Reviewerly/exHarmony huggingface dataset:
Toxic Sentiment Identification Using R Programming

Text messaging has become a universal staple. WhatsApp is regularly becoming a news delivery channel as users rely on its broadcast messages to share both local and international news. Today we are not utilizing and operating it, but it is operating us which can confirm to be very unsafe for us. Most of the fake news spread rapidly by WhatsApp. So, there is requirement to examine WhatsApp chat by user’s sentiment or opinion. WhatsApp is such an application which is used widely for transferring media, text, files as well as audio calling. WhatsApp is progressively becoming a turning point in numerous sectors like healthcare, education and business. So, there is requirement to inspect WhatsApp chat by user’s sentiment or opinion. The advent of the internet had played a huge role in expanding the usage of text messaging to instant messaging on mobile devices. WhatsApp chat sentiment analysis to increase improved insights regarding their employees and strive to stay away from unanticipated conflicts due to various redundancies and insufficiency of business processes. Sentiment analysis is most popular branches of textual analytics which with the aid of information and natural language processing observe and categorize the unorganized written data into different sentiments. It is as well as acknowledged as opinion mining. Most of the false news increase rapidly by WhatsApp. Therefore, there is call for to observe and examine WhatsApp chat to find user’s sentiment or opinion. Firstly, chat from WhatsApp is selected and exported to a system which is an easy task and can be done either by phone or WhatsApp for the computer system. Following this, the processes are fairly simple and have been explained with all the coding details needed to analyze the texts. In this project, chat of WhatsApp has been used as database by using R, sentiments and emotions are being analyzed.
Matrix Factorization Enriched with Item Features

This paper presents a novel matrix factorization (MF) recommendation model, FeatureMF, which extends item latent vectors with item representation learned from metadata. By taking into account item features, the model addresses the cold-start item problem and data-sparsity problem of collaborative filtering (CF). Extensive experiments conducted on a public dataset with two testing views confirm that FeatureMF achieves better prediction accuracy than some of the popular state-of-the-art MF-based recommendation models.
Multi-threaded Multilayer Neural Network for Character Recognition

New techniques are evolving day by day to recognize characters in any image, text, cards, etc. Optical character recognition is a well-accepted technique to recognize characters in scene or optically captured image. In this present work, we have evolved new method which employs feed forward back propagation (FFPB) technique interlaced with multi-threaded multilayer perceptron neural network (MTMLNN) to recognize the characters with speedup in operations and better accuracy in character recognition. In present methodology, we have employed structured similarity index measure (SSIM) in MTMLNN to compare image and sigmoid function for neuron activation with stochastic gradient descent method for optimization, to train the neural network to recognize the input character with better accuracy.
Subset Sampling for Progressive Neural Network Learning

Progressive Neural Network Learning is a class of algorithms that incrementally construct the network's topology and optimize its parameters based on the training data. While this approach exempts the users from the manual task of designing and validating multiple network topologies, it often requires an enormous number of computations. In this paper, we propose to speed up this process by exploiting subsets of training data at each incremental training step. Three different sampling strategies for selecting the training samples according to different criteria are proposed and evaluated. We also propose to perform online hyperparameter selection during the network progression, which further reduces the overall training time. Experimental results in object, scene and face recognition problems demonstrate that the proposed approach speeds up the optimization procedure considerably while operating on par with the baseline approach exploiting the entire training set throughout the training process.
Analysis of the Effect of Topic Modeling on General Corpus Mixed with In-Domain Text for English-Hindi Translation

Statistical Machine translation (SMT) has attracted attention of many researches associated with Natural Language processing (NLP). As indicated by its name, SMT systems use statistical methods to translate text from source language to target language. For any system which uses statistical methods, size of data is very critical. Many approaches, e.g., domain adaptation, are proposed to improve translation quality without changing the size of corpus. In this paper, we have created a phrase-based topic model system which is incorporated into baseline phrase-based system. The system is applied to a mixture of general and small size special corpuses. The system has shown improved results in comparison to not only baseline system but also topic model system applied to general corpus.
Self-Training Of Graph Neural Networks Using Similarity Reference For Robust Training With Noisy Labels

Filtering noisy labels is crucial for robust training of deep neural networks. To train networks with noisy labels, sampling methods have been introduced, which sample the reliable instances to update networks using only sampled data. Since they rarely employ the non-sampled data for training, these methods have a fundamental limitation that they reduce the amount of the training data. To alleviate this problem, our approach aims to fully utilize the whole dataset by leveraging the information of the sampled data. To this end, we propose a novel graph-based learning framework that enables networks to propagate the label information of the sampled data to adjacent data, whether they are sampled or not. Also, we propose a novel self-training strategy to utilize the non-sampled data without labels and to regularize the network update using the information of the sampled data. Our method outperforms state-of-the-art sampling methods.
Learned Weight Sharing for Deep Multi-Task Learning by Natural Evolution Strategy and Stochastic Gradient Descent

