The address verification tools market is experiencing robust growth, driven by the increasing need for accurate and reliable address data across various industries. The market's expansion is fueled by several key factors, including the rise of e-commerce, stringent regulatory compliance requirements, and the growing adoption of cloud-based solutions. Businesses across all sizes, from large enterprises to SMEs, are increasingly relying on these tools to improve data quality, reduce fraud, and enhance customer experiences. The shift towards digitalization and the expanding global reach of businesses further contribute to the market's expansion. A significant trend is the integration of address verification tools with other business solutions, creating a more streamlined and efficient workflow. This integration enhances data accuracy and reduces manual intervention, leading to cost savings and increased productivity. While the market faces challenges such as data privacy concerns and the complexities of handling international addresses, innovative solutions are emerging to address these limitations. The market is segmented by application (large enterprises, SMEs) and type (cloud-based, on-premise), with cloud-based solutions gaining significant traction due to their scalability, flexibility, and cost-effectiveness. The North American market currently holds a substantial share, driven by the high adoption of e-commerce and advanced technological infrastructure; however, other regions, particularly Asia Pacific, are showing promising growth potential. The competitive landscape is diverse, with both established players and emerging startups vying for market share. Overall, the address verification tools market presents a compelling investment opportunity, exhibiting strong growth prospects in the coming years. The projected Compound Annual Growth Rate (CAGR) suggests a substantial increase in market value over the forecast period (2025-2033). Considering the mentioned companies and the market segmentation, it is reasonable to anticipate continued market penetration across various sectors, including finance, logistics, healthcare, and government. The market will likely see further innovation in areas such as AI-powered address verification and enhanced integration with other data management solutions. The ongoing focus on data security and privacy will also shape the development and adoption of address verification tools, leading to more robust and compliant solutions. Competition will likely intensify, with companies focusing on differentiation through superior accuracy, ease of use, and comprehensive functionality. Geographic expansion into emerging markets will also play a significant role in overall market growth, driven by the increasing adoption of digital technologies and the expanding e-commerce sector in those regions.

The dataset is sourced from the event matching part of the 2021 Sohu Campus Text Matching Algorithm Competition（https://www.biendata.xyz/competition/sohu_2021/). The event matching datasets released in the preliminary and final rounds were merged, and the event matching parts of short text and short text, short text and long text, and long text and long text were selected. 20% were used as the test set, the remaining 20% were used as the validation set, and the rest were used as the training set.

The address verification software market is experiencing robust growth, driven by the increasing need for accurate and reliable address data across various sectors. The market's expansion is fueled by several key factors, including the rising adoption of e-commerce, stringent regulatory compliance requirements demanding accurate address information, and the increasing prevalence of fraud prevention initiatives. Businesses, particularly SMEs and large enterprises, are increasingly reliant on address verification software to streamline operations, reduce errors, and improve customer experience. Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of integration with existing systems. The market is segmented by application (SMEs and large enterprises) and type (cloud-based and on-premise). While on-premise solutions still hold a market share, the shift towards cloud-based offerings is undeniable, reflecting the broader technological trends favoring cloud infrastructure. Competitive landscape analysis reveals a mix of established players and emerging companies vying for market share, indicating a dynamic and innovative market environment. North America currently dominates the market, followed by Europe and Asia-Pacific, reflecting the higher adoption rates in these regions. However, growth potential in emerging markets remains substantial, presenting lucrative opportunities for existing and new market entrants. Geographic expansion, enhanced features such as real-time data updates and advanced fraud detection capabilities, and strategic partnerships are key strategies being adopted by companies to increase their market share and solidify their position in this rapidly evolving market. The forecast period of 2025-2033 suggests continued expansion, driven by the increasing adoption of digital technologies, growing data volumes, and the need for enhanced data quality. While the market faces some restraints, such as initial implementation costs and the need for ongoing maintenance, the overall benefits of improved accuracy, efficiency gains, and reduced risks associated with inaccurate address data outweigh these challenges. Therefore, continued growth is expected, with a focus on innovation and development of more advanced and integrated solutions. The market is poised for significant expansion as more businesses recognize the crucial role of accurate address data in improving their bottom line and ensuring regulatory compliance.

