Community detection is a significant methodology in network science. Traditional methods show limitations in dealing with multi-scale and high-dimensional complex data. As one of the most popular unsupervised algorithms, affinity propagation algorithm (AP) has been widely applied in community detection. However, its negative Euclidean similarity and inflexible parameter may lead to high time or memory

This editorial introduces the Virtual Special Issue: “Hierarchical Representations: New Results and Challenges for Image Analysis” that was handled between May 2019 and June 2020.

With the intense competition of global intellectual property, the increasing number of patents promotes the potential of patent transactions. However,an unknown patent value degrades the patent transaction rate. Automatic patent valuation faces some challenges, including the following: 1) how to represent a valuation object, 2) how to construct the valuation scenario, and 3) how to generate and measure

Urban underground pipelines have complex structures, long service lives, and are susceptible to illegal interference, corrosion, and external force damage. Therefore, they are a constant security risk that seriously threaten the public security of the city. Due to the complexity of the underground environment, lack of various monitoring technologies, high cost, backwardness of emergency technology

The reconstruction of epicardial and endocardial extracellular potentials (EEP) by noninvasive methods has become a significant topic in cardiac electrophysiology over recent years. It is of great importance for the diagnosis of arrhythmia and for guidance of radiofrequency ablation, based on the difference in potentials between different locations on the heart's surface. In this study, we propose

Malaria is a life-threatening mosquito (Anopheles)-borne blood disease caused by the plasmodium parasite. Microscopic examination of peripheral blood smears by experts helps to identify parasites precisely. The manual assessment technique is a tedious and time-consuming process. The present study focuses on developing a hybrid screening algorithm for automated identification and classification of malaria

A fast and accurate algorithm is presented to register scans from an RGB-D camera, which rotates and scans the entire scene in an automated fashion on a pan-tilt platform. The proposed algorithm, Axis Bound Registration, exploits the movement of the camera that is bound by the two rotation axes of pan-tilt servos, so as to realize fast and accurate registration of acquired point clouds. The rotation

In recent years, analyzing time-series data has become an ever important research topic due to an increased number of temporal datasets in science and engineering. Segmentation is widely used in time-series data analysis because it provides a more compact representation by dividing the series into segments. Segmentation approaches based on genetic algorithms have been proposed to extract segments and

Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behaviour of these models depends on the initialisation of the corresponding network. In this work, we consider the impact of noise regularisation (e.g. dropout) on NNGPs, and relate their behaviour to signal propagation theory

Accurate diagnosis of autism spectrum disorder (ASD), which is a neurodevelopmental disorder and often accompanied by abnormal social skills, communication skills, interests and behavior patterns, has always been a challenging task in clinical practice. Recent studies have shown great potential for using fMRI data to distinguish ASD from typical control (TC). However, it has always been a challenging

Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks. However, existing joint methods fail to effectively take the advantage of the multiple granularity of information, e.g., character, word and subword, which has been proven prominently useful. In this paper, we propose to improve the joint tasks

Pedestrian Attribute Recognition (PAR) plays an important role in intelligent video surveillance. This paper tackles two severe challenges in it i.e., complex relations between images and attributes, and imbalanced distribution of pedestrian attributes. Specifically, a new multiple time steps attention mechanism is proposed to boost the modeling of the relations. Different from existing attention approaches

A smart city is engineered to be a self-sustained ecosystem driven by Internet-of-Things (IoT) devices. Smooth functioning of smart cities is conditioned on seamless communication between users and devices. Smart devices equipped with biometric authentication can offer security as well as personalized experience to the end users. Currently, a number of smart devices employ face, fingerprint, and voice

The paper presents a method to learn the substitution, deletion and insertion costs on nodes and edges applied to the graph edit distance. We model the learning strategy as a general model and then we concretise it in two different architectures: the first architecture is based on a neural network and the second architecture is based on a multivariate normal distribution, which have been previously

Unsupervised learning of 3D shape feature is a challenging yet important problem for organizing a large collection of 3D shape models that do not have annotations. Recently proposed neural network-based approaches attempt to learn meaningful 3D shape feature by autoencoding a single 3D shape representation such as voxel, 3D point set, or multiview 2D images. However, using single shape representation

Few-Shot Learning (FSL) aims at recognizing the target classes that only a few samples are available for training. The current approaches mostly address FSL by learning a generalized class-level metric while neglect the intra-class distribution information. In this work, we propose Improved Prototypical Networks (IPN) to address this issue. Inspired by the observation that the intra-class samples differ

Anastylosis is an archaeological technique which focuses on the reconstruction of collapsed building and destroyed artworks, starting from the original pieces. Many digital approaches have been developed in the last decade, mainly based on 2D and 3D analysis of the structure of the fragments. These techniques aim at supporting the priceless work of the involved operators, mainly in the decision processes

