Reviewerly/exHarmony huggingface dataset:
Occluded thermal face recognition using BoCNN and radial derivative Gaussian feature descriptor

In this work, we propose a Radial derivative Gaussian feature (RDGF) descriptor, a novel handcrafted feature descriptor for disguised thermal face recognition. The feature encoding has been done so that the performance is least affected by noise and works well over challenging datasets. We propose a cascaded framework that combines two modules, namely BoCNN and the RDGF descriptor. The cascading architecture estimates the performance of BoCNN before classification. It also uses a dynamic classifier selector in run time to choose between handcrafted features and the CNN framework to enhance the overall performance. We also propose a thermal face dataset with partial occlusion. We have compared the performance of the RDGF descriptor with state-of-the-art descriptors on the IIIT-Delhi disguised thermal face dataset and our proposed dataset. RDGF exhibits better performance compared to other state-of-the-art descriptors. Our proposed descriptor shows relative increment of 56.84%, 64.92%, 67.25%, 64.03%, 48.06%, and 7.28% on IIIT-D Occluded Thermal Dataset when compared with LBP, LDP, LBDP, LVP, LGHP, and HOG, respectively. A similar enhancement of accuracy has been observed on our proposed dataset as well. An exhaustive comparison based on the performance of the cascaded framework with state-of-the-art CNN models has also been done in a similar fashion.
Automatic schema construction of electrical graph data platform based on multi-source relational data models

Data storage and management in power systems usually adopt relational databases. However, the relational database requires ample storage space and has low data retrieval and query efficiency. An electrical graph data platform can describe the complicated relationships between concepts and entities involved in power systems with the form of an association graph, which provides a better ability to organize, manage, and apply massive amounts of information. Since the construction of the top-level ontology model or schema for a specific field graph data platform is cumbersome, complex, and generally requires lots of association analysis and expert system intervention, it is insufficiently automated, time-consuming, and unable to cope with large-scale electric power knowledge. This paper proposes a method for automatically constructing the schema of an electrical graph data platform, which uses the diverse table structure information from the relational database and SQL language descriptions to extract ontologies to form the ontology candidate set automatically. Then the method utilizes ontology clustering and disambiguation to initial an ontology graph model and automatically update ontology and relationship expressions. Meanwhile, the model layering is used to construct a hierarchical model based on different business needs, and the schema optimization is applied according to expert comments.
A new method for improving prediction performance in neural networks with insufficient data

This paper proposes Simultaneous Trainings of Identical Neural Networks (STNN) that aims to predict when sufficient data is not available for training neural networks (NN). While predictive applications of neural networks are growing, a common assumption in the NN algorithms is to have a training dataset that is large enough to sufficiently represent the population. However, in practice, this is difficult or expensive where the size of datasets is limited by the complexity and cost of large-scale experiments or data collections. Lacking sufficient data commits the NN training to two issues; namely parameter initialization and training sequence. STNN selects the outperforming NN out of several training episodes of the selected identical NN design by changing parameter initialization and training sequence. STNN has been evaluated by comparing with alternative methods in the literature. The results demonstrate improvement in prediction of STNN compared to other alternatives.
Combined CNN/RNN video privacy protection evaluation method for monitoring home scene violence

Rapid developments in technology have led to increasing numbers of video surveillance devices in the home environment. The importance of video privacy protection has spurred the development of various video privacy protection methods. This paper proposes a method for evaluating the degree of privacy protection for multilayer compressed sensing video. The combination of CNN convolutional neural network and RNN convolutional network was used to extract video spatial-temporal feature mapping visual privacy protection scores, and the same model was used to map video practicality scores through classifiers. Compared with previous methods, the proposed approach achieves a better prediction effect and generalization performance for videos. Finally, an association model is established between visual privacy protection score and practicability score. This model quantifies the relationship between these aspects, providing suggestions for practical application and enabling the evaluation of other video privacy protection methods.
Advanced Face Mask Detection Model Using Hybrid Dilation Convolution Based Method

A face-mask object detection model incorporating hybrid dilation convolutional network termed ResNet Hybrid-dilation-convolution Face-mask-detector (RHF) is proposed in this paper.Furthermore, a lightweight face-mask dataset named Light Masked Face Dataset (LMFD) and a medium-sized face-mask dataset named Masked Face Dataset (MFD) with data augmentation methods applied is also constructed in this paper.The hybrid dilation convolutional network is able to expand the perception of the convolutional kernel without concern about the discontinuity of image information during the convolution process.For the given two datasets being constructed above, the trained models are significantly optimized in terms of detection performance, training time, and other related metrics.By using the MFD dataset of 55,905 images, the RHF model requires roughly 10 hours less training time compared to ResNet50 with better detection results with mAP of 93.45%.
Movie Poster Classification Using Federated Learning

