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In this work, an assembly of FTO/α-Fe2O3/PB|M+ electrolyte|PB/α-Fe2O3/FTO (M+ = Li+, Na+, K+) secondary photocell was done to improve the charge capacity in the presence of illumination. Our previous results with these electrodes demonstrated the possibility of transferring photogenerated carriers from hematite to Prussian blue. This cell was characterized by incident photon-to-current efficiency (IPCE) and galvanostatic charge/discharge measurements under illumination and compared in darkness. A higher IPCE when potassium ions are employed in the cell was founded, which suggests an easy insertion/desertion of these ions inside/outside of the hexacyanometallate framework, related to the more compact water coordination sphere for this ion. The voltage cell increased when the system was charged under illumination, indicating an increase in the oxidation/reduction of iron centers in the hexacyanometallate structure due to the increase of photogenerated carriers in hematite. The cell's specific capacity reaches around 12% of the theoretical capacity, with a voltage close to the theoretical (1.5 V) and about 95% of coulombic efficiency when the cell was charged and discharged at longer times (8 h). [ABSTRACT FROM AUTHOR]

Decision making models are constrained by taking the expert evaluations with pre-defined numerical or linguistic terms. We claim that the use of sentiment analysis will allow decision making models to consider expert evaluations in natural language. Accordingly, we propose the Sentiment Analysis based Multi-person Multi-criteria Decision Making (SA-MpMcDM) methodology for smarter decision aid, which builds the expert evaluations from their natural language reviews, and even from their numerical ratings if they are available. The SA-MpMcDM methodology incorporates an end-to-end multi-task deep learning model for aspect based sentiment analysis, named DOC-ABSADeepL model, able to identify the aspect categories mentioned in an expert review, and to distill their opinions and criteria. The individual evaluations are aggregated via the procedure named criteria weighting through the attention of the experts. We evaluate the methodology in a case study of restaurant choice using TripAdvisor reviews, hence we build, manually annotate, and release the TripR-2020 dataset of restaurant reviews. We analyze the SA-MpMcDM methodology in different scenarios using and not using natural language and numerical evaluations. The analysis shows that the combination of both sources of information results in a higher quality preference vector. • Decision making models are limited by pre-defined numerical and linguistic terms. • We propose a methodology to deal with natural language and numerical assessments. • We design a deep learning model for distilling opinions from written assessments. • We present and release a dataset, which can be used for evaluating MpMcDM models. [ABSTRACT FROM AUTHOR]

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyperparametric configurations with improved performance for a given task, to the optimization of the model's parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: (a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, (b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and (c) challenges and new directions of research (What can be done, and what for?). In summary, three axes – optimization and taxonomy, critical analysis, and challenges – which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research. • We thoroughly examine the fusion between Deep Learning and bioinspired optimization. • Definitions and a taxonomy of Deep Learning optimization problems are provided. • We perform a critical methodological analysis of contributions made so far. • Learned lessons and recommendations are drawn from our analysis and two study cases. • Challenges and research directions are given in this fusion of technologies. [ABSTRACT FROM AUTHOR]

The present work reports iron tungstate (FeWO 4) nanostructures with reduced graphene oxide (rGO) as a novel anode material for enhancing the electrochemical properties of asymmetric supercapacitors. The FeWO 4 -rGO composite nanostructures were successfully synthesized by the one-pot sonochemical method. The synthesized nanocomposites crystal structure and phase purity were investigated using the powder X-ray diffraction (XRD) technique. The Fourier transform infrared (FT-IR) spectrograms demonstrate the presence of functional groups in the composite. The composite's morphology was examined using the high-resolution transmission electron microscopy (HR-TEM) and the field emission scanning electron microscopy (FE-SEM), and it was observed that the FeWO 4 nanostructures were uniformly distributed on the reduced graphene oxide surface. The cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) analyses were used to evaluate the electrochemical performance. After 5000 cycles at 10 mA cm− 2, the FeWO 4 -rGO composite achieved a better rate of efficiency and outstanding cycling performance, with capacitance retained at 68% and 77.8%, respectively. In 1 M Na 2 SO 4 , an asymmetric device (CuCo 2 O 4 //FeWO 4 -rGO composite) achieved a high energy density of 21.5 W.h Kg−1 and a power density of 147 W Kg−1. In the FeWO 4 -rGO nanocomposite, the reduced graphene oxide could enhance the conductivity and the free diffusion processes for the quick ion transport and easy ion access to the storage sites. The obtained results indicated that the FeWO 4 -rGO nanocomposite could be a good anode electrode material for the next-generation energy storage applications. [Display omitted] • FeWO 4 nanostructures with rGO are synthesized by sonochemical synthesis. • FeWO 4 -rGO composite exhibited a specific capacitance of 414 Fg−1 at a current density of 1 mA cm−2. • FeWO 4 -rGO composite delivered an improved rate capability of 68% and excellent cycling stability of 77.8%. • An asymmetric device CuCO 2 O 4 // FeWO 4 -rGO device provided a high energy density. [ABSTRACT FROM AUTHOR]

