Large display interaction has undergone considerable growth, whereas distant pointing poses challenges due to distance limitations and input devices. Smart devices with superior computing power and rich input capabilities aim to surmount these restrictions. However, pointing interactions in single-layer have limited accuracy in large display scenarios. We investigate a hierarchical pointing approach that couples multiple control modes of rotation attitude, touch operation, and pressure simulation with different cursor strategies. Specifically, it covers the dual-layer cursor with the hierarchical structure, the pressure-dependent multistage gain cursor, and the cursor projection technique based on the target acquisition mechanism. We contrast seven distant pointing techniques in two groups of experiments. Our results indicate that the hierarchical interaction strategy improves user performance, and cursor projection achieves an optimal trade-off between velocity and accuracy for high Fitts' index of difficulty (ID) contexts.

Due to the difficulty and high cost of conducting sufficient real-world road tests, it is widely accepted in the industry to use a digital twin testing system that combines virtual simulation testing with real-world road testing to test and evaluate autonomous driving systems. Digital twin testing can simulate a road test environment using technologies such as sensor simulation and vehicle dynamics simulation, advanced graphics processing, traffic flow simulation, digital simulation, and road modelling. We integrated simulation testing tools, V2X communication devices, and real testing vehicles to achieve digital twin autonomous driving testing based on virtual simulation and real environments. It generates scene data based on virtual scene simulation software, constructs simulated scenarios mapping to reality, and uses V2X communication devices to transmit simulated scene environment data in real time to the real testing vehicles. It also receives real-time status data from the vehicles and feeds them back to the scene controllers, enabling autonomous vehicles to test different scenarios on real roads. In active safety-related testing, virtual scenes can avoid accidents and save unnecessary equipment losses. The system has high repeatability, and after a large number of repetitive in-vehicle tests, it can significantly reduce functional defects in the tested system, reducing the workload of actual vehicle field testing and road testing and improving testing efficiency and cost savings.

Unmanned aerial vehicles (UAVs) Communications and networks are of paramount importance in the 5G/6G networks. However, how to deploy the limited on-board energy and restricted transmit power of the UAV so that enhancing the communication efficiency is still a key issue. In this work, we consider a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). For achieving a better energy efficiency, we formulate a hovering and motion energy consumption multiobjective optimization problem (HMECMOP) of UAV-enable CB to simultaneously minimize the total hovering and motion energy consumptions of UAVs by jointly optimizing the positions, excitation current weights of UAVs and the order of communicating with different BSs. Then, we propose an improved multiobjective multi-verse optimizer (IMOMVO) to solve the formulated HMECMOP. IMOMVO uses the vertical and horizontal renewal strategy and nearest neighbor procedure (NNP) to deal with the complex solution space which contains continuous and discrete solutions, so that making the algorithm more suitable for solving the formulated optimization problem. Simulation results demonstrate that the proposed algorithm is effective for solving the HMECMOP and it has better performance than some other comparison algorithms.

Edge computing has recently gained momentum as it provides computing services for mobile devices through high-speed networks. In edge computing system optimization, deep reinforcement learning(DRL) enhances the quality of services(QoS) and shorts the age of information(AoI). However, loosely coupled edge servers saturate a noisy data space for DRL exploration, and learning a reasonable solution is enormously costly. Most existing works assume that the edge is an exact observation system and harvests well-labeled data for the pretraining of DRL neural networks. However, this assumption stands in opposition to the motivation of driving DRL to explore unknown information and increases the scheduling and computing costs in large-scale dynamic systems. This article leverages DRL with a distillation module to drive learning efficiency for edge computing with partial observation. We formulate the deadline-aware offloading problem as a decentralized partially observable Markov decision process (Dec-POMDP) with distillation, called fast decentralized reinforcement distillation(Fast-DRD). Each edge server decides makes offloading decisions in accordance with its own observations and learning strategies in a decentralized manner. By defining trajectory observation history(TOH) distillation and trust distillation to avoid overfitting, Fast-DRD learns a suitable offloading model in a noisy partially observed edge system and reduces the cost for communication among servers. Finally, experimental simulations are presented to evaluate and compare the effectiveness and complexity of Fast-DRD.

