 may require the controller to rectify, complete, update, block or delete personal data concerning them, as the case may be, if the data is inaccurate, incomplete, equivocal, out of date, or if their collection, use, communication or storage is prohibited, including with regard to third parties to whom such data have been communicated (Article 21 of the 2021 Act).
In the event of a dispute, the burden of proof lies with the controller to whom the right of access is exercised (Article 21 of the 2021 Act).
Data subjects have the right to obtain from the controller the removal of personal data relating to their private life and past activities, made public on a website, whether or not accessible by a search engine.
Data subjects have the right to object on legitimate grounds to the processing of their personal data (Article 20 of the 2021 Act). In the case of direct marketing, objections may be made on any grounds.
The controller may refuse the request to object where the processing is justified on legitimate grounds which override the interests, fundamental rights and freedoms of the data subject (Article 20 of the 2021 Act).
as the sole basis for any administrative or private decision involving an assessment of human behaviour.
publication of the sanction in several newspapers at the expense of the sanctioned person.
The CIL may impose the following administrative fines amounting to 1% of the turnover excluding tax of the previous financial year and 5% of the turnover excluding tax for the previous financial year in the event of a repeat offence.
Other sanctions can also reach up to CFAF 100,000,000 (approx. €154560) and 5 years' imprisonment.Thousands of girls participated in the Girl Scout Cookie Program this year and one Girl Scout, Ziyanece A. of Webster Troop 65105, achieved a groundbreaking accomplishment when she sold an astounding 7,125 packages of cookies – the most cookies ever sold in Girl Scout history for the Western New York Region (covering Cattaraugus, Chautauqua, Erie, Genesee, Livingston, Monroe, Niagara, Orleans and Wyoming counties).
At the age of 8, Ziyanece put her ingenuity and entrepreneurial skills to work as she sold her cookies online and in-person.
Ziyanece and her family were recently invited to celebrate her success at the Girl Scouts of Western New York Rochester Service Center by the GSWNY CEO Alison Wilcox; two of her service unit managers, Jennifer Shaw and Beth Cunico; and GSWNY staff members Mary Jo Brown (executive vice president of product program and customer service) and Megan Hall (product program manager).
Three-thousand cookie packages was the highest amount of packages sold for the Western New York region until Ziyanece set a new bar. Girl Scouts of Western New York applauds Ziyanece and all the girls and volunteers who worked so hard in a pandemic and participated in the 2021 Girl Scout Cookie Program.
√ Girl Scout service unit with the highest amount of cookie packages sold per girl was Cattaraugus County West with 508 per girl.
√ Troop with the highest packages sold was Troop 34213 of Lackawanna South Buffalo with 10,832 packages of cookies sold.
√ Service units who made an impact in a big way through service unit community cookie booths were Pittsford, Webster, Millwheel, Big Bend, Como Lake and Kenton.
A press release stated, “The Girl Scout Cookie Program powers amazing experiences for girls. When customers buy cookies, they are doing so much more than purchasing a box of cookies. They are helping girls become confident leaders, by learning the five key life skills, like goal-setting, decision-making, money management, people skills and business ethics.
“As largest entrepreneurial program for girls in the world – the Girl Scout Cookie Program powers hands-on and life-changing experiences in a variety of skills areas for girls all year long. With the help of proceeds from the cookie program, girls gain real-world entrepreneurial experience, learn critical life skills, explore the great outdoors, and dive into fun, hands-on science, technology, engineering and math (STEM) activities that unleash their imaginations and broaden their worlds. When customers make a local Girl Scout Cookie purchase, they are helping the next generation of entrepreneurs get an important taste of what it takes to become successful in business.
To learn more about Girl Scout Summer Camp, girl membership and adult volunteerism, visit www.gswny.org or call 1-888-837-6410.
Write a text based on "tanzania urged to fight offshore drug trafficking"

Text:
the tanzanian maritime foundation, a non-governmental organization, friday called on the government to establish a coast guard unit to curb drug trafficking on the coastline and offshore islands.
\section{Introduction}

Learning word representations has become a fundamental problem in processing natural languages. 
These semantic representations, which map a word into a point in a linear space, have been widely applied in downstream applications, including named entity
recognition ~\cite{guo2014}, document ranking ~\cite{NMCC16}, sentiment analysis~\cite{IrsoyCardie14}, question answering~\cite{VQA}, and image captioning~\cite{karpathy2015deep}. 



