Data Preparation. (s,t)∈→−x x=1 i=1 P ((i, j)|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There-
Data Preparation. The Contractor approaches data preparation in a way that is ongoing, automated wherever feasible, scalable, and auditable. The Contractor’s preparation approach must be flexible and extensible to future data sources as well, including State datasets and systems. For the CCRS, data preparation will consist of the following at a minimum:
1. The ability to perform data matching, deduplication, cleaning, and other needed data processing across both current datasets and future State datasets for identified data.
2. Reports that monitor ongoing data preparation processes, including, for example, the success of data matching, de-duplication, and more (e.g., metadata).
3. Workflow for onboarding new datasets into the existing data preparation process.
4. Data preparation activities apply to both Phase 1 and Phase 2.
5. Volume Transaction fee is included as part of the monthly ODX and Diameter transaction fee up to 150,000 message per day. (please see Costing sheet line #13).
6. In excess of the 150,000 messages per day, additional Tiered pricing transaction fee applies and is available on the CA Costing sheet. The additional Transaction per day Fee is based on the: Low and High message volume will be based off of average daily transaction for month. Tier pricing does not apply to Phase 1 historical data conversion.
7. In excess of 150,000 messages per day, Optum will calculate the average daily transaction for the month and will provide a Work Order Authorization (WOA) document reporting the daily excess messages. CDPH will review the excess messages report and the associated tiered pricing. Approval of the excess message report will be provided through WAD by CDPH and will be used by Optum for invoicing.
Data Preparation. We manually selected 10 conversations from the CallHome Corpus based on their audio quality. Each conversation lasts around 30 minutes, but the reference transcript only covers 10 minutes of the audio. Therefore, we cut each of the audio into a 10- minute clip and transcribe it using Amazon and RevAI separately. The transcription output from both RevAI and Amazon Transcribe is provided in JSON format. For RevAI, the output consists of a list of monologues, with each monologue containing speaker information and a list of elements representing indi- vidual tokens, including text, punctuation, and timestamps. In contrast, Amazon’s output is structured with separate lists for transcripts, speaker labels, and items. The transcripts list contains the entire transcript as a single string, while the speaker labels list stores diarization results as speaker segments. The items list contains individual tokens with timestamps and confidence scores.
Data Preparation. The data available from various sources was collected. The ground maps, contour information, etc. were scanned, digitized and registered as per the requirement. Data was prepared depending on the level of accuracy required and any corrections required were made. All the layers were geo-referenced and brought to a common scale (real coordinates), so that overlay could be performed. A computer programme was used to estimate the soil loss. The formats of outputs from each layer were firmed up to match the formats of inputs in the program. The grid size to be used was also decided to match the level of accuracy required, the data availability and the software and time limitations. The format of output was finalized. Ground truthing and data collection was also included in the procedure.
Data Preparation. Organize the data by property type and attribute land values accordingly. Organize the data into easily understandable charts and maps that will be used for land use to valuation comparisons.
Data Preparation s,t⟩∈→−x x=1 i=1 P (⟨i, j⟩|f (t), e(s); ←θ−),, (31) Although it is appealing to apply our approach to dealing with real-world non-parallel corpora, it is time-consuming and labor-intensive to manually construct a ground truth parallel corpus. There- where P ( i, x x(s), f (t); →−θ ) is source-to-target link posterior probability of the link i, j be- ing present (or absent) in the word align- ment according to the source-to-target model, P ( i, x x (t), e(s); ←θ−) is target-to-source link pos- terior probability. We follow Xxxxx et al. (2006) to use the product of link posteriors to encourage the agreement at the level of word alignment. xxxx, we follow Xxxx et al. (2015) to build syn- thetic E, F , and G to facilitate the evaluation. We first extract a set of parallel phrases from a sentence-level parallel corpus using the state- of-the-art phrase-based translation system Xxxxx (Xxxxx et al., 2007) and discard low-probability parallel phrases. Then, E and F can be con- structed by corrupting the parallel phrase set by 0.40 0.35 agreement ratio 0.30 0.25 noise inner outer no agreement iteration C → E E → C Outer Inner 0 10K 41.0 54.4 83.6 83.8 0 20K 28.3 48.3 80.1 81.2 10K 0 54.7 43.1 84.9 84.3 20K 0 50.4 31.4 83.8 83.6 10K 10K 34.9 34.4 80.0 79.7 20K 20K 22.4 23.1 73.6 74.3 Table 2: Effect of noise in terms of F1 on the de- velopment set. Figure 4: Comparison of agreement ratios on the development set. seed C → E E → C Outer Inner Table 1: Effect of seed lexicon size in terms of F1 on the development set. adding irrelevant source and target phrases ran- domly. Note that the parallel phrase set can serve as the ground truth parallel corpus G. We refer to the non-parallel phrases in E and F as noise. From LDC Chinese-English parallel corpora, we constructed a development set and a test set. The development set contains 20K paral- lel phrases, 20K noisy Chinese phrases, and 20K noisy English phrases. The test test contains 20K parallel phrases, 180K noisy Chinese phrases, and 180K noisy English phrases. The seed parallel lex- icon contains 1K entries.
Data Preparation. In preparing the data for subsequent analyses, several iterations were required to detect potential outliers, errors and other data anomalies. Reviews included multiple scatter plot comparisons, source plot card reviews, as well as between-measurement data checks. Corrections were made where noted, and plot measurement deletions only occurred in a few instances. SAS programs were written so that compilations could be easily adjusted or modified (e.g., changes in utilization standards). All SAS programs and input data files will be made available to ASRD.
