Purpose: Authorship can be defined as a combination of content and style . Modern open-source transformer foundational authorship models apply contrastive learning techniques. When naively contrasting texts to an authorship task some amount of semantic leakage is present, as authors frequently repeat their topic preferences. Our aim is to reduce spurious correlations due to topic leakage born from contrastive objectives. Methodology: We present a technique to modify a well-established contrastive learning objective (InfoNCE) using synthetic hard negative examples for in-domain topic leakage improvements, while preserving competitive out of domain performance. This topic leakage mitigation technique aims to distance the content embedding space from the style embedding space. Our experiments aim to demonstrate this technique in detail, using exclusively affordable encoder-only models instead of costly hard negative mining. Results: We showcase the performance with ablations on two different datasets and compare them on out-of-domain challenges. We improve on challenging evaluations with prolific authors, with up to 10% increase in accuracy on highly diverse bodies of work . Trials with standard challenges also demonstrate the preservation of zero-shot capabilities of this method as fine-tuning.

ABSTRACT Deepfake detection models achieve high accuracy, yet their interpretability remains underexplored. This study presents a unified evaluation pipeline for post hoc visual explanations, grounded in the Co‐12 attributes of explanation quality and operationalised through a structured framework that assesses Coherence, Composition, Correctness, Completeness, Compactness, Covariate completeness and Continuity. Applying this pipeline to 16 representative explanation methods reveals systematic differences across methodological categories. CAM‐based approaches demonstrate strong spatial coherence and temporal continuity; gradient‐based techniques such as Guided Backprop and LRP yield compact and accurate attributions; and redistribution‐based methods including ExcitationBP and Deep Taylor maintain high consistency across evaluation conditions. In contrast, perturbation‐based approaches such as SHAP and LIME exhibit weaker localisation and reduced temporal stability. By enabling controlled, attribute‐level comparison of explanation methods, the proposed pipeline bridges conceptual interpretability frameworks and empirical analysis, offering practical guidance for the development and deployment of interpretable deepfake detectors in forensic and auditing applications. The source code is publicly available at: https://github.com/junxinchenieee/EAI‐Deepfake‐Detection .

ABSTRACT Federated learning (FL) has emerged as an impactful paradigm for privacy‐preserving machine learning, and allows model training without the need to share raw data. However, data heterogeneity across clients challenges practical FL deployment. Data space heterogeneity and statistical heterogeneity create significant training difficulties. System heterogeneity imposes additional external constraints. These combined factors impair convergence and reduce model performance. They also raise concerns regarding fairness, scalability and robustness. Focused on data heterogeneity, this review provides a structured analysis of FL. It encompasses three key areas: core categorizations of data heterogeneity, algorithmic advances (e.g., personalized FL, mixture‐of‐experts architectures, transfer learning‐based solutions) and system‐level techniques spanning communication optimization, resource adaptation and secure collaboration. We further synthesize benchmark efforts and real‐world applications in healthcare, finance, nuclear power and the Internet of Things (IoT)/edge computing to highlight the practical implications of heterogeneity‐aware FL. Finally, we identify key challenges and outline promising research directions towards scalable, fair and adaptive FL systems capable of operating in complex real‐world settings. This survey aims to serve as a reference point and conceptual roadmap for future research in heterogeneous FL.

ABSTRACT This study sheds light on a fundamental problem in image segmentation known as multilevel image thresholding. With the rapid growth of artificial intelligence applications that rely on image processing such as medical imaging, remote sensing, and pattern recognition the demand for more effective techniques has become increasingly urgent. Traditional methods suffer from significant limitations, including slow convergence and premature convergence to local optima, particularly when applied to complex or high‐dimensional images. To address these challenges, this study proposes a novel approach based on metaheuristic algorithms, specifically elephant herding optimization (EHO) and symbiotic organism search (SOS). Although these algorithms have shown promising results due to their adaptability and exploratory capabilities, they still face performance bottlenecks resulting from insufficient diversity in the search process. To overcome these limitations, enhanced variants of EHO and SOS are introduced by integrating opposition‐based learning (OBL) and chaos theory to achieve a better balance between exploration and exploitation. These improved algorithms, OCEHO and OCSOS, are applied to the multilevel thresholding problem using Otsu's variance, Kapur's entropy and Masi's entropy as objective functions. The proposed methods are evaluated on 75 standard benchmark images, with segmentation quality evaluated using PSNR, SSIM, and FSIM metrics. Experimental results on 75 standard benchmark images show that the proposed OCEHO algorithm achieves PSNR values up to 37.51 dB, SSIM scores of 0.972, and FSIM values of 0.986, significantly outperforming baseline and hybrid variants. Furthermore, statistical analyzes, including the Wilcoxon rank sum test, confirm the superior stability and convergence speed of OCEHO over its counterparts. These results validate the effectiveness and robustness of the proposed approach for high‐quality image segmentation.

ABSTRACT Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images. While this technological progress has captured significant interest, it has also raised concerns about the high challenge in distinguishing real images from their synthetic counterparts. This paper takes inspiration from the potent convergence capabilities between vision and language, coupled with the zero‐shot nature of vision‐language models (VLMs). We introduce an innovative method called Bi‐LORA that leverages VLMs, combined with low‐rank adaptation (LORA) tuning techniques, to enhance the precision of synthetic image detection for unseen model‐generated images. The pivotal conceptual shift in our methodology revolves around reframing binary classification as an image captioning task, leveraging the distinctive capabilities of cutting‐edge VLM, notably bootstrapping language image pre‐training (BLIP)2. Rigorous and comprehensive experiments are conducted to validate the effectiveness of our proposed approach, particularly in detecting unseen diffusion‐generated images from unknown diffusion‐based generative models during training, showcasing robustness to noise, and demonstrating generalisation capabilities to GANs. The experiments show that Bi‐LORA outperforms state of the art models in cross‐generator tasks because it leverages multi‐modal learning, open‐world visual knowledge, and benefits from robust, high‐level semantic understanding. By combining visual and textual knowledge, it can handle variations in the data distribution (such as those caused by different generators) and maintain strong performance across different domains. Its ability to transfer knowledge, robustly extract features and perform zero‐shot learning also contributes to its generalisation capabilities, making it more adaptable to new generators. The experimental results showcase an impressive average accuracy of 93.41% in synthetic image detection on unseen generation models. The code and models associated with this research can be publicly accessed at https://github.com/Mamadou‐Keita/VLM‐DETECT .

ABSTRACT Federated Learning (FL) has emerged as an innovative approach for distributed neural networks, allowing multiple clients to collaboratively train a model without centralising their data, thus preserving decentralisation and data privacy. This review provides a comprehensive discussion of FL's core concepts, including its components, key challenges, and distinctions from traditional machine learning. The paper outlines the various types of FL, highlighting applications in privacy‐sensitive fields like healthcare and finance. It also addresses recent advancements in self‐supervised learning, personalisation, and multi‐modal applications within FL, as well as the integration of blockchain technology for enhanced privacy. Key advantages of FL are discussed, such as reduced communication overhead through the transmission of model parameters instead of raw data, which minimises network load and enhances privacy protection. Furthermore, the paper explores emerging questions for FL development, including scalability, fairness, and system standardisation. Real‐world examples, such as Google Gboard and brain tumour segmentation, are presented to illustrate FL's practical impact. Finally, the paper discusses future directions, including potential integration with other AI techniques like reinforcement learning and transfer learning. This review provides valuable insights for researchers and professionals who are new to FL or seek a broader understanding of its ecosystem. While there are few studies that explore limited aspect of FL, this review adopts a holistic approach and covers all aspects of FL including foundational concepts, implementation, challenges faced by FL, and real‐world implementation. The broader scope, which spans FL from concepts to practical implementation, makes it particularly distinctive and a valuable contribution.

ABSTRACT Deep learning models have achieved significant success in time series forecasting, substantially improving predictive accuracy across several applications. The interpretability of these intricate models continues to pose a significant problem, especially in high‐stakes fields where transparency and trust are essential. Current explanation strategies are often limited to static assessments, lacking consistency and semantic significance into model behaviour. This article introduces SUE‐TS (Surrogate‐based Universal Explanation for Time Series), a versatile interpretability framework that develops a simple and interpretable surrogate to replicate the predictive behaviour of opaque forecasting models, therefore addressing existing restrictions. SUE‐TS incorporates a SHAP‐based closed‐loop feedback mechanism that enhances prediction accuracy and semantic consistency within the explanation domain. The methodology guarantees fidelity via dual‐consistency evaluation: predictive consistency, which evaluates numerical concordance between surrogate and black‐box models, and explanation‐space consistency, which confirms semantic coherence through high‐dimensional representation analysis. Comprehensive studies on seven benchmark time series datasets and six advanced forecasting systems reveal that SUE‐TS consistently attains high approximation fidelity, interpretability, and robustness. The surrogate models reproduce output behaviours and maintain the reasoning logic of the original models, even in complex temporal dynamics. SUE‐TS offers a scalable, model‐agnostic solution for reliable time series forecasting by reconciling performance with explainability. It provides a significant resource for enhancing interpretability in actual AI applications, facilitating downstream activities such as model audits, knowledge distillation, and decision‐support system integration.