Anomaly detection aims to identify observations that differ significantly from the majority of the data. Time series, which are data with a temporal component, is often used for anomaly detection. Identifying anomalies is not perfect and may produce many false positives, which labels standard data as anomalous. In this context, false positive mitigation is the task of reducing the number of false positives tagged by the anomaly detector, and thus both problems are closely linked. Moreover, current techniques for false positive mitigation are ad-hoc solutions for specific data sets. In this paper, we propose a novel two-stage methodology for Multivariate Anomaly Detection for Time Series and False Positive Mitigation, namely F A D F P M methodology, which creates the fusion of two learning models. The first stage is a multivariate anomaly detection stage. The second stage consists of training a new classifier on the false and true positives from the anomaly detector, which refines the observations labeled as anomalous by the anomaly detector to obtain more accurate and higher-quality results. Experiments using two benchmark data sets, as well as a real-world case study have shown the performance and validity of the proposal. • Proposed methodology decreases impact from FPs in anomaly detection. • A thorough comparison with latest SOA methods is performed. • We also provide a series of hints for applying the methodology. • High sensitivity methods are more benefited from the proposal. • A real-world case of study provided by ArcelorMIttal is analyzed. [ABSTRACT FROM AUTHOR]

Due to their high adaptability to varied settings and effective optimization algorithm, Convolutional Neural Networks (CNNs) have set the state-of-the-art on image processing jobs for the previous decade. CNNs work in a sequential fashion, alternating between extracting significant features from an input image and aggregating these features locally through "pooling" functions, in order to produce a more compact representation. Functions like the arithmetic mean or, more typically, the maximum are commonly used to perform this downsampling operation. Despite the fact that many studies have been devoted to the development of alternative pooling algorithms, in practice, "max-pooling" still equals or exceeds most of these possibilities, and has become the standard for CNN construction. In this paper we focus on the properties that make the maximum such an efficient solution in the context of CNN feature downsampling and propose its replacement by grouping functions, a family of functions that share those desirable properties. In order to adapt these functions to the context of CNNs, we present (a , b) -grouping functions, an extension of grouping functions to work with real valued data. We present different construction methods for (a , b) -grouping functions, and demonstrate their empirical applicability for replacing max-pooling by using them to replace the pooling function of many well-known CNN architectures, finding promising results. • Grouping pooling generalizes maximum pooling while considering all inputs. • Aggregation functions extended to a real domain (a, b) can act as pooling operator. • Different methods for constructing (a, b)-grouping functions are presented. • (a, b)-grouping poolings improve the gradient flow of max-pooling. [ABSTRACT FROM AUTHOR]

Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and a social perspective. However, attaining truly trustworthy AI concerns a wider vision that comprises the trustworthiness of all processes and actors that are part of the system's life cycle, and considers previous aspects from different lenses. A more holistic vision contemplates four essential axes: the global principles for ethical use and development of AI-based systems, a philosophical take on AI ethics, a risk-based approach to AI regulation, and the mentioned pillars and requirements. The seven requirements (human agency and oversight; robustness and safety; privacy and data governance; transparency; diversity, non-discrimination and fairness; societal and environmental wellbeing; and accountability) are analyzed from a triple perspective: What each requirement for trustworthy AI is, Why it is needed, and How each requirement can be implemented in practice. On the other hand, a practical approach to implement trustworthy AI systems allows defining the concept of responsibility of AI-based systems facing the law, through a given auditing process. Therefore, a responsible AI system is the resulting notion we introduce in this work, and a concept of utmost necessity that can be realized through auditing processes, subject to the challenges posed by the use of regulatory sandboxes. Our multidisciplinary vision of trustworthy AI culminates in a debate on the diverging views published lately about the future of AI. Our reflections in this matter conclude that regulation is a key for reaching a consensus among these views, and that trustworthy and responsible AI systems will be crucial for the present and future of our society. • A holistic vision of trustworthy AI with principles for ethical use and development. • A philosophical reflection on AI Ethics. • An analysis of regulatory efforts around trustworthy AI focused on the European AI Act. • An examination of the fundamental pillars and requirements for trustworthy AI. • A definition of responsible AI systems and the role of regulatory sandboxes. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model's decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data. • A novel four-axis framework to examine a model for robustness and explainability. • Formulation of research questions at each axis and its corresponding taxonomy. • Discussion of different explainability assessment methods. • A novel methodological workflow for determining the model and explainability criteria. • Revisited discussion on challenges and future directions of XAI and Trustworthy AI. [ABSTRACT FROM AUTHOR]

