Reviewerly/exHarmony huggingface dataset:
Temporal Semantics Auto-Encoding based Moving Objects Detection in Urban Driving Scenario

Detecting moving objects from a moving vehicle is a challenging problem and crucial for autonomous driving, especially in urban scenarios. The current literature has focused on this task as many approaches have been dedicated to moving object detection. These approaches consist of multistage pipelines, including semantic segmentation and optical flow estimation, and require multiple sources of information from active and passive sensors. However, they fail to accurately segment moving objects due to the large ego- camera motion, in addition to the high processing time. In this work, we propose a novel approach to moving object detection by processing information only from a camera. Our approach is based on integrating an encoder-decoder network (EDNet) with a semantic segmentation model (Mask R-CNN), where Mask R-CNN detects the objects of interest and the EDNet classifies their motion (moving/static) over two consecutive frames. We compare the results of our proposed model with existing MOD models on three SOTA benchmarks. We achieved SOTA performance in terms of visual quality and accuracy with competitive speed.
A Back-Transliteration Based Manipuri Meetei Mayek Keyboard IME

Meetei Mayek is a recently revived script for Manipuri language/Meeteilon. The script was added to the Unicode Standard in 2009 only and has very scarce openly available digital tools. An Input Method Editor (IME) is a software that enables users to enter text in languages that are difficult to do with a standard QWERTY keyboard. The proposed and developed Meetei Mayek Keyboard is an IME to type Unicode compliant Meetei Mayek texts. The underlying core architecture of the input method is based on the phonological Back-Transliteration based mapping of Meetei Mayek alphabets to Latin keys of QWERTY. The Unigram frequency distribution of Meetei Mayek letters over a corpus of 10 Million words is used for secondary enhancement and layout performance testing purposes. The resulting software is an IME for Meetei Mayek with easy setup and use by common mass for general Meetei Mayek text input purposes.
Breast Mass Classification with Deep Transfer Feature Extractor Model and Random Forest Classifier

Breast Cancer is the most common type of cancer & leading cause of deaths in women worldwide. Early diagnosis of breast cancer and proper treatment play a vital role in death rate reduction. The success of Deep Convolutional Neural Networks (CNN) models in image classification tasks with state of art level accuracy has always attracted researchers to use them for disease diagnosis in the field of medical imaging. The proposed method uses CNN based fixed feature extraction technique, a type of deep transfer learning approach to perform binary classification of breast masses using Mammography Images. Mammography images are obtained from three publicly available datasets namely Mammographic Image Analysis Society (MIAS), Digital Database for Screening Mammography (DDSM) and Inbreast. The pretrained CNN models: VGG16, VGG19 & Resnet-50 performs feature extraction from the mammography images. The extracted features from CNN models are then classified into malignant & benign masses using Random Forest machine learning classifier. The models performances have been summarized with performance matrices (Sensitivity, Specificity, F1-score, and Accuracy), onfusion matrices and ROC (Receiver Operating Characteristics) curves. The combination of pretrained models, VGG16/VGG19/Resnet-50 & RF classifier gave model accuracies of: 0.81%, 0.80%,0.83% for the MIAS dataset, 0.994%, 0.986%, 0.996% for DDSM Dataset and 0.83%, 0.81%,0.87% for Inbreast dataset respectively. Automated classification of breast mass from the mammography images can be used by the doctors as a quick and efficient method for breast cancer screening.
Application of Decision Tree ID3 Algorithm in Tax Policy Document Recognition

In recent years, the tax system has vigorously promoted office automation, and more and more data needs to be processed. This puts forward higher requirements for the classification of massive data and the output of tax knowledge, but the current tax system is still in the simple data search stage. The processing of official documents in the taxation system is the core of office automation and the main method for issuing tax policies. Since the operation of the tax system, a large amount of official document data has been generated, which contains unknown and possibly useful tax information. Taxpayers must keep abreast of and implement the latest tax policies through formal document processing. Therefore, how to quickly and effectively identify tax policies is a major challenge facing current tax information. This article explores and studies the decision tree ID3 algorithm in the recognition of tax policy official documents. It has a general understanding of the text recognition process on the basis of literature data, and then proposes the application advantages of the decision tree ID3 algorithm on the basis of it, which provides the following experiments. The idea is that according to the tax policy document recognition experiment based on the decision tree ID3 algorithm, the actual tax policy samples are 43, and the decision tree ID3 algorithm has identified 41, with an accuracy of 97%. In order to further verify the superiority of the algorithm, on the basis of comparison with the other two algorithms, the experimental results show that the classification accuracy of WNB, SWNB and decision tree ID3 are compared. It is not difficult to find that the classification of official documents in the field. Decision tree ID3 has achieved good classification results, and its performance is significantly better than WNB classification algorithm and SWNB classification algorithm.
Enhanced Neuro-Fuzzy-Based Crop Ontology for Effective Information Retrieval

Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers. Familiarizing ontology as information retrieval (IR) aids in augmenting the searching effects of user-required relevant information. The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet, mapping the connection between keywords and information, and categorizing the retrieval outcomes. The prevailing procedures for IR consume considerable time, and they could not recover information proficiently. In this study, through applying a modified neuro-fuzzy algorithm (MNFA), the IR time is mitigated, and the retrieval accuracy is enhanced for trouncing the above-stated downsides. The proposed method encompasses three phases: i) development of a crop ontology, ii) implementation of the IR system, and iii) processing of user query. In the initial phase, a crop ontology is developed and evaluated by gathering crop information. In the next phase, a hash tree is constructed using closed frequent patterns (CFPs), and MNFA is used to train the database. In the last phase, for a specified user query, CFP is calculated, and similarity assessment results are retrieved using the database. The performance of the proposed system is measured and compared with that of existing techniques. Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.
Progressive Diversified Augmentation for General Robustness of DNNs: A Unified Approach

Adversarial images are imperceptible perturbations to mislead deep neural networks (DNNs), which have attracted great attention in recent years. Although several defense strategies achieved encouraging robustness against adversarial samples, most of them still failed to consider the robustness on common corruptions (e.g. noise, blur, and weather/digital effects). To address this problem, we propose a simple yet effective method, named Progressive Diversified Augmentation (PDA), which improves the robustness of DNNs by progressively injecting diverse adversarial noises during training. In other words, DNNs trained with PDA achieve better general robustness against both adversarial attacks and common corruptions than other strategies. In addition, PDA also enjoys the advantages of spending less training time and keeping high standard accuracy on clean examples. Further, we theoretically prove that PDA can control the perturbation bound and guarantee better robustness. Extensive results on CIFAR-10, SVHN, ImageNet, CIFAR-10-C and ImageNet-C have demonstrated that PDA comprehensively outperforms its counterparts on the robustness against adversarial examples and common corruptions as well as clean images. More experiments on the frequency-based perturbations and visualized gradients further prove that PDA achieves general robustness and is more aligned with the human visual system.
Assigned Values in the <i>GeoPT</i> Proficiency Testing Scheme

Geo PT , the proficiency test organised by the International Association of Geoanalysts (IAG), provides z ‐scores that indicate a participant’s performance on a scale expressing fitness for purpose. Analytical results are converted into z ‐scores by using an “assigned value” and a standard deviation set by the IAG as a criterion of fitness for purpose. The assigned value is a consensus derived from the participants’ results themselves, intended to be the best possible estimate of the true value that can be derived from the results and methodological information reported by the participants. In some instances where no assigned value is produced, a “provisional value” is made available. This paper discusses the different statistical ways in which an assigned value can be determined and concludes that as well as the judicious use of statistics, demonstrable expertise in the analytical chemistry and a detailed knowledge of the composition of the test materials are required in the assessment of assigned values.
An accurate and robust registration framework based on outlier removal and feature point adjustment for remote sensing images

The reliability of feature matching can decide the accuracy and robustness of the feature-based registration result. Aiming at the problem that the number of final feature matches preserved by many popular outlier removal methods is small, and the position accuracy of final feature matches is not high enough, we propose an accurate and robust image registration framework based on outlier removal and feature point adjustment in this paper. This framework increases the number and improves the position accuracy of inliers while eliminating most outliers. The increased number of inliers improves the robustness of image registration, and high accurate inliers improves the accuracy of image registration. Firstly, the initial feature matches are extracted by a commonly used feature-based registration method, such as the scale-invariant feature transform (SIFT)-based method. Then, outliers of the initial feature matches are eliminated by a frequency domain similarity measure, called PHase-based Structural SIMilarity (PH-SSIM) proposed in this paper. Considering the inherent error of the feature matches that still exist after the outlier elimination, a PH-SSIM-based feature point adjustment strategy is designed to fine-adjust the position of the preserved feature points in the reference image. Finally, the registration parameters are calculated by the fine-adjusted feature matches. The proposed framework has been evaluated by several remote sensing images with different resolution, grey-scale, texture, and scene, and compared with four state-of-the-art image registration methods. Experimental result demonstrates the high accuracy and robustness of the proposed method.
Evaluation Framework for Public Interactive Installations

