A complete list of live websites using the Same But Different technology, compiled through global website indexing conducted by WebTechSurvey.

Saving you time and space on HHD! Экономлю ваше время и место на диске!
"-cl" = clear (no models / other languages) / очишенное (без моделей / лишних языков) Моя личная подборка портативных приложений и ИИ!Перепаковывал и уменьшал размер архивов лично я!Поддержите меня: Boosty или Donationalerts
My personal selection of portable apps and AI's!I personally repacked and reduced the size of the archives!Support me: Boosty or Donationalerts

Files authors / Авторы… See the full description on the dataset page: https://huggingface.co/datasets/Derur/all-portable-apps-and-ai-in-one-url.

This API is providing the information of press releases issued by the authorized institutions and other similar press releases issued by the HKMA in the past regarding fraudulent bank websites, phishing E-mails and similar scams information.

Data sets used for experimental evaluation in the related publication: Evaluating Web Table Annotation Methods: From Entity Lookups to Entity EmbeddingsThe data sets are contained within archive folders corresponding the three gold standard data sets used in the related publication. Each is presented in both .csv and .json formats.The gold standard data sets are collections of web tables:T2D consists of a schema-level gold standard of 1,748Web tables, manually annotated with class- and property-mappings, as well as an entity-level gold standard of 233 Web tables. Limaye consists of 400 manually annotated Web tables with entity-, class-, and property-level correspondences, where single cells (not rows) are mapped to entities. The corrected version of this gold standard is adapted to annotate rows with entities, from the annotations of the label column cells. WikipediaGS is an instance-level gold standard developed from 485K Wikipedia tables, in which links in the label column are used to infer the annotation of a row to a DBpedia entity. Data formatCSV:The .csv files are formatted as double quoted (' " ') fields, separated by commas (',').In the tables files, each file corresponds to one table, each field represents a column, and each line represents a different row.In the entities files, there are only three fields:"DBpedia uri","cell string","row number"representing the correct annotation, the string of the label column cell, and the row (starting from 0) in which this mapping is found, respectively.Tables and entities files that correspond to the same table have the same filename.The same formatting and naming convention is used in T2D gold standard (http://webdatacommons.org/webtables/goldstandard.html).JSON:Each line in a .json file corresponds to a table, written as a JSONObject. T2D and Limaye tables files contain only one line (table) per file, while the Wikipedia gold standard contains multiple lines (tables) per .json file. In T2D and Limaye, the entity mappings of those tables can be found in the entities files with the same filename, while in Wikipedia, the entity mappings of each table can be found the line of the entities files having the "tableId" field as the one of the corresponding table.The contents of a table in .json are given as a two-dimensional array (a JSONArray of JSONArray s), called "contents". Each JSONArray in the contents represents a table row. Each element of this array is a JSONObject, representing one cell of the row. The field "data" of each cell contains the cell string contents, while there may also be a field "isHeader" to denote of the current cell is in a header row. In the Wikipedia gold standard there may also be a "wikiPageId" field, denoting the existing hyperlink of this cell to a Wikipedia page. It only contains the suffix of a Wikipedia URL, skipping the first part "https://en.wikipedia.org/wiki/".The entity mappings files are in the same format as in csv:["DBpedia uri","cell string",row number] inside the "mappings" field of a json file. Note on license: please refer to the README.txt. Data is derived from Wikipedia and other sources may have different licenses.

Wikipedia contents can be shared under the terms of Creative Commons Attribution-ShareAlike License as outlined on Wikipedia: https://en.wikipedia.org/wiki/Wikipedia:Reusing_Wikipedia_content

The correspondences of the T2D Gold standard is provided under the terms of the Apache license. The Web tables are provided according the same terms of use, disclaimer of warranties and limitation of liabilities that apply to the Common Crawl corpus. The DBpedia subset is licensed under the terms of the Creative Commons Attribution-ShareAlike License and the GNU Free Documentation License that applies to DBpedia. Limaye gold standard is downloaded from: http://websail-fe.cs.northwestern.edu/TabEL/ (download date: August 25, 2016). Please refer to the original website and the following paper for more details and citation information: G. Limaye, S. Sarawagi, and S. Chakrabarti. Annotating and Searching Web Tables Using Entities, Types and Relationships. PVLDB, 3(1):1338–1347, 2010.Also: THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Data dictionary: Page_Title: Title of webpage used for pages of the website www.cityofrochester.gov Pageviews: Total number of pages viewed over the course of the calendar year listed in the year column. Repeated views of a single page are counted. Unique_Pageviews: Unique Pageviews - The number of sessions during which a specified page was viewed at least once. A unique pageview is counted for each URL and page title combination. Avg_Time: Average amount of time users spent looking at a specified page or screen. Entrances: The number of times visitors entered the website through a specified page.Bounce_Rate: " A bounce is a single-page session on your site. In Google Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Google Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Google Analytics server during that session. Bounce rate is single-page sessions on a page divided by all sessions that started with that page, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Google Analytics server. These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Google Analytics calculate the length of the session. "Exit_Rate: The number of exits from a page divided by the number of pageviews for the page. This is inclusive of sessions that started on different pages, as well as “bounce” sessions that start and end on the same page. For all pageviews to the page, Exit Rate is the percentage that were the last in the session. Year: Calendar year over which the data was collected. Data reflects the counts for each metric from January 1st through December 31st.