Extracting useful and accurate information from scanned geologic and other earth science maps is a time-consuming and laborious process involving manual human effort. To address this limitation, the USGS partnered with the Defense Advanced Research Projects Agency (DARPA) to run the AI for Critical Mineral Assessment Competition, soliciting innovative solutions for automatically georeferencing and extracting features from maps. The competition opened for registration in August 2022 and concluded in December 2022. Training and validation data from the competition are provided here, as well as competition details and baseline solutions. The data are derived from published sources and are provided to the public to support continued development of automated georeferencing and feature extraction tools. References for all maps are included with the data.

The USPS Address Verification market is experiencing robust growth, driven by the increasing need for accurate and reliable address data across various sectors. E-commerce, logistics, and financial institutions are key drivers, demanding high-quality address validation to streamline operations, reduce errors, and improve customer experience. The market's expansion is fueled by the rising adoption of cloud-based solutions, offering scalability and cost-effectiveness compared to on-premise systems. Furthermore, the growth of SMEs and large enterprises adopting digital transformation strategies necessitates robust address verification capabilities for improved data quality and compliance. While the market shows strong potential, challenges remain, including data privacy concerns and the complexities of maintaining accuracy across constantly evolving address databases. Competition is fierce amongst established players and emerging technology providers, leading to innovation in areas such as AI-powered address correction and real-time verification. The market segmentation reveals a clear preference for cloud-based solutions due to their flexibility and ease of integration. Large enterprises represent a significant revenue segment, contributing to the overall market size, estimated at $2.5 billion in 2025 and projected to reach $3.5 billion by 2033, indicating a healthy CAGR. North America currently dominates the market, with the United States being the key contributor. However, significant growth opportunities are anticipated in Asia-Pacific and Europe, driven by increasing e-commerce penetration and digitalization initiatives within these regions. This expansion is despite certain restraints such as the cost of implementation and maintenance of address verification systems, as well as the ongoing need for continuous data updates to ensure accuracy.

The USPS Address Verification market is experiencing robust growth, driven by increasing e-commerce transactions and the stringent need for accurate address data across various industries. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $6.5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of e-commerce necessitates precise address validation to minimize delivery failures and return shipping costs, a significant concern for businesses of all sizes. Secondly, regulatory compliance mandates accurate addressing for various industries, including financial services and healthcare, driving adoption of address verification solutions. Thirdly, the increasing availability of cloud-based solutions offers greater accessibility and scalability, attracting both Small and Medium-sized Enterprises (SMEs) and large enterprises. The market segmentation reveals a significant share captured by cloud-based solutions due to their flexibility and cost-effectiveness, while large enterprises represent a larger revenue segment due to their higher transaction volumes. Geographic expansion is another significant trend, with North America currently holding the largest market share, followed by Europe and Asia Pacific, showing strong growth potential in developing economies. However, challenges such as data privacy concerns and the complexity of integrating address verification systems into existing workflows act as restraints to the market's growth. Competition in the USPS Address Verification market is intense, with a mix of established players like Experian and LexisNexis, and specialized providers like Smarty and Melissa Data. These companies are constantly innovating to improve accuracy, speed, and integration capabilities of their solutions. Furthermore, the market is witnessing increased adoption of advanced technologies such as AI and machine learning to enhance address validation accuracy and efficiency. The overall market outlook remains positive, with continued growth expected driven by the unwavering demand for reliable address data across various sectors and technological advancements in address verification solutions. The increasing focus on data security and compliance will also play a significant role in shaping the future of this dynamic market.