An important part of art history can be discovered through the visual information in monument facades. However, the analysis of this visual information, i.e, morphology and architectural elements, requires high expert knowledge. An automatic system for identifying the architectural style or detecting the architectural elements of a monument based on one image will certainly help improving our knowledge in art and history. Building such tool is challenging as some styles share architectural elements, the bad conservation state of some monuments and the noise included in the image itself. The aim of this paper is to introduce MonuMAI (Monument with Mathematics and Artificial Intelligence) framework. In particular, (i) we designed MonuMAI dataset rich with expert knowledge considering the proposed architectural styles taxonomy and key elements relationship, which allows addressing several tasks, e.g., monument style classification and architectural elements detection, (ii) we developed MonuMAI deep learning pipeline based on lightweight MonuNet architecture for monument style classification and MonuMAI Key Elements Detection (MonuMAI-KED) model, and (iii) we built citizen science based MonuMAI mobile app that uses the proposed MonuMAI deep learning pipeline trained on MonuMAI dataset for performing in real life conditions. Our experiments show that both MonuNet architecture and the detection model achieve very good results under real life conditions. [ABSTRACT FROM AUTHOR]

Cadmium telluride quantum dots (CdTe-QDs) can be deposited from colloidal solutions on glassy carbon (GC) electrodes potentiostatically. The immobilization of QDs can be performed at potentials positive and negative from the potential of zero charge (PZC = 0.45 V vs. Ag/AgCl), including the open circuit potential. The surface coverage of QDs depends on the deposition potential and time. The amount of QD layers was determined by following the redox process of cadmium by cyclic voltammetry and chronoamperometry. Additionally, the QD layers were characterized by scanning electrochemical microscopy (SECM) indicating a decrease of the surface conductivity of GC electrodes. [ABSTRACT FROM AUTHOR]

• We analyze the origin and taxonomy of distributed linguistic representations. • We review the key elements of distributed linguistic representations in decision making. • We review the applications of distributed linguistic representations in decision making. • We propose ongoing challenges for distributed linguistic representations. Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence. [ABSTRACT FROM AUTHOR]

This article provides a brief tour through the main fuzzy and linguistic decision-making trends, studies, methodologies, and models developed in the last 50 years. Fuzzy and linguistic decision-making approaches allow to address complex real-world decision problems where humans exhibit vagueness, imprecision, and/or use natural language to assess decision alternatives, criteria, etc. The aim of this article is threefold. First, the main fuzzy set theory and computing with words-based representation paradigms of decision information, with their different levels of expressive richness and complexity, are reviewed. Second, three core decision-making frameworks are examined: 1) multicriteria decision making; 2) group consensus-driven decision making; and 3) multiperson multicriteria decision making. Third, the article discusses new complex decision-making frameworks that have emerged in recent years, where decisions are guided by the “wisdom of the crowd”: their associated challenges are highlighted and considerations on much needed key guidelines for future research in the field are provided. [ABSTRACT FROM AUTHOR]

Ensembles, especially ensembles of decision trees, are one of the most popular and successful techniques in machine learning. Recently, the number of ensemble-based proposals has grown steadily. Therefore, it is necessary to identify which are the appropriate algorithms for a certain problem. In this paper, we aim to help practitioners to choose the best ensemble technique according to their problem characteristics and their workflow. To do so, we revise the most renowned bagging and boosting algorithms and their software tools. These ensembles are described in detail within their variants and improvements available in the literature. Their online-available software tools are reviewed attending to the implemented versions and features. They are categorized according to their supported programming languages and computing paradigms. The performance of 14 different bagging and boosting based ensembles, including XGBoost, LightGBM and Random Forest, is empirically analyzed in terms of predictive capability and efficiency. This comparison is done under the same software environment with 76 different classification tasks. Their predictive capabilities are evaluated with a wide variety of scenarios, such as standard multi-class problems, scenarios with categorical features and big size data. The efficiency of these methods is analyzed with considerably large data-sets. Several practical perspectives and opportunities are also exposed for ensemble learning. • We present an overview of recent and iconic bagging and boosting based algorithms. • We list their software tools available online and features. • We perform a performance study, extracting some lessons learned. • Some perspectives on ensemble learning are discussed. • Some opportunities and future fields are examined for ensembles in machine learning. [ABSTRACT FROM AUTHOR]

