Reviewerly/exHarmony huggingface dataset:
Cognitive learning and reasoning models applied to biomedical engineering

In this chapter we will focus on many “ prestudies and preanalysis of different Biomedical Engineering problems that need to be developed with specialized research projects applying Cognitive Learning and Reasoning (CL&R) algorithms, that can be integrated to the Proposed General Architecture framework of a Cognitive Computing Agents System (AI-CCAS) ,” with a special emphasis on “ Cognitive Learning and its relationship with the neuroscience of reasoning, proposed as Cognitive Learning–Reasoning ( CL&R ) using Cognitive Computing (CC) .” Through this book, we have studied many interactions of different “ human illnesses, diseases, and disorders ,” where “ human illness ” is defined as body damage that needs to be cured, such as infections, injuries, cell degeneration, etc.; “ human diseases ” are defined as states or reactions that must be managed, such as pain, discomfort, weakness, fatigue, etc.; and “ human disorders ” are defined as functional abnormalities that must be treated, such as physical, mental, genetic, emotional/behavioral, and functional issues. The complexity of the analysis needed is boundless, and it can only be analyzed through multidisciplinary sciences, via the interactions of fields such as “ Biomedical Engineering ,” “ Neurology ,” “ Cognitive Sciences ,” and “ Computer Science ” using tools with “ exponential technologies ” such as “ Artificial intelligence (AI) ” and others through its “continuous exponential evolution” that includes “ Machine Learning ”, “ Deep Learning ”, and “ Cognitive Computing ”, with the main purpose of obtaining useful “ AI models ” that can help to analyze human health problems. Now it is the time to apply them and many others in research challenge projects.
A Novel Conditional Knowledge Graph Representation and Construction

Many statements in the massive scientific text data are in the form of conditional sentences. Conditions are of great importance to facts. Existing conditional knowledge graphs have introduced condition triples, but ignore the latent semantic relations between fact and condition triples and the logical relationships among condition triples. To address these issues, we propose a novel conditional knowledge graph representation, which is a nested hierarchical triple. We design a new extraction strategy that employs a text hierarchy parsing module to extract the semantic relations between facts and conditions and a triple extraction module to extract fact and condition triples. Moreover, we provide a corresponding knowledge storage scheme which can store conditional knowledge. Experimental results on our constructed conditional dataset show that our model can not only capture semantic relations between fact and condition triples as well as logical relationships among condition triples, but also significantly improve the accuracy of triple extractions compared to baselines.
Smartphone Mammography for Breast Cancer Screening

In 2020 alone approximately 2.3 million women were diagnosed with breast cancer which caused over 685,000 deaths worldwide. Breast cancer affects women in developing countries more severely than in developed country such that over 60% of deaths due to breast cancer occur in developing countries. Deaths due to breast cancer can be reduced significantly if it is diagnosed at an early stage. However, in developing countries cancer is often diagnosed when it is in the advanced stage due to limited medical resources available to women, lack of awareness, financial constraints as well as cultural stigma associated with traditional screening methods. Our paper aims to provide an alternative to women that is easily available to them, affordable, safe, non-invasive and can be self-administered. We propose the use of a smartphone's inbuilt camera and flashlight for breast cancer screening before any signs or symptoms begin to appear. This is a novel approach as there is presently no device that can be used by women themselves without any supervision from a medical professional and uses a smartphone without any additional external devices for breast cancer screening. The smartphone mammography brings the screening facility to the user such that it can be used at the comfort and privacy of their homes without the need to travel long distances to hospitals or diagnostic centers. The theory of the system is that when visible light penetrates through the skin into the breast tissue, it reflects back differently in normal breast tissue as compared to tissue with anomalies. A phantom breast model, which mimics real human breast tissue, is used to develop the modality. We make use of computer vision and image processing techniques to analyze the difference between an image taken of a normal breast and that of one with irregularities in order to detect lumps in the breast tissue and also make some diagnosis on its size, density and the location.
National bibliography in the digital age: international recommendations and domestic experience

The main directions of the development of the national bibliogra-phy in the modern world, the problems of the formation of the system of the national bibliography in Russia, issues related to the current national bibliogra-phy, retrospective national bibliography are presented. The activity of the IFLA Bibliography Section on the regulation of the activity of national bibliographic agencies and the creation of national bibliographic resources is covered. The article describes the activity of the National Bibliographic Agency in Russia (Russian Book Chamber), the National Library of Russia and their role in the creation of a national bibliography in the digital age.
A Visualization and Analysis tool for VCL Auto-generation Code Framework

