To understand the operating status of the road network and measure the traffic congestion problem, an intelligent calculation method for the intelligent traffic flow index based on big data mining is proposed. According to the error data discriminating rules, the error data in the traffic flow data is discriminated, all lanes are detected according to the data discriminating result, the traffic data

As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride-hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance

The research on uncertainty is a hot spot in artificial intelligence, especially in actual applications. Most research focus on measuring the uncertainty between evidence, ignoring the structure of evidence. When a sample has multiple features, the traditional structure of the focal element cannot handle and display the uncertainty between multiple features directly. Hence, we proposed a new structure

Very large neural network models such as GPT-3, which have many billions of parameters, are on the rise, but so far only big tech has the resources to train, deploy and study such models. This needs to change, say Stanford AI researchers, who call for an investment in academic collaborations to build and study large neural networks.

Functional subsystems of the macroscale human brain connectome are mapped onto a recurrent neural network and found to perform optimally in a critical regime at the edge of chaos.

Neuromorphic chips that use spikes to encode information could provide fast and energy-efficient computing for ubiquitous embedded systems. A bio-plausible spike-timing solution for training spiking neural networks that makes the most of sparsity is implemented on the BrainScaleS-2 hardware platform.

For a biological agent operating under environmental pressure, energy consumption and reaction times are of critical importance. Similarly, engineered systems are optimized for short time-to-solution and low energy-to-solution characteristics. At the level of neuronal implementation, this implies achieving the desired results with as few and as early spikes as possible. With time-to-first-spike coding

Despite the rapid increase of data available to train machine-learning algorithms in many domains, several applications suffer from a paucity of representative and diverse data. The medical and financial sectors are, for example, constrained by legal, ethical, regulatory and privacy concerns preventing data sharing between institutions. Collaborative learning systems, such as federated learning, are

We consider the subset selection problem for function f with constraint bound B that changes over time. Within the area of submodular optimization, various greedy approaches are commonly used. For dynamic environments we observe that the adaptive variants of these greedy approaches are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach

Hacked data—data obtained in an unauthorized manner through illicit access to a computer or computer network—are increasingly being used in scientific research. Examples include conflict modelling studies based on WikiLeaks datasets, and studies on sexual behaviour based on data leaked from Ashley Madison, a dating website hacked in 2015. Studies of this kind are prima facie lawful because the researchers

Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal phases, or mixtures thereof, in X-ray diffraction measurements of synthesized materials. Phase mapping algorithms have been developed that excel at solving systems with up to several unique phase mixtures, where each phase has a readily distinguishable diffraction pattern. However

In this paper, we propose an efficient method for robust and accurate 3D self-portraits using a single RGBD camera. Our method can generate detailed and realistic 3D self-portraits in seconds and shows the ability to handle subjects wearing extremely loose clothes. To achieve highly efficient and robust reconstruction, we propose PIFusion, which combines learning-based 3D recovery with volumetric non-rigid

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg

Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization towards a target task. In this paper, we go further and extract synthetic data by leveraging

Multilabel feature selection plays an essential role in high-dimensional multilabel learning tasks. Existing multilabel feature selection approaches mainly either explore the feature-label and feature-feature correlations or the label-label and feature-feature correlations. A few of them are able to deal with all three types of correlations simultaneously. To address this problem, in this article,

Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNNs) to extract statistical features from data and often neglect the potential topological structure information between different land cover classes. CNN-based approaches generally only model the local spatial relationships

Multitask joint learning technology continues gaining more attention as a paradigm shift and has shown promising performance in many applications. Depth estimation and semantic understanding from monocular images emerge as a challenging problem in computer vision. While the other joint learning frameworks establish the relationship between the semantics and depth from stereo pairs, the lack of learning

Meta-path-based random walk strategy has attracted tremendous attention in heterogeneous network representation, which can capture network semantics with heterogeneous neighborhoods of nodes. Despite the success of meta-path-based random walk strategy in plain heterogeneous networks which contain no attributes, it remains unexplored how meta-path-based random walk strategy could be utilized on attributed

Current state-of-the-art class-imbalanced loss functions for deep models require exhaustive tuning on hyperparameters for high model performance, resulting in low training efficiency and impracticality for nonexpert users. To tackle this issue, a parameter-free loss (PF-loss) function is proposed, which works for both binary and multiclass-imbalanced deep learning for image classification tasks. PF-loss

