Reviewerly/exHarmony huggingface dataset:
Deep Learning Framework for Classification of Emoji Based Sentiments

Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS).Social media users are widely using emoji based sentiments (EBS) in between text messages, tweets and posts.Although tiny pictures of emoji contains sufficient information to be considered for construction of classification model; but due to the wide range of dissimilar, heterogynous and complex patterns of emoji with similar meanings (SM) have become one of the significant research areas of machine vision.This paper proposes an approach to provide meticulous assistance to social media application (SMA) users to classify the EBS sentiments.Proposed methodology consists upon three layers where first layer deals with data cleaning and feature selection techniques to detect dissimilar emoji patterns (DEP) with similar meanings (SM).In first sub step we input set of emoji, in second sub step every emoji has to qualify user defined threshold, in third sub step algorithm detects every emoji by considering as objects and in fourth step emoji images are cropped, after data cleaning these tiny images are saved as emoji images.In second step we build classification model by using convolutional neural networks (CNN) to explore hidden knowledge of emoji datasets.In third step we present results visualization by using confusion matrix and other estimations.This paper contributes (1) data cleaning method to detect EBS;(2) highest classification accuracy for emoji classification measured as 97.63%.
Univariate and Multivariate Filtering Techniques for Feature Selection and Their Applications in Field of Machine Learning

Machine learning's feature selection technique aids in the selection of a subset of original features in order to decrease high-dimensional data space. As per the literature, there are two basic strategies for feature selection: supervised and unsupervised. This chapter will focus on supervised filtering approaches only. Filter, intrinsic, and wrapper are the three types of supervised filtering algorithms. Filtering strategies are the subject of this chapter. The chapter covers the most popular univariate filtering algorithms with examples, advantages and downsides, and R implementation. The chapter compares univariate filtering techniques with number of parameters. The chapter also depicts two popular multivariate filtering techniques: minimum redundancy and maximum relevance (mRMR) and correlation-based feature selection (CFS) using appropriate example and implementation with R programming. Finally, the chapter deals with prominent applications of filtering techniques in context to machine learning.
Brand LOGO Image Recognition and Inquiry Creative Teaching Design Based on Deep Learning

In order to develop more reasonable and scientific teaching equipment and software and improve the diversity of teaching process, LOGO image recognition technology is used to build a development environment in experiment, with Unity 3D as the development platform and Vuforia AR SDK as the development tool. LOGO image recognition technology is applied to the case study of high school inquiry teaching. By the way of classroom teaching practice and teacher-student interview, the application effect of LOGO image recognition technology in teaching is evaluated, and the effectiveness of this technology in inquiry teaching practice is verified by practical data. The results show that compared with YOLO (You Only Look Once), R-CNN (Region-CNN), and Faster R-CNN, image recognition algorithms based on deep learning theory, LOGO image recognition ability is stronger and the result is more accurate. Using this teaching mode can significantly improve students’ academic performance, and this method is correct, reasonable, and scientific. The application of LOGO image recognition technology based on deep learning in teaching can provide research ideas for the combination of AI and education.
Remote Control Architecture for Virtual Reality Application for Ankle Therapy

Worldwide, as the average life expectancy increases, the number of people in need of physical therapy rises. As a means of turning therapy more efficient and engaging, serious games have been developed for virtual reality environments. At the same time, in order to aid the user to perform the exercising movements, robotic devices have been employed along with these rehabilitation games. In the context of telerehabilitation, communication between devices is crucial since a seamless communication among many computers is a requisite. Having this in mind, this work proposes a remote control architecture for computers connected to the same local network, which will serve as a proof-of-concept for further applications where the communication among the robot device, the virtual reality application and other auxiliary computers is needed. This paper introduces briefly a robotic device for ankle rehabilitation and the control scheme, shows the development of a virtual reality racing game and introduces a third element, the physiotherapist computer, which can change remotely the robot resistance, thus increasing the game difficulty. It is crucial to guarantee that the physiotherapist has control over the robot parameters as well as access to the patients’ data in order to adapt their experience according to their performance.
Object Detection and Area Perception in Subway Operation Space Based on the Multi-task Model

At present, it is straightforward for the subway platform door and train door to clamp foreign objects, or the foreign objects fall in the gap between the two doors that cause major traffic accidents. To improve the accuracy of foreign object detection and area perception, this paper proposes a real-time and accurate multi-task model that can simultaneously complete foreign object detection, area segmentation and area line detection. It has a shared encoder to extract deep features between related tasks and three encoders for the specific tasks. The Cross Stage Partial module is applied in the encoder to enhance the learning ability of CNN. Then, use the Spatial Pyramid Pooling and Feature Pyramid Network to merge features further. Besides, Lightweight models are used in these decoders to improve the inference speed of the model. The accuracy of the model almost exceeds the existing state-of-the-art methods while the inference speed of the model meets the real-time requirements.
ANN-EMOA: Evolving Neural Networks Efficiently

