Federated learning has emerged as a distributed learning paradigm by training at each client and aggregating at a parameter server. System heterogeneity hinders stragglers from responding to the server in time with huge communication costs. Although client grouping in federated learning can solve the straggler problem, the stochastic selection strategy in client grouping neglects the impact of data distribution within each group. Besides, current client grouping approaches make clients suffer unfair participation, leading to biased performances for different clients. In order to guarantee the fairness of client participation and mitigate biased local performances, we propose a federated dynamic client selection method based on data representativity (FedSDR). FedSDR clusters clients into groups correlated with their own local computational efficiency. To estimate the significance of client datasets, we design a novel data representativity evaluation scheme based on local data distribution. Furthermore, the two most representative clients in each group are selected to optimize the global model. Finally, the DYNAMIC-SELECT algorithm updates local computational efficiency and data representativity states to regroup clients after periodic average aggregation. Evaluations on real datasets show that FedSDR improves client participation by 27.4%, 37.9%, and 23.3% compared with FedAvg, TiFL, and FedSS, respectively, taking fairness into account in federated learning. In addition, FedSDR surpasses FedAvg, FedGS, and FedMS by 21.32%, 20.4%, and 6.90%, respectively, in local test accuracy variance, balancing the performance bias of the global model across clients.

When a human body moves within the coverage range of Wi-Fi signals, the reflected Wi-Fi signals by the various parts of the human body change the propagation path, so analysis of the channel state data can achieve the perception of the human motion. By extracting the Channel State Information (CSI) related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm, the human motion in the spatial environment can be perceived. On the basis of this theory, this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning. This algorithm collects the environmental information containing upward or downward wave in a conference room scene, uses the local outlier factor detection algorithm to segment the actions, and then the time domain features are extracted to train Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classification modules. The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%, and the SVM module can reach 89%, so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

Friend recommendation plays a key role in promoting user experience in online social networks (OSNs). However, existing studies usually neglect users' fine-grained interest as well as the evolving feature of interest, which may cause unsuitable recommendation. In particular, some OSNs, such as the online learning community, even have little work on friend recommendation. To this end, we strive to improve friend recommendation with fine-grained evolving interest in this paper. We take the online learning community as an application scenario, which is a special type of OSNs for people to learn courses online. Learning partners can help improve learners' learning effect and improve the attractiveness of platforms. We propose a learning partner recommendation framework based on the evolution of fine-grained learning interest (LPRF-E for short). We extract a sequence of learning interest tags that changes over time. Then, we explore the time feature to predict evolving learning interest. Next, we recommend learning partners by fine-grained interest similarity. We also refine the learning partner recommendation framework with users' social influence (denoted as LPRF-F for differentiation). Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy (i.e., approximate 50% improvements on the precision and the recall) and can recommend learning partners with high quality (e.g., more experienced and helpful).

Sustaining an operational wireless sensor network (WSN) is challenging due to the persistent need of the battery-powered sensors to be charged from time to time. The procedure of exploiting mobile chargers (MCs) that traverse to the fixed sensors of the network and wirelessly transfer energy in an efficient matter has been considered widely as a promising way to tackle this challenge. An optimization problem, called the mobile charger coverage problem, arises naturally to keep all of the sensors alive with an objective of determining both the minimum number of MCs required meeting the sensor recharge frequency and the schedule of these MCs. It is shown that this optimization problem becomes NP-hard in high-dimensional spaces. Moreover, the special case of the homogeneous recharge frequency of the sensors has already been proven to have a tractable algorithm if we consider whether the 1-dimensional space is a line or a ring. In this work, we seek to find a delicate border between the tractable and the intractable problem space. Specifically, we study the special case of heterogeneous sensors that take frequencies of 1's and 2's (lifetime of 1 and 0.5 time units) on a line, conjecture the special case's NP-hardness, propose a novel brute-force optimal algorithm, and present a linear-time greedy algorithm that gives a 1.5-approximation solution for the problem. Afterwards, we introduce the energy optimization problem of the MCs with the minimized number and solve it optimally. Comprehensive simulation is conducted to verify the efficiency of using our proposed algorithms that minimize the number of MCs.

The volume of information that needs to be processed in big data clusters increases rapidly nowadays. It is critical to execute the data analysis in a time-efficient manner. However, simply adding more computation resources may not speed up the data analysis significantly. The data analysis jobs usually consist of multiple stages which are organized as a directed acyclic graph (DAG). The precedence relationships between stages cause scheduling challenges. General DAG scheduling is a well-known NP-hard problem. Moreover, we observe that in some parallel computing frameworks such as Spark, the execution of a stage in DAG contains multiple phases that use different resources. We notice that carefully arranging the execution of those resources in pipeline can reduce their idle time and improve the average resource utilization. Therefore, we propose a resource pipeline scheme with the objective of minimizing the job makespan. For perfectly parallel stages, we propose a contention-free scheduler with detailed theoretical analysis. Moreover, we extend the contention-free scheduler for three-phase stages, considering the computation phase of some stages can be partitioned. Additionally, we are aware that job stages in real-world applications are usually not perfectly parallel. We need to frequently adjust the parallelism levels during the DAG execution. Considering reinforcement learning (RL) techniques can adjust the scheduling policy on the fly, we investigate a scheduler based on RL for online arrival jobs. The RL-based scheduler can adjust the resource contention adaptively. We evaluate both contention-free and RL-based schedulers on a Spark cluster. In the evaluation, a real-world cluster trace dataset is used to simulate different DAG styles. Evaluation results show that our pipelined scheme can significantly improve CPU and network utilization.