Equivalency is a powerful approach that can transform an original problem into another problem that is relatively more ready to be resolved. In recent years, Zhang neurodynamics equivalency (ZNE), in the form of neurodynamics or recurrent neural networks (RNNs), has been investigated, abstracted, and proposed as a process that can equivalently solve equations at different levels. After long-term research

Controlling and processing of time-variant problem is universal in the fields of engineering and science, and the discrete-time recurrent neural network (RNN) model has been proven as an effective method for handling a variety of discrete time-variant problems. However, such model usually originates from the discretization research of continuous time-variant problem, and there is little research on

In recent years, web servers pay attention to privacy and anonymity protection and choose to rely on hidden service to avoid exposure of the real geographic locations. Several studies have confirmed that hidden service is vulnerable to flow correlation attacks, specifically, the attacker has the ability to synchronize the behavior of both sides of the communication after observing the flow for an extended

The best-worst method (BWM) is a multi-criteria decision-making (MCDM) approach for solving several types of real-life decision-making problems. The basic BWM determines the priority order by pairwise comparisons of the best and worst criteria. The strength of the preference assigned via numerical value for linguistic interpretations of relative comparisons ranges from 1 to 9, representing preferences

Fault section diagnosis (FSD) is a critical part of the power system dispatching and control. To diagnose the faulty section(s) correctly, an improved binary variant of gaining-sharing knowledge-based algorithm (GSK) named LOBGSK is presented in this paper. It stretches the original GSK over binary search space so as to solve the 0-1 integer programming FSD problem. In LOBGSK, individuals are encoded

Most of existing multi-view clustering methods assume that different feature views of data are fully observed. However, it is common that only portions of data features can be obtained in many practical applications. The presence of incomplete feature views hinders the performance of the conventional multi-view clustering methods to a large extent. Recently proposed incomplete multi-view clustering

In a small operational space, e.g., mesoscale or microscale, we need to control movements carefully because of fragile objects. This article proposes a novel structure based on spiking neural networks to imitate the joint function of multiple brain regions in visual guiding in the small operational space and offers two channels to achieve collision-free movements. For the state sensation, we simulate

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling

This article addresses the batch-based learning consensus for linear and nonlinear multiagent systems (MASs) with faded neighborhood information. The motivation comes from the observation that agents exchange information via wireless networks, which inevitably introduces random fading effect and channel additive noise to the transmitted signals. It is therefore of great significance to investigate

Social relations are ubiquitous and form the basis of social structure in our daily life. However, existing studies mainly focus on recognizing social relations from still images and movie clips, which are different from real-world scenarios. For example, movie-based datasets define the task as the video classification, only recognizing one relation in the scene. In this article, we aim to study the

This article reconsiders synchronization problem of linear complex networks with time-varying delay on time scales. For different types of time scales, aperiodically intermittent control scheme is established by using a matrix-based convex combination method, which has great potential in reducing control consumption and saving communication bandwidth. By employing a common Lyapunov function, aperiodically

This article addresses the safety-critical containment maneuvering of multiple underactuated autonomous surface vehicles (ASVs) in the presence of multiple stationary/moving obstacles. In a complex marine environment, every ASV suffers from model uncertainties, external disturbances, and input constraints. A safety-critical control method is proposed for achieving a collision-free containment formation

Event-based neural networks are currently being explored as efficient solutions for performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural networks coupled to adequate preprocessing must be investigated. Within this context, we demonstrate a 4-b-weight spiking neural network (SNN) for radar gesture recognition, achieving a state-of-the-art 93% accuracy within

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the performance of existing autoencoder approaches for manipulator control may be still largely dependent on the

This research focuses on the application of artificial neural networks (ANNs) on parameters extraction of photovoltaic (PV) models. Extracting parameters of the PV models accurately is crucial to control and optimize PV systems. Although many algorithms have been proposed to address this issue, how to extract the parameters of the PV models accurately and reliably is still a great challenge. Neural

Existing deep reinforcement learning (RL) are devoted to research applications on video games, e.g., The Open Racing Car Simulator (TORCS) and Atari games. However, it remains under-explored for vision-based autonomous urban driving navigation (VB-AUDN). VB-AUDN requires a sophisticated agent working safely in structured, changing, and unpredictable environments; otherwise, inappropriate operations

In this article, the problem of tracking control for a class of nonlinear time-varying full state constrained systems is investigated. By constructing the time-varying asymmetric barrier Lyapunov function (BLF) and combining it with the backstepping algorithm, the intelligent controller and adaptive law are developed. Neural networks (NNs) are utilized to approximate the uncertain function. It is well

