Atenção

Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.

create table TY(C int primary key not null, A int)

create table TX(A int primary key not null, B int,

foreign key (B) references TY(C)

on delete cascade

)

insert into TY values (1,0)

insert into TY(C) values (2)

insert into TY(C) values (3)

insert into TY values (5,NULL)

insert into TY values (6,NULL)

insert into TX values (1,2)

insert into TX values (2,1)

insert into TX values (3,2)

insert into TX values (4,2)

Com referência às tabelas TX e TY, como descritas anteriormente, analise o comando SQL a seguir.

insert into TX(A, B)

select C,A FROM TY

where C not in (select A from TX)

or A in (select A from TX)

O conjunto de linhas inseridas é:

Atenção

Quando referidas, considere as tabelas relacionais TX e TY, criadas e instanciadas com o script SQL a seguir.

create table TY(C int primary key not null, A int)

create table TX(A int primary key not null, B int,

foreign key (B) references TY(C)

on delete cascade

)

insert into TY values (1,0)

insert into TY(C) values (2)

insert into TY(C) values (3)

insert into TY values (5,NULL)

insert into TY values (6,NULL)

insert into TX values (1,2)

insert into TX values (2,1)

insert into TX values (3,2)

insert into TX values (4,2)

Com referência às tabelas TX e TY, como descritas anteriormente, analise o comando SQL a seguir.

select count(*)

from TX t1 left join TY t2 on t1.B=t2.A

O valor exibido pela execução desse comando é:

O sistema SisBRAVO foi desenvolvido aderente ao preconizado na Lei nº 13.709/2018 – Lei Geral de Proteção de Dados (LGPD). O SisBRAVO solicita autorização para coleta de dados pessoais inseridos pelos usuários.

Sendo assim, o SisBRAVO atende requisitos tipificados como:

O TCE SP deseja aprimorar a gestão de pessoal utilizando um novo software. Para isso, o Setor Geral de Pessoal delegou à Diretoria de Gestão de Pessoas (DGP) a tarefa de determinar os meios pelos quais este software será implementado. A DGP decidiu contratar a empresa SisPesSoft para desenvolver o software em parceria com a equipe interna da Diretoria de Tecnologia da Informação.

De acordo com a Lei nº 13.709/2018 – Lei Geral de Proteção de Dados (LGPD), nesse contexto, a empresa SisPesSoft atua como:

Uma peça é colocada na casa 1 de um tabuleiro de 10 casas. Ela se move com a seguinte regra de probabilidade: a peça avança uma casa se um número par é obtido no lançamento de um dado e a peça avança duas casas se o número obtido for ímpar. Seja C(j) a probabilidade de a peça cair na casa j.

Então, é correto afirmar que:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

The aim of the last paragraph is to:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

The word “downsides” in “There are downsides to both categories” (7^th paragraph) means:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

In the question “Or create laws or a code of conduct that probably would be ignored?” (5^th paragraph), the authors imply that these laws and code of conduct may be:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

When the authors refer to the use of deepfake in education (2^nd paragraph), they state that ultimately teachers may find it:

READ THE TEXT AND ANSWER THE FOLLOWING QUESTION:

Is It Live, or Is It Deepfake?

It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape. Today we have something new to be in awe of: deepfakes. Deepfakes include hyperrealistic videos that use artificial intelligence (AI) to create fake digital content that looks and sounds real. The word is a portmanteau of “deep learning” and “fake.” Deepfakes are everywhere: from TV news to advertising, from national election campaigns to wars between states, and from cybercriminals’ phishing campaigns to insurance claims that fraudsters file. And deepfakes come in all shapes and sizes — videos, pictures, audio, text, and any other digital material that can be manipulated with AI. One estimate suggests that deepfake content online is growing at the rate of 400% annually.

There appear to be legitimate uses of deepfakes, such as in the medical industry to improve the diagnostic accuracy of AI algorithms in identifying periodontal disease or to help medical professionals create artificial patients (from real patient data) to safely test new diagnoses and treatments or help physicians make medical decisions. Deepfakes are also used to entertain, as seen recently on America’s Got Talent, and there may be future uses where deepfake could help teachers address the personal needs and preferences of specific students.

Unfortunately, there is also the obvious downside, where the most visible examples represent malicious and illegitimate uses. Examples already exist.

Deepfakes also involve voice phishing, also known as vishing, which has been among the most common techniques for cybercriminals. This technique involves using cloned voices over the phone to exploit the victim’s professional or personal relationships by impersonating trusted individuals. In March 2019, cybercriminals were able to use a deepfake to fool the CEO of a U.K.-based energy firm into making a US$234,000 wire transfer. The British CEO who was victimized thought that the person speaking on the phone was the chief executive of the firm’s German parent company. The deepfake caller asked him to transfer the funds to a Hungarian supplier within an hour, emphasizing that the matter was extremely urgent. The fraudsters used AI-based software to successfully imitate the German executive’s voice. […]

What can be done to combat deepfakes? Could we create deepfake detectors? Or create laws or a code of conduct that probably would be ignored?

There are tools that can analyze the blood flow in a subject’s face and then compare it to human blood flow activity to detect a fake. Also, the European Union is working on addressing manipulative behaviors.

There are downsides to both categories of solutions, but clearly something needs to be done to build trust in this emerging and disruptive technology. The problem isn’t going away. It is only increasing.

Authors

Nit Kshetri, Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, USA

Joanna F. DeFranco, Software Engineering, The Pennsylvania State University, Malvern, PA, USA Jeffrey Voas, NIST, USA

Adapted from:

https://www.computer.org/csdl/magazine/co/2023/07/10154234/ 1O1wTOn6ynC

In the 1^st sentence (“It’s been four decades since society was in awe of the quality of recordings available from a cassette recorder tape”), the reaction of society is described as being one of: