Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as convolutional neural networks, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering them less suitable for resource-constrained mobile health applications. This study introduces HARMamba, an innovative lightweight and versatile HAR architecture that combines selective bidirectional state-space model and hardware-aware design. To optimize real-time resource consumption in practical scenarios, HARMamba employs linear recursive mechanisms and parameter discretization, allowing it to selectively focus on relevant input sequences while efficiently fusing scan and recompute operations. The model employs independent channels to process sensor data streams, dividing each channel into patches and appending classification tokens to the end of the sequence. It utilizes position embedding to represent the sequence order. The patch sequence is subsequently processed by HARMamba Block, and the classification head finally outputs the activity category. The HARMamba Block serves as the fundamental component of the HARMamba architecture, enabling the effective capture of more discriminative activity sequence features. HARMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands. Its effectiveness has been extensively validated on four publicly available data sets, namely, PAMAP2, WISDM, UNIMIB SHAR, and UCI. The F1 scores of HARMamba on the four data sets are 99.74%, 99.20%, 88.23%, and 97.01%, respectively.

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields, such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This article introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in an efficient single-task model. P2LHAP divides sensor data streams into a sequence of “patches,” served as input tokens, and outputs a sequence of patch-level activity labels, including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on the three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications.

The massive generation of time-series data by large-scale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the channel dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the channel independence (CI) strategy. The CI strategy treats channel multichannel series as separate single-channel series, expanding the data set to improve generalization performance and avoiding interchannel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to mitigate interchannel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture interchannel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.

Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for corresponding recognition modeling. Labeled activity data is expensive to collect. To address this challenge, we propose an effective and robust semi-supervised active learning method, which combines the mainstream semi-supervised learning method with expert collaboration. Our method takes a user's trajectory as the only input. In addition, this novel method uses expert collaboration to judge the valuable samples further to enhance its performance. Our method relies on very few semantic activities, outperforms all baseline activity recognition methods, and is close to the performance of supervised learning methods. On the adlnormal dataset with 200 semantic activities data, our work achieved an accuracy of 89.07%, and supervised learning has 91.77%. Our ablation study validated the components in our method using a query strategy and a data fusion approach.

Acupoints (APs) prove to have positive effects on disease diagnosis and treatment, while intelligent techniques for the automatic detection of APs are not yet mature, making them more dependent on manual positioning. In this paper, we realize the skin conductance-based APs and non-APs recognition with machine learning, which could assist in APs detection and localization in clinical practice. Firstly, we collect skin conductance of traditional Five-Shu Point and their corresponding non-APs with wearable sensors, establishing a dataset containing over 36000 samples of 12 different AP types. Then, electrical features are extracted from the time domain, frequency domain, and nonlinear perspective respectively, following which typical machine learning algorithms (SVM, RF, KNN, NB, and XGBoost) are demonstrated to recognize APs and non-APs. The results demonstrate XGBoost with the best precision of 66.38%. Moreover, we also quantify the impacts of the differences among AP types and individuals, and propose a pairwise feature generation method to weaken the impacts on recognition precision. By using generated pairwise features, the recognition precision could be improved by 7.17%. The research systematically realizes the automatic recognition of APs and non-APs, and is conducive to pushing forward the intelligent development of APs and Traditional Chinese Medicine theories.

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm44https://github.com/DMiC-Lab-HFUT/SMDE-Transfer.

Contrastive learning is an emerging and important self-supervised learning paradigm that has been successfully applied to sensor-based human activity recognition (HAR) because it can achieve competitive performance relative to supervised learning. Contrastive learning methods generally involve instance discrimination, which means that the instances are regarded as negatives of each other, and thus their representations are pulled away from each other during the training process. However, instance discrimination could cause overclustering, meaning that the representations of instances from the same class could be overly separated. To alleviate this overclustering phenomenon, we propose a new contrastive learning framework to select negatives by clustering in HAR, which is named clustering for contrastive learning in human activity recognition (ClusterCLHAR). First, ClusterCLHAR clusters the instance representations, and for each instance, only those from different clusters are regarded as negatives. Second, a new contrastive loss function is proposed to mask the same-cluster instances from the negative pairs. We evaluate ClusterCLHAR on three popular benchmark data sets: 1) USC-HAD; 2) MotionSense; and 3) UCI-HAR, using the mean F1-score as an evaluation metric for downstream tasks. The experimental results show that ClusterCLHAR outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.