Multi-objective neuroevolution is a research field of growing importance within reinforcement learning. This paper introduces ANN-EMOA, a novel multi-objective neuroevolutionary algorithm that is inspired by nNEAT and aims at high efficiency, usability, and comprehensibility. To that end it applies a simple encoding and efficient variation operators. Diversity plays a key role in evolutionary computation. For this reason, we apply the Riesz s-energy to foster diversity explicitly. This paper also develops a new efficient approach to determine the individual Riesz s-energy contribution of each solution within a set. To assess the performance of the new ANN-EMOA it is compared to nNEAT and NEAT-MODS, two multi-objective variants of NEAT, in the multi-objective Double Pole Balancing problem. While other domains and more complex test cases need to be investigated, these promising first results show that ANN-EMOA does not only converge faster and to higher quality-levels than its competitors, but it also maintains more compact network-genomes and shows convincing performance even with comparably small populations.
Implementation of Decision Tree Classifier on FPGA

This paper proposed the implementation of decision tree classifier on FPGA. A comma separated value format is accepted by the framework and then it does many steps to form a trained model. Subsequently the framework forms two formats of decision tree based classifier viz Joint Photographic Experts Group and Verilog/VHDL. The hierarchy of generated tree is represented by JPEG (image) representation and VHDL /Verilog code is used as a the representation of trained model hardware description. The current framework uses the Verilog/VHDL in FPGA DESIGN VALIDATION FLOW. The novelty framework bridges the gap between M.L. model training and its hardware designing.
Opportunities and Advantages of Cloud Migration of a Smart Restaurant System

The restaurant sector, despite being increasingly developing nowadays, is still very ineffective due to the low use of technology. The latter has introduced significant improvements in this sector through the simple use of applications, cloud computing, NFC sensors and IoT. For this reason, it was proposed to design a system that would allow automated and intelligent control and management of a restaurant using IoT for product safety in the kitchen and booking of parking spaces, tables and meals through an interactive Menu. So, something that would provide ease of organisation in the process of ordering food, regularly checking and monitoring its quality to maintain proper hygiene. In addition, there will also be the possibility to collect information about the availability of parking spaces, which will be detected through infrared proximity sensors, which will avoid a lack of organisation. These improvements in restaurant management have greater results through the use of cloud services, in fact in the article, the migration of a restaurant application to the cloud is proposed using the services and patterns offered by Amazon. The use the latter allows an increase in performance, availability and security of the entire system.
Correction

This article refers to:What Frustrates Hospitality Students at the Mandatory Synchronous Online Classes?
Various-Level Spatio-Temporal Alignment for Cross-Domain Action Recognition

Cross-domain action recognition is a less explored field of research until recently. The previous approaches usually feed the pre-extracted video-level or segment-level feature vectors to shallow networks for regenerating them. These approaches cannot directly affect the full capability of the action recognition models. Considering the recent researches, CNN has an inductive bias towards the texture of images. In a domain-changing situations, this information is affected and changed easily. Moreover theses low-level information is mainly encoded in intermediate features and is also needed to be aligned. For exploring the effect of the adaptation between various levels of spatial dimensions of the feature map, we divided the model into several parts and performed adaptation for each step. However, not every stage play the important role in action recognition in the temporal axis. To more sensitive adaptation, we propose a similarity-based weighting strategy. We first calculate the discrimination loss for each stage. Next, these discrimination losses are weighted by their similarity. The discrimination losses become large if the source and target sample’s similarity values are small in a certain stage. The proposed method achieves state-of-the-art performance on UCF101-HMDB $$_{full}$$ dataset.
Cybercrime: Thieves, Swindlers, Bandits, and Privateers in Cyberspace

Abstract This chapter describes the definitions and scope of cybercrime including an outline of the history of hackers and the role of criminal networks and markets in the dissemination of malicious software and other contraband such as illicit drugs, stolen credit cards and personal identification, firearms, and criminal services. Different cybercrime types and methods are described, including the widespread use of ‘social engineering’ or deception in computer misuse and identity theft. The challenges facing law enforcement in the suppression of cybercrime and the important role of private and public partnerships, as well as cross-national cooperation in the suppression of cybercrime is illustrated.
3D cell morphology detection by association for embryo heart morphogenesis