Biogeography-based optimization (BBO) is a swarm intelligence optimization algorithm based on migration and mutation operations, which is usually used to solve the complex optimization problems. However, it is also a challenging task for conventional BBO to solve some complex and diversified optimization problems with a perfect balance between the performance of exploration and exploitation. In this paper, we propose a variant of BBO approach called BBO with improved migration and adaptive mutation (BBOIMAM) to improve the performance of conventional BBO for dealing with different optimization problems. First, BBOIMAM introduces an improved migration strategy which includes the generalized sinusoidal migration model and immigration strategy based on elite-learning mechanism for the improvement of the local search ability. Second, we propose an adaptive mutation strategy based on the spring vibration to further enhance the population diversity, so that improving the global search ability of the algorithm. By the combination of the proposed improved migration and adaptive mutation strategies, the exploration and exploitation performance of the algorithm can be balanced. We make a large number of experiments on a set of various kinds of benchmark functions, and the experimental results demonstrate that the proposed BBOIMAM approach achieves better performance than several state-of-the-art peer algorithms on CEC 2017 and CEC 2020 test function sets and three cases of the antenna array beam pattern optimization problems.

Dengue as an acute infectious disease threatens global public health and has sparked broad research interest. However, existing studies generally ignore the spatial dependencies involved in dengue forecast, and consideration of temporal periodicity is absent. In this work, we propose a spatiotemporal component fusion model (STCFM) to solve the dengue risk forecast issue. Considering that mosquitoes are an important vector of dengue transmission, we introduce feature factors involving mosquito abundance and spatiotemporal lags to model temporal trends and spatial distributions separately on the basis of statistical properties. Specifically, we conduct multiscale modeling of temporal dependencies to enhance the forecast capability of relevant periods by capturing the historical variation patterns of the data across different segments in the temporal dimension. In the spatial dimension, we quantify the multivariate spatial correlation analysis as additional features to strengthen the spatial feature representation and adopt the ConvLSTM model to learn spatial dependencies adequately. The final forecast results are obtained by stacking strategy fusion in ensemble learning. We conduct experiments on real dengue datasets. The results indicate that STCFM improves prediction accuracy through effective spatiotemporal feature representations and outperforms candidate models with a reasonable component construction strategy.

Mobile edge computing is emerging to provide cloud-computing capabilities to mobile users, so that they can offload computation intensive tasks to close proximity for execution. However, most existing works imply that a transmission-finished task still occupies the channel until all users on the same channel finish the transmission, leading to severe channel resource waste. To solve this problem, we propose an efficient computation offloading mechanism which releases the channel resources of transmission-finished tasks for transmission-unfinished tasks, and aims to minimize the response time and energy consumption for each user. Specifically, we formulate the computation offloading problem as a game, analyze its structural properties and show how it possesses a Nash equilibrium and admits the finite improvement property, in the cases of elastic cloud and non-elastic cloud respectively. We then propose a Distributed Multi-channel Computation Offloading (DMCO) algorithm, which can converge to a Nash equilibrium, and find the upper bound of the convergence time. We further evaluate the performance of DMCO using the price of anarchy. Numerical results show that DMCO scales well with the number of users, and outperforms existing works, for example, benefits 13.3 percent more users and reduces cost by 23.7 percent than CO, one of the best existing works.

Aspect-based sentiment analysis (ABSA) is a fine-grained task whose main target is to identify the sentiment polarity associated with a given aspect in a sentence. Its main challenge is aspect feature extraction of sentences. For the problem of insufficient aspect-level information extraction, such as long dependencies problems, we propose a novel model that enhances aspect information from semantic and syntactic perspectives to address this issue. In terms of semantics, we provide a mechanism to actively mask aspect words and enable the model to capture specific properties in context. In terms of syntax, we advance a new aspect weight graph that can directly connect aspect words with syntactically related words. Furthermore, we integrate semantic extraction and syntactic analysis methods to enhance the informative representation of aspect-level words. We evaluate the effectiveness of our method on publicly available datasets. Experimental results show that our model achieves excellent results on the ABSA tasks.

Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Lévy flight strategy and Euclidean distance‐based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP.

Affective computing is an emerging field in recent years, it is proposed to allow computers understanding human emotions. At present, the most commonly used method of affective computing with textual signals is based on a sentiment lexicons. The polarity of emotion words can be divided into two types: positivity and negativity. The general method is to store common emotional words and their polarities in a list, taking them out of the list for calculation when needed. This approach can be used in basic affective computing , but it cannot solve the problems of polysemy. In view of this situation, we propose a new approach that is using a graphic dictionary to store sentiment words. The graph data structure is composed of two parts: vertices and edges, and the weights of several edges connecting the same vertex can be different from each other. The feature of the graph can solve the problem that one word may not have only one sentiment tendencies in different context. This paper constructs a graph sentiment dictionary based on the characteristics of the graph, then extracts the targets, features, sentiment words in the text as the vertices, and extracts the relationship between them as the edges to build a graph sentiment dictionary. By this way, the problem of polysemy and the lack of keywords can be solved. Compared with the traditional emotional dictionary, the effect of the graph sentiment dictionary will be better.

With the development of the Intelligent Transportation System, various distributed sensors (including GPS, radar, infrared sensors) process massive data and make decisions for emergencies. Federated learning is a new distributed machine learning paradigm, in which system heterogeneity is the difficulty of fairness design. This paper designs a system heterogeneous fair federated learning algorithm (SHFF). SHFF introduces the equipment influence factor I into the optimization target and dynamically adjusts the equipment proportion with other performance. By changing the global fairness parameter θ, the algorithm can control fairness according to the actual needs. Experimental results show that, compared with the popular q-FedAvg algorithm, the SHFF algorithm proposed in this paper improves the average accuracy of the Worst 10% by 26% and reduces the variance by 61%.

In order to improve the classification effect of potato early blight (mild, moderate, severe), the study improved the GhostNet model of the lightweight convolutional neural network, including the use of ReLU activation function to replace the sigmoid activation function of the SE attention mechanism, Dropout measure to optimize the network. At present, the most commonly used cross entropy loss function does not consider whether the data is balanced. In order to solve the problem of data imbalance, the LS-CCE (Label Smoothing Complement Cross Entropy) loss function is proposed. Through training on potato early blight, late blight, and health image data, a potato disease classification model is obtained. Its recognition accuracy rate can reach 97.17%, which is 1.046% higher than the average accuracy rate before improvement. Using transfer learning, the trained model is used to classify the degree of potato early blight, and its recognition accuracy can reach 93.27%.

Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a challenge. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proven as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces three improved items that are the opposition-based learning model, attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for solving the formulated CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for dealing with the formulated joint optimization problem. Moreover, the superiority of IFA is verified by tests.

Secure transmissions are important for visible light communication (VLC) systems. However, conventional information security technologies may be not suitable for VLCs due to the limited hardware resources especially for the receivers. In this letter, we conduct a security zone in the room and propose a physical layer security approach based on optical beamforming to achieve secure transmissions. First, we formulate an optimization problem to maximize the received signal power of the security zone so that the eavesdroppers in the insecurity zone can not obtain enough signal power for decoding. Then, a cuckoo search with adaptive searching and population mutation (CSASPM) algorithm is proposed to solve the formulated optimization problem. CSASPM introduces the adaptive searching and population mutation factors to improve the performance of conventional cuckoo search algorithm so that making it more suitable for the secure transmission optimization of VLC. Simulation results demonstrate that the proposed approach is effective and has better performance than other comparison methods.

This paper proposes an improved GRPS protocol based on the ant colony algorithm, OVGPSR. In this protocol, various factors are considered in the routing calculation, and the parameters such as vehicle speed, energy consumption, and deflection angle are introduced into the ant colony algorithm. The algorithm predicts vehicle intermediate nodes, improves the ability of a vehicle ad hoc network, and reduces energy consumption. The results show that OVGPSR decreases delay and enhance throughput.