This dataset describes all the matches made available. Each match is a document consisting of the following fields:- competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field "wyId" of the competition document;- date and dateutc: the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT+2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss; - duration: the duration of the match. It can be "Regular" (matches of regular duration of 90 minutes + stoppage time), "ExtraTime" (matches with supplementary times, as it may happen for matches in continental or international competitions), or "Penalities" (matches which end at penalty kicks, as it may happen for continental or international competitions);- gameweek: the week of the league, starting from the beginning of the league;- label: contains the name of the two clubs and the result of the match (e.g., "Lazio - Internazionale, 2 - 3");- roundID: indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i + 1, even tough some matches can be anticipated or postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the "roundID" indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);- seasonId: indicates the season of the match;- status: it can be "Played" (the match has officially finished), "Cancelled" (the match has been canceled for some reason), "Postponed" (the match has been postponed and no new date and time is available yet) or "Suspended" (the match has been suspended and no new date and time is available yet);- venue: the stadium where the match was held (e.g., "Stadio Olimpico");- winner: the identifier of the team which won the game, or 0 if the match ended with a draw;- wyId: the identifier of the match, assigned by Wyscout;- teamsData: it contains several subfields describing information about each team that is playing that match: such as lineup, bench composition, list of substitutions, coach and scores: - hasFormation: it has value 0 if no formation (lineups and benches) is present, and 1 otherwise; - score: the number of goals scored by the team during the match (not counting penalties); - scoreET: the number of goals scored by the team during the match, including the extra time (not counting penalties); - scoreHT: the number of goals scored by the team during the first half of the match; - scoreP: the total number of goals scored by the team after the penalties; - side: the team side in the match (it can be "home" or "away"); - teamId: the identifier of the team; - coachId: the identifier of the team's coach; - bench: the list of the team's players that started the match in the bench and some basic statistics about their performance during the match (goals, own goals, cards); - lineup: the list of the team's players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards); - substitutions: the list of team's substitutions during the match, describing the players involved and the minute of the substitution.

This package contains the datasets and supplementary materials used in the IPIN 2016 Competition (Alcalá, Spain).

Contents:

1. Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.
2. Track3_TechnicalAnnex.pdf: Technical annex describing the competition
3. 01-Logfiles: This folder contains a subfolder with the 17 training logfiles and a subfolder with the 9 blind evaluation logfiles as provided to competitors.
4. 02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps and the visualization of the training routes.
5. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 578 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.

Please, cite the following works when using the datasets included in this package:

• Torres-Sospedra, J.; Jiménez, A.; Knauth, A.; Moreira, A.; Beer, Y.; Fetzer, T.; Ta, V.-C.; Montoliu, R.; Seco, F.; Mendoza, G.; Belmonte, O.; Koukofikis, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Ebner, F.; Deinzer, F.; Vaufreydaz, D.; Dao, T.-K.; and Castelli, E. The Smartphone-based Off-Line Indoor Location Competition at IPIN 2016: Analysis and Future work Sensors Vol. 17(3), 2017. http://dx.doi.org/10.3390/s17030557
• Jimenez, A.R.; Mendoza-Silva, G.M.; Montoliu, R.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2016 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.2791530

Additional information can be found at:

• http://evaal.aaloa.org/2016/competition-home
• http://indoorloc.uji.es/ipin2016track3/

For any further questions about the database and this competition track, please contact:

• Joaquín Torres (jtorres@uji.es) Institute of New Imaging Technologies, Universitat Jaume I, Spain.
• Antonio R. Jiménez (antonio.jimenez@csic.es) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.

1) Data Introduction • The FRC Match Dataset is based on the FRST Robotics Competition (FRC) competition records from 2018 to 2025, and is a robot competition match data that includes various information such as EPA (Expected Score Contribution), match win rate, team composition, and match results for each match.

2) Data Utilization (1) FRC Match Data has characteristics that: • Each row contains numerical and categorical variables such as year, event, playoff status, match stage, winning team, EPA-based probability of victory, team name and composition, and match results, which together provide team/match performance and forecasting indicators. (2) FRC Match Data can be used to: • Prediction and Assessment of Match Results: Using EPA and past match data, machine learning models can predict match wins and losses, and prediction models can be evaluated for reliability with indicators such as Brier score. • Team Strategy and Performance Analysis: By analyzing EPA, win rate, and matchup data for each team, you can use it to understand the strategic contribution, cooperation effects, seasonal trends, and strong and weak team characteristics.

