Mirror Symmetry and the Classification of Orbifold del Pezzo Surfaces

We state a number of conjectures that together allow one to classify a broad class of del Pezzo surfaces with cyclic quotient singularities using mirror symmetry. We prove our conjectures in the simplest cases. The conjectures relate mutation-equivalence classes of Fano polygons with Q-Gorenstein deformation classes of del Pezzo surfaces

Basic tasks of sentiment analysis

Subjectivity detection is the task of identifying objective and subjective sentences. Objective sentences are those which do not exhibit any sentiment. So, it is desired for a sentiment analysis engine to find and separate the objective sentences for further analysis, e.g., polarity detection. In subjective sentences, opinions can often be expressed on one or multiple topics. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about

Betti numbers for numerical semigroup rings

We survey results related to the magnitude of the Betti numbers of numerical semigroup rings and of their tangent cones.Comment: 22 pages; v2: updated references. To appear in Multigraded Algebra and Applications (V. Ene, E. Miller Eds.

The benefits of adversarial defense in generalization

Recent research has shown that models induced by machine learning, and in particular by deep learning, can be easily fooled by an adversary who carefully crafts imperceptible, at least from the human perspective, or physically plausible modifications of the input data. This discovery gave birth to a new field of research, the adversarial machine learning, where new methods of attacks and defense are developed continuously, mimicking what is happening from a long time in cybersecurity. In this paper we will show that the drawbacks of inducing models from data less prone to be misled can actually provide some benefits when it comes to assessing their generalization abilities. We will show these benefits both from a theoretical perspective, using state-of-the-art statistical learning theory, and both with practical examples

ReForeSt: Random forests in apache spark

Random Forests (RF) of tree classifiers are a popular ensemble method for classification. RF are usually preferred with respect to other classification techniques because of their limited hyperparameter sensitivity, high numerical robustness, native capacity of dealing with numerical and categorical features, and effectiveness in many real world classification problems. In this work we present ReForeSt, a Random Forests Apache Spark implementation which is easier to tune, faster, and less memory consuming with respect to MLlib, the de facto standard Apache Spark machine learning library. We perform an extensive comparison between ReForeSt and MLlib by taking advantage of the Google Cloud Platform (https://cloud.google.com). In particular, we test ReForeSt and MLlib with different library settings, on different real world datasets, and with a different number of machines equipped with different number of cores. Results confirm that ReForeSt outperforms MLlib in all the above mentioned aspects. ReForeSt is made publicly available via GitHub (https://github.com/alessandrolulli/reforest)

Deep Learning for the Generation of Heuristics in Answer Set Programming: A Case Study of Graph Coloring

Answer Set Programming (ASP) is a well-established declarative AI formalism for knowledge representation and reasoning. ASP systems were successfully applied to both industrial and academic problems. Nonetheless, their performance can be improved by embedding domain-specific heuristics into their solving process. However, the development of domain-specific heuristics often requires both a deep knowledge of the domain at hand and a good understanding of the fundamental working principles of the ASP solvers. In this paper, we investigate the use of deep learning techniques to automatically generate domain-specific heuristics for ASP solvers targeting the well-known graph coloring problem. Empirical results show that the idea is promising: the performance of the ASP solver wasp can be improved

Randomized learning and generalization of fair and private classifiers: From PAC-Bayes to stability and differential privacy

We address the problem of randomized learning and generalization of fair and private classifiers. From one side we want to ensure that sensitive information does not unfairly influence the outcome of a classifier. From the other side we have to learn from data while preserving the privacy of individual observations. We initially face this issue in the PAC-Bayes framework presenting an approach which trades off and bounds the risk and the fairness of the randomized (Gibbs) classifier. Our new approach is able to handle several different state-of-the-art fairness measures. For this purpose, we further develop the idea that the PAC-Bayes prior can be defined based on the data-generating distribution without actually knowing it. In particular, we define a prior and a posterior which give more weight to functions with good generalization and fairness properties. Furthermore, we will show that this randomized classifier possesses interesting stability properties using the algorithmic distribution stability theory. Finally, we will show that the new posterior can be exploited to define a randomized accurate and fair algorithm. Differential privacy theory will allow us to derive that the latter algorithm has interesting privacy preserving properties ensuring our threefold goal of good generalization, fairness, and privacy of the final model

Energy Efficient Smartphone-Based Activity Recognition Using Fixed-Point Arithmetic

In this paper we propose a novel energy efficient approach for the recog- nition of human activities using smartphones as wearable sensing devices, targeting assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The method exploits fixed-point arithmetic to propose a modified multiclass Support Vector Machine (SVM) learning algorithm, allowing to better pre- serve the smartphone battery lifetime with respect to the conventional floating-point based formulation while maintaining comparable system accuracy levels. Experiments show comparative results between this approach and the traditional SVM in terms of recognition performance and battery consumption, highlighting the advantages of the proposed method

Fair graph representation learning: Empowering NIFTY via Biased Edge Dropout and Fair Attribute Preprocessing

The increasing complexity and amount of data available in modern applications strongly demand Trustworthy Learning algorithms that can be fed directly with complex and large graphs data. In fact, on one hand, machine learning models must meet high technical standards (e.g., high accuracy with limited computational requirements), but, at the same time, they must be sure not to discriminate against subgroups of the population (e.g., based on gender or ethnicity). Graph Neural Networks (GNNs) are currently the most effective solution to meet the technical requirements, even if it has been demonstrated that they inherit and amplify the biases contained in the data as a reflection of societal inequities. In fact, when dealing with graph data, these biases can be hidden not only in the node attributes but also in the connections between entities. Several Fair GNNs have been proposed in the literature, with uNIfying Fairness and stabiliTY (NIFTY) (Agarwal et al., 2021) being one of the most effective. In this paper, we will empower NIFTY's fairness with two new strategies. The first one is a Biased Edge Dropout, namely, we drop graph edges to balance homophilous and heterophilous sensitive connections, mitigating the bias induced by subgroup node cardinality. The second one is Attributes Preprocessing, which is the process of learning a fair transformation of the original node attributes. The effectiveness of our proposal will be tested on a series of datasets with increasingly challenging scenarios. These scenarios will deal with different levels of knowledge about the entire graph, i.e., how many portions of the graph are known and which sub-portion is labelled at the training and forward phases

An Efficient Hybrid Planning Framework for In-Station Train Dispatching

In-station train dispatching is the problem of optimising the effective utilisation of available railway infrastructures for mitigating incidents and delays. This is a fundamental problem for the whole railway network efficiency, and in turn for the transportation of goods and passengers, given that stations are among the most critical points in networks since a high number of interconnections of trains’ routes holds therein. Despite such importance, nowadays in-station train dispatching is mainly managed manually by human operators. In this paper we present a framework for solving in-station train dispatching problems, to support human operators in dealing with such task. We employ automated planning languages and tools for solving the task: PDDL+ for the specification of the problem, and the ENHSP planning engine, enhanced by domain-specific techniques, for solving the problem. We carry out a in-depth analysis using real data of a station of the North West of Italy, that shows the effectiveness of our approach and the contribution that domain-specific techniques may have in efficiently solving the various instances of the problem. Finally, we also present a visualisation tool for graphically inspecting the generated plans