Abstract Advances in tissue engineering for cardiac regenerative medicine require cellular-level understanding of the mechanism of cardiac muscle growth during embryonic developmental stage. Computational methods to automatize cell segmentation in 3D and deliver accurate, quantitative morphology of cardiomyocytes, are imperative to provide insight into cell behavior underlying cardiac tissue growth. Detecting individual cells from volumetric images of dense tissue, poised with low signal-to-noise ratio and severe intensity in homogeneity, is a challenging task. In this article, we develop a robust segmentation tool capable of extracting cellular morphological parameters from 3D multifluorescence images of murine heart, captured via light-sheet microscopy. The proposed pipeline incorporates a neural network for 2D detection of nuclei and cell membranes. A graph-based global association employs the 2D nuclei detections to reconstruct 3D nuclei. A novel optimization embedding the network flow algorithm in an alternating direction method of multipliers is proposed to solve the global object association problem. The associated 3D nuclei serve as the initialization of an active mesh model to obtain the 3D segmentation of individual myocardial cells. The efficiency of our method over the state-of-the-art methods is observed via various qualitative and quantitative evaluation.
Natural Language Based Corpus Model on a Wide Range of Medical Corpus

In 2018, BERT (Bidirectional Encoder Representation from Transformers) for NLP was created and published by Google. BERT has two English language models. For training general purpose language representation models in the large volume of unannotated content on the web to help bridge this data gap (known as pre-training) a variety of strategies has been devised. Instead of training BERT on the data from scratch the model should be pre trained on a small dataset, then it focuses on question answering and sentiment analysis in NLP on significant growth in accuracy. The purpose of this study is to conduct a descriptive analysis of BERT in certain applications and to train the language model. Throughout the job, we are referring to a well-known medical book called "Harrison's Principles of Internal Medicine." High Configuration Computation has been used into the research i.e., Nvidia Tesla DGX-V100 GPU. Sometimes BERT gives improper resultant, due to insufficient availability of data. So, a diverse corpus must require in order to train a BERT language models.
The Research on Underground Safety Inspection and Gas Methane Detection Based on Industrial Internet Technology

By using industrial Internet technology and integrating coal mine safety monitoring system, UWB underground personnel accurate positioning system, MIS system data, electronic watermark, etc., it is realized to strictly restrict underground security inspectors from carrying out safety inspection at the specified time and place. To realize the authenticity and uniqueness of underground safety inspection and gas inspection data, we can use the digital watermark of inspection place, inspection time and inspector&#x0027;s name which is added to the picture by using the photographing function. And prevent missed inspection and data fraud, so as to ensure the safety of underground production and avoid accidents.
A Wearable Fall Detection Device : From Research Advances and Public Datasets to a Senior Design Project

A fall is one of the most serious accidents for elders and the fall might occur at any moment. With the growing ageing population, an urgent need for the development of fall detection systems is inevitable. Fall detection and monitoring is an active field of research, so this work used available research advances and public datasets as a launch pad for a senior-year design project. There were two main goals in this work, to develop a fall detection algorithm and to build a wearable fall detection device. A public dataset was used to test the performance of this threshold-based classification. The developed algorithm had 86.95% sensitivity, 96.08% specificity and 90.83% accuracy. The chosen thresholds were later programmed into the embedded hardware. This fall detection device employed ADXL345 accelerometer, ITG3200 gyroscope and ESP8266 and all components were fitted into a 3D printed case as a wearable belt buckle. The detector monitors the elder’s normal and abnormal movements through the embedded algorithm. In the event of fall, the alarm will turn on, to attract attention and help from people nearby as well as an Internet-based notification message being sent to family members and/or caregivers through the Wi-Fi connection. In case of a false alarm, this device was equipped with a decision delay, a posture verification and a button to switch off the alert.
Design of a Low Power Double AES Crypto-Processor SoC

System on a Chip (SoC) is the state of the art in VLSI design and automation today due to its impressive performance matrix in terms of power, speed, area, and design time. This paper presents the design of a SoC implementing the double AES crypto processor for better security than its single core. AES has been proved the most secured symmetric cryptographic algorithm certified by USA Govt. So, its hardware implementation using SoC technology is expected to offer much better performance than that of software implementation. The chip is designed using industry-standard Verilog HDL and simulated in Altera Quartus II EDA environment. Two different symmetric keys are used to encrypt and decrypt plaintext. Moreover, mixed columns, and substitution byte functions are simplified to minimize the complexity of the proposed design. The experimental study shows that the proposed AES crypto-processor SoC outperforms the existing works in terms of power requirement.
Predictors of Software Metric Correlation: A Non-parametric Analysis

