Reviewerly/exHarmony huggingface dataset:
Extensions

This chapter discusses a number of extensions and variations of the models and techniques discussed in the previous chapters. First, in Sect. 7.1, we discuss higher-order hidden Markov models, in which the current state depends not only on the immediately preceding states, but other states further back in time as well. In Sect. 7.2, we then focus on models which assume that underlying each observation is not a single state, but multiple simultaneous states. For both higher-order HMMs, and HMMs with multiple simultaneous states, we discuss how these models can be transformed into a standard first-order HMM, by redefining the states. Section 7.3 discusses how to deal with practical estimation issues, such as those illustrated in Sect. 3.3 . The final part of this chapter discusses alternatives to maximum likelihood estimation. Section 7.4 introduces what can be called the “classification likelihood” approach to estimating mixture and hidden Markov models. Here, each observation is assigned uniquely to a single state (a “hard” classification), rather than allowing for uncertainty about the underlying state (“soft” classification). If the objective is purely to classify observations, this approach can be reasonable and will often converge more quickly than an EM algorithm using the standard likelihood. Finally, Sect. 7.5 touches on Bayesian estimation as another alternative to maximum likelihood estimation.
Carlin, Witwatersrand, and Some Other Gold-only Examples

Goldfields in greenstone belts and metasedimentary rocks have been used to build models for the origin of gold-only deposits, but these two types are only a sub-section of gold-only deposits globally. It is now possible to place the important Carlin goldfields of Northern Nevada USA and the gold porphyry and low-sulfidation deposits into a similar context of thermal events, deformation, and hydrothermal fluids. In addition, some initial observations about the Witwatersrand goldfields of South Africa illustrate their obvious differences but also similarities. In superficial appearance each grouping of deposits is quite different, but all share most of the five characteristics of gold-only deposits already described.
Explaining CNN Classifications by Propositional Rules Generated from DCT Feature Maps

So far, propositional rules have been extracted from Multi Layer Perceptrons to explain how they achieve to classify data. This type of explanation technique is much less prevalent with deep models, such as convolutional neural networks (CNNs). In this work, we propose to transfer the feature maps generated by a CNN to a simpler neural network model from which propositional rules have been generated. To enable the execution of our rule extraction algorithm in a reasonable time, the Discrete Cosine Transform (DCT) was applied to the feature maps retaining a small number of low spatial frequencies. As a proof of concept, this technique was applied to the MNIST benchmark problem. In the frequency domain, for the 60000 training samples, about 1860 unordered rules were generated. The predictive accuracy of the rules was very close to that of a trained CNN, but with about 3% of the testing samples without explanation. In another series of experiments, a slightly lower predictive accuracy was obtained, but with explanations in the form of propositional rules for 99.99% of the testing samples. Finally, we illustrated an example of a propositional rule, for which each antecedent in the frequency domain implies in its variation an incidence on several pixels in the luminosity domain.
Improved Regularization of Convolutional Neural Networks with Point Mask

Image Data are critical data for computer vision. Above all, occluded Image Data is still challenging. This paper, introduces Point Mask, a new data augmentation method for training the convolutional neural network (CNN). In training, Point Mask selects some corners region in an image and erases its pixels with zero values. In this process, training images with various places of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Point Mask is extremely easy to implement and can be integrated with most CNN-based recognition models. Point Mask is very effective and consistently improves over solid baselines in image classification, object detection, and person re-identification. The extensive experiments have demonstrated the new method's the generality and effectiveness.
A supervised and unsupervised image quality assessment framework in real-time

In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to process a large number of images in real-time while estimating the No-Reference image quality. Our quality evaluation is measured using a combined neural network: 1) A Convolutional Neural Network (CNN) representing the supervised network, and 2) An unsupervised network. In addition, a face alignment anomaly detection metric is used to assess the facial features. Experimental results on the LIVE dataset show that our proposed method outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Coefficient (SROCC) with subjective human scores of almost 0.977 and 0.973. These results are further confirmed also on the TID2013 dataset.
Learning Scale-Invariant Object Representations with a Single-Shot Convolutional Generative Model