Privacy is an important aspect in the movie industry, with production companies going the extra mile to ensure that their data isn't leaked before the intended date of release. With machine learning branching out in almost every industrial field, the movie industry is no exception. However, at present, most project implementations require collaborators, such as the production firms, to make their data available to the development team in order to train the intended model. We investigate the replacement of traditional centralized machine learning models with a decentralized approach of Federated Learning, wherein models can be trained on client devices without the need for each client to send their personal data to the central server. The research has been carried out on the problem statement of classifying genres of movie posters by using neural networks and optimizing the use of color theory. This preserves a protected environment for movie productions' design teams to determine whether their poster designs align with the genre of the movie which would potentially lead to the reach of the movie to the intended audience.
Answer Prediction for Questions from Tamil and Hindi Passages

Predicting answers for questions based on passages from Tamil and Hindi is a Question Answering (QA) task. It helps us to elicit information that is relevant to the queries from the passage. Answers are predicted by identifying the answer span from the context based on the questions. In this paper, four different approaches are used to solve the QA task for Tamil and Hindi Passages. Zero-shot transfer of QA models has been tested on the QA datasets available for Hindi and Tamil. Comparative analysis between Multilingual models that are fine-tuned using Tamil and Hindi QA datasets is carried out. In addition to that, the performance has also been evaluated after increasing the Hindi data points. As there are very few QA datasets available in Indic Languages, especially Tamil, a possible solution to overcome this challenge was tried and tested using the creation of a QA dataset for Tamil using Indic Language Translation models. Multilingual models were then used for fine-tuning with the translated Tamil QA dataset generated using Language translation and the results have been discussed. The predicted answers will be evaluated using Jaccard Similarity, Exact match (EM), and F1 Scores.
Effect of Reconstruction Losses in Discriminative and Generative Learning based Networks for the Person Re-identification

The Person Re-identification (Re-ID) task has gained popularity in recent times. Researchers are continuously looking to improve the accuracy of the existing person Re-ID systems. Identifying the person from the surveillance footage can be essential to security concerns. Currently, there are many state-of-art Person Re-ID systems available. Deep learning frameworks are also adopted for designing Re-ID systems. Apart from deep learning-based approaches, the Generative Adversarial Networks (GAN) based approach also gained substantial interest in Person Re-ID tasks. Augmentation of training data has significantly improved the performance of the system. Our primary objective is to analyze the effect of applying different reconstruction losses and their combinations on the GAN-based approach. The Discriminative and Generative Learning (DG-Net) approach is chosen for carrying out this study from other existing GAN-based systems. DG-Net is currently considered benchmarked in the GAN-based method for person Re-ID. Analysis shows that the proposed idea of using a variety of reconstruction losses simultaneously significantly improves the existing system's performance. Using the proposed technique of fusing multiple Losses simultaneously, we achieved a massive performance gain of 20.57% over the current benchmarked approach on the Market1501 dataset. This paper includes a thorough study of different loss functions and their effect on the generated images for performing Person Re-ID tasks.
An Emphatic Attempt with Cognizance of the Marathi Language for Named Entity Recognition

Recent developments in the field of artificial intelligence has led to renewed interest in natural language processing. Named entity recognition (NER) is a classic problem in natural language processing. Investigating named entities is a continuing concern within information extraction. Names of people, organizations, locations, events, etc. are named entities (NEs). Although extensive research has been carried out on NER, limited study exists that explores NER in the Marathi language. The paper aims to provide a conceptual theoretical framework based on the gazetteer matching technique for named entity recognition system development for the Marathi language. A combination of rules and regular expressions is considered for temporal and numerical pattern recognition. The techniques presented in this paper are systematic, clear, and effective for morphologically rich language processing tool development. The system described in the paper reports a satisfactory performance with 62.64% NE identification and 72.27% NE classification accuracy.
Gujarati Script Recognition