Data Preparation. All documents, instruments and data supplied by Client to TCS will be supplied in accordance with the previously agreed upon time requirements and specifications set forth in Schedule 1. Client shall be responsible for all consequences of its failure to supply TCS with accurate documents and data within prescribed time periods. Client agrees to retain duplicate copies of all documents, instruments and data supplied by Client to TCS hereunder; or, if the production and retention of such copies is not practical, Client holds TCS blameless for loss or damage to said documents. Client is responsible for the accuracy and completeness of its own information and documents and Client is responsible for all of its acts, omissions and representations pertaining to or contained in all such information or documents. Unless Client previously informs TCS in writing of exceptions or qualifications, TCS has the right to rely upon the accuracy and completeness of the information and documents provided by Client and TCS assumes no liability for services performed in reliance thereon. TCS shall inform Client of any erroneous, inaccurate or incomplete information or documents from the Client to the extent such becomes apparent or known to TCS. However, unless expressly accepted in writing as a part of the service to be performed, TCS shall have no obligation to audit or review Client's information or documents for accuracy or completeness.
Data Preparation. Esri will support the City with preparing the source data requested as part of Task 2. The prepared data will then be published as feature services to the City’s ArcGIS Online Organization (AGOL), enabling these services to be used and manipulated by ArcGIS Urban once it has been deployed. It is anticipated the following data preparation steps will be performed: Reproject data to appropriate coordinate system. Clean up parcel geometries using geoprocessing tools (repair geometry, generalize, multipart to single part, etc.). Assign standard road classification to centerlines. Assign parcel edge information. Interpret zoning code parameters (e.g., floor area ratio [FAR], setbacks, heights, coverage) for up to 5 zones, 1 overlay, 5 current land uses, and 5 future land uses. Prepare approximately 10 residential and nonresidential space uses and building types based on the development typologies identified in Task 2. Load parcel, zoning, project, plan, and indicator geometries and attributes into the ArcGIS Urban data model. Publish loaded layers as feature services to the City’s AGOL. Once all necessary feature services are published, Esri will support the City by conducting the following ArcGIS Urban deployment tasks: Populate ArcGIS Urban configuration tables to read to the previously published services, including previously created services for existing 3D buildings. Configure ArcGIS Online permissions, enabling specified groups and accounts to access the ArcGIS Urban Web application. Configure the plan area, focused project, and up to four custom indicators identified during the project kickoff meeting and deployed to ArcGIS Urban. Esri anticipates configuration include tasks such as adding descriptions, URL links, charts, etc., to the deployed features using the Web-based interface.
Data Preparation. HDM-4’s required input is organized into data sets that describe road networks, vehicle fleets, pavement preservation standards, traffic and speed flow patterns, and climate conditions. Most of the required pavement performance information was obtained from 2002 data within the Washington State Pavement Management System (WSPMS) (Xxxxxxxxxxxx et al., 2002). Other data were obtained through available literature and interviews with WSDOT personnel. used for HDM-4 input and included passenger cars, single-unit trucks, double-unit trucks, and truck trains (Xxxxxxxxxxxx et al., 2003). Specific inputs shown in Table 1 are not described in this report. Table 1: Maintenance standard of 45-mm HMA overlay in HDM-4 version 1.3 General Name: 45-mm HMA Overlay Short Code: 45 OVER Intervention Type: Responsive Design Surface Material: Asphalt Concrete Thickness: 45 mm Dry Season a: 0.44 CDS: 1 Intervention Responsive Criteria: Total cracked area ≥ 10% or Rutting ≥ 10 mm or IRI ≥ 3.5 m/km Min. Interval: 1 Max. Interval: 9999 Last Year: 2099 Max Roughness: 16 m/km Min ADT: 0 Max ADT: 500,000 Costs Overlay Economic: 19 dollars/m2 * Financial: 19 dollars/m2 * Economic: 47 dollars/m2 * Financial: 47 dollars/m2 * Economic: 47 dollars/m2 Financial: 47 dollars/m2 Effects Roughness: Use generalized bilinear model a0 = 0.5244 a1 = 0.5353 a2 = 0.5244 a3 = 0.5353 Rutting: Use rutting reset coefficient = 0 Texture Depth: Use default values (0.7 mm) Skid Resistance: Use default value (0.5 mm) A BST surface application is triggered when the total area of pavement cracking is ≥ 10 percent of the total roadway area. Table 2 lists the major inputs. Table 2: Maintenance standard of BST resurfacing in HDM-4 version 1.3 General Name: BST resurfacing Short Code: BSTCRA Intervention Type: Responsive Design Surface Material: Double Bituminous Surface Treatment Thickness: 12.5 mm Dry Season a: 0.2 CDS: 1 Intervention Responsive Criteria: total cracked area ≥ 10% Min. Interval: 1 Max. Interval: 100 Max Roughness: 16 m/km Max ADT: 100,000 BST Economic: 2.04 dollars/m2 * BST Financial: 2.04 dollars/m2 * Patching Economic: 47 dollars/m2 * Patching Financial: 47 dollars/m2 * Edge Repair Economic: 47 dollars/m2 * Edge Repair Financial: 47 dollars/m2 * Crack Seal Economic: 8.5 dollars/m2 * Crack Seal Financial: 8.5 dollars/m2 * Effects Roughness: Use user defined method Roughness: 2 m/km Mean rut depth: 0 mm Texture Depth: 0.7mm Skid Resistance: 0.5mm different road widths (narrow, standard, and wide)...
