SIAM Journal on Matrix Analysis and Applications, 2013
Approximations of matrix-valued functions of the form W T f (A)W , where A ∈ R m×m is symmetric, ... more Approximations of matrix-valued functions of the form W T f (A)W , where A ∈ R m×m is symmetric, W ∈ R m×k , with m large and k m, has orthonormal columns, and f is a function, can be computed by applying a few steps of the symmetric block Lanczos method to A with initial block-vector W ∈ R m×k . Golub and Meurant have shown that the approximants obtained in this manner may be considered block Gauss quadrature rules associated with a matrix-valued measure. This paper generalizes anti-Gauss quadrature rules, introduced by Laurie for real-valued measures, to matrix-valued measures, and shows that under suitable conditions pairs of block Gauss and block anti-Gauss rules provide upper and lower bounds for the entries of the desired matrix-valued function. Extensions to matrix-valued functions of the form W T f (A)V , where A ∈ R m×m may be nonsymmetric, and the matrices V, W ∈ R m×k satisfy V T W = I k are also discussed. Approximations of the latter functions are computed by applying a few steps of the nonsymmetric block Lanczos method to A with initial block-vectors V and W . We describe applications to the evaluation of functions of a symmetric or nonsymmetric adjacency matrix for a network. Numerical examples illustrate that a combination of block Gauss and anti-Gauss quadrature rules typically provides upper and lower bounds for such problems. We introduce some new quantities that describe properties of nodes in directed or undirected networks, and demonstrate how these and other quantities can be computed inexpensively with the quadrature rules of the present paper.
Journal of Advances in Information Technology, Dec 31, 2022
The ever-increasing use of services based on computer networks, even in crucial areas unthinkable... more The ever-increasing use of services based on computer networks, even in crucial areas unthinkable until a few years ago, has made the security of these networks a crucial element for anyone, also in consideration of the increasingly sophisticated techniques and strategies available to attackers. In this context, Intrusion Detection Systems (IDSs) play a primary role since they are responsible for analyzing and classifying each network activity as legitimate or illegitimate, allowing us to take the necessary countermeasures at the appropriate time. However, these systems are not infallible due to several reasons, the most important of which are the constant evolution of the attacks (e.g., zero-day attacks) and the problem that many of the attacks have behavior similar to those of legitimate activities, and therefore they are very hard to identify. This work relies on the hypothesis that the subdivision of the training data used for the IDS classification model definition into a certain number of partitions, in terms of events and features, can improve the characterization of the network events, improving the system performance. The non-overlapping data partitions train independent classification models, classifying the event according to a majority-voting rule. A series of experiments conducted on a benchmark real-world dataset support the initial hypothesis, showing a performance improvement with respect to a canonical training approach.
The outbreak of COVID-19 has significantly impacted on education, training, and mobility opportun... more The outbreak of COVID-19 has significantly impacted on education, training, and mobility opportunities provided to learners, teachers, and educators. In response to this situation, synchronous online learning has been massively adopted in universities and, therefore, the analysis of student behavior in this context is becoming essential. However, the literature has mainly tackled student behavior modelling in asynchronous online learning (e.g., interactions with pre-recorded videos), making how students learn in synchronous online learning so far under-explored. Grounding on the experience on online learning at the University of Cagliari, this paper proposes a preliminary study on student participation in synchronous lessons, covering more than 80 courses, and identifies patterns and strategies of engagement in classes over the semester. Then, by means of clustering techniques applied to data from more than 25,000 students, we model fine-grained behavioral strategies and discuss how they are related with the student's experience across courses and whether planning elements (e.g., the hour of the day a lesson is delivered) influence their level of participation. We expect that this study will support the educational stakeholders with preliminary data-driven informed decisions and pose the basis for data-driven personalization for students and teachers involved in online synchronous learning.
There are different types of information systems, such as those that perform group recommendation... more There are different types of information systems, such as those that perform group recommendations and market segmentations, which operate with groups of users. In order to combine the individual preferences and properly address suggestions to users, group modeling strategies are employed. Nowadays, data is characterized by large amounts in terms of volume, speed, and variety (the so-called big data issue). In this paper, we are going to tackle the problem of modeling group preferences in big data scenarios. This study will present the existing strategies, and we are going to present criteria to design the algorithms that implement them when big amounts of data have to be combined. Moreover, a set of best practices discusses under which conditions the presented strategies can be adopted in big data scenarios.
In this paper, we describe a solution for engineering and modelling user interfaces for supportin... more In this paper, we describe a solution for engineering and modelling user interfaces for supporting input collected through gesture recognition hardware. We describe how we applied such approach by extending the MARIA UIDL, and how the modelling solution can be applied to other UI toolkits. In addition, we detail the model-to-code transformation for obtaining a running application through an example case study.
