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.

/pdf/deep-physical-informed-neural-networks-for-metamaterial-3juq2c4qnv.pdf

Deep Physical Informed Neural Networks for Metamaterial Design

Social media have been growing rapidly during the past decade. However, it remains unclear whether social media make people more emotionally healthy or less. This study aims to explore the effect o...

https://journals.sagepub.com/doi/pdf/10.1177/2056305120912488

The Effects of Instagram Use, Social Comparison, and Self-Esteem on Social Anxiety: A Survey Study in Singapore:

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques has shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate the changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks.

/pdf/deep-learning-for-link-prediction-in-dynamic-networks-using-3pq6tebls1.pdf

Deep Learning for Link Prediction in Dynamic Networks Using Weak Estimators

The recognition accuracy of a single biometric authentication system is often much reduced due to the environment, user mode and physiological defects. In this paper, we combine face and iris features for developing a multimode biometric approach, which is able to diminish the drawback of single biometric approach as well as to improve the performance of authentication system. We combine a face database ORL and iris database CASIA to construct a multimodal biometric experimental database with which we validate the proposed approach and evaluate the multimodal biometrics performance. The experimental results reveal the multimodal biometrics verification is much more reliable and precise than single biometric approach.

Fusion of face and iris features for multimodal biometrics

Wide adoption of Internet of Things (IoT) devices and applications encounters security vulnerabilities as roadblocks. The heterogeneous nature of IoT systems prevents common benchmarks, such as the NSL-KDD dataset, from being used to test and verify the performance of different Network Intrusion Detection Systems (NIDS). In order to bridge this gap, in this paper, we examine specific attacks in the NSL-KDD dataset that can impact sensor nodes and networks in IoT settings. Furthermore, in order to detect the introduced attacks, we study eleven machine learning algorithms and report the results. Through numerical analysis, we show that tree-based methods and ensemble methods outperform the rest of the studied machine learning methods. Among the supervised algorithms, XGBoost ranks the first with 97% accuracy, 90.5% Matthews correlation coefficient (MCC), and 99.6% Area Under the Curve (AUC) performance. Moreover, a notable research finding of this study is that the Expectation-Maximization (EM) algorithm, which is an unsupervised method, also performs reasonably well in the detection of the attacks in the NSL-KDD dataset and outperforms the accuracy of the Naive Bayes classifier by 22.0%.

Machine learning-driven intrusion detection for Contiki-NG-based IoT networks exposed to NSL-KDD dataset

Hand gestures are the most common forms of communication and have great importance in our world. They can help in building safe and comfortable user interfaces for a multitude of applications. Various computer vision algorithms have employed color and depth camera for hand gesture recognition, but robust classification of gestures from different subjects is still challenging. I propose an algorithm for real-time hand gesture recognition using convolutional neural networks (CNNs). The proposed CNN achieves an average accuracy of 98.76% on the dataset comprising of 9 hand gestures and 500 images for each gesture.

Hand Gesture Recognition with Convolution Neural Networks

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.

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

A sizable amount of current literature on online drift detection tools thrive on unrealistic parametric strictures such as normality or on non-parametric methods whose power performance is questionable. Using minimal realistic assumptions such as unimodality, we have strived to proffer an alternative, through a novel application of Bernstein’s inequality. Simulations from such parametric densities as Beta and Logit-normal as well as real-data analyses demonstrate this new method’s superiority over similar techniques relying on bounds, such as Hoeffding’s. Improvements are apparent in terms of higher power, efficient sample sizes, and sensitivity to parameter values.

A Novel Online and Non-Parametric Approach for Drift Detection in Big Data

Many studies have been conducted on Handwritten Signature Verification. Researchers have taken many different approaches to accurately identify valid signatures from skilled forgeries, which closely resemble the real signature. The purpose of this paper is to suggest a method for validating written signatures on bank checks. This model uses a convolutional neural network (CNN) to analyze pixels from a signature image to recognize abnormalities. We believe the feature extraction capabilities of a CNN can optimize processing time and feature analysis of signature verification. Unique characteristics from signatures can be accurately and rapidly analyzed with multiple layers of receptive fields and hidden layers. Our method was able to correctly detect the validity of the inputted signature approximately 83 percent of the time. We tested our method using the SIGCOMP 2011 dataset. The main contribution of this method is to detect and decrease fraud committed, especially in the banking industry. Future uses of signature verification could include legal documents and the justice system.

Toward data quality analytics in signature verification using a convolutional neural network

Multimodal data can be used to gain additional perspective on a phenomenon. For applications, such as security and the detection of suspicious activity, the need to aggregate and analyze data from multiple modes is vital. Recent research in suspicious behavior detection has introduced methods for identifying and scoring dense blocks in multivariate tensors, which are consistent indicators of suspicious activity. None yet, however, have proposed a method for the merging and analysis of multiple modes of data for suspicious behavior, especially when the set of items described in each data set do not match—that is, the data is partially paired—which is common when data sets originate from different sources. Neither has a method been described for dealing with the similar case of incomplete data. This paper introduces a technique for multimodal data analysis for suspicious activity detection when the data are only partially paired and/or incomplete. The method is applied to synthetic and real data, demonstrating strong precision and recall even in poorly paired cases.

Felix Zhan

Papers

Hand Gesture Recognition with Convolution Neural Networks

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

A Novel Online and Non-Parametric Approach for Drift Detection in Big Data

Toward data quality analytics in signature verification using a convolutional neural network

Uncovering Suspicious Activity From Partially Paired and Incomplete Multimodal Data