Reviewerly/exHarmony huggingface dataset:
An Initial Deep CNN Design Approach for Identification of Vehicle Color and Type for Amber and Silver Alerts

According to the Amber alert report in 2018, of the 132 cases, children in 68 cases were recovered within three or fewer hours from when the amber alert was activated, where 85% involved vehicles. In North Carolina, of the 128 silver alerts, 118 seniors were safely recovered in 2008. Colors, types, and license plate details play a vital role in both Amber and Silver alerts. Currently, children and seniors were recovered when someone recognized the vehicle in the alert. The process can be automated through deep learning modeling to classify a vehicle's color and type with faster detection. The system should also recognize each vehicle's license plate from camera feeds under different weather conditions and find possible matches involved in these emergency alerts to return a child and older adult safely. The automation will help find the number of cases involved in the alert faster and decrease recovery time. The paper focuses on comparing and analyzing the vehicle's color and type classification using Convolution Neural Network (CNN) models for the initial work. Vehicle classification model can be converted to vehicle detection model using Keras, TensorFlow, and OpenCV. Pre-processing includes haze removal, and data augmentation helps to increase testing accuracy and increase the training samples. The best results of each of the models for the color cyan: the precision is 1.00, recall is 0.98, and F1-score is 0.99, and average testing accuracy is 95% type SUV: precision is 0.95, recall is 0.98 and F1-score is 0.96, and average testing accuracy is 89%.
A Machine Learning Based Implementation of Product and Service Recommendation Models

Most of the internet based companies are now relying on the capabilities of recommendation models to increase their product sales. By applying efficient recommendation models businesses can track their customer preferences and effectively recommend products to users thereby increasing their sales. This paper describes the prototype implementation of two recommendations models using machine learning algorithms. The first prototype system is a banking service recommendation system and the second one is a movie recommendation system. These prototype implementations are evidence of how effectively machine learning algorithms can be applied for designing recommendation models.
Should the Non‐Classical Logician be Embarrassed?*

Abstract Non‐classical logicians do not typically reject classically valid logical principles across the board. In fact, they sometimes suggest that their preferred logic recovers classical reasoning in most circumstances. This idea has come to be known in the literature as ‘classical recapture’. Recently, classical logicians have raised various doubts about it. The main problem is said to be that no rigorous explanation has been given of how is it exactly that classical logic can be recovered. The goal of the paper is to address this problem. First, I discuss a number of different ways to characterize the idea of classical recapture and I examine how far it can be pushed. Secondly, I argue that a palatable form of classical recapture is given by the thought that classical logic can be retained for statements that are grounded (in Kripke's sense). To substantiate this view I provide a formal fixed‐point semantics for a language containing a predicate standing for the concept of groundedness and I address a couple of objections that have been deployed against the claim that classical recapture can aid the non‐classical logician's cause.
Error analysis of elitist randomized search heuristics

For complicated problems that cannot be solved in polynomial first hitting time (FHT)/running time(RT), a remedy is to perform approximate FHT/TH analysis for given approximation ratio. However, approximate FHT/RT analysis of randomized search heuristics (RSHs) is not flexible enough because polynomial FHT/RT is not always available for any given approximation ratio. In this paper, the error analysis, which focuses on estimation of the expected approximation error of RSHs, is proposed to accommodate the requirement of flexible analysis. By diagonalizing one-step transition matrix of the Markov chain model, a tight estimation of the expected approximation error can be obtained via estimation of the multi-step transition matrix. For both uni- and multi-modal problems, error analysis leads to precise estimations of approximation error instead of asymptotic results on fitness values, which demonstrates its competitiveness to FHT/RT analysis as well as the fixed budget analysis.
Foreground Feature Attention Module Based on Unsupervised Saliency Detector for Few-Shot Learning

In recent years, few-shot learning is proposed to solve the problem of lacking samples in deep learning. However, previous works are mainly concentrated on optimizing neural network structures or augmenting the dataset while ignoring the local relationship of the images. Considering that humans pay more attention to the foreground or prominent features of the images during image recognition, we proposed the foreground feature attention module (FFAM) based on an unsupervised saliency detector for few-shot learning. The FFAM consists of two parts: the foreground extraction module and the features attention module. More specifically, we first extract the foreground images by Robust Background Detector (RBD), one of the best unsupervised saliency detectors. Secondly, we employ the same embedding module to extract the features of both original images and foreground images. Finally, we introduce three improvements to enhance the foreground features and make our network focus on the foreground features without losing background information. Our proposed FFAM is more sensitive to the foreground features than previous approaches. Hence, it effectively recognizes those images with similar backgrounds. Extensive experiments are conducted on miniImagenet and tieredImagenet datasets. It is demonstrated that our proposed FFAM greatly improves the accuracy performance over baseline systems for both one-shot and few-shot classification tasks without increasing the network complexity.
Secure Data Storage in Cloud using Encryption Algorithm