Persuasive health technologies in the sport domain focus mostly on motivating and supporting peop... more Persuasive health technologies in the sport domain focus mostly on motivating and supporting people in reaching an active lifestyle. In this paper, we exploit a real-world dataset made available by a commercial persuasive ecosystem called u4fit. u4fit allows coaches to create tailored workout plans and to constantly monitor and support their sportsmen remotely. Occasional sportsmen often and suddenly abandon their workout routines, without giving any prior notice to the coach, frequently because of a decline in motivation. In this paper, we tackle this issue by developing an approach able to spot users' behavioral changes and predict if one will soon stop exercising. These predictions can be further elaborated and provided as a recommendation to the user's coach, to let her get in touch with the sportsmen and prevent such a situation. Experiments, validated through standard accuracy metrics, revealed that behavioral changes in training patterns represent one of the main markers that lead sportsmen to abandon.
Online educational platforms are playing a primary role in mediating the success of individuals' ... more Online educational platforms are playing a primary role in mediating the success of individuals' careers. Therefore, while building overlying content recommendation services, it becomes essential to guarantee that learners are provided with equal recommended learning opportunities, according to the platform values, context, and pedagogy. Though the importance of ensuring equality of learning opportunities has been well investigated in traditional institutions, how this equality can be operationalized in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize educational principles that model recommendations' learning properties, and a novel fairness metric that combines them in order to monitor the equality of recommended learning opportunities among learners. Then, we envision a scenario wherein an educational platform should be arranged in such a way that the generated recommendations meet each principle to a certain degree for all learners, constrained to their individual preferences. Under this view, we explore the learning opportunities provided by recommender systems in a large-scale course platform, uncovering systematic inequalities. To reduce this effect, we propose a novel post-processing approach that balances personalization and equality of recommended opportunities. Experiments show that our approach leads to higher equality, with a negligible loss in personalization. Our study moves a step forward in operationalizing the ethics of human learning in recommendations, a core unit of intelligent educational systems.
Online educational platforms are promising to play a primary role in mediating the success of ind... more Online educational platforms are promising to play a primary role in mediating the success of individuals' careers. Hence, while building overlying content recommendation services, it becomes essential to ensure that learners are provided with equal learning opportunities, according to the platform values, context, and pedagogy. Even though the importance of creating equality of learning opportunities has been well investigated in traditional institutions, how it can be operationalized scalably in online learning ecosystems through recommender systems is still under-explored. In this paper, we formalize principles, that aim to model a range of learning opportunity properties in recommendations, and a metric that combines them to quantify the equality of learning opportunities among learners. Then, we envision a scenario wherein platform owners seek to guarantee that the generated recommendations meet each principle, to a certain degree, for all learners, constrained to their individual preferences. Under this view, we provide observations on learning opportunities in a real-world online dataset, highlighting inequalities among learners. To mitigate this effect, we propose a post-processing approach that balances personalization and learning opportunity equality in recommendations. Experiments on a large-scale dataset demonstrate that our approach leads to higher equality of learning opportunity, with a small loss in personalization.
In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can ... more In recent years, there has been a stimulating discussion on how artificial intelligence (AI) can support the science and engineering of intelligent educational applications. Many studies in the field are proposing actionable data mining pipelines and machine-learning models driven by learning-related data. The potential of these pipelines and models to amplify unfairness for certain categories of students is however receiving increasing attention. If AI applications are to have a positive impact on education, it is crucial that their design considers fairness at every step. Through anonymous surveys and interviews with experts (researchers and practitioners) who have published their research at top-tier educational conferences in the last year, we conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice, that lead the community to have a clear understanding on the main questions raising doubts in this topic. Based on these findings, we highlighted directions that will facilitate the ongoing research towards fairer AI for education.
Fig. . GNNUERS updates the perturbation vector such that the removed user-item interactions from ... more Fig. . GNNUERS updates the perturbation vector such that the removed user-item interactions from the graph lead the trained GNN to generate fairer recommendations. The perturbation vector represents the counterfactual explanation of the prior unfairness across demographic groups. Nowadays, research into personalization has been focusing on explainability and fairness. Several approaches proposed in recent works are able to explain individual recommendations in a post-hoc manner or by explanation paths. However, explainability techniques applied to unfairness in recommendation have been limited to finding user/item features mostly related to biased recommendations. In this paper, we devised a novel algorithm that leverages counterfactuality methods to discover user unfairness explanations in the
In today's society digital services have become the key to the success of anyone. Hence, for bein... more In today's society digital services have become the key to the success of anyone. Hence, for being competitive it is important that these services are available, employ the latest technology and are low cost. Unfortunately, it often happens that these good intentions do not correspond to reality. In this paper an information system is proposed, targeted at those small realities affected by the digital divide and at those companies that employ out of date, high cost technologies, that provides data and voice services in a unified manner using heterogeneous devices. The system utilizes innovative technologies, in particular wireless technology, to deliver low cost solutions. The distinctive feature is that it does not depend on the network hardware infrastructure and the underlying platform. Furthermore, it deals with the configuration, accounting, security, management, and monitoring aspects while maintaining its flexibility and simplicity of use both for the administrator and end user.
