of properties may be used for linking resources to othe... more In the context of the Web of Data, plenty of properties may be used for linking resources to other resources but also to literals that specify their attributes. However the scale and inherent nature of the setting is also characterized by a large amount of missing and incorrect information. To tackle these problems, learning models and rules for predicting unknown values of numeric features can be used for approximating the values and enriching the schema of a knowledge base yielding an increase of the expressiveness, e.g. by eliciting SWRL rules. In this work, we tackle the problem of predicting unknown values and deriving rules concerning numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. To this purpose, we adapted learning predictive clustering trees for solving multi-target regression problems in the context of knowledge bases of the Web of Data. The approach has been experimentally evaluated showing interesting results.
Proceedings of the AAAI Conference on Artificial Intelligence
While all kinds of mixed data---from personal data, over panel and scientific data, to public and... more While all kinds of mixed data---from personal data, over panel and scientific data, to public and commercial data---are collected and stored, building probabilistic graphical models for these hybrid domains becomes more difficult. Users spend significant amounts of time in identifying the parametric form of the random variables (Gaussian, Poisson, Logit, etc.) involved and learning the mixed models. To make this difficult task easier, we propose the first trainable probabilistic deep architecture for hybrid domains that features tractable queries. It is based on Sum-Product Networks (SPNs) with piecewise polynomial leaf distributions together with novel nonparametric decomposition and conditioning steps using the Hirschfeld-Gebelein-Renyi Maximum Correlation Coefficient. This relieves the user from deciding a-priori the parametric form of the random variables but is still expressive enough to effectively approximate any distribution and permits efficient learning and inference.Our e...
Proceedings of the AAAI Conference on Artificial Intelligence
Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been success... more Sum-Product Networks (SPNs) are a deep probabilistic architecture that up to now has been successfully employed for tractable inference. Here, we extend their scope towards unsupervised representation learning: we encode samples into continuous and categorical embeddings and show that they can also be decoded back into the original input space by leveraging MPE inference. We characterize when this Sum-Product Autoencoding (SPAE) leads to equivalent reconstructions and extend it towards dealing with missing embedding information. Our experimental results on several multi-label classification problems demonstrate that SPAE is competitive with state-of-the-art autoencoder architectures, even if the SPNs were never trained to reconstruct their inputs.
Recent studies suggest that robots play an important role to cope Autistic Spectrum Disorder (ASD... more Recent studies suggest that robots play an important role to cope Autistic Spectrum Disorder (ASD). This paper presents a multimodal interface based on a multilevel treatment protocol customized to improve eye contact, joint attention, and imitation. An evaluation of the system has been performed involving 6 high functioning children with autism spectrum disorders. The experiments carried out make it possible to evaluate the behavioral response of the children in the eye contact exercise. ASD children had achieved better results than traditional therapy thanks to the multimodal interface.
Discussions on social Web platforms carry a lot of information which is more and more difficult t... more Discussions on social Web platforms carry a lot of information which is more and more difficult to analyze. Given a virtual community of users that discuss a particular topic of interest, an important task is to extract a model of the whole debate in order to automatically evaluate what are the most reliable claims. This paper proposes to approach this task using abstract argumentation, and define a new argument system, called Bipolar Weighted Argumentation Framework. It is able to capture all the useful information from a discussion thread, including the strength of positive (i.e., supports) and negative (i.e., attacks) relations between arguments. It also provides a way to assess an acceptability degree for each argument by means of the strength propagation of indirect relations ending to it, and a strategy to build such a framework from an online debate with a hierarchical structure. A model obtained from a real life discussion (a Reddit thread) is discussed and qualitatively eva...
The possibility for people to leave comments in blogs and forums on the Internet allows to study ... more The possibility for people to leave comments in blogs and forums on the Internet allows to study their attitude (in terms of valence or even of specific feelings) on various topics. For some digital libraries this may be a precious opportunity to understand how their content is perceived by their users and, as a consequence, to suitably direct their future strategic choices. So, libraries might want to enrich their sites with the possibility, for their users, to provide feedback on the items they have consulted. Of course, manually analyzing all the available comments would be infeasible. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at automatically analyzing and classifying text documents based on the underlying opinions expressed by their authors.
Sum-Product networks (SPNs) are expressive deep architectures for representing probability distri... more Sum-Product networks (SPNs) are expressive deep architectures for representing probability distributions, yet allowing exact and efficient inference. SPNs have been successfully applied in several domains, however always as black-box distribution estimators. In this paper, we argue that due to their recursive definition, SPNs can also be naturally employed as hierarchical feature extractors and thus for unsupervised representation learning. Moreover, when converted into Max-Product Networks (MPNs), it is possible to decode such representations back into the original input space. In this way, MPNs can be interpreted as a kind of generative autoencoder, even if they were never trained to reconstruct the input data. We show how these learned representations, if visualized, indeed correspond to "meaningful parts" of the training data. They also yield a large improvement when used in structured prediction tasks. As shown in extensive experiments, SPN and MPN encoding and decodi...
In this work, we tackle the problem of predicting unknown values of numeric features expressed as... more In this work, we tackle the problem of predicting unknown values of numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. However, solving such problems singularly does not allow to exploit likely correlations existing among the target values. To this purpose, we propose a method for learning predictive clustering trees for solving multi-target regression problems. Such trees are characterized by leaf nodes containing prototype vectors for local regression while inner nodes (test nodes) contain concept descriptions to be used for traversing the tree and select specific regions of the ideal instance-space. The feasibility of the approach has been experimentally evaluated on Linked Data Cloud datasets, showing interesting results.
