algorithm for task assignment

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A modified greedy algorithm for the task assignment problem..

Allison M. Douglas , University of Louisville

Date on Master's Thesis/Doctoral Dissertation

Document type.

Master's Thesis

Degree Name

Industrial Engineering

Committee Chair

Depuy, Gail W.

Production management; Personnel management--Data processing

Assigning workers to tasks in an efficient and cost effective manner is a problem that nearly every company faces. This task assignment problem can be very time consuming to solve optimally. This difficulty increases as problem size increases. Most companies are large enough that it isn't feasible to find an optimal assignment; therefore a good heuristic method is needed. This project involved creating a new heuristic to solve this problem by combining the Greedy Algorithm with the Meta-RaPS method. The Greedy Algorithm is a near-sighted assignment procedure that chooses the best assignment at each step until a full solution is found. Although the Greedy Algorithm finds a good solution for small to medium sized problems, introducing randomness using the meta-heuristic Meta-RaPS results in a better solution. The new heuristic runs 5000 iterations and reports the best solution. The final Excel® VBA program solves a small sized problem in less than one minute, and is within 10% of the optimal solution, making it a good alternative to time consuming manual assignments. Although larger, more realistic problems will take longer to solve, good solutions will be available in a fraction of the time compared to solving them optimally.

Recommended Citation

Douglas, Allison M., "A modified greedy algorithm for the task assignment problem." (2007). Electronic Theses and Dissertations. Paper 369. https://doi.org/10.18297/etd/369

Since February 12, 2015

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Is there a greedy algorithm to solve the assignment problem?

The assignment problem is defined as:

There are n people who need to be assigned to n jobs, one person per job. The cost that would accrue if the ith person is assigned to the jth job is a known quantity C[i,j] for each pair i, j = 1, 2, ..., n. The problem is to find an assignment with the minimum total cost.

There is a question asking to design a greedy algorithm to solve the problem. It also asks if the greedy algorithm always yields an optimal solution and for the performance class of the algorithm. Here is my attempt at designing an algorithm:

Am I correct in saying that my algorithm is of O(n^2)? Am I also correct in saying that a greedy algorithm does not always yield an optimal solution? I used my algorithm on the following cost matrix and it is clearly not the optimal solution. Did I Do something wrong?

• greedy-algorithms
• assignment-problem

• $\begingroup$ Algorithms for maximum weight bipartite maximum matching are unfortunately more complicated than that. They don't really follow the greedy paradigm. $\endgroup$ –  Yuval Filmus Commented Apr 7, 2017 at 6:05

The answer of your post question (already given in Yuval comment) is that there is no greedy techniques providing you the optimal answer to an assignment problem.

The commonly used solution is the Hungarian algorithm, see

Harold W. Kuhn, "The Hungarian Method for the assignment problem", Naval Research Logistics Quarterly, 2: 83–97, 1955

for the original paper.

Otherwise your solution seems correct to me, and as usually with greedy algorithms, it will provide you a feasible solution which you may hope to not be "too far" from the global optimal solution.

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What is Task Assignment Approach in Distributed System?

A Distributed System is a Network of Machines that can exchange information with each other through Message-passing. It can be very useful as it helps in resource sharing. In this article, we will see the concept of the Task Assignment Approach in Distributed systems.

Resource Management:

One of the functions of system management in distributed systems is Resource Management. When a user requests the execution of the process, the resource manager performs the allocation of resources to the process submitted by the user for execution. In addition, the resource manager routes process to appropriate nodes (processors) based on assignments. 

Multiple resources are available in the distributed system so there is a need for system transparency for the user. There can be a logical or a physical resource in the system. For example, data files in sharing mode, Central Processing Unit (CPU), etc.

As the name implies, the task assignment approach is based on the division of the process into multiple tasks. These tasks are assigned to appropriate processors to improve performance and efficiency. This approach has a major setback in that it needs prior knowledge about the features of all the participating processes. Furthermore, it does not take into account the dynamically changing state of the system. This approach’s major objective is to allocate tasks of a single process in the best possible manner as it is based on the division of tasks in a system. For that, there is a need to identify the optimal policy for its implementation.

Working of Task Assignment Approach:

In the working of the Task Assignment Approach, the following are the assumptions:

• The division of an individual process into tasks.
• Each task’s computing requirements and the performance in terms of the speed of each processor are known.
• The cost incurred in the processing of each task performed on every node of the system is known.
• The IPC (Inter-Process Communication) cost is known for every pair of tasks performed between nodes.
• Other limitations are also familiar, such as job resource requirements and available resources at each node, task priority connections, and so on.

Goals of Task Assignment Algorithms:

• Reducing Inter-Process Communication (IPC) Cost
• Quick Turnaround Time or Response Time for the whole process
• A high degree of Parallelism
• Utilization of System Resources in an effective manner

The above-mentioned goals time and again conflict. To exemplify, let us consider the goal-1 using which all the tasks of a process need to be allocated to a single node for reducing the Inter-Process Communication (IPC) Cost. If we consider goal-4 which is based on the efficient utilization of system resources that implies all the tasks of a process to be divided and processed by appropriate nodes in a system.

Note: The possible number of assignments of tasks to nodes:

But in practice, the possible number of assignments of tasks to nodes < m x n because of the constraint for allocation of tasks to the appropriate nodes in a system due to their particular requirements like memory space, etc.