ABSTRACT No‐reference image quality assessment (NR‐IQA) has garnered significant attention due to its critical role in various image processing applications. This survey provides a comprehensive and systematic review of NR‐IQA methods, datasets, and challenges, offering new perspectives and insights for the field. Specifically, we propose a novel taxonomy for NR‐IQA methods based on distortion scenarios and design principles, which distinguishes this work from previous surveys. Representative methods within each category are thoroughly examined, with a focus on their strengths, limitations, and performance characteristics. Additionally, we review 20 widely used NR‐IQA datasets that serve as benchmarks for evaluating these methods, providing detailed information on the number of images, distortion types, and distortion levels for each dataset. Furthermore, we identify and discuss key challenges currently faced by NR‐IQA methods, such as handling diverse and complex distortions, ensuring generalisation across datasets and devices, and achieving real‐time performance. We also suggest potential future research directions to address these issues. In summary, this survey offers a comprehensive and systematic examination of NR‐IQA methods, datasets, and challenges, offering valuable insights and guidance for researchers and practitioners working in the NR‐IQA domain.

ABSTRACT Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in‐depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in‐depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours.

Diagnosing Parkinson’s disease (PD) remains challenging due to its complex motor symptoms and the reliance on subjective clinical evaluations. To address this issue, this study proposes the multiscale frequency-sharing attention network (MSF-CANet), an end-to-end framework designed to identify PD and healthy control subjects using smartwatch-based inertial sensor data. MSF-CANet integrates a multiscale perception module to capture temporal features of tremors at different frequencies, a frequency-aware module to enhance PD-specific tremor signals within the 3–7 Hz range, and a shared channel attention mechanism to focus on key sensor channels while ensuring computational efficiency. The model was trained and evaluated on the PADS dataset using nested 5-fold cross-validation. The proposed method achieved an accuracy of 92.39% and an AUC of 0.9797, outperforming existing methods. The findings indicate that dual-hand data significantly improves detection performance compared to single-hand data, and dynamic tasks like “Drink from Glass” and “cross and extend both arms” achieved higher accuracy than static activities. These findings underscore the potential of MSF-CANet as a robust, noninvasive tool for real-time PD monitoring through wearable devices.

An informative 3-D avatar, closely mirroring real-world traits, plays a pivotal role in accessing the metaverse. Traditional methods for creating 3-D avatars usually employ one-to-one training, which restricts avatar diversity. To enhance style diversity in generated 3-D avatars, we utilize synthesized images derived with prompts from ChatGPT in a conversational manner, ultimately resulting in a broader range of 3-D variations. Rather than creating models from scratch, we devise a training-free framework that utilizes established large-scale foundation models. Specifically, we employ a real-world image synthesis technique guided by text prompts that are generated by ChatGPT in a conversational manner, to describe the desired characteristics of the synthesized image. As a result, these informative latent representations can accurately reflect the distinct style of the synthesized image, and further lead to the creation of photorealistic and diverse 3D avatars. Our training-free design allows this proposed method to achieve competitive performance compared to existing generation models, while requiring minimal computational resources.

The precise observation of thunderstorm charge motion remains challenging due to the limitations of single-modal remote sensing techniques. This paper proposes a novel multimodal remote sensing framework integrating radar echo intensity (REI) and atmospheric electric field (AEF) data for three-dimensional (3D) charge localization. Our approach tackles critical challenges in meteorological remote sensing by introducing a high-precision spatiotemporal calibration protocol to align high-frequency AEF with low-frequency radar scans, along with physics-constrained feature fusion that incorporates an AEF-REI interaction term to enhance robustness. We develop a Bayesian-optimized random forest model to improve localization accuracy under non-stationary conditions. Validated via 3D charge trajectory reconstruction, our method achieves strong consistency with radar-observed storm core movements, demonstrating its potential as a new remote sensing paradigm for thunderstorm monitoring.

The rapid advancement of deepfake technology has enabled the creation of highly realistic forged face images or videos. While deepfake technology adds entertainment to people’s lives, it also poses a potential threat to social security. Deepfake detection is a crucial technology for identifying forged images. However, existing deep learning-based models for deepfake detection often overlook subtle forged traces. To solve this problem, we propose a Progressive Attention Network (PANet). The PANet incorporates two attention modules, namely the Efficient Multi-Scale Attention Module (EMAM) and the Spatial and Channel Attention Module (SCAM), in a progressive manner. The EMAM focuses on crucial facial regions, such as the eyes, nose, and mouth, rather than the entire face. The SCAM facilitates fine-grained feature extraction. Experimental results demonstrate that the proposed method achieves state-of-the-art results on deepfake detection datasets.

Memes are an increasingly prevalent element of online discourse in social networks, especially among young audiences. They carry ideas and messages that range from humorous to hateful, and are widely consumed. Their potentially high impact requires adequate means of control to moderate their use in large scale. In this work, we propose SimCLIP a deep learning-based architecture for cross-modal understanding of memes, leveraging a pre-trained CLIP encoder to produce context-aware embeddings and a Siamese fusion technique to capture the interactions between text and image. We perform an extensive experimentation on seven meme classification tasks across six datasets. We establish a new state of the art in Memotion7k with a 7.25% relative F1-score improvement, and achieve super-human performance on Harm-P with 13.73% F1-Score improvement. Our approach demonstrates the potential for compact meme classification models, enabling accurate and efficient meme monitoring. We share our code at https://github.com/jahuerta92/meme-classification-simclip.

Imagined speech production with electrocorticography (ECoG) plays a crucial role in brain-computer interface system. A challenging issue is the great variation underlying the frequency bands of the ECoG signals’ encode information, which makes current methods difficult to generate imagined speech with stable quality among different persons. To this end, we propose a robust model to generate high-quality imagined speech from ECoG. A frequency enhancement branch is first designed to adaptively modulate the frequency information, whose product is fed into the following multi-scale channel attention module for robust feature extraction and fusion. By incorporating both the mel-spectrum and audio as training constraints, a multi-constraint decoder branch is finally constructed for imagined speech production. The performance of our model is evaluated on a high-quality dateset, i.e, Single Word Production Dutch-iBIDS. It yields Pearson correlation scores that are all above 0.8, and the standard deviationsare are all below 0.2 in different volunteers. Experimental results demonstrate that our model is effective and robust for ECoG based imagined speech production, and has advantages over peer methods.

Abstract Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision‐making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more conveniently and efficiently. Despite multiple works on automatic heart sound analysis, there are few summarization and review works. This article attempts to give a thorough overview of various heart sound analysis subtasks and examine the improvements made in each subtask by both machine learning techniques and deep learning algorithms. It goals to highlight the potential of AI to revolutionize cardiovascular healthcare by enabling accurate and automated analysis of heart sounds. The findings of this review are beneficial for researchers, clinicians, and engineers in the development and application of AI‐based solutions for improved heart sound classification and diagnosis.

Abstract Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT‐based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.

Abstract Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning‐based approaches. We aim to broaden the state‐of‐the‐art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.

Abstract This editorial summarizes and analyzes 17 articles selected for a special issue on machine learning advances for Industry 4.0 applications. The diverse articles cover fault detection, deep learning optimisation, IoT networking, vehicle control, recommendation systems and domain knowledge integration. Key methods represented include neural networks, deep learning, reinforcement learning and explainable AI. Real‐world industrial case studies showcase machine learning's versatility in enabling intelligent automation, control, and decision‐making across manufacturing, healthcare, transportation and other sectors. While highlighting theoretical innovations, the contributions also demonstrate machine learning's transformative potential for intelligent, connected, self‐optimising next generation production systems. This editorial concisely overviews the latest trends represented in this special issue.

Benefiting from the progresses of sensing and sustainable computing technologies, recent years have witnessed the dramatic progresses of artificial intelligence of things (AIoT). As a typical AIoT application, WiFi-based human activity recognition has increasing popularities in smart homes. However, WiFibased action recognition often has unstable performance due to environmental interference. To this end, a robust deep learning framework called MSF-Net is proposed for coarse and fine activity recognition using channel state information (CSI) information. First, a dual-stream structure incorporating short-time Fourier transform and discrete wavelet transform is developed to highlight abnormal information in the CSI data. Then, a Transformer is employed as the backbone to effectively extract high-level features. In addition, an attention-based fusion branch is designed to enhance cross-model fusion. Experimental results show that MSF-Net achieves Cohens Kappa scores of 91.82%, 69.76%, 85.91%, and 75.66% on the SignFi, Widar3.0, UT-HAR, and NTU-HAR datasets, respectively. These performance records demonstrate the advantages of MSF-Net over existing methods for coarse and fine activity recognition based on WiFi data.

We introduce the Style Transformer for Authorship Representations (STAR) to detect and characterize writing style in social media. The model is trained on a heterogeneous large corpus derived from public sources with 4.5⋅106 authored texts from 70k authors leveraging Supervised Contrastive Loss to minimize the distance between texts authored by the same individual. This pretext pre-training task yields competitive performance at zero-shot with PAN challenges on attribution and clustering. We attain promising results on PAN verification challenges using STAR as a feature extractor. Finally, we present results from our test partition on Reddit, where using a support base of 8 documents of 512 tokens, we can discern authors from sets of up to 1616 authors with at least 80% accuracy. We share our pre-trained model at huggingface AIDA-UPM/star and our code is available at jahuerta92/star.

