Reviewerly/exHarmony huggingface dataset:
Data Collection and Development of Bengali ASR and TTS for Conversational AI-based Automated Advisories in the Agriculture domain

This paper presents an indigenous work of text and speech data collection and organization in the agriculture domain and developing a structured Agriculture Knowledge Repository (AKR), Automatic Speech Recognition (ASR), and Text-To-Speech synthesis (TTS) systems in Bengali language, applicable to agriculture-related automated advisory systems. Authors have searched available data sources, interacted with real farmers and experts at local farming fields and agriculture institutes, collected feedback on actual advisory requirements, and designed an initial text corpus which is then used to prepare 10,000 numbers of unique query-answer pairs with 5000 agri-keywords in the final AKR. The DNN-HMM based ASR is trained by merging general domain speech data with 40.5 hours of agriculture data newly collected using a mobile app, web-based and Interactive Voice Response (IVR) based data collection setups. TTS is developed in one male and one female voice with 20 hours of studio speech data using DNN-based architecture. Both the ASR and TTS are tested with end users in real environments and are having encouraging results. Corpus collection and system development methodology is language invariant and developed sub-systems can be readily used for web or mobile chatbot-based and IVR-based automated advisory applications in the agriculture domain.
Intentional knowledge map representation learning method based on fusion of action roadmap

Affective knowledge graph constructs the objective world from the perspective of knowledge representation learning, and has certain reasoning and analysis capabilities. The reasoning analysis of the objective world includes intention analysis. The action and behavior route of the intention analysis is constructed as an event knowledge graph, and the relationship between the nodes in the event knowledge graph is used to explain the intention, and the intention analysis task can be completed by constructing the knowledge representation. However, in intention analysis, the number of actions and the position of actions in the overall action route are important indicators in the task of intention analysis. However, most of the knowledge graphs are two-node relationships, and the study of the position of nodes in the relationship only considers Symmetrical relationship. Aiming at the problem, a representation model of super nodes is proposed, in which information is integrated into the model, and attention is paid to network data set to test knowledge link prediction tasks. Experiments show that the model also achieves good results in the link prediction task.
Sphinx-Based Evaluation of Efficient Acoustic Modeling Parameters for LibriSpeech Corpus

In this paper we are assessing the efficient parameters i.e., the number of senones and number of gaussian densities for a well-known audiobook corpus "LibriSpeech" based Automatic Speech Recognition System (ASR) using the open-source tool Sphinx. Sphinx is a Hidden Markov Model (HMM) based offline large vocabulary language and speaker independent continuous ASR system with a support for low-resource handheld devices. We have trained the acoustic model by varying the parameters and examined the quality of the models using Word Error Rate (WER). The best achieved WER of the model is observed as 9.5% with 2000 senones and 64 gaussian distributions.
A Transfer Learning based Model for Knowledge Graph in Power Grid

In view of the limited utilization of fault knowledge value in the power field, this paper explores the automatic term extraction for knowledge graph in the power field to solve the cold start problem of lack of large-scale annotated corpus. We design a TTL model consisting of text representation, feature extraction, and sequence labeling, which realizes the knowledge transfer from the annotated corpora in the source domain to the target domain in the power grid domain. With Transformer as the skeleton, the model is used to adequately model semantic associations between words. The experiments on a real dataset show the effectiveness of the proposed model.
Face Recognition Based on Collaborative Sparse Representation with Dictionary Learning

For sparse face recognition algorithm lacking of identification information, face recognition based on collaborative representation with dictionary learning is proposed. Firstly the LC-KSVD dictionary learning algorithm is used to get a discriminant dictionary. The collaborative sparse representation algorithm is conditioned to complete the classification, the sparse coefficients of the sample in the dictionary are solved by using the L2 norm instead of the L1 norm. The collaborative representation is accustomed to calculate the residuals of each category. Finally, the category is got by comparison. Experimental results on the common face database show that the algorithm can effectively improve the recognition rate without reducing the speed.
A 3D Model-Driven Integration and Visualization Framework for Aerospace Engineering Data

