Artificial Intelligence

2107 Submissions

[4] viXra:2107.0124 [pdf] submitted on 2021-07-22 18:37:33

Breaking Free from the Stability-Plasticity Dilemma with Incremental Domain Inference on Sequential Data

Authors: Romain Mouret
Comments: 5 Pages.

We make the case for identifying the input domain prior to running downstream models and propose an architecture that opens the door to lifelong learning systems that forget at a decreasing rate as the tasks grow in complexity. Our model accurately identifies domains and is compatible with other continual learning algorithms, provided they benefit from knowing the current domain beforehand.
Category: Artificial Intelligence

[3] viXra:2107.0122 [pdf] submitted on 2021-07-21 19:07:21

Open Science with Respect to Artificial Intelligence

Authors: Sagnik Mazumder
Comments: 4 Pages.

Artificial Intelligence is one of those fields in computer science that is currently being extensively studied. In this paper, the author attempts to summarise the current state of research in the field with respect to openness to the general community, and has found a profound lack of opportunity to contribute to the field as a novice, and a near monopoly of effective research by large industries while production environments continue to largely remain safe from such influences.
Category: Artificial Intelligence

[2] viXra:2107.0097 [pdf] submitted on 2021-07-16 15:11:10

Smart Contracts on Algorand

Authors: Archie Chaudhury, Brian Haney
Comments: 16 Pages. Blockchain, Computation, and Cryptocurrency

This Paper makes three main contributions. First, this Paper surveys Algorand Smart Contracts and the Algorand Network, including software systems and algorithmic architectures. Second, this Paper discusses various software mechanisms enabling developers to execute transfers on the Algorand Network. Third, this Paper advances Algorand Smart Contracts by introducing the Algogeneous Smart Contract. Algogeneous Smart Contracts are a new type of Algorand Smart Contract, which are simpler to develop and utilize artificial intelligence to ensure contracts are legally compliant and enforceable.
Category: Artificial Intelligence

[1] viXra:2107.0058 [pdf] submitted on 2021-07-10 13:40:51

Twitter Sentiment Analysis using Deep Learning

Authors: Vedurumudi Priyanka
Comments: 17 Pages.

In this report, address the problem of sentiment classification on twitter dataset. used a number of machine learning and deep learning methods to perform sentiment analysis. In the end, used a majority vote ensemble method with 5 of our best models to achieve the classification accuracy of 83.58% on kaggle public leaderboard. compared various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a CSV file of type tweet_id, sentiment, tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a CSV file of type tweet_id, tweet. Please note that CSV headers are not expected and should be removed from the training and test datasets. used Anaconda distribution of Python for datasets for library requirements specific to some methods such as keras with TensorFlow backend for Logistic Regression, MLP, RNN (LSTM), and CNN. and xgboost for XGBoost. Usage of preprocessing, baseline, Naive Bayes, Maximum entropy, Decision Tree, random forest, multi-layer perception etc are implemented
Category: Artificial Intelligence