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The trend of drone applications in recent years has begun to change. The application of Drone or UAV (Unmanned Vehicle) brings flexibility in those areas that are difficult to reach or manage. The use of drones enables the structure of a semi-autonomous system, where workers in the performance of many tasks can be replaced by drones, affecting time savings and flexibility at work. This research proposes an efficient algorithm that can be applied to drones to transport materials into the indoor environment. This algorithm optimizes the time and reduces power consumption when sharing and completing tasks between different drones. In this research, the results will be achieved based on the "Earliest Time Algorithm". We have modified this algorithm, where we have managed to get much better results in terms of saving time while performing various tasks from the drone. It achieves by changing the logic of the impact of weight on the lifetime of the drone battery from linear to dynamic. Also, we have implemented the possibility of a partial charge of the drone battery. The experiments are performed in a test environment for various tasks performed by the drone. In this case, the impact of weight on algorithm performance as well as the impact of partial charging on time optimization in performing various tasks from the drone will be tested. The performance of the algorithm is tested and analyzed for three different types of drone tasks depending on the weight the drone carries. The results achieved with our algorithm are compared with the results of the existing algorithm. [ABSTRACT FROM AUTHOR]

Real-time task scheduling is a critical part of systems since processes must finish their work at a certain time. Large data streams require high energy efficiency and rapid response times. Real-time system performance must be improved through task scheduling on resources. Numerous researchers have recently and rapidly proposed enormous efforts with enhancements in numerous task scheduling methods. Round Robin algorithm has been commonly utilized in task scheduling. In this paper, a Modified Median Mean Round Robin (MMMRR) algorithm is proposed to significantly enhance the performance of the Round Robin algorithm. The proposed algorithm finds an ideal dynamic time quantum └(median+ mean)/2)┘ and generated for each round depending on the remaining burst time of the processes. The system performance has been enhanced in terms of waiting time, turnaround time and context switching. The experimental results show that the proposed algorithm outperforms ADRR, HYRR, EDRR, MARR and MMRR algorithms. [ABSTRACT FROM AUTHOR]

SUMMARY: This research article formulates contemporary approach named multi‐objective reliability‐based workflow scheduler. Numerous strategies have been proposed in the past to prioritize and map the tasks to cloud resources. Though the recent studies lead to efficient solutions however they are restrained in terms of performance due to lack of resource consideration based on utilization rate and reliability index. It is crucial to consider reliability parameter while mapping tasks onto the virtual machines and not just the reliability value, but the cost incurred must also be minimized. To this end, the proposed strategy has been categorized into four modules, (i) scrutiny of reliable VMs, (ii) task ranking, (iii) optimizing the task re‐ordering using flower pollination optimization, and (iv) task mapping onto the VM. It intends to map task onto the most suitable machine in terms of makespan, efficiency, and incurred cost. In the experimental setup, four scientific workflows have been considered namely, LIGO, Genome, Cybershake, and Montage, they have been tested on the proposed approach while making comparison with the existing approaches namely FPA, GWO, and GA. The simulation results justified the claims by allocating resources to the cloudlets efficiently and stabilizing all the aforementioned parameters by attaining performance measures adequately. [ABSTRACT FROM AUTHOR]

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]

Partial domain adaptation (PDA) is a framework for mitigating the covariate shift problem when target labels are contained in source labels. For this task, adversarial neural network (ANN) methods proposed in the literature have been proven to be flexible and effective. In this work, we adapt such methods to tackle the more general problem of open-set domain adaptation (OSDA), which further allows the existence of target instances with labels outside the source labels. The aim in OSDA is to mitigate the covariate shift problem and to identify target instances with labels outside the source label space. We show that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and we propose an adaptation for effective OSDA. [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]

The descriptions of complex events usually span sentences, so we need to extract complete event information from the whole document. To address the challenges of document-level event extraction, we propose a novel framework named Document-level Event Extraction as Relation Extraction (DEERE), which is suitable for document-level event extraction tasks without trigger-word labelling. By well-designed task transformation, DEERE remodels event extraction as single-stage relation extraction, which can mitigate error propagation. A long text supported encoder is adopted in the relation extraction model to aware the global context effectively. A fault-tolerant event integration algorithm is designed to improve the prediction accuracy. Experimental results show that our approach advances the SOTA for the ChFinAnn dataset by an average F1-score of 3.7. The code and data are available at https://github.com/maomaotfntfn/DEERE. [ABSTRACT FROM AUTHOR]