The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. Hence, we present the Sherpa.ai Federated Learning framework that is built upon a holistic view of federated learning and differential privacy. It results from both the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases. • We analyse the most recent federated learning and differential privacy frameworks. • We present Sherpa.ai FL, a unified framework for federated learning and differential privacy. • We discuss the adaptation of machine learning models to federated learning. • We define the guidelines for privacy-preserving artificial intelligence services. [ABSTRACT FROM AUTHOR]

Crowd behaviour analysis is an emerging research area. Due to its novelty, a proper taxonomy to organise its different sub-tasks is still missing. This paper proposes a taxonomic organisation of existing works following a pipeline, where sub-problems in last stages benefit from the results in previous ones. Models that employ Deep Learning to solve crowd anomaly detection, one of the proposed stages, are reviewed in depth, and the few works that address emotional aspects of crowds are outlined. The importance of bringing emotional aspects into the study of crowd behaviour is remarked, together with the necessity of producing real-world, challenging datasets in order to improve the current solutions. Opportunities for fusing these models into already functioning video analytics systems are proposed. • Proposal of hierarchical taxonomy for crowd behaviour analysis subtasks. • Review and numeric comparison of Deep Learning models for crowd anomaly detection. • Discussion of current limitations in datasets and importance of going beyond. • Discussion of the importance of using emotional aspects in crowd behaviour analysis. • Proposals of fusion crowd analysis models into existing video analytics solutions. [ABSTRACT FROM AUTHOR]

• It provides an overview of the most popular ensemble methods. • It analyzes several fusion schemes using MNIST as guiding thread. • It introduces MNIST-NET10, a complex heterogeneous fusion architecture. • MNIST-NET10 reaches a new record in MNIST with only 10 misclassified images. Ensemble methods have been widely used for improving the results of the best single classification model. A large body of works have achieved better performance mainly by applying one specific ensemble method. However, very few works have explored complex fusion schemes using heterogeneous ensembles with new aggregation strategies. This paper is three-fold: 1) It provides an overview of the most popular ensemble methods, 2) analyzes several fusion schemes using MNIST as guiding thread and 3) introduces MNIST-NET10, a complex heterogeneous fusion architecture based on a degree of certainty aggregation approach; it combines two heterogeneous schemes from the perspective of data, model and fusion strategy. MNIST-NET10 reaches a new record in MNIST with only 10 misclassified images. Our analysis shows that such complex heterogeneous fusion architectures based on the degree of certainty can be considered as a way of taking benefit from diversity. [ABSTRACT FROM AUTHOR]

In many machine learning tasks, learning a good representation of the data can be the key to building a well-performant solution. This is because most learning algorithms operate with the features in order to find models for the data. For instance, classification performance can improve if the data is mapped to a space where classes are easily separated, and regression can be facilitated by finding a manifold of data in the feature space. As a general rule, features are transformed by means of statistical methods such as principal component analysis, or manifold learning techniques such as Isomap or locally linear embedding. From a plethora of representation learning methods, one of the most versatile tools is the autoencoder. In this paper we aim to demonstrate how to influence its learned representations to achieve the desired learning behavior. To this end, we present a series of learning tasks: data embedding for visualization, image denoising, semantic hashing, detection of abnormal behaviors and instance generation. We model them from the representation learning perspective, following the state of the art methodologies in each field. A solution is proposed for each task employing autoencoders as the only learning method. The theoretical developments are put into practice using a selection of datasets for the different problems and implementing each solution, followed by a discussion of the results in each case study and a brief explanation of other six learning applications. We also explore the current challenges and approaches to explainability in the context of autoencoders. All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space. [ABSTRACT FROM AUTHOR]

• We analyze the origin of feedback mechanism with minimum adjustment or cost (FMMA/C). • We review FMMA/C in classical group decision making problems. • We review FMMA/C in complex group decision making problems. • We propose some open problems on FMMA/C. Consensus reaching process is a very powerful decision tool to eliminate the preference conflict in group decision making. In general, the consensus is achieved by the decision makers modifying their preferences (or opinions) toward a point of mutual consent, and the feedback mechanism aims to provide preference-modifications suggestions. In many situations, the preference-modifications mean cost and the resources for the consensus reaching process are limited. So, in the last decade, the feedback mechanism with minimum adjustment or cost (FMMA/C) has been developed and widely used in various group decision making contexts to improve consensus efficiency. In this review, we first analyze the origin and basic research paradigm of the FMMA/C. Then, we review the FMMA/C in two decision contexts: (1) the FMMA/C in classical group decision making problems, and (2) the FMMA/C in complex group decision making problems (e.g., social network, large-scale, and opinion dynamic group decision making problems). Finally, we identify research challenges and propose future research direction. [ABSTRACT FROM AUTHOR]

