Global Assignment Research Articles

A Collaborative Online International Learning Experience for Doctoral Nursing Students and Faculty From Three Countries: Reshaping the Educational Landscape

Abstract The COVID-19 pandemic presented opportunities for educational innovations and the development of intercultural learning experiences. A global health assignment guided by a collaborative online international learning pedagogy was assigned to doctoral nursing students from three different countries. Icebreaker activities, along with the Culturally You diagram, commenced the team-building process. Students, mentored by course faculty, completed the assignment, a 10-minute presentation and reflections. Positive trends in intercultural sensitivity scores were observed, highlighting exposure to a new teaching platform, global health issues, and the value of faculty mentorship. While challenges existed, successful collaborations could thrive even during a pandemic.

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Placement legalization for heterogeneous cells of non-integer multiple-heights

It is intuitively clear that a circuit to be implemented by selectively utilizing standard cells of various non-integer multiple-heights (NIMH) (e.g., mixed use of 6-track, 7.5-track, and 9-track cells) is able to provide a better opportunity in optimizing power, performance, and area over that by using cells of single-height only or of integer multiple-heights only. However, from the cell placement legalization point of view, the issues to be addressed for placement legalization on NIMH designs are very complex. And this paper primarily focuses on introducing novel ideas for row placement when utilizing NIMH cells, which involves determining the row pattern. The most inter-dependent and critical tasks, which are rather unique to the NIMH cell placement legalization problem, are (task 1) for the cells of the same height, distributing and assigning them to a set of distinct rows on a die and (task 2) determining the location of the rows containing cells of the same height. We solve the legalization problem by, starting from an initial row placement, iteratively solving the two tasks by formulating task 1 into an instance of row-capacity constrained network flow problem, followed by solving task 2 which leads to an optimal vertical displacement of the cells in the rows. Meanwhile, through experiments, it is shown that our network flow driven global cell assignment to rows for NIMH cell placement problem tightly linking the optimal determination of row location is able to reduce the total amount of cell displacements from the global (initial) placement by 9.5% and 51.2% in comparison with that produced by a greedy approach and the conventional state-of-the-art NIMH cell placement legalization method, respectively.

A collapse risk assessment method for subway foundation pit based on cloud model and improved Dempster–Shafer evidence theory

Collapse is a major engineering hazard in open-cut foundation pit construction, and risk assessment is crucial for considerably reducing engineering hazards. This study aims to address the ambiguity problem of qualitative index quantification and the failure of high-conflict evidence fusion in risk assessment. Thus, a fast-converging and high-reliability multi-source data fusion method based on the cloud model (CM) and improved Dempster–Shafer evidence theory is proposed. The method can achieve an accurate assessment of subway pit collapse risks. First, the CM is introduced to quantify the qualitative metrics. Then, a new correction parameter is defined for improving the conflicts among evidence bodies based on conflict degree, discrepancy degree and uncertainty, while a fine-tuning term is added to reduce the subjective effect of global focal element assignment. Finally, the risk assessment result is obtained according to the maximum affiliation principle. The method is successfully applied to Luochongwei Station, where the difference between the maximum value and the second largest value of the basic probability assignment is 0.624, and the global uncertainty degree is 0.087. Both values satisfy the decision evaluation condition; however, values of other methods only satisfy one or neither condition. In addition, the proposed method requires only four cycles to reach the steady state by fusing data of the same index, which has faster convergence compared with that of other methods. The proposed method has good universality and effectiveness in subway pit collapse risk assessment.

A Comparison of Global Nearest Neighbor and Linear Assignment Problem Solution Methods on a Model Tracking Problem

A Comparison of Global Nearest Neighbor and Linear Assignment Problem Solution Methods on a Model Tracking Problem