A recently introduced classifier, called SS3, has shown to be well suited to deal with early risk detection (ERD) problems on text streams. It obtained state-of-the-art performance on early depression and anorexia detection on Reddit in the CLEF’s eRisk open tasks. SS3 was created to deal with ERD problems naturally since: it supports incremental training and classification over text streams, and it

The minimum spanning tree (MST) is one the most popular data structure used to extract hierarchical information from images. This work addresses MST construction in streaming for images. First, we focus on the problem of computing a MST of the union of two graphs with a non-empty intersection. Then we show how our solution can be applied to streaming images. The proposed solution relies on the decomposition

We propose a practicable method for generating synthetic CT images from modified Dixon (mDixon) MR data for the challenging body section of the abdomen and extending into the pelvis. Attenuation correction is necessary to make quantitatively accurate PET but is problematic withPET/MR scanning as MR data lack the information of photon attenuation. Multiple methods were proposed to generate synthetic

This paper deals with the problem of computing the shortest reducts of a decision system. The shortest reducts are useful for attribute reduction in classification problems and data size reduction. Unfortunately, finding all the shortest reducts is an NP-hard problem. There are some algorithms reported in the literature to overcome the complexity of computing the shortest reducts. However, most of

Providing pixel–level supervisions for scene text segmentation is inherently difficult and costly, so that only few small datasets are available for this task. To face the scarcity of training data, previous approaches based on Convolutional Neural Networks (CNNs) rely on the use of a synthetic dataset for pre–training. However, synthetic data cannot reproduce the complexity and variability of natural

Association rule mining is one of the most important and active research areas in data mining. In the literature, several association rule miners have been proposed; among them, those based on particle swarm optimization (PSO) have reported the best results. However, these algorithms tend to prematurely fall into local solutions, avoiding a wide exploration that could produce even better results. In

This paper is the editorial of the virtual special issue (VSI) “Pattern recognition and artificial intelligence techniques for cultural heritage”, of which the authors of this paper have been the guest editors. It aims to bring together the work of experts from the fields of pattern recognition and artificial intelligence and that of cultural heritage. This multidisciplinary subject covers a wide spectrum

Aspects related to prognostics are becoming a crucial part in the industrial sector. In this sense, Industry 4.0 is considered as a new paradigm that leverages on the IoT to propose increasingly more solutions to provide an estimate on the working conditions of an industrial plant. However, in context like the industrial sector where the number and heterogeneity of sensors can be very large, and the

One of the most difficult tasks for the physicians is to acquire a quality foetal electrocardiogram (fECG) to analyze, manage and plan according to the condition of the foetus in the womb. Hence the foetal electrocardiogram signal is not preferred to execute the analysis to monitor the Foetal condition. Other traditional methods are being used to access the foetal condition. The foetal electrocardiogram

Human hair is a crucial biometric characteristic with rich color and texture information. In this paper, we propose a novel hair segmentation approach integrating a deep shape prior into a carefully designed two-stage Fully Convolutional Neural Network (FCNN) pipeline. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module to train a human hair shape prior based on a specific

In this study we introduce the k-Strongest Strengths (kSS) Classification Algorithm, a novel approach for classification problems based on the well-known k-Nearest Neighbor (kNN) classifier. The proposed kSS method is motivated by an analogy to the Law of Universal Gravitation. The novelty of kSS resides in that instead of only using the neighbors’ labels to classify an unseen object it uses gravitation

Recent work in signal propagation theory has shown that dropout limits the depth to which information can propagate through a neural network. In this paper, we investigate the effect of initialisation on training speed and generalisation for ReLU networks within this depth limit. We ask the following research question: given that critical initialisation is crucial for training at large depth, if dropout

Image set classification has recently received much attention due to its various applications in pattern recognition and computer vision. To compare and match image sets, the major challenges are to devise an effective and efficient representation and to define a measure of similarity between image sets. In this paper, we propose a method for representing image sets based on block-diagonal Covariance

Food image recognition has recently attracted the attention of many researchers, due to the challenging problem it poses, the ease collection of food images, and its numerous applications to health and leisure. In real applications, it is necessary to analyze and recognize thousands of different foods. For this purpose, we propose a novel prediction scheme based on a class hierarchy that considers

Neural networks have improved the accuracy of human pose estimation from a single RGB image. However, such estimation remains difficult, especially when the human body is only partially visible due to a limited field of view of the camera or occlusions. In this paper, we introduce a data augmentation method called body-cropping augmentation (BCA), which generalizes the dataset for effective training

Joint domain adaptation aims to learn a high-quality classifier for an unlabeled dataset with the help of auxiliary data. Most methods reduce domain shifts through some carefully designed distance measures. Adversarial learning, which is rarely used for joint domain adaptation, can learn more transferable features while avoiding explicit distance measures. However, it usually suffers from a gradient