A number of authors hypothesize and experimentally confirm that Cyclomatic Complexity (CC) has a very strong correlation with Lines of Code (LOC), justifying the use of LOC in place of CC. Others report on a moderate correlation and advocate for the use of both metrics. These studies have, for the most part, studied production code, and we suspect different results may be observed for test code. With 40 different, large open-source subjects and five subjects from industry partners, we collected metric values for LOC, CC and Halstead Effort (HE) and measured their correlation. In test code, contrary to production code, there exist a very weak (or almost no) correlation between (a) LOC and CC, and weak (nearly moderate) correlation for (b) HE and CC, and (c) LOC and HE. We therefore argue and propose that the level of correlation depends on at least three factors namely: the kind of code (i.e., production code vs test code), the kind of software (open-source vs industry) and the kind of metric (LOC, CC, HE). Given the weak monotonicity between CC, LOC and HE we observe, we aspire to challenge the viewpoint that CC and Halstead metrics are redundant with LOC, as some studies suggest, at least on test code. We therefore advocate for using CC over LOC (or both, or cyclomatic density) when studying test code, as CC is perceived to better reflect cognitive complexity, numerical complexity, interdependency and code refactoring that cannot be accounted for simply by LOC.
Stereo Seam Coupling and Depth Distortion Score in 3D Image Retargeting Using DMA Algorithm

Preserving the highlighted contents in left and right stereo images by using seam carving technique is the challenging job in current digital era. The seam carving technique facilitates to minimise the trouble by eliminating unwanted seam patches from the stereo images by using saliency detection method. These images are popularly known as stereoscopic images. This paper addresses the issues in 2-D planer images and emphasised on 3-D stereoscopic images. We method enforces on the foreground pixels which are highly considerable by calculating the high energy pixels. The depth distortion estimation is performed by adjusting the aspect ratio. The viewpoints of left and right stereo images are somewhat different. Disparity Map Acquisition (DMA) algorithm maps the pixel points from the left stereo image to its corresponding right image pixels. The fusion of the left and right images is incorporating to minimise the depth distortion score.
Sammon Quadratic Recurrent Multilayer Deep Classifier for Legal Document Analytics

In recent years, machine learning algorithms and in particular deep learning has shown promising results when used in the field of legal domain.The legal field is strongly affected by the problem of information overload, due to the large amount of legal material stored in textual form.Legal text processing is essential in the legal domain to analyze the texts of the court events to automatically predict smart decisions.With an increasing number of digitally available documents, legal text processing is essential to analyze documents which helps to automate various legal domain tasks.Legal document classification is a valuable tool in legal services for enhancing the quality and efficiency of legal document review.In this paper, we propose Sammon Keyword Mapping-based Quadratic Discriminant Recurrent Multilayer Perceptive Deep Neural Classifier (SKM-QDRMPDNC), a system that applies deep neural methods to the problem of legal document classification.The SKM-QDRMPDNC technique consists of many layers to perform the keyword extraction and classification.First, the set of legal documents are collected from the dataset.Then the keyword extraction is performed using Sammon Mapping technique based on the distance measure.With the extracted features, Quadratic Discriminant analysis is applied to perform the document classification based on the likelihood ratio test.Finally, the classified legal documents are obtained at the output layer.This process is repeated until minimum error is attained.The experimental assessment is carried out using various performance metrics such as accuracy, precision, recall, F-measure, and computational time based on several legal documents collected from the dataset.The observed results validated that the proposed SKM-QDRMPDNC technique provides improved performance in terms of achieving higher accuracy, precision, recall, and F-measure with minimum computation time when compared to existing methods.
3D Flattering Amplified Neural Network-Based Segmentation of Amygdala and Hippocampus

Abstract Recent emergence in deep learning resulted in significant improvement in the segmentation accuracy of sub cortical brain structures like hippocampus and amygdala. The traditional methods of segmentation cannot produce an ideal segmentation result that exhibits issues like redundant computations, inconsistencies, coefficient variations and motion artifacts. Therefore, in this paper, an improved 3D Flatteringly Amplified Neural Network model for biomedical imaging is efficiently proposed, which can make full use of the 3D spatial information of MRI image itself to overcome the inconsistency of segmented images along with equalizing the coefficient variation of tiny region of brain image segmentation. Also while equalizing the coefficient, certain significant minute details are lost due to motion artifacts hence, the robust Amyg-Hippo Seg algorithm has been introducing that extracts the features through deep learning, and achieve high-precision segmentation, it reduced the computational complexity without neglecting minute features. In addition, the Daytona dropout function provides uncertainty information and reduces over-fitting problems. The outcome of the proposed work efficiently segments the most significant regions of hippocampus and amygdala with 97.4% accuracy.