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Recognizing the need for control, this report focuses on international obligations regarding deep-sea fisheries and biodiversity conservation, and discusses provisions that require national-level implementation. It analyses policy and legal instruments, and identifies implementation challenges.
Review and analysis of recently developed evaluation techniques for the performance recording of low income or minority group trainees enrolled in various vocational training programmes in the USA - appraises current practices and testing methods for predicting workers adaptation, success, etc., considers several new approaches, and includes recommendations for further research. Annotated bibliography pp. 148 to 174.
This book presents the recent achievements on the processing of representative user generated content (UGC) on E-commerce websites. This large size of UGC is valuable information for data mining to help customer/object profiling. It provides a comprehensive overview on the concept of customer credibility, object-oriented review summarization technology and content-based collaborative filtering algorithm. It covers a feedback mechanism which is designed to discover customer credibility, which is used to define the professional degree of review content; product-oriented review summarization for restaurants or trip arrangements, and introduced content-based collaborative filtering for product recommendation.
Foreign direct investment (FDI) in China has been growing during the past years. Nowadays, China is the second-largest recipient of FDI. Consequently, the increasing number of foreign direct investments in China and the economic development of the country influence the decision to promulgate a new investment law. This book pretends to guide investors and entrepreneurs to make adequate decisions in the field of investment law. The book approaches the importance of the national security review. Then, it makes a comparative study between China and Germany on the ground of the National Security Review Regimes of Foreign Investment. It pretends to demonstrate the similarities and differences in the selected jurisdictions.
The Research and Analysis (R&A) program managed by NASA's Planetary Science Division (PSD), supports a broad range of planetary science activities, including the analysis of data from past and current spacecraft; laboratory research; theoretical, modeling, and computational studies; geological and astrobiological fieldwork in planetary analog environments on Earth; geological mapping of planetary bodies; analysis of data from Earth- and space-based telescopes; and development of flight instruments and technology needed for future planetary science missions. The primary role of the PSD R&A program is to address NASA's strategic objective for planetary science and PSD's science goals. Recently, PSD reorganized the R&A program to provide better alignment with the strategic goals for planetary sciences. The major changes in the R&A program involved consolidating a number of prior program elements, many of which were organized by subdiscipline, into a smaller number of thematic core research program elements. Despite numerous efforts by PSD to communicate the rationale for the reorganization and articulate clearly the new processes, there has been significant resistance from the planetary science community and concerns in some sectors regarding the major realignment of funding priorities. Review of NASA's Planetary Science Division's Restructured Research and Analysis Programs examines the new R&A program and determines if it appropriately aligns with the agency's strategic goals, supports existing flight programs, and enables future missions. This report explores whether any specific research areas or subdisciplinary groups that are critical to NASA's strategic objectives for planetary science and PSD's science goals are not supported appropriately in the current program or have been inadvertently disenfranchised through the reorganization.
Myanmar is in need of a structural transformation from an agrarian economy to one based more on a mix of modern activities, including manufacturing and services. Modernising the agricultural sector by building linkages to complementary non-agricultural activities – an “agricultural value chain” ...
Paraguay has set itself ambitious development goals for 2030. To achieve them, it will have to tackle two major challenges: buttressing sources of sustainable economic prosperity and putting the country on a more inclusive development path. Progressing towards a more inclusive society will require a broad and vigorous reform agenda. First, the country’s healthcare system requires systemic reform to widen its coverage, reduce Paraguayans’ vulnerability in the face of health risks and increase the efficiency of health service provision. Second, the social protection system needs to overcome its fragmentation and become more effective in delivering the right services and risk management tools to citizens according to their needs. In particular, the pension system requires reforms to increase its coverage and become more equitable and more sustainable.
The data used in this project is the data published by Anurag Sharma about hotel reviews that were given by costumers. The data is given in two files, a train and test. The train.csv is the training data, containing unique User_ID for each entry with the review entered by a costumer and the browser and device used. The target variable is Is_Response, a variable that states whether the costumers was happy or not happy while staying in the hotel. This type of variable makes the project to a classification problem. The test.csv is the testing data, contains similar headings as the train data, without the target variable. The models used in this project are K-Nearest Neighbor, Random Forest, Naive Bayes, Logistic Regression, Decision Tree, Support Vector Machine, Adaboost, LGBM classifier, Gradient Boosting, and XGB classifier, and LSTM. Three vectorizers used in machine learning are Hashing Vectorizer, Count Vectorizer, and TFID Vectorizer. Finally, you will develop a GUI using PyQt5 to plot cross validation score, predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.