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This paper proposes a three‐stage algorithm based on clustering decomposition and task allocation—improved clustering planning algorithm (iK‐iD‐N), aiming at the optimization task allocation problem of drones in actual application to meet the task demand constraints. The algorithm solves the problem of the number of drones demanded and the initial delivery range of each drone by introducing dual‐objective planning into the clustering decomposition. Combining improved Dijkstra algorithm (iK‐D) with neighbourhood insertion algorithm into task allocation, to get high‐quality solutions and solve efficiently. Compared with the existing ant colony algorithm, the iK‐iD‐N algorithm proposed in this paper is more efficient and can obtain the best and stable solutions while evenly distributing tasks. Then it is compared with the improved clustering algorithm combined with the basic iK‐D to get better solutions of the iK‐iD‐N algorithm at any time, and compared with the basic clustering algorithm with the improved task allocation algorithm (K‐iD‐N) that iK‐ iD‐N can get a better solution with high probability. The thesis also simulates and analyzes the impact of uncertainty requirements on the solutions based on drone demand and task allocation models, and discusses the impact of drone load capability and endurance capability constraints on the final solutions. [ABSTRACT FROM AUTHOR]

Various researches have been conducted to discover the machinery that led to the evolvement of non-symmetric formation of groups by uncountable marine animals. The huge of tasks comes per unit of time brought obstacles to assign each to particular server, while task assignment have needed a fast strategy to make decision. Artificial fish affect the environment through their behavior and the behavior of their peers. Creating a synthetic fish model has two parts: variables and functions which could be used for task assignment. This paper present improved fish swarm algorithm (IFSA) for task assignment to reduce the latency in cloud computing that could achieve one green computing goals. The research trying to reduce the pending job numbers compared with exist research. [ABSTRACT FROM AUTHOR]

This paper proposes a quantum-inspired evolutionary algorithm (QiEA) to solve an optimal service-matching task-assignment problem. Our proposed algorithm comes with the advantage of generating always feasible population individuals and, thus, eliminating the necessity for a repair step. That is, with respect to other quantum-inspired evolutionary algorithms, our proposed QiEA algorithm presents a new way of collapsing the quantum state that integrates the problem constraints in order to avoid later adjusting operations of the system to make it feasible. This results in lower computations and also faster convergence. We compare our proposed QiEA algorithm with three commonly used benchmark methods: the greedy algorithm, Hungarian method and Simplex, in five different case studies. The results show that the quantum approach presents better scalability and interesting properties that can be used in a wider class of assignment problems where the matching is not perfect. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min–max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal–dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min–max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time. [ABSTRACT FROM AUTHOR]

A number of scheduling algorithms have been proposed for real-time parallel tasks modeled as Directed Acyclic Graphs (DAGs). Many of them focus on scheduling DAG tasks with implicit deadlines. Fewer studies have considered DAG tasks with constrained deadlines or arbitrary deadlines. In this study, we propose a scheduling strategy based on fluid scheduling theory and we target DAG tasks with constrained or arbitrary deadlines. We prove that the proposed algorithm has a capacity augmentation bound of $\frac{1}{2}(1+\beta +\sqrt{(1+\beta)^2-\frac{4}{m}})$ 1 2 (1 + β + (1 + β) 2 - 4 m ) when scheduling multiple DAG tasks with constrained deadlines, in which $m$ m is the number of processors and $\beta$ β is the maximum ratio of task period to deadline. This value is lower than the current best result $\beta +2\sqrt{(\beta +1-\frac{1}{m})(1-\frac{1}{m})}$ β + 2 (β + 1 - 1 m) (1 - 1 m) . We also prove that a capacity augmentation bound of $\frac{1}{2}(1+\sqrt{2}+\sqrt{(1+\sqrt{2})^2-\frac{4\sqrt{2}}{m}})$ 1 2 (1 + 2 + (1 + 2) 2 - 4 2 m ) is guaranteed by our algorithm in the case of scheduling multiple DAG tasks with deadlines greater than periods. To the best of our knowledge, this is the first capacity augmentation bound that has been proven for scheduling multiple DAG tasks with deadlines greater than periods. Our experiments show that our algorithm outperforms the state of the art scheduling algorithms in the percentage of schedulable task sets. [ABSTRACT FROM AUTHOR]

In this work, we extend the topology-based framework and method for the quantification and classification of general resilient asynchronous complexity. We present the arbitrary resilient asynchronous complexity theorem, applied to decision tasks in an iterated delayed model which is based on a series of communicating objects, each of which mainly consists of the delayed algorithm. In order to do this, we first introduce two topological structures, delayed complex and reduced delayed complex, and build the topological computability model, and then investigate some properties of those structures and the computing power of that model. Our theorem states that the time complexity of any arbitrary resilient asynchronous algorithm is proportional to the level of a reduced delayed complex necessary to allow a simplicial map from a task's input complex to its output complex. As an application, we use it to derive the bounds on time complexity to approximate agreement with n + 1 processes. [ABSTRACT FROM AUTHOR]

Mutual information-based filtering feature selection algorithms are often limited to the metric of mutual information. In order to circumvent the limitations of adopting only mutual information, a distance metric-based algorithm RReliefF is introduced on the basis of mutual information to obtain better filtering criteria. RReliefF is used for the classification tasks to measure the relevance between features and labels. In addition, maximal information coefficient(MIC) is used to measure the redundancy between features and the relevance between features and labels. Finally, entropy weight method is applied to objectively weigh the MIC and RReliefF. On this basis, a filtering feature selection algorithm based on entropy weight method(FFSBEWM) is proposed. Comparing experiments carried out on 13 data sets show that the average classification accuracy and highest classification accuracy of the feature subsets selected by the proposed algorithm are higher than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

At present, most link-prediction based models on anomaly detection in social networks lack the ability to consider the influence of abnormal nodes evolution. With the limitation of network scale and complexity, the detection efficiency of traditional models is generally low. To address these issues, this paper proposed an anomaly detection method based on the spatial scale coarse granulation and weighting mechanism on abnormal nodes. Firstly, the method introduced a cohesive community discovery algorithm, Louvain algorithm, to coarsely granulate process to streamline network. Subsequently, it identified abnormal nodes with different evolution behaviors in the processed network following the quantification of abnormal evolution process. Finally, it applied the link prediction method combined with a weighting mechanism of abnormal nodes for final abnormal detection. Compared with different LinkEvent-based strategy adjustment algorithms and NESO_ED method on three real social network data sets VAST, Email-EU ( dept1 and dept2 ) and Enron, the proposed method outperforms other state-of-the-art methods, can take into account both stability and sensitivity on anomaly detection tasks and more reasonably describe the network evolution process. [ABSTRACT FROM AUTHOR]

Mutual information-based filtering feature selection algorithms are often limited to the metric of mutual information. In order to circumvent the limitations of adopting only mutual information， a distance metric-based algorithm RReliefF is introduced on the basis of mutual information to obtain better filtering criteria. RReliefF is used for the classification tasks to measure the relevance between features and labels. In addition， maximal information coefficient(MIC) is used to measure the redundancy between features and the relevance between features and labels. Finally， entropy weight method is applied to objectively weigh the MIC and RReliefF. On this basis， a filtering feature selection algorithm based on entropy weight method(FFSBEWM) is proposed. Comparing experiments carried out on 13 data sets show that the average classification accuracy and highest classification accuracy of the feature subsets selected by the proposed algorithm are higher than those of the comparison algorithms. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

With the advancement of networking technologies and large-scale social computing, multi-user collaboration is becoming popular. In large-scale and dynamic collaborative environments, users may arbitrarily join or quit a collaboration group and work on common tasks for a long time. When users synchronize their work with the other collaborators, a large number of concurrent updates from the collaborators must be reconciled or merged to produce a consistent document state, which could challenge existing consistency maintenance approaches. Our idea is to accelerate the commutative replicated data type (CRDT)-based consistency maintenance algorithms with multi-core processors. We theoretically prove a general commutative condition for CRDT operations. One typical CRDT algorithm, replicated growable array (RGA), is transformed into Parallel RGA (PRGA) under the guidance of the general commutative condition. PRGA allows a batch of RGA operations to be executed in parallel. Experimental results showed that when processing millions of concurrent updates, PRGA achieves approximately 4 × performance gains over RGA on an ordinary four-core processor. [ABSTRACT FROM AUTHOR]

Efficiently implementing the divide-and-conquer pattern of parallelism in distributed memory systems is very relevant, given its ubiquity, and difficult, given its recursive nature and the need to exchange tasks and data among the processors. This task is noticeably further complicated in the presence of multi-core systems, where hybrid parallelism must be exploited to attain the best performance, and when unbalanced and deep workloads are considered, as additional measures must be taken to load balance and avoid deep recursion problems. In this manuscript a parallel skeleton that fulfills all these requirements while providing high levels of usability is presented. In fact, the evaluation shows that our proposal is on average 415.32% faster than MPI codes and 229.18% faster than MPI + OpenMP benchmarks, while offering an average improvement in the programmability metrics of 131.04% over MPI alternatives and 155.18% over MPI + OpenMP solutions. [ABSTRACT FROM AUTHOR]

In recent years, there have been constant disputes over college student cadres. Starting from the public opinion crisis of College Students' autonomy and using the "K-core" algorithm, this paper found that the college student cadres could not realize their own correct roles. The fuzzy cognitive map (FCM) model was used to analyze 49 influencing factors of N University's retreat action. It is found that the five factors of mass support, ability, ideology and morality, work tasks, and performance evaluation are important factors leading to the retreat action. This research proposes role reconstruction and system rectification strategies for the five variables in order to achieve a true feeling of student autonomy and good governance. [ABSTRACT FROM AUTHOR]

In order to fully mine nodes' features and better integrate these features simultaneously in the heterogeneous information network, this paper proposed a AMF L& GRec Firstly, AMFL & GRec used the Leader Rank algorithm to extract the target node' global sequence, and used a meta-path-bas ed heterogeneous information network embedding model to extract the node' local sequence,and used the skip-gram model to learn the node' global and local features. And then it used the self-attention mechanism to learn the preference of the target nodes' local and global features to obtain the feature representation of the target node in a single meta-path. Secondly, it used the self-attention mechanism to fuse the representation of the same node under different meta-paths to obtain the final feature representation. Finally, it utilized a multi-layer perceptron to achieve the task of rating prediction. This paper conducted a large number of experiments on two real datasets. The experimental results verify that the AMFL & GRec algorithm can not only capture the micro(local) structure of densely connected nodes, but also capture the global structure of the node in the network, and finally obtain nodes' overall (local +global) characteristics. At the same time, the experimental results also prove that the AMFL & GRec's rating prediction performance is better than the baselines. It proves that in the heterogeneous information network utilizing the self-attention mechanism to consider the nodes' preferences for local and global features and meta-paths can improve the accuracy of rating prediction. [ABSTRACT FROM AUTHOR]

In attribute network, the attribute information associated with nodes is essential to improve the performance of network embedding tasks. Nevertheless, network is a graph structure, in which nodes not only contain attribute information but also embrace the rich structural information. In order to make full use of the structural information, firstly, this paper defined influential node characteristics, spatial relationships, and constructed similarity matrix based on the definition of link prediction. Then it mapped correlation similarity vector associated with nodes in the binary group to the relationship space of the adjacency matrix, so as to maintain the node vector matrix structure information feature. Based on the definition of normalized graph Laplacian, it fused the attribute information and label feature and integrated the above three kinds of information into an optimization framework. Finally, it inferenced the projection matrix by calculating the local maximum value through a second order derivative. Experimental results indicate that the proposed algorithm can effectively utilize information of the adjacency structure with the binary group of nodes, and compared with other benchmark network embedding algorithms, it also can achieve better results on the node classification task. [ABSTRACT FROM AUTHOR]

Well connected users are generally discovered in communities which is one of the most important tasks for network data analytics and has tremendous real applications. In recent years, community search in attributed graphs has begun to attract attention, which aims to find communities that are both structure and attribute cohesive. Meanwhile, searching a community that is structure cohesive but attribute diversified, denoted as attribute diversified community search, is still at an early stage. In this paper, we introduce our recent effort for discovering attribute diversified community. In fact, for different applications, the needs of attribute diversification for modelling the community are quite different. We introduce three attribute diversified community models in which attribute diversification takes different roles for presenting as an objective and as a constraint. We also discuss major techniques for speeding up the attribute diversified community search. We conduct extensive experiments to show the effectiveness and efficiency of our algorithms for finding attribute diversified communities in various settings. [ABSTRACT FROM AUTHOR]

Task allocation is a critical issue of spatial crowdsourcing. Although the batching strategy performs better than the real-time matching mode, it still has the following two drawbacks: (1) Because the granularity of the batch size set obtained by batching is too coarse, it will result in poor matching accuracy. However, roughly designing the batch size for all possible delays will result in a large computational overhead. (2) Ignoring non-stationary factors will lead to a change in optimal batch size that cannot be found as soon as possible. Therefore, this paper proposes a fine-grained, batching-based task allocation algorithm (FGBTA), considering non-stationary setting. In the batch method, the algorithm first uses variable step size to allow for fine-grained exploration within the predicted value given by the multi-armed bandit (MAB) algorithm and uses the results of pseudo-matching to calculate the batch utility. Then, the batch size with higher utility is selected, and the exact maximum weight matching algorithm is used to obtain the allocation result within the batch. In order to cope with the non-stationary changes, we use the sliding window (SW) method to retain the latest batch utility and discard the historical information that is too far away, so as to finally achieve refined batching and adapt to temporal changes. In addition, we also take into account the benefits of requesters, workers, and the platform. Experiments on real data and synthetic data show that this method can accomplish the task assignment of spatial crowdsourcing effectively and can adapt to the non-stationary setting as soon as possible. This paper mainly focuses on the spatial crowdsourcing task of ride-hailing. [ABSTRACT FROM AUTHOR]

Due to the limited computing resources and very different capabilities of the space-earth integration network, the processing power of complex tasks is not strong and important tasks fail to be processed. Therefore, a three-layer computational offloading overhead model that offloads tasks to local-backbone-edge access nodes was established and the optimal offloading strategy was formulated through the optimal offloading algorithm based on DQN ( deep Q-leaming network). Firstly, based on the characteristics of the three types of computing nodes( offloading sites) in the network, including the space-based backbone nodes, edge access nodes and ground-based backbone nodes, the expressions of the delay, energy consumption and the corresponding expressions of different offload sites were given. Then, based on this, the DQN algorithm was proposed to complete the low-delay, low-energy offloading process. Finally, simulation results show that the DQN algorithm can improve the speed of task execution, reduce the energy consumption of terminal equipment, and effectively improve the current situation of computing node resources in the network. [ABSTRACT FROM AUTHOR]

Estimating the reliability of cognitive task datasets is commonly done via split-half methods. We review four methods that differ in how the trials are split into parts: a first-second half split, an odd-even trial split, a permutated split, and a Monte Carlo-based split. Additionally, each splitting method could be combined with stratification by task design. These methods are reviewed in terms of the degree to which they are confounded with four effects that may occur in cognitive tasks: effects of time, task design, trial sampling, and non-linear scoring. Based on the theoretical review, we recommend Monte Carlo splitting (possibly in combination with stratification by task design) as being the most robust method with respect to the four confounds considered. Next, we estimated the reliabilities of the main outcome variables from four cognitive task datasets, each (typically) scored with a different non-linear algorithm, by systematically applying each splitting method. Differences between methods were interpreted in terms of confounding effects inflating or attenuating reliability estimates. For three task datasets, our findings were consistent with our model of confounding effects. Evidence for confounding effects was strong for time and task design and weak for non-linear scoring. When confounding effects occurred, they attenuated reliability estimates. For one task dataset, findings were inconsistent with our model but they may offer indicators for assessing whether a split-half reliability estimate is appropriate. Additionally, we make suggestions on further research of reliability estimation, supported by a compendium R package that implements each of the splitting methods reviewed here. [ABSTRACT FROM AUTHOR]

