Reviewerly/exHarmony huggingface dataset:
Question Answering Chatbot using Deep Learning with NLP

In spite of the number of techniques, models and datasets, Question Answering is still an exacting problem because of the issues in understanding the question and extracting the correct answer. It refers to creating platforms that when given a question in a natural language by humans, can automatically answer it. While many information retrieval chatbots achieve the task, recently, deep learning has earned a lot of attention to question answering due to its capability to learn optimal representation for the given task. This paper aims to build a closed domain, factoid Question Answering system. We recruit NLP methods of pattern matching and information retrieval to create an answer candidate pool. Before scoring similarities between the question and answers, we map them into some feature space. Our approach solves this task through distributional representations of the words and sentences wherein encodings store their lexical, semantic, and syntactic aspects. We use a convolutional neural network architecture to rank these candidate answers. Our model learns an optimal representation for the input question and answer sentences and a matching function to relate each such pair in a supervised manner from training data. Our model does not require any manual feature engineering or language sensitive data; hence can be extended to various domains. Training and testing on TREC QA, a Question Answering dataset, showed very promising metrics for our model.
Linking User Accounts across Social Media Platforms

To improve social media analysis across diverse platforms, an effective method to evaluate the possibility that different accounts belong to the same users is required. This might be used to support fake news detection or other nefarious activities. In this paper, we present an approach to calculate the probability that different social media accounts on diverse social media platforms belong to the same user. We consider various platform aspects related to user accounts that can be used for user matching including the selected username, the avatar or profile picture, the content of platform posts and related metadata such as the writing style, account binding and use of hyperlinks across platforms. The experimental results showed that the approach is able to distinguish whether the same person has different platform accounts with an F1-score of up to 0.937.
Accurate Bounding Box Prediction for Single-Shot Object Detection

Accurate single-shot object detection is an extremely challenging task in real environments because of complex scenes, occlusion, ambiguities, blur, and shadow, i.e., these factors are called uncertainty problem. It leads to unreliable labeling of bounding box annotation and makes detectors arduous to learn bounding box localization. Previous methods viewed the ground truth box coordinates as a rigid distribution omitting localization uncertainty in real datasets. This article proposes a novel bounding box encoding algorithm integrated into the single-shot detector (BBENet) to consider the flexible distribution of bounding box localization. First, discretized ground truth labels are generated by decomposing each object’s boundary into multiple boundaries. The new representation of ground truth boxes is more arbitrary and flexible to cover any case of complex scenes. During training, the detector directly learns discretized box locations instead of continuous domain. Second, the bounding box encoding algorithm reorganizes bounding box predictions to be more accurate. Furthermore, another problem in existing methods is inconsistency in estimating detection quality. The single-shot detection consists of classification and localization tasks, but the popular detectors consider the classification score as the final detection quality. Thus, it lacks localization quality and hinders the overall performance because both tasks have a positive correlation. To overcome this problem, BBENet introduces detection quality by combining the localization and classification quality to rank detection during nonmaximum suppression. The localization quality is computed based on how uncertain the predicted boxes are, which is a new perspective in detection literature. The proposed BBENet is evaluated on three benchmark datasets, i.e., MS-COCO, Pascal VOC, and CrowdHuman. Without bells and whistles, BBENet outperforms the existing methods by a large margin with comparable speed, achieving the state-of-the-art single-shot detector.
WindTunnel

While deep neural networks (DNNs) have shown to be successful in several domains like computer vision, non-DNN models such as linear models and gradient boosting trees are still considered state-of-the-art over tabular data. When using these models, data scientists often author machine learning (ML) pipelines: DAG of ML operators comprising data transforms and ML models, whereby each operator is sequentially trained one-at-a-time. Conversely, when training DNNs, layers composing the neural networks are simultaneously trained using backpropagation. In this paper, we argue that the training scheme of ML pipelines is sub-optimal because it tries to optimize a single operator at a time thus losing the chance of global optimization. We therefore propose WindTunnel: a system that translates a trained ML pipeline into a pipeline of neural network modules and jointly optimizes the modules using backpropagation. We also suggest translation methodologies for several non-differentiable operators such as gradient boosting trees and categorical feature encoders. Our experiments show that fine-tuning of the translated WindTunnel pipelines is a promising technique able to increase the final accuracy.
A Customizable Web Platform to Manage Standards Compliance of Information Security and Cybersecurity Auditing