Contemporary machine learning literature highlights learning object-centric image representations' benefits, i.e. interpretability, and the improved generalization performance. In the current work, we develop a neural network architecture that effectively addresses the task of multi-object representation learning in scenes containing multiple objects of varying types and sizes. In particular, we combine SPAIR and SPACE ideas, which do not scale well to such complex images, and blend them with recent developments in single-shot object detection. The method overcomes the limitations of fixed-scale glimpses' processing by learning representations using a feature pyramid-based approach, allowing more feasible parallelization than all other state-of-the-art methods. Moreover, the method can focus on learning representations of only a selected subset of types of objects coexisting in scenes. Through a series of experiments, we demonstrate the superior performance of our architecture over SPAIR and SPACE, especially in terms of latent representation and inferring on images with objects of varying sizes.
Modular Information Fusion Model of Landscape Design Based on Genetic Algorithm

With the acceleration of China’s urbanization process, urban landscape design has also ushered in a new era, and it is an important link in the modular information management of landscape. This paper makes a comprehensive analysis and research based on genetic algorithm and ant colony theory. Firstly, it expounds the relevant contents of this subject and the development status at home and abroad. Then the concept, characteristics and functions of landscape design are introduced, and then the application of genetic algorithm is studied. Based on genetic algorithm, a modular information fusion model of landscape is designed, and the performance of the model is simulated. Finally, the experimental results show that the informatization degree of each module of the landscape design is relatively high, and the soft landscape and hard landscape are excellent in data fusion, data cleaning, data fusion and data synchronization. The full score of the experimental performance is 5, and the two main modules are more than 4, which shows that the performance of the landscape design module based on genetic algorithm is better, which can meet the needs.
Regularizing Deep Text Models by Encouraging Competition

The difficulty in acquiring a large amount of labelled training data and the demand of complex neural network models in text learning make developing effective regularization techniques an important research topic. In this paper, we present a novel regularization scheme for supervised text learning, Competitive Word Dropout, or CWD. Experiments on three different natural language learning tasks demonstrate that CWD outperforms significantly the standard regularization schemes such as weight decay and dropout. The CWD scheme has another unique advantage, namely that it can be interpreted semantically.
Marlo’s Networks of Expectations

Marlo’s networks of expectation have been developed from a heterogeneous reasoning perspective, allowing a perfect integration of visual, linguistic, chromatic, and numerical information. These networks are logical tree diagrams whose structure consists of nested sets formed by three types of logical nodes: Objects, Or, And. Any node can receive a numerical value that goes from −1 to +1 passing through zero, which can be translated to a chromatic value between red and blue, passing through yellow. These values correspond to natural language expressions: false, probably false, uncertain, probably true, true, or even absurd. That is, the network can be interpreted both through natural language propositions and through rigorous and precise mathematical formulas. In any case, we must bear in mind that cognitive systems, to generate adaptive expectations about resources and threats, have to encode both the qualities of the stimuli and the correlations of their presences (+1) and absences (−1). As a result, we have built a system in which any first-order logical inference can be represented using a very limited number of logic gates. Deductive, inductive, abductive, and even statistical inferences can be explained by the activation and inhibition relationships between the nodes of the sets that we propose here.
Sentiment Analysis By Using Modified RNN And A Tree LSTM

In recent times, there has been an exponential growth in the number of complex documents, texts, and user-generated content, mainly through social media, blogs and review websites, etc., that require a deeper understanding of deep learning methods to be able to accurately classify texts. This research work approaches problems of fine-grained sentiment analysis. We explore how the sentiment of the user-generated text can be identified using Deep Learning. This paper contributes sentiment analysis, which aims to extract opinions from the text. E-commerce, social media, and other many online user-generated contents provide a powerful way for collecting people&#x2019;s thoughts and their feelings towards users or customers. Powerful sentiment analysis helps many businesses with regard to sentiment, so they will understand users&#x2019; perceptions of the product. It&#x2019;s also very helpful for other customers to get reviews of products and get to know about services they offer. Lots of websites provide users with reviews but it&#x2019;s very difficult to make some decisions from them. Deep Learning sentiment helps to identify the pros and cons of a product. Nowadays Automated sentiment analysis systems are tools for decision-making for many parties. The deep learning algorithms are able to handle complex sequential data and determine non-linear relationships within data. Recurrent Neural Networks (RNN) are widely used in natural language processing (NLP) because they are very suitable to process variable-length text. Doing Fine-grained sentiment analysis is a challenging task. We use a modified Recurrent Neural Network (Tree-LSTM) for fine-grained analysis. In this work, we provide a brief overview of deep learning classification algorithms and also conclude that Tree-LSTM gives state-of-the-art accuracy for fine-grained sentiment analysis.
Analysis on Influencing Factors of Happiness of Rural Empty Nest Elderly Based on Health Internet of Things Data Mining

