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This work surveys the research contributions of the last decade to the prediction of customer churn and adds a perspective toward what is yet to be reached. The main objective of this article is to report on (1) the methods and algorithms studied, the evaluation metrics adopted, and the results achieved, (2) the data used, and (3) the issues and limitations identified. Furthermore, the work highlights the gaps in the current literature and suggests a direction for future research.

Artificial intelligence (AI) is gaining lots of interest across multiple domains, such as consumer electronics, the Internet of Things (IoT), blockchain, edge computing, remote sensing, etc. Bringing adaptiveness to AI-based learning makes the overall system autonomous and robust. In parallel, Blockchain is one of the vital solutions of current age computing to make the end-to-end process decentralize. It is unavoidable to integrate AI with Blockchain when the system designing focuses on decentralized approaches, such as decentralized storage, security, and processing. Blockchain is the only solution in the current age to make the end-to-end system decentralized. This article gives a close view of Blockchain properties, data structures, IoT use cases, AI integration, and the potential future aspects.

The use of telerobots for surgery, military missions or rescue activities are increasingly commonplace, and so are guidelines to achieve an ergonomic design and optimal system-operator performance. However, in medical device manufacturing, in particular for fine-manipulation and highly precise operations, the existing human-machine interface and interaction (HMII) design standards to date are insufficient to ensure a cost-effective ergonomic teleoperation solution, that complies with the learnability, usability, dependability and efficiency required for this case-study. We analyze the most relevant human-system interface and interaction requirements for telerobotic systems applied to medical device manufacturing, and expand on the current standards with knowledge from recent research results in this field. We further our contribution by proposing a use-case-based telerobotic system architecture and experimental setup for human-centered evaluation of the telerobotic system HMII design.

The Internet of Things (IoT) has recently emerged as a new family of technologies that allow a great number of things to be connected over heterogeneous networks. Conventional networks, however, face a technological barrier in efficiently handling such a large number of devices. The approach based on software-defined networks (SDNs), with its speed and flexibility, has recently been introduced into IoT area to potentially achieve scalability and adaptability, resulting in the SDN-IoT, a unique IoT design. In this work, we describe a new multiple-path routing model for the SDN-IoT. This model consists of two components: 1) a congested source path discovery technique; 2) a multi-path selection method taking into account route similarity and priority. In this paper we first describe the SDN based Quality of Service and highlight the unique features of this routing technique, then report on the performance evaluation of such system, which achieves an approximate 17% decrease in packet loss ratio over the existing approaches.

Recently, due to the rapid development of deep learning methods, there has been a growing interest in Neuro-symbolic Artificial Intelligence, which takes advantage of both explicit symbolic knowledge and statistical sub-symbolic neural knowledge representations. In sensor-based human performance prediction (HPP) for safety-critical applications, where maintaining optimal human and system performance is a major concern, neuro-symbolic AI systems can improve sensor-based HPP tasks in complex working settings. In this paper, we focus on the advantages of hybrid neuro-symbolic AI systems, present the outstanding challenges and propose possible solutions for HPP in the safety-critical application domain.

Given a class label y assigned by a classifier to a point x in feature space, the counterfactual generation task, in its simplest form, consists of finding the minimal edit that moves the feature vector to a new point x′, which the classifier maps to a pre-specified target class y′≠ y. Counterfactuals provide a local explanation to a classifier model, by answering the questions “Why did the model choose y instead of y′ : what changes to x would make the difference?". An important aspect in classification is ambiguity: typically, the description of an instance is compatible with more than one class. When ambiguity is too high, a suitably designed classifier can map an instance x to a class set Y of alternatives, rather than to a single class, so as to reduce the likelihood of wrong decisions. In this context, known as set-based classification, one can discuss set-based counterfactuals. In this work, we extend the counterfactual generation problem – normally expressed as a constrained optimization problem – to set-based counterfactuals. Using non-singleton counterfactuals, rather than singletons, makes the problem richer under several aspects, related to the fact that non-singleton sets allow for a wider spectrum of relationships among them: (1) the specification of the target set-based class Y′ is more varied (2) the target solution x′ that ought to be mapped to Y′ is not granted to exist, and, in that case, (3) since one might end up with the availability of a number of feasible alternatives to Y′, one has to include the degree of partial fulfillment of the solution into the loss function of the optimization problem.