A YOLO-NL object detector for real-time detection

In recent years, YOLO object detection models have undergone significant advancement due to the success of novel deep convolutional networks. The success of these YOLO models is often attributed to their use of guidance techniques, such as expertly tailored deeper backbone and meticulously crafted detector head, which provides effective mechanisms to tradeoff between accuracy and efficiency. However, these sluggish-reasoning models are not capable of handling false detection and negative phenomena, facing challenges include improving the robustness of scaled objects detection against occlude and densely sophisticated scenarios.To address these limitations, we propose a novel object detector, You Only Look Once and None Left (YOLO-NL). Our model includes a novel global dynamic label assignment strategy, which allocates labels for specific anchors to maintain a balance between higher precision detection and finer localization. To enhance the detection capability of multi-scale objects in complex scenes, we separately upgrade CSPNet and PANet using the shortest-longest gradient strategy and self-attention mechanism. To meet the need for fast inference, we propose the Rep-CSPNet network using the reparameterization method to convert residual convolutions to ghost linear operations. Additionally, we accelerate the feature extraction process by deploying the serial SSPP structure. The proposed model is robust to scale objects against negative effectives such as dust, dense, ambiguous, and obstructed scenes. YOLO-NL achieved a mAP of 52.9% on the COCO 2017 test dataset, exhibiting a significant improvement of 2.64% compared to the baseline YOLOX. It is worth noting that YOLO-NL can perform high-accuracy and high-speed face mask detection in real-life scenarios. The YOLO-NL model was employed on self-built FMD and large open-source datasets, and the results show that it outperforms the other state-of-the-art methods, achieving 98.8% accuracy while maintaining 130 FPS.

Competition and Cooperation: Global Task Assignment in Spatial Crowdsourcing

Online spatial crowdsourcing platforms provide popular O2O services in people's daily. Users submit real-time tasks through the Internet and require the platform to immediately assign workers to serve them. However, the imbalance distribution of tasks and workers leads to the rejection of some tasks, which reduces the profit of the platform. In this paper, we propose that similar platforms can form an alliance to make full use of the global service supply through cooperation. We name the problem as <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> lobal <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ask <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> ssignment (GTA), in which platforms are allowed to hire idle workers from other platforms to improve the profit of all the platforms together. Different from relevant works, the decision-makers in GTA are platforms rather than individual workers, which can better assign workers in all platforms and improve the overall profit. We design an auction-based incentive mechanism (AIM), to motivate platforms to rent idle workers to other platforms so that increase their own profit. Based on the mechanism, we propose a greedy-based assignment algorithm (BaseGTA), in which platforms greedily maximizes their current profit. We further propose a prediction-based assignment algorithm (ImpGTA), in which platforms make decisions based on the spatial-temporal distribution in the future time. Experimental results show that platforms using our algorithms can achieve higher profit than the existing studies.

ActionMixer: Temporal action detection with Optimal Action Segment Assignment and mixers

In this paper, we propose a novel method for dynamic label assignment in temporal action detection (TAD) called Optimal Action Segment Assignment (OASA). The OASA method converts label assignment into an optimal transportation problem by computing the cost matrix between predicted temporal action segments and groundtruths. The unit transportation cost between a predicted temporal segment and a groundtruth pair is defined as the weighted summation of action classification loss and temporal localization loss. Additionally, we deploy Adaptive Estimation of Candidate Segment Number (AE-CSN) to adaptively determine the number of positive samples for each groundtruth. After formulation, the label assignment problem is converted to find a global optimal assignment plan by minimizing the cost. Therefore, OASA eliminates the need for manually designed prior parameters, which exist in fixed label assignment methods, and improves the generalization of the algorithm between different datasets. To evaluate OASA, we also introduce a simple anchor-free temporal action detector called ActionMixer. It consists of two components: Temporal Mixer and Channel Mixer. The Temporal Mixer employs depth-wise convolution layers with large kernels to capture temporal information, while the Channel Mixer mixes and extracts features across the channel dimension. Extensive experiments conducted on the THUMOS-14, ActivityNet-1.3, and EPIC-Kitchens-100 datasets show that ActionMixer equipped with OASA achieves state-of-the-art performance, surpassing other advanced temporal action detection methods.

RAT-Cycle-GAN for text to image

We present a method that enhances the RATGAN using the idea of CycleGAN to prevent the model from collapsing. The generator with the proposed recurrent affine transformation for text-to-image synthesis and the fusion blocks are connected by an RNN during the generation of fake images to assure the global assignment of text information. The transformation from the phony images to the text is then accomplished using a CNN and encoder with LSTM. A comparison of the generated text with the original text ensures the generator’s preciseness. In order to achieve the state of the art, we evaluated both unconditional and conditional generation’s effectiveness in relation to the accepted standards.