• Definition and formal characterization of Large-Scale Decision Making (LSDM) framework. • Taxonomy of key elements found in LSDM models and processes. • Classification of preference representations used in LSDM. • Discussion of challenges and future directions from an AI and applications perspective. The last decade witnessed tremendous developments in social media and e-democracy technologies. A fundamental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM research was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior management, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided. [ABSTRACT FROM AUTHOR]

• We review concepts related to the explainability of AI methods (XAI). • We comprehensive analyze the XAI literature organized in two taxonomies. • We identify future research directions of the XAI field. • We discuss potential implications of XAI and privacy in data fusion contexts. • We identify Responsible AI as a concept promoting XAI and other AI principles in practical settings. In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence , namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability. [ABSTRACT FROM AUTHOR]

The problem of Multiple Object Tracking (MOT) consists in following the trajectory of different objects in a sequence, usually a video. In recent years, with the rise of Deep Learning, the algorithms that provide a solution to this problem have benefited from the representational power of deep models. This paper provides a comprehensive survey on works that employ Deep Learning models to solve the task of MOT on single-camera videos. Four main steps in MOT algorithms are identified, and an in-depth review of how Deep Learning was employed in each one of these stages is presented. A complete experimental comparison of the presented works on the three MOTChallenge datasets is also provided, identifying a number of similarities among the top-performing methods and presenting some possible future research directions. [ABSTRACT FROM AUTHOR]

This paper presents otsad, the first R package which implements a set of novel online detection algorithms for univariate time-series. Moreover, this package also provides advanced functionalities and contents such as new false positive reduction algorithm and the novel NAB detectors measurement technique which is specifically designed to measure online time-series anomaly detectors. [ABSTRACT FROM AUTHOR]

Transcriptional regulation, determined by the chromatin structure and regulatory elements interacting at promoter regions, is a key step in plant responses to environmental cues. Nitrate (NO 3 −) is a nutrient signal that regulates the expression of hundreds of genes in Arabidopsis thaliana. Here, we integrate mRNA sequencing, genome-wide RNA polymerase II (RNPII), chromatin immunoprecipitation sequencing, and DNase sequencing datasets to establish the relationship between RNPII occupancy and chromatin accessibility in response to NO 3 − treatments in Arabidopsis roots. Genomic footprinting allowed us to identify in vivo regulatory elements controlling gene expression in response to NO 3 − treatments. NO 3 −-modulated transcription factor (TF) footprints are important for a rapid increase in RNPII occupancy and transcript accumulation over time. We mapped key TF regulatory interactions and functionally validated the role of NAP, an NAC-domain containing TF, as a new regulatory factor in NO 3 − transport. Taken together, our study provides a comprehensive view of transcriptional networks in response to a nutrient signal in Arabidopsis roots. NO 3 − treatments in Arabidopsis root produce changes in transcription factor (TF) footprints. This study mapped key TF regulatory interactions and validated the role of new TFs in the nitrate response in roots. [ABSTRACT FROM AUTHOR]

While used in minor proportion respect to other components in cathode formulations, binders play a crucial role in lithium batteries cathode. This is particularly true in Li-O2 batteries which represent a very harsh environment mainly because of the formation, upon cycling, of very aggressive superoxide radicals. In such batteries, the use of catalyst in the cathode formulation is quite usual to help oxygen reduction reaction and oxygen evolution reaction (ORR and OER) to proceed rapidly and reversibly. However, the slightest binder degradation upon cycling can hinder the catalyst effects dramatically, modifying them to act on other reactions associated with the generated side-products, thus deteriorating cell performances and preventing researchers from drawing the right conclusions about catalytic properties of new materials. In this work, the influence of different catalysts in the degradation of the PVDF binder, on the performance of Li-O2 batteries, was investigated. The results obtained were compared with the ones of cathodes prepared with the same catalysts but Li-Nafion binder instead (already reported as stable), to further demonstrate that the choice of binder must be strongly linked to the nature of the catalyst. The catalysts employed for this study were α-MnO2, and commercial Co3O4, and Co phthalocyanines (CoPc). [ABSTRACT FROM AUTHOR]