Terms of Use Score from Open Data Watch (ODW). Openness element 5 measures whether data are available with an open terms of use. Generally, terms of use (TOU) will apply to an entire website or data portal (unless otherwise specified). In these cases, all data found on the same website and/or portal will receive the same score. If a portal is located on the same domain as the NSO website, the terms of use on the NSO site will apply. If the data are located on a portal or website on a different domain, another terms of use will need to be present. For a policy/ license to be accepted as a terms of use, it must clearly refer to the data found on the website. Terms of use that refer to nondata content (such as pictures, logos, etc.) of the website are not considered. A copyright symbol at the bottom of the page is not sufficient. A sentence indicating a recommended citation format is not sufficient. Terms of use are classified the following ways: (1) Not Available, (2) Restrictive, (3) Semi-Restrictive, and (4) Open. If the TOU contains one or more restrictive clauses, it receives 0 points and is classified as “restrictive.”

This dataset contains a collection of around 2,000 HTML pages: these web pages contain the search results obtained in return to queries for different products, searched by a set of synthetic users surfing Google Shopping (US version) from different locations, in July, 2016.

Each file in the collection has a name where there is indicated the location from where the search has been done, the userID, and the searched product: no_email_LOCATION_USERID.PRODUCT.shopping_testing.#.html

The locations are Philippines (PHI), United States (US), India (IN). The userIDs: 26 to 30 for users searching from Philippines, 1 to 5 from US, 11 to 15 from India.

Products have been choice following 130 keywords (e.g., MP3 player, MP4 Watch, Personal organizer, Television, etc.).

In the following, we describe how the search results have been collected.

Each user has a fresh profile. The creation of a new profile corresponds to launch a new, isolated, web browser client instance and open the Google Shopping US web page.

To mimic real users, the synthetic users can browse, scroll pages, stay on a page, and click on links.

A fully-fledged web browser is used to get the correct desktop version of the website under investigation. This is because websites could be designed to behave according to user agents, as witnessed by the differences between the mobile and desktop versions of the same website.

The prices are the retail ones displayed by Google Shopping in US dollars (thus, excluding shipping fees).

Several frameworks have been proposed for interacting with web browsers and analysing results from search engines. This research adopts OpenWPM. OpenWPM is automatised with Selenium to efficiently create and manage different users with isolated Firefox and Chrome client instances, each of them with their own associated cookies.

The experiments run, on average, 24 hours. In each of them, the software runs on our local server, but the browser's traffic is redirected to the designated remote servers (i.e., to India), via tunneling in SOCKS proxies. This way, all commands are simultaneously distributed over all proxies. The experiments adopt the Mozilla Firefox browser (version 45.0) for the web browsing tasks and run under Ubuntu 14.04. Also, for each query, we consider the first page of results, counting 40 products. Among them, the focus of the experiments is mostly on the top 10 and top 3 results.

Due to connection errors, one of the Philippine profiles have no associated results. Also, for Philippines, a few keywords did not lead to any results: videocassette recorders, totes, umbrellas. Similarly, for US, no results were for totes and umbrellas.

The search results have been analyzed in order to check if there were evidence of price steering, based on users' location.

One term of usage applies:

In any research product whose findings are based on this dataset, please cite

@inproceedings{DBLP:conf/ircdl/CozzaHPN19, author = {Vittoria Cozza and Van Tien Hoang and Marinella Petrocchi and Rocco {De Nicola}}, title = {Transparency in Keyword Faceted Search: An Investigation on Google Shopping}, booktitle = {Digital Libraries: Supporting Open Science - 15th Italian Research Conference on Digital Libraries, {IRCDL} 2019, Pisa, Italy, January 31 - February 1, 2019, Proceedings}, pages = {29--43}, year = {2019}, crossref = {DBLP:conf/ircdl/2019}, url = {https://doi.org/10.1007/978-3-030-11226-4_3}, doi = {10.1007/978-3-030-11226-4_3}, timestamp = {Fri, 18 Jan 2019 23:22:50 +0100}, biburl = {https://dblp.org/rec/bib/conf/ircdl/CozzaHPN19}, bibsource = {dblp computer science bibliography, https://dblp.org} }

Web Based Construction Management Software Market Outlook

As of 2023, the global market size for Web Based Construction Management Software is valued at approximately USD 5.2 billion, with a projected reach of USD 12.4 billion by 2032, growing at a CAGR of 10% during the forecast period. This growth is primarily driven by the increasing need for real-time project tracking, efficient resource management, and the rising adoption of digital solutions in the construction industry.