An ISO 9001 audit can be seen as an independent risk assessment on the business, where each ‘Nonconformity” or “Opportunity For Improvement” is considered as a potential risk. Nevertheless, their actual impact on the business remains difficult to determine; as a consequence, the urgency of a mitigation plan at corporate level can sometimes be underestimated. This paper proposes a semi-quantitative risk assessment methodology on the ISO 9001 findings relying on a selected panel of experts. The experts’ responses are analyzed and validated using a specific statistics test for inter-rater reliability. The proposed methodology has been applied on real findings coming from ISO 9001 internal audits, involving 10 subject matter experts of 7 different countries.

Saffron production from Crocus sativus flowers produces large amounts of by-products that may represent an excellent source of polyphenols. The aim of this work was to evaluate infusions originating from different brewing processes and from different saffron flower portions, in terms of both functional and sensory traits. For this aim, total polyphenols and total flavonoids, in vitro antioxidant assays and an untargeted phenolic profiling were applied. In general, tepals showed higher polyphenol and flavonoid content than stamen infusions, and their bioactive content depended more on brewing temperature than brewing time. These findings were consistent with both antioxidant capacity and phenolic profiling. Multivariate statistics highlighted polyphenols discriminating ‘boiled’ vs. ‘cold’ infusions, being mainly flavonoids, phenolic acids and the alkylphenol 5-pentadecylresorcinol (showing a strong down-accumulation at the higher brewing temperatures). Positive correlations could be highlighted between anthocyanins, flavones, flavonols and lignans, and the in vitro antioxidant assays. In general, cold brewing was successful in extracting phenolic compounds and provided better sensory properties, thus indicating that this may represent a valuable strategy to develop saffron-based functional beverages with better consumers' acceptability.

Structural network analysis retrieves the holistic patterns of interactions among network instances. Due to the unprecedented growth of data availability, it is time to take advantage of Machine Learning to integrate the outcome of the structural analysis with better predictions on the upcoming states of large networks. Concerning the existing challenges of adopting methods embracing multi-dimensional, multi-task, transparent representations within incremental procedures, in our recent study, we proposed the AVPRA algorithm. It works as an embedder of both the network structure and domain-specific features making the aforementioned challenges feasible to address. In this paper, we elaborate on the validation of AVPRA by adopting it in multiple downstream Machine Learning tasks on the Twitter network of the Italian Parliament. Comparing the outcome with state-of-the-art algorithms of graph embedding, the capability of AVPRA in retaining either network structure properties or domain-specific features of the nodes is promising. In addition, the method is incremental and transparent.

Given a class label y assigned by a classifier to a point x in feature space, the counterfactual generation task, in its simplest form, consists of finding the minimal edit that moves the feature vector to a new point x′, which the classifier maps to a pre-specified target class y′≠ y. Counterfactuals provide a local explanation to a classifier model, by answering the questions “Why did the model choose y instead of y′ : what changes to x would make the difference?". An important aspect in classification is ambiguity: typically, the description of an instance is compatible with more than one class. When ambiguity is too high, a suitably designed classifier can map an instance x to a class set Y of alternatives, rather than to a single class, so as to reduce the likelihood of wrong decisions. In this context, known as set-based classification, one can discuss set-based counterfactuals. In this work, we extend the counterfactual generation problem – normally expressed as a constrained optimization problem – to set-based counterfactuals. Using non-singleton counterfactuals, rather than singletons, makes the problem richer under several aspects, related to the fact that non-singleton sets allow for a wider spectrum of relationships among them: (1) the specification of the target set-based class Y′ is more varied (2) the target solution x′ that ought to be mapped to Y′ is not granted to exist, and, in that case, (3) since one might end up with the availability of a number of feasible alternatives to Y′, one has to include the degree of partial fulfillment of the solution into the loss function of the optimization problem.

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy.

In this paper, we present a smart connected parking lots solution to automatically count and notify drivers about empty parking spots in major cities. As its name implies, the proposed smart IoT system has two operating phases: (i) continuous counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification through a lightweight MQTT mechanism. This notification system relies only on information collected from the pre-installed multimedia devices (no other apparatus installation or maintenance such as ground sensors is required). To validate the proper operation of our solution, we have implemented a small-scale version of it and assessed its performance while considering different classical and lightweight deep learning techniques (MobileNetV2, ResNet-50, YOLOv3, SSD-MobileNetV2, Tiny-YOLO, SqueezeDet, and SqueezeDet pruned with l1-norm). The experiments have confirmed the proper operation, efficiency, ease of deployment, and ease of extension of our system. They also confirmed that lightweight deep learning solutions are more adequate for small-sized resource-constrained embedded systems. They are more efficient in terms of inference time, size, resource consumption, and yield an accuracy that is close to that of classical solutions.