Tricks Of The Trade Synonym

Tricks Of The Trade Synonym – The new venture aims to hyperbitcoinize by combining the speed of the Lightning Network with the architecture of an open, peer-to-peer platform.

Synonym Software Ltd., a company founded by stablecoin issuer Tether Holdings Limited, officially launched on Tuesday, launching a very ambitious project to bring Bitcoin (BTC) transactions into the mainstream through an independent financial platform that uses the Lightning Network.

Tricks Of The Trade Synonym

Tricks Of The Trade Synonym

The company announced on Tuesday that Synonym’s stated goal is to provide self-ownership and control over crypto-assets by creating an open financial ecosystem that uses Bitcoin and the Lightning Network. CEO John Carvalho said, “Hyperbitcoinization will not magically happen on its own. To live in a world without big banks, oppressive regulations or big technology dominating our lives, we need a strategy and ecosystem to replace the outdated economy. That’s where Synonym comes in.”

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The first protocol launched by Synonym, called Slashtags, is an interaction framework for private networks that does not rely on blockchain technology and can be used by any platform for coordination, privacy and consensus.

The Bitcoin network recently completed the long-awaited Taproot update, which aims to improve transaction efficiency, privacy and smart contract functionality. Taproot marks the first major upgrade to the Bitcoin network since Segregated Witness, also known as SegWit, in 2018. SegWit eventually culminated in the launch of the Lightning Network, Bitcoin’s second layer scaling solution.

Scalability has been cited as one of the biggest barriers to mass adoption of Bitcoin as a transactional currency. The Lightning Network aims to solve the scalability problem by enabling off-chain transactions. The number of Lightning Network nodes opening payment channels with each other has increased by 128% in the past 12 months, according to industry sources.

At the time of writing, Bitcoin has more than 43% of the total cryptocurrency market capitalization. The premier digital currency recently hit new all-time highs north of $69,000 amid growing widespread adoption and recognition by the financial elite that crypto is a new asset class. Design and Implementation of an IoT-Oriented Smart Voltage Sensor with Research Capabilities for Energy Harvesting and Magnetorheological Elastomeric Transducers

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Tricks Of The Trade Synonym

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Received: 20 May 2020 / Revised: 23 June 2020 / Accepted: 23 June 2020 / Published: 26 June 2020

Embedding dictionary words as vectors in a space has become an active area of ​​research due to its repeated use in several natural language processing programs. The distances between the vectors should reflect the relatedness between the corresponding words. The problem with existing word embedding methods is that they often fail to distinguish between synonymous, antonymous, and unrelated word pairs. Meanwhile, polarity detection is crucial for applications such as sentiment analysis. In this work, we propose an embedding approach that is designed to account for the polarity problem. The approach is based on embedding word vectors into a sphere, as a result of which the dot product between any vectors represents similarity. Vectors corresponding to synonymous words would be close to each other on the sphere, and the word and its antonym would lie on opposite poles of the sphere. The approach used to design the vectors is a simple relaxation algorithm. The proposed word embedding successfully distinguishes between synonyms, antonyms and unrelated word pairs. It achieves results that are better than some of the state-of-the-art methods and competes well with others.

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Word vector embedding seeks to model each word as a multidimensional vector. Modeling words as vectors has many obvious advantages. Implementing natural language processing (NLP) algorithms using machine learning models requires converting a text word or sentence into a numeric format. Moreover, this transformation must be significant. For example, words with similar meanings should have vectors that are in close proximity in the embedded space. This is because such words should produce the same results when applied to a machine learning model. In addition, embedding words in a complete dictionary will provide a complete representation of the corpus. At the same time, each word is assigned its unique position in the vector space, which reflects its cumulative relationship with all other words in one coherent structure. Word’s vector spaces have been very useful in many applications; e.g., machine translation [1], sentiment analysis [2, 3, 4, 5], question answering [6, 7], information retrieval [8, 9], spelling correction [10], crowdsourcing [11], named entity recognition (NER) [12], text annotation [13, 14, 15], etc.

The problem with existing word vector embedding methods is that word polarity is not properly taken into account. Two antonyms are considered polar opposites and should be modeled as such. However, most modern methods do not solve this problem. For example, they do not differentiate between two unrelated words and two opposite words by similarity scores.

In this work, we propose a new embedding that takes the polarity problem into account. The new approach is based on embedding words in a sphere, as a result of which the scalar product of the corresponding vectors represents the similarity of meaning between any two words. The polar nature of the sphere is the main motivation for such an embedding, as in this way antonymic relations can be fixed in such a way that a word and its antonym are placed at opposite poles of the sphere. This polarity locking feature is important for some applications such as sentiment analysis. Recently, word embedding techniques have begun to penetrate the field of sentiment analysis at the expense of traditional machine learning algorithms that rely on manually annotated sentiment polarity data and expensive feature development. With word embeddings, a sentence can be mapped to a series of features based on its word embeddings. However, these plugins must reflect the polarity of the words in order to perform the task of sentiment analysis. As mentioned earlier, this issue of polarity is fully addressed in our approach to word embedding.

Tricks Of The Trade Synonym

Figure 1 illustrates the concept of the proposed approach. As shown in the figure, the two vectors corresponding to the words “happy” and “joyful” have a close similarity because they are synonyms. On the other hand, the vectors corresponding to “happy” and “sad” are at opposite poles and have a similarity score of −1 because they are antonyms. The algorithm can deduce the new relationship that “happy” and “sad” are also antonyms, since their corresponding vectors are also located close to opposite poles of the sphere.

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Two words that are similar in meaning are assigned vectors with a similarity measure equal to 1. Two unrelated words will be assigned a similarity close to zero, and two antonyms will be assigned a similarity close to -1. In other words, we specifically address the issue of polarity and distinguish between “opposite” and “unrelated.”

The fact that the similarity between opposite words or antonyms is close to -1 is consistent with the logically appealing fact that negation equals multiplication by -1. Double negation means multiplying −1 twice, which gives a similarity close to unity (an antonym of an antonym is a synonym). In our geometry, negation can also be thought of as a reflection around a coordinate. Therefore, the developed model makes logical sense. Another question to investigate is whether unrelated words should have close to zero similarity. The answer is yes. Logically, unrelated words such as “train” and “star” would have their vectors far apart, due to our principle that similarity or proximity of vectors should reflect relatedness. This is consistent with the theory that with high dimensionality, the scalar product of randomly occurring unit vectors on the sphere is close to zero [16]. The algorithm has a number of useful functions:

One of the earliest vector embeddings of words is the work of Mikalov et al. [17] who published the word representation of Google word2vec. Theirs

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