The growth factors fueling the Web Based Construction Management Software market are multifaceted. Firstly, technological advancements and the integration of artificial intelligence (AI) and machine learning (ML) in construction management software have significantly enhanced predictive analytics and automation capabilities. These technologies enable construction managers to make informed decisions, optimize resource allocation, and predict potential project delays. Consequently, the demand for advanced software solutions that can streamline complex construction processes is on the rise.

Additionally, the growing emphasis on sustainable construction practices is propelling the adoption of web-based construction management software. The software aids in better planning, tracking, and reporting, ensuring that construction projects meet environmental regulations and sustainability goals. With governments and organizations globally pushing for greener construction methods, the market for such software is anticipated to expand rapidly. The ability to monitor and reduce waste, manage energy consumption, and ensure compliance with sustainability standards is becoming increasingly crucial.

Moreover, the rising trend of remote work and decentralized project teams has accelerated the need for web-based solutions that provide seamless access to project data from any location. This shift has been further amplified by the COVID-19 pandemic, which highlighted the necessity for digital collaboration tools. Web-based construction management software facilitates real-time collaboration among stakeholders, ensuring that all team members are on the same page despite geographical barriers. The scalability and flexibility offered by cloud-based solutions make them particularly attractive for construction companies of all sizes.

Construction Scheduling Software plays a pivotal role in enhancing the efficiency of web-based construction management systems. By providing tools for detailed project timelines, resource allocation, and task prioritization, this software ensures that all project phases are meticulously planned and executed. The integration of scheduling software with other construction management tools allows for seamless updates and adjustments, accommodating any unforeseen changes in project scope or timelines. This adaptability is crucial in the fast-paced construction environment, where delays can lead to significant cost overruns. As the industry moves towards more complex and larger-scale projects, the demand for robust scheduling solutions is expected to grow, reinforcing the importance of integrating such software into existing management systems.

From a regional perspective, North America is expected to maintain a significant share of the market due to the early adoption of technology and the presence of key market players. However, the Asia Pacific region is projected to witness the highest growth rate, driven by rapid urbanization, infrastructural development, and increasing investments in smart city projects. Developing economies in this region are recognizing the benefits of adopting digital construction management solutions to enhance efficiency and competitiveness in the construction sector.

Component Analysis

The Web Based Construction Management Software market can be segmented by components into Software and Services. The software segment includes various applications and tools designed to manage different aspects of construction projects. This segment is expected to dominate the market due to the increasing demand for comprehensive project management solutions. These software solutions offer functionalities such as scheduling, budgeting, resource allocation, and document management, which are essential for efficient project execution.

On the other hand, the services segment comprises consulting, training, support, and maintenance services provided by vendors to ensure the effective implementation and use of constru

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Squarespace (SQSP): Building a Web Presence, One Website at a Time

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

BackgroundThis study aimed to investigate the quality and readability of online English health information about dental sensitivity and how patients evaluate and utilize these web-based information.MethodsThe credibility and readability of health information was obtained from three search engines. We conducted searches in "incognito" mode to reduce the possibility of biases. Quality assessment utilized JAMA benchmarks, the DISCERN tool, and HONcode. Readability was analyzed using the SMOG, FRE, and FKGL indices.ResultsOut of 600 websites, 90 were included, with 62.2% affiliated with dental or medical centers, among these websites, 80% exclusively related to dental implant treatments. Regarding JAMA benchmarks, currency was the most commonly achieved and 87.8% of websites fell into the "moderate quality" category. Word and sentence counts ranged widely with a mean of 815.7 (±435.4) and 60.2 (±33.3), respectively. FKGL averaging 8.6 (±1.6), SMOG scores averaging 7.6 (±1.1), and FRE scale showed a mean of 58.28 (±9.1), with "fair difficult" being the most common category.ConclusionThe overall evaluation using DISCERN indicated a moderate quality level, with a notable absence of referencing. JAMA benchmarks revealed a general non-adherence among websites, as none of the websites met all of the four criteria. Only one website was HON code certified, suggesting a lack of reliable sources for web-based health information accuracy. Readability assessments showed varying results, with the majority being "fair difficult". Although readability did not significantly differ across affiliations, a wide range of the number of words and sentences count was observed between them.