Each year, wildfires destroy larger areas of Spain, threatening numerous ecosystems. Humans cause 90% of them (negligence or provoked) and the behaviour of individuals is unpredictable. However, atmospheric and environmental variables affect the spread of wildfires, and they can be analysed by using deep learning. In order to mitigate the damage of these events, we proposed the novel Wildfire Assessment Model (WAM). Our aim is to anticipate the economic and ecological impact of a wildfire, assisting managers in resource allocation and decision-making for dangerous regions in Spain, Castilla y León and Andalucía. The WAM uses a residual-style convolutional network architecture to perform regression over atmospheric variables and the greenness index, computing necessary resources, the control and extinction time, and the expected burnt surface area. It is first pre-trained with self-supervision over 100,000 examples of unlabelled data with a masked patch prediction objective and fine-tuned using a very small dataset, composed of 445 samples. The pretraining allows the model to understand situations, outclassing baselines with a 1,4%, 3,7% and 9% improvement estimating human, heavy and aerial resources; 21% and 10,2% in expected extinction and control time; and 18,8% in expected burnt area. Using the WAM we provide an example assessment map of Castilla y León, visualizing the expected resources over an entire region.

Summary Managers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

Summary Collaborative filtering recommendation systems, which analyze sets of user ratings, have been applied to various domains and have resulted in considerable improvements in the traditional recommendation system. However, they still have problems with data sparsity and cold‐start of the user ratings. To solve these problems, we present a hybrid recommendation approach by combining collaborative filtering methods and word embedding‐based content analysis. This study focuses on the movie domain, and therefore, the contents of the items are represented as a set of features such as titles, genres, directors, actors, and plots. The main aim of this paper is to understand the content of the movie plot using a word embedding to improve the measurement of similarity of each plot content to other plot content (called plot embedding). To enhance the accuracy in measuring the similarity between movies, we also consider other features such as titles, genres, directors, and actors extracted from movies. In the experiments, the movie dataset was collected by our crowdsourcing platform, which is the OMS platform. The experimental findings indicate that the proposed approach can enhance the efficiency of applied collaborative filtering recommendation systems.

Abstract The appearance of complex attention‐based language models such as BERT, RoBERTa or GPT‐3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever‐changing stream of information written with informal and complex language, where each message requires careful evaluation to be understood even by humans given the important role that context plays. Addressing tasks in this domain through Natural Language Processing involves severe challenges. When powerful state‐of‐the‐art multilingual language models are applied to this scenario, language specific nuances get lost in translation. To face these challenges we present BERTuit, the largest transformer proposed so far for Spanish language, pre‐trained on a massive dataset of 230 M Spanish tweets using RoBERTa optimization. Our motivation is to provide a powerful resource to better understand Spanish Twitter and to be used on applications focused on this social network, with special emphasis on solutions devoted to tackle the spreading of misinformation in this platform. BERTuit is evaluated on several tasks and compared against M‐BERT, XLM‐RoBERTa and XLM‐T, very competitive multilingual transformers. The utility of our approach is shown with applications, in this case: an unsupervised methodology to visualize groups of hoaxes; and supervised profiling of authors spreading disinformation.

Images have been repeatedly used as the perfect environment to hide information through the use of steganography techniques. Whether messages, documents or even other images, the bitmap of an digital picture provides a place where hidden data can be embedded without human notice. So far, a plethora of steganography methods can be found in the state-of-the-art literature, together with steganalysis techniques, devoted to detect the presence of hidden information in files. Recent steganography techniques rely on Convolutional Neural Networks, trying to embed as information as possible while minimising visual changes in the image. Following this trend, this article tries to demonstrate that a Generative Adversarial Network (GAN) can be used to improve the ability of a spatial domain steganalysis method and to insert secret information with minimal image alteration. Through a training process, the GAN learns how to adapt an image to later introduce a message using the Least Significant Bit steganography algorithm. The results evidence that the approach is successful at avoiding detection by a state-of-the-art Deep Learning steganalysis architecture.

Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-box algorithms, i.e. it is impossible to understand how the algorithm has achieved the final result. To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques. Most of the work in the medical field focuses on binary or multiclass classification problems. However, in many real-life situations, such as chest X-rays, radiological signs of different diseases can appear at the same time. This gives rise to what is known as ”multilabel classification problems”. A disadvantage of these tasks is class imbalance, i.e. different labels do not have the same number of samples. The main contribution of this paper is a Deep Learning methodology for imbalanced, multilabel chest X-ray datasets. It establishes a baseline for the currently underutilised PadChest dataset and a new eXplainable AI technique based on heatmaps. This technique also includes probabilities and inter-model matching. The results of our system are promising, especially considering the number of labels used. Furthermore, the heatmaps match the expected areas, i.e. they mark the areas that an expert would use to make a decision.

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with an interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://github.com/vrodriguezf/deepvats.

Deep learning technology has become one of the core driving forces to promote the in-depth development of industrial automation. In [A1], Wang et al. interpreted the decision process of the convolutional neural network (CNN) by constructing a percolation model from a statistical physics perspective. In this perspective, the decision-making basis of CNN is difficult to understand, because CNN is usually used as a black box model. Furthermore, a novel concept of the differentiation degree and summarized an empirical formula for quantifying the differentiation degree is presented and discussed.

One possible approach to deflect the trajectory of an asteroid on a collision course with the Earth, and prevent a potentially devastating impact, is the use of a kinetic impactor. The upcoming NASA DART and ESA Hera space missions will be the first to study and demonstrate this technique, by driving a spacecraft into the moon of a binary asteroid system with the aim of altering its momentum, and knocking it off course. In this work, we seek to predict critical parameters associated with such an impact, namely the momentum transfer efficiency and axial ratio of the target body, based on light curve data observed from ground before and after the impact in order to give insights into the real effect of the deflection effort. We present here our approach to this problem, which we address from a purely data-driven perspective based on simulated data provided as a part of the Andrea Milani Planetary Defence Challenge, organised by the EU H2020 Stardust-R research network in conjunction with ESA. Formulating the problem as a time series regression task, we develop an end-to-end deep learning pipeline in which we apply the latest advances in deep learning for time series, such as the use of the Transformer architecture as well as ensembling and self-supervised learning techniques. Exploiting these techniques for the challenge, we achieved second place out of the student teams, and fifth place overall without relying on any a priori knowledge of the physics of the asteroid system.

Authorship attribution and profiling tools provide useful instruments with wide areas of application, such as disinformation spreaders detection. Models developed until now to fulfil these tasks usually rely on manually crafted features or on a training process restricted and limited by the number of authors involved. Besides, current methods have limited capacity to generate a broad representation of the author, without considering a great variety of features that can be extracted from their texts. In this paper, we propose a contrastive training method to generate representative embeddings of the authorship of a text and able to generalize to unseen authors. The core of this method is the Transformer architecture, which is known to generate very powerful semantically-aware text representations. Using a pretrained RoBERTa-large model, we evaluate our method on the environment of the standardized Gutenberg corpus, detecting the authorship of literary works. Representations generated by our proposal can be used to extract representative authorship embeddings and to visualize meaningful relationships between authors, genres and books. Furthermore, the embedding method achieves zero-shot 79% accuracy and 94% top-5 accuracy when tasked to distinguish a text piece from a set of 100 authored texts. Our code is readily available on GitHub11https://github.com/jahuerta92/authorship-embedding

The dissemination of fake news is one of the most concerning issues in current digital media platforms, originating from the quick and easy spread of unverified information therethrough. Consequently, intense research efforts have been invested towards automating the process of identifying fake news from textual data by means of Artificial Intelligence methods. Among the manifold approaches proposed for this purpose to date, a large fraction of studies have examined the performance of modern deep neural network architectures, mostly relying on pretrained word embeddings and neural processing modules of diverse kind. Unfortunately, such sophisticated Deep Learning methods often require intense computational efforts for training. In this work we explore a novel approach based on randomization-based recurrent neural networks. Specifically, our proposal consists of a weighted ensemble of bidirectional Echo State Networks learned from word sequences processed through pretrained embeddings. Experiments over two fake news detection datasets reveal that competitive detection statistics are obtained by our proposed approach when compared to shallow learning and avant-garde Deep Learning models, but at a dramatically less computational complexity in their training phase.

Our society produces and shares overwhelming amounts of information through Online Social Networks (OSNs). Within this environment, misinformation and disinformation have proliferated, becoming a public safety concern in most countries. Allowing the public and professionals to efficiently find reliable evidence about the factual veracity of a claim is a crucial step to mitigate this harmful spread. To this end, we propose FacTeR-Check, a multilingual architecture for semi-automated fact-checking and hoaxes propagation analysis that can be used to implement applications designed for both the general public and for fact-checking organisations. FacTeR-Check implements three different modules relying on the XLM-RoBERTa Transformer architecture to evaluate semantic similarity, to calculate natural language inference and to build search queries through automatic keywords extraction and Named-Entity Recognition. The three modules have been validated using state-of-the-art benchmark datasets, exhibiting good performance in all of them. Besides, FacTeR-Check is employed to collect and label a dataset, called NLI19-SP, composed of more than 40,000 tweets supporting or denying 60 hoaxes related to COVID-19, released publicly. Finally, an analysis of the data collected in this dataset is provided, which allows to obtain a deep insight of how disinformation operated during the COVID-19 pandemic in Spanish-speaking countries.