The address verification tool market is experiencing robust growth, driven by the increasing need for businesses to ensure accurate customer data for various applications. The expanding e-commerce sector, coupled with stricter regulatory compliance requirements concerning KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations, are key catalysts. Businesses across all sizes, from SMEs to large enterprises, are adopting these tools to mitigate risks associated with inaccurate addresses, such as failed deliveries, fraud prevention, and improved customer experience. The market's segmentation reveals a preference for software-based solutions, owing to their flexibility and integration capabilities with existing systems. Platform-based solutions are also gaining traction, particularly among enterprises seeking comprehensive address verification capabilities within a broader suite of services. Geographically, North America and Europe currently dominate the market due to established e-commerce infrastructure and stringent regulatory frameworks. However, Asia Pacific is projected to witness significant growth in the coming years, driven by the rapid expansion of e-commerce and digitalization across the region. The market's competitive landscape is characterized by a mix of established players and emerging innovators, each offering a range of solutions tailored to specific industry needs. This competitive pressure fosters innovation and drives down prices, making address verification tools increasingly accessible to a broader range of businesses. Looking ahead, the market is poised for continued expansion. Advancements in AI and machine learning are expected to enhance the accuracy and efficiency of address verification tools, leading to improved data quality and reduced operational costs. Increased integration with other business systems, such as CRM and ERP platforms, will further streamline workflows and enhance data management capabilities. The rising adoption of cloud-based solutions is also contributing to the market's growth, offering scalability and cost-effectiveness. Challenges remain, including the need to address data privacy concerns and the ongoing evolution of address formats across different regions. However, the overarching trend points toward sustained growth, driven by the imperative for businesses to maintain accurate data in an increasingly digital world.

This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).

Contents:

1. IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition
2. 01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.
3. 02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.
4. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.
5. 03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.

Please, cite the following works when using the datasets included in this package:

• Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.2823964
• Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389

Additional information can be found at:

• http://evaal.aaloa.org/2018/call-for-competitions
• http://ipin-conference.org/2018/ipincompetition/

For any further questions about the database and this competition track, please contact:

• Joaquín Torres (jtorres@uji.es) Institute of New Imaging Technologies, Universitat Jaume I, Spain.
• Antonio R. Jiménez (antonio.jimenez@csic.es) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.

SV-COMP 2025

Competition Results

This file describes the contents of an archive of the 14th Competition on Software Verification (SV-COMP 2025). https://sv-comp.sosy-lab.org/2025/

The competition was organized by Dirk Beyer, LMU Munich, Germany and Jan Strejček, Masaryk University, Czechia. More information is available in the following article: Dirk Beyer and Jan Strejček. Improvements in Software Verification and Witness Validation: SV-COMP 2025. In Proceedings of the 31st International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2025, Hamilton, Canada, May 3–8), 2024. Springer.

Copyright (C) 2025 Dirk Beyer and Jan Strejček https://www.sosy-lab.org/people/beyer/ https://www.fi.muni.cz/~xstrejc/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0

To browse the competition results with a web browser, there are two options:

start a local web server using php -S localhost:8000 in order to view the data in this archive, or

browse https://sv-comp.sosy-lab.org/2025/results/ in order to view the data on the SV-COMP web page.