IYOLO-NL: An improved you only look once and none left object detector for real-time face mask detection

Object detection is a fundamental task in computer vision that aims to locate and classify objects in images or videos. The one-stage You Only Look Once (YOLO) models are popular approaches to object detection. Real-time monitoring of mask wearing is necessary, especially for preventing the spread of the COVID-19 virus. While YOLO detectors facing challenges include improving the robustness of object detectors against occlusion, scale variation, handling false detection and false negative, and maintaining the balance between higher precision detection and faster inference time. In this study, a novel object detection model called Improved You Only Look Once and None Left (IYOLO-NL) based on YOLOv5 was proposed for real-time mask wearing detection. To fulfill the requirement of real-time detection, the lightweight IYOLO-NL was developed by using novel CSPNet-Ghost and SSPP bottleneck architecture. To prevent any missed correct results, IYOLO-NL integrates the proposed PANet-SC with a multi-level prediction scheme. To achieve high precision and handle sample allocation properly, the proposed global dynamic-k label assignment strategy was utilized in an anchor-free manner. A large dataset of face masks (FMD) was created, consisting of 6130 images, for use in conducting experiments on IYOLO-NL and other models. The experiment results show that IYOLO-NL surpasses other state-of-the-art (SOTA) methods and achieves 98.8% accuracy while maintaining 130 FPS.

Dynamic Task Assignment Framework for Mobile Crowdsensing with Deep Reinforcement Learning

Task assignment is a key issue in mobile crowdsensing (MCS). Previous task assignment methods were mainly static offline assignment. However, the MCS platform needs to process dynamically changing workers and tasks online in the actual assignment process. Hence, a reliable dynamic assignment strategy is crucial to improving the platform’s efficiency. This paper proposes an MCS dynamic task assignment framework to solve the task maximization assignment problem with spatiotemporal properties. First, a single worker is modeled for the Markov decision process, and a deep reinforcement learning algorithm (DDQN) is used to perform offline learning on historical task data. Then, in the dynamic assignment process, we consider the impact of current decisions on future decisions. Use the maximum flow model to maximize the number of tasks completed in each period while maximizing the expected Q value of all workers to achieve the optimal global assignment. Experiments show that the strategy proposed in this paper has good performance compared with the baseline strategy under different conditions.

Faster goal-oriented shortest path search for bulk and incremental detailed routing

We develop new algorithmic techniques for VLSI detailed routing. First, we improve the goal-oriented version of Dijkstra’s algorithm to find shortest paths in huge incomplete grid graphs with edge costs depending on the direction and the layer, and possibly on rectangular regions. We devise estimates of the distance to the targets that offer better trade-offs between running time and quality than previously known methods, leading to an overall speed-up. Second, we combine the advantages of the two classical detailed routing approaches—global shortest path search and track assignment with local corrections—by treating input wires (such as the output of track assignment) as reservations that can be used at a discount by the respective net. We show how to implement this new approach efficiently.

Reconstructing charged-particle trajectories in the PANDA Straw Tube Tracker using the LOcal Track Finder (LOTF) algorithm

We present the LOcal Track Finder (lotf) algorithm, a method that performs charged-particle trajectory reconstruction using the Straw Tube Tracker, one of the central trackers of the antiProton ANnihilation at DArmstadt (PANDA) detector. The algorithm builds upon the neighboring relations of the tubes to connect individual hits and form track candidates. In addition, it uses a local fitting procedure to handle regions where several tracks overlap and utilizes a system of virtual nodes to reconstruct the z-information of the particle trajectories. We generated 30,000 events to assess the performance of our approach and compared its global track assignment efficiency with respect to two other track reconstruction methods. lotf has (1) an average of 85% of found tracks, (2) the largest number of Fully Pure tracks, (3) the lowest amount of incorrect reconstructions, and (4) is significantly faster than the other two approaches. Further, we compared the z-reconstruction performance with one of the two alternative methods and show that lotf improves the median z-error by a factor of 8.7. Finally, we tested our method using 3750 data sets composed of 4 events each, demonstrating that our approach handles cases in which events are mixed. The raw (without parallelization) average reconstruction rate is about 68,000 hits/s, which makes the present algorithm promising for online data selection and processing.