• We review additive consistency measurements of various reciprocal preference relations. • We review decision making methods based on additive consistency measurements. • We propose some open problems on consistency of reciprocal preference relations. The reciprocal preference relation (RPR) is a powerful tool to represent decision makers' preferences in decision making problems. In recent years, various types of RPRs have been reported and investigated, some of them being the 'classical' RPRs, interval-valued RPRs and hesitant RPRs. Additive consistency is one of the most commonly used property to measure the consistency of RPRs, with many methods developed to manage additive consistency of RPRs. To provide a clear perspective on additive consistency issues of RPRs, this paper reviews the consistency measurements of the different types of RPRs. Then, consistency-driven decision making and information fusion methods are also reviewed and classified into four main types: consistency improving methods; consistency-based methods to manage incomplete RPRs; consistency control in consensus decision making methods; and consistency-driven linguistic decision making methods. Finally, with respect to insights gained from prior researches, further directions for the research are proposed. [ABSTRACT FROM AUTHOR]

• Construct consensus evolution networks (CENs) managing consensus thresholds. • Introduce new consensus index numerically and structurally based on CENs. • Design a pairwise adjustment method based on the complementary sensitive CEN. • Give reference for the agreed consensus threshold based on the sparsity of CENs. The consensus reaching process (CRP) is a critical part of group decision making (GDM). In order to explore the evolution of consensus, a new CRP tool is proposed based on consensus evolution networks (CENs). The CENs are built based on the consensus degrees among decision makers (DMs) and allow us to manage the consensus thresholds and its evolution. A new consensus index is introduced based on the structured and numerical aspects of the CENs. The new consensus index can not only deeply analyze the constitution of consensus, but also determine the weights of DMs. According to the clustering coefficient, the sensitive consensus threshold is identified and the sensitive consensus evolution network (SCEN) is built. Based on the complementary SCEN, a pairwise feedback adjustment method is proposed to improve consensus. Besides, the sparsity of the CENs can act as a reference to determine the agreed consensus thresholds, which is considered an important issue in traditional models. A numerical example is used to verify the usefulness of the proposed CRP tool. The numerical results show that the evolution of consensus can be clearly found based on CENs and the pairwise method can improve consensus in only four rounds. [ABSTRACT FROM AUTHOR]

• We prepare on overview of MULTIMOORA by analyzing 106 main studies. • We scrutinize the theoretical features of MULTIMOORA. • We discuss the developments of MULTIMOORA based on seven uncertainty theories. • We present the practical applications of MULTIMOORA categorized in five sectors. • The challenges are posed concerning theoretical features and practical applications. MULTIMOORA is a useful multi-criteria decision-making technique. The output of the MULTIMOORA is a ranking obtained by aggregating the results of the ternary ranking methods: Ratio System, Reference Point Approach, and Full Multiplicative Form. In the literature of MULTIMOORA, there is not a comprehensive review study. In this paper, we conduct an overview of MULTIMOORA by categorizing and analyzing main researches, theoretically and practically. First, we go through an theoretical survey of MULTIMOORA in terms of the subordinate ranking methods, ranking aggregation tools, weighting methods, group decision-making, combination with other models, and the robustness of the method. We scrutinize the developments of MULTIMOORA based on uncertainty theories accompanied by analyzing the mathematical formulations of breakthrough models. Practical problems of MULTIMOORA are categorized into application sectors concerning industries, economics, civil services and environmental policy-making, healthcare management, and information and communications technologies. Bibliometric analyses are implemented into all studies. Also, we pose major theoretical and practical challenges. From the theoretical viewpoint, extensions of Reference Point Approach, cooperative group decision-making structure, and utilization of new uncertainty sets in MULTIMOORA model are the main challenges. From the practical viewpoint, industrial and socio-economic fields are appealing to be studied intensively. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Highlights • A social network and sparse representation-based conflict investigation process (S-CRIP) is presented. • The defineded opinion and behavior conflicts can be detected and classified exactly by S-CRIP. • A novel conflict degree-based consensus reaching process (CRP) is presented. • Conflict degree is used to check the consensus is reached or not in CRPs for LSDM events. • The large-scale decision making model can be used for any numerical evaluation environments. Abstract Large-Scale Decision Making (LSDM) scenarios, such as public participation events, are becoming increasingly common in human life. Decision makers (DMs) in LSDM events present different interest preferences, leading to different relationships being created between them. In LSDM scenarios, a conflict relationship, which is a type of negative relationship among DMs, has the biggest negative impact on reaching the consensus. The conflict relationships can be divided into two parts: the opinion conflict and the behavior conflict. In this paper, a Social network analysis-based Conflict Relationship Investigation Process (S-CRIP) is presented to detect the conflict relationships among DMs for LSDM events, in which sparse representation is used. Besides, a Conflict Degree-based Consensus Reaching Process (CD-CRP) is proposed for LSDM problems, which is using group conflict degree to check whether the consensus is reached or not. In the decision selection process, DMs' weights are calculated by their conflict performances, which can reduce the negative influence of those DMs that present conflict in the LSDM event. The proposed S-CRIP can not only investigate the conflict relationships among DMs, but can also recognize the two types of conflict relationships according to their features. The three processes constitute the S-CRIP and CD-CRIP-based LSDM model, which is suitable for any numerical representations. Illustrative experiments not only show the feasibility and veracity of S-CRIP in LSDM scenarios, but also prove the practicability and effectiveness of S-CRIP and CD-CRP-based LSDM model. [ABSTRACT FROM AUTHOR]