Twitter is currently one of the most popular microblogging platforms allowing people to post short messages, news, thoughts, and so on. The Twitter user community is growing very fast. It has an average of 328 million active accounts today, making it one of the most common media for getting information during any influential or important event. Because it is freely used by the public, some credibility checking is required, especially when it comes to events of high importance. Automatic rumor detection in Arabic tweets is a challenging task due to the changes in the structural and morphological nature of the Arabic language, which makes the detection of rumors more difficult than in other languages. In this article, we proposed an effective approach for rumor detection of Arabic tweets using an eXtreme gradient boosting (XGBoost) classifier. We conducted a set of experiments on a public dataset that contained a large number of rumor and non-rumor tweets. The model uses a comprehensive set of features, including content-based, user-based, and topic-based features, allowing one to look at credibility from different angles. The experimental results demonstrated that the proposed XGBoost-based approach achieves 97.18% accuracy on 60% of the dataset as a training set, which is the highest accuracy rate compared with the other methods used in recent related work.

Industrial prognosis refers to the prediction of failures of an industrial asset based on data collected by Internet of Things sensors. Prognostic models can experience the undesired effects of concept drift, namely, the presence of nonstationary phenomena that affects the data collected over time. Consequently, fault patterns learned from data become obsolete. To overcome this issue, contextual and operational changes must be detected and managed, triggering rapid model adaptation mechanisms. This article presents an adaptive learning approach based on a dendritic cell algorithm for drift detection and a deep neural network model that dynamically adapts to new operational conditions. A kernel density estimator with drift-based bandwidth is used to generate synthetic data for a faster adaptation, focusing on fine-tuning the lowest neural layers. Experimental results over a real-world industrial problem shed light on the outperforming behavior of the proposed approach when compared to other drift detectors and classification models.

This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data are not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead, they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data and the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time-series log is carried out, highlighting the benefits of this change of paradigm.

The adjustment of the hyperparameters and network structure of Convolutional Neural Networks (CNNs) composes an important step towards building effective, but still efficient learning models. The selection of the best configuration is a problem-dependent task that involves to explore an enormous and complex search space. Due to this reason, the use of heuristic-based search fits perfectly within this task, seeking to obtain a near to optimal solution in a complex and large exploratory space. This paper presents SCRODeep, a self-adapting algorithm based on a statistically-driven Coral Reef Optimisation algorithm (SCRO), for the selection of the most adequate CNNs architecture in a particular domain. This metaheuristic has been designed to navigate through a search space where the architecture (defining the particular set of layers, including convolutional or pooling layers), and the hyperparameters of the network (i.e. activation functions, number of units or the kernel initializer, among others) are represented, but where the connections weights and bias are inferred using typical CNNs optimisation algorithms. In contrast to other approaches, where the use of a metaheuristic implies in turn to fix a series of hyperparameters (i.e. the mutation probability in a genetic algorithm), our approach follows a self-parametrisation perspective, thus removing the necessity of fixing these values. The method has been tested in the design of CNNs for image classification, showing that SCRODeep is able to find competitive solutions, while the complexity of the architectures found is constrained.

Summary In this work, we conducted a literature review about deep learning (DNN, RNN, CNN, and so on) for analyzing EEG data for decoding the activity of human's brain and diagnosing disease and explained details about various architectures for understanding the details of CNN and RNN. It has analyzed a word, which presented a model based on CNN and LSTM methods, and how these methods can be used to both optimize and set up the hyper parameters of deep learning architecture. Later, it is studied how semi‐supervised learning on EEG data analytics can be applied. We review some studies about different methods of semi‐supervised learning on EEG data analytics and discussing the importance of semi‐supervised learning for analyzing EEG data. In this paper, we also discuss the most common applications for human EEG research and review some papers about the application of EEG data analytics such as Neuromarketing, human factors, social interaction, and BCI. Finally, some future trends of development and research in this area, according to the theoretical background on deep learning, are given.

Abstract In this study, we propose a computational intelligence model for the Internet of Things applications by applying the concept of swarm intelligence (SI) into connected devices. Particularly, decentralized management of smart home energy management system (HEMS) is taken into account in which connected appliances, by sharing information with each other, make the individual decisions for optimizing electricity prices of smart HEMS. Specifically, the study includes two main issues: (a) We propose a framework for decentralized management in smart HEMS; and (b) artificial bee colony (ABC) algorithm, a typical algorithm of SI techniques, has been applied for connected appliances in terms of communication and collaboration with each other to optimize the performance of the energy management system. Moreover, regarding the implementation, we develop and simulate a connected environment of smart home systems to evaluate the proposed approach. The simulation indicates the promising results in terms of optimizing the load balancing problem comparing with the conventional approach of the decentralized management system in smart home applications.

Abstract This paper addresses a multiattribute vehicle routing problem, the rich vehicle routing problem, with time constraints, heterogeneous fleet, multiple depots, multiple routes, and incompatibilities of goods. Four different approaches are presented and applied to 15 real datasets. They are based on two meta‐heuristics, ant colony optimization (ACO) and genetic algorithm (GA), that are applied in their standard formulation and combined as hybrid meta‐heuristics to solve the problem. As such ACO‐GA is a hybrid meta‐heuristic using ACO as main approach and GA as local search. GA‐ACO is a memetic algorithm using GA as main approach and ACO as local search. The results regarding quality and computation time are compared with two commercial tools currently used to solve the problem. Considering the number of customers served, one of the tools and the ACO‐GA approach outperforms the others. Considering the cost, ACO, GA, and GA‐ACO provide better results. Regarding computation time, GA and GA‐ACO have been found the most competitive among the benchmark.

Industry is undergoing the so-called “fourth revolution” with a trend towards fully automated cyber-physical systems (CPS) and an augmented data exchange provided by the internet of things (IoT). This revolution is highly correlated and supported by an increasing adoption of machine learning techniques that allow, on the one hand, to generate valuable predictions for the daily work in smart factories, and on the other hand, to help the operators make the right decisions, or even to take decisions on their own. While research in machine learning is rapidly evolving, the transfer to industry is still slow. To overcome this issue, researchers and factories must work together to get the most of both sides. This way, industries can add value to their data and processes, and researchers can study ways of facilitating the application of theoretical results to real world scenarios. The aim of this special issue is to integrate cutting edge research from the fields of machine learning and deep learning into industrial production and manufacturing processes, to leverage their technological transformation towards the new era of smart factories. We cordially invite researchers to contribute original research papers that report novel systems, applications, as well as survey papers that review the novel technologies and new trends on the intersection between these two areas. A strong focus should be on applicability and transferability, so that the interested readers should find it easy to reproduce and replicate the published results of the paper into their own use industrial cases. Submitted papers must be unpublished and not submitted anywhere else for publication. If a shorter version of the paper has been accepted or published in a conference, then the submission must contain at least 80% new material as compared to the conference publication this must be mentioned when submitting the paper, as well as the name of the conference and the title of publication. Deadline for submission: December 31st, 2020.

Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively used in many commercial applications due to their manageability and risk avoidance. One of the main problems considered is the Mission Planning for multiple UAVs, where a solution plan must be found satisfying the different constraints of the problem. This problem has multiple variables that must be optimized simultaneously, such as the makespan, the cost of the mission or the risk. Therefore, the problem has a lot of possible optimal solutions, and the operator must select the final solution to be executed among them. In order to reduce the workload of the operator in this decision process, a Decision Support System (DSS) becomes necessary. In this work, a DSS consisting of ranking and filtering systems, which order and reduce the optimal solutions, has been designed. With regard to the ranking system, a wide range of Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are compared on a multi-UAV mission planning scenario, in order to study which method could fit better in a multi-UAV decision support system. Expert operators have evaluated the solutions returned, and the results show, on the one hand, that fuzzy methods generally achieve better average scores, and on the other, that all of the tested methods perform better when the preferences of the operators are biased towards a specific variable, and worse when their preferences are balanced. For the filtering system, a similarity function based on the proximity of the solutions has been designed, and on top of that, a threshold is tuned empirically to decide how to filter solutions without losing much of the hypervolume of the space of solutions.

The interest in Community Detection Problems on networks that evolves over time has experienced an increasing attention over the last years. Multi-Objective Genetic Algorithms and other bio-inspired methods have been successfully applied to tackle the community finding problem in static networks. Although, there are a large number of evolutionary and bio-inspired approaches that combine Local Search Strategies and other techniques from graph theory to handle the community detection problems in static networks, few research has been done related to the application of these algorithms over temporal, or dynamic, networks. This work is focused on the design, implementation, and the empirical analysis of a new Multi-Objective Genetic Algorithm that combines an Immigrant's scheme with local search strategies for dynamic community detection. The main contribution of this new algorithm is to address the adaptation of these strategies to dynamic networks. On the one hand, the Immigrant's scheme motif is to reuse previously acquired information to reduce computational time. On the other hand, in a dynamic environment is possible that a valid solution became invalid due to some changes in the environment, for example, if some nodes or edges have been removed or added to the network. Therefore, the aim of the local search operator used in the new algorithm is to transform an invalid solution, due to a change happened on the network, into a valid one maintaining the highest possible quality. Finally, the proposed algorithm has been tested using several synthetic and real-world networks, and compared against several algorithms (DYNMOGA, ALPA, Infomap) from the state of the art.