Information security and cybersecurity are key subjects in modern enterprises’ management, being ISO-27001:2013, NIST Cybersecurity Framework and ISO-27009 some of the most implemented international frameworks and standards. Their main goal is to globally reduce the risk, by leveraging enterprises’ competitiveness in global markets and enhancing business processes and collaborators’ cyber awareness. Auditing processes examine and assess a list of predefined controls. For each control, a set of corrective measures could be proposed, to increase its compliance with the standard being used. These processes are time-consuming, involve on-site intervention by specialized consulting teams on the intervened enterprises, and a set of status reports of all the interventions should be elaborated and delivered. The existing auditing information systems are not developed to meet Small and Medium-sized Enterprises (SME) requirements, as they are mostly proprietary and expensive, ground usually on off-the-shelf applications, and are not generic to be used by several standards with different checklists and auditing methodologies. In this paper, a generic and web-integrated cybersecurity auditing information system is described. Its architecture, design, and data model enable it to be used in a wide set of auditing processes, by loading a predefined controls checklist assessment and their corresponding mitigation tasks list. It was designed to meet both SMEs and large enterprises’ requirements, and stores auditing and intervention-related data in a relational database. The information system was tested on an ISO-27001:2013 information security auditing project, which has integrated fifty SMEs. The results obtained during the project are promising and reveal the appropriateness of using this information system in further similar auditing processes.
LPI Radar Waveform Recognition Based on Neural Architecture Search

In order to reach the intelligent recognition, the deep learning classifiers adopted by radar waveform are normally trained with transfer learning, where the pretrained convolutional neural network on an external large-scale classification dataset (e.g., ImageNet) is used as the backbone. Though transfer learning could effectively avoid overfitting, transferred models are usually redundant and might not generalize well. To eliminate the dependence on transfer learning and achieve high generalization ability, this paper introduced neural architecture search (NAS) to search the suitable classifier of radar waveforms for the first time. Firstly, one of the innovative technologies in NAS called differentiable architecture search (DARTS) was used to design the classifier for 15 kinds of low probability intercept radar waveforms automatically. Then, a method with an auxiliary classifier called flexible-DARTS was proposed. By adding an auxiliary classifier in the middle layer, the flexible-DARTS has a better performance in designing well-generalized classifiers than the standard DARTS. Finally, the performance of the classifier in practical application was compared with related work. Simulation proves that the model based on flexible-DARTS has a better performance, and the accuracy rate for 15 kinds of radar waveforms can reach 79.2% under the -9 dB SNR which proved the effectiveness of the method proposed in this paper for the recognition of radar waveforms.
k-NN based Writer Independent Offline Signature Verification System

Signature verification is a difficult research area since two people’s signatures may be similar, but an individual’s signature might vary depending on the situation. The accuracy of the signature verification framework is largely determined by the classifier and feature extraction scheme employed in the classification process. With this in mind, the current study looks into the effectiveness of the k-Nearest Neighbors classifier in conjunction with the Local Binary Pattern feature set for the development of a writer-independent offline signature verification system. To evaluate the system’s performance, two signature databases of 100 and 260 writers are used. Genuine signatures, as well as random forgery, unskilled forgery, and simulated forgery signatures, are considered for the development of the desired system, while genuine signatures, as well as random forgery, unskilled forgery, and simulated forgery signatures, are used to test the performance of the developed system. In simulation study false acceptance rate of 2.00%, 11.00% and 12.00% for random, unskilled, and simulated forgery signatures, respectively is obtained whereas the false rejection rate of 0.00% is achieved using Local Binary Pattern feature set.
A Novel One-Dimensional Convolutional Neural Network for Breast Cancer Classification

Breast cancer is one of the most common and leading causes of death in women worldwide. Accurate classification of breast cancer is important in diagnosis, and classification of benign and malignant tumors can prevent patients from undergoing unnecessary testing. The most important part of cancer detection is to distinguish between benign and malignant tumors. Recently, the use of deep neural networks in various machine learning applications has improved significantly. In this article, a novel deep learning architecture uses 1D-CNN to classify healthy and cancerous breast cancer patients to overcome the limitations of classic methods. First, the Wisconsin Breast Cancer Data Set (WBCD) from the UCI Machine Learning Library was used in the study. Then the proposed 1D-CNN model and seven machine learning algorithms were trained and tested with the same dataset for comparison. Experimental result shows that all machine learning algorithms perform best, with a test accuracy rate of over 90%. Based on the results obtained in this study. The best overall diagnostic accuracy of 98.25%, 100% precision and 95.34% recall has been achieved using the 1D-CNN model. Accuracy and other performance metrics were higher than other machine learning methods. The study also performed well when compared to the other existing methods. This study demonstrates that the proposed method can be reliably used by physicians to effectively classify breast cancer and lay the foundation for future automated cancer diagnosis.
Intelligent Indian Sign Language Recognition Systems: A Critical Review

