Contrastive self-supervised representation learning without negative samples for multimodal human action recognition.

Action recognition is an important component of human-computer interaction, and multimodal feature representation and learning methods can be used to improve recognition performance due to the interrelation and complementarity between different modalities. However, due to the lack of large-scale labeled samples, the performance of existing ConvNets-based methods are severely constrained. In this paper, a novel and effective multi-modal feature representation and contrastive self-supervised learning framework is proposed to improve the action recognition performance of models and the generalization ability of application scenarios.

Multi-scale and attention enhanced graph convolution network for skeleton-based violence action recognition.

Graph convolution networks (GCNs) have been widely used in the field of skeleton-based human action recognition. However, it is still difficult to improve recognition performance and reduce parameter complexity. In this paper, a novel multi-scale attention spatiotemporal GCN (MSA-STGCN) is proposed for human violence action recognition by learning spatiotemporal features from four different skeleton modality variants.

Optimization of Graphene Nanoplatelets Dispersion and Its Performance in Cement Mortars.

As promising next-generation conducting materials, Graphene Nanoplatelets (GNPs) have been widely used to enhance the mechanical and pressure-sensitive properties of cement-based materials. However, this beneficial effect highly depended on its dispersion. In this study, polyvinyl pyrrolidone (PVP) surfactant, high-speed shear, and ultrasonication were used to disperse GNPs.

A Feasibility Study of Low Cement Content Foamed Concrete Using High Volume of Waste Lime Mud and Fly Ash for Road Embankment.

This paper aims to study the feasibility of low cement content foamed concrete using waste lime mud (LM) and fly ash (FA) as mineral additives. The LM/FA ratio was first optimized based on the compressive strength. Isothermal calorimetry test, ESEM, and XRD were used to investigate the role of LM during hydration.

Ant Colony Evacuation Planner: An Ant Colony System With Incremental Flow Assignment for Multipath Crowd Evacuation.

Evacuation path optimization (EPO) is a crucial problem in crowd and disaster management. With the consideration of dynamic evacuee velocity, the EPO problem becomes nondeterministic polynomial-time hard (NP-Hard). Furthermore, since not only one single evacuation path but multiple mutually restricted paths should be found, the crowd evacuation problem becomes even challenging in both solution spatial encoding and optimal solution searching.

Evolutionary Divide-and-Conquer Algorithm for Virus Spreading Control Over Networks.

The control of virus spreading over complex networks with a limited budget has attracted much attention but remains challenging. This article aims at addressing the combinatorial, discrete resource allocation problems (RAPs) in virus spreading control. To meet the challenges of increasing network scales and improve the solving efficiency, an evolutionary divide-and-conquer algorithm is proposed, namely, a coevolutionary algorithm with network-community-based decomposition (NCD-CEA).

A Distributed Swarm Optimizer With Adaptive Communication for Large-Scale Optimization.

Large-scale optimization with high dimensionality and high computational cost becomes ubiquitous nowadays. To tackle such challenging problems efficiently, devising distributed evolutionary computation algorithms is imperative. To this end, this paper proposes a distributed swarm optimizer based on a special master-slave model.

Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors.

Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly defined on static QoS data without the consideration of such temporal dynamics. To address this issue, this paper presents a biased non-negative latent factorization of tensors (BNLFTs) model for temporal pattern-aware QoS prediction.

Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach.

Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost.

DECAL: Decomposition-Based Coevolutionary Algorithm for Many-Objective Optimization.

This paper develops a decomposition-based coevolutionary algorithm for many-objective optimization, which evolves a number of subpopulations in parallel for approaching the set of Pareto optimal solutions. The many-objective problem is decomposed into a number of subproblems using a set of well-distributed weight vectors. Accordingly, each subpopulation of the algorithm is associated with a weight vector and is responsible for solving the corresponding subproblem.

Set-Based Discrete Particle Swarm Optimization Based on Decomposition for Permutation-Based Multiobjective Combinatorial Optimization Problems.

This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g.