Contents

index.html: directs to the overview web pages of the verification and validation track

LICENSE-results.txt: specifies the license

README-results.txt: this file

results-validated/: results of validation runs

results-verified/: results of verification runs

The folder results-validated/ contains the results from validation runs:

index.html: overview web page with rankings and score table

design.css: HTML style definitions

*.results.txt: TXT results from BenchExec

*.xml.bz2: XML results from BenchExec

*.fixed.xml.bz2: XML results from BenchExec, status adjusted according to the validation results

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

*.table.html: HTML views of the full benchmark set (all categories) for each validator

*.table.html: HTML views of the benchmark set for each category over all validators

*.xml: XML table definitions for the above tables

validators.*: Statistics of the validator runs (obsolete)

correctness: Infix for validation of correctness witnesses

violation: Infix for validation of violation witnesses

1.0: Infix for validation of v1.0 witnesses

2.0: Infix for validation of v2.0 witnesses

quantilePlot-*: score-based quantile plots as visualization of the results

quantilePlotShow.gp: example Gnuplot script to generate a plot

score*: accumulated score results in various formats

witness-database.csv: data base of all witnesses

witness-classification.csv: data base of all witnesses with their classification into correct, wrong, unknown

The folder results-verified/ contains the results from verification runs and aggregated results:

index.html: overview web page with rankings and score table

design.css: HTML style definitions

*.results.txt: TXT results from BenchExec

*.xml.bz2: XML results from BenchExec

*.fixed.xml.bz2: XML results from BenchExec, status adjusted according to the validation results

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

*.xml.bz2.table.html: HTML views on the detailed results data as generated by BenchExec’s table generator

*.table.html: HTML views of the full benchmark set (all categories) for each verifier

META_*.table.html: HTML views of the benchmark set for each meta category for each verifier, and over all verifiers

*.table.html: HTML views of the benchmark set for each category over all verifiers

*.xml: XML table definitions for the above tables

results-per-tool.php: List of results for each tool for review process in pre-run phase

.list.html: List of results for a tool in HTML format with links

quantilePlot-*: score-based quantile plots as visualization of the results

quantilePlotShow.gp: example Gnuplot script to generate a plot

score*: accumulated score results in various formats

The hashes of the file names (in the files *.json.gz) are useful for

validating the exact contents of a file and

accessing the files from the witness store.

Related Archives

Overview of archives from SV-COMP 2025 that are available at Zenodo:

https://doi.org/10.5281/zenodo.15012077 Verification Witnesses from SV-COMP 2025 Verification Tools. Witness store (containing the generated verification witnesses)

https://doi.org/10.5281/zenodo.15055359 Verifiers and Validators: FM-Tools Data Set for SV-COMP 2025. Metadata snapshot of the evaluated tools (DOIs, options, etc.)

https://doi.org/10.5281/zenodo.15012085 Results of the 14th Intl. Competition on Software Verification (SV-COMP 2025). Results (XML result files, log files, file mappings, HTML tables)

https://doi.org/10.5281/zenodo.15012096 SV-Benchmarks: Benchmark Set of SV-COMP 2025. Verification tasks, version svcomp24

https://doi.org/10.5281/zenodo.15007216 BenchExec, version 3.29. Benchmarking framework

All benchmarks were executed for SV-COMP 2025 https://sv-comp.sosy-lab.org/2025/ by Dirk Beyer, LMU Munich, based on the following components:

https://gitlab.com/sosy-lab/benchmarking/fm-tools 2.2

https://gitlab.com/sosy-lab/benchmarking/sv-benchmarks svcomp25

https://gitlab.com/sosy-lab/sv-comp/bench-defs svcomp25

https://gitlab.com/sosy-lab/software/benchexec 3.29

https://gitlab.com/sosy-lab/software/benchcloud 1.3.0

https://gitlab.com/sosy-lab/benchmarking/sv-witnesses 2.0.3

https://gitlab.com/sosy-lab/software/coveriteam 1.2.1

https://gitlab.com/sosy-lab/benchmarking/competition-scripts svcomp25

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

This dataset was generated using Gemini 2.0 Flash to be used as an extra validation of submissions to the Drawing with LLMs competition

The source is a kaggle notebook created as Capstone project for the Gen AI Intensive Course 2025 Q1

This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).

Contents:

1. Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.
2. Track3_TechnicalAnnex.pdf: Technical annex describing the competition
3. 01-Logfiles: This folder contains a subfolder with the 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.
4. 02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).
5. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.