Sign language (SL) is an alternative way of communication for hearing/speech impaired people. SL varies from country to country. Thus, different SLs came into existence such as American Sign Language (ASL), Chinese Sign Language (CSL), British Sign Language (BSL) and so on. In India, people use Indian Sign Language (ISL) as an alternative communication medium. Approximately, 2.7 million Indian people are deaf/dumb, out of which 98% population uses ISL for communication. But human interpreters are not available or affordable for identifying ISL. Thus, researchers are focussing on Sign Language Recognition System (SLRS) for the identification of ISL. But designing an SLRS for ISL is very difficult in comparison with other SL. ISL is very complex as it consists of single- and double-handed gesture and has an extensive vocabulary with similar gestures. This paper aims to provide a thorough study of ISL, its syntax/vocabulary and different trending techniques for designing an ISL recognition system. This paper also discusses various feature extraction techniques, classification methods used in ISL recognition and points out some challenges researchers face while developing an ISL recognition system.
Dual distribution matching GAN

Generative Adversarial Network (GAN) has become the dominant generative model in recent years. Although GAN is capable of generating sharp and realistic images, it faces several problems such as training instability and mode collapse. To address these issues, aside from the usual distribution matching via GAN’s adversarial training in a high-dimensional data space, we propose to perform distribution matching within a low-dimensional latent representation space as well. Such a low-dimensional latent representation space is obtained through training an Autoencoder (AE), which not only captures salient features and modes of the data distribution but can also be regularized to learn a nice latent manifold structure of the data. Based on that, we develop a novel hybrid generative model that combines AE and GAN, namely Dual Distribution Matching GAN (DM2GAN), that performs distribution matching in both data and latent space simultaneously. We theoretically show that the optimum of the proposed distribution matching constraint in the latent space is attained if and only if the generated and the real data distribution match exactly. The empirical evaluations on the 2D synthetic data, MNIST-1K, and several real-world datasets demonstrate the effectiveness of the proposed method to stabilize the training and increase mode coverage for GAN.
The utility of <scp>3D</scp>, <scp>haptic‐enabled</scp>, virtual reality technologies for student knowledge gains in the complex biological system of the human heart

Abstract Background Knowledge of the structure and function of the human heart is fundamental to accurately understanding human physiology. As a complex biological system, naïve conceptions abound regarding cardiac anatomy and physiology for K‐12 learners and medical students alike. Objective Textbooks and lectures, as well models and simulations, have had limited success in aiding learners in constructing accurate and cohesive knowledge of the human heart. Three dimensional (3D) modelling, haptic‐enabled (HE) feedback, and interactive virtual reality (VR) experiences aid tertiary learners, yet it is unknown if secondary learners benefit from learning with these technologies. Methods An exploratory study examined secondary student knowledge of cardiac anatomy and physiology after participation in an interactive lesson on cardiac structure and function using a 3D, HE, VR technology system. Students from sixth grade (11–12 years old; n = 75) and ninth grade (14–15 years old; n = 76) completed a pre‐ and post‐assessment on cardiac knowledge, anatomy, and physiology punctuated by technology‐delivered instruction on the human heart. Results and Conclusions Significant gains were found in knowledge from both groups in cardiac anatomy and blood circulation within the chambers of the heart; however, only ninth grade students demonstrated significant knowledge gains in pulmonary circulation. Takeaways Results suggest that 3D HE VR technologies provide learners robust representations of and student‐driven interactions with complex biological systems that are innovative instructionally for strong conceptual and systematic learning. This study offers insight on technology‐assisted science visualizations for the promotion of knowledge acquisition and systems thinking of the human heart among secondary science students.
Class-E Power Amplifiers Incorporating Fingerprint Augmentation With Combinatorial Security Primitives for Machine-Learning-Based Authentication in 65 nm CMOS

One means by which the security of Internet-of-Things (IoT)-enabled devices may be augmented is through radio-frequency fingerprinting-based authentication methods. As variability in CMOS processes increases with technology scaling, the hardware imperfections that form RF fingerprints can be controlled with small reconfigurable elements, enabling the feasibility of RF fingerprinting as a low overhead security measure for device authentication. To achieve rapid RF identification, we present an inherently secure RF power amplifier and a convolutional neural network-based machine learning classifier through an exploration of combinatorial randomness and self-aware detection mechanisms. By selecting different subsets of thinly sliced power amplifier elements, combinations of random process variations are exploited and updated to form a large search space of distinct RF fingerprints and improve fingerprint prominence. The rich features enabled by augmented device primitives are updated in a time-varying manner to strengthen built-in hardware security. Measurement results demonstrate the effectiveness of this approach at generating distinguishable RF fingerprints across a significant number of configurations.
Human Firewall: Cyber Awareness using WhatApp AI Chatbot