This paper documents the development of a framework for the evaluation of interactive installations in a public setting through a small scope literature survey. The aim of this is to outline the most generally used methods and metrics through which these types of implementations are evaluated and, following this, create a framework that will allow this researchers of this blossoming field to more easily prepare and specify an evaluation for the artifact in question.
Research on Short Text Similarity Calculation Method for Power Intelligent Question Answering

With the development of artificial intelligence, the question answering system has penetrated into various industries and has become an important production factor. In the electricity field, problems such as the diversification of power equipment failures and the complicated terminology of the power industry are challenging the traditional power question answering system solutions. Therefore, it is of great significance to construct a question answering system based on the knowledge base in the electricity field. However, there are two problems to be solved in the question answering system in this field: (1) How to accurately segment the vocabulary (2) How to effectively match the sentence similarity. To solve the above problems, this paper proposes an algorithm model of cosine similarity combined with TF-IDF. First, add a custom electricity power dictionary in the word segmentation stage, secondly use the space vector model (VSM)-based TD-IDF algorithm for vectorization, and finally, use cosine similarity degree to perform similarity comparison. This method is verified on the electricity power question answering data set, and compared with the LDA model, TF -IDF algorithm and LSI model respectively. The experimental results show that the accuracy of the method proposed in this paper reaches 75.8%, which is significantly better than the other three. It proves that the research model can accurately match user questions, effectively reduce labor costs, and help electric power workers better solve the problems encountered in their work.
Short-term Power Prediction using ANN

An accurate prediction of solar energy becomes imperative for the planning and optimization of solar-based energy systems. The present research involves the implementation of Artificial Neural Network (ANN) models employing a cascade forward backpropagation algorithm for predicting short-term PV power using meteorological parameters based on distinct weather conditions. Prediction of solar energy during clear weather is easily done; however, the challenge lies in prediction under cloudy weather conditions. Therefore, the present work involves the prediction of power in solar PV systems for clear, hazy, partly and fully cloudy weather in composite climatic zone. Models are developed by simulating in MATLAB platform and for validating the accuracy of the results, statistical evaluation indices are used. The model can be used easily for predicting power for the preliminary design of solar-based applications.
Video Frame Deletion Detection using Correlation Coefficients

In this paper, we propose feature-based machine learning models for detecting frame deletion tampering in videos. The work investigates inconsistency in correlations between adjacent frames that occurs when frames are dropped from a continuous sequence. As a result, the correlation pattern of the original and counterfeit videos differs slightly. Three machine learning models namely Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolution Neural Network (CNN) have been implemented to predict the authenticity of video shots. Experiments have been conducted on a large dataset of 600 videos each of 25-frame deletion and 100-frame deletion. The results show that the CNN model can classify between authentic and forged sequences more accurately than SVM and MLP with the highest accuracy of 97% for 100-frame deletion.
World Class Hutti Gold Deposit—An Archean Orogenic Gold Deposit in Hutti-Maski Greenstone Belt, Karnataka, India

Archean orogenic gold deposits are widespread in the Dharwar Craton and account for almost 80% of cumulative Indian gold production. The Dharwar Craton hosts significant Gold prospects and the Hutti Gold deposit stand as the only Gold producing Mine. A close examination of the geological characteristics of world class gold deposit reveals many common features: (i) strong structural control of orebodies, (ii) relative timing of gold mineralization with respect to peak metamorphism, (iii) consistent metal association and (iv) broadly uniform, low-moderate salinity, mixed aqueous-carbonic hydrothermal fluid. In the past 15 years, however, detailed investigations reveal that the world class deposits in the Dharwar Craton display a diverse range of depositional site characteristics (host lithologies, structural setting, mineralization styles), at both the camp- and deposit-scales, and a range in relative- and absolute-timings. This contribution explores the variability in deposit characteristics, and its implications for generic genetic models for this deposit-class. Gold mineralization is confined to sub-parallel reefs hosted within sheared metabasalts. Mining is in progress in six out of nine reefs with a metallurgical plant having capacity of processing 2500 tons of ore per day. The results of exploration, research and existing geological knowledge together indicate further persistence of gold mineralization to depth and strike.
A Graph Convolutional Neural Network Based Approach for Traffic Monitoring Using Augmented Detections with Optical Flow