As the amount of data available exponentially grows, data scientists are aware that finding the value in the data is key to a successful data exploiting. However, the data rarely presents itself in a ordered, clean way. In opposition to dealing with raw data, the term smart data is becoming more and more visible both in the specialized literature and companies. While software packages publicly exist to deal with raw data, there is no unified framework that encompasses all the required fields to transform such raw data to smart data. In this paper, the novel smartdata package is introduced. Written in R and available at CRAN repository, it includes the most recent and relevant algorithms to treat raw data from multiple perspectives, now unified under a simple yet powerful API, which enables the data scientist to easily pipeline their application. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript. [ABSTRACT FROM AUTHOR]

Because of the increasing complexity of real-world decision-making environment, there is a trend that a large number of decision-makers are becoming involved in group decision making problems. In large-scale group decision making problems, owing to various backgrounds and psychological cognition, it is natural to use heterogeneous representation forms (quantitative or qualitative) to express distinct preference information for different decision-makers. In this paper, we investigate the consensus reaching process in the environment of heterogeneous large-scale group decision making. A novel ordinal consensus measure with an objective threshold based on preference orderings is proposed. This process contains five parts: (1) obtaining ordinal preferences; (2) classifying all decision-makers into several subgroups using the ordinal k-means clustering algorithm; (3) measuring consensus levels of subgroups and the global group using novel ordinal consensus indexes; (4) providing suggestions for decision-makers to revise preferences using feedback strategies; (5) obtaining final decision result. An illustrative example is provided to verify the implementation of the proposed consensus model. Lastly, we discuss the approaches to determine the appropriate number of clusters and the initialization center points for the proposed ordinal k-means clustering algorithm. [ABSTRACT FROM AUTHOR]

Highlights • This paper proposes a novel binocular image approach that makes the detection model focus on the area of interest. • We built a low cost symmetric dual camera system to compute the disparity map and exploit that information to improve the selection of candidate regions in the input frames. • The proposed approach reduces the number of false positives in the test videos by 49.47%. Abstract Object detection models have known important improvements in the recent years. The state-of-the art detectors are end-to-end Convolutional Neural Network based models that reach good mean average precisions, around 73%, on benchmarks of high quality images. However, these models still produce a large number of false positives in low quality videos such as, surveillance videos. This paper proposes a novel image fusion approach to make the detection model focus on the area of interest where the action is more likely to happen in the scene. We propose building a low cost symmetric dual camera system to compute the disparity map and exploit this information to improve the selection of candidate regions from the input frames. From our results, the proposed approach not only reduces the number of false positives but also improves the overall performance of the detection model which make it appropriate for object detection in surveillance videos. [ABSTRACT FROM AUTHOR]

TripAdvisor is an opinion source frequently used in Sentiment Analysis. On this social network, users explain their experiences in hotels, restaurants or touristic attractions. They write texts of 200 character minimum and score the overall of their review with a numeric scale that ranks from 1 (Terrible) to 5 (Excellent). In this work, we aim that this score, which we define as the User Polarity, may not be representative of the sentiment of all the sentences that make up the opinion. We analyze opinions from six Italian and Spanish monument reviews and detect that there exist inconsistencies between the User Polarity and Sentiment Analysis Methods that automatically extract polarities. The fact is that users tend to rate their visit positively, but in some cases negative sentences and aspects appear, which are detected by these methods. To address these problems, we propose a Polarity Aggregation Model that takes into account both polarities guided by the geometrical mean. We study its performance by extracting aspects of monuments reviews and assigning to them the aggregated polarities. The advantage is that it matches together the sentiment of the context (User Polarity) and the sentiment extracted by a pre-trained method (SAM Polarity). We also show that this score fixes inconsistencies and it may be applied for discovering trustworthy insights from aspects, considering both general and specific context. [ABSTRACT FROM AUTHOR]