In recent years, with the attention of our government and academia to the quality of life of residents, the problem of well-being has also attracted the attention of many domestic researchers. Happiness is an important indicator of social and individual quality of life, which has been studied in the West for more than half a century. The overall process of health IOT data processing is to receive the massive heterogeneous data from the intelligent collection terminal of health IOT, store and filter the data in a distributed manner, and then conduct distributed data mining. This study investigated the influencing factors of happiness of rural empty nesters.
Virtual shoe test system

In order to solve the problem of uncomfortable wearing shoes caused by foot type or shoe type of consumers, a virtual shoe test system is developed based on computer graphics knowledge. If the online shoe test has problems, it can provide personal customization for consumers. Firstly, the shoe database is constructed to store shoe last model and key parts data for data comparison and fast data query. Secondly, the method based on OBB and AABB bounding box and plane fitting is proposed. Based on the foot bottom surface, combined with the foot shape law and foot skeleton composition of Chinese people, the automatic extraction of key data and the automatic matching of foot and shoe last are realized. Finally, the OpenGL function is realized in the MFC program, and the 3D visualization of shoes and boots is realized. The mouse control function is added to the system, which improves the human-computer interaction experience.
Hand Gesture Recognition for User Interaction in Augmented Reality (AR) Experience

Augmented Reality (AR) has gained a lot of attraction in the recent past. Arguably, the most important tool that can make AR a household gadget is its interaction with the user. This may lead to two possible interaction methodologies: (i) Using an extra device for interaction; (ii) Using human hands as interaction. Former is probably the easy method, but it may increase the cost of the AR device, limiting its target users. Therefore, hand gestures are a feasible and efficient mode of interaction. However, for accurate and pleasant interaction, the AR device should be capable of hand gesture understanding. In this direction, we propose a hand gesture classification method, based on Convolutional Neural Networks (CNNs) that takes advantage of the pre-trained network weights for faster and efficient training, which also helps improve the quality of gesture classification. Moreover, the proposed approach takes advantage of hand detection for background elimination and efficient gesture recognition. The proposed approach is evaluated on the Hand gesture classification task for three datasets that differ in terms of the number of data samples, amount of gestures, and data quality. The obtained results show that our method outperforms state-of-the-art methods in most of the experimentation cases.
Metamorphic Testing of Image Classification and Consistency Analysis Using Clustering

Testing deep learning systems requires expensive labeled data. In recent years, researchers began to leverage metamorphic testing to address this issue. However, metamorphic relations on image data remain poorly understood. To gain a deeper understanding of these metamorphic relations, we survey common image operations modeling covariate shift, manually classify and categorize the underlying metamorphic relations, and conduct experiments to validate our classifications. In our experiments, we train three popular convolutional neural network architectures on an image classification task. Next, we apply metamorphic operations on input test images and measure the change in classification accuracy and cross-entropy loss. A hierarchical clustering algorithm cluster these results and plots a dendrogram. We compare the groups from manual classification and the clusters from the algorithm to provide key insights. We find that Affine and Noise relations are consistent. Furthermore, we recommend metamorphic relationships to save time and better test deep learning systems in the future.
National Language Statistical System Based on Fuzzy Cluster Analysis Algorithm