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company's and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this article, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.

Detecting radicalization on social networks is crucial to the fight against violent extremism and terrorism. In most cases, online radicalization has clear warning indicators that can be detected at the early stages of the radicalization process. In this paper, we focus on mining radicalization indicators from online messages by exploiting structured domain knowledge. More precisely, we propose an approach to automatically annotate social messages with concepts from a domain ontology. Annotations are then exploited within an inference phase to identify the messages exhibiting a radicalization indicator. We conducted a set of experiments on a sample extracted from a public dataset that contains radicalized individuals along with their social messages (i.e. Tweets). Obtained results show the effectiveness of our approach compared to a baseline method.

Distributed Systems current face new challenges of adapting and reusing research results in the area of Intelligent Systems. Intelligent Systems use methods and technology derived from Knowledge-based and Computational Intelligence. Distributed Computing develops methods and technology to build systems composed of collaborating components. The fast growth of both Big Data and Data Mining have created interesting challenges for classical methods, algorithms, and frameworks from Distributed Computing, which makes especially interesting analysis and research into new trends and innovations that have recently appeared in this area. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the roles of Knowledge and Intelligence in Distributed Systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. This issue presents a expanded versions of these papers from the best of those presented at the 10th International Symposium on Intelligent Distributed Computing (IDC 2016), which was held in Paris (France). As the special issue editors, we would like to take this opportunity to thank the various authors for their papers and the reviewers for their work. We are also grateful to Jon Hall, Editor-in-Chief of the Wiley journal Expert Systems. We would like to particularly thank the IDC'16 programme committee members for their hard work and dedication. To conclude, we would like to acknowledge the financial support received from Spanish Ministry of Economy and Competitiveness (MINECO) projects: EphemeCH (TIN2014-56494-C4-{1…4}-P) and DeepBio (TIN2017-85727-C4-{1…4}-P), both under the European Regional Development Fund FEDER and the support by COMPETE: POCI-01-0145-FEDER-007043 and FCT Fundao para a Cincia e Tecnologia within the Project Scope: UID/CEC/00319/2013.

The Community Detection Problem (CDP) in Social Networks has been widely studied from different areas such as Data Mining, Graph Theory Physics, or Social Network Analysis, among others. This problem tries to divide a graph into different groups of nodes (communities), according to the graph topology. A partition is a division of the graph where each node belongs to only one community. However, a common feature observed in real-world networks is the existence of overlapping communities, where a given node can belong to more than one community. This paper presents a new Multi-Objective Genetic Algorithm (MOGA-OCD) designed to detect overlapping communities, by using measures related to the network connectivity. For this purpose, the proposed algorithm uses a phenotype-type encoding based on the edge information, and a new fitness function focused on optimizing two classical objectives in CDP: the first one is used to maximize the internal connectivity of the communities, whereas the second one is used to minimize the external connections to the rest of the graph. To select the most appropriate metrics for these objectives, a comparative assessment of several connectivity metrics has been carried out using real-world networks. Finally, the algorithm has been evaluated against other well-known algorithms from the state of the art in CDP. The experimental results show that the proposed approach improves overall the accuracy and quality of alternative methods in CDP, showing its effectiveness as a new powerful algorithm for detecting structured overlapping communities.

An operating procedure (OP), also known as checklist or action plan, is a list of actions or criteria arranged in a systematic way, commonly used in areas such as aviation or healthcare to ensure the success of critical tasks and to help decrease human errors. In these areas, operators are hardly trained to follow the OP carefully, but the evaluation of how they are following, it is usually performed manually by an expert instructor. Automating this evaluation process would lead to an objective and scalable analysis of the operator performance, which is extremely important in areas where the number of operators to evaluate is high. This problem, which can be referred as automatic procedure following evaluation, needs of new techniques and formalizations due to current conformance checking methods do not fit well with some aspects of a OP. In this paper, OPs are modeled as Petri Net-based workflows, and interact with the data log of the system to allow an automatic evaluation of the progress and time spent following the OP. In order to illustrate the contributions of this paper, a case study is carried out designing and modeling an emergency OP for an unmanned aircraft system, and evaluating the proposed approach with a battery of tests.

Deep Neural Networks (DNN) have become a powerful, widely used, and successful mechanism to solve problems of different nature and varied complexity. Their ability to build models adapted to complex non-linear problems, have made them a technique widely applied and studied. One of the fields where this technique is currently being applied is in the malware classification problem. The malware classification problem has an increasing complexity, due to the growing number of features needed to represent the behaviour of the application as exhaustively as possible. Although other classification methods, as those based on SVM, have been traditionally used, the DNN pose a promising tool in this field. However, the parameters and architecture setting of these DNNs present a serious restriction, due to the necessary time to find the most appropriate configuration. This paper proposes a new genetic algorithm designed to evolve the parameters, and the architecture, of a DNN with the goal of maximising the malware classification accuracy, and minimizing the complexity of the model. This model is tested against a dataset of malware samples, which are represented using a set of static features, so the DNN has been trained to perform a static malware classification task. The experiments carried out using this dataset show that the genetic algorithm is able to select the parameters and the DNN architecture settings, achieving a 91% accuracy.

The increasing interest in the use of Unmanned Aerial Vehicles (UAV) in the last years has opened up a new complex area of research applications. Many works have been focused on the applicability of new Artificial Intelligence techniques to facilitate the successfully execution of UAV operations from the Ground Control Stations (GCSs). Some of the most demanded applications in this field are the reduction of the workload of operators and the automation of training processes. This paper presents new algorithms focused on this field: a Multi-Objective Genetic Algorithm for solving Mission Planning and Replanning problems and a Procedure Following Evaluation methodology based on Petri Nets. This paper is based on a framework that simulates a GCS with support for multiple UAVs. The functionality of this framework has been extended in two different directions: on the one hand, to deal with Mission Designing, Automated Mission Planning and Replanning, and Alert Generation; and, on the other hand, to perform different analysis tasks of the UAV operators. Using this framework, a test mission has been executed and debriefed, focusing on the main AI-based issues described in this work.

The current boom of Unmanned Aerial Vehicles (UAVs) is increasing the number of potential industrial and research applications. One of the most demanded topics in this area is related to the automated planning of a UAVs swarm, controlled by one or several Ground Control Stations (GCSs). In this context, there are several variables that influence the selection of the most appropriate plan, such as the makespan, the cost or the risk of the mission. This problem can be seen as a Multi-Objective Optimization Problem (MOP). On previous approaches, the problem was modelled as a Constraint Satisfaction Problem (CSP) and solved using a Multi-Objective Genetic Algorithm (MOGA), so a Pareto Optimal Frontier (POF) was obtained. The main problem with this approach is based on the large number of obtained solutions, which hinders the selection of the best solution. This paper presents a new algorithm that has been designed to obtain the most significant solutions in the POF. This approach is based on Knee Points applied to MOGA. The new algorithm has been proved in a real scenario with different number of optimization variables, the experimental results show a significant improvement of the algorithm performance.

Existing group recommender systems generate a consensus function to aggregate individual preference into group preference. However, the systems encounter difficulty in gathering rating-scores and validating their reliability, since the aggregation strategy requires user rating-scores. To solve these problems, we propose Group Recommendation based on Social Affinity and Trustworthiness (GRSAT) based on social affinity and trustworthiness, which is obtained from the user’s watching-history and content features, without rating-score. Our experiment proves that GRSAT has outstanding performance for group recommendation compared with the other consensus functions, in terms of the number of the movies and users, on both biased and unbiased groups.

Summary Smart traffic light control at intersections is 1 of the major issues in Intelligent Transportation System. In this paper, on the basis of the new emerging technologies of Internet of Things, we introduce a new approach for smart traffic light control at intersection. In particular, we firstly propose a connected intersection system where every objects such as vehicles, sensors, and traffic lights will be connected and sharing information to one another. By this way, the controller is able to collect effectively and mobility traffic flow at intersection in real‐time. Secondly, we propose the optimization algorithms for traffic lights by applying algorithmic game theory. Specially, 2 game models (which are Cournot Model and Stackelberg Model) are proposed to deal with difference scenarios of traffic flow. In this regard, based on the density of vehicles, controller will make real‐time decisions for the time durations of traffic lights to optimize traffic flow. To evaluate our approach, we have used Netlogo simulator, an agent‐based modeling environment for designing and implementing a simple working traffic. The simulation results shows that our approach achieves potential performance with various situations of traffic flow.

Due to the complexity of theoretical approaches in evolutionary computation (EC), research has being largely performed on experimental basis. One popular measure used by the EC community is the success rate (SR), which is used alone or as part of more complex measures such as Koza’s computational effort in genetic programming. A common practice in EC is to report just a punctual estimation of the SR, without additional information about its associated uncertainty. We aim to motivate EC researchers to adopt more rigorous practices when working with SRs. In particular, we introduce the importance of correctly reporting this measure and highlight its binomial nature. Unfortunately, this fact is usually overlooked in the literature. Considering the binomiality of the SR opens the whole corpus of binomial statistics to EA research and practice. In particular, we focus on studying several methods to compute SR confidence intervals, the factors that determine their quality in terms of coverage probability and interval length. Due to its practical interest, we also briefly discuss the number of required runs to build confidence intervals with a certain quality, providing a sound method to set the number of runs, one of the most important experimental settings in EC. Evidence suggests that Wilson is, on average, a reliable and simple method to bound an estimation of SR with confidence intervals, while the standard method, which is quite popular because of its conceptual simplicity, should be avoided in any case. However, other methods can also be of interest under certain circumstances. We encourage to report the number of trials and successes, as well as the interval, to ease further comparability of the results.