Please, cite the following works when using the datasets included in this package:

• Torres-Sospedra, J.; Jiménez, A. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. http://dx.doi.org/10.3390/s18020487
• Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.2823924

Additional information can be found at:

• http://evaal.aaloa.org/2017/2017-competition-home
• http://indoorloc.uji.es/ipin2017track3/

For any further questions about the database and this competition track, please contact:

• Joaquín Torres (jtorres@uji.es) Institute of New Imaging Technologies, Universitat Jaume I, Spain.
• Antonio R. Jiménez (antonio.jimenez@csic.es) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.

Competition Results

This file describes the contents of an archive of the 10th Competition on Software Verification (SV-COMP 2021). https://sv-comp.sosy-lab.org/2021/

The competition was run by Dirk Beyer, LMU Munich, Germany. More information is available in the following article: Dirk Beyer. Software Verification: 10th Comparative Evaluation (SV-COMP 2021). In Proceedings of the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2021, Luxembourg, March 27 - April 1), 2021. Springer.

Copyright (C) Dirk Beyer https://www.sosy-lab.org/people/beyer/

SPDX-License-Identifier: CC-BY-4.0 https://spdx.org/licenses/CC-BY-4.0.html

To browse the competition results with a web browser, there are two options:

start a local web server using php -S localhost:8000 in order to view the data in this archive, or

browse https://sv-comp.sosy-lab.org/2021/results/ in order to view the data on the SV-COMP web page.

Contents

index.html: directs to the overview web page

LICENSE.txt: specifies the license

README.txt: this file

results-validated/: results of validation runs

results-verified/: results of verification runs and aggregated results

The folder results-validated/ contains the results from validation runs:

*.xml.bz2: XML results from BenchExec

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

The folder results-verified/ contains the results from verification runs and aggregated results:

index.html: overview web page with rankings and score table

*.xml.bz2: XML results from BenchExec

*.merged.xml.bz2: XML results from BenchExec, status adjusted according to the validation results

*.logfiles.zip: output from tools

*.json.gz: mapping from files names to SHA 256 hashes for the file content

*.xml.bz2.table.html: HTML views on the detailed results data as generated by BenchExec’s table generator

*.All.table.html: HTML views of the full benchmark set (all categories) for each tool

META_*.table.html: HTML views of the benchmark set for each meta category for each tool, and over all tools

*.table.html: HTML views of the benchmark set for each category over all tools

iZeCa0gaey.html: HTML views per tool

validatorStatistics.html: Statictics of the validator runs

quantilePlot-*: score-based quantile plots as visualization of the results

quantilePlotShow.gp: example Gnuplot script to generate a plot

score*: accumulated score results in various formats

The hashes of the file names (in the files *.json.gz) are useful for

validating the exact contents of a file and

accessing the files from the witness store.

Other Archives

Overview over archives from SV-COMP 2021 that are available at Zenodo:

https://doi.org/10.5281/zenodo.4459196 Witness store (containing the generated verification witnesses)

https://doi.org/10.5281/zenodo.4458215 Results (XML result files, log files, file mappings, HTML tables)

https://doi.org/10.5281/zenodo.4459126 Verification tasks, version svcomp21

https://doi.org/10.5281/zenodo.4317433 BenchExec, version 3.6

All benchmarks were executed for SV-COMP 2021 https://sv-comp.sosy-lab.org/2021/ by Dirk Beyer, LMU Munich, based on the following components:

https://gitlab.com/sosy-lab/sv-comp/archives-2021 svcomp21-0-g08c7a98

https://gitlab.com/sosy-lab/software/sv-benchmarks svcomp21-0-g4cc6b6d96a

https://gitlab.com/sosy-lab/software/benchexec 3.6-0-gb278ebbb

https://gitlab.com/sosy-lab/benchmarking/competition-scripts svcomp21-0-g8339740

https://gitlab.com/sosy-lab/sv-comp/bench-defs svcomp21-0-ga57fe48

Contact

Feel free to contact me in case of questions: https://www.sosy-lab.org/people/beyer/