Need for Task Assignment in a Distributed System:

The need for task management in distributed systems was raised for achieving the set performance goals. For that optimal assignments should be carried out concerning cost and time functions such as task assignment to minimize the total execution and communication costs, completion task time, total cost of 3 (execution, communication, and interference), total execution and communication costs with the limit imposed on the number of tasks assigned to each processor, and a weighted product of cost functions of total execution and communication costs and completion task time. All these factors are countable in task allocation and turn, resulting in the best outcome of the system.

Example of Task Assignment Approach:

Let us suppose, there are two nodes namely n1 and n2, and six tasks namely t1, t2, t3, t4, t5, and t6. The two task assignment parameters are:

• execution cost: x ab refers to the cost of executing a task an on node b.
• inter-task communication cost: c ij refers to inter-task communication cost between tasks i and j.

Note: The execution of the task (t2) on the node (n2) and the execution of the task (t6) on the node (n1) is not possible as it can be seen from the above table of Execution costs that resources are not available.

Case1: Serial Assignment

Cost of Execution in Serial Assignment:

Cost of Communication in Serial Assignment:

Case2: Optimal Assignment

Cost of Execution in Optimal Assignment:

Cost of Communication in Optimal Assignment:

Optimal Assignment using Minimal Cutset:

Cutset: The cutset of a graph refers to the set of edges that when removed makes the graph disconnected.

Minimal Cutset: The minimal cutset of a graph refers to the cut which is minimum among all the cuts of the graph.

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Enhancing unmanned aerial vehicle task assignment with the adaptive sampling-based task rationality review algorithm.

1. Introduction

• Introducing task path decision variables and incorporating real-world constraints such as collaborative action constraints into the traditional model, improving the rigor and completeness of the task assignment mathematical model.
• Proposing an adaptive sampling strategy with dynamically adjusted sampling probabilities based on task importance, effectively avoiding the loss of high-value tasks due to low sampling probabilities, and ensuring a balance between computational efficiency and maximizing task value.
• Presenting a task review and classification method to address coherence issues in UAV task paths that are common in existing auction algorithms, significantly enhancing overall task benefits.
• Proposing a crossover path exchange strategy to reduce crossovers between UAV task paths, further optimizing the task assignment scheme and improving overall benefits.

2. Preliminary

2.1. variable definitions, 2.2. symbol descriptions, 2.3. assumptions, 3. problem formulation, 3.1. decision variables, 3.2. objective function, 3.3. constraints, 3.4. mathematical model of task assignment, 4. algorithm and analysis, 4.1. basic idea and framework, 4.1.1. introduction of lsta.

• Initialization stage : In the first stage, each UAV randomly samples the task set with a pre-set probability to form its own task sample set, followed by initializing the initial task sample set.
• Task assignment auction stage : In the second stage, UAVs calculate and rank the marginal gains [ 41 ] for the initial task sample set formed in the first stage. Through negotiation, the task with the highest marginal value and its corresponding UAV are selected, followed by conflict detection. Finally, global consensus and update are performed, adding the task with the highest marginal value to the corresponding UAV’s task set while removing the same task from the sample sets of other UAVs involved in the conflict detection.

4.1.2. Algorithm Overall Process

• Initialization stage : We propose an adaptive sampling strategy that dynamically adjusts the sampling probability. This strategy increases the sampling probability for high-value tasks and decreases it for low-value tasks, minimizing the risk of missing high-value tasks due to a low sampling probability and reducing the benefit loss caused by probability adjustment.
• Task assignment auction stage : This stage follows the same process as that of the LSTA algorithm. During each iteration, tasks are auctioned and allocated to the UAV with the highest bid, and the same tasks are removed from the task sample sets of other UAVs. This iterative process continues, completing the initial allocation through repeated auctions.
• Task review and classification stage : We propose a task review and classification method for tasks that are unreasonably allocated during the auction stage. After the first round of auctions for all tasks, tasks are reviewed for coherence using heading change angles and flight distances as indicators. Tasks identified as having coherence issues in the UAV task sequence and tasks assigned to UAVs with underutilized load capacities are classified into two categories, creating a coherence issue task pool and a load-balanced task pool. These two pools undergo a new round of auctions independently, improving the quality of task allocation.
• Crossover path exchange stage : After the task review and classification stage, we introduce a crossover path exchange strategy. This strategy is inspired by the lazy-based review consensus algorithm (LRCA) [ 42 ] proposed by Xu et al. for vehicle task allocation and the crossover step in genetic algorithms. The maximum consensus strategy assigns the best task in each iteration to the UAV with the highest bid, which may cause intersections between UAV task paths. By adding a crossover path exchange strategy, the allocation results can be further optimized.

4.2. Adaptive Sampling Strategy

4.3. Task Review and Classification Method

4.4. Crossover Path Exchange Strategy

5. Experiments and Analysis

5.1. validation of effectiveness, 5.2. comparative analysis, 5.3. dynamic reassignment after damage, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Sun, C.; Yao, Y.; Zheng, E. Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm. Drones 2024 , 8 , 422. https://doi.org/10.3390/drones8090422

Sun C, Yao Y, Zheng E. Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm. Drones . 2024; 8(9):422. https://doi.org/10.3390/drones8090422

Sun, Cheng, Yuwen Yao, and Enhui Zheng. 2024. "Enhancing Unmanned Aerial Vehicle Task Assignment with the Adaptive Sampling-Based Task Rationality Review Algorithm" Drones 8, no. 9: 422. https://doi.org/10.3390/drones8090422

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• Image Processing Essentials: Edge Detection, Algorithm Implementation, and ROC Analysis

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1. Analyzing the Task

2. understanding the theoretical background, 3. planning your approach, 1. writing functions for image processing, 2. experimenting with edge detection techniques, 3. evaluating the performance of edge detectors, 1. structuring the report, 2. including visual and quantitative data, 3. proofreading and final checks, final submission and best practices.

