Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall.

The EU is facing a backlash over new AI rules that allow for limited use of facial recognition by authorities -- with opponents warning the carveouts could usher in a new age of biometric surveillance. A coalition of digital rights and consumer protection groups across the globe, including Latin America, Africa and Asia are calling for a global ban on biometric recognition technologies that enable mass and discriminatory surveillance by both governments and corporations. In an open letter, 170 signatories in 55 countries argue that the use of technologies like facial recognition in public places goes against human rights and civil liberties. "It shows that organizations, groups, people, activists, technologists around the world who are concerned with human rights, agree to this call," said Daniel Leufer of U.S. digital rights group Access Now, which co-authored the letter. The use of facial recognition technology is becoming widespread.

Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Malvinas/Falkland Islands off Argentina's coast. The Malvinas/Falklands are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%. All told, the automated counts were within 5% of human counts about 90% of the time.

DURHAM, N.C. - Using drones and artificial intelligence to monitor large colonies of seabirds can be as effective as traditional on-the-ground methods, while reducing costs, labor and the risk of human error, a new study finds. Scientists at Duke University and the Wildlife Conservation Society (WCS) used a deep-learning algorithm--a form of artificial intelligence--to analyze more than 10,000 drone images of mixed colonies of seabirds in the Falkland Islands off Argentina's coast. The Falklands, also known as the Malvinas, are home to the world's largest colonies of black-browed albatrosses (Thalassarche melanophris) and second-largest colonies of southern rockhopper penguins (Eudyptes c. chrysocome). Hundreds of thousands of birds breed on the islands in densely interspersed groups. The deep-learning algorithm correctly identified and counted the albatrosses with 97% accuracy and the penguins with 87%.

In a wide variety of applications including online advertising, contractual hiring, and wireless scheduling, the controller is constrained by a stringent budget constraint on the available resources, which are consumed in a random amount by each action, and a stochastic feasibility constraint that may impose important operational limitations on decision-making. In this work, we consider a general model to address such problems, where each action returns a random reward, cost, and penalty from an unknown joint distribution, and the decision-maker aims to maximize the total reward under a budget constraint $B$ on the total cost and a stochastic constraint on the time-average penalty. We propose a novel low-complexity algorithm based on Lyapunov optimization methodology, named ${\tt LyOn}$, and prove that it achieves $O(\sqrt{B\log B})$ regret and $O(\log B/B)$ constraint-violation. The low computational cost and sharp performance bounds of ${\tt LyOn}$ suggest that Lyapunov-based algorithm design methodology can be effective in solving constrained bandit optimization problems.

MR Accuracy Reports recently introduced new title on "Global Cloud Machine Learning Market Report 2020 Market: Industry Analysis, Size, Share, Growth, Trends, and Forecasts 2021-2027" from its database utilizing diverse methodologies aims to examine and put forth in-depth and accurate data regarding the global Cloud Machine Learning Market Report 2020 market. The report provides study with in-depth overview, describing about the Product / Industry Scope and elaborates market outlook and status (2021-2026). Cloud Machine Learning Market Report 2020 Market research report which provides an in-depth examination of the market scenario regarding market size, share, demand, growth, trends, and forecast for 2020-2026. The report covers the impact analysis of the COVID-19 pandemic. The COVID-19 pandemic has affected export imports, demands, and industry trends and is expected to have an economic impact on the market. The report provides a comprehensive analysis of the impact of the pandemic on the entire industry and provides an overview of a post-COVID-19 market scenario.

This Machine Learning market report provides a thorough insight of the market, allowing key players to keep informed and keep their competitive advantage. It focuses on present trends by forecasting future trends, market size, and market features. Such meticulous Market Analysis creates a comprehensive picture of market policies and supports industries in making larger earnings than before. The greatest way to gain insight into the current market situation and take a position in it is to read this Machine Learning market Research Report. It strengthens corporate positions and assists various industry participants in understanding future and current market situations.

A newly published study on Global AI & Machine Learning Operationalization Software Market the report observes numerous in-depth, influential and inducing factors that outline the market and industry. All of the findings, data, and information provided in the report are validated and revalidated with the help of trustworthy sources. The analysts who have authored the report took a unique and industry-best research and analysis approach for an in-depth study of the global AI & Machine Learning Operationalization Software market. This report forecasts demands, Trends, and revenue growth at regional & country levels and provides an analysis of the industry trends in each of the sub-segments from 2021 to 2026. The global AI & Machine Learning Operationalization Software Market to grow with a CAGR of 44.2% over the forecast period of 2021-2026.

Although Artificial Intelligence (AI) has existed for more than sixty years, today, thanks to greater computing power, as well as the availability of large amounts of data and more sophisticated algorithms, the applications of this field of computing have proliferated considerably and have had a direct impact on the workplace . The need to meet the demand for products and services that integrate this technology has forced companies to recruit qualified professionals and experts in this field . That is why many companies are currently looking to fill positions related to Artificial Intelligence and Machine Learning programming, as well as Data Scientists . According to the latest report from the World Economic Forum, Jobs of Tomorrow: Mapping Opportunity in the New Economy (2020), by 2022 133 million new jobs will be created worldwide, of which 16% will be within of the Data and Artificial Intelligence sector . According to the latest analysis by the Federal Institute of Telecommunications, published in January 2020, the Internet is the most used Information and Communication Technology (ICT) in Mexico, since 66 out of 100 people aged 6 years and over use it; which accounts for the importance of this network in the lives of Mexicans.

Graph convolutional network (GCN) has become popular in various natural language processing (NLP) tasks with its superiority in long-term and non-consecutive word interactions. However, existing single-hop graph reasoning in GCN may miss some important non-consecutive dependencies. In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer. To alleviate the over-smoothing in high-order Chebyshev approximation, a multi-vote-based cross-attention (MVCAttn) with linear computation complexity is also proposed. The empirical results on four transductive and inductive NLP tasks and the ablation study verify the efficacy of the proposed model. Our source code is available at https://github.com/MathIsAll/HDGCN-pytorch.