Traditional graph regularization methods use Euclidean distance to measure the similarity of sample space, and can not accurately preserve the neighborhood information of complex data sets, which easily lead to the degradation of the generalization performance of the model in complex shape data and non convex data sets. This paper proposed an improved graph regularization algorithm, which used isometric feature mapping to preserve the neighborhood information of the sample space and help the model learn manifold. The simultaneous used of KL constraints further smoothed the external structure of the data representation, thereby capturing more sparse and advanced feature representations. Experimental results on MNIST and YaleB datasets show that compared with several popular feature extraction algorithms, the proposed algorithm can extract more meaningful and robust features. It has advantages in classification and clustering tasks, and also has better anti-interference capability. [ABSTRACT FROM AUTHOR]

The computing power of mobile devices is too limited to execute computation tasks fast. Mobile edge computing (MEC) allows mobile devices to offload tasks to near servers to reduce the completion time of the tasks (a.k.a makespan). The input data of some critical tasks should be encrypted before the offloading. Aiming at the security critical tasks in the MEC composed of multiple servers, this paper addresses to minimize the makespan by scheduling security-critical tasks. We provide the formulation of the problem which is generally an integer programming problem, and three effective composite heuristics CH1–CH3 are proposed to solve the problem. Task permutations are considered as solutions. We construct a greedy heuristic algorithm to calculate the value of the objective. These three composite heuristics consist of two phases: solution initialization and solution improvement. In the first phase, the solutions of all the proposed algorithms are generated by a task arrangement rule called Biggest data Task First (BTF), and then in the second phase, improved by three searching methods based on different neighborhoods including a insertion neighborhood, a swap neighborhood and a hybrid neighborhood, respectively. Experimental results show that CH1–CH3 outperform the well-known RoundRobin algorithm. Particularly, BTF is demonstrated to initialize highly qualified solutions, making contributions to the high effectiveness. Meanwhile, all the improvement methods are justified to be effective and the method based on the hybrid neighborhood achieves the best effectiveness. [ABSTRACT FROM AUTHOR]

Cloud computing will continue to play a critical role due to its ability to deliver various types of on-demand information technology (IT) services according to customer needs. Unfortunately, cloud computing has faced many challenges. The task scheduling problem is considered one of the main challenges because the user has to pay for a resource. Therefore, a good mapping of users' tasks on the available resources is required to reduce the total execution time of the users' tasks (i.e., reduce makespan), execution cost, enhance load balancing, increase resource utilization, etc. Many heuristic algorithms have been existed to solve task scheduling problems. Although, the Heterogeneous Earliest Finish Time (HEFT) heuristic algorithm is considered one of the best task scheduling algorithms in a heterogeneous environment, it does not concern load balancing. In this paper, a modification has been introduced to the HEFT algorithm to satisfy load balancing. The modified algorithm is called Load Balancing HEFT (LB-HEFT). A comparative study has been done among the proposed LB-HEFT, the Enhancement Heterogeneous Earliest Finish Time (E-HEFT), and HEFT algorithms to evaluate the performance of the proposed LB-HEFT algorithm. According to the comparative results, it is found that the proposed LB-HEFT algorithm outperforms the existing E-HEFT and HEFT algorithms by improving load balancing by 43.49% and 72.59%, respectively in average, enhancing resource utilization by 2.28% and 5.61%, respectively in average, and reducing makespan by 7.55%, and 3.75% respectively in average. [ABSTRACT FROM AUTHOR]

This study deals with the task assignment problem in a heterogeneous unmanned aerial vehicle system under the multiple constraints of limited resources, effective task execution scope, obstacle avoidance and task priority. Considering the nonlinearity, multimoding and high complexity of the task assignment problem, many existing optimization methods may fall into local optimum and even fail to provide satisfactory solutions. To address the issues, the discrete adaptive search whale optimization algorithm is proposed. First, we present the obstacle-avoiding distance estimation method to provide precise task scenario information and reduce the computational complexity. Then, the crossover-based updating method is presented to enable the algorithm to solve discrete problems such as task assignment. In addition, the search intensity adaptive mechanism is presented to optimize and balance the exploration and exploitation intensities in the solution space reasonably, thereby achieving high-quality task assignment results quickly. Further, joint leadership mechanism and local search mechanism are proposed to enhance the exploration ability of the algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results. [ABSTRACT FROM AUTHOR]

Maintaining the quality of service (QoS) related parameters is an important issue in cloud management systems. The lack of such QoS parameters discourages cloud users from using the services of cloud service providers. The proposed task scheduling algorithms consider QoS parameters such as the latency, make-span, and load balancing to satisfy the user requirements. These parameters cannot sufficiently guarantee the desired user experience or that a task will be completed within a predetermined time. Therefore, this study considered the cost-enabled QoS-aware task (job) scheduling algorithm to enhance user satisfaction and maximize the profit of commercial cloud providers. The proposed scheduling algorithm estimates the cost-enabled QoS metrics of the virtual resources available from the unified resource layer in real-time. Moreover, the virtual machine (VM) manager frequently updates the current state-of-the art information about resources in the proposed scheduler to make appropriate decisions. Hence, the proposed approach guarantees profit for cloud providers in addition to providing QoS parameters such as make-span, cloud utilization, and cloud utility, as demonstrated through a comparison with existing time-and cost-based task scheduling algorithms. [ABSTRACT FROM AUTHOR]

Recent advancements of virtualization technologies for parallel processing involve scheduling containerized tasks in a workflow. Since a container can include multiple tasks, it can be reused or shared among applications. If every task in a workflow uses its dedicated container without sharing among any tasks, each container image must be downloaded for each task. As a result, many computational resources are required to process and the communication latency related to container image downloading can become a bottleneck for the makespan. In task scheduling algorithms for workflows, this characteristic produces a new challenging issue that how effectively shares containers among tasks to avoid redundant container image download processes and redundant task allocations. One of the fundamental problems is that no policy has been established for simultaneously satisfying effective container sharing, maintaining the degree of task parallelism, and effective computational resource utilization. In this paper, we propose a clustering-based containerized task scheduling algorithm for clouds, namely, shareable functional task clustering for utilizing virtualized resources (SF-CUV). The objective of SF-CUV is to minimize the makespan with less computational resources and containers than other algorithms by clustering tasks and sharing each container among tasks. SF-CUV consists of two phases: (i)task clustering and pre-virtual CPU (vCPU) allocation phase to derive an accurate scheduling priority, and (ii)task ordering and actual task reallocation phase. Experimental results obtained via simulation and in a real environment show that SF-CUV can utilize both vCPUs and containers with a shorter makespan compared with other approaches. [ABSTRACT FROM AUTHOR]

This paper considers the task allocation problem under the requirement that the assignments of some critical tasks must be maximized when the network capacity cannot accommodate all tasks due to the limited capacity for each unmanned aerial vehicle (UAV). To solve this problem, this paper proposes an extended performance impact algorithm with critical tasks (EPIAC) based on the traditional performance impact algorithm. A novel task list resizing phase is developed in EPIAC to deal with the constraint on the limited capacity of each UAV and maximize the assignments of critical tasks. Numerical simulations demonstrate the outstanding performance of EPIAC compared with other algorithms. [ABSTRACT FROM AUTHOR]

The problem of precisely computing the worst-case blocking time that tasks may experience is one of the fundamental issues of schedulability analysis of real-time applications. While exact methods have been proposed for more sophisticated protocols, the problem is indeed complex in case of the Priority Inheritance Protocol, even restricting the attention to uniprocessor systems, non-nested resource accesses, and non-self-suspending tasks. Besides a very simple method leading in general to loose upper bounds, only one algorithm of exponential complexity has been so far reported in literature to tighten such bounds. In this article, we describe a novel approach which, leveraging an operational research technique for modeling the problem, computes the same tight bounds in polynomial time. We then discuss the scenarios in which, assuming no conditional statements in the tasks’ code, the computed bounds derive from an actually impossible blocking chain, and we refine the initial model to more precisely compute the worst-case blocking times for any task set in any possible operating condition. [ABSTRACT FROM AUTHOR]

Meta reinforcement learning（Meta-RL）aims at automatically learning induction bias for a new reinforcement learning task from a set of different but related tasks. It plays an important role in improving the sample efficiency and generalization of reinforcement learning algorithm in difficult scenarios. This paper first introduces a framework in which three key components of Meta-RL are identified，i.e.，learning experience （related tasks），inductive bias and learning objective. Based on this，current research progress in this field is analyzed and reviewed，and the principles and characteristics of several representative algorithms are described. The paper also gives a detailed account of commonly used benchmark environments and performance evaluation methods for meta-RL. The limitation of current research and potential future development directions are also discussed. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low data quality and high incentive costs caused by the low enthusiasm of participants in mobile crowd sensing, a new task recommendation framework is proposed in this paper. First, the participants' historical behaviors are analyzed, assuming that user behaviors can be quantified as the user's willingness to participate, and the cosine similarity theorem is used to calculate the similarity between participants, thereby constructing a user-hybrid model. Secondly, probabilistic matrix factorization is developed to predict the willingness of participants, and a ranking model is obtained through learn-to-rank algorithm. Finally, a task recommendation list is generated according to the ranking model, which serves as the target participant's preferred task list for sensing task recommendation. The experiment in this paper is carried out on the MATLAB platform based on two real check-in datasets, Gowalla and Brightkite. The results show that the average allocation precision rate can reach 96%, and the sensing user participation rate is about 97%. Meantime, the user's mobile cost is reduced, and the overall goal of maximizing accuracy and minimizing perceived cost is achieved. [ABSTRACT FROM AUTHOR]

• Addressing the worst-case performance of labeling methods like the Coffman-Graham algorithm. • Focus on delayed precedence constraints in the single- and multi-processor case. • New lower bounds on worst-case approximation guarantees are derived. • The results extend to more general level-based or critical-path scheduling techniques. • An open problem by Bernstein, Rodeh, and Gertner from 1989 is resolved. There is some evidence that labeling schemes as employed for instance in the famous Coffman-Graham algorithm may provide superior worst-case approximation guarantees than purely path- or level-based list schedules in the presence of (delayed) precedence constraints. In 1989, Bernstein, Rodeh, and Gertner proved that this also holds true for their labeling scheme targeting unit execution time tasks on a single processor provided that all delays imposed by a single task on all of its successors are uniformly either zero or some fixed positive integer. They further conjectured that this superiority is preserved when allowing the delays imposed by a task to differ successor-wise. It is shown in this note however that their labeling scheme as well as more general ones may perform as bad as any list schedule in this case. Moreover, a new lower bound on the worst-case performance of labeling methods in the multiprocessor setting is derived. [ABSTRACT FROM AUTHOR]

To create a precise model structure and perform fault monitoring algorithms for a wide range of complex systems along with dynamic behavioural characteristics, the causal graph-based methods were considered herein. In this paper, a new scheme was devised based on fast fault detection mechanism relying on the Directed Hypergraph Observer model. The performance of the suggested pattern is illustrated by a case study including systems with single energy. Noting that the modern methods possess a large range of applications for the reliability and energy effectiveness analysis related to multi-energy systems. The Directed Hypergraph model architecture was exploited to generate the diagnostic condition based on a graphical observer. [ABSTRACT FROM AUTHOR]

Over the past decades, learning to rank (LTR) algorithms have been gradually applied to bioinformatics. Such methods have shown significant advantages in multiple research tasks in this field. Therefore, it is necessary to summarize and discuss the application of these algorithms so that these algorithms are convenient and contribute to bioinformatics. In this paper, the characteristics of LTR algorithms and their strengths over other types of algorithms are analyzed based on the application of multiple perspectives in bioinformatics. Finally, the paper further discusses the shortcomings of the LTR algorithms, the methods and means to better use the algorithms and some open problems that currently exist. [ABSTRACT FROM AUTHOR]

With the explosive growth in the scale of cloud computing infrastructures, reliability and energy efficiency have become important concerns considering the great complexity of cloud data centers. There is an urgent need for efficient task assignment that can dispatch tasks to appropriate cloud data center servers, which is critical to achieve reliability and energy efficiency in current cloud data centers. Most of the research on task assignment focuses on only one of the objectives of reliability and energy efficiency, while the two objectives are intrinsically conflicting with each other. In this paper, we deal with the problem of task assignment in data centers, with the objective of minimizing the energy consumption while providing failure tolerance to task execution failure. We propose a reliability-aware and energy-efficient task replica assignment algorithm based on running task replicas at a low speed and enabling multiple task replicas to share the same server resources. Each task in a job processed by the cloud computing platform has two instances: main task and task replica (shadow). Each main task runs on an individual server, and the task replica associated with the main task is assigned on a different server. The main tasks run at the full server speed, while the task replicas run at a lower rate than the main tasks. The task replicas can be mapped onto dedicated backup servers or be assigned to the servers on which the main tasks are running. Multiple task replicas can share the same server resources to reduce the number of servers required. We conduct experiments through simulations. Experimental results demonstrate that the proposed algorithm can effectively reduce the energy consumption, while achieving a good balance between the number of servers used and job completion time. [ABSTRACT FROM AUTHOR]

This research paper proposes a novel approach named priority-based load balancing (PLB) for cloud computing environment. The PLB provides a resilient and adaptive task scheduling using multi-queues. Numerous strategies have already been proposed in the past researches to prioritize the tasks and mapping all the tasks to different resources available on the cloud. There is still a hindrance in the performance due to the negligible attention paid to the unused resources and tasks having low priority, eventually leading to starvation problem. To this end, the PLB algorithm has been partitioned into four sub-procedures, namely (i) Starvation-free task allocation, (ii) Inserting tasks into the dispatcher, (iii) Re-ordering tasks inside the queues and eventually, (iv) Mapping tasks onto the Virtual Machines (VMs) calculating the cost incurred for all the corresponding VMs. The sole motivation of this research work is to optimize the performance parameters by allocating all the jobs to all the available resources in the workflow model. It also consolidates the job categorization in the priority-based multi-queues, while filtering tasks from all the queues to overcome the deprivation of low priority tasks. In this paper, a test-bed setup has been deployed using CloudSim 3 and TCS WAN emulator for experimentation and results evaluation. The experimental setup imbibes different aspects such as performance measures, average response time, makespan time in order to ascertain efficiency, resource utilization ratio and bandwidth of the workflow model. The obtained results are further compared with five different approaches including- First Come First Serve, Round Robin, Min–Min, Max–Min and ACO and it was observed that the proposed strategy yielded more efficiency and accuracy in most of the cases. The experimental results have been further validated and demonstrated in order to justify the claims of the proposed approach, being able to tackle out different priority tasks and resource allocation in a stable and optimum manner. [ABSTRACT FROM AUTHOR]

The use of Q-learning methods in multi-robot systems is a challenging area. Multi-robot systems have dynamic and partially observable nature because of robot's independent decision-making and acting mechanisms. Whereas, Q-learning is defined on Markovian environments theoretically. One way to apply Q-learning in multi robot systems is centralized learning. It learns optimal Q-values for state space of overall system and joint action spaces of all agents. In this case, the system can be considered as stationary and optimal solutions can be converged. But, centralized learning requires full knowledge of the environment, perfect inter-robot communication and good computational power. Especially for large systems, the computational cost becomes huge because of exponentially growing learning space size with the number of robots. The proposed approach in this study, subG-CQL, divides the overall system into small-sized sub-groups without adversely affecting the system's task performing abilities. Each sub-group consists of less number of robots performing less tasks and learns in centralized manner for its own team. So, the learning space dimension is reduced to a reasonable level and required communication remains limited to the robots in the same the sub-group. Due the centralized learning is used, it is expected that the successful results are achieved. Experimental studies show that the proposed algorithm provides increase in the task assignment performance of the system and efficient use of system resources. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of the existing task allocation strategies, this paper proposed a multi-task allocation strategy based on deferred acceptance in mobile crowd sensing. Firstly, based on the different needs of platform and workers, this paper proposed a method to achieve the two-side matching of tasks and workers. Secondly, considering the urgency of the task, this paper designed a hybrid deferred acceptance algorithm. Finally, in order to verify the performance of the proposed strategy, it was compared with the two baseline task allocation methods. Experimental results show that the proposed task allocation strategy is effective in improving platform utility and worker satisfaction. [ABSTRACT FROM AUTHOR]