Image processing is a critical area in computer science, often involving complex algorithms and sophisticated techniques to analyze, enhance, or manipulate images. Assignments in this domain require not just theoretical knowledge but also practical skills in coding, experimentation, and analysis. For those seeking help with image processing assignment, understanding how to integrate machine learning techniques into image processing can be particularly beneficial. This guide will walk you through the steps to successfully tackle image processing assignments, similar to the one described in the provided brief, by providing a comprehensive approach to handling such tasks effectively.

Understanding the Assignment Requirements

Before solving any programming assignment , it's essential to thoroughly understand what is required. Image processing assignments often include multiple components such as coding, applying algorithms to images, analyzing results, and presenting findings in a structured report. To navigate these tasks effectively, one must first break down the assignment requirements into manageable parts.

The first step in handling any assignment is to carefully analyze the task at hand. Understand the specific objectives of the assignment. For example, an image processing assignment may require you to implement various edge detection algorithms, apply them to a set of images, and then compare the results against a "Ground Truth" to evaluate their performance.

• Task Analysis: Read through the assignment brief carefully. Identify keywords such as "implement," "apply," "compare," and "analyze." These words indicate the actions you need to take.
• Objectives Identification: Determine the specific goals of each task. In the case of edge detection, the objective might be to find the most effective method for detecting edges in noisy images.

Having a solid grasp of the theoretical background is crucial for any image processing assignment. This knowledge will guide your coding and experimentation.

• Review Lecture Notes and Textbooks: Make sure you understand the theoretical concepts behind the techniques you're required to implement. For example, the Laplacian of Gaussian (LoG) is a combination of Gaussian smoothing and Laplacian for edge detection, which helps in reducing noise before detecting edges.
• Research Additional Resources: Utilize online resources, tutorials, and academic papers to enhance your understanding. Platforms like Coursera, edX, or YouTube offer valuable tutorials on image processing techniques.

After understanding the theoretical foundation, the next step is to plan your approach to the assignment. This involves deciding on the tools and libraries you will use, structuring your code, and outlining your report.

• Choosing Tools and Libraries: Depending on your familiarity, choose a programming language like Python or MATLAB that supports robust image processing libraries (such as OpenCV for Python or the Image Processing Toolbox for MATLAB).
• Structuring Your Code: Plan the organization of your code into functions that handle specific tasks, such as loading images, applying filters, and displaying results. This modular approach makes debugging easier and enhances code readability.
• Outlining the Report: Sketch a rough outline of your report, noting where you will include descriptions of methods, results, and discussions. This will help you stay within the page limit and maintain a logical flow.

Implementation and Experimentation

The core of any image processing assignment is the implementation of algorithms and conducting experiments. This section will guide you through coding the necessary functions and experimenting with different techniques to achieve the best results.

Implementing image processing algorithms involves writing functions that perform specific tasks, such as filtering or edge detection.

• Implementing Basic Filters: Start by writing functions for basic filters. For instance, a Laplacian of Gaussian filter can be implemented by first applying a Gaussian blur to an image and then applying the Laplacian operator.
• Modular Code Design: Use a modular approach to code design. Create separate functions for each operation. For example, write one function to load an image, another to apply a specific filter, and another to save or display the processed image.
• Adding Comments and Documentation: Good coding practices are essential. Ensure your code is well-commented, explaining each step of the process. This not only helps you understand your code later but is also beneficial for anyone who reviews your code.

Once the basic functions are in place, experiment with different edge detection techniques to determine which one works best for the given images.

• Applying Edge Detectors: Use the functions you wrote to apply different edge detectors, such as Roberts, Sobel, Gaussian, Laplacian, and Laplacian of Gaussian, to the provided images. Observe how each filter affects the image and note any differences.
• Noise Removal Techniques: Experiment with noise removal techniques before applying edge detection. Methods like Gaussian blurring or median filtering can help reduce noise, making edge detection more effective.
• Documenting Your Findings: Keep detailed notes of your observations. For each edge detection technique, describe the steps taken and discuss the effectiveness of the method. Note any challenges or interesting observations, such as the effect of noise on edge detection.

The final step in experimentation is to evaluate the performance of the edge detectors you have implemented.

• Thresholding and Binarization: Convert the processed images to binary format using thresholding techniques. This step simplifies the comparison of detected edges with the ground truth.
• ROC Analysis: Implement Receiver Operator Characteristic (ROC) analysis to evaluate the sensitivity and specificity of each edge detector. This analysis involves comparing your edge-detected images to the labeled ground truth images and calculating the number of true positives, false positives, true negatives, and false negatives.
• Comparing Against Ground Truth: Use statistical measures to compare the edges detected by your algorithms against the ground truth. This comparison helps determine the accuracy and robustness of each edge detector under different conditions.

Writing the Experimental Report

Writing a concise and comprehensive report is just as important as the coding and experimentation itself. The report should clearly convey your objectives, methods, results, and conclusions in a structured manner.

A well-structured report enhances readability and ensures that all key points are covered within the page limit.

• Introduction: Begin with a brief introduction that outlines the aim of the assignment and provides an overview of the tasks performed.
• Methods: Describe the methods used for each task, including the algorithms implemented and the reasoning behind choosing specific techniques. Provide enough detail for someone else to replicate your work.
• Results: Present your findings, including both visual (images) and quantitative results (such as ROC curves and performance metrics). Make sure to label all graphs and images clearly.
• Discussion and Conclusion: Discuss the implications of your findings. Which edge detector worked best, and why? What were the challenges faced, and how could they be addressed in future work? Provide a concise conclusion summarizing the key takeaways.

