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Mera Bharat Desh Mahan
There are so many writers who write and remain unknown (unsaid). They present themselves with their words and unsaid feelings. These writers hide their actual potential , their real side of passion . we the compilers of book Raushan singh and kanishka goyal, were their for those who were in need of talent writers to express themselves to the world of writings with their names. This has all the emotions of the writers who wanted to be a part of this book. Writers present themselves, the love for our nation . There may be so many words and writeups that you may find close enough to connect with your real life. Our writers have written this unique pieces which We are presenting through this book. Read their writeups and try to relate yourself with their words. We have worked hard together to bring this book. A book that may become collection of your unsaid moments. All in all, I hope you like the efforts. This book is a “TRIBUTE TO OUR MOTHER EARTH". Mera Bharat Mahan is a tribute to all such unknown and unsung heroes, who make true Bharat. Let’s appreciate them, emulate them and pray for their tribe to increase.
Federated Learning
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
All of Us in Our Own Lives
A beautiful story of strangers who shape each other’s lives in fateful ways, All of Us in Our Own Lives delves deeply into the lives of women and men in Nepal and into the world of international aid. Ava Berriden, a Canadian lawyer, quits her corporate job in Toronto to move to Nepal, from where she was adopted as a baby. There she struggles to adapt to her new career in international aid and forge a connection with the country of her birth. Ava’s work brings her into contact with Indira Sharma, who has ambitions of becoming the first Nepali woman director of a NGO; Sapana Karki, a bright young teenager living a small village; and Gyanu, Sapana’s brother, who has returned home from Dubai to settle his sister’s future after their father’s death. Their journeys collide in unexpected ways. All of Us in Our Own Lives is a stunning, keenly observant novel about human interconnectedness, about privilege, and about the ethics of international aid (the earnestness and idealism and yet its cynical, moneyed nature).
Graph Representation Learning
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical sy...
Pearl River Mansion
None
Representation Learning for Natural Language Processing
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.
2019 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT)
The main objective of the Conference is to stimulate and facilitate active exchange, interaction and comparison of approaches, methods and ideas related to specific topics, both theoretical and applied, in the general areas related to the networking, intelligent techniques, computing technologies, Software Engineering and other contemporary issues like High Performance Computing, Bio inspired Computing, Green Computing, Distributed Computing and Grid Computing to foster the exchange of concepts and ideas The main aim of this International Conference is to contribute to academic arena, business world, and industrial community and in turn to the society
2020 IEEE International Conference on Big Data (Big Data)
We solicit high quality original research papers (and significant work in progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor IoT IoE, and multimedia (audio, video, image, etc ) big data systems and applications