As the main health threat to the elderly living alone and performing indoor activities, falls have attracted great attention from institutions and society. Currently, fall detection systems are mainly based on wear sensors, environmental sensors, and computer vision, which need to be worn or require complex equipment construction. However, they have limitations and will interfere with the daily life of the elderly. On the basis of the indoor propagation theory of wireless signals, this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior. The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive, robustness, universality, and low price. The module combines digital signal processing technology and machine learning technology. This paper analyzes and processes the channel state information (CSI) data of wireless signals, and the local outlier factor algorithm is used to find the abnormal CSI sequence. The support vector machine and extreme gradient boosting algorithms are used for classification, recognition, and comparative research. Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%. This work has important social significance in ensuring the safety of the elderly.

The intelligent transportation system (ITS) integrates a variety of advanced science and technology to support and monitor road traffic systems and accelerate the urbanization process of various countries. This paper analyzes the shortcomings of ITS, introduces the principle of quantum computing and the performance of universal quantum computer and special-purpose quantum computer, and shows how to use quantum advantages to improve the existing ITS. The application of quantum computer in transportation field is reviewed from three application directions: path planning, transportation operation management, and transportation facility layout. Due to the slow development of the current universal quantum computer, the D-Wave quantum machine is used as a breakthrough in the practical application. This paper makes it clear that quantum computing is a powerful tool to promote the development of ITS, emphasizes the importance and necessity of introducing quantum computing into intelligent transportation, and discusses the possible development direction in the future.

The Kinetic Monte Carlo (KMC) is one of the commonly used methods for simulating radiation damage of materials. Our team develops a parallel KMC software named Crystal-KMC, which supports the Embedded Atom Method (EAM) potential energy and utilizes the Message Passing Interface (MPI) technology to simulate the vacancy transition of the Copper (Cu) element under neutron radiation. To make better use of the computing power of modern supercomputers, we develop the parallel efficiency optimization model for the Crystal-KMC on Tianhe-2, to achieve a larger simulation of the damage process of materials under irradiation environment. Firstly, we analyze the performance bottleneck of the Crystal-KMC software and use the MIC offload statement to implement the operation of key modules of the software on the MIC coprocessor. We use OpenMP to develop parallel optimization for the Crystal-KMC, combined with existing MPI inter-process communication optimization, finally achieving hybrid parallel optimization. The experimental results show that in the single-node CPU and MIC collaborative parallel mode, the speedup of the calculation hotspot reaches 30.1, and the speedup of the overall software reaches 7.43.

The radiation damage effect of key structural materials is one of the main research subjects of the numerical reactor. From the perspective of experimental safety and feasibility, Molecular Dynamics (MD) in the materials field is an ideal method for simulating the radiation damage of structural materials. The Crystal-MD represents a massive parallel MD simulation software based on the key material characteristics of reactors. Compared with the Large-scale Atomic/Molecurlar Massively Parallel Simulator (LAMMPS) and ITAP Molecular Dynamics (IMD) software, the Crystal-MD reduces the memory required for software operation to a certain extent, but it is very time-consuming. Moreover, the calculation results of the Crystal-MD have large deviations, and there are also some problems, such as memory limitation and frequent communication during its migration and optimization. In this paper, in order to solve the above problems, the memory access mode of the Crystal-MD software is studied. Based on the memory access mode, a memory access optimization strategy is proposed for a unique architecture of China’s supercomputer Sunway TaihuLight. The proposed optimization strategy is verified by the experiments, and experimental results show that the running speed of the Crystal-MD is increased significantly by using the proposed optimization strategy.

With the quick development of the sharing economy, ride-hailing services have been increasingly popular worldwide. Although the service provides convenience for users, one concern from the public is whether the location privacy of passengers would be protected. Service providers (SPs) such as Didi and Uber need to acquire passenger and driver locations before they could successfully dispatch passenger orders. To protect passengers’ privacy based on their requirements, we propose a cloaking region based order dispatch scheme. In our scheme, a passenger sends the SP a cloaking region in which his/her actual location is not distinguishable. The trade-off of the enhanced privacy is the loss of social welfare, i.e., the increase in the overall pick-up distance. To optimize our scheme, we propose to maximize the social welfare under passengers’ privacy requirements. We investigate a bipartite matching based approach. A theoretical bound on the matching performance under specific privacy requirements is shown. Besides passengers’ privacy, we allow drivers to set up their maximum pick-up distance in our extended scheme. The extended scheme could be applied when the number of drivers exceeds the number of passengers. Nevertheless, the global matching based scheme does not consider the interest of each individual passenger. The passengers with low privacy requirements may be matched with drivers far from them. To this end, a pricing scheme including three strategies is proposed to make up for the individual loss by allocating discounts on their riding fares. Extensive experiments on both real-world and synthetic datasets show the efficiency of our scheme.