Highlights • We present a method to calculate the hesitant degree of HFLTS. • We propose a novel score function for HFLTS, named Score-HeDLiSF. • A hesitant degree-based weighting method is developed to derive the weights. • We use Score-HeDLiSF in an unbalanced HFL-MULTIMOORA method integrated by ORESTE. • The unbalanced HFL-MULTIMOORA is applied to the shared bicycle selection problem. Abstract The Hesitant Fuzzy Linguistic Term Set (HFLTS) is a powerful tool to depict experts' cognitive complex linguistic information. This paper aims to propose a new score function of HFLTS to eliminate the defects of the subscript-based operations on HFLTSs. Hesitant degree is an intrinsic feature of HFLTS, and the greater the hesitant degree is, the lower the quality of the HFLTS will be. The asymmetric and non-uniform distributed linguistic term set is commonly used when expressing cognitive complex linguistic information. Considering both the hesitant degrees and the unbalanced linguistic terms in evaluations, a new score function of HFLTS, named the Score-HeDLiSF, is proposed based on the psychology of experts. The Score-HeDLiSF shows many advantages over the existing score function of HFLTS in terms of representing both the balanced and unbalanced linguistic information with hesitant degree and linguistic scale functions. Afterward, a hesitant degree-based weighting method is proposed to determine the weights of experts and criteria. To derive robust decision results, the MULTIMOORA method is improved by integrating the ORESTE method, and then we extend it to the unbalanced hesitant fuzzy linguistic context based on the introduced score function of HFLTS. Finally, an investment problem regarding the shared bicycles is solved by the proposed unbalanced HFL-MULTIMOORA method. The advantages of the unbalanced HFL-MULTIMOORA are highlighted by comparative analyses with two well-known multi-criteria decision-making methods. [ABSTRACT FROM AUTHOR]

There exist a high demand to provide explainability to artificial intelligence systems, where decision making models are included. This paper focuses on crowd decision making using natural language evaluations from social media with the aim to provide explainability. We present the Explainable Crowd Decision Making based on Subgroup Discovery and Attention Mechanisms (ECDM-SDAM) methodology as an a posteriori explainable process that captures the wisdom of crowds that is naturally provided in social media opinions. It extracts the opinions from social media texts using a deep learning based sentiment analysis approach called Attention based Sentiment Analysis Method. The methodology includes a backward process that provides explanations to justify its sense-making procedure by applying mainly the attention mechanism on texts and subgroup discovery on opinions. We evaluate the methodology in the real case study of the TripR-2020Large dataset for restaurant choice. The results show that the ECDM-SDAM methodology provides easy understandable explanations that elucidates the key reasons that support the output of the decision process. • Explainability in decision making is essential to increase its use and understanding. • Attention mechanisms and subgroup discovery can generate explainable decision making. • We propose a methodology that offers explanations of its internal decision mechanism. • The proposed methodology captures the wisdom of crowds from social media. • Natural language with sentiment analysis and deep learning enriches expert evaluation. [ABSTRACT FROM AUTHOR]

• We provide five new mechanisms to deal with changing dynamic (streams) or static data that can be integrated in anomaly detection or ML tasks. • We propose a novel unsupervised anomaly detection method, named Multi-step Histogram Based Outlier Score. It takes advantage from the mechanisms indicated above. • We validate our proposal against statistical and ML-based anomaly detection methods. • We release a new dataset for unsupervised anomaly detection named TINA, based on real data from ArcelorMittal™. Anomaly detection is the task of detecting samples that behave differently from the rest of the data or that include abnormal values. Unsupervised anomaly detection is the most common scenario, which implies that the algorithms cannot train with a labeled input and do not know the anomaly behavior beforehand. Histogram-based methods are one of the most approaches in unsupervised anomaly detection, remarking a good performance and a low runtime. Despite the good performance, histogram-based anomaly detectors are not capable of processing data flows while updating their knowledge and cannot deal with a high amount of samples. In this paper, we propose a new histogram-based approach for addressing the aforementioned problems by introducing the ability to update the information inside a histogram. We have applied these strategies to design a new algorithm called Multi-step Histogram Based Outlier Scores (MHBOS), including five new histogram update mechanisms. The results have shown the performance and validity of MHBOS as well as the proposed strategies in terms of performance and computing times. [ABSTRACT FROM AUTHOR]