What went wrong: Execution failed for task ‘:app:processDebugGoogleServices’.

No matching client found for package name ‘com.example.myapp ‘

Step -1: check your google-services.json and build.gradle matching applicationId and package_name match

this is google-services.json file

this is build.gradle file

So no matching applicationId and package name get the match so remove com.

The post Execution failed for task’:app:processDebugGoogleServices’.> No matching client found for package name’com.cotocus.holidaylandmark.publisher.holidaylandmark_publisher’ android studio-SOLVED appeared first on Artificial Intelligence.

and my code is given in below

i added isExpanded: true, inside dropdown widget

now ,finally my issue is fixed

Refrence:https://stackoverflow.com/questions/58747973/right-overflowed-by-23-pixels-in-flutter-listview

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App Annie, the leading mobile data and analytics company helping brands and publishers create winning experiences on mobile, announces its newest innovation designed to put data-driven insights in the hands of executives — the App Annie Pulse app.

For busy executives, time is of the essence. To keep pace with the ever-changing app economy, it’s important to access critical information about trends, markets and the competition in real time. With over USD 143 billion spent on mobile and 218 billion new app downloads in 2020, unlocking mobile insights and capitalizing quickly on opportunities is more important than ever.

Now available on the Apple App Store, App Annie Pulse provides one touch access to mobile market data. This is the industry’s only answer to benchmark competition, track market movers, and identify insights that are powered by the industry’s best estimates and data science.

This is the first mobile app to fully leverage AI-driven features:

• App Annie Performance Score: A composite metric, based on sentiment, acquisition, monetization, and engagement metrics. This allows businesses to quantify and benchmark their performance. The score can be tailored to your favorite metrics.
• App Annie Insights: Automatically monitors shifts in data and identifies potential causality

“We are unmatched in our data science capabilities – the only market data provider that derives all estimates and insights from a pure AI model,” said Ted Krantz, CEO, App Annie. “App Annie Pulse marks the introduction of the industry’s first Mobile Performance Score and ability to track your own custom metrics.”

The company is also announcing the appointment of Ketaki Rao as Chief Product Officer. Ketaki will lead the way on innovation and continue to deliver the most advanced products on the market. Ketaki joins with over 20 years of experience in product development at technology companies including Salesforce, Amazon, and Sun Microsystems.

“App Annie’s best-in-class intelligence for mobile, recent breakthroughs in AI technology, privacy-forward approach, and a bold product vision are primed to meet the new needs of a rapidly changing digital landscape. I am thrilled to join the talented and energetic App Annie team as the Chief Product Officer.” Ketaki Rao, Chief Product Officer, App Annie.

Look for the company to continue to unveil more intuitive and streamlined user experience redesigns for all its products. App Annie Intelligence redesign beta launches today and will become generally available in H2 2021. App Annie Pulse will be available on Android at the end of Q2.

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According to article 484 of the Interpretations of the Supreme People’s Court on the Application of the Civil Procedure Law, after the enforcement process begins, the person subject to enforcement shall appear before the court to accept investigation and questioning after being lawfully summoned by the court.

In reality, however, there are a number of major enforcement difficulties to be addressed, including the difficulty in locating the person subject to enforcement, discovering properties to be enforced, penalizing the person for evading enforcement and cashing the assets to be enforced, of which how to locate the person subject to enforcement and discover the properties to be enforced are the key problems to be solved in enforcement.

Innovative enforcement

The current frequently used Apps, such as WeChat and Taobao, collect general basic data in several categories including personal basic information, mobile numbers, emails, identity profiles, biometrics, online identities, physical health, educational backgrounds, jobs, property, contacts, internet use records, commonly used devices, locations and motor vehicle information. Information useful for enforcement can be acquired by collecting, screening, analysing and studying different data.

Legal basis. The Standardization Administration of China has officially issued the Information Security Technology – Personal Information Security Specification, which clearly stipulates that any sharing, transfer and public disclosure of personal information shall be subject to prior authorisation or approval, but in circumstances directly related to crime investigation, prosecution, trial and enforcement of judgment, the controller of personal information is exempt from the relevant person’s prior authorisation or approval for such sharing, transfer and public disclosure of the person’s personal information.

In addition, article 485 of the interpretations, and articles 1 and 12 of the Provisions of the Supreme People’s Court on Issues Concerning Property Investigation During Enforcement in Civil Procedures, have all made it clear that the court may not only search information in the online enforcement inquiry and control system, but also inquire to the relevant entities and individuals about personal information of the person subject to enforcement, including identity and property information.

Developing intelligent algorithms suitable for enforcement. Advanced big data-based statistical methods can be employed to extract data from the information records of the persons subject to enforcement, and then establish a data model so as to identify the behavioural habits of the persons subject to enforcement, and predict their possible behavioural trends in the future. This requires that a scientific and effective intelligent enforcement algorithm be developed before capturing data, so as to screen out the data meeting the enforcement requirements, and draw a possible general conclusion through smart inference, thus providing data support for the enforcement judge to make further decisions.

Locating the person subject to enforcement. The online identity and accounts of the person subject to enforcement can be found based on identity data such as personal ID number, date of birth, native place and mobile number. Also, the real-time location, whereabouts, vehicle trajectory, parking place and immovable property address of the person subject to enforcement can be searched by using the positioning data in Baidu Map, Amap and Didi Chuxing Apps.

Searching clues to the property of the person subject to enforcement. Apps such as Alipay and WeChat can be used to collect property-related big data spanning hundreds of scenarios (for example, payment, mobility, accommodation and shopping), including e-commerce data, internet finance data, public institution data and user-uploaded data. These data together can well depict the property and credit profile of the person subject to enforcement.