Aerospace engineering data contains huge application value. Designing approaches and tools for visualizing aerospace engineering data presents a unique set of challenges due to their multi-source and heterogeneous characteristics. An integration framework is proposed in this article to connect people and aerospace engineering data through visualization technology. A unified data retrieval unit was designed to implement unified retrieval and processing service of all kinds of aerospace engineering data. A data visualization chart engine was implemented to integrate various visualization charts and provide a unified API through function customization to make it easy to use. The model-data-visualization chart association based on configuration files was designed to implement the mapping between models, data, and visualization charts, which implements the dynamic binding between the three, and supports the dynamic expansion of the database. Finally, an application system is implemented, which achieves the integration and visualization of multi-source and heterogeneous aerospace engineering data. The validity of the proposed framework was verified by the practical application.
A Multi-level Deep Network Algorithm For Image Segmentation In Coal Mine Operation Scenarios

The main problem of current coal mine operation scene is safety, however, as the site environment is characterized by high noise, multiple exposure, low brightness by working reasons, which makes the traditional coal mine operation scene image segmentation algorithm is difficult to achieve the expected effect. In addition, whether the construction of a single network model is generalizable to the processing of multiple environments in coal mine operation scenes is also a pressing issue. To this end, a multi-level depth network algorithm for image segmentation in coal mine operation scenes is proposed. The algorithm firstly divides the enhancement layer, backbone layer, neck layer, head classification layer and loss layer. Secondly, the algorithm scheduling layer and algorithm interpretation layer are constructed, which groups and concatenates different kinds of models to form a variety of network models. Then, the annotated data is transferred to the network model for training to realize the training of different network models. Finally, the trained multiple models are analyzed and processed to judge the suitability of the models. This method can produce a large number of network models in very short time, which helps to expand beyond the original number of network models in a limited number of network models and increase the generalizability of the models.
Design and Implementation of Group Video Animation Based on Intelligent Algorithm

In today's society, intelligent algorithms have become an indispensable part of modern technology development and production life, and for intelligent algorithms, they are mainly used to achieve control goals by adjusting parameters. However, in most cases, it cannot meet the requirements of communication and collaboration among different audience groups, so the development of intelligent algorithms in the application field is limited. The production and implementation based on intelligent algorithms can effectively improve the quality of film and animation creation and reduce the cost by adjusting the parameters. In this paper, we study the design and production of film and television animation based on intelligent algorithms.
PN code tracking aided by GMSK signal algorithm based on GMSK+PN-FH

GMSK+PN-FH has the development potential with the advantages of constant envelope, high spectral efficiency, high precision ranging and time synchronization, and anti- interference. It is a research hotspot in the field of covert anti- interference communication ranging integration system. GMSK+PN-FH requires accurate carrier synchronization. However, the existing carrier tracking loop assisted code tracking loop algorithm cannot achieve accurate carrier synchronization at high hop speed, which limits the improvement of hop speed and the ability of covert anti-interference. This paper proposes an algorithm named PN code tracking aided by GMSK signal algorithm based on GMSK+PN-FH, which sacrifices 25% of the communication efficiency and can work stably in a high-speed frequency-hopping (FH) system where one period PN chip is transmitted in four FH time slots, and the mean square error of the code tracking error is less than 0.0419 chip.
A method of integrating machine learning algorithm to predict user behavior

At present, mobile operators usually use data mining algorithms and expert experience to analyze and predict user behavior. Market frontline of two kinds of algorithms. The application satisfaction and effect are limited. This paper fully combines the advantages of the two methods, and proposes to carry out user behavior prediction based on the recursive partition feature of distribution degree. It improves the prediction efficiency of the model.
OPABP-Optimizing Parameters, to Improve Accuracy in Bug Prediction using Machine Learning

Predicting a bug and attaining a successful application is critical in today's scenario during the development phase of a program. This can only be accomplished by foreseeing some of the shortcomings in the early stages of development, resulting in software that is dependable, efficient, and of high quality. A challenging aspect is to develop a sophisticated model capable to determine the error and producing effective software. A few ML methods are utilized to achieve this, and they produce accuracy with both trained and test datasets. The novelty of this approach is to demonstrate the applicability of machine learning algorithms namely Neural Network, SVM, Decision Tree and Cubist in using different performance metrics i.e. R, R square, Root Mean Square Error, Accuracy and obtaining the optimal outcome-based algorithm for a Bug report on diversion dataset from PROMISE repository. Findings reveal that SVM is giving significantly higher accuracy among all the algorithms in the ANT dataset and integrates the existing work on detecting a bug in software by providing information about various aforementioned methods in bug prediction The proposed work is highlighting the accuracy obtained by the current approaches that are significant for research scholars and solution providers.
A Survey of Morphological Analysis for Marathi Language