Visual and quantitative data are crucial for supporting your findings and providing a clear understanding of the results.

• Using Images Effectively: Include images that showcase the effects of different edge detectors. Ensure the images are clear, well-labeled, and directly relevant to the points you're discussing.
• Graphs and Tables: Use graphs and tables to present quantitative data, such as the results of your ROC analysis. These visual aids can help illustrate trends and make complex data more accessible.
• Adhering to Page Limits: Remember that your report must be concise. Focus on the most important results and discussion points, and avoid unnecessary details. This will help you stay within the page limit while still providing a comprehensive overview of your work.

Before submitting your report, ensure it is polished and free of errors.

• Review for Clarity and Conciseness: Make sure your writing is clear and concise. Avoid jargon and overly complex sentences that could confuse the reader.
• Check for Grammar and Spelling Errors: Proofread your report to catch any grammatical or spelling errors. A well-written report reflects professionalism and attention to detail.
• Confirm Adherence to Formatting Guidelines: Ensure your report follows all formatting guidelines provided in the assignment brief, such as font size, margins, and page limits.

The final steps in completing your assignment involve ensuring everything is ready for submission according to the specified guidelines.

• Code Submission: Make sure to submit your code along with the report. Follow the guidelines for code submission, such as including all necessary files and ensuring your code is well-documented.
• Double-Check Submission Requirements: Review the submission requirements on your course platform (like Canvas) to ensure you have followed all instructions. This includes file formats, naming conventions, and deadlines.
• Avoid Common Pitfalls: Common pitfalls in image processing assignments include not adequately testing code, submitting incomplete or poorly documented code, and exceeding page limits. Avoid these by thoroughly testing your code, following good documentation practices, and adhering strictly to the assignment guidelines.

By following these steps, you can effectively approach and complete image processing assignments. This structured approach not only helps in achieving better grades but also in developing a deeper understanding of image processing techniques and their practical applications.

This extended guide provides a comprehensive approach to tackling image processing assignments. It emphasizes the importance of understanding the assignment requirements, planning, implementation, experimentation, and clear reporting. By following this guide, students can develop the skills needed to successfully complete their assignments and gain valuable experience in image processing.

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New Classical Algorithm Enhances Understanding of Quantum Computing’s Future

In an exciting development for quantum computing, researchers from the University of Chicago’s Department of Computer Science , Pritzker School of Molecular Engineering , and Argonne National Laboratory have introduced a groundbreaking classical algorithm that simulates Gaussian boson sampling (GBS) experiments. This achievement not only helps clarify the complexities of current quantum systems but also represents a significant step forward in our understanding of how quantum and classical computing can work together. The research just appeared in the prominent Nature Physics Journal this past June.

The Challenge of Gaussian Boson Sampling

Gaussian boson sampling has gained attention as a promising approach to demonstrating quantum advantage, meaning the ability of quantum computers to perform tasks that classical computers cannot do efficiently. The journey leading up to this breakthrough has been marked by a series of innovative experiments that tested the limits of quantum systems. Previous studies indicated that GBS is challenging for classical computers to simulate under ideal conditions. However, Assistant Professor and author Bill Fefferman pointed out that the noise and photon loss present in actual experiments create additional challenges that require careful analysis.

Notably, experiments ( such as these ) conducted by teams at major research centers from the University of Science and Technology of China and Xanadu, a Canadian quantum company, have shown that while quantum devices can produce outputs consistent with GBS predictions, the presence of noise often obscures these results, leading to questions about the claimed quantum advantage. These experiments served as a foundation for the current research, driving scientists to refine their approaches to GBS and better understand its limitations.

Understanding Noise in Quantum Experiments

“While the theoretical groundwork has established that quantum systems can outperform classical ones, the noise present in actual experiments introduces complexities that require rigorous analysis,” explained Fefferman. “​​Understanding how noise affects performance is crucial as we strive for practical applications of quantum computing.”

This new algorithm addresses these complexities by leveraging the high photon loss rates common in current GBS experiments to provide a more efficient and accurate simulation. The researchers employed a classical tensor-network approach that capitalizes on the behavior of quantum states in these noisy environments, making the simulation more efficient and manageable with available computational resources.

Breakthrough Results

Remarkably, the researchers found that their classical simulation performed better than some state-of-the-art GBS experiments in various benchmarks.

“What we’re seeing is not a failure of quantum computing, but rather an opportunity to refine our understanding of its capabilities,” Fefferman emphasized. “It allows us to improve our algorithms and push the boundaries of what we can achieve.”

The algorithm outperformed experiments by accurately capturing the ideal distribution of GBS output states, raising questions about the claimed quantum advantage of existing experiments. This insight opens doors for improving the design of future quantum experiments, suggesting that enhancing photon transmission rates and increasing the number of squeezed states could significantly boost their effectiveness.

Implications for Future Technologies

The implications of these findings extend beyond the realm of quantum computing. As quantum technologies continue to evolve, they hold the potential to revolutionize fields such as cryptography, materials science, and drug discovery. For instance, quantum computing could lead to breakthroughs in secure communication methods, enabling more robust protection of sensitive data. In materials science, quantum simulations can help discover new materials with unique properties, paving the way for advancements in technology, energy storage, and manufacturing. By advancing our understanding of these systems, researchers are laying the groundwork for practical applications that could change the way we approach complex problems in various sectors.

