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In wireless visual sensor networks (WVSNs), feature aggregation instead of image collection can reduce network traffic, and thus, network lifetime can be prolonged, and network delay can be shortened. However, it requires the deployment of a number of processing nodes (PNs) near camera nodes (CNs) for image feature extraction. In this article, we investigate how to schedule such a WVSN to maximize network lifetime and minimize network delay while ensuring that each target is covered, and the features of each image are extracted and sent to a base station (BS). This problem requires the optimal arrangement of cover sets, and each cover set involves the joint optimization of the selection of sensing nodes and their working directions, image offloading, and image feature data routing. We formulate it as a mixed integer nonlinear program and propose a maximum-lifetime and minimum-delay network scheduling algorithm to solve it. Extensive simulation results show that this algorithm is more promising than two baseline algorithms.

Population aging has highlighted the significance of developing home-care robots to assist human life. However, traditional home-care robots work according to fixed procedures, which lack human–robot interaction to mimic human hand action. Human–robot interfaces provide an opportunity for interactions between robots and users, which play an important role in home-care robots. Traditional human–robot interfaces are based on rigid, cumbersome, and high-priced machines, which prevent their wide application in performing home-care tasks. An e-skin sensor with 11 stretchable resistors evenly distributed on the back of the human hand, which could be applied to monitor human finger motion and control robotic fingers to dexterously grasp diverse objects, is presented in this article. A 3D printer is employed to pattern high-performance wires into structurally soft and stretchable filamentary serpentines. The stretchable resistors are fabricated by a thin sensing film of mixed multiwalled carbon nanotubes (MWCNTs) and silicone elastomer. The e-skin sensor is applied in a hand-in-the-loop system for controlling a robotic hand to help grasp an object synchronously. A robot system is developed to connect the e-skin with the robotic hand to perform grasp actions with a success rate of more than 80%. By analyzing the experimental results of the e-skin sensor and robotic hand grasp, this work obtains insights showing that the skin sensor can help engineers design a home-care assistant plan for patients according to monitoring value or a human–robot interaction mode by robotic fingers mimicking human finger actions.

Myocardial scar regions appear in cardiac magnetic resonance images of patients with myocardial infarction. Implantable cardioverter defibrillators (ICDs) can be used to effectively prevent arrhythmias and even death caused by myocardial infarction. Whether or not to implant an ICD and deciding the precise location of implantation are huge clinical challenges. This work proposes a noise-estimation-dominated fuzzy segmentation strategy for ICD implantation. It achieves accurate noise estimation in cardiac magnetic resonance image segmentation by weighting mixed noise distributions and adding a spatial information constraint. To be specific, a weighted $\ell _{2}$-norm regularization term is proposed to form a universal noise-estimation-based fuzzy $C$-means algorithm that can perform accurate segmentation of images subject to mixed or unknown noise. Through region growth and flood fill in order, the region and volume of myocardial scars are precisely obtained. Thus, the ICD implantation is accurately estimated. Finally, a criterion for ICD implantation estimation is reported. Experimental results on different myocardial infarction datasets show that the proposed strategy is more effective and efficient than its competitive peers.

To perform well, deep learning (DL) models have to be trained well. Which optimizer should be adopted? We answer this question by discussing how optimizers have evolved from traditional methods like gradient descent to more advanced techniques to address challenges posed by high-dimensional and non-convex problem space. Ongoing challenges include their hyperparameter sensitivity, balancing between convergence and generalization performance, and improving interpretability of optimization processes. Researchers continue to seek robust, efficient, and universally applicable optimizers to advance the field of DL across various domains.

Contract Bridge, a four-player imperfect information game, comprises two phases: bidding and playing. While computer programs excel at playing, bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents. In this work, we introduce a Bridge bidding agent that combines supervised learning, deep reinforcement learning via self-play, and a test-time search approach. Our experiments demonstrate that our agent outperforms WBridge5, a highly regarded computer Bridge software that has won multiple world championships, by a performance of 0.98 IMPs (international match points) per deal over 10 000 deals, with a much cost-effective approach. The performance significantly surpasses previous state-of-the-art (0.85 IMPs per deal). Note 0.1 IMPs per deal is a significant improvement in Bridge bidding.

With the rapid development of service computing, the demand for service recommendation is increasing. Quality of Service (QoS) prediction has been one of the key challenges for service recommendation. Existing deep learning-based methods have been proposed for QoS prediction, but further improvement of their neural network structures is still needed to improve the prediction accuracy. This work introduces multi-stage multi-scale feature fusion with individual evaluations to a deep learning model for accurate QoS prediction. In our model, non-negative matrix factorization is used to extract three-scale (i.e., global, local, and individual) features; distance similarity is exploited to find similar users and services; a multi-stage deep neural network is designed to fuse multi-scale features, where individual evaluations are input to each stage to correct QoS features. Finally, our model is compared with often-cited prediction methods, and the experimental results show that it can more accurately predict QoS than its peers. Note to Practitioners—Accurate QoS prediction is very helpful to recommend the most suitable services to users among many similar services. Affected by the sparsity of historical data, the accuracy of existing QoS prediction methods is often limited. The multi-scale features of users and services can be used to improve prediction accuracy. This work proposes a new QoS prediction method to do so. Specifically, it first extracts global and individual features through non-negative matrix factorization and uses distance similarity to obtain local features. Then, it proposes a new deep neural network that fuses the extracted multi-scale features in each learning stage, thereby improving QoS prediction for services recommendation.

Community is a fundamental and highly desired pattern in a Large-scale Undirected Network (LUN). Community detection is a vital issue when LUN representation learning is performed. Owing to its good scalability and interpretability, a Symmetric and Non-negative Matrix Factorization model is frequently utilized to tackle this issue. It adopts a unique Latent Factor (LF) matrix for precisely representing LUN’s symmetry, which, unfortunately, leads to a reduced LF space that decreases its representation learning ability to a target LUN. Motivated by this discovery, this study proposes a Symmetry and Graph Bi-regularized Non-negative Matrix Factorization (B-NMF) method that: a) leverages multiple LF matrices when representing LUN, thereby boosting the representation learning ability; b) constructs a symmetry regularization term that implies the equality constraint among its multiple LF matrices, thereby illustrating LUN’s intrinsic symmetry; and c) incorporates graph regularization into its learning objective, thereby illustrating LUN’s local geometry. A theoretical proof is given to theoretically validate B-NMF’s convergence ability. The regularization hyperparameters are selected by validating model modularity, thereby guaranteeing B-NMF’s practicability in addressing real application issues. Extensive experimental results on ten LUNs from real applications demonstrate that the proposed B-NMF-based community detector significantly outperforms several baseline and state-of-the-art models in achieving highly-accurate community detection results. Note to Practitioners—LUNs are very-commonly seen in real applications like a social network system. Communities in LUNs are vital for various knowledge discovery-related applications. For accurately detecting them, a detector should guarantee its high representation learning ability to a target LUN. To do so, this paper presents a B-NMF model that is able to perform precise representation learning to LUNs, thereby achieving accurate community detection results. In comparison with conventional Symmetric and Non-negative Matrix Factorization-based community detectors, a B-NMF-based community detector enjoys its enlarged latent feature space, which ensures its higher representation ability to a target LUN. It depends on two regularization hyperparameters, which can be selected by performing grid-search on the target LUN via its modularity evaluation. This paper gives the empirical values of B-NMF’s regularization hyperparameters based on the parametersensitivity tests on the involved experimental datasets. The proposed B-NMF model is shown to be highly suitable for addressing community detection and clustering tasks on LUNs from real applications.