Studies have shown that deep neural networks (DNNs) are susceptible to adversarial attacks, which can cause misclassification. The adversarial attack problem can be regarded as an optimization problem, then the genetic algorithm (GA) that is problem-independent can naturally be designed to solve the optimization problem to generate effective adversarial examples. Considering the dimensionality curse in the image processing field, traditional genetic algorithms in high-dimensional problems often fall into local optima. Therefore, we propose a GA with multiple fitness functions (MF-GA). Specifically, we divide the evolution process into three stages, i.e., exploration stage, exploitation stage, and stable stage. Besides, different fitness functions are used for different stages, which could help the GA to jump away from the local optimum.Experiments are conducted on three datasets, and four classic algorithms as well as the basic GA are adopted for comparisons. Experimental results demonstrate that MF-GA is an effective black-box attack method. Furthermore, although MF-GA is a black-box attack method, experimental results demonstrate the performance of MF-GA under the black-box environments is competitive when comparing to four classic algorithms under the white-box attack environments. This shows that evolutionary algorithms have great potential in adversarial attacks.

Although followership has been widely studied in sociology and management, the problem of finding followers has not drawn attention in the field of artificial intelligence. We refer to the problem of finding followers of a given object as followers search. In sociology, followers are close to their leaders, and leaders are superior to their followers. In this article, aimed at finding followers of a given object, we formulate followers on the basis of both superiority and closeness. The former means that the given object should be superior to followers, and the latter means that followers should be close to the given object. We present a followers search algorithm to find the followers of the given object. Furthermore, we apply the ideas of followers to the market basket and recommender system datasets. The experimental results demonstrate the rationality of the discovered followers on the market basket dataset and the improved performance of the TrustPMF algorithm by adopting followership on recommender system datasets, which indicate a promising future for followers search.

Considering the uncertainty of power generations, in addition to the uncertainty of loads, is more and more important because of the increasing use of renewable energy sources. Most existing works on dynamic optimal power flow (DOPF) have only focused on either the uncertainty of loads (called demand-side uncertainty) or the uncertainty of power generations (called supply-side uncertainty). As far as we know, only a little work on the dynamic OPF problems considered both uncertainties simultaneously. It might be because the combination of variable uncertainties could lead to a huge problem size for existing methods. In this paper, inspired by the ideas in the field of evolutionary dynamic optimization (EDO), we attempt to deal with uncertain parameters from the perspective of tracking the moving optimum. A species and memory enhanced differential evolutionary algorithm (called SMDE) is specially designed to solve the DOPF with double-sided uncertainties. Specifically, in order to deal with the double-sided uncertainties, a modified memory strategy and an improved multi-population strategy were introduced, where the multi-population strategy includes two versions. The experimental results on the modified IEEE 57-bus and 118-bus systems show that the proposed algorithms perform much better than the comparison algorithms for most cases.

Both Particle Swarm Optimization (PSO) and Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) exhibit good performance when solving global optimization problems. However, PSO could be misled by historical information and falls into a local optimum. Further, CMA-ES cannot fully utilize global information. Therefore, in this paper, we first propose a time-window PSO (TW-PSO) as an improvement of PSO, which could enhance the exploration ability of the algorithm. Second, we design a hybrid algorithm of TW-PSO, PSO and CMA-ES, i.e., HTPC, which combines the advantages of TW-PSO, PSO, and CMA-ES. We test HTPC on single-objective optimization problems from the CEC-2019 100-Digit Challenge, and the experimental results show that the performance of HTPC is competitive.

