A model of assessment of collateral damage on power grids based on complex network theory

As power grids are gradually adjusted to fit into a smart grid paradigm, a common problem is to identify locations where it is most beneficial to introduce distributed generation. In order to assist in such a decision, we work on a graph model of a regional power grid, and propose a method to assess collateral damage to the network resulting from a localized failure. We perform complex network analysis on multiple instances of the network, looking for correlations between estimated damages and betweenness centrality indices, attempting to determine which model is best suited to predict features of our network

A portable wireless-based architecture for solving minimum digital divide problems.

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system is proposed, targeted at those small realities affected by the digital divide and at those companies that employ out of date, high cost technologies, that provides data and voice services in a unified manner using heterogeneous devices.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

A model of second-degree Virtual Cut Nodes applied to Complex Networks in Ecological Landscape

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Graph models of network behavior in environmental planning

Policies to protect the environment in Europe and in the rest of the world have been adjusted to take into account the network behavior of conglomerates of nature protection areas. Network behavior can emerge from the natural configuration of habitat patches, or be induced by the establishment of habitat corridors. Careful planning is required to protect and improve the network behavior in existing sites; this has prompted researchers to build graph models of ecological networks, and apply complex network analysis to improve the understanding of their features. However, the most common approach is to keep the focus on a single species, meant to be representative of most species within the area under analysis, or especially important with respect to conservation issues. In this paper, data pertaining to land use types found within sites making up the "Natura 2000" ecological network is used to provide a high-level view of the network, and propose a framework for study, in which similarity measures are used as a criterion to suggest guidelines for land management

The More Secure, The Less Equally Usable: Gender and Ethnicity (Un)fairness of Deep Face Recognition along Security Thresholds

Face biometrics are playing a key role in making modern smart city applications more secure and usable. Commonly, the recognition threshold of a face recognition system is adjusted based on the degree of security for the considered use case. The likelihood of a match can be for instance decreased by setting a high threshold in case of a payment transaction verification. Prior work in face recognition has unfortunately showed that error rates are usually higher for certain demographic groups. These disparities have hence brought into question the fairness of systems empowered with face biometrics. In this paper, we investigate the extent to which disparities among demographic groups change under different security levels. Our analysis includes ten face recognition models, three security thresholds, and six demographic groups based on gender and ethnicity. Experiments show that the higher the security of the system is, the higher the disparities in usability among demographic groups are. Compelling unfairness issues hence exist and urge countermeasures in real-world high-stakes environments requiring severe security levels.Comment: Accepted as a full paper at the 2nd International Workshop on Artificial Intelligence Methods for Smart Cities (AISC 2022

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

Evaluation framework for context-aware speaker recognition in noisy smart living environments

The integration of voice control into connected devices is expected to improve the efficiency and comfort of our daily lives. However, the underlying biometric systems often impose constraints on the individual or the environment during interaction (e.g., quiet surroundings). Such constraints have to be surmounted in order to seamlessly recognize individuals. In this paper, we propose an evaluation framework for speaker recognition in noisy smart living environments. To this end, we designed a taxonomy of sounds (e.g., home-related, mechanical) that characterize representative indoor and outdoor environments where speaker recognition is adopted. Then, we devised an approach for off-line simulation of challenging noisy conditions in vocal audios originally collected under controlled environments, by leveraging our taxonomy. Our approach adds a (combination of) sound(s) belonging to the target environment into the current vocal example. Experiments on a large-scale public dataset and two state-of-the-art speaker recognition models show that adding certain background sounds to clean vocal audio leads to a substantial deterioration of recognition performance. In several noisy settings, our findings reveal that a speaker recognition model might end up to make unreliable decisions. Our framework is intended to help system designers evaluate performance deterioration and develop speaker recognition models more robust to smart living environments

Interplay between upsampling and regularization for provider fairness in recommender systems

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user–item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user–item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility

A design pattern for multimodal and multidevice user interfaces

In this paper, we introduce the MVIC pattern for creating multidevice and multimodal interfaces. We discuss the advantages provided by introducing a new component to the MVC pattern for those interfaces which must adapt to different devices and modalities. The proposed solution is based on an input model defining equivalent and complementary sequence of inputs for the same interaction. In addition, we discuss Djestit, a javascript library which allows creating multidevice and multimodal input models for web applications, applying the aforementioned pattern. The library supports the integration of multiple devices (Kinect 2, Leap Motion, touchscreens) and different modalities (gestural, vocal and touch). Copyright is held by the owner/author(s)