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[7] viXra:1912.0542 [pdf] submitted on 2019-12-31 13:46:47
Authors: Bheemaiah, Anil Kumar
Comments: 9 Pages.
IaC is also CaC, Circuits as Code, we introduce a uniform framework for IOT based sensor fusion and automated persistence to AWS S3 using the per-observer design pattern defined in reactive streams. A uniform scalable IoT architecture is in the automated code generation of Alexa Skills with both AlexaPi and the flex echo. We introduce CaC using the TOMU board, an open source ARM v7 based architecture.
Keywords: IaC, CloudFormation, CaC, Circuits as Code, AWS, stack, SaaS, AIaaS, IoT, ARM
What:
The Amazon Echo Flex, retailing at $24.99 is presented as an attractive scalable IOT platform for use with grid compatible IOT solutions, in conjunction with the open source Tomu and Fomu platforms for multi sensor integrated IOT with Alexa skills and AWS Lambda cloud functions. We compare this with the AlexPi solution, enabling IOT with inexpensive hardware like the raspberry pi zero W or orange pi, or any scalable browser based device, as an IOT solution with Alexa. The marketing pitch for the echo flex, remains as a power source for all the hardware supported by AlexaPi or any other computing needing a 5v USB-A bus and thus supplements the AlexaPi.
A case study of flooding in the Lilydale Regional Park in St Paul is presented where a scalable network of Tomu based water level indicators is hypothesized to predict flooding for closure of the park.
How:
Simple resistive water level monitoring sensors are integrated with Tomu and Echo Flex boards using the Alexa Gadget API, USB function for an Rx formulation of Alexa IOT interactions. This allows periodic uploading of water level information to AWS S3 for data mining and predictive analytics.
A hypothetical model to compute the operations of such a ‘N’ node grid is presented with a case study of lilydale park and the Mississippi river.
Why:
Simplicity , robustness and cost effective solutions , lead to the evolution of the IOT or things network, while Lora or SigFox are touted as solutions we present a simpler approach using WiFi and VUI based echo flex units for IOT.
Applications:
IoT for AI for Earth series, starring Early Bird Warning for Flood Prediction Analytics, an AIaaS data mining Lambda of S3 data from a network of IoT nodes with river level sensors.
Category: Artificial Intelligence
[6] viXra:1912.0486 [pdf] submitted on 2019-12-28 03:46:03
Authors: George Rajna
Comments: 54 Pages.
A research team led by Professor Hongzhe Sun from the Department of Chemistry at the University of Hong Kong (HKU), in collaboration with Professor Junwen Wang from Mayo Clinic, Arizona in the United States (a former HKU colleague), implemented a robust deep learning approach to predict disease-associated mutations of the metal-binding sites in a protein. [33]
Researchers at the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) employed a suite of deep-learning techniques to identify and observe these temporary yet notable structures. [32]
As part of a team of scientists from IBM and New York University, my colleagues and I are looking at new ways AI could be used to help ophthalmologists and optometrists further utilize eye images, and potentially help to speed the process for detecting glaucoma in images. [31] A team of EPFL scientists has now written a machine-learning program that can predict, in record time, how atoms will respond to an applied magnetic field. [30] Researchers from the University of Luxembourg, Technische Universität Berlin, and the Fritz Haber Institute of the Max Planck Society have combined machine learning and quantum mechanics to predict the dynamics and atomic interactions in molecules. [29] For the first time, physicists have demonstrated that machine learning can reconstruct a quantum system based on relatively few experimental measurements. [28] AlphaZero plays very unusually; not like a human, but also not like a typical computer. Instead, it plays with "real artificial" intelligence. [27] Predictions for an AI-dominated future are increasingly common, but Antoine Blondeau has experience in reading, and arguably manipulating, the runes-he helped develop technology that evolved into predictive texting and Apple's Siri. [26] Artificial intelligence can improve health care by analyzing data from apps, smartphones and wearable technology. [25] Now, researchers at Google's DeepMind have developed a simple algorithm to handle such reasoning-and it has already beaten humans at a complex image comprehension test. [24]
Category: Artificial Intelligence
[5] viXra:1912.0477 [pdf] submitted on 2019-12-27 02:44:18
Authors: George Rajna
Comments: 31 Pages.