Character recognition is the extraction of printed or handwritten text from images into machine-readable format. The extracted text can be easily edited, modified and efficiently stored. While there are several Optical Character Recognition (OCR) and Handwritten Character Recognition (HCR) systems available for the English language, such systems are not well developed for Indian languages such as Gujarati. This work deals with text recognition of the Gujarati Script. Two different models have been analyzed in this work for the task of recognition of Gujarati text: CNN based EfficientNet B3 and YOLO v4. The system has been developed using the EfficientNet B3 model which gives better accuracy and efficiency. The input to the system is an image having optical Gujarati text and the system produces an editable text document having the contents of the recognized text in the image. The system has been successfully implemented for the task of creating a digital library of Gujarati newspapers articles from their images. This novel project is a step toward the cultural and linguistic preservation of the Gujarati language.
Identifying Fake Reviews in Relation with Property and Political Data Using Deep Learning

Modern deep learning algorithms have achieved tremendous success in many visual applications by training a model with all relevant task-specific data. In this module fake reviews evolving in online political and property data will be discussed. Regarding political information there are many differences while switching from offline to online mode. The views and comments of the politicians in social media act as a question mark “whether it is true or not?”. In some cases, it appears to be real and sometimes it may be a false/fake statement. All because, the online information is often considered as the most important source of info for the users these kinds of reviews and comments affects the trust factors of social media. Now days in social media, more of toxic contents and unwanted information were present rather than the useful information. These toxic comments acts as a threat for users using the application. Hence users do not come forward to post their thoughts and actions or to share the information. This module is used to distinguish reviews, identify the factor in online mode of political and property comments in social media. The LSTM and BERT (Bidirectional Encoder Representations from Transformers) algorithms are used in the first module because they offer a wide variety of parameters such as learning rates, input and output biases, and text categorization. Additionally, GPT2 (Generative Pre-Trained Transformer 2) is implemented which helps in text generation, increasing the size of dataset for training in different classification models. Thus it is important to understand for us that ultimately the model will be getting trained in such a way that it is able to give accurate results while we are undergoing classification of text and also while we are undergoing the identification of fake/authentic comments that are there in the source of our data.
Link Prediction based on bipartite graph for recommendation system using optimized SVD++

To alleviate the big data difficulties that have created a potential problem for many Internet users, it is necessary to filter, rank, and efficiently communicate the relevant information on the Web, where the diversity of possibilities is overwhelming. Recommender systems, which sift through enormous amounts of dynamically generated data to give consumers with personalised information and services, are able to overcome this problem. Bipartite graphs are currently generally used to store and understand this data due to its sparse nature. Data are mapped to a bipartite user-item interaction network where the graph topology captures detailed information about user-item associations, transforming a recommendation issue into a link prediction problem. Earlier, approaches for link prediction in bipartite graphs for various recommendation systems were developed, but the efficacy of the prediction methodology was not close to the standards required by real-time recommenders. So, the primary goal of this research is to offer an effective link prediction-based recommendation system that takes advantage of SVD++ along with K-nearest neighbors and reduces the system's error rate, leading to better outcomes. The proposed system is sorely tested on the MovieLens dataset and compared to some traditional recommendation methods. The results demonstrate that the suggested strategy exceeds all traditional approaches in terms of accuracy, and the actual suggestions are equally encouraging.
Optimal Path Planning for Mobile Robot Navigation Using FA-TPM in Cluttered Dynamic Environments

Mobile Robot Navigation(MRN) in moving obstacles environment is a very difficult research area in field of robotics. Nowadays, mostly Nature-inspired algorithms are used methods used for autonomous MRN. Many algorithms in MRN are developed by simulating the swarm behavior of various creatures like ants, fireflies, honey bees, cuckoos, spider monkeys, whales and the outcomes are very motivating. The paper presents an hybrid of Firefly algorithm (FA)along with Three Path Method (TPM) in [35] for MRN in cluttered dynamic environment. TPM check the free path in the environment and whenever all the paths are blocked by obstacles, FA is used for avoiding obstacles. The key concept of the proposed method is the searching of brighter firefly from the groups. Several experiments are performed by considering two environments to validate the performance of the proposed hybrid method and the results are compared with existing navigation methods. It is observed that the proposed hybrid method shows superior performance in terms of reduced path length and lower computational cost.
Experimental Analysis of Fuzzy Clustering Techniques for Outlier Detection

