Joshua Holley

A Block Withholding attack is when a miner on a pool intentionally does not submit any blocks he finds to the pool.

A Block Withholding attack occurs when a miner on a pool intentionally does not submit any blocks he finds to the pool. The intent of the attack is to cause the profitability of the mining pool to drop. This type of attack, if sustained for a long enough period, can bankrupt a pay-per-share pool. Mitigation of a Block Withholding attack is complex because of the random nature of mining, but some methods have been developed.

Mitigation of the block withholding attack is complex because of the random nature of mining, but some methods have been developed.

OKcoin is a digital asset trading platform. Itthat provides trading services between USD, EURO and Japanese Yen as flat currencies, and supports different virtual currencies such as Bitcoin, Litecoin and Ethereum. OKcoin iswas foundfounded by Mingxing Xu in June, 2013.

Markov decision processes serve to provide mathematical frameworks where an outcomes or set of outcomes is in part random and in part under control that can be used in model decision making.

A model-free reinforcement technique in machine learning and data mining that compares available actions to states of the expected actions for a given machine state. Q-learning is able to find the optimal set of states for any given finite Markov decision process (MDP).

Text to speech (TTS) is form of speech synthesis. It is a system that converts text into spoken voice output. TTS firstsystems realwere useinitially wasused in reading systems for the blind, in which the system reads some text from a book andby convertconverting it into speech. TTS applications include voice-enabled e-mail and spoken prompts in voice response systems. TTS is often used with voice recognition programs.

TTS system is built by creating a database of recorded voices (speaking whole sentences to syllables). The recordings are stored, sorted, labeled and segmented by phones, syllables, morphemes, words, phrases, and sentences. It will reproduce words from a text by carrying out a sophisticated linguistic analysis, and natural language processing to understand the structure of the sentences and to determine the context of the word for pronunciation. And afterAfter the natural language processing, the system will search and choosematch the text to the database of speech units to produce speech fitted to the text input.

GPU computing is the use of graphics processing unitunits (GPU) and central processing unitunits (CPU) to perform scientific calculations, engineering computingcomputation, and the processing of large volumes of data. GPUs have massive-parallel architecture. By using GPUs, the completion of computationally intensive assignments that benefit from parallel computation is faster compared with conventional central processing units (CPUs).

GPU computing is recognized offor having enormous potential in other fields such as medical imaging. ComputerDue to the increased clock speeds now present in modern GPUs coupled with optimized parallel architectures, computer scientists and researchers arehave nowbegun using GPU computing asfor some high performance computing (HPC) forthat generalwas purposestraditionally andperformed establishedby a CPU utilizing specialized chips known as general-purpose computing on graphics processing unitunits (GPGPU).

The simple method of speech synthesis relies on a machine analyzing the words of input phrases and grouping letters based on common usage together. These letters are then matched to a specific sound in the machine's database, which creates the synthesized audio. In this version of speech synthesis, the machine is merely converting the most common sounds that letters make together into audio, which results in the uneven and robotic tones and odd mispronunciations present in simpler systems.

In order to introduce smooth and more natural speech patterns, modern speech synthesis systems have begun to deploy Hidden Markov models to determine the most likely phrase that needs to be "spoken" by the synthesizer. Hidden Markov models are finite state machines that can be used to analyze segments of text that are broken down into a series based on time. The state machine determines the actual word that has been typed using phonetic analysis and its place within the typed phrase based on probability. This allows the machine to string the sounds along in a more naturally paced manner that matches the intent of the text to the audio being produced.

HMM-Based Speech Synthesis: Fundamentals and Its Recent Advances

The four states of analysis to produce audio based on Hidden Markov models are text, phonetic, prosodic, and speech. Text analysis converts the text into a form usable by the machine and utilizes probability to determine the linguistic meaning of the text and the context of the text. Phonetic analysis converts the literal typed letters into phonetic symbols that the machine can relate to certain sounds. Prosodic analysis seeks to use the linguistic meaning in conjunction with the context and phonetic sounds to determine the most probable rhythms, stress patterns, and intonation. Speech analysis combines the results of the previous states to generate the speech signal.