The Cloud Computing is a self-motivated term, which gives argument free information outsourcing service, which keep the client from burdens of nearby storage issues. Presently, cloud computing is utilized in numerous areas like military, hospitals, industry, colleges and so on to putting away huge amount of data or information. On request of client, the data or information can be accessed from cloud. Generally, Information can be classified as personal, public, and confidential and all the information is in cloud are stored in someone else’s infrastructure. It is always very difficult to trust on third party cloud providers for confidential and personal data. Even the Big players of cloud industry say that security is the responsibility of both company as well as of their client. Therefore, from the client perspective, when the information is stored on cloud then it should be crypted well so that no other user can be able to read it if they get anyhow access to it. When information stored on cloud it faces so many issues like security of data. For these issues many algorithms designed. However, utilization of one calculation or algorithm is not successful for significant phase of security in cloud. In this research work, with the help of cryptographic algorithms the security issues are reduced. Proposed system improves the security in cloud storage framework using different encryption algorithm like AES algorithm with S- box and Feistel Algorithm. The structure utilizes the information-transferring, cutting, ordering, encryption, merging, unscrambling and recovery cycle to make sure about the enormous information put away in the multi cloud. Cryptographic algorithm is mostly used for sending data over a network in a secure manner and to store data in system in non-human readable form. In addition, cloud computing is a combination of Network, Server, and Storage at Infrastructure level. Therefore, this research work is proposed using cryptographic algorithm to store data in secure manner on cloud.
Geochemical domains and their characteristics in the middle-southern part of the Sanjiang orogenic belt in southwestern China

Principal component analysis (PCA) was used to extract geological information about regional strata and mineralization from many high-quality stream sediment geochemical data in the middle-southern part of the Sanjiang orogenic belt. According to the geological reality, the 32 main ore-forming elements in the study area were grouped into seven element associations that reflected the stratum features and mineralization. The study area was further divided into six geochemical domains that reflected the geological background. The strata and structure were combined with mineral data and revealed that the geological domains proposed in this paper represented the geological background of the study area. The corresponding elements within these geochemical domains have higher concentrations and enrichment factors, showing that the geological domains in the study area are reasonable. In the study area, the observed anomalies in the geochemical domains highlighted effective anomalies and reduced false anomalies. These anomalies can not only be used to identify potential metallogenic areas, but also provide information on tectonics; for instance, the middle axial fault of the Lanping-Simao basin was reflected in the linear anomalies of the LiB element association. The results of this study can help understand the combination and distribution characteristics of ore-forming elements and the regional geological structure of the middle-southern part of the Sanjiang orogenic belt.
SEA WAF: The Prevention of SQL Injection Attacks on Web Applications

The security of website application has become important in the last decades.According to the Open Web Application Security Project (OWASP), the SQL Injection is classified as one of the major vulnerabilities found in web application security.This research is focused on improving website security in dealing with SQL Injection attacks by stopping, monitoring, and dividing types of SQL Injection attacks using the features provided by the proposed Web Application Firewall (WAF).The architecture is designed to detect and prevent some types of SQL Injection attacks, including Tautologies, Logically Incorrect Queries, Union Queries, Piggy Backed Queries, Stored Procedures.For the testing scenario, this experiment uses an application that has become an industry standard in identifying and validating security holes on a website.The result of this research is that the proposed system is able to increase the website security from SQL Injection.
Learning Mathematics in an Immersive Way