In this paper, we tackle the problem of clustering individual resources in the context of the Web... more In this paper, we tackle the problem of clustering individual resources in the context of the Web of Data, that is characterized by a huge amount of data published in a standard data model with a well-defined semantics based on Web ontologies. In fact, clustering methods offer an effective solution to support a lot of complex related activities, such as ontology construction, debugging and evolution, taking into account the inherent incompleteness underlying the representation. Web ontologies already encode a hierarchical organization of the resources by means of the subsumption hierarchy of the classes, which may be expressed explicitly, with proper subsumption axioms, or it must be detected indirectly, by reasoning on the available axioms that define the classes (classification). However it frequently happens that such classes are sparsely populated as the hierarchy often reflect a view of the knowledge engineer prior to the actual introduction of assertions involving the individu...
Designing robot-based treatments for children with Autistic Spectrum Disorder (ASD) is a growing ... more Designing robot-based treatments for children with Autistic Spectrum Disorder (ASD) is a growing research field. This paper presents an artificial intelligence system based on a robot-assisted treatment of autism. The robot acts as a social mediator, trying to elicit specific behaviors in autistic children. A first preliminary evaluation of the system has been performed involving 3 high functioning children with autism spectrum disorders. The experiments carried out make it possible to evaluate the behavioral response of the children in the eye contact exercise.
Abstract. In the phase of evaluation of accepted arguments, one may find that not all the argumen... more Abstract. In the phase of evaluation of accepted arguments, one may find that not all the arguments of discussion are essential when drawing conclusions. Especially when the cardinality of the set of arguments is high, the task of identifying the most relevant arguments of the whole discussion in huge Argument Systems through the analysis of its synthesis may favor better interpretability and may allow us to extract semantics that include the the strongest arguments. We propose a new matrix interpretation of argumentation graphs and exploit a matrix decomposition technique, i.e. the Singular Value Decomposition, in order to yield a synthetized argument system with only the most prominent arguments.
Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamic... more Building a diversified portfolio is an appealing strategy in the analysis of stock market dynamics. It aims at reducing risk in market capital investments. Grouping stocks by similar latent trend can be cast into a clustering problem. The classical K-Means clustering algorithm does not fit the task of financial data analysis. Hence, we investigate Non-negative Matrix Factorization (NMF) techniques which, contrary to K-Means, turn out to be very effective when applied to stock data. In particular, recently developed NMF techniques, which incorporate convexity constraints, generate more disjoint latent trend groupings than the traditional sector-based groupings. In this paper, the NMF technique and its variants are applied to NASDAQ stock data (i.e., daily closing prices). Experimental results confirm that (convex ) NMF techniques are highly recommended to produce trend based assets and build a good diversified portfolio.
Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We fo... more Knowledge Graphs are a widely used formalism for representing knowledge in the Web of Data. We focus on the problem of predicting missing links in large knowledge graphs, so to discover new facts about the world. Recently, representation learning models that embed entities and predicates in continuous vector spaces achieved new state-of-the-art results on this problem. A major limitation in these models is that the training process, which consists in learning the optimal entity and predicate embeddings for a given knowledge graph, can be very computationally expensive: it may even require days of computations for large knowledge graphs. In this work, by leveraging adaptive learning rates, we propose a principled method for reducing the training time by an order of magnitude, while learning more accurate link prediction models. Furthermore, we employ the proposed training method for evaluating a set of novel and scalable models. Our evaluations show significant improvements over stat...
Computational models of argument aim at engaging argumentation-related activities with human user... more Computational models of argument aim at engaging argumentation-related activities with human users. In the present work we propose a new generalized version of abstract argument system, called Trust-affected Bipolar Weighted Argumentation Framework (T-BWAF). In this framework, two mainly interacting components are exploited to reason about the acceptability of arguments. The former is the BWAF, which combines and extends the theoretical models and properties of bipolar and weighted Argumentation Frameworks. The latter is the Trust Users Graph, which allow us to quantify gradual pieces of information regarding the source (who is the origin) of an argument. The synergy between them allow us to consider further gradual information which lead to a definition of intrinsic strength of an argument. For this reason, the evaluation of arguments for T-BWAF is defined under a ranking-based semantics, i.e. by assigning a numerical acceptability degree to each argument.
Proceedings of the International Workshop on Social Learning and Multimodal Interaction for Designing Artificial Agents, 2016
Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorde... more Several studies suggest that robots can play a relevant role to address Autistic Spectrum Disorder (ASD). This paper presents a humanoid social robot-assisted behavioral system based on a therapeutic multilevel treatment protocol customized to improve eye contact, joint attention, symbolic play, and basic emotion recognition. In the system, the robot acts as a social mediator, trying to elicit specific behaviors in child, taking into account his/her multimodal signals. Statistical differences in eye contact and facial expression imitation behaviors after the use of the system are reported as preliminary results.
In the context of the Web of Data, plenty of properties may be used for linking resources to othe... more In the context of the Web of Data, plenty of properties may be used for linking resources to other resources but also to literals that specify their attributes. However the scale and inherent nature of the setting is also characterized by a large amount of missing and incorrect information. To tackle these problems, learning models and rules for predicting unknown values of numeric features can be used for approximating the values and enriching the schema of a knowledge base yielding an increase of the expressiveness, e.g. by eliciting SWRL rules. In this work, we tackle the problem of predicting unknown values and deriving rules concerning numeric features expressed as datatype properties. The task can be cast as a regression problem for which suitable solutions have been devised, for instance, in the related context of RDBs. To this purpose, we adapted learning predictive clustering trees for solving multi-target regression problems in the context of knowledge bases of the Web of Data. The approach has been experimentally evaluated showing interesting results.
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Papers by Floriana Esposito