Task scheduling is important in cloud manufacturing because of customers' increasingly individualized demands. However, when various changes occur, a previous optimal schedule may become non-optimal or even infeasible owing to the uncertainty of the real manufacturing environment where dynamic task arrival over time is a vital source. In this paper, we propose a novel collaborative task scheduling (CTS) model dealing with new task arrival which considers multi-supply chain collaboration. We present an improved multi-population biogeography-based optimization (IMPBBO) algorithm that uses a matrix-based solution representation and integrates the multi-population strategy, local search for the best solution, and the collaboration mechanism, for determining the optimal schedule. A series of experiments are conducted for verifying the effectiveness of the IMPBBO algorithm for solving the CTS model by comparing it with five other algorithms. The experimental results concerning average best values obtained by the IMPBBO algorithm are better than that obtained by comparison algorithms for 41 out of 45 cases, showing its superior performance. Wilcoxon-test has been employed to strengthen the fact that IMPBBO algorithm performs better than five comparison algorithms. [ABSTRACT FROM AUTHOR]

Motivation Pairwise comparison problems arise in many areas of science. In genomics, datasets are already large and getting larger, and so operations that require pairwise comparisons—either on pairs of SNPs or pairs of individuals—are extremely computationally challenging. We propose a generic algorithm for addressing pairwise comparison problems that breaks a large problem (of order n 2 comparisons) into multiple smaller ones (each of order n comparisons), allowing for massive parallelization. Results We demonstrated that this approach is very efficient for calling identical by descent (IBD) segments between all pairs of individuals in the UK Biobank dataset, with a 250-fold savings in time and 750-fold savings in memory over the standard approach to detecting such segments across the full dataset. This efficiency should extend to other methods of IBD calling and, more generally, to other pairwise comparison tasks in genomics or other areas of science. Availability and Implementation A GitHub page is available at https://github.com/emmanuelsapin with the code to generate data needed for the implementation [ABSTRACT FROM AUTHOR]

For the large-scale search and rescue (S&R) scenarios, the centralized and distributed multi-UAV multitask assignment algorithms for multi-UAV systems have the problems of heavy computational load and massive communication burden, which make it hard to guarantee the effectiveness and convergence speed of their task assignment results. To address this issue, this paper proposes a hierarchical task assignment strategy. Firstly, a model decoupling algorithm based on density clustering and negotiation mechanism is raised to decompose the large-scale task assignment problem into several nonintersection and complete small-scale task assignment problems, which effectively reduces the required computational amount and communication cost. Then, a cluster head selection method based on multiattribute decision is put forward to select the cluster head for each UAV team. These cluster heads will communicate with the central control station about the latest assignment information to guarantee the completion of S&R mission. At last, considering that a few targets cannot be effectively allocated due to UAVs' limited and unbalanced resources, an auction-based task sharing scheme among UAV teams is presented to guarantee the mission coverage of the multi-UAV system. Simulation results and analyses comprehensively verify the feasibility and effectiveness of the proposed hierarchical task assignment strategy in large-scale S&R scenarios with dispersed clustering targets. [ABSTRACT FROM AUTHOR]

The vision of Industry 4.0 is to materialize the notion of a lot-size of one through enhanced adaptability and resilience of manufacturing and logistics operations to dynamic changes or deviations on the shop floor. This article is motivated by the lack of formal methods for efficient transfer of knowledge across different yet interrelated tasks, with special reference to collaborative robotic operations such as material handling, machine tending, assembly, and inspection. We propose a meta reinforcement learning framework to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules. Our experiments on the OpenAI Gym Robotics environments Reach, Push, and Pick-and-Place indicate an average 75% reduction in the number of iterations to achieve a 60% success rate as well as a 50%-80% improvement in task completion efficiency, compared to the deep deterministic policy gradient (DDPG) algorithm as a baseline. The significant improvements achieved in the jumpstart and asymptotic performance of the robot create new opportunities for investigating the current limitations of learning robots in industrial settings, associated with sample inefficiency and specialization on one task through modularization and transfer learning. [ABSTRACT FROM AUTHOR]

Finger tapping is a task widely used in a variety of experimental paradigms, in particular to understand sensorimotor synchronization and time processing in the range of hundreds of milliseconds (millisecond timing). Normally, subjects do not receive any instruction about what to attend to and the results are seldom interpreted taking into account the possible effects of attention. In this work we show that attention can be oriented to the purely temporal aspects of a paced finger‐tapping task and that it affects performance. Specifically, time‐oriented attention improves the accuracy in paced finger tapping and it also increases the resynchronization efficiency after a period perturbation. We use two markers of the attention level: auditory ERPs and subjective report of the mental workload. In addition, we propose a novel algorithm to separate the auditory, stimulus‐related components from the somatosensory, response‐related ones, which are naturally overlapped in the recorded EEG. [ABSTRACT FROM AUTHOR]