Antenna arrays are able to improve the directivity performance and reduce the cost of wireless communication systems. However, how to reduce the maximum sidelobe level (SLL) of the beam pattern is a key problem in antenna arrays. In this paper, three kinds of antenna arrays that are linear antenna array (LAA), circular antenna array (CAA) and random antenna array (RAA) are investigated. First, we formulate the SLL suppression optimization problems of LAA, CAA and RAA, respectively. Then, we propose a novel method called improved chicken swarm optimization (ICSO) approach to solve the formulated optimization problems. ICSO introduces four enhanced strategies including the local search factor, weighting factor and global search factor into the update method of conventional chicken swarm optimization (CSO) algorithm, respectively, for achieving better beam pattern optimization results of antenna arrays. Moreover, a variation mechanism is proposed to enhance the population diversity so that further improving the performance of the algorithm. We conduct simulations to evaluate the performance of the proposed ICSO for the maximum SLL suppressions of LAAs, CAAs and RAAs, and the results show that ICSO obtains lower maximum SLLs for different antenna array cases with different numbers of antenna elements compared to several other algorithms.

Task offloading could optimize computational resource utilization in edge computing environments. However, how to assign and offload tasks for different behavior users is an essential problem since the systems dynamic, intelligent application diversity, and user personality. With user behavior prediction, this paper proposes soCoM, a semi-online Computational Offloading Model. We explore the user behaviors in sophisticated action space by reinforcement learning for catching unknown environment information. With Dueling Deep-Q Network, both the prediction accuracy of users' behaviors and the server load balance are well-considered, while increasing the computational efficiency and decreasing the resource costing. We propose a dynamic simulation environment of edge computing to demonstrate that user behavior is the critical factor for impacting system utilization. As the action space increasing, Dueling DQN performs better than state-of-art DQN and other improved strategies, and also load balance in multiple different server scenario.

The nodes in mobile wireless sensor networks (MWSNs) are usually with limited hardware resources, which leads to the limitation of transmission range and energy. To extend the communication distance of a single sensor node, distributed collaborative beamforming (DCB) based on a virtual node antenna array (VNAA) can be used in MWSNs. The locations and excitation current weights are the key factors that affect the performance of DCB, thus the nodes of a MWSN can move to better locations to achieve a lower maximum sidelobe level (SLL) of the beam pattern, thereby reducing the communication interferences and enhancing the directivity. However, the moving energy consumption will be increased. In this paper, a joint optimization problem for optimizing the maximum SLL of beam pattern, transmission power and moving energy consumption of DCB nodes in MWSNs is proposed, and the NP-hardness of the formulated problem is proven. Then, we propose a distributed parallel cuckoo search algorithm (DPCSA), which is a nature-inspired approach, to solve the formulated joint optimization problem. Simulation results verify that the maximum SLL, transmission power and moving energy consumption of DCB nodes can be optimized effectively. Moreover, the performance and stability of the proposed DPCSA are evaluated.

Task scheduling between edge devices and remote servers is a common application scenario in edge computing or cloud computing, also known as computational offloading. A reasonable scheduling strategy can effectively shorten task completion time, reduce energy consumption, and improve user experience. However, the traditional offline task scheduling algorithm is NP-hard, and the decision requires obtaining all the information of the task and the device (such as task computing amount, data amount, device computing resources, etc.), which is challenging to meet in practical applications. The semi-online algorithm describes the task scheduling method when the system cannot obtain all the information. In this paper, we propose an Efficient Semi-online algorithm for Multi-users task offloading (ESaM), which includes two specific implementations: ESaM-I as known server-side idle time, and ESaM-O for known task computing amount. Because ESaM-I has obtained server information, it is better than ESaM-O in performance for most of the scenarios. The experimental results show that ESaM-I and ESaM-O are superior to the well-known semi-online scheduling algorithm SPaC in task completion time. As the remote processor computing ability increases, the average makespan converges to 0.875, 0.742, 0.782 for SPaC-M, ESaM-O, and ESaM-I in the simulation.