The pursuit of quantum advantage is not just an academic endeavor; it has tangible implications for industries that rely on complex computations. As quantum technologies mature, they have the potential to play a crucial role in optimizing supply chains, enhancing artificial intelligence algorithms, and improving climate modeling. The collaboration between quantum and classical computing is crucial for realizing these advancements, as it allows researchers to harness the strengths of both paradigms.

A Cumulative Research Effort

Fefferman worked closely with Professor Liang Jiang from the Pritzker School of Molecular Engineering and former postdoc Changhun Oh , currently an Assistant Professor at the Korea Advanced Institute of Science and Technology, on previous work that culminated in this piece of research.

In 2021, they examined the computational power of noisy intermediate-scale quantum (NISQ) devices through lossy boson sampling . The paper revealed that photon loss affects classical simulation costs depending on the number of input photons, which could lead to exponential savings in classical time complexity. Following this, their second paper focused on the impact of noise in experiments designed to demonstrate quantum supremacy, showing that even with significant noise, quantum devices can still produce results that are difficult for classical computers to match. In their third article, they explored Gaussian boson sampling (GBS) by proposing a new architecture that improves programmability and resilience against photon loss, making large scale experiments more feasible. They then introduced a classical algorithm in their fourth paper that generates outcomes closely aligned with ideal boson sampling, enhancing benchmarking techniques and emphasizing the importance of carefully selecting experiment sizes to preserve the quantum signal amidst noise.Finally, in their latest study, they developed quantum-inspired classical algorithms to tackle graph-theoretical problems like finding the densest k-subgraph and the maximum weight clique and a quantum chemistry problem called the molecular vibronic spectra generation . Their findings suggested that the claimed advantages of quantum methods may not be as significant as previously thought, with their classical sampler performing similarly to the Gaussian boson sampler.

Looking Ahead

The development of the classical simulation algorithm not only enhances our understanding of Gaussian boson sampling experiments but also highlights the importance of continued research in both quantum and classical computing. The ability to simulate GBS more effectively serves as a bridge toward more powerful quantum technologies, ultimately helping us navigate the complexities of modern challenges. As we explore these insights, we move closer to realizing the full potential of quantum technologies, which could lead to innovative solutions that benefit society as a whole. Each step forward in this journey brings us closer to a future where quantum computing plays a vital role in addressing some of the world’s most pressing challenges. With ongoing research and collaboration, the future of quantum computing looks promising, unlocking new realms of possibility for science and society.

Related News

Ian Foster and Rick Stevens Named to HPCwire’s 35 Legends List

University of Chicago to Develop Software for Effort to Create a National Quantum Virtual Laboratory

Fred Chong Receives Quantrell Award for Excellence in Teaching

Non-Unital Noise Adds a New Wrinkle to the Quantum Supremacy Debate

Argonne scientists use AI to identify new materials for carbon capture

New research unites quantum engineering and artificial intelligence

Group From UChicago CS To Present Four Papers at Most Prestigious International Quantum Conference

UChicago Scientists Make New Discovery Proving Entanglement Is Responsible for Computational Hardness In Quantum Systems

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Algorithm for task assigning

I have 5 activity-cases to be mapped to 2 users and my senior suggested to use round robin algorithm to do this

Like 1st case - 1st user

2nd case - 2nd user

3rd case - 1st user

4th case - 2nd user

5th case - 1st user

I want to know , is there any other algorithm best suited for such scenarios ?

• data-structures

• Is it just a assignment task or users need to complete each activity before he can take new activity? –  Ravi Rane Commented Aug 30, 2018 at 4:09
• 1 Define "best suited". Many algorithms are possible, including unfair ones like giving all cases to user 1. –  Henry Commented Aug 30, 2018 at 4:14
• Hi Ravi -- its an assignment task , there is no completion activity to be monitored , only putting x tasks on each user's bucket –  Ajinkya Karode Commented Aug 30, 2018 at 4:56
• Hi Henry -- best suited in the sense no user should sit idle , also no user should be overloaded with tasks –  Ajinkya Karode Commented Aug 30, 2018 at 4:57
• One thing that comes to my mind as well is a queue where you just pull the next task and assign it to a user when he has completed his previous one (or has currently none assigned). –  Glains Commented Aug 30, 2018 at 5:28

2 Answers 2

This appears analogous to process scheduling to me, so you might find my answer more along that track.

Case 1: All activities are equally important

With this assumption, RR is the good enough scheduling algorithm.

Case 2: Each activity has a priority

Most scheduling problems that occur in real-world come under this case. I'm considering priority is proportional to time taken to complete activity, I can think of:

• Shortest job first
• Priority - based scheduling

Recommended Reading:

Modern Operating Systems (4th Edition) by A.S.Tanenbaum - Section 2.4

Round robin is probably your best choice if you want the number of cases to be the same for each user. Unless someone works more efficiently and you want to give them more work, then I would just stick with that.

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Why children can’t pay attention to the task at hand

Researchers closer to understanding why kids ‘over explore’.

Scientists have learned that children find it hard to focus on a task, and often take in information that won’t help them complete their assignment. But the question is, why?

In a new study, researchers found that this “distributed attention” wasn’t because children’s brains weren’t mature enough to understand the task or pay attention, and it wasn’t because they were easily distracted and lacked the control to focus.

It now appears that kids distribute their attention broadly either out of simple curiosity or because their working memory isn’t developed enough to complete a task without “over exploring.”