In the last few years virus writers have made use of new obfuscation techniques with the aim of hindering malware in order to difficult their detection by Anti-Virus engines. Strategies to reverse this trend involve executing potentially malicious programs and monitor the actions they perform in runtime, what is known as dynamic analysis. In this paper we present a method able to reach a high accuracy rate without using this kind of analysis. Instead we use a static analysis approach, which discards those samples that cannot be classified with enough certainty and need, certainly, a dynamic analysis. The K-means clustering algorithm has been used to group samples into regions according to their features. Then a boosting process, guided by a genetic algorithm, is executed in each region that are evaluated using a test dataset discarding those regions which do not reach a minimum accuracy threshold.

The study of user behavior based on his/her interactions with a system is widely extended over several fields of research. Often, it is useful to have an underlying model to generate behavioral predictions, allowing the system to automatically adapt to the user and to detect deviations from an expected behavior. In this work, we develop a general method to create, select and validate a Hidden Semi-Markov Model (HSMM) to predict behavior in interactive environments, based on previously seen interactions. The method is completely data-driven, unrestricted by any prior knowledge of the model structure, and easy to automate once some parameters has been adjusted. To test the proposed method, a multi-UAV mission simulator has been used, obtaining a model able to perform adequate predictions in terms of quality and time.

Billions of computing elements continuously collect data and elaborate information nowadays. They range from personal computers to high-performance machines, from virtual machines to physical clusters and from smartphones to embedded systems. Most of them are connected, and this number increases continuously. Such an unlimited amount of spread resources offers challenging opportunities for building new kinds of computing overlay, for inferring collective knowledge and for developing emergent applications. In this context, the emergent field of Intelligent Distributed Computing focuses on the development of a new generation of intelligent distributed systems. It faces the challenges of adapting and combining research in the fields of Intelligent Computing and Distributed Computing. Intelligent Computing develops methods and technology ranging from classical artificial intelligence, computational intelligence and multi-agent systems to game theory. The field of Distributed Computing develops methods and technology to build systems that are composed of interacting and collaborating components. The International Symposium on Intelligent Distributed Computing (IDC) has a special interest in (but will not be limited to) novel architectures, systems and methods that facilitate distributed/parallel/multi-agent biocomputing for solving complex computational and real-life problems. The eighth edition of this conference was held in 2014 in Madrid under the auspicious of Autonomous University of Madrid (www.uam.es). A careful selection from some of the best paper presented at IDC2014 welcomes focused on ‘Intelligent Distributed Computing’ that were selected and invited to be extended for its potential publication at Concurrency and Computation: Practice and Experience journal. The special issue received submissions of original papers on all aspects of IDC ranging from concepts and theoretical developments to advanced technologies and innovative applications; some of the most relevant areas cover by this special issue includes the following: Intelligent Distributed High-performance Architectures; Organization and Management of Intelligent Distributed Systems; Intelligent Distributed Knowledge Representation and Processing; Networked and Distributed Intelligence and Intelligent Distributed Applications and Case Studies. From the received papers, those with the highest quality were selected and finally accepted. In the next section, a short description of final accepted papers is briefly outlined. Pokahr 8 focuses on the effective utilization of theoretically unlimited distributed and virtual resources, which are provided by the Cloud computing paradigm. In this context, intelligence is necessary to allow for exploiting the elastic computing infrastructure by a distributed application. This paper by Alexander Pokahr and Lars Braubach presents a component-based Cloud platform that provides autonomic management of applications using scale-out and on-demand deployment of computing resources at IaaS (Infrastructure as a Service) level. An implementation has been developed extending a multi-agent middleware towards a PaaS (Platform As A Service) Cloud infrastructure. Improving performance in distributed system is the objective of the paper 6. In fact, there are relevant issues also in data bound applications, because of the increasing number of users who are always connected and continuously publish contents in social networks or in their own remote repositories. Paper 6, authored by Randi Karlsen, David Sundby and Joan Nordbotten, deals with design and development of image retrieval algorithms of full set of thematic images, from huge collections from millions of users, which can be found in many social networks nowadays. The proposed solution can automatically generate an image collection description suitable for distributed search. This approach enhances the image tag sets collected by the host system for development of a collection description that provides an extended vocabulary to match search query terms. The retrieval process first selects collections that are relevant to the query, before retrieval of relevant images from those collections. This two-step process improves query processing efficiency, because irrelevant collections need not be searched. Annotation of contents with metadata is as relevant as difficult to achieve, because of a large amount of data exists on social networks services without annotations. An example of meta-information is geographical location of contents. It has become common for users to geotag resources on many online social networking services, but automatic annotation is still an open issue. Tri Nguyen Tuong, Dosam Hwang and Jason J. Jung 7 propose a method to predict the location of unlabeled resources on social networking services. The described approach uses the Naive Bayes and Support Vector Machine methods to classify the resources that are collected by using the term frequency of the tags in each class. Calculation for these methods is improved by using the values of the term frequency, and the class frequency is inverted to optimize the input data. These results can be applied to tag unlabelled resources on social networking services. Distributed intelligence can be exploited also on improved human users' performance in workplace contexts. In fact, usually, workers use computers connected to the network to carry out their tasks. This provides the possibility to collect information and plan actions to improve the development of working activities. In 3, Davide Carneiro, André Pimenta, Sérgio Gonçalves, José Neves and Paulo Novais discuss about monitoring and management of individual's performance in workplace contexts. The proposed approach is based on the observation of the worker's interaction with computer. Musical selection is investigated as an effective method for improving performance in the workplace. The described infrastructure allows team coordinators to assess and manage their co-workers' performance continuously and in real time, using a distributed service-based architecture. Performance results are discussed by experimental activities in real scenarios. We can understand, just from these works the relevance of a monitoring infrastructure. It is needed to build the necessary knowledge base for reasoning about effective actions to take for reconfiguration or optimization of distributed systems. Paper 5, authored by Kai Jander, Lars Braubach and Winfried Lamersdorf, focuses on distributed monitoring and workflow analysis and re-engineering of business processes. The research contribution deals with the utilization goal-oriented processes for modelling and executing workflow of collaborative business processes. The proposed solution allows for real-time association of occurring actions with business goals in the process. It uses drill-down analysis of events resulting from the execution of goal-oriented workflows in a distributed workflow environment After monitoring, learning becomes the next relevant problem. In fact, the availability of heterogeneous data, which have been collected by computers, smartphones and embedded devices, represent a precious source of information, which can be exploited in many application domains. Ricardo Aler, Ricardo Martin, Jose Valls and Ines Galván in 1 develop machine learning algorithms for forecasting of solar energy in the contexts of renewable energy sources. The prediction of solar energy is derived from numerical weather prediction models, which predicts meteorological variables for nodes in a grid. The paper investigates how prediction accuracy improves depending on the number of grid nodes and on the number and types of attributes. Many feature selection attributes are tested, and experimental results on historical data are used to build models for prediction of solar energy production in new locations. Distributed intelligence and exploitation of collective knowledge is the topic of the next research work. Authors use software agents to exploit collective intelligence for optimizing green energy utilization in smart-grids. In particular, the paper 2 authored by Alba Amato, Beniamino Di Martino, Marco Scialdone and Salvatore Venticinque focuses on a P2P network of users and agents for energy monitoring and management in smart solar powered micro-grids. Software agents are delegated to learn and predict energy profiles and to optimize the share of green energy within a neighbourhood. The emergent behaviour of the multi-agent system is an optimal schedule of consuming devices according to users' preferences and constraints. The platform design and the technological stack of the prototypal implementation are presented. The last cited papers demonstrate the proliferation of many kinds of social networks, where services are used by users, robots and even physical objects, which join the so-called Internet of Things. In such a cyber-physical system, security plays a key role. In the paper 9, Esther Villar-Rodriguez, Javier Del Ser and Sancho Salcedo-Sanz describe how this trend has given rise to a myriad of fraudulent strategies aimed at getting some sorts of benefit from the attacked individual. Stealing the credentials of the victim and assuming his or her identity to obtain access to resources (e.g. relationships or confidential information), credit and other benefits in that person's name are the objective of attackers. The paper delves into a machine learning approach that permits to efficiently detect this kind of attacks by solely relying on connection time information of the potential victim. Authors demonstrate how these learning algorithms – in particular, support vector classifiers – can be of great help to understand and detect impersonation attacks without compromising the user privacy of social networks. Because of problems like the ones presented previously, many mechanisms have been designed and implemented to increase the strength of the authentication mechanisms. A well-known example is Secure-images as text CAPTCHA. In this context paper 4 authored by Carlos Javier Hernández-Castro, David F. Barrero, María D. R-Moreno deals with the usage of Human Interactive Proofs (HIPs) to avoid automatic attacks. Civil Rights CAPTCHA is proposed here to aim at higher security. Empathy capacity of humans is exploited to further strengthen the security of text CAPTCHA. Fundamental design flaws are analysed from a security perspective using several well-known Machine Learning algorithms. Authors show that there is no need to solve the problem of neither OCR nor empathy analysis for computers to break this HIP. On the opposite, Machine Learning has been successfully used to break an HIP that uses both with a side-channel attack. This special issue has been achieved by a number of fruitful collaborations. We would like to thank the Editor in Chief of Concurrency and Computation: Practice and Experience, Prof. Geoffrey C. Fox, for his kind support and help during the whole process of publication. The guest editors would like to thank the reviewers for their valuable contribution, which has given this special issue the high quality we were expecting. Finally, this work has been partially supported by several research projects: Comunidad Autónoma de Madrid under project CIBERDINE S2013/ICE-3095 and by Spanish Ministry of Science and Education under project code TIN2014-56494-C4-4-P.