In deep multi-task learning, weights of task-specific networks are shared between tasks to improve performance on each single one. Since the question, which weights to share between layers, is difficult to answer, human-designed architectures often share everything but a last task-specific layer. In many cases, this simplistic approach severely limits performance. Instead, we propose an algorithm to learn the assignment between a shared set of weights and task-specific layers. To optimize the non-differentiable assignment and at the same time train the differentiable weights, learning takes place via a combination of natural evolution strategy and stochastic gradient descent. The end result are task-specific networks that share weights but allow independent inference. They achieve lower test errors than baselines and methods from literature on three multi-task learning datasets.
Contrast Enhancement for Color Dermascopy Images using Equalization based on Luminosity

Contrast Enhancement is one of the crucial and important pre-processing steps. This method is applied to improve the perceiving quality of the image. In this paper we have proposed a method to preserve the details of an image while adjusting luminosity of the image. The main benefit of this method is that this can be applied in colored skin lesion image and while contrasting process is applied the details will not be lost. To study the success of the proposed system, the entropies of the test input images are compared with the entropies of the enhanced image.
An Outlier Detection Algorithm based on KNN-kernel Density Estimation

The importance of outlier detection is growing significantly in a various fields, such as military surveillance,tax fraud detection, telecommunications, terrorist activities, medical and commercial sectors. Focusing on this has resulted in the growth of several outlier detection algorithms, mostly based on distance or density strategies. But for each approach, there are inherent weaknesses. The distance-based techniques have a local density issue, while the density-based method has a low-density pattern issue. In this article, we present an unsupervised density-based outlier detection algorithm to address these shortcomings. In the proposed approach, each object is assigned a local outlying degree, which indicates how much one point in its locality deviates from the other. The local outlying degree focuses explicitly on the concept of local density, which is defined as a relative measure of the local density of the object to the local density of its neighbour. The proposed approach uses a measure of k nearest neighbour kernel density (NKD) to estimate the density. Besides, our proposed algorithm used three different categories of nearest neighbours, k nearest neighbour (kNN), reverse nearest neighbour (RNN), and shared nearest neighbour (SNN) to make our systems more flexible in modeling different local data patterns. Formal analysis and extensive experiments on artificial and UCI machine learning repository datasets show that this technique can achieve better outlier detection performance.
Belöningssystem inom språkinlärning

Rewards can be used to navigate a person’s behaviour through stimulation. Knowledge about how humans experience and react to rewards has been of use in many areas, one such example is how retailers ...
Maximum Likelihood Estimation of State-Space Models

We have seen in Chap. 2 that most state-space models depend on some parameter θ; typically Open image in new window. In certain applications, θ is known, while in others it must be learnt from data, either in one go (from some data-set y0:T), or sequentially (as datapoints y0, y1, … arrive).This chapter covers maximum likelihood estimation of state-space models. Maximum likelihood estimation is popular in many areas of Statistics, but it is a bit cumbersome for state-space models. For one thing, the likelihood is intractable for such models. Another, perhaps more fundamental problem is the likelihood is rarely a well behaved function (at least as maximisation is concerned). In particular, if one wishes to fully account for parameter uncertainty, one may be better off by adopting a Bayesian approach (covered in Chap. 16).Methods to compute the maximum likelihood estimator usually combine some particular instance of a general optimisation scheme with some particular implementation of a particle algorithm. Covering all possible combinations, down to the smallest detail, would be tedious. Rather, we provide a general overview, and stress the main ideas. One such idea is that there is a strong connection between maximum likelihood estimation and smoothing. In fact, most approaches will require running one of the smoothing algorithms covered in Chap. 12.
Research on nondestructive measurement solving algorithm based on grid slice volume

Abstract In this paper, an improved discrete binary particle swarm optimization (BPSO) for large scale 0-1 integer programming is proposed. Firstly, a large-scale 0-1 integer programming model based on the multi-directional slice volume of the virtual grid element is constructed, and the optimization objective function is constructed to change the problem of solving the equations into the problem of seeking the optimal solution. Secondly, the 0-1 model intelligent solution algorithm based on the improved BPSO algorithm is designed, and the model solution system based on the grid slice volume measurement is designed. Finally, through the analysis of the experimental results, it shows that the improved discrete particle swarm optimization algorithm has a significant improvement on the convergence and calculation efficiency of the fitness function, which shows that the algorithm has a good optimization performance.
Compressive Rolling Shutter Camera Array