Using a set of code templates, the Variable Configuration Language (VCL) framework is capable of automatically generating a full running web system from definitions of entities, properties, relationships, and system definitions. Existing development environments provide limited support for the development of code auto-generation features, and configuration of the auto-generated code itself. Herein a tool is presented that supports the VCL framework that aims to reduce development cost while also facilitating efficient learning of VCL and its auto-generation framework. In this work, the V ACVIS tool is developed to encourage exploration through the hierarchical structure of the VCL framework in a visual and interactive approach. Visualizations aid developers in understanding the framework during the development process, hence enhancing the auto-generation and maintenance of the systems produced with VCL. The tool developed is further enhanced and integrated into Visual Studio IDE as an extension.
Improving human perception of GAN generated facial image synthesis by filtering the training set considering facial attributes

Data anonymization methods have been investigated as strategies to provide data privacy and to minimize prejudices against minorities. Recent works on machine learning have shown impressive results in generating new examples of images using Generative Adversarial Networks (GAN). Creating synthetic facial images that resembles a group, but no one in particular seem an interesting strategy to anonymize a face. Nevertheless, the image synthesis process is computationally expensive and requires a very large training dataset to produce a human-acceptable outcome. This paper presents an investigation on the trade-off between the required number of instances (size) and the images’ inter-variability of a training set for using Deep Convolutional Generative Adversarial Network (DCGAN) to generate human-acceptable synthetic images. We report an experiment using the Appen crowdsourcing platform to evaluate the human acceptance of the synthetic images generated by our DCGAN trained using different samples of the CelebA public dataset, taken according to facial attributes, such as cheekbones protuberance, face shape and eyebrow thickness. The results indicate that (1) Facial attributes have a significant effect on diminishing the images’ inter-variability, specially face shape; (2) smaller images’ inter-variability leads to smaller required training dataset and (3) filtering the DCGAN’s training datasets using oval face shape led to a required dataset almost 50% lesser than without filtering for generating images humanly acceptable. We believe this result may help individuals to anonymize their visual data maintaining certain characteristics but keeping their privacy at a lower computational cost.
Data representation using robust nonnegative matrix factorization for edge computing

As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.
Emotion Recognition in Conversation Using Capsule Networks and Gated Recurrent Units

Emotion recognition in conversations (ERC) task is receiving increasing attention from the academic community because of its critical role in health, education, social media, and other areas. However, previous studies have overlooked the problem of unbalanced data distribution and insufficient data in the ERC task. In this paper, we propose a Capsule Network with Gated Recurrent Unit (CapsGRU) framework for the ERC task. Both the advanced semantic information and the contextual information of utterances can be learned by the CapsGRU framework. To take advantage of the features learned from the large corpus, we also design the CapsGRU-BERT model. A series of experiments are performed on three benchmark datasets, and our model achieves competitive results over the compared baseline methods. The evaluation results show that our method can learn both the advanced semantic information and the contextual information of utterances, and it can better recognize small classes and reduce the impact of data imbalance. Our results additionally show that combining capsule network and BERT can obtain further improvements and alleviate the problem of insufficient data.
L2 Czech Annotation for Automatic Feedback on Pronunciation

Abstract In this paper, we would like to provide a brief overview of the current state of pronunciation teaching in e-learning and demonstrate a new approach to building tools for automatic feedback concerning correct pronunciation based on the most frequent or typical errors in speech production made by non-native speakers. We will illustrate this in the process of designing annotation for a sound recognition tool to provide feedback on pronunciation. At the end of the paper, we will also present how we have tried to apply this annotation to the tool, what caveats we have found and what our plans are.
A Formal Approach to Good Practices in Pseudo-Labeling for Unsupervised Domain Adaptive Re-Identification

The use of pseudo-labels prevails in order to tackle Unsupervised Domain Adaptive (UDA) Re-Identification (re-ID) with the best performance. Indeed, this family of approaches has given rise to several UDA re-ID specific frameworks, which are effective.In these works, research directions to improve Pseudo-Labeling UDA re-ID performance are varied and mostly based on intuition and experiments: refining pseudo-labels, reducing the impact of errors in pseudo-labels... It can be hard to deduce from them general good practices, which can be implemented in any Pseudo-Labeling method, to consistently improve its performance. To address this key question, a new theoretical view on Pseudo-Labeling UDA re-ID is proposed. The contributions are threefold: (i) A novel theoretical framework for Pseudo-Labeling UDA re-ID, formalized through a new general learning upper-bound on the UDA re-ID performance. (ii) General good practices for Pseudo-Labeling, directly deduced from the interpretation of the proposed theoretical framework, in order to improve the target re-ID performance. (iii) Extensive experiments on challenging person and vehicle cross-dataset re-ID tasks, showing consistent performance improvements for various state-of-the-art methods and various proposed implementations of good practices.
Distributed Orchestration of Regression Models Over Administrative Boundaries