The use of technology is a necessary component for the success of business operations. Yet cyber risks related to technology are well known. In previous works, three different layers were defined to be responsible of the cyber physical security of any given system: the network layer, the device layer and the human factor layer. However, statistics and experience have proven that the third factor is the weakest link in the cyber security of any company. This factor is divided into two subsections: the ignorance of the user (which will be treated in this paper) and the malignancy of the user. As the two previous layers were presented in previous works, this paper will focus on enhancing security by raising the cyber awareness of users thus creating a human firewall. For that, an AI-based conversational bot that focuses on cyber threat awareness and acts as a personal assistant relating to security issues was developed. This bot is based on AI-approach and it is used to make the interaction with the user appealing and friendly through the use of WhatsApp as a mean of communication. The implementation and testing of this bot has shown great interest for the users who have tried it due to its simplicity, the quality of deliverable data and the evaluation technique.
Artificial Intelligence approaches on Ultrasound for Breast Cancer Diagnosis

Among all the cancer types, breast cancer is the leading cause of death for women worldwide. Breast cancer affects women of all ages after puberty in every country on the planet, with rates rising as they become older. Early detection of breast cancer, before it spreads, allows for more effective therapies and hence improves survival rates dramatically. Breast cancer screening with mammography, ultrasound, and magnetic resonance imaging (MRI) can considerably improve a patient’s prognosis. Artificial Intelligence (AI) has consistently outperformed the competition in classification tasks a nd is currently being researched for use in breast cancer screening. Mammography is the most widely adopted imaging technique to check for breast cancer and early detection, but it has limitations and, like MRI technologies, it is not always accessible, especially in environments with limited resources. For these reasons, ultrasound has a significant impact on the diagnosis of breast cancer, both as an additional modality to mammography and MRI, and as a primary imaging modality in some areas. Breast ultrasound interpretation is a difficult task. Due to the difficulty of reading the images and the results, the use of breast ultrasound has been criticized for the increase in the number of false positives. In the present paper we refer to its applications in breast medical ultrasound imaging, such as lesion detection, segmentation, and classification, breast density evaluation, and breast cancer risk assessment; we refer to recent AI proposals aiming to predict the risk that a woman could develop breast cancer, also examining the limitations and future prospects of using AI in breast medical imaging.
Latent Pattern Sensing: Deepfake Video Detection via Predictive Representation Learning

Increasingly advanced deepfake approaches have made the detection of deepfake videos very challenging. We observe that the general deepfake videos often exhibit appearance-level temporal inconsistencies in some facial components between frames, resulting in discriminable spatiotemporal latent patterns among semantic-level feature maps. Inspired by this finding, we propose a predictive representative learning approach termed Latent Pattern Sensing to capture these semantic change characteristics for deepfake video detection. The approach cascades a CNN-based encoder, a ConvGRU-based aggregator and a single-layer binary classifier. The encoder and aggregator are pre-trained in a self-supervised manner to form the representative spatiotemporal context features. Finally, the classifier is trained to classify the context features, distinguishing fake videos from real ones. In this manner, the extracted features can simultaneously describe the latent patterns of videos across frames spatially and temporally in a unified way, leading to an effective deepfake video detector. Extensive experiments prove our approach's effectiveness, e.g., surpassing 10 state-of-the-arts at least 7.92%@AUC on challenging Celeb-DF(v2) benchmark.
Development of Intelligent Telegram Chatbot Using Natural Language Processing

Intelligent chatbots have been gaining interest in the past years due to the advance of artificial intelligence algorithms. As a result, many studies have been conducted on emotional transition and dialog structures. One of the benefits is on the medical applications, in which psychological assessment, clinical counseling, autism diagnostics, and advanced cognitive models could be provided. On the other hand, chatbot knowledge generally comes from a web-based information repository, in which the information is reliable, but it is rather not versatile as it does not contain emotions. The objective of this paper is to develop an intelligent chatbot using natural language processing and Telegram API. First, text processing using Telegram API on Python was developed. Next, emotion recognition was performed on the recorded chats. The appropriate response is then sent to the user. Results showed that our Telegram chatbot could interact smoothly with the users and identify the user's emotions.