Fuzzy clustering is an effective clustering approach that divides the dataset into fuzzy segments. Fuzzy clustering and outlier detection are two interconnected processes. Outlier detection is essential because it discloses hidden patterns and crucial information about a dataset and is beneficial in a wide range of applications like fraud detection, military surveillance, identifying computer network intrusions, image processing, insurance or health care, etc. Many fuzzy clustering techniques are robust to anomalies since they limit the outlier's impact on the cluster's centroid. In this paper, we have compared four fuzzy clustering techniques namely Fuzzy C-means (FCM), Noise Clustering (NC), Credibilistic Fuzzy C-means (CFCM), and Density Oriented Fuzzy C - Means (DOFCM) on the basis of some crucial properties that are essential for efficient outlier detection. To better evaluate the feasibility of all these techniques, we experimentally evaluated these techniques on six datasets (two Real and four Synthetic) in the presence of noise and outliers. The results of the comparative analysis show that DOFCM outperforms all other techniques in terms of outlier detection and cluster formation. This research will surely benefit any researcher willing to detect outliers in their study using fuzzy clustering or either implicitly or explicitly working on fuzzy clusters.
Indian News Headlines Classification using Word Embedding Techniques and LSTM Model

Newspapers introduce us to the latest happenings around the world. Going paperless creates more opportunities for newspapers, like broadcasting news coverage and presenting breaking news conveniently. News headlines are considered under the short text category and are vibrant subjects for researchers. Creating a dense vector from short texts has become a challenging and essential task in many applications such as recommender systems, context analysis, decision making, text classification, etc. This work not only targeted creating a classification model for the short text but also categorized the headlines with the ‘unknown’ category. Our work uses Bidirectional Encoder Representations from Transformers (BERT), cosine similarity index, word embedding, and Long Short-Term Memory (LSTM) network to classify news headlines in multiple categories. Our proposed method outperforms labeling the unlabeled data with the help of a BERT sentence encoder. The system uses LSTM to learn the headlines as input vectors and classify the headline text by the classifier. At the end of this experiment, the designed pipeline achieves remarkable precision at the class level.
English-Assamese Multimodal Neural Machine Translation using Transliteration-based Phrase Augmentation Approach

Neural machine translation (NMT) is a popular machine translation method due to its contextual analyzing ability and end-to-end process flexibility. However, NMT suffers poor translation quality in low-resource contexts, particularly for diverse language pairs. To overcome this issue, multimodal concept has been introduced in NMT, wherein leverage information from different modalities like image or speech in addition to text to enhance automatic translation quality. In this paper, we have investigated multimodal NMT for a low-resource language diverse pair, English-Assamese, by addressing data scarcity and word-order divergence issues. To tackle such issues, a transliteration-based phrase augmentation approach is proposed, that leverages the sub-word level tokens sharing among source-target sequences in the training process via transliteration and provides more word alignment information by the addition of phrase pairs. Also, the relevant image features corresponding to the phrase pairs are augmented by considering a filtering step. With the proposed approach, state-of-the-art multimodal NMT results are attained for both directions of English-Assamese pair translation.
White Blood Cell Classification based on Gray Level Co-occurrence Matrix with Zero Phase Component Analysis Approach

This paper presents an approach to WBC classification by employing Gray Level Co-occurrence Matrix (GLCM) along with the Analysis of Variance (ANOVA) test and Zero Phase Component Analysis (ZCA) whitening. Moreover, the performance is evaluated through the K- Nearest Neighbor (K-NN) classifier. The proposed approach has achieved an accuracy of 94.25% on the Blood Cell Count and Detection (BCCD) dataset for the classification of four categories of WBC namely, lymphocytes, monocytes, neutrophils, and eosinophils. The experimental results reveal an improvement in accuracy (11.05, 8.15, and 14.25%) in comparison to the state-of-the-art approaches i.e., Watershed segmentation, Local Binary Pattern (LBP), and Extreme Learning Machines (ELMs) respectively.
Analysis of Students’ Critical Thinking Skills on Virtual Reality Learning Media

This study aims to describe students’ critical thinking skills and the corresponding prior knowledge of Virtual Reality (VR) media used for learning purposes in a classroom setting. A total of 65 high school students from Grade XI Science 1 and Science 2 were involved as respondents in this preliminary research using qualitative-descriptive analysis. The instrument used included questionnaires (distributed over the respondents) containing 12 questions on VR relevant to the indicators of critical thinking skills. The results showed students' skills in critical thinking at the intermediate level. The indicator of analytical ability has the highest percentage in the medium category. The number of students with critical thinking skills in the high category is 14, and in the medium category, as many as 51 students. Students' prior knowledge of VR media (familiar with the technology) was reported to 46.00% of all the respondents, and the remaining 54.00% did not know what it was. However, all the respondents were interested and happy with VR media as a learning tool. Improvement of critical thinking skills could be achieved through contextual learning and VR learning media. The implication of the present study for future use of VR education media in schools is possible as VR media is considered to be technology-assisted learning, applicable to science and physics teachers when delivering learning materials in class.