Natural language processing is a field which studies how machines can understand the natural languages used in human-to-human interaction. Any language consists of meaningful sentences. Sentences are made of elementary parts called words. Morphemes are the building blocks of words. Marathi is a morphologically rich language since it has various root words and affixes that come together to form a word. The properties of words change according to their role in the sentence (gerund, adjective, verb, preposition, etc.). Morphology studies the formation of words. The paper focuses on the survey of morphological features of Marathi, which have proven useful in applications like speech synthesis, machine translation, information retrieval and spell checking.
Anomaly Detection of Power Big Data Based on Improved Support Vector Machine

To reduce the false negative rate in power data anomaly detection, enhance the overall detection accuracy and reliability, and create a more stable data detection environment, this paper designs a power big data anomaly detection method based on improved support vector machine technology. The abnormal features are extracted in advance, combined with the changes of power data, the multi-target anomaly detection nodes are laid, and on this basis, the improved support vector machine anomaly detection model is constructed. The anomaly detection is realized by combining the normalization processing of the equivalent vector. The final test results show that compared with the traditional clustering algorithm big data anomaly detection test group and the traditional multi-domain feature extraction big data anomaly detection test group, the final false negative rate of the improved support vector machine big data exception detection test group designed in this paper is only 2.04, which shows that the effect of the anomaly detection method is better. It is more accurate and reliable for testing in a complex power environment and has practical application value.
Research on an Enhancement Algorithm of Color Low Illuminance Image Based on Bilateral Filtering and Defog Model

Low illuminance image is a kind of common image in dark light condition, usually has high noise, texture loss and other low quality problems. Low illumination image can not be directly used as the input for extracting beneficial information such as feature extraction and image segmentation.The processing of low illumination image usually involves two parts: denoising and enhancement. In the denoising part, this paper firstly preprocesses the L channel of the image, so as to retain more color information in the subsequent processing. Secondly, according to the noise distribution characteristics of low illumination image, the image is denoised by adaptive bilateral filtering. Finally, since the inversion image of the low illumination image called the pseudo fog image is very similar to the fog image. In this paper, the de-fog model based on the image brightness information is used to process the pseudo fog image, and the final low-illumination enhanced image is obtained by reversing the no-fog image. Through experimental simulation, the enhanced image can maintain good texture and color information while de-noising.
Image Privacy Regions Encryption Scheme Based on SIFT Features and Face Detection

Privacy protection of cloud images is widely concerned, while their usability is often neglected. Therefore, an image privacy regions encryption scheme based on SIFT features and face detection is proposed. Firstly, the algorithm detects the faces of the image and obtains the face regions. Then the SIFT feature points of the image are extracted, and the image is divided into several blocks. Meanwhile, the image blocks with more SIFT feature points are selected as SIFT feature regions. The face regions and the SIFT feature regions are viewed as the regions of interest (ROIs) that require privacy protection. In the scrambling encryption step, the ROIs are combined to improve the scrambling effect. Each ROI adopts a different chaotic sequence in the diffusible encryption step to enhance chaos complexity. Experiments show that the encryption scheme saves running time, and the usability of the encrypted image is preserved. This scheme is suitable for the encryption of images such as ID cards and invoices.
Optimized implementations of stream cipher ZUC-256 algorithm

ZUC-256 is a more secure stream cipher based on ZUC-128 developed independently by China, which emerged mainly to cope with the advent of 5G communication and the post-quantum cipher era. Based on the ZUC-256 encryption algorithm, this paper focuses on its working phase and proposes two optimization methods for the working phase, one is a pipelined structure that focuses more on increasing the frequency, and the other is based on the specificity of the modulo operation in the algorithm, which focuses on reducing the resource overhead. The final validation results show that the first method increased frequency by 37.2% and the second method reduced resource overhead by 18.7%. These two optimization methods show that the ZUC algorithm is quite flexible in balancing different throughputs with resource overheads.
Disguised Face Detection using Machine Learning

The crime rate has been increasing day by day. According to India today, the police have said that 3,06,389 crime cases were reported in 2021 alone. To reduce the crime rate, faster action and stricter law policies are required. Due to the increasing crime rate, it becomes a very difficult task for the police to catch the criminals and address the issue quickly. One strong piece of evidence that helps the Police identify criminals is the CCTV footage. Unfortunately, the images in CCTV footage are generally blurred. Also, when criminals disguise themselves and commit a crime, it becomes even more difficult in identifying their faces. Hence, more time is required to resolve cases. To solve this problem, a tool was developed for disguised detection of images using machine learning algorithms. This tool includes age and gender detection of disguised images, an image search engine that can retrieve similar images, and the calculation of percentage similarity between the images. In our paper testing is performed using various algorithms like CNN, KNN, SVM, Logistic Regression and Decision tree [1]. Apart from this, a dataset is also generated with real time images, and further applied various noises, blurred images, and tested the model. We have observed that using SVM, an accuracy of around 81% is achieved and CNN has performed extremely well with an accuracy of 90.6%.
Noise Reduction and dehazing of Visual Data