Over the past few years, various approaches have been proposed to learn word vectors~(e.g., ~\cite{pennington2014glove,Mikolov2013,OL14a,ji2015wordrank}) based on co-occurrence information between words observed on the training corpus. The intuition behind this is to represent words with similar vectors if they have similar contexts. To learn a good word embedding, most approaches assume a large collection of text is freely available, such that the estimation of word co-occurrences is accurate. For example, the Google Word2Vec model~\cite{Mikolov2013}  is trained on the Google News dataset, which contains around 100 billion tokens, and the GloVe embedding~\cite{pennington2014glove} is trained on a crawled corpus that contains 840 billion tokens in total.
However, such an assumption may not hold for low-resource languages such as Inuit or Sindhi, which are not spoken by many people or have not been put into a digital format.
For those languages, usually, only a limited size corpus is available. Training word vectors under such a setting is a challenging problem.




One key restriction of the existing approaches is that they often mainly rely on the word pairs that are observed to co-occur on the training data. When the size of the text corpus is small, most word pairs are unobserved, resulting in an extremely sparse  co-occurrence matrix (i.e., most entries are zero)\footnote{Note that the zero term can mean either the pairs of words cannot co-occur or the co-occurrence is not observed in the training corpus.}. For example, the text8\footnote{http://mattmahoney.net/dc/text8.zip} corpus has about 17,000,000 tokens and 71,000 distinct words. The corresponding co-occurrence matrix has more than five billion entries, but only about 45,000,000 are non-zeros (observed on the training corpus). Most existing approaches, such as Glove and Skip-gram, cannot handle a vast number of zero terms in the co-occurrence matrix; therefore, they only sub-sample a small subset of zero entries during the training. 


In contrast, we argue that the {\it unobserved} word pairs can provide valuable information for training a word embedding model, especially when the co-occurrence matrix is very sparse. Inspired by the success of Positive-Unlabeled Learning (PU-Learning) in collaborative filtering applications~\cite{RP08a-short,YH08a,RP09a,QT10a,UP13a-short,hsieh2015pu}, we design an algorithm to effectively learn  word embeddings from both positive (observed terms) and unlabeled (unobserved/zero terms) examples. 
Essentially, by using the square loss to model the unobserved terms and designing an efficient update rule based on linear algebra operations, the proposed PU-Learning framework can be trained efficiently and effectively. 



We evaluate the performance of the proposed approach in English\footnote{Although English is not a resource-scarce language, we simulate the low-resource setting in an English corpus. In this way, we leverage the existing evaluation methods to evaluate the proposed approach.} and  other three resource-scarce languages. We collected unlabeled language corpora from Wikipedia and compared the proposed approach with popular approaches, the Glove and the Skip-gram models, for training word embeddings. The experimental results show that our approach significantly outperforms the baseline models, especially  when the size of the training corpus is small.

Our key contributions are summarized below.
\begin{itemize}
    \item We propose a PU-Learning framework for learning word embedding.
    \item We tailor the coordinate descent algorithm~\cite{Yu} for solving the corresponding optimization problem. 
    \item Our experimental results show that PU-Learning improves the word embedding training in the low-resource setting.
\end{itemize}


\section{Related work}


\paragraph{Learning word vectors.}
The idea of learning word representations can be traced back to Latent Semantic Analysis (LSA)~\cite{deerwester1990indexing} and Hyperspace Analogue to Language (HAL)~\cite{lund1996producing}, where word vectors are generated by factorizing a word-document and word-word co-occurrence matrix,