The detection speed of detection algorithms based on artificial design features is generally slow, and the detection accuracy needs to be improved, which can no longer meet the needs of today's industrial production. The detection technology based on deep learning cannot be deployed on resource-constrained devices because it requires a lot of computing and storage space. In response to these problems, this paper cited a channel pruning method to realize the lightweight of the YOL0v3 detection network, and obtained the pruning model Slim YOLOv3, and proposed to use Slim YOLOv3 for real-time detection tasks in industrial scenarios. The method enhanced the channel-level sparsity of the convolutional layer by applying L, regularization to the channel scaling factor, and pruned the feature channels with less information, and obtained a lightweight network model. Compared with the original model, SlimYOLOv3 gives a 60% reduction in model size and a 50% reduction in computing operations, the detection speed is 1. 7 times of the original model. It is more suitable for real-time detection of complex targets in intelligent industrial scenes. [ABSTRACT FROM AUTHOR]

Task allocation plays a vital role in crowd computing by determining its performance. The power of crowd computing stems from a large number of workers potentially available to provide high quality of service and reduce costs. An important challenge in the crowdsourcing market today is the task allocation of crowdsourcing workflows. Task allocation aims to maximize the completion quality of the entire workflow and minimize its total cost. Trust can affect the quality of the produced results and costs. Selecting workers with high levels of trust could provide better solution to the workflow and increase the budget. Crowdsourcing workflow needs to balance the two conflicting objectives. In this paper, we propose an alternative greedy approach with four heuristic strategies to address the issue. In particular, the proposed approach aims to monitor the current status of workflow execution and use heuristic strategies to adjust the parameters of task allocation. We design a two-phase allocation model to accurately match the tasks with workers. T-Aware allocates each task to the worker that maximizes the trust level, while minimizing the cost. We conduct extensive experiments to quantitatively evaluate the proposed algorithms in terms of running time, task failure ratio, trust and cost using a customer objective function on WorkflowSim, a well-known cloud simulation tool. Experimental results based on real-world workflows show that T-Aware outperforms other optimal solutions on finding the tradeoff between trust and cost, which is 3 to 6% better than the best competitor algorithm. [ABSTRACT FROM AUTHOR]

Training text simplification model based on seq2seq requires large-scale parallel corpora. However, current task lacks large-scale and well-labeled parallel corpora. To address the above issues, this paper proposed an unsupervised text simplification algorithm that made the learning of the model only need simple and complex sentence datasets without labels. First, the method used denoising autoencoder to learn from simple sentence corpus and complex sentence corpus, respectively, to obtain a simple sentence autoencoder and a complex sentence autoencoder. Then, it combined the two autoencoders to form an initial text simplification model and a text complication model. Finally, it used back-translation to convert the unsupervised text simplification problem into a supervised problem, and iteratively optimized the text simplification model. Experiments on the standard dataset show that the method is superior to the existing unsupervised model on the general indicators BLEU and SARI, and the model has simplified effects at both the lexical and syntactic level. [ABSTRACT FROM AUTHOR]

Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise with the help of scheduling algorithms to their respective processors. For task assignment, every scheduling algorithm follows different protocols like upper bound of CPU utilization, assigning priorities, etc. In this paper, author has worked on such scheduling algorithms. Previously, the author evaluated the performance of algorithms on the basis of transactions (group of tasks). In this paper, the author re-evaluates joint EDF–RM scheduling algorithm, where its performance is calculated on the execution of individual task basis. For comparative analysis, similar algorithms are considered, i.e., joint EDF–RMS, earliest deadline first (EDF) and rate monotonic scheduling (RMS). These mentioned algorithms are simulated and analyzed with the help of statistical analysis, and turnaround time of periodic tasks is evaluated. Additionally, migration distribution and CPU utilization on the basis of scheduling algorithms' upper bounds are also calculated. [ABSTRACT FROM AUTHOR]

The mixed-criticality system provides multiple real-time applications with different criticalities in a single system. Poor energy-saving performance of the previous studies on mixed-criticality sporadic tasks are mainly due to the fact that the slack time generated from the random arrival of sporadic tasks is not taken into account. In this article, we focus on scheduling energy aware mixed-criticality sporadic tasks and take the random arrival of sporadic tasks into account. We proposed a dynamically frequency updating mixed-criticality algorithm (DFU). DFU based on the earliest deadline first scheme can exploit the slack time generated from high criticality tasks in a low criticality mode to reduce processor frequency. In addition, it also can dynamically update the utilization of sporadic tasks set to further reduce processor frequency. The simulation experiments are conducted to evaluate the performance of DFU and experimental results show that DFU consumes 34.29% less energy than that of the existing algorithms. [ABSTRACT FROM AUTHOR]

Aiming at the problem of the discriminator' s poor classification accuracy on a small number of labeled samples and insufficient robustness to the local perturbation of manifolds in the generative adversarial network, this paper proposed a generative adversarial network based on variable loss and manifold regularization. The algorithm used a variable loss instead of the original discriminator to solve the adverse effect of the poorly trained classifier on the semi-supervised classification task. In addition, on the basis of variable loss of discriminator, it added manifold regular terms to improve the robustness of discriminator to local disturbance by punishing the variation of classification decision of discriminator on manifold. Using the quality of the generated samples and the semi-supervised classification accuracy as the evaluation criteria of the algorithm, it performed numerical experiments on the dataset SVHN and CIFAR-10. Comparing with other semi-supervised algorithms, the results show that the proposed algorithm can obtain higher quality generated samples and higher precision classification results with a small amount of labeled data. [ABSTRACT FROM AUTHOR]