An Analysis of Features and Tendencies in Mobile Banking Apps

Mobile devices such as smartphones and tablets are being employed alongside personal computers, and even replacing them in some applications. Banks are increasingly investing on mobility, by enabling the mobile web and mobile app channels for online banking, and by providing new mobile payment services. In this paper, the services for off-branch banking offered by several Italian banks are analyzed, showing that mobile apps have surpassed the mobile web channel in completeness of the offer, due to the fact that additional capabilities of mobile devices make possible advanced features and applications. An outlook on the near future is provided, remarking that mobile marketing and mobile recommender systems can greatly take advantage of being run natively on devices, making it desirable for businesses to invest on designing mobile apps

Combining mitigation treatments against biases in personalized rankings: Use case on item popularity

Historical interactions leveraged by recommender systems are often non-uniformly distributed across items. Though they are of interest for consumers, certain items end up therefore being biasedly under-recommended. Existing treatments for mitigating these biases act at a single step of the pipeline (either pre-, in-, or post-processing), and it remains unanswered whether simultaneously introducing treatments throughout the pipeline leads to a better mitigation. In this paper, we analyze the impact of bias treatments along the steps of the pipeline under a use case on popularity bias. Experiments show that, with small losses in accuracy, the combination of treatments leads to better trade-offs than treatments applied separately. Our findings call for treatments rooting out bias at different steps simultaneously

SoftNet: A Package for the Analysis of Complex Networks

Identifying the most important nodes according to specific centrality indices is an important issue in network analysis. Node metrics based on the computation of functions of the adjacency matrix of a network were defined by Estrada and his collaborators in various papers. This paper describes a MATLAB toolbox for computing such centrality indices using efficient numerical algorithms based on the connection between the Lanczos method and Gauss-type quadrature rules

Block Gauss and anti-Gauss quadrature with application to networks

Approximations of matrix-valued functions of the form WT f(A)W, where A ∈Rm×m is symmetric, W ∈ Rm×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 ∈ Rm×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 WT f(A)V , where A ∈ Rm×m may be nonsymmetric, and the matrices V, W ∈ Rm×k satisfy VT W = Ik also are 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

Influencing brain waves by evoked potentials as biometric approach: taking stock of the last six years of research

The scientific advances of recent years have made available to anyone affordable hardware devices capable of doing something unthinkable until a few years ago, the reading of brain waves. It means that through small wearable devices it is possible to perform an electroencephalography (EEG), albeit with less potential than those offered by high-cost professional devices. Such devices make it possible for researchers a huge number of experiments that were once impossible in many areas due to the high costs of the necessary hardware. Many studies in the literature explore the use of EEG data as a biometric approach for people identification, but, unfortunately, it presents problems mainly related to the difficulty of extracting unique and stable patterns from users, despite the adoption of sophisticated techniques. An approach to face this problem is based on the evoked potentials (EPs), external stimuli applied during the EEG reading, a noninvasive technique used for many years in clinical routine, in combination with other diagnostic tests, to evaluate the electrical activity related to some areas of the brain and spinal cord to diagnose neurological disorders. In consideration of the growing number of works in the literature that combine the EEG and EP approaches for biometric purposes, this work aims to evaluate the practical feasibility of such approaches as reliable biometric instruments for user identification by surveying the state of the art of the last 6 years, also providing an overview of the elements and concepts related to this research area

Recency, Popularity, and Diversity of Explanations in Knowledge-based Recommendation

Modern knowledge-based recommender systems enable the end-to-end generation of textual explanations. These explanations are created from learnt paths between an already experience product and a recommended product in a knowledge graph, for a given user. However, none of the existing studies has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with the already experience product) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explanation quality. In this paper, we summarize our previous work on conceptualizing three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposing re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, while preserving recommendation utility. Source code and data: https://github.com/giacoballoccu/explanation-quality-recsys

Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations

Existing explainable recommender systems have mainly modeled relationships between recommended and already experienced products, and shaped explanation types accordingly (e.g., movie "x"starred by actress "y"recommended to a user because that user watched other movies with "y"as an actress). However, none of these systems has investigated the extent to which properties of a single explanation (e.g., the recency of interaction with that actress) and of a group of explanations for a recommended list (e.g., the diversity of the explanation types) can influence the perceived explaination quality. In this paper, we conceptualized three novel properties that model the quality of the explanations (linking interaction recency, shared entity popularity, and explanation type diversity) and proposed re-ranking approaches able to optimize for these properties. Experiments on two public data sets showed that our approaches can increase explanation quality according to the proposed properties, fairly across demographic groups, while preserving recommendation utility. The source code and data are available at https: //github.com/giacoballoccu/explanation-quality-recsys

XRecSys: A framework for path reasoning quality in explainable recommendation

There is increasing evidence that recommendations accompanied by explanations positively impact on businesses in terms of trust, guidance, and persuasion. This advance has been made possible by traditional models representing user–product interactions augmented with external knowledge modeled as knowledge graphs. However, these models produce textual explanations on top of reasoning paths extracted from the knowledge graph without considering relevant properties of the path entities. In this paper, we present XRecSys, a Python framework for the optimization of the reasoning path selection process according to properties deemed relevant by users (e.g., time relevance of the linking interaction or popularity of the entity linked to the explanation). Our framework leads to a higher reasoning path quality in terms of the considered properties and, consequently, textual explanations more relevant for the users

Shell-supported footbridges

Architects and engineers have been always attracted by concrete shell structures due to their high efficiency and plastic shapes. In this paper the possibility to use concrete shells to support footbridges is explored. Starting from Musmeci's fundamental research andwork in shell bridge design, the use of numerical formfinding methods is analysed. The form-finding of a shellsupported footbridge shaped following Musmeci's work is first introduced. Coupling Musmeci's and Nervi's experiences, an easy construction method using a stay-inplace ferrocement formwork is proposed. Moreover, the advantage of inserting holes in the shell through topology optimization to remove less exploited concrete has been considered. Curved shell-supported footbridges have been also studied, and the possibility of supporting the deck with the shell top edge, that is along a single curve only, has been investigated. The form-finding of curved shell-supported footbridges has been performed using a Particle-Spring System and Thrust Network Analysis. Finally, the form-finding of curved shell-supported footbridges subjected to both vertical and horizontal forces (i.e. earthquake action) has been implemented

Time-Dependent Analysis of Precast Segmental Bridges

Prestressed segmentally constructed balanced cantilever bridges are often subjected to larger deflections than those predicted by calculations, especially for long-term effects. In this paper, the case of modular balanced cantilever bridges, which are prestressed segmental bridges obtained through a repetition of the same double cantilever, is investigated. The considered bridges are two typical cases of modular balanced cantilever both subjected to large deformations during their lifetime. In this case, due to the unusual employed static scheme, creep deflections indefinitely evolve over time particularly at the end of the cantilevers and in correspondence with the central joint. These remarkable deflections cause discomfort for vehicular traffic and in certain cases can lead to the bridge collapse. Important extraordinary maintenance interventions were necessary to restore the viability of the bridges and to replace the viaduct design configuration. To this aim, the static schemes of the structures were varied, introducing new constraints, new tendons, and carbon fiber reinforcements. In the present work, time analysis was performed to compare the time-dependent behavior of the bridge according to two different creep models, the CEB-FIP Model Code 2010 and the RILEM Model B3, with the real-time-dependent behavior of the bridge observed during its lifetime. The two different employed models exhibit different behaviors in terms of displacements and bending moments acting on the bridge. Interesting considerations are made on their reliability in simulating the long-term creep effects that evolve indefinitely over time. Moreover, retrofitting techniques have been proposed and modeled to predict their effectiveness in reducing time-dependent deflections