Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Sort & Filter functions

Waste Sorting

## Overview

Waste Sorting is a dataset for object detection tasks - it contains Waste annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [BY-NC-SA 4.0 license](https://creativecommons.org/licenses/BY-NC-SA 4.0).

Weight Sorting

## Overview

Weight Sorting is a dataset for object detection tasks - it contains Size1 Size2 Size3 annotations for 704 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

184 views (3 recent) Dataset extent Map data © OpenStreetMap contributors. What is mobile sorting? It is a trailer fitted out and towed by a vehicle. Once on the site, the trailer unfolds to allow the public to be received and a ramp gives access to a sorting platform. This system allows residents to deposit their small bulky items in the specially designed boxes and crates adapted for the different types of waste, in order to encourage sorting and recycling.

We propose a novel methodology to uncover the sorting pattern in labor markets. We identify the strength of sorting solely from a ranking of firms by profits. To discern the sign of sorting, we build a noisy ranking of workers from wage data. Our test for the sign of sorting is consistent even with noisy worker rankings. We apply our approach to a panel dataset that combines social security earnings records with detailed financial data for firms in the Veneto region of Italy. We find robust evidence of positive sorting. The correlation between worker and firm types is about 52%.

Replication package for Submission "Identifying Energy Efficiency Patterns in Sorting Algorithms via Abstract Syntax Tree Mining".

Authors kept anonymous for review.

There are 1212 different arrays in each of the two data sets. These arrays are categorized into 5 different groups. In the first group, we have 100 arrays with different sizes. The first array in this group consists of 1000 numbers and the size of each successive array increases by 1000 and the size of the last array is 100000. In the second group, we have 180 arrays with different sizes. The first array in the second group consists of 105000 numbers and the size of each successive array increases by 5000 and the size of the last array is 1000000. In the third group, we have 100 arrays with different sizes. The first array in this group consists of 1010000 numbers and the size of each successive array increases by 10000 and the size of the last array is 2000000. In the fourth group, we have 20 arrays with different sizes. These sizes are 2050000, 2100000, 2150000,…, 3000000, respectively. The dimensions of 4 arrays in the last group with different sizes are 3250000, 3500000, 3750000, and 4000000. Thus, in each group, there are 404 arrays of different sizes. We obtained 1212 arrays in each data set by randomly generating three arrays of each size. In the tests, the algorithms were run for 3 arrays of the same size, and the average of the running times was taken as the running time for an array of this size. The numbers in both data sets are floating point numbers and are not ordered because they are randomly generated. In the first data set with a uniform distribution, the numbers vary randomly between 0 and 30000000. A standard normal distribution was used for the numbers with a Gaussian distribution.

Citation: Please give an refernces for the using the dataset to authors of the dataset: Sahin Emrah AMRAHOV, Yilmaz AR, Bulent TUGRUL, Bekir Emirhan AKAY, Nermin KARTLI Future Generation Computer Systems

DOI: https://doi.org/10.1016/j.future.2024.03.049

Source: Time.perf_counter() Work: Myself Form: csv Img: graph of example Code from other ide spder. Will be pasted as notebook.

This code is implements the nondominated sorting genetic algorithm (NSGA-II) in the R statistical programming language. The function is theoretically applicable to any number of objectives without modification. The function automatically detects the number of objectives from the population matrix used in the function call. NSGA-II has been applied in ARS research for automatic calibration of hydrolgic models (whittaker link) and economic optimization (whittaker link). Resources in this dataset:Resource Title: Non-dominated Sorting Genetic Algorithm-II. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=393&modecode=20-72-05-00 download page

Meet Thomas McElrath, a insect collection manager at the Illinois Natural History Survey and beetle researcher. Tommy explains the value of data standards while discussing beetles and variations in sex.

Comprehensive dataset containing sorting algorithm performance metrics, natural sorting patterns, and multi-level sorting efficiency data used in the Advanced Text Sorter tool.

1) Data Introduction • The Harry Potter Sorting Dataset contains various attributes and Hogwarts dorm assignments of 1,000 virtual students in Harry Potter's worldview, and is designed to be used in machine learning classification exercises such as dorm classification based on student tendencies.