“Children can’t seem to stop themselves from gathering more information than they need to complete a task, even when they know exactly what they need,” said Vladimir Sloutsky , co-author of the study and professor of psychology at The Ohio State University .

 Sloutsky and his colleagues have done several studies in the past documenting how children distribute their attention broadly, and don’t seem to have the ability of adults to efficiently complete tasks by ignoring anything that is not relevant to their mission.

In this new research, Sloutsky and Wan confirmed that even when children successfully learn how to focus their attention on a task to earn small rewards such as stickers, they still “over explore” and don’t concentrate just on what is needed to complete their assignment.

One goal of this study was to see if children’s distractibility could be the explanation.

One study involved 4- to 6-year-old children and adults. Participants were told they were going to identify two types of bird-like creatures called Hibi or Gora. Each type had a unique combination of colors and shapes for their horn, head, beak, body, wing, feet and tail. For six of the seven body parts, the combination of color and shape predicted whether it was a Hibi or Gora with 66% accuracy. But one body part always was a perfect match to only one of the creatures, which both children and adults quickly learned to identify in the first part of the study.

In order to test whether children were easily distracted, the researchers covered up each body part, meaning the study participants had to uncover them one by one to identify which creature it was. They were rewarded for identifying the creature as quickly as possible.

For adults, the task was easy. If they knew the tail was the body part that was always matched perfectly with one of the two types of creatures, they always uncovered the tail and correctly identified the creature.

But the children were different. If they had learned the tail was the body part that always identified a creature perfectly, they would uncover that first – but they would still uncover other body parts before they made their choice.

“There was nothing to distract the children – everything was covered up. They could do like the adults and only click on the body part that identified the creature, but they did not,” Sloutsky said.

“They just kept uncovering more body parts before they made their choice.”

Another possibility is that children just like tapping on the buttons, Sloutsky said. So, in another study, they gave adults and children the opportunity to make just one tap on an “express” button to reveal the whole creature and all of its parts, or to tap on each body part individually to reveal it.

Children predominantly chose the express option to just tap once to reveal the creature to make their decision of what type it was. So, the kids weren’t just clicking for the fun of it.

Future studies will look at whether this unneeded exploration is simple curiosity, Sloutsky said. But he said he thinks the more likely explanation is that working memory is not fully developed in children. That means they don’t hold information they need to complete a task in their memory for very long, at least not as long as adults.

“The children learned that one body part will tell them what the creature is, but they may be concerned that they don’t remember correctly. Their working memory is still under development,” Sloutsky said.

“They want to resolve this uncertainty by continuing to sample, by looking at other body parts to see if they line up with what they think.”

As children’s working memory matures, they feel more confident in their ability to retain information for a longer time, he said, and act more like adults do.

The future research should resolve the question of whether the issue is curiosity or working memory, Sloutsky said.

This study was supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development .

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Introduction

The Worker business object is complex with around 30 components in the object hierarchy. This tutorial doesn't attempt to cover all available components of this complex object but provides guidance on the rules you need to follow when uploading new hires.

• WorkRelationship
• Create and upload a HCM Data Loader Worker.dat file to load new hires.
• Understand the rules specific to loading new hires.

Prerequisites

To complete this tutorial, you will require:

• Access to import and load data using HCM Data Loader.
• Access to Setup and Maintenance to update a lookup type.
• A text editor to create your files.
• A file compressor to zip your business object data files.

Task 1: Create the Source System Owner

• In the application, click on your username and click Setup and Maintenance .

• Click on the side drawer icon and click Search .

• Search for and select the Manage Common Lookups task.
• Search for the Lookup Type HRC_SOURCE_SYSTEM_OWNER .
• Click the Add icon in the Lookup Codes table.

• Specify EMP in the lookup code and meaning, specify a Start Date of 01/01/2000 .
• Click Save .

Task 2: Create the Worker File

The Worker component identifies the worker with the Person Number and Date of Birth.

Create a new file and add these file lines: METADATA|Worker|SourceSystemOwner|SourceSystemId|EffectiveStartDate|PersonNumber|StartDate|DateOfBirth|ActionCode MERGE|Worker|EMP|HDL001|2001/09/08|HDL-1001|2001/09/08|1952/05/21|HIRE MERGE|Worker|EMP|HDL002|2005/02/08|HDL-1002|2005/02/08|1966/04/21|HIRE

• Each Worker record is uniquely identified by a source key using attributes SourceSystemOwner and SourceSystemId . For example, EMP and HDL001, EMP and HDL002.
• The PersonNumber attribute is the user key and is also provided, but if you are auto-generating person numbers, you don't need to supply it.
• An ActionCode of HIRE is needed for new hires.
• The value for EffectiveStartDate must match the StartDate value when creating new hires.

Person Name

The PersonName component records your employee's name.

Add these file lines to your file: METADATA|PersonName|SourceSystemOwner|SourceSystemId|EffectiveStartDate|PersonId(SourceSystemId)|NameType|LegislationCode|Title|LastName|FirstName MERGE|PersonName|EMP|HDL001_NME|2001/09/08|HDL001|GLOBAL|US|MR.|Wells|Christopher MERGE|PersonName|EMP|HDL002_NME|2005/02/08|HDL002|GLOBAL|US|MRS.|Hugh|Lorraine

• Each PersonName record is uniquely identified by a source key using attributes SourceSystemOwner and SourceSystemId . For example, EMP, HDL001_NME .
• To identify the Worker record each PersonName is for, the source system ID is supplied to the parent surrogate ID attribute PersonId with the (SourceSystemId) hint. For example, HDL001 , or HDL002 .
• The LegislationCode determines the valid values for the Title attribute, so if you change the legislation code you may need to change the value for the Title attribute.
• The value for EffectiveStartDate on the PersonName record must match that of the Worker record for new hires.

