In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.

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Deep Physical Informed Neural Networks for Metamaterial Design

Advances in communication technologies have enabled peoples to deliver more. Due to this phenomenon, an increasing amount of data are easily disseminated and published on the internet, which encouraged the practice of paraphrasing. It allows the original sentence to be concealed by alternative expressions of the same meaning. Its detection consists in identifying the degree of semantic similarity between them. It is one of the complex tasks of automatic natural language processing and artificial intelligence. Despite the fact that Arabic language is spoken by a large population around the world, it is rich of grammars and semantics that made hard its sentences modeling and similarity computing. In this paper, an Arabic extrinsic paraphrase identification method is proposed. It is based on a Siamese recurrent neural networks architecture seeing its performance in processing variable size of textual sequences. Indeed, pertinent features are firstly extracted using global word vector that used a global co-occurrence matrix based on a local context window. Then, bidirectional long short-term memory is introduced that incorporated efficiently long-term dependent relationships and captured meaningful contextual semantics between words. For paraphrase identification, cosine measure is used as a merge function. It was useful for identifying semantic similarity between the obtained source and suspect vectors. To address the lack of free and publicly Arabic paraphrased datasets, word2vec algorithm and part-of-speech tagging are combined to generate suspect sentences. For its validation, its quality is compared to the SemEval benchmark. Experiments demonstrated the effectiveness of our proposal’s methods.

BLSTM-API: Bi-LSTM Recurrent Neural Network-Based Approach for Arabic Paraphrase Identification

Neural networks are the cutting edge of artificial intelligence, demonstrated to reliably outperform other techniques in machine learning. Within the domain of neural networks, many different classes of architectures have been developed for various tasks in specific subfields, as well as a multitude of diversity in the way of activation functions, loss functions, and other such hyperparameters. These networks are often large and computationally expensive to train and deploy, restricting their utility. Furthermore, the fundamental theory behind the effectiveness of particular network architectures and hyperparameters are often not well understood, and as such, practitioners frequently resort to trial-and-error techniques to optimize their model performance. To address these concerns, we propose the use of compact directed acyclic graph neural networks (DAG-NNs) and an evolutionary approach for automating the optimization of their structure and parameters. Our experimental results demonstrate that our approach consistently outperforms conventional neural networks, even while employing fewer nodes.

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An Evolutionary Approach to Compact DAG Neural Network Optimization

Ensemble methods for machine learning serve to increase the predictive power of preexisting models by applying a meta-algorithm on top of the underlying workings of one or many prediction models. These ensemble models show promise for anomaly detection over the simpler prediction models they are built on-top of without much more admin work and theory, making them ideal for network intrusion detection. This study looks at the advantages of these methods when applied to the cybersecurity domain by using the widely used NSL-KDD intrusion detection dataset. The types of ensemble methods studied are voting, bagging, and boosting, specifically the algorithms experimented with are the Voting classifier, boosting, Random forest classifier, and AdaBoost classifier.

A Study of Ensemble Methods for Cyber Security

Sustaining a society requires reusing, reducing, and recycling waste. Waste disposal has always been a problem in developing countries because of inadequate infrastructure. By utilizing artificial intelligence to detect hazardous waste, more individuals will be protected from the negative effects of it. To help mitigate this problem, we experimented with Keras, to create a convolutional neural network, and OpenCV, to create real-time videos, that identifies hazardous waste from other recyclable materials. Through the use of machine learning, our model is able to categorize different recyclable materials with about 90% accuracy. Objects within the video receive a prediction for 3 classifications which includes batteries, syringes, and nonhazardous waste. Then, the category with the highest category is what the network will classify it as. In conclusion, the model is able to identify hazardous objects and recyclable items within a pile of trash to help protect all individuals.

Segregating Hazardous Waste Using Deep Neural Networks in Real-Time Video

Paraphrase Identification or Natural Language Sentence Matching (NLSM) is one of the important and challenging tasks in Natural Language Processing where the task is to identify if a sentence is a paraphrase of another sentence in a given pair of sentences. Paraphrase of a sentence conveys the same meaning but its structure and the sequence of words varies. It is a challenging task as it is difficult to infer the proper context about a sentence given its short length. Also, coming up with similarity metrics for the inferred context of a pair of sentences is not straightforward as well. Whereas, its applications are numerous. This work explores various machine learning algorithms to model the task and also applies different input encoding scheme. Specifically, we created the models using Logistic Regression, Support Vector Machines, and different architectures of Neural Networks. Among the compared models, as expected, Recurrent Neural Network (RNN) is best suited for our paraphrase identification task. Also, we propose that Plagiarism detection is one of the areas where Paraphrase Identification can be effectively implemented.

Ethan Hunt

Papers

Machine Learning Models for Paraphrase Identification and its Applications on Plagiarism Detection