Quantitative biologists Justin B. Kinney and Ammar Tareen have a strategy to design advanced machine learning algorithms that are easier for biologists to understand. [21] Marculescu, along with ECE Ph.D. student Chieh Lo, has developed a machine learning algorithm-called MPLasso-that uses data to infer associations and interactions between microbes in the GI microbiome. [20] A team of researchers from the University of Muenster in Germany has now demonstrated that this combination is extremely well suited to planning chemical syntheses-so-called retrosyntheses-with unprecedented efficiency. [19] Two physicists at ETH Zurich and the Hebrew University of Jerusalem have developed a novel machine-learning algorithm that analyses large data sets describing a physical system and extract from them the essential information needed to understand the underlying physics. [18]
Category: Artificial Intelligence
[4] viXra:1912.0400 [pdf] submitted on 2019-12-22 07:00:51
Authors: George Rajna
Comments: 47 Pages.
With their experimental setup, the neurobiologists disassembled a complex behavior into individual components recognizable by the computer. [29]
Biomedical engineers at Duke University have developed an automated process that can trace the shapes of active neurons as accurately as human researchers can, but in a fraction of the time. [28]
Imagine a future technology that would provide instant access to the world's knowledge and artificial intelligence, simply by thinking about a specific topic or question. [27]
Just like living ecosystems, web services form a complex artificial system consisting of tags and the user-generated media associated with them, such as photographs, movies and web pages. [26]
Category: Artificial Intelligence
[3] viXra:1912.0204 [pdf] replaced on 2020-10-10 08:53:27
Authors: Jeongik Cho, Kyoungro Yoon
Comments: 9 Pages.
Conditional Generative Adversarial Network (GAN) is a GAN that generates data with the desired condition from the latent vector. The auxiliary classifier GAN is the most used among the variations of conditional GANs. In this study, we explain the problem of auxiliary classifier GAN and propose conditional activation GAN that can replace auxiliary classifier GAN to reduce the number of hyperparameters and improve training speed. The loss function of conditional activation GAN is defined as the sum of the loss of each GAN created for each condition. Since each GAN shares all hidden layers, the GANs can be considered as a single GAN and it does not increase the amount of computation much. Also, in order to prevent ignorance of conditions in the discriminator of conditional GANs with batch normalization, we propose a mixed batch training, in which each batch for discriminator is always configured to have the same ratio of real data and generated data so that each batch always has the similar condition distribution
Category: Artificial Intelligence
[2] viXra:1912.0179 [pdf] submitted on 2019-12-09 12:44:11
Authors: Priyanshi Bhola, Garima
Comments: 10 Pages.
In this paper, we are going to present and review a more feasible method to predict the stock movement with higher accuracy. The first thing we have taken into account is the dataset of the stock market prices from the previous year. The dataset was pre-processed and tuned up for real analysis.
Stock market prediction is an act of trying to determine the future value of a stock other financial instrument traded on a financial exchange. This paper explains the prediction of a stock using Machine Learning. The technical and fundamental or the time series analysis is used by most of the stockbrokers while making the stock predictions. The programming language is used to predict the stock market using machine learning is Python. In this paper, we propose a Machine Learning (ML) approach that will be trained from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In this context this study uses a machine learning technique called Support Vector Machine (SVM) to predict stock prices for the large and small capitalizations and in the three different markets, employing prices with both daily and up-to-the-minute frequencies.
Category: Artificial Intelligence
[1] viXra:1912.0128 [pdf] submitted on 2019-12-06 12:11:38
Authors: Diptanu Sarkar
Comments: 7 Pages.
Language Identification (LID) in Natural Language Processing (NLP)
is the process of identifying the spoken language in speech utterances. In the last decade, the interest and functional application of speech processing systems have grown exponentially. The proliferated use of handsfree voice-operated devices, speech-to-speech translation systems requires low latency, reliable automatic speech identification systems. This article examines three different models to recognize languages automatically in speech. The first model uses Dynamic Hidden Markov Networks (DHMNet) for LID in utterances. Another model utilizes Deep Neural Network (DNN), and the third uses the recently developed Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). Finally, comparing three different models, it is shown that a fusion of LSTM RNN and DNN model gives better results than the state-of-the-art models when applied to short utterances.
Category: Artificial Intelligence
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