National language is the carrier and tool to inherit and carry forward Chinese culture and civilization and carry out interpersonal communication in social life. Minority languages are the main way to distinguish ethnic minorities and the basis for the protection of minority language rights. This paper analyzes the fuzzy clustering analysis algorithm and the concept of dynamic fuzzy set, discusses the average speech model and natural language processing, and studies the learning methods of Uygur language. The results show that the respondents basically participate in Uighur language learning, and their learning styles are also different.
Tablext: A Combined Neural Network and Heuristic Based Table Extractor

A significant portion of the data available today is found within tables. Therefore, it is necessary to use automated table extraction to obtain thorough results when data-mining. Today's popular state-of-the-art methods for table extraction struggle to adequately extract tables with machine-readable text and structural data. Moreover, many tables do not have machine-readable data, such as tables saved as images, making most extraction methods completely ineffective. To address these issues, a novel, general format table extractor tool, Tablext, is proposed. This tool uses a combination of computer vision techniques and machine learning methods to efficientlyÂ identify and extract data from tables. Tablext begins by using a custom Convolutional Neural Network (CNN) to identify and separate all potential tables. The identification process is optimized by combining the custom CNN with the YOLO object detection network. Then, the high-level structure of each table is identified with computer vision methods. This high-level, structural meta-data is used by another CNN to identify exact cell locations. As a final step, Optical Characters Recognition (OCR) is performed on every individual cell to extract their content without needing machine-readable text. This multi-stage algorithm allows for the neural networks to focus on completing complex tasks, while letting image processing methods efficiently complete the simpler ones. This leads to the proposed approach to be general-purpose enough to handle a large batch of tables regardless of their internal encodings or their layout complexity. Additionally, it becomes accurate enough to outperform competing state-of-the-art table extractors on the ICDAR 2013 table dataset.
NER in Archival Finding Aids: Next Level

Currently, there is a vast amount of archival finding aids in Portuguese archives, however, these documents lack structure (are not annotated) making them hard to process and work with. In this way, we intend to extract and classify entities of interest, like geographical locations, people’s names, dates, etc. For this, we will use an architecture that has been revolutionizing several NLP tasks, Transformers, presenting several models in order to achieve high results. It is also intended to understand what will be the degree of improvement that this new mechanism will present in comparison with previous architectures. Can Transformer-based models replace the LSTMs in NER? We intend to answer this question along this paper.
English Scores by Distributed ID3 Algorithm of Computer Network

With the rapid development of cloud computing, big data and the Internet of Things, networked distributed storage technology will be widely developed. ID3 algorithm is to process information through a computer network system, transform a complex data set into a simple digital array, and store it in the memory. This article intends to study the effect of the algorithm on the analysis of English scores. Its purpose is to improve the accuracy of English score testing, conduct a detailed analysis of students’ English scores, and find problems. This article mainly uses the experimental method to analyze the role of the network distributed ID3 algorithm in the analysis of English scores, and analyzes the students’ English scores in detail through statistical methods. The results showed that the students’ listening and reading scores reached more than 64%, while the writing and vocabulary scores were a bit unstable. This shows that students’ English scores still need to be reinforced, and vocabulary learning is not enough.
A Search Cryptographically Significant S-Boxes with Improved DPA Resistance Based on Genetic Algorithm

Finding an S-box that is resistant against DPA attacks is an important research topic in cryptography. Until now, the notion of the transparency order including transparency order (TO) and modified transparency order (MTO) has received considerable attention and is one of the best metrics to measure the resistance of S-boxes against DPA attacks. Recently, Li et al. revisited the concepts of TO and MTO. They spotted a flaw that is overlooked and provided a revised definition (revised transparency order, RTO). There are few works on evolving cryptographically strong S-boxes with good RTO values. In fact, generating high nonlinear balanced S-boxes with low differential uniformity and improved DPA resistance is an extremely challenging problem. In this paper, we propose to use the genetic algorithm to search cryptographically significant S-boxes. Particularly, we generate an 8 $$\times $$ 8 balanced S-box, with algebraic degree of 7, nonlinearity of 112 and differential uniformity of 4, absolute indicator 32, RTO 7.5222, SNR 8.8869 (the RTO of the S-box of AES is 7.5402 and SNR is 9.6000), which improves the resistance to DPA attack.