Constraining and contributing factors of an expatriate assignment life cycle

Orientation: Globalisation necessitated the physical movement of talented employees, leading to expatriate assignments to foreign countries. Investigative studies and strategy development were needed to moderate the high failure rate of such assignments.Research purpose: To explore the constraining and contributing factors of an expatriate assignment lifecycle.Motivation for the study: With only 40% of assignments being successful, there is an urgent need to find and rectify the reasons for the failure in order to mitigate the associated financial losses and threatened sustainability of businesses.Research approach, design and method: A qualitative research methodology within an exploratory research design was used. The opinions of the expatriates were compared using subjective, interpretivist and phenomenological dimensions.Main findings: Based on the lived experience of the expatriates, the constraining factors were a lack of training and mentoring, inadequate cultural acclimatisation, ambiguous policies, unsupportive host organisation, low-quality logistics support and ineffective re-induction of the expatriates into the home country. The expatriates strongly consider career path planning, effective ongoing training and mentoring, a caring organisational culture, clear assignment policies and social relations to be decisive contributing factors.Practical and managerial implications: The international human resource management process can use the findings to reduce the global assignment failure rates. It provides clear directions for driving relevant strategies and processes for successful expatriation operations.Contribution: The study contributes to the existing body of knowledge on the constraining and contributing factors of the expatriate assignment. It sets a course to initiate further studies to evolve a sustainable framework for an efficient global mobility programme.

Self-organized multi-target trapping of swarm robots with density-based interaction

AbstractThe task of multi-target trapping in swarm robots can often be solved by global shape planning and target assignment, but it still remains a challenge to achieve fully self-organized multi-target trapping behavior based on local information. In this paper, inspired by the concept of spatial density in physics and biology, we proposed a novel density-based method to enable the swarm robots to entrap multiple targets with either single-ring, multi-ring or multi-subgroup formation in a distributed and self-organized way while neither communication among robots nor encirclement function is required. Each robot’s local spatial density is considered as the main clue for the individual’s motion decision-making and the enclosed configurations emerge from such individual-level interactions rather than being explicitly designed. Numerical simulations and real robotic experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed self-organized trapping method allows a swarm of robots to entrap multiple moving targets in a stable, flexible, noise-tolerate and size-scalable fashion.

An explanatory approach to modeling the fleet assignment in the global air transportation system

Airlines’ fleet assignment heavily affects the economic and ecological performance of the global air transportation system (ATS). Consequently, it is inevitable to include potential changes of the fleet assignment when modeling and assessing future global ATS scenarios. Therefore, this article presents a novel explanatory approach to modeling the fleet assignment in the global ATS. The presented approach is based on formulating and solving an optimization problem, which describes the fleet assignment in the ATS through a suitable combination of objective function and constraints. While the objective function combines both the airline and the passenger perspective on the fleet assignment, the constraints include additional operational and technological aspects. In comparison to the available global fleet assignment models in the literature, which rely on statistical approaches, the advantages of the presented approach via an optimization problem lie in the overall scenario capability and the consideration of explicit aircraft types instead of simplifying seat categories. To calibrate and validate our model, we use 10 years of historic flight schedule data. The results underline the strengths and weaknesses of the presented approach and indicate potential for future improvement.

Simulating the Impact of the Sustained Melting Arctic on the Global Container Sea–Rail Intermodal Shipping

Global warming trends and the rapid reduction of summer Arctic sea ice extent have increased the feasibility of transarctic transport. How the process of glacier melting affects the existing containerized sea–rail shipping network and container flow assignment has become a challenging economic and policy issue. This paper first examines the meteorological influences on glacier melting and the assignment of container flow over the existing sea–rail network. Then, a three-layer simulation framework is constructed, with the upper layer simulating glacier melting based on the raster grid, the middle layer combining a grid and topology analysis to simulate the evolution of the global sea–rail network and the lower layer establishing a concave cost network flow model to simulate the container flow assignment. Finally, we use MicroCity to achieve the dynamic optimization and simulation of global container flow assignment, solving the large-scale sea–rail shipping network traffic assignment problem. The simulation results show that the proposed model and solution algorithm are feasible and effective, revealing the variation of container flow assignment in the global sea–rail shipping network under different Arctic ice melting scenarios. For instance, in the summer of 2050, the Arctic routes will share the global container flows, resulting in a significant reduction of container flows in the Malacca Strait, Suez Canal and Panama Canal.