We propose a CMOS-camera array architecture exploiting rolling shutter diversity to generate a compressive forward model that can increase the temporal resolution of the sensed data.
Requirement Prioritization Based on Cost using Artificial Bee Colony Algorithm

The most critical aspect in Software Development Life Cycle (SDLC) model is of Requirement Engineering Phase various kind of complication occurred in software projects, numerous categories of requirements for software projects containing few constraints just like cost, time and a lot of more. Thus it is crucial for choosing such a lot valuable ones for implementation with intention to the delivery of a high-value system. When an instance of lack of cost and time it turns into necessary for prioritizing requirement. The proposed approach prioritize the requirement consistent with higher preference and prioritizing the Non-Functional Requirement w.r.t. Functional Requirement where cost having the principal task for prioritizing and the approach referred to as Artificial Bee Colony (ABC) algorithm based on Population meta-heuristic policy motivated by foraging behavior of bees.
Asynchronous Brain Computer Interfaces Using Echo State Networks

Decoding brain activity for brain computer interfaces (BCI) is a challenging machine learning task. Many BCIs are designed in a synchronous setting, which allows to output a command from the brain to the computer at pre-determined, specific time points. The task becomes substantially more demanding when designing asynchronous BCIs, which allow control over the interface at any given time. Most BCIs use static classifiers, in which the readout is driven only by the current input. However, due to the complex nature of ongoing brain activity, dynamic classifiers, which capture the temporal dynamics of the input signal, are likely to be more suitable for the task. To examine this notion, we designed a classification framework based on Echo State Networks (ESNs), which have demonstrated strong performance in similar tasks in other domains. We evaluated the performance of this approach on pre-recorded EEG data of an asynchronous motor imagery (MI) BCI task, and compared our approach to a conventional method. ESN-based architectures exhibited superior performance in comparison. Our findings suggest that ESNs may prove useful in other BCI paradigms in virtue of their ability to reduce the detrimental effect of non-stationarity in brain signals.
Latent Weights Generating for Few Shot Learning Using Information Theory

Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many metalearning approaches for few shot image classification due to its simplicity and effectiveness. However, fixed classification weights for different query samples within one task might be sub-optimal, due to the few shot challenge, and it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples. In this work, we introduce latent weights generating using information theory (LWGIT) for few shot learning which addresses current issues by generating different classification weights for different query samples by letting each of query samples attends to the whole support set. The experiment results demonstrate the effectiveness of LWGIT, thereby contributing to exceed the performances of the existing state-of-the-art models.
Feature Integration Via Geometrical Supervised Multi-View Multi-Label Canonical Correlation For Incomplete Label Assignment

This paper presents feature integration via geometrical supervised multi-view multi-label canonical correlation analysis (GSM2CCA) for incomplete label assignment. The problem of incomplete labels is frequently encountered in the multi-label classification problem where the training labels are obtained via crowd-sourcing. In such a situation, consideration of only the label correlation, which is the basic approach, is not suitable for improvement of representation ability of features. For dealing with the incomplete label assignment, GSM2CCA constructs effective feature embedding space providing the discriminant ability by introducing both the multi-label correlation and feature similarity of the original feature space into its objective function. Since novel integrated features with high discriminant ability can be calculated by our GSM2CCA, performance improvement of multi-label classification with the incomplete label assignment is realized. The main contribution of this paper is the realization of the effective feature integration via the adoption of the combination use of label similarity and locality preserving projection of heterogeneous features for solving the problem of the incomplete label assignment. The effectiveness of GSM2CCA by applying GSM2CCA-based feature integration to heterogeneous features calculated from various convolutional neural network models is verified via experimental results.
Improving the Clustering Algorithms Automatic Generation Process with Cluster Quality Indexes

AutoClustering is a computational tool for the automatic generation of clustering algorithms, which combines and evaluates the main parts of density-based algorithms to generate more appropriate solutions for a given dataset for clustering tasks. AutoClustering uses the Estimation of Distribution Algorithms (EDA) evolutionary technique to create the algorithms (individuals), and the adapted CLEST method (originally determines the best number of groups for a dataset) to compute individual fitness, using a decision-tree classifier. Thus, as the motivation to improve the quality of the results generated by AutoClustering, and to avoid possible bias by the adoption of a classifier, this work proposes to increase the efficiency of the evaluation process by the addition of a quality metric based on a fusion of three quality indexes of solution clusters. The three quality indexes are Silhouette, Dunn, and Davies-Bouldin, which assess the situation Intra and Inter clusters, with algorithms based on distance and independent of the generation of the groups. A final score for a specific solution (algorithm + parameters) is the average of normalized quality metric and normalized fitness. Besides, the results of the proposal presented solutions with higher cluster quality metrics, higher fitness average, and higher diversity of generated individuals (clustering algorithms) when compared with traditional Autocluestering.
Redistributing and Re-Stylizing Features for Training a Fast Photorealistic Stylizer