Cloud computing is one of the most commonly used infrastructures for carrying out activities using virtual machines known as processing units. One of the most fundamental issues with cloud computing is task scheduling. The optimal determination of scheduling criteria in cloud computing is a non-deterministic polynomial-time (NP)-complete optimization problem, and several procedures to manage this problem have been suggested by researchers in the past. Among these methods, the Heterogeneous Earliest Finish Time (HEFT) algorithm is recognized to produce optimal outcomes in a shorter time period for scheduling tasks in a heterogeneous environment. Literature shows that HEFT gives extraordinary results in terms of quality of schedule and execution time. However, in some cases, the average computation cost and selection of the first idle slot may not produce a good solution. Therefore, here we propose modified versions of the HEFT algorithm that can obtain improved results. In the rank generation phase, we implement different methodologies for calculating ranks, while in the processor selection phase, we modify the way of selecting idle slots for scheduling the tasks. This paper suggests enhanced versions of the HEFT algorithm under user-required financial constraints to minimize the makespan of a specified workflow submission on virtual machines. Our findings also suggest that enhanced versions of the HEFT algorithm perform better than the basic HEFT method in terms of lesser schedule length of the workflow problems running on various virtual machines. [ABSTRACT FROM AUTHOR]

In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user's requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods. [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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Romanian Journal of Information Technology & Automatic Control / Revista Română de Informatică și Automatică is the property of National Institute for Research & Development in Informatics - ICI Bucharest and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Logistics problems lie at the core of industries' everyday operations. These types of problems are also one of the main points of interest in the operational research field of study. In recent years, the workforce scheduling and routing problems (WSRP) class of optimization problems has gained significant attention from researchers. In this paper, a recently proposed WSRP is studied: the multiperiod WSRP with dependent tasks. In this problem, a given company provides services by means of heterogeneously skilled mobile teams. Service requests are known beforehand and each service is composed of one or more activities. Execution of one activity may depend on the completion of another. A feasible schedule in which the requested activities are completed in the minimum amount of days must be provided. Two matheuristic algorithms are proposed. Several new upper bounds for a set of instances from the literature are discovered. [ABSTRACT FROM AUTHOR]

The task allocation process in the Witkey mode is dynamic and open, in which Witkey is a rational person. Due to Witkey's individual rationality, in order to make the result of task allocation stable, task allocation must reach the Nash equilibrium. However, the Nash equilibrium point does not necessarily have the highest total system revenue. In order to make the task allocation result stable and have a high total system revenue, this paper proposes an incentive measure based on integral ranking to improve user participation. When Witkey adopts the best response strategy to select task, the order of participating in the selection will affect the individual income of Witkey to a certain extent. The higher the order, the greater the probability of obtaining higher system income. Based on this idea, a dynamic task allocation algorithm with complex tasks in the Witkey mode is proposed by combining incentive measures with best response strategy and reasonable benefit allocation strategy. The finally simulation results verified the effectiveness of the proposed algorithm, and the impact of incentive measures on the total revenue of the system was also examined. [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]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

In the era of enhancing demand for online resources cloud is emerging as the greatest cutting edge technologies to serve the requirements. With volatility in the usage of the cloud resources, it is a tough task to serve the user requirements. Heuristic algorithms, Meta heuristic algorithms are trending these days, as they are helping to find the nearby optimal solutions within a reasonable time. But they suffer from either slow convergence or with premature solutions. It is evident that NP-complete algorithms take exponential time to find an efficient and optimal solution. This paper hybridizes grey wolf optimization along with Crow search algorithm. This balances the exploration and exploitation. The experimental results prove that the proposed algorithm is on par with existing algorithms and at times it shows better performance. [ABSTRACT FROM AUTHOR]

We consider the problem of fairly dividing a set of indivisible items. Much of the fair division literature assumes that the items are "goods" that yield positive utility for the agents. There is also some work in which the items are "chores" that yield negative utility for the agents. In this paper, we consider a more general scenario in which an agent may have positive or negative utility for each item. This framework captures, e.g., fair task assignment, where agents can experience both positive and negative utility for each task. We demonstrate that whereas some of the positive axiomatic and computational results extend to this more general setting, others do not. We present several new and efficient algorithms for finding fair allocations in this general setting. We also point out several gaps in the literature regarding the existence of allocations that satisfy certain fairness and efficiency properties and examine the complexity of computing such allocations. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

Multi-label classification is the task of inferring the set of unseen instances using the knowledge obtained through the analysis of a set of training examples with known label sets. In this paper, a multi-label classifier fusion ensemble approach named decision templates for ensemble of classifier chains is presented, which is derived from the decision templates method. The proposed method estimates two decision templates per class, one representing the presence of the class and the other representing its absence, based on the same examples used for training the set of classifiers. For each unseen instance, a new decision profile is created and the similarity between the decision templates and the decision profile determines the resulting label set. The method is incorporated into a traditional multi-label classifier algorithm: the ensemble of classifier chains. Empirical evidence indicates that the use of the proposed decision templates adaptation can improve the performance over the traditionally used combining schemes, especially for domains with a large number of instances available, improving the performance of an already high-performing multi-label learning method. [ABSTRACT FROM AUTHOR]