Person Legislative Data

The PersonLegislativeData component is where you capture your employee's marital status and gender.

Add these files lines to your file: METADATA|PersonLegislativeData|SourceSystemOwner|SourceSystemId|EffectiveStartDate|PersonId(SourceSystemId)|LegislationCode|Sex|MaritalStatus MERGE|PersonLegislativeData|EMP|HDL001_LEG|2001/09/08|HDL001|US|M|M MERGE|PersonLegislativeData|EMP|HDL002_LEG|2005/02/08|HDL002|US|F|S

• Each PersonLegislativeData record is uniquely identified by a source key. For example, EMP, HDL001_LEG.
• The parent record is Worker, so you reference the parent record in the same way as for PersonName, using the PersonId(SourceSystemId) attribute.
• The LegislationCode determines the valid values for the Sex and MaritalStatus attributes.
• The value for EffectiveStartDate on the PersonLegislativeData record must match that of the Worker record for new hires.

Work Relationship

The WorkRelationship component specifies the legal employer for your employee.

Add these file lines to your file: METADATA|WorkRelationship|SourceSystemOwner|SourceSystemId|PersonId(SourceSystemId)|LegalEmployerName|DateStart|WorkerType|PrimaryFlag MERGE|WorkRelationship|EMP|HDL001_POS|HDL001|Vision Corporation|2001/09/08|E|Y MERGE|WorkRelationship|EMP|HDL002_POS|HDL002|Vision Corporation|2005/02/08|E|Y

• Each WorkRelationship record is uniquely identified by a source key. For example, EMP, HDL001_POS.
• The parent record is still the Worker record, so you reference the parent record using the PersonId(SourceSystemId) attribute.
• The DateStart attribute value must match the StartDate supplied on the parent Worker record for new hires.

Employment Terms

Employment Terms are no longer visible in Oracle HCM, but you still need to supply a skeleton WorkTerms record to link the WorkRelationship and Assignment records.

Add these file lines to your file: METADATA|WorkTerms|SourceSystemOwner|SourceSystemId|PeriodOfServiceId(SourceSystemId)|ActionCode|EffectiveStartDate|EffectiveSequence|EffectiveLatestChange|AssignmentName|AssignmentNumber|PrimaryWorkTermsFlag MERGE|WorkTerms|EMP|HDL001_TRM|HDL001_POS|HIRE|2001/09/08|1|Y|ET-HDL001|ET-HDL001|Y MERGE|WorkTerms|EMP|HDL002_TRM|HDL002_POS|HIRE|2005/02/08|1|Y|ET-HDL002|ET-HDL002|Y

• Each WorkTerms record is uniquely identified by a source key. For example, EMP, HDL001_TRM.
• The parent for the WorkTerms is the WorkRelationship , the parent record is identified by the PeriodOfServiceId attribute. The SourceSystemId hint is added, so you can supply the source key to identify the parent work relationship.
• As the parent work relationship record and the local employment terms record both use the same source system owner, both references use the same SourceSystemOwner attribute.
• The EffectiveStartDate value must match that on the WorkRelationship record.

The Assignment record type is where you specify your employee's department, location, position, job etc.

Add these file lines to your file: METADATA|Assignment|SourceSystemOwner|SourceSystemId|ActionCode|EffectiveStartDate|EffectiveSequence|EffectiveLatestChange|WorkTermsAssignmentId(SourceSystemId)|AssignmentName|AssignmentNumber|AssignmentStatusTypeCode|PersonTypeCode|BusinessUnitShortCode|PrimaryAssignmentFlag|JobId(SourceSystemId)|JobId(SourceSystemOwner) MERGE|Assignment|EMP|HDL001_ASG|HIRE|2001/09/08|1|Y|HDL001_TRM|HDL001|HDL001|ACTIVE_PROCESS|Employee|Vision Operations|Y|SCN|VISION MERGE|Assignment|EMP|HDL002_ASG|HIRE|2005/02/08|1|Y|HDL002_TRM|HDL002|HDL002|ACTIVE_PROCESS|Employee|Vision Operations|Y|MGR|VISION

• Each Assignment record is uniquely identified by a source key. For example, EMP, HDL001_ASG.
• The parent for the Assignment is the WorkTerms , the parent record is identified by the WorkTermsAssignmentId attribute. Using the SourceSystemId hint, you can use the source key to identify the parent WorkTerms record.

To specify a different source system owner for a foreign object reference, add the SourceSystemOwner hint to the attribute that identifies the foreign object, i.e., JobId(SourceSystemOwner) .

• The EffectiveStartDate value must match that on the WorkTerms record.

Task 3: Save and Upload Your File

• Save your file, naming it Worker.dat . Alternatively, download and edit the Worker.dat file.
• Compress (zip) the Worker.dat into a filename of your choice, but it must have a .zip file extension.
• Upload your file using HCM Data Loader.

The following tutorials will further expand your HCM Data Loader knowledge:

• Understanding HCM Data Loader (HDL) Business Objects
• Loading Flexfield Segments, Images and Attachments with HCM Data Loader (HDL)
• Make Date-Effective Changes with HCM Data Loader (HDL)

Related Links

• HCM Data Loader Oracle by Example Tutorials
• Overview of HCM Data Loader
• Overview of Loading Workers

Acknowledgements

• Authors - Ema Johnson (Senior Principal Product Manager)

More Learning Resources

Explore other labs on docs.oracle.com/learn or access more free learning content on the Oracle Learning YouTube channel . Additionally, visit education.oracle.com/learning-explorer to become an Oracle Learning Explorer.