Brazilian Soccer Data

This repository consists of collecting the history and current data of all the most important competitions that Brazilian teams compete, the principal competitions are:

• Brasileirão(Brazilian soccer league)
• Libertatodes(Principal South america Competition)
• Sudamericana(South American secondary competition)
• Copa do Brasil(Brazilian Cup)

Next Steps: - structure the collection of the games of the sudamericana and copa do brasil - Gather data from the main state championships(SP, RJ, MG, RS) - Gather more data from these championships, such as match statistics

Any questions or suggestions are welcome, feel free to collaborate on the github repository

By using and downloading this dataset, you are accepting the rules of the competition "Shopee - Price Match Guarantee"

The data and programs replicate tables and figures from "Group Size and Matching Protocol in Contests", by Baik, Chowdhury, and Ramalingam. Please see the ReadMe file for additional details.

This package contains the datasets and supplementary materials used in the IPIN 2019 Competition (Pisa, Italy).

Contents:

1. IPIN2019_Call4Competition: Call for competition and main rules
2. IPIN2019_Track03_TechnicalAnnex: Technical annex describing the Track 3 of the competition.
3. 01-Logfiles: This folder contains a subfolder with the 50 (40 + 10) training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.
4. 02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.
5. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.

Please, cite the following works when using the datasets included in this package:

• Jiménez, A. R.; Perez-Navarro, A.; Crivello, A.; Mendoza-Silva, G.; Ortiz, M.; Perul, J.; Seco, F. and Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2019 Competition Track 3 (Smartphone-based, off-site), Zenodo 2019. http://dx.doi.org/10.5281/zenodo.3606765
• Potorti, F.; Park, S.; Palumbo, F.; Girolami, M.; Barsocchi, P.; Lee, S.; Torres-Sospedra, J.; Jimenez Ruiz, A. R.; Perez-Navarro, A.; Mendoza-Silva, G. M.; Seco, F.; Ortiz, M.; Perul, J.; Renaudin, V.; Kang, H.; Park, S. Y.; Lee, J. H.; Park, C. G.; Ha, J.; Han, J.; Park, C.; KIM, K.; Lee, Y.; GYE, S.; Lee, K.; Kim, E.; Choi, J.-S.; Choi, Y.-S.; Talwar, S.; Cho, S. Y.; Ben-Moshe, B.; Sansano, E.; Chidlovskii, B.; Kronenwett, N.; Prophet, S.; Landay, Y.; Marbel, R.; Peng, A.; Wu, B.; MA, C.; Poslad, S.; Selviah, D.; Wu, W.; Ma, Z.; Zhang, W.; Wei, D.; Yuan, H.; Jiang, J.-B.; Liu, J.-W.; Su, K.-W.; Leu, J.-S.; Nishiguchi, K.; Bousselham, W.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.-I.; Cortés, V.; Lungenstrass, T.; Ashraf, I.; Lee, C.; Usman Ali, M.; Im, Y.; Kim, G.; Eom, J.; Hur, S.; Park, Y.; Opiela, M.; Moreira, A.; Nicolau, M. J.; Pendão, C.; Silva, I.; Meneses, F.; Costa, A.; Trogh, J.; Plets, D.; Chien, Y.-R.; Chang, T.-Y.; Fang, S.-H.; Tsao, Y. The IPIN 2019 Indoor Localisation Competition - Description and Results IEEE Access Vol. 8, pp. 206674-206718, 2020. https://doi.org/10.1109/ACCESS.2020.3037221

Additional information can be found at:

• http://evaal.aaloa.org/2019/call-for-competitions

For any further questions about the database and this competition track, please contact:

• Joaquín Torres (jtorres@uji.es,torres@ubikgs.com) UBIK Geospatial Solutions S.L., Spain
  Institute of New Imaging Technologies, Universitat Jaume I, Spain.
• Antonio R. Jiménez (antonio.jimenez@csic.es) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain.

Context

This data is scraped from The Blue Alliance, with python scripts which are published on my Github. Go there for more data, notebooks, models, and analysis.