In this work we introduce a learning system based on Virtual Reality and Augmented Reality for studying analytical-geometric structures that are part of the curriculum in mathematics and physics in high school classes. We believe that an immersive study environment has several advantages with respect to traditional two-dimensional environments (such as a book or the simple screen of a PC or even a tablet), such as the spatial understanding of the concepts exposed, more peripheral awareness and moreover an evident decreasing in the information dispersion phenomenon. This does not mean that our teaching proposal has to substitute the traditional approaches, but it can be seen as a robust tool to support learning. In the first phase of our research we have sought to understand which mathematical objects and which tools could have been used to enhance the teaching of mathematics, in order to demonstrate that the use of Virtual Reality and Augmented Reality techniques significantly improves the level of understanding of the mathematical subject being studied by the students. The system which provides for the integration of two machine levels, hardware and software, was subsequently tested by a representative sample of students who then provided feedback through a questionnaire.
MelGlow: Efficient Waveform Generative Network Based On Location-Variable Convolution

Recent neural vocoders usually use a WaveNet-like network to capture the long-term dependencies of the waveform, but a large number of parameters are required to obtain good modeling capabilities. In this paper, an efficient network, named location-variable convolution, is proposed to model the dependencies of waveforms. Different from the use of unified convolution kernels in WaveNet to capture the dependencies of arbitrary waveforms, location-variable convolutions utilizes a kernel predictor to generate multiple sets of convolution kernels based on the melspectrum, where each set of convolution kernels is used to perform convolution operations on the associated waveform intervals. Combining WaveGlow and location-variable convolutions, an efficient vocoder, named MelGlow, is designed. Experiments on the LJSpeech dataset show that MelGlow achieves better performance than WaveGlow at small model sizes, which verifies the effectiveness and potential optimization space of location-variable convolutions.
Shannon, Rényi entropies, and Fisher information calculations of the Li<sup>1+</sup> and Be<sup>2+</sup> ions screened by the ion-sphere plasma model

Abstract We show results for the Shannon entropy, Rényi entropy, Fisher information, and their relative relations in the position space for the Li 1+ and Be 2+ ions embedded in a strong plasma. This medium is simulated by the ion-sphere screening model as a function of the plasma electron density, N e . We solve the Hartree–Fock equations using a numerical grid method in a self-consisting manner and the simple Hylleraas-type correlated wave function is applied. Our findings show a reduction of the total energy for both ions as the electron density is increased reaching the ionization limit. We find interesting features in the description of the Shannon entropy and Fisher information where the correlation effects of the embedded ions are relevant. The Shannon-Fisher product results indicate that the electron-electron correlation decreases as N e increases and similar behavior is found for the Rényi-Fisher product. Finally, in the absence of the plasma medium, our results are in excellent agreement with those available in the literature.
Compression for Very Sparse Big Social Data

Technological advancements in the current era of big data have led to rapid generation and collection of very large amounts of valuable data from a wide variety of rich data sources. As rich data sources, social networks consist of social entities that are linked by some social relationships (e.g., kinship, colleagueship, co-authorship, friendship, followship). Usually, these networks are very big but also very sparse. Embedded in the very sparse but very big networks are implicit, previously unknown and potentially useful information and knowledge that can be discovered by social network analysis and mining. In this paper, we aim to discover interesting social relationships from very sparse but very big social network data. Due to the sparsity of the data, we effectively compress bitmaps representing social entities in the data, from which useful information can be mined and interesting knowledge can be discovered. Evaluation results show the effectiveness of our compression scheme for very sparse but very big social network data.
LF3Net: Leader-follower feature fusing network for fast saliency detection

Recently, convolutional neural networks (CNNs) have been widely used for saliency detection. Most of existing saliency detection methods produce saliency maps from the complementary multi-level convolutional features. However, it is still a challenging task to accurately integrate multi-level features for saliency detection. In this paper, we explore the intrinsic relationships between multi-level features and introduce the Stackelberg game theory as a new strategy to fuse multi-level features for saliency detection. Based on the theory, we propose a leader-follower feature fusing network (LF3Net) to obtain saliency maps. We first apply a multi-scale context-aware leader-follower attention module (MCLAM) to select multi-scale spatial and semantic information. Then, we propose a leader-follower feature fusing module (LF3M) to integrate the multi-level features. Extensive experiments on five datasets show that the proposed method outperforms the state-of-the-art approaches under different evaluation metrics. In addition, our network can run fast at the real-time speed of 75 FPS.
An Augmented Reality Implementation of the <i>Pong</i> Game

This article presents the development of an interactive, augmented reality, and collaborative Pong game. In our approach, an overhead projector displays the game on the floor, showing the game's current state as it develops. In the meantime, a computer vision system sensing throughout an overhead depth camera detects the players and their movements. We tested our system in a roofed but wall-less environment. Our assessment of the gaming experience includes natural language processing on interviews with children and qualitative analysis of their verbal responses.
Deep Learning Enabled Autoencoder Architecture for Collaborative Filtering Recommendation in IoT Environment