For product documentation, visit Oracle Help Center .

Create and Load New Hires with HCM Data Loader (HDL)

August 2024

‘Let the N***ers Dig’: Florida Construction Company That Allegedly Made Black Workers Do ‘Degrading’ Tasks Must Pay $1.6M to Settle Civil Rights Lawsuit

Black and Hispanic workers at a Florida construction company who endured racial and ethnic slurs and were given “degrading” assignments were awarded $1.6 million in a settlement of a civil rights lawsuit filed on their behalf by the U.S. Equal Employment Opportunity Commission (EEOC).

J.A. Croson, a Sorrento, Florida-based plumbing and HVAC contractor, agreed last month to pay $850,000 to three Black employees who sued for racial discrimination and retaliation after they were fired or forced to quit. The company must also set up a $750,000 fund to compensate other former Black and Hispanic employees who said they suffered harassment and discrimination in a hostile work environment.

According to the lawsuit filed in the U.S. District Court for the Middle District of Florida in 2022, several managers who supervised plumbers and plumber’s helpers working in an apprenticeship program at the company’s construction projects regularly called Black employees “n***er,” “boy,” and “biscuit lips,” and referred to Hispanic workers as “wetbacks,” “stupid Mexicans,” and “f-cking Puerto Ricans.”

Some managers openly displayed Confederate flags on their personal vehicles and held team meetings in workplace parking lots “in view of the Confederate flags,” the complaint alleged.

Black and Hispanic employees were disproportionately assigned manual labor assignments that didn’t count as credit towards the company’s apprenticeship program, the complaint said. White employees were generally assigned favorable tasks, such as plumbing and pipework, while Black and Hispanic employees performed more laborious work, such as digging deep ditches with a shovel and using a jackhammer for days at a time.

Joseph Hollis, one of the lead plaintiffs, reported in an amended complaint filed in March 2023 that he heard one supervisor say, “Let the n***ers dig,” in reference to Black workers being given such assignments, the lawsuit said.

Hollis also said he was ordered to haul hundreds of toilets on his own “with no motorized tools of any kind from the roof of a building to the first floor” and that managers “spewed racist comments to employees” in and outside of the workplace.

White employees also got regular access to company trailers, while Black and Hispanic workers did not, the complaint said.

When two Black employees, Ernest Hankerson and Cyrus Hawthorne, complained in December of 2020 to management about the ongoing racial harassment and unfair workload, they were initially given “less desirable work assignments” and then fired.

Hollis, who had worked at J.A. Croson since 2013, complained to supervisors about his grueling workload and also about a manager’s display of the Confederate flag in the workplace. He quit in 2021 due to the ongoing harassment and inequitable work practices.

The EEOC alleged that the conduct reported by J.A. Croson’s Black and Hispanic employees violated Title VII of the Civil Rights Act of 1964, which prohibits racial harassment and retaliation and requires employers to promptly investigate and stop misconduct after they become aware of it, the commission noted in a press release .

In its answer to the EEOC complaint in November of 2022, J.A. Croson categorically denied all of the allegations by its employees and said that all of its employment decisions were “based on legitimate, non-discriminatory and non-retaliatory reasons” unrelated to the plaintiff’s race or national origin.

The company said it “took affirmative measures to provide a workplace free from unlawful discrimination, harassment and retaliation” and that “any adverse employment action” suffered by the plaintiffs was “the direct and proximate result of their own misconduct in the workplace.”

In the consent decree settling the case in July, the company again denied the allegations in the lawsuit and that it had violated Title VII.

“It is J.A. Croson’s position that settlements are favored over continued, costly and uncertain litigation,” the agreement stated.

Besides paying $1.6 million to compensate 17 Black and Hispanic former employees, the three-year consent decree requires J.A.Croson to set up an employee relations hotline for employees to submit complaints, assign an investigator to address harassment and retaliation complaints and provide training to its employees on recognizing workplace harassment. The EEOC will monitor the company’s compliance with these new equal opportunity employment policies and practices over the next three years.

The consent decree says J.A. Croson’s employees and managers are forbidden to use racial or ethnic slurs referring to the race or national origin of its workers and specifically prohibits the use of the N-word. The company is also barred from making work assignments on the basis of race or national origin.

U.S. District Court Judge Marcia Morales Howard ordered J.A. Croson to pay a total sum of $850,000 in back pay and compensatory damages to Hankerson, Hawthorne, and Hollis, who are free to reapply for jobs at the company and to talk about their complaints.

Howard also ordered the company to deposit $750,000 in a qualified settlement fund, which the company’s other Black and Hispanic employees can make claims on. The EEOC will evaluate their claims of discrimination and harassment and determine if and how much those employees will be compensated based on the severity and duration of their experiences and the alleged emotional harm they suffered.

Miami employment lawyer Gary A. Costales, who represented three former employees in the case, told McClatchy News on Aug. 28 that his clients “are hard-working people who had never been involved in a matter as this one. … We were fortunate to have the EEOC spring into action in the public interest. We are gratified to have achieved this result,” the Bellingham Herald reported .

“Unfortunately, the reality is that race and national origin harassment continues to pervade the construction industry,” said EEOC Miami Regional Attorney Kristen Foslid. “This consent decree sends a message that employers must ensure that workers at construction sites are free from harassment and receive fair work assignments.”

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