2) Data Utilization (1) Harry Potter Sorting Dataset has characteristics that: • Each student contains the actual assigned Hogwarts dorm (House) information, along with several attribute columns, including name, gender, ancestry, region of origin, personality traits, and magic-related abilities. • The House is divided into four categories: Gryffindor, Slytherin, Ravenclaw, and Hufflepuff. (2) Harry Potter Sorting Dataset can be used to: • Development of boarding classification model: Using student characteristic data, we can build a Hogwarts House classification machine learning model and evaluate prediction accuracy. • Data Science Practice and Training: It can be used for data science and machine learning training practices, such as characteristic selection, data preprocessing, and classification modeling.

Bottle Sorting

## Overview

Bottle Sorting is a dataset for object detection tasks - it contains Bottles annotations for 2,498 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [MIT license](https://creativecommons.org/licenses/MIT).

Knuth introduced the problem of sorting with a sequence of stacks. Tarjan extended this idea to sorting with acyclic networks of stacks (and queues), where items to be sorted move from a source through the network to a sink while they may be stored temporarily at nodes (the stacks). Both characterized which permutations are sortable; but complexity of sorting was not an issue.

Sorting in sequential data mining is significantly improved through hardware acceleration, which becomes essential as data volume and complexity increase. This paper presents a scalable hybrid sorting network that maintains or improves performance while reducing computational load and hardware requirements. The network is composed of the pre-comparison odd--even sorting network (P-OESN) and the bidirectional insertion sorting network (BISN). A pre-comparison layer is introduced to the original OESN. This layer aims to place larger values in the first half of the input sequence and smaller values in the latter half. The number of iterations is reduced when the P-OESN transitions from fully parallel execution to iterative execution. A novel pipelined BISN architecture is proposed, which leads to enhanced operating frequency and throughput. The experimental results show that the pre-comparison layer reduces the number of iterations by 6\% to 50\%. Throughput is improved by more than four times, and operating frequency is increased by more than two times due to the pipelined BISN. The proposed hybrid sorting network reduces sorting time or resource usage, while enabling the sorting of large-scale data sets that other methods cannot support.

The order-of-addition (OofA) experiment has received a great deal of attention in the recent literature. The primary goal of the OofA experiment is to identify the optimal order in a sequence of m components. All the existing methods are model-dependent and are limited to small number of components. The appropriateness of the resulting optimal order heavily depends on (a) the correctness of the underlying assumed model, and (b) the goodness of model fitting. Moreover, these methods are not applicable to deal with large m (e.g., m≥7). With this in mind, this article proposes an efficient adaptive methodology, building upon the quick-sort algorithm, to explore the optimal order without any model specification. Compared to the existing work, the run sizes of the proposed method needed to achieve the optimal order are much smaller. Theoretical supports are given to illustrate the effectiveness of the proposed method. The proposed method is able to obtain the optimal order for large m (e.g., m≥20). Numerical experiments are used to demonstrate the effectiveness of the proposed method.

The data-requirement sort operation aggregates and returns the parameters and data requirements for a resource and all its dependencies as a single module definition.

Waste Sorting

## Overview

Waste Sorting is a dataset for object detection tasks - it contains Glass Metal Paper Plastic annotations for 1,198 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Data and code for replicating `Sorting with Teams'

FLO wants to set a roadmap for sales and marketing activities. In order for the company to make a medium-long-term plan, it is necessary to estimate the potential value that existing customers will provide to the company in the future.

The dataset consists of information obtained from the past shopping behavior of customers who made their last purchases from OmniChannel (both online and offline) between 2020 and 2021.

Columns - master_id: Unique client number - order_channel : Which channel of the shopping platform is used (Android, ios, Desktop, Mobile, Offline) - last_order_channel : The channel where the last purchase was made - first_order_date : The date of the first purchase made by the customer - last_order_date : The date of the last purchase made by the customer - last_order_date_online : The date of the last purchase made by the customer on the online platform - last_order_date_offline : The date of the last purchase made by the customer on the offline platform - order_num_total_ever_online : The total number of purchases made by the customer on the online platform - order_num_total_ever_offline : Total number of purchases made by the customer offline - customer_value_total_ever_offline : The total price paid by the customer for offline purchases - customer_value_total_ever_online : The total price paid by the customer for their online shopping - interested_in_categories_12 : List of categories the customer has shopped in the last 12 months