Style transfer studies can be categorized into two types-artistic and photorealistic. The high-speed transfer has been well-studied for artistic styles but remains challenging for photorealistic styles. To guarantee semantic accuracy and style faithfulness, prior photorealistic style transfer techniques often rely on intensive feature matching, hierarchical stylization, and complex auxiliary smoothing. Such high design complexity severely limits the space of transfer speed improvement. In this paper, we propose to accelerate the transfer through a single-level stylization without complex auxiliary smoothing. We design a two-stage "stylization and re-stylization" training pipeline to enhance style faithfulness. The stylization/re-stylization stage consists of two core steps: feature aggregation and redistribution. A new type of layers, Feature Aggregation (FA) layers, is proposed to gradually aggregate multi-scale style features into content features at each spatial location. A Spatially coherent Content-style Preserving (SCP) loss at feature map level is then used to preserve semantic accuracy. The SCP loss provides effective guidance on redistributing the aggregated features between locations to enforce spatial coherence of style-sensitive content semantic. Experimental results show that compared to previous competitive methods, our method reduces at least 72% run time while achieving better image synthesis quality based on both subjective and objective evaluation metrics. Ablation studies validate the major contribution of our proposed SCP loss and re-stylization to the quality of our synthesized images.
Enhancing Surface and Thin Film Analysis Through In-Situ Complementary Raman Spectroscopy

Full characterization of complex materials with ultra-thin film structures or nanostructures can rarely be accomplished with a single analysis technique.X-ray Photoelectron Spectroscopy (XPS) has often been complemented with Ultra-violet Photoelectron Spectroscopy (UPS), ion scattering data or electron microscopy.This presentation will show how the addition of in-situ Raman spectroscopy to an XPS surface analysis system can help to characterize 2D nano-materials such as graphene, by adding chemical and bulk information to surface information all obtained from the same analysis position on the sample.
A Contract Based User-Centric Computational Trust Towards E-Governance

E-Government services are persistent targets of the organized crime by hackers, which hinders the delivery of services. Computational trust is an important technique for the security work of service providers (SPs). However, it relies on data collection about users' past behaviors conventionally from other SPs, which incurs the uncertainty of data and thus impacts the quality of data. Motivated by this issue, this paper proposes a novel smart contract based user-centric computational trust framework (UCCT) which collects the behavioral data of the user. It uses smart contract as a rational trustworthy agent to automatically monitor and manage the user's behaviors on the user side, so as to provide deterministic data quality assurance services for the computational trust. Furthermore, a privacy-preserving way of the data sharing is provided for the user and a personalized security mechanism for the SP. A new ledger is also introduced to provide a user-centric and efficient search. The results of experiments conducted on a Hyperledger Fabric based blockchain platform demonstrate that the time cost of user-centric ledger in UCCT can be less than 1 s. Moreover, even if a more complicated contract is provided, the improvement of transaction per second (TPS), which is made by UCCT, is not less than 8%.
ReFuzzTiD: A Recurrent Neurofuzzy Model for Anomaly Detection in Time Series

In this paper a recurrent neurofuzzy model is proposed, for time series anomaly detection. ReFuzzTiD comprises fuzzy rules whose consequent parts are simple three-layer neural networks with internal feedback at the neurons of the hidden layer. ReFuzzTid is trained by the Simulated Annealing Dynamic Resilient Propagation algorithm. The model learns the dynamics of the time series such that it can classify them by detecting the anomaly points. A comparative analysis with a series of time series anomaly detection models is given, highlighting the characteristics of the proposed detector.
Newspaper Identification in Hindi

This paper reports the creation of a newspaper identification model in Hindi. This work has two broad angles, namely, the detection of online content in Indian languages, and second, the use of world-class data. Data is a primary problem in Indic computing. This work tries to show with the use of proper data, which may remove the data-driven challenge of Indic computing. The work used annotated and unannotated gold standard Hindi data. Hand-annotated Hindi data for Named Entity Recognition (NER) is used to carry out this task. The work shows a detailed analysis of entity types present in each online newspaper. One generative and another discriminative model, namely Naïve Bayes (NB) and Support Vector Machine (SVM) based classifier, are applied here. In both the models, the success of around 90.0 in F-measure is received. The work is done to help the ever-growing electronic document user community. It will help a user to classify the Indic document types.