The problem of haziness is a poorly presented problem that has been broadly contemplated in recent years. Images of dim scenes normally experience low contrast which corrupts the perceivability of the scene. Because of light scattering and absorption by climatic particles, pictures/recordings acquired from video surveillance, traffic checking, galactic imaging, clinical imaging, and remote detecting have poor perceivability. Noise and haze both degraded the overall quality of an image. So, the objective of this paper is to give a review with respect to these issues by reviewing over eleven techniques of image dehazing on different datasets. Through this paper, a thorough study and analysis of unsupervised as well as supervised techniques have been presented.
Map-Reduce Based Parallel Firefly Algorithm For Fast Recommendations

The Recommendation System is a strong tool that aids the decision-making process across a variety of situations. Using the aid of aspects such as prior experiences of the user, their ratings, comparable interests, etc., we can acquire the most relevant results upon the application of various optimization techniques. A movie recommendation system is a useful tool/software that aids users in rapidly obtaining optimum results with comparable interests. Using the Firefly clustering technique, this study focuses on a movie recommendation system whose major goal is to propose movies of comparable interest to the active user. Although much study has been done in the topic of recommendation systems, there are still several issues with producing suitable results. To address these issues, we suggested a strategy that uses a meta-heuristic approach to get optimal outcomes. Instead of utilising K-means, fuzzy C-means, and other algorithms, we present the Firefly clustering method in this research to provide the best optimum outcomes in recommendation systems. For performance analysis, many measurements such as t-value, RMSE, SD, and MAE are utilised.
Depthwise Separable Convolutions based relation network for few-shot learning

In recent years, few-shot learning has attracted more and more attention, because it is very similar to human learning mechanism, requiring only a small amount of data to learn concepts. Relation network is a very classic few-shot learning model, which adopts end-to-end training method. Recent research in the field of meta-learning shows that exploring the relationship between features can effectively improve the accuracy of the model. In this paper, a depthwise separable convolution based relation network is proposed to learn the relationship between the channel features of the support set and the query set. First, the wide residual network is introduced to extract more abundant features, and the depthwise separable convolution is used to replace the original relational model. Experiments show that the performance of the model proposed in this paper is better than the original relational network. Our method pushes 5-way 1-shot mini-Imagenet accuracy to 52.25% (2.09% higher than the baseline) and 5-way 5-shot mini-Imagenet accuracy to 66.79% (1.47% higher than the baseline) .
Computation Analysis for Identifying the Protagonist and Antagonist and their Sentiments in Harry Potter Books

The main goal of sentiment analysis is to find the text polarity (positive or negative). Sentiment analysis can be Natural language processing and computational literary studies have long been interested in sentiment analysis, which can be used to infer relationships between fictional characters. This study uses sentence-based semantic analysis to analyze sentiment in the Harry Potter series. The findings showed that among the primary characters with heroic attributes, Harry Potter scored the most positive polarity; Voldemort scored the most negative polarity, and Hermoine scored the neutral polarity.
Image Classification Algorithm combining Local Feature and Attention

In order to solve the problem of low accuracy of image classification caused by incomplete feature information extracted by convolutional neural network and indistinguishable feature information, an image classification algorithm combining local feature and attention was proposed. Firstly, patch layer is added to obtain multiple patch images before extracting image features, and feature extraction is carried out at both global and local levels. VGG16 is used as the backbone network to extract patch image features. Then, for each patch image, feature attention is carried out in the channel domain and spatial domain respectively to obtain important patch channel information and spatial information. In addition, in order to further constrain the feature attention of patch image, the patch attention aggregation loss is proposed to penalize the weights of irrelevant regions and strengthen the attention of important patch regions, which makes the extracted feature information more discriminative, so as to facilitate classification and improve the final classification performance. The experimental results on CIFAR-10, CIFAR-100 and RAF-DB datasets show that the classification performance of the proposed algorithm is significantly improved compared with other advanced methods.