Revealing the links between the person subject to enforcement and his/her properties. For court enforcement, the internet of things (IoT) App-based big data combine the three different key elements, namely target individual, motor vehicles and real properties, together for the first time. The community data profoundly reveal the interaction between the three elements. Meanwhile, locating the person subject to enforcement, his/her vehicles and real properties are of great realistic significance for increasing the efficiency and fulfilment rate of enforcement.

Penalising poor credit. Through data docking with credit reporting Apps via private line, the information of a person with poor credit and subject to enforcement can be shared to enable application platforms to impose comprehensive restrictions on the dishonest person under a variety of scenarios, including consumer finance, microcredit, credit cards, hotels, rental house and car rental, thus reducing the living space of that person subject to enforcement.

The poorly credited person subject to enforcement will be restricted from applying for loans, financing or other financial services, restricted from purchasing via Apps any air tickets, soft-sleeper train tickets, insurance or wealth management products, as well as motor vehicles and tourist products, which are not indispensable for business operation, and restricted from booking any hotels rated three stars or above via Apps.

Serving legal papers regarding enforcement. Traditional service requires delivery of mail to the registered office address, registered household address or any other valid address provided by the applicant for enforcement.When delivery fails or is refused, enforcement officers may even have to serve court papers by hand.

All the mobile numbers registered under the name of the person concerned, as well as the WeChat/Microblog accounts, e-commerce accounts and email addresses linked to these mobile numbers can be automatically retrieved according to the concerned person’s name, ID card number and other information provided in case filing, and these frequently used e-addresses can be taken as the logical starting point for electronic delivery. Legal papers can be electronically delivered to part or all of these electronic accounts, depending on their activity level, or to the actual residence identified through data comparison.

Accurate push of public notification offering reward for enforcement. According to the Civil Procedure Law and the relevant rules of the Supreme People’s Court, the court may issue a public notification offering a reward for enforcement, based on the application for enforcement, by offering a reward submitted by the enforcement applicant.

By drawing reference from the reward for photographing traffic violations, a reward can be offered publicly for clues to the property of the dishonest person subject to enforcement, so as to leverage social forces to solicit clues to motor vehicles, real properties and other community properties and solve the enforcement difficulties. The properties of a poorly credited person subject to enforcement are best known to his/her acquaintances. The offer of reward for enforcement can be pushed to those on the address list of the dishonest person. Meanwhile, the court should keep confidential the personal information of informants.

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Back in the day, Gallery apps came with only one function: to efficiently manage and organize the photos in descending order within device folders. Today, the Gallery apps aren’t just limited to photos management. They must package image editing, face reorganization, automation, AI, and more.

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WASHINGTON: For its updated news application, Google is doubling down on the use of artificial intelligence as part of an effort to weed our disinformation and help users get viewpoints beyond their own “filter bubble.”

Google chief Sundar Pichai, who unveiled the updated Google News earlier this month, said the app now “surfaces the news you care about from trusted sources while still giving you a full range of perspectives on events.”

It marks Google’s latest effort to be at the center of online news and includes a new push to help publishers get paid subscribers through the tech giant’s platform.

According to product chief Trystan Upstill, the news app “uses the best of artificial intelligence to find the best of human intelligence — the great reporting done by journalists around the globe.”

While the app will enable users to get “personalized” news, it will also include top stories for all readers, aiming to break the so-called filter bubble of information designed to reinforce people’s biases.

“Having a productive conversation or debate requires everyone to have access to the same information,” Upstill said.

He said the “full coverage” feed would be the same for everyone — “an unpersonalized view of events from a range of trusted news sources.”

Some journalism industry veterans were skeptical about the effort to replace human editors with machine curators.

“There’s been a fantasy of (algorithmic) personalized news for a log time,” said New York University journalism professor Meredith Broussard.

“Nobody has ever gotten it right. I think that news designers and home page editors do a good job of curating already.”

Google and Facebook have also been criticized for scooping up most online ad revenues and for enabling false information to spread.

Recently, News Corp. CEO Robert Thomson called for an “algorithm review board” that would “oversee these historically influential digital platforms and ensure that there is no algorithmic abuse or censorship.”
Changing news ecosystem 
In the new app, Google’s “newsstand” addresses some concerns by allowing users to sign up for subscriptions using their Google accounts, and will enable publishers to connect with readers directly.

Dan Kennedy, a Northeastern University journalism professor, said the revamped news app appears to be positive for the news ecosystem.

“For many years, Google refused to share ad revenues with news organizations on the grounds that Google was driving traffic to them, and that it was up to those organizations to figure out how to monetize that traffic,” Kennedy said.

“Now, with more than 90 percent of all new digital ad revenues going to Google and Facebook, Google is finally acknowledging that it’s time to try something else.”

Nicholas Diakopoulos, a Northwestern University professor specializing in computational and data journalism, said the impact of Google’s changes remain to be seen.

Diakopoulos said algorithmic and personalized news can be positive for “engagement” but may only benefit a handful of news organizations.

His research found that “Google concentrates its attention on a relatively small number of publishers, it’s quite concentrated.”

Google’s effort to identify and prioritize “trusted” news sources may also be problematic, according to Diakopoulos.

“Maybe it’s good for the big guys, or the (publishers) who have figured out how to game the algorithm,” he said. “But what about the local news sites, what about the new news sites that don’t have a long track record?”

Diakopoulos said that while AI can offer some advantages in news curation, “you still need people involved in many different ways. you need to reflect human values, editorial values… you can’t quantify every aspect that might be important to an editorial decision.”

The growing importance of the platforms raise the questions of whether Google and Facebook are “media” companies and not simply technology firms, a moniker both have resisted.

Diakopoulos said Google “is becoming more and more like a media company,” although that is sometimes difficult to define.

“Yahoo started as a tech company and became a media company, and maybe Google is headed in that direction,” he said.

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