Recently, Data analysis is one of the most growing fields. The big amounts of data are making their analysis a really challenging area. The most relevant techniques are mainly divided in two sub-domains: Classification and Clustering. Even though Classification is currently growing and evolving, one of the promising techniques to deal with the Large Data Analysis is Clustering, because Classification needs human supervision, which makes the analysis more expensive. Clustering is a blind process used to group data by similarity. Currently, the most relevant methods are those based on manifold identification. The main idea behind these techniques is to group data using the form they define in the space. In order to achieve this goal, there are several techniques based on Spectral Analysis which deal with this problem. However, these techniques are not suitable for Large Data, due to they require a lot of memory to determine the groups. Besides, there are some problems of local minima convergence in these techniques which are common in statistical methodologies. This work is focused on combining Genetic Algorithms with spectral-based methodologies to deal with the Large Data Analysis problem. Here, we will combine the Nyström method with the Spectrum to generate an approximation of the problem to an accurate summary of the search space. Also a genetic algorithm is used to reduce the local minimum convergence problem in the new search space. The performance of this methodology has been evaluated using the accuracy with both, synthetic and real-world datasets extracted from the literature.

The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAVs) comprises a set of locations to visit in different time windows, and the actions that the vehicle can perform based on its features, such as sensors, speed or fuel consumption. Although this problem is increasingly more supported by Artificial Intelligence systems, nowadays human factors are still critical to guarantee the success of the designed plan. Studying and analyzing how humans solve this problem is sometimes difficult due to the complexity of the problem and the lack of data available. To overcome this problem, we have developed an analysis framework for Multi-UAV Cooperative Mission Planning Problem (MCMPP) based on a videogame that gamifies the problem and allows a player to design plans for multiple UAVs intuitively. On the other hand, we have also developed a mission planner algorithm based on Constraint Satisfaction Problems (CSPs) and solved with a Multi-Objective Branch & Bound (MOBB) method which optimizes the objective variables of the problem and gets the best solutions in the Pareto Optimal Frontier (POF). To prove the environment potential, we have performed a comparative study between the plans generated by a heterogenous group of human players and the solutions obtained by this planner.

In this paper, we propose a constraint-based model for the combination of a course timetabling problem and a course allocation problem for secondary schools in Switzerland. Course timetabling has been widely studied for Universities, but for schools, solutions have only been proposed for specific countries or even for specific schools. In fact, timetabling problems presents the difficulty to be case-specific through specific constraints satisfaction. In addition, due to a recent reform of the education system in Switzerland, the timetabling problem is combined with a course allocation problem that has an important impact on timetables. Indeed, some topics are defined by a curriculum but each student may be assigned to up to five different options depending on past grades and students choices. Both problems, timetabling and allocation must be solved simultaneously and educational objectives are related to both composition of classes and timetables of students. A description of the problem and the educational objectives to consider are presented. Finally, a complete constraint-based model based on hard and soft constraints has been designed to solve the combined problem, both types of constraints are described in detail and a qualitative complexity analysis of this model is given.

Resource-Constrained Project Scheduling Problem (RCPSP) is a NP-hard combinatorial problem that consists in scheduling different activities in such a way the resource, precedence, and temporal constraints are satisfied. The main problem when dealing with NP-hard problems is the exponential growth of the computational resources needed to solve the problems. This work is an extension of a previous one, where a new CSP graph-based representation to solve Constraint Satisfaction Problems (CSP) by using Ant Colony Optimization (ACO) were proposed. This paper studies the behaviour of the CSP graph-based representation when it is applied to a real-world complex problem, in this case the RCPSP. The dataset used in this work has been extracted from Project Scheduling Problem Library (PSPLIB). Experimental results show that the proposed approach provides excellent results, closer to the optimum values published in the PSPLIB repository. Also, it has been analysed how the number of jobs and the number of different execution modes affect the performance of the algorithm.

Clustering is a field of Data Mining that deals with the problem of extract knowledge from data blindly. Basically, clustering identifies similar data in a dataset and groups them in sets named clusters. The high number of clustering practical applications has made it a fertile research topic with several approaches. One recent method that is gaining popularity in the research community is Spectral Clustering (SC). It is a clustering method that builds a similarity graph and applies spectral analysis to preserve the data continuity in the cluster. This work presents a new algorithm inspired by SC algorithm, the Co-Evolutionary Multi-Objective Genetic Graph-based Clustering (CEMOG) algorithm, which is based on the Multi-Objective Genetic Graph-based Clustering (MOGGC) algorithm and extends it by introducing an adaptative number of clusters. CEMOG takes an island-model approach where each island keeps a population of candidate solutions for kiclusters. Individuals in the islands can migrate to encourage genetic diversity and the propagation of individuals around promising search regions. This new approach shows its competitive performance, compared to several classical clustering algorithms (EM, SC and K-means), through a set of experiments involving synthetic and real datasets.

Automatic summarization is emerging as a feasible instrument to help biomedical researchers to access online literature and face information overload. The Natural Language Processing community is actively working toward the development of effective summarization applications; however, automatic summaries are sometimes less informative than the user needs. In this work, our aim is to improve a summarization graph-based process combining genetic clustering with graph connectivity information. In this way, while genetic clustering allows us to identify the different topics that are dealt with in a document, connectivity information (in particular, degree centrality) allows us to asses and exploit the relevance of the different topics. Our automatic summaries are compared with others produced by commercial and research applications, to demonstrate the appropriateness of using this combination of techniques for automatic summarization.

The problem of community detection has become highly relevant due to the growing interest in social networks. The information contained in a social network is often represented as a graph. The idea of graph partitioning of graph theory can be apply to split a graph into node groups based on its topology information. In this paper the problem of detecting communities within a social network is handled applying graph clustering algorithms based on this idea. The new approach proposed is based on a genetic algorithm. A new fitness function has been designed to guide the clustering process combining different measures of network topology (Density, Centralization, Heterogeneity, Neighbourhood, Clustering Coefficient). These different network measures have been experimentally tested using a real-world social network. Experimental results show that the proposed approach is able to detect communities and the results obtained in previous work have been improved.

Image Segmentation is one of the most challenging problems in Computer Vision. This process consists in dividing an image in different parts which share a common property, for example, identify a concrete object within a photo. Different approaches have been developed over the last years. This work is focused on Unsupervised Data Mining methodologies, specially on Graph Clustering methods, and their application to previous problems. These techniques blindly divide the image into different parts according to a criterion. This work applies a Multi-Objective Genetic Algorithm in order to perform good clustering results comparing to classical and modern clustering algorithms. The algorithm is analysed and compared against different clustering methods, using a precision and recall evaluation, and the Berkeley Image Database to carry out the experimental evaluation.

The problem of Mission Planning for a large number of Unmanned Air Vehicles (UAV) can be formulated as a Temporal Constraint Satisfaction Problem (TCSP). It consists on a set of locations that should visit in different time windows, and the actions that the vehicle can perform based on its features such as the payload, speed or fuel capacity. In this paper, a temporal constraint model is implemented and tested by performing Backtracking search in several missions where its complexity has been incrementally modified. The experimental phase consists on two different phases. On the one hand, several mission simulations containing (n) UAVs using different sensors and characteristics located in different waypoints, and (m) requested tasks varying mission priorities have been carried out. On the other hand, the second experimental phase uses a backtracking algorithm to look through the whole solutions space to measure the scalability of the problem. This scalability has been measured as a relation between the number of tasks to be performed in the mission and the number of UAVs needed to perform it.

Abstract Radio frequency identification (RFID) is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational capabilities. This issue is particularly challenging from the security point of view. Security protocols in RFID environments have to deal with strong computational limitations, and classical protocols cannot be used in this context. There have been several attempts to overcome these limitations in the form of new lightweight security protocols designed to be used in very constrained (sometimes called ultra‐lightweight) RFID environments. One of these proposals is the David–Prasad ultra‐lightweight authentication protocol. This protocol was successfully attacked using a cryptanalysis technique named Tango attack. The capacity of the attack depends on a set of boolean approximations. In this paper, we present an enhanced version of the Tango attack, named Genetic Tango attack, that uses Genetic Programming to design those approximations, easing the generation of automatic cryptanalysis and improving its power compared to a manually designed attack. Experimental results are given to illustrate the effectiveness of this new attack.