The classification and recognition of foliar diseases is an increasingly developing field of rese... more The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific r... more HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Over the last few years, the impact of climate change has increased rapidly. It is influencing al... more Over the last few years, the impact of climate change has increased rapidly. It is influencing all steps of plant production and forcing farmers to change and adapt their crop management practices using new technologies based on data analytics. This study aims to classify plant diseases based on images collected directly in the field using deep learning. To this end, an ensemble learning paradigm is investigated to build a robust network in order to predict four different pear leaf diseases. Several convolutional neural network architectures, named EfficientNetB0, InceptionV3, MobileNetV2 and VGG19, were compared and ensembled to improve the predictive performance by adopting the bagging strategy and weighted averaging. Quantitative experiments were conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. Data augmentation was adopted to improve the generalization of the model. The results, evaluated with a range of metrics, including accuracy, recall, precison and f1-score, showed that the proposed ensemble convolutional neural network outperformed the single convolutional neural network in classifying diseases in real field-condition with variation in brightness, disease similarity, complex background, and multiple leaves.
Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only inte... more Numerous Knowledge Graphs (KGs) are being created to make Recommender Systems (RSs) not only intelligent but also knowledgeable. Integrating a KG in the recommendation process allows the underlying model to extract reasoning paths between recommended products and already experienced products from the KG. These paths can be leveraged to generate textual explanations to be provided to the user for a given recommendation. However, the existing explainable recommendation approaches based on KG merely optimize the selected reasoning paths for product relevance, without considering any user-level property of the paths for explanation. In this paper, we propose a series of quantitative properties that monitor the quality of the reasoning paths from an explanation perspective, based on recency, popularity, and diversity. We then combine in-and post-processing approaches to optimize for both recommendation quality and reasoning path quality. Experiments on three public data sets show that our approaches significantly increase reasoning path quality according to the proposed properties, while preserving recommendation quality. Source code, data sets, and KGs are available at .
Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
Detecting learners in need of support is a complex process for both teachers and machines. Most p... more Detecting learners in need of support is a complex process for both teachers and machines. Most prior work has devised visualization tools that allow teachers to do so by analyzing educational indicators. Other recent efforts have been devoted to models that predict whether learners might be at risk. However, the question on how teacher-like is the model behaving under this detection task still remains unanswered. In this paper, we investigate the (dis)agreement between teachers and model decisions, using a realworld flipped course as a case study. From the model perspective, we considered a well-known neural network, trained on educational indicators extracted from online pre-class logs. To gather teachers' understanding, we employed a crowd sourcing approach including over 360 human intelligence tasks from 60 university teachers. We asked each recruited teacher to analyze visualizations pertaining to four relevant educational indicators of a given learner, and reason about their probability of failing the course (and so requiring support). Learners presented to teachers were selected to address different aspects of model confidence and (in)accuracy. Our results show that teacher and model predictions diverged for students who passed the course, while predictions were similar for students who failed the course. Moreover, confidence and correctness were more aligned in teachers than the model, reducing the unknown risks originally present in models. The source code is available at . • Applied computing → Education; • Computing methodologies → Machine learning.
1st International Workshop on Enabling Data-Driven Decisions from Learning on the Web, L2D 2021, 2021
The outbreak of COVID-19 has significantly impacted on education, training, and mobility opportun... more The outbreak of COVID-19 has significantly impacted on education, training, and mobility opportunities provided to learners, teachers, and educators. In response to this situation, synchronous online learning has been massively adopted in universities and, therefore, the analysis of student behavior in this context is becoming essential. However, the literature has mainly tackled student behavior modelling in asynchronous online learning (e.g., interactions with pre-recorded videos), making how students learn in synchronous online learning so far under-explored. Grounding on the experience on online learning at the University of Cagliari, this paper proposes a preliminary study on student participation in synchronous lessons, covering more than 80 courses, and identifies patterns and strategies of engagement in classes over the semester. Then, by means of clustering techniques applied to data from more than 25,000 students, we model fine-grained behavioral strategies and discuss how...
Human Computer interaction is typically constrained to the use of sight, hear, and touch. This pa... more Human Computer interaction is typically constrained to the use of sight, hear, and touch. This paper describes an attempt to get over these limitations. We introduce the smell in the interaction with the aim of obtaining information from scents, i.e. giving meaning to odours and understand how people would appreciate such extensions. We discuss the design and implementation of our prototype system. The system is able to represent/manage an immersive environment, where the user interacts by means of visual, hearing and olfactory informations. We have implemented an odour emitter controlled by a presence sensor device. When the system perceives the presence of a user it activates audio/visual contents to encourage engaging in interaction. Then a specific scent is diffused in the air to augment the perceive reality of the experience. We discuss technical difficulties and initial empirical observations.
The classification and recognition of foliar diseases is an increasingly developing field of rese... more The classification and recognition of foliar diseases is an increasingly developing field of research, where the concepts of machine and deep learning are used to support agricultural stakeholders. Datasets are the fuel for the development of these technologies. In this paper, we release and make publicly available the field dataset collected to diagnose and monitor plant symptoms, called DiaMOS Plant, consisting of 3505 images of pear fruit and leaves affected by four diseases. In addition, we perform a comparative analysis of existing literature datasets designed for the classification and recognition of leaf diseases, highlighting the main features that maximize the value and information content of the collected data. This study provides guidelines that will be useful to the research community in the context of the selection and construction of datasets.
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Papers by Gianni Fenu