An improved optimization model for crowd evacuation considering individual exit choice preference

AbstractGuidance‐assisted crowd evacuation is a process of combining individual exit choice behavior with managers' exit assignment control. The knowledge of individual exit choice preference is of great significance for optimizing global exit assignment planning. This study proposes an improved optimization model for crowd evacuation by integrating the individual‐level exit choice preference analysis with system‐level exit assignment optimization to represent more realistic crowd evacuation decisions. First, the impact factors of individual exit choice behavior are considered in a mixed logit model to predict the probability of each individual choosing each exit in specific situations. Second, a preference‐based exit filtering strategy is designed to analyze the sensible alternative exits for individuals or groups in multi‐scale evacuation cells. Finally, to pursue optimal exit assignment planning, a multi‐objective particle swarm optimization algorithm and an improved social force model are adopted to simulate the process of crowd evacuation and evaluate the performance of the specific exit assignment plans. The case study of an outdoor multiple‐exit scenario in Xi'an, China, indicates that the proposed model can help managers to understand the heterogeneity of individual evacuation behaviors. Furthermore, it will support more reliable and realistic evacuation decisions in real‐life situations than conventional plans that typically implement the top‐n strategy.

Avrupa’daki Eğitim Kurumlarında Eğitim Yöneticisi Seçme ve Atama Kriterlerinin İncelenmesi

This study aims to examine the criteria and qualifications for choosing an educational administrator in educational institutions on an international global scale on the basis of some countries in Europe. In this context, the studies conducted in the international framework on determining the criteria and qualifications for choosing managers in educational institutions have been examined and some suggestions have been made in accordance with the results obtained within the scope of the documents. In addition, selection criteria and training in educational institutions within the framework of administrative qualifications with managers to these features by examining international data within the training manager training on a global scale and assignment practices a literature search was conducted. As a result of the study countries examined within the scope of the subject from leading and significant contributions on educational administration and Management Science the European continent in the UK, Germany and France within the framework of administrative qualifications in educational institutions in selection criteria and the basic conditions of being a graduate of a training manager to become among it has been observed that graduate. In the same way, the roles played by education administrators in educational institutions in the countries studied and mentioned in the subject are emphasized as educational and educational leadership processes, while social leadership qualities have been assigned to education administrators. Key Words: Education Management, European Countries, Assignment Criteria

Local worker perspectives from Nicaraguan surf tourism: revisiting career anchors in non-standard work contexts

PurposeThis qualitative study explores, through the lens of Schein's (1978) career anchor theory, the internal career perceptions (self-perceived values, challenges and capabilities) of local surf workers in the highly internationalized sector of surf tourism in Nicaragua.Design/methodology/approachSemi-structured interviews were carried out with 22 local surf tourism workers. Participant experiences were analyzed using thematic analysis to distinguish their career anchor orientations.FindingsThe results indicate the sustained value and instrumentality of Schein's original career anchor theory, specifically in terms of the interconnectedness of dominant and supporting anchors and the relevance of anchor groupings for workers in non-standard working environments. The anchors of lifestyle, entrepreneurial creativity, and security and stability were closely interrelated and complementary, as participants from this context were ultimately striving for security and stability.Research limitations/implicationsFuture research should consider more explicitly the role of the socio-political, environmental or economic context in shaping the internal career self-concepts and experiences of workers.Practical implicationsThis study sheds light on the internal career drivers — the unique dilemmas, challenges, passions and motives — of local workers in a resource-constrained environment. Managers, business owners and other economic actors stand to gain important insights into the realities of workers they employ, but do not intimately understand. Such insights could be generalizable to a variety of work settings in which there are high material, social or cultural constraints.Social implicationsNon-standard work contexts and local worker voices are both thematically underrepresented in the careers scholarship. Research on these topics can contribute to broader discussions of sustainability, sustainable development goals and decolonial perspectives in social science scholarship. Bringing local workers from the Global South into view means turning scholarly attention towards less-visible “others” working alongside those having received the lion's share of academic discussion, i.e. expatriate workers on a global assignment or self-initiated expatriates, most often from the Global North.Originality/valueThis is one of the first studies to explore the career anchors of local workers in the Global South in a non-standard, non-bureaucratic vocational setting. The study sheds light on local workers' career decisions, an often-neglected perspective within international human resource management.