Difficult images with complicated environments and occlusion have significant impacts on the performance of algorithms. They obey the long-tail distribution in the widely used datasets, which results in rare samples being overwhelmed during training. This paper presents a new approach to generate plausible occluded images with annotation as a kind of data augmentation with scenes semantics. To achieve

According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine

Multilabel classification is the task of assigning relevant labels to an instance, and it has received considerable attention in recent years. This task can be performed by extending a single-label classifier, such as the naïve Bayes classifier, to utilize the useful relations among labels for achieving better multilabel classification accuracy. However, the conventional multilabel naïve Bayes classifier

In the last years, the consolidation of deep neural network architectures for information extraction in document images has brought big improvements in the performance of each of the tasks involved in this process, consisting of text localization, transcription, and named entity recognition. However, this process is traditionally performed with separate methods for each task. In this work we propose

The paper presents Imbalance-XGBoost, a Python package that combines the powerful XGBoost software with weighted and focal losses to tackle binary label-imbalanced classification tasks. Though a small-scale program in terms of size, the package is, to the best of our knowledge, the first of its kind which provides an integrated implementation for the two loss functions on XGBoost and brings a general-purpose

Open Set Domain Adaptation (OSDA) focuses on bridging the domain gap between a labeled source domain and an unlabeled target domain, while also rejecting target classes that are not present in the source as unknown. The challenges of this task are closely related to those of Positive-Unlabeled (PU) learning where it is essential to discriminate between positive (known) and negative (unknown) class

Scene text recognition has become a research hotspot owing to its abundant semantic information and various applications. Recent methods of scene text recognition usually focus on handling shape distortion, attention drift, or background noise, ignoring that text recognition encounters character scale-variation problem. To address this issue, in this paper, we propose a new scale-aware hierarchical

We introduce a novel multimodal machine translation model that integrates image features modulated by its caption. Generally, images contain vastly more information rather than just their description. Furthermore, in multimodal machine translation task, feature maps are commonly extracted from pre-trained network for objects. Therefore, it is not appropriate to utilize these feature map directly. To

Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, velocity and variety of the data collected in many of these applications pose significant and unique challenges that must be carefully addressed for each of them. In this work, a novel Temporal Logistic Neural Bag-of-Features approach

Data classification, where the goal is to divide data into predefined classes, is a fundamental problem in machine learning with many applications, including the classification of 3D sensory data. In this paper, we present a data classification method which can be applied to both semi-supervised and unsupervised learning tasks. The algorithm is derived by unifying complementary region-based and edge-based

Icons are widely used as a UI component in mobile applications. However, it is considerably difficult to detect icons because of their small size. Existing rapid object detection frameworks, such as YOLO and SSD, cannot perform well on small objects, such as icons, because of the multiple downsampling layers of convolutional networks and prediction grids. This paper summarizes our recent efforts toward

Semantic segmentation methods have achieved outstanding performance thanks to deep learning. Nevertheless, when such algorithms are deployed to new contexts not seen during training, it is necessary to collect and label scene-specific data in order to adapt them to the new domain using fine-tuning. This process is required whenever an already installed camera is moved or a new camera is introduced

Coffee production estimation is an essential task for coffee farmers in terms of money investment and planning time. In Colombia, the traditional methodology to estimate the total amount of cherry coffee beans is through direct measurements in the field; leave out the cherry beans collected of coffee production (destructive sampling). The cherry coffee dropped in this process cannot be harvest by the

Long short-term memory(LSTM) is an effective solution to time sequence prediction. Considering the data perturbations, in this letter, a variant model of LSTM is proposed to achieve robustness of prediction. Specifically, data processing procedure in the recurrent unit of proposed model is reformulated, the gates are controlled by only one variable, and the variable is the sum of long-term memory and

In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional

The Matthews Correlation Coefficient (MCC) is one of the popular measurements for classification accuracy. It has been generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The study of this paper finds that this is not true. MCC deteriorates seriously when the dataset in classification are imbalanced. Experiment results and analysis show that MCC

Number partitioning is a classic problem in artificial intelligence. And balanced multi-way number partitioning problem (BMNP) aims to partition a set of numbers into multiple subsets, such that (1) each subset contains the same number of numbers and (2) the subset sums are equal. The BMNP problem has various applications in real world scenarios, including task allocation, CPU scheduling, file placement

In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated

Multi-task learning (MTL) significantly pre-dates the deep learning era, and it has seen a resurgence in the past few years as researchers have been applying MTL to deep learning solutions for natural language tasks. While steady MTL research has always been present, there is a growing interest driven by the impressive successes published in the related fields of transfer learning and pre-training

Melanoma skin cancer is one of the most common diseases in the world. It is essential to diagnose melanoma at an early stage. Visual inspection during the medical examination of skin lesions is not a simple task, as there is a similarity between lesions. Also, medical experience and disposition can result in inaccurate diagnoses. Technologies such as the Internet of Things (IoT) have helped to create