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.

Constraint Satisfaction Problems (CSP) have been widely studied in several research areas like Artificial Intelligence or Operational Research due their complexity and industrial interest. From previous research areas, heuristic (informed) search methods have been particularly active looking for feasible approaches. One of the critical problems to work with CSP is related to the exponential growth of computational resources needed to solve even the simplest problems. This paper presents a new efficient CSP graph-based representation to solve CSP by using Ant Colony Optimization (ACO) algorithms. This paper presents also a new heuristic (called Oblivion Rate), that have been designed to improve the current state-of-the-art in the application of ACO algorithms on these domains. The presented graph construction provides a strong reduction in both, the number of connections and the number of nodes needed to model the CSP. Also, the new heuristic is used to reduce the number of pheromones in the system (allowing to solve problems with an increasing complexity). This new approach has been tested, as case study, using the classical N-Queens Problem. Experimental results show how the new approach works in both, reducing the complexity of the resulting CSP graph and solving problems with increasing complexity through the utilization of the Oblivion Rate.

Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.

SUMMARY Network communications have been widely studied in the last decades in different research fields: artificial intelligence, computer science, biology, medicine and psychology among others. Some important efforts have been carried out to analyse communication features such as overhead, connectivity or communication protocols in these areas from their own perspectives. When this problem is restricted to intelligent agents or multi‐agent systems, networks are built by a set of interconnected agents that can be software or hardware. In multi‐agent systems, communication optimization is used to improve the overall performance of the system by reducing the information sharing (i.e. number of messages or message size) between the agents. This paper analyses a scale‐free network topology of agents to solve a multi‐sorting problem. The agents use their local information as well as a bio‐inspired identity discrimination process to select only those messages that are relevant for each agent to solve jigsaw puzzles. We provide a comprehensive study on the influence of some essential parameters (memory information size and reconnection probability) in an agent network, and how they can be set to obtain a better performance in the system. The experiments show that this strategy contributes to reduce the number of iterations needed to solve the problem. Copyright © 2011 John Wiley & Sons, Ltd.

The graph clustering problem has become highly relevant due to the growing interest of several research communities in social networks and their possible applications. Overlapped graph clustering algorithms try to find subsets of nodes that can belong to different clusters. In social network-based applications it is quite usual for a node of the network to belong to different groups, or communities, in the graph. Therefore, algorithms trying to discover, or analyze, the behavior of these networks needed to handle this feature, detecting and identifying the overlapped nodes. This paper shows a soft clustering approach based on a genetic algorithm where a new encoding is designed to achieve two main goals: first, the automatic adaptation of the number of communities that can be detected and second, the definition of several fitness functions that guide the searching process using some measures extracted from graph theory. Finally, our approach has been experimentally tested using the Eurovision contest dataset, a well-known social-based data network, to show how overlapped communities can be found using our method.

Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.

Regular expressions, or regexes, have been used traditionally as a pattern matching tool to search for structures in a set of objects, like flies, text documents or folders. Pattern matching can be used to look for flies whose name contains a given string, to search flies that contain a specific pattern within them, or simply to extract text in a set of documents. It is very popular to apply regexes to detect and extract patterns that represent phone numbers, URLs, email addresses, etc. These kind of information can be characterized because it has a well defined structure. Nevertheless, regexes are not very frequently used because its high complexity in both, syntax and grammatical rules, makes regexes difficult to understand. For this reason, the development of programs able to automatically generate, and evaluate, regexes has become a valuable task. This work analyzes the performance of different grammatical evolutionary approaches in the generation of regexes able to extract URL patterns. Four different types of grammars have been evaluated: a context-free grammar, a context-free grammar with a penalized fitness function, an extensible context-free grammar, and a Christiansen grammar. For the considered problem, the experimental results show that the best performance of the system, measured as cumulative success rate, is achieved using Christiansen grammars.

Compression distances have been widely used in knowledge discovery and data mining. They are parameter-free, widely applicable, and very effective in several domains. However, little has been done to interpret their results or to explain their behavior. In this paper, we take a step toward understanding compression distances by performing an experimental evaluation of the impact of several kinds of information distortion on compression-based text clustering. We show how progressively removing words in such a way that the complexity of a document is slowly reduced helps the compression-based text clustering and improves its accuracy. In fact, we show how the nondistorted text clustering can be improved by means of annealing text distortion. The experimental results shown in this paper are consistent using different data sets, and different compression algorithms belonging to the most important compression families: Lempel-Ziv, Statistical and Block-Sorting.

Multimedia Information Retrieval is currently a hot research topic due the popularity of the World Wide Web and the huge amount of multimedia data available. There exists an increasing interest to design and develop new methods and techniques to represent and classify this kind of information. Among the different sources of multimedia information currently available, we have decided to work with music audio files. Three different music representations (binary code, wave information, and SAX) have been used to study how the selection of a particular representation could affect a clustering process based on a set of similarity clusters. Two different algorithms (a hierarchical clustering method based on the quartet tree method and a genetic algorithm) have been applied to automatically perform the clustering. A compression distance, the Normalized Compression Distance (NCD), has been used to generate the similarities among the music files. This distance is parameter-free and widely applicable so we can use it directly with different formats and representations. The paper shows some experimental results using these representations and compares the behavior of both clustering methods.

Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studied.

Current web application development requires highly qualified staff, dealing with an extensive number of architectures and technologies. When these applications incorporate semantic data, the list of skill requirements becomes even larger, leading to a high adoption barrier for the development of semantically enabled Web applications. This paper describes VPOET, a tool focused mainly on two types of users: web designers and web application developers. By using this tool, web designers do not need specific skills in semantic web technologies to create web templates to handle semantic data. Web application developers incorporate those templates into their web applications, by means of a simple mechanism based in HTTP messages. End-users can use these templates through a Google Gadget. As web designers play a key role in the system, an experimental evaluation has been conducted, showing that VPOET provides good usability features for a representative group of web designers in a wide range of competencies in client-side technologies, ranging from amateur HTML developers to professional web designers.

This work presents an Information Retrieval technique based on algorithmic information theory (using the normalized compression distance), statistical data outlier detection, and a novel database structure. The paper shows how they all can be integrated to retrieve information from generic databases using long text-based queries. Two important problems are addressed. On the one hand, we analyze and tyr to solve the detection of a particular case of false positives: when the distance among two documents is outlyingly low but there is not actual similarity. On the other hand, we propose a way to structure the database such that the similarity distance estimation scales well with the length of the size of the query. All design choices are justified with an experimental evaluation.

This article presents a new type of Frequently Asked Questions (FAQ) System, called DynJAQ (Dynamic Java Asked Questions) that has been designed with the purpose of making learning more appealing to beginner students of engineering disciplines and overcome the inconvenience of these systems. DynJAQ is able to generate dynamically several HTML guides that can be used to answer any possible question about a particular programming language (Java), although it can be easily extended to any other topic. DynJAQ integrates case-based knowledge into a graph-based representation that can be easily learned and managed. The combination of both case-based knowledge and graphs allows it to implement a flexible hierarchical structures (or learning graphs) that have been applied to implement a new kind of Frequently Asked Questions Systems. In these systems the output is dynamically built from the user query, using as basis structures the knowledge retrieved from a Case Base. The management of these cases allows enriching the knowledge base. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 303–318, 2007.

In this paper we describe SHAMASH, a tool for modeling and automatically optimizing Business Processes. The main features that differentiate it from most current related tools are its ability to define and use organisation standards, and functional structure, and make automatic model simulations and optimisation of them. SHAMASH is a knowledge based system, and we include a discussion on how knowledge acquisition takes place. Furthermore, we introduce a high level description of the architecture, the conceptual model, and other important modules of the system.

Solving problems in dynamic and heterogeneous environments where information sources change their format representation and stored data is very complex. In previous work we presented a system called MAPWeb (Multiagent Planning on the Web) that tried to solve these problems by integrating artificial intelligence planning techniques within the multiagent framework. Basically, MAPWeb allows cooperative work between planning agents and Web agents. The purpose of MAPWeb is to find solutions to travel problems. In order to give detailed solutions, MAPWeb uses information gathering techniques to retrieve travel information that is made available by many different companies. However, Web access to the information sources is quite time expensive. In this paper, we try to minimize the number of Web queries by using caching techniques based on relational databases. Experimental results show that the reduction in Web access time is quite important, while maintaining the number of solutions found.

It is possible to find any kind of useful information in the Web. However, there are serious problems in retrieving, managing and using this information, due to its vastness. Different approaches have been developed to avoid those problems (search engines, metasearch engines, spiders, softbots, intelligent agents or Web agents). This paper is based on one of these systems which uses a set of heterogeneous intelligent software agents to achieve these previous tasks. Two different agents compose the system: Web agents developed to retrieve information from a specific Web source and meta Web agents developed to select the appropriated Web agent to search the necessary information. Each Web agent retrieves, filters and stores the information from the Web to improve system performance. The meta Web agents need to represent and classify the behavior of different Web agents. In this work a fuzzy system that helps to classify the behavior of the Web agents is presented. The meta Web agent calculates the appropriateness of existing agent behavior, using different distances that are analyzed in the paper. The behavior classification is used to decide which Web agent is requested for information by the meta Web agent.