This paper presents a novel method for Multi-Object Tracking (MOT) using Graph Convolutional Neural Network based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the “context” information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of the proposed framework is the use of the Sinkhorn algorithm for end-to-end learning of the associations among objects during model training. The network is trained to predict object associations by taking into account constraints specific to the MOT task. To increase the detector's sensitivity, a new approach is also presented that propagates previous frame detections into each new frame using optical flow. These are treated as added object proposals which are then classified as objects. A new traffic monitoring dataset is additionally provided, which includes naturalistic video footage from current infrastructure cameras in Virginia Beach City. Experimental evaluation demonstrates the efficacy of the proposed approaches on the provided dataset and the popular MOT Challenge Benchmark.
Extreme Learning Machine-Based Age-Invariant Face Recognition With Deep Convolutional Descriptors

The principal intention of this paper is to study face recognition across age progression at two levels: feature extraction and classification. In other words, this work aims to prove the benefit of replacing the Softmax layer of the Deep-Convolutional Neural Networks (CNN) by Extreme Learning Machine (ELM) classifier based on deep features computed from fully-connected layer of pre-trained AlexNet CNN model, in a context of age-invariant face recognition. Experimental results indicate that the ELM classifier combined with feature extracted by the pre-trained AlexNet CNN model worked effectively for face recognition across age progression. As significant highest mean accuracy rates are always obtained using ELM classifier. These results are more significant, following a 95% confidence level hypothesis test.
Role of Machine Learning in Software Project Management

Abstract Monitoring and control project work is both critical and challenging. The greatest asset of any organization is its human resource that ensures the success to achieve the goals and objectives of the company. Managers have to manage and organize the team to enhance productivity. The effective human resource enables their employees to contribute effectively and achieve desired growth of an organization. The assignment of role to a process depends on the skills and competence of an individual. If these details of an individual are known to the manager, then it will an added point to him while assigning a new task. It is observed from the previous study that managers spend a lot of time in allocation which is performed according to the experts experience rather than following some specific criteria which lead to inefficient utilization of the resource available, so if a model is developed and trained using machine learning algorithms to handle this routine task so that the manager can spend time in other developmental activities rather than spending time on routine tasks.
Aspect Based Sentiment Analysis – An Incremental Model Learning Approach Using LSTM-RNN

Analysing user opinions have become a necessity in order to understand customer satisfaction and requirements. Sentiment Analysis is the process where framework is capable of analysing and predicting sentiments of a sentence. Several researchers have attempted to perform sentimental analysis with various classification techniques especially machine learning techniques, however still issues faced with respect to analysis of data and decision-making process. Hence, this work attempts to improve the accuracy of classification process by implementing an incremental based novel framework. In this work, sentiments of restaurant reviews are processed and this will help in identifying satisfaction level of customers automatically. Artificial intelligence is used by implementing Recurrent Neural Network and Long Short-Term Memory. The hybrid LSTM-RNN is capable of analysing and predicting polarity of aspects with maximum possible accuracy. Various transformations and pre-processing techniques are applied for cleaning text-data as per required input to classifier. From reviews, lots of different aspects are obtained and trained to the LTSM-RNN classifier. When latest reviews are fed into the classifier, sentiments of each aspects are identified as either positive, negative or neutral aspects. The obtained accuracy is seen to be around 76% which is comparable with existing algorithms. Apart from good accuracy, major outcome is - multiple aspects are correctly extracted from multi-sentence lengthy reviews.
A formal representation of the divine comedy’s primary sources: The Hypermedia Dante Network ontology

Abstract Hypermedia Dante Network (HDN) is a 3-year Italian National Research Project, started in 2020, which aims to enrich the functionalities of the DanteSources Digital Library to efficiently represent knowledge about the primary sources of Dante’s Comedy. DanteSources allows users to retrieve and visualize the list and the distribution of Dante's primary sources that have been identified by recent commentaries of five of Dante’s minor works (i.e. Vita nova, De vulgari eloquentia, Convivio, De Monarchia, and Rime). The digital library is based on a formal ontology expressed in Resource Description Framework Schema (RDFS) language. Based on the DanteSources experience, the HDN project aims to formally represent the primary sources of the Divine Comedy whose identification is based on several commentaries included in the Dartmouth Dante Project corpus. To reach this goal, we restructured and extended the DanteSources ontology to provide a wider and more complete representation of the knowledge concerning the primary sources of the Comedy. In this article, we present the result of this effort, i.e. the HDN ontology. The ontology is expressed in OWL and has as reference ontologies the CIDOC CRM and its extension FRBRoo, including its in-progress reformulation LRMoo. We also briefly describe the semi-automatic tool that will be used by the scholars to populate the ontology.