Cyberspace is a new basic space after the three traditional basic spaces-physical, social, and thinking spaces (PST spaces). It is a trend that entities (objects) and PST spaces they are living in to be cyberized. On the one hand, the rapidly developing of cyberspace has the increasingly significant influences to PST spaces. On the other hand, the cyberization of objects in PST spaces have been continuously deepening and strengthening. Cyberization leads to the convergence of the four basic spaces, which also called cyberspace and cyber-enabled physical-social-thinking spaces (CPST spaces). In recent years, the philosophy research on CPST spaces and objects (short for cyber philosophy) has been developing rapidly while some researchers try to figure cyber science and its fundamental issues. Up to now, the bridge, fundament logic from cyber philosophy to cyber science, has not yet formed. This paper proposes a new concept of “cyberlogic” for establishing a bridge from cyber philosophy to cyber science. The etymology, concept, contents, and methods of cyberlogic are presented, and the cyberlogic for the CPST spaces is shown. Moreover, main issues and methodologies for cyberlogic are discussed.

Space and time are crucial characteristics of the physical objects. Considering only the spatial dimension will lead to an ambiguity when objects are mapped from the physical world to cyber world, therefore the temporal and logical dimensions also should be addressed in the mapping process. In this context we propose STLF, a spatial-temporal-logical framework that observe the relations that holds among objects in the physical space to properly map them to the cyberspace, and furthermore, we discuss the methods to map the object's changing properties, we conclude by advocating Perdurance-based mapping.

Evolutionary Algorithms (EAs) with gradient-based repair, which utilize the gradient information of the constraints set, have been proved to be effective. It is known that it would be time-consuming if all infeasible individuals are repaired. Therefore, so far the infeasible individuals to be repaired are randomly selected from the population and the strategy of choosing individuals to be repaired has not been studied yet. In this paper, the Species-based Repair Strategy (SRS) is proposed to select representative infeasible individuals instead of the random selection for gradient-based repair. The proposed SRS strategy has been applied to εDEag which repairs the random selected individuals using the gradient-based repair. The new algorithm is named SRS-εDEag. Experimental results show that SRS-εDEag outperforms εDEag in most benchmarks. Meanwhile, the number of repaired individuals is reduced markedly.

Evolutionary clustering is a hot research topic that clusters the time-stamped data and it is essential to some important applications such as data streams clustering and social network analysis. An evolutionary clustering should accurately reflect the current data at any time step while simultaneously not deviate too drastically from the recent past. In this paper, the differential evolution (DE) is applied to deal with the evolutionary clustering problem. Comparing with the typical k-means, evolutionary clustering based on DE (deEC) could perform a global search in the solution space. Experimental results over synthetic and real-world data sets demonstrate that the deEC provides robust and adaptive solutions.

Both multipopulation and memory are widely used approaches in the field of evolutionary dynamic optimization. It would be interesting to examine the effect of the combinations of multipopulation algorithms (MPAs) and memory schemes. However, since most of the existing memory schemes are proposed with single population algorithms, straightforwardly applying them to MPAs may cause problems. By addressing the possible problems, a new memory scheme is proposed for MPAs in this paper. In the experiments, several existing memory schemes and the newly proposed scheme are combined with a MPA, i.e. the Species-based Particle Swarm Optimizer (SPSO), and these combinations are tested on cyclic and acyclic problems. The experimental results indicate that 1) straightforwardly using the existing memory schemes sometimes degrades the performance of SPSO even on cyclic problems; 2) the newly proposed memory scheme is very competitive.

The Shortest Path (SP) problems are conventional combinatorial optimization problems. There are many deterministic algorithms for solving the shortest path problems in static topologies. However, in dynamic topologies, these deterministic algorithms are not efficient due to the necessity of restart. In this paper, an improved Genetic Algorithm (GA) with four local search operators for Dynamic Shortest Path (DSP) problems is proposed. The local search operators are inspired by Dijkstra's Algorithm and carried out when the topology changes to generate local shortest path trees, which are used to promote the performance of the individuals in the population. The experimental results show that the proposed algorithm could obtain the solutions which adapt to new environments rapidly and produce high-quality solutions after environmental changes.