The era of the Internet of things (IoT) has marked a continued exploration of applications and services that can make people’s lives more convenient than ever before. However, the exploration of IoT services also means that... | Find, read and cite all the research you need on Tech Science Press
Elastic Embedding through Graph Convolution-based Regression for Semi-supervised Classification

This article introduces a scheme for semi-supervised learning by estimating a flexible non-linear data representation that exploits Spectral Graph Convolutions structure. Structured data are exploited in order to determine non-linear and linear models. The introduced scheme takes advantage of data-driven graphs at two levels. First, it incorporates manifold smoothness that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are derived from the data and their associated graph. The proposed semi-supervised embedding can tackle the problem of over-fitting on neighborhood structures for image data. The proposed Graph Convolution-based Semi-supervised Embedding paves the way to new theoretical and application perspectives related to the non-linear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with some state-of-the-art semi-supervised approaches. This empirical evaluation shows the effectiveness of the proposed embedding scheme.
A multi-feature hybrid classification data mining technique for human-emotion

Abstract Background and objectives The ideal treatment of illnesses is the interest of every era. Data innovation in medical care has become extremely quick to analyze diverse diseases from the most recent twenty years. In such a finding, past and current information assume an essential job is utilizing and information mining strategies. We are inadequate in diagnosing the enthusiastic mental unsettling influence precisely in the beginning phases. In this manner, the underlying conclusion of misery expressively positions an extraordinary clinical and Scientific research issue. This work is dedicated to tackling the same issue utilizing the AI strategy. Individuals’ dependence on passionate stages has been successfully characterized into various gatherings in the data innovation climate. Methods A notable AI multi-include cross breed classifier is utilized to execute half and half order by having the passionate incitement as pessimistic or positive individuals. A troupe learning calculation helps to pick the more appropriate highlights from the accessible classes feeling information on online media to improve order. We split the Dataset into preparing and testing sets for the best proactive model. Results The execution assessment is applied to check the proposed framework through measurements of execution assessment. This exploration is done on the Class Labels MovieLens dataset. The exploratory outcomes show that the used group technique gives ideal order execution by picking the highlights’ greatest separation. The supposed results demonstrated the projected framework’s distinction, which originates from the picking-related highlights chosen by the incorporated learning calculation. Conclusion The proposed approach is utilized to precisely and successfully analyze the downturn in its beginning phase. It will assist in the recovery and action of discouraged individuals. We presume that the future strategy’s utilization is exceptionally appropriate in all data innovation-based E-medical services for discouraging incitement.
Synth2Aug: Cross-Domain Speaker Recognition with TTS Synthesized Speech

In recent years, Text-To-Speech (TTS) has been used as a data augmentation technique for speech recognition to help complement inadequacies in the training data. Correspondingly, we investigate the use of a multi-speaker TTS system to synthesize speech in support of speaker recognition. In this study we focus the analysis on tasks where a relatively small number of speakers is available for training. We observe on our datasets that TTS synthesized speech improves cross-domain speaker recognition performance and can be combined effectively with multi-style training. Additionally, we explore the effectiveness of different types of text transcripts used for TTS synthesis. Results suggest that matching the textual content of the target domain is a good practice, and if that is not feasible, a transcript with a sufficiently large vocabulary is recommended.
Hybrid Model for Generation of Verbs of Dogri Language

The hybrid model for generation of the verbs of Dogri language is proposed and applied for generating all the inflected forms of verb. During the course of investigation we found that the inflectional phenomena for the generation of the verbs cannot be handled by one general model. The Indian languages differ in the syntactic expression of the semantic properties. Thus the mapping between the syntax and semantics becomes very important for the automatic generation and identifications of the verbs. In this paper we are identifying the finite verbs and all its inflectional forms, and also whether the finite verb in the text is contextually correct. The abstract idea for finding the inflections in any language can be solved by designing the Finite State Transducers. The corpus based approach is used for testing the results for the identification of the proposed model for the generation of the verbs in Dogri. We have tested the model on five sets of data and then computed the average of the all the values for the accuracy, precision, recall and F-Score is obtained as 69.213, 93.861, 75.278 and 83.500 respectively.