As one of the most popular parallel data processing models, data analysis system MapReduce has been widely used in many fields. Task scheduling is the core module in MapReduce system, and the quality of the scheduling algorithm directly affects the processing capacity of the system. Since new nodes need to be continuously added in the cluster to improve the processing capacity of the cluster, objectively, the heterogeneity of the cluster is caused. Heterogeneous environment is common in practical application scenarios, but there has been little research on task scheduling in heterogeneous environment. For this reason, this paper presents an in-depth study of task scheduling in heterogeneous environment and proposes a new task scheduling algorithm HTD. First, we give a formal definition of the throughput-driven task scheduling problem in a heterogeneous environment. Second, we design the scheduling algorithm HTD, which quickly obtains the completion sequence of a jobs set and optimizes the task scheduling details in heterogeneous environment. Finally, a series of experiments show the efficiency and effectiveness of the algorithm. [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]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of | G | groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl 2) time for k iterations, p repetitions and l tasks. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The effectiveness of the Wolf Pack Algorithm (WPA) in high-dimensional discrete optimization problems has been verified in previous studies; however, it usually takes too long to obtain the best solution. This paper proposes the Multi-Population Parallel Wolf Pack Algorithm (MPPWPA), in which the size of the wolf population is reduced by dividing the population into multiple sub-populations that optimize independently at the same time. Using the approximate average division method, the population is divided into multiple equal mass sub-populations whose better individuals constitute an elite sub-population. Through the elite-mass population distribution, those better individuals are optimized twice by the elite sub-population and mass sub-populations, which can accelerate the convergence. In order to maintain the population diversity, population pretreatment is proposed. The sub-populations migrate according to a constant migration probability and the migration of sub-populations are equivalent to the re-division of the confluent population. Finally, the proposed algorithm is carried out in a synchronous parallel system. Through the simulation experiments on the task assignment of the UAV swarm in three scenarios whose dimensions of solution space are 8, 30 and 150, the MPPWPA is verified as being effective in improving the optimization performance. [ABSTRACT FROM AUTHOR]

The efficiency of task scheduling under cloud computing is related to the effectiveness of users. Aiming at the problems of long scheduling time, high cost consumption, and large virtual machine load in cloud computing task scheduling, an improved scheduling efficiency algorithm (called the improved whale optimization algorithm, referred to as IWC) is proposed. Firstly, a cloud computing task scheduling and distribution model with time, cost, and virtual machines as the main factors is constructed. Secondly, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; in order to better find the optimal individual, we use the inertial weight strategy for the whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence; we use the add operator and delete operator to screen individuals after each iteration which is completed and updated to improve the quality of understanding. In the simulation experiment, IWC was compared with the ant colony algorithm, particle swarm algorithm, and whale optimization algorithm under a different number of tasks. The results showed that the IWC algorithm has good results in terms of task scheduling time, scheduling cost, and virtual machine. The application is in cloud computing task scheduling. [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]

In mobile crowdsensing, it is a challenge to assign tasks to appropriate smartphones. Existing task allocation mechanisms mainly aim at optimizing the global system performance, while ignoring the personal preferences of individual crowdsensing tasks and smartphone users. Nevertheless, in an open crowdsensing system, a task assignment is prone to be unstable if smartphone users or tasks have incentives to deviate from the global assignment, and seek for alternative choices to improve their own utilities. Besides that, during task competition, the rational smartphone users might choose to adjust their payments after the first few failures, which however, brings new challenges in achieving the stability. To address these issues, this paper constructs a distributed many-to-many matching model to capture the interaction between crowdsensing tasks and smartphone users, taking into account the budget constraints of tasks. Then, we design a stable matching algorithm to allocate the tasks to the users, and determine their payments. We prove that the proposed algorithm achieves several desirable properties including individual rationality, stability, and convergency. It is also proved that the proposed scheme achieves at least half of the optimal system efficiency when each smartphone provides homogeneous service quality. Finally, simulation results confirm the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

How to solve the task scheduling problem under resource constraints to ensure the efficient execution of multiple tasks in the presence of conflicts in resource use, among which reasonable task scheduling and resource conflict resolution are the key factors that affect the effect of task execution. Based on the workflow graph model, a framework for task scheduling under resource constraints is proposed, and for resource conflicts generated in the scheduling process, two task scheduling algorithms are proposed; one algorithm determines priority by task criticality, is based on greedy thinking and adjusting the topological structure of the workflow graph, and determines the task scheduling plan before the task starts; the other algorithm adopts the flexible resource scheduling method, so that the conflicting task will be executed first under the condition of insufficient resources, and the task will be scheduled and executed alternately. Finally, the feasibility of related algorithms is verified through earthquake rescue cases, contrasting experiments with typical representatives of two types of typical resource-constrained project scheduling algorithms are conducted, and the advantages and significance of the two algorithms proposed in this paper are analyzed. The simulation results show that the algorithm proposed in this paper has the advantage of being suitable for cases with the shortage of earthquake rescue resources. [ABSTRACT FROM AUTHOR]