Scene parsing, or semantic segmentation, aims at labeling all pixels in an image with the predefined categories of things and stuff. Learning a robust representation for each pixel is crucial for this task. Existing state-of-the-art (SOTA) algorithms employ deep neural networks to learn (discover) the representations needed for parsing from raw data. Nevertheless, these networks discover desired features

In inverse reinforcement learning (RL), there are two agents. An expert target agent has a performance cost function and exhibits control and state behaviors to a learner. The learner agent does not know the expert's performance cost function but seeks to reconstruct it by observing the expert's behaviors and tries to imitate these behaviors optimally by its own response. In this article, we formulate

A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP presumes that the locations of customers and the traveling time among customers are fixed and constant. In real-life cases, however, the traffic conditions and customer requests may change over time. To find the most economic route, the decisions can be made constantly upon the time-point when the salesman completes

In this paper, we study fuzzy congruence relations and their classes; so-called fuzzy congruence classes in universal algebras whose truth values are in a complete lattice satisfying the infinite meet distributive law. Fuzzy congruence relations generated by a fuzzy relation are fully characterized in different ways. The main result in this paper is that, we give several Mal'cev-type characterizations

The real-world optimal problems frequently encountered by various industries are the nonlinear constrained optimization problems (NCOPs), where the constraints represent the limitations of practical resources. Many researchers have attempted to improve particle swarm optimization (PSO) in the past decades; however, in solving the NCOPs, the PSO-based approaches often cause premature convergences. The

The search for food stimulated by hunger is a common phenomenon in the animal world. Mimicking the concept, recently, an optimization algorithm Hunger Games Search (HGS) has been proposed for global optimization. On the other side, the Whale Optimization Algorithm (WOA) is a commonly utilized nature-inspired algorithm portrayed by a straightforward construction with easy parameters imitating the hunting

The notions of the bipolar complex fuzzy set (BCFS) and complex bipolar fuzzy set (CBFS) have been already given, but these notions of BCFS and CBFS have the problem that they contradict the basic definition of the complex numbers which we discussed in this article, and then we defined the new definition of BCFS. Our defined notion of BCFS is more closed to bipolarity as compared with already existing

In this paper, we discussconsensus measures for typical hesitant fuzzy elements (THFE), which are the finite and nonempty fuzzy membership degrees under the scope of typical hesitant fuzzy sets (THFS). In our approach, we present a model that formally constructs consensus measures by means of aggregations functions, fuzzy implication-like functions and fuzzy negations, using admissible orders to compare

The popularity of mutual funds, which are necessarily portfolios, has been drawing more attention from the people of India over the last three decades. In a mutual fund, one can invest his or her money in the securities of different sectors traded mainly in the stock exchange markets to get expected return bearing tolerable risks. A mutual fund other than the passive mutual funds is directed by an

The Internet of Things (IoT) is an emerging domain in recent days as they provided a huge number of applications in day-to-day lives. In contrast to the agricultural sector, the automatic techniques for recognizing plant disease have different benefits and pose several issues. In addition, inappropriate diagnoses are ineffectual in treating the disease and may affect the crop yield. This paper presents

Variance reduction is popular in accelerating gradient descent and stochastic gradient descent for optimization problems defined on both Euclidean space and Riemannian manifold. This paper further improves on existing variance reduction methods for non-convex Riemannian optimization, including R-SVRG and R-SRG/R-SPIDER by providing a unified framework for batch size adaptation. Such framework is more

The regulatory landscape for artificial intelligence (AI) is shaping up on both sides of the Atlantic, urgently awaited by the scientific and industrial community. Commonalities and differences start to crystallize in the approaches to AI in medicine.

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The papers in this special section are aimed at contributions from computational neuroscience and psychology, artificial intelligence, machine learning, and affective computing, challenging and expanding current research on interpretation and estimation of human affective behavior from longitudinal data, i.e., single or multiple modalities captured over extended periods of time allowing efficient representation

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The focus on evolutionary neural architecture search in this issue has driven me to ponder the evolution of biological neural networks, or rather, the human brain. Researchers in neuroanatomy found that the cognitive and mental development of humans is attributed to the increase of our brain size throughout evolution. Yet bigger is not always better. It was also discovered that, once the brain reaches

Ethics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums. It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI). The latter was promoted by DARPA, and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are

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Republishes 7 book reviews first appearing in various IEEE journals.