Geospatial data collections are now available in a multiplicity of domains. The accompanying data volumes, variety, and diversity of encoding formats within these collections have all continued to grow. These data offer opportunities to extract patterns, understand phenomena, and inform decision making by fitting models to the data. To ensure accuracy and effectiveness, these models need to be constructed at geospatial extents/scopes that are aligned with the nature of decision-making — administrative boundaries such as census tracts, towns, counties, states etc. This entails construction of a large number of models and orchestrating their accompanying resource requirements (CPU, RAM and I/O) within shared computing clusters. In this study, we describe our methodology to facilitate model construction at scale by substantively alleviating resource requirements while preserving accuracy. Our benchmarks demonstrate the suitability of our methodology.
Multi-label Text Classification Optimization Model Fusing ALBERT and LDA

In the traditional multi-label text classification model, there are some problems such as insufficient representation of characteristic information and high network time complexity in feature extraction. To solve this problem, this paper proposes a deep learning of multi-label text classification model (DL-MLTC) based on CNN to modeling feature matrix fusion simultaneously from the global granularity of “word” to “topic”. Firstly, training global semantic word vector based on ALRERT model, and training LDA topic vector based on Gibbs sampling method, then merging through four fusion mechanism. Finally inputting into already processed convolution dimension reduction of CNN and completing feature extraction and classification. DL-MLTC model proposed in this paper are tested on the data sets such as CAIL2018 evaluation, the experimental results show that DL-MLTC model can optimize the modeling time complexity and speed up its training time under the precondition of better classification accuracy.
Rethinking Pooling Operation for Liver and Liver-Tumor Segmentations

Deep convolutional neural networks (DCNNs) have been widely used in medical image segmentation due to their excellent feature learning ability. In these DCNNs, the pooling operation is usually used for image down-sampling, which can gradually reduce the image resolution and thus expands the receptive field of convolution kernel. Although the pooling operation has the above advantages, it inevitably causes information loss during the down-sampling of the pooling process. This paper proposes an effective weighted pooling operation to address the problem of information loss. First, we set up a pooling window with learnable parameters, and then update these parameters during the training process. Secondly, we use weighted pooling to improve the full-scale skip connection and enhance the multi-scale feature fusion. We evaluated weighted pooling on two public benchmark datasets, the LiTS2017 and the CHAOS. The experimental results show that the proposed weighted pooling operation effectively improve network performance and improve the accuracy of liver and liver-tumor segmentation.
Super-pixel Segmentation based Skin texture pattern recognition

Super-pixel segmentation is widely used nowadays in image processing to enhance segmentation accuracy. A new detection model is proposed for skin texture pattern recognition of Leopard, Cheetah, and Jaguar. In this model, a combination of Histogram of Gradient (HOG) and superpixel segmentation is used for extracting the features and the segmentation task of the target animal. This method does not require several superpixels to be created in advance, whereas it can automatically partition the image to its content into a suitable number of superpixels without any over or under segmentation. Then, the obtained features are fed into a Support Vector Machine (SVM) classifier to classify the skin texture pattern of Leopard, Cheetah, and Jaguar. The validation is performed which shows that the classifier achieves an accuracy of 96.67 %.
Assessing the Impact of Information and Communication Technologies on Human Development: A Regional Analysis for Africa

This study examines the socio-economic effects of information and communication technologies (ICT) in Africa by region. Analysis-based data from countries in five regions of Africa show that the contribution of ICTs to socio-economic indicators varies according to the type of ICT and the region where these technologies are used. For example, Internet usage is prominent in the Middle, East, and North Africa, while telecommunication technologies are at the forefront in South, East, North, and West Africa. The study, therefore, contributes to the formation of policies according to the regional dynamics of the continent. Besides, this study argues that, contrary to the study performed by Njoh (2018) for the African continent, IC technologies can be effective on HDI with different types of ICT according to the regional dynamics of the continent. The result of this study is that using ICT alone does not adequately support development. Therefore, IC technologies should also be used in sectors such as agriculture, health, and education. Digital transformation in the agriculture sector can be a significant factor in solving the food problem facing the continent. Mobile phones and Internet use in the agricultural field can enable farmers to learn about new agricultural techniques and to use agricultural areas more efficiently. In healthcare, mobile phones and the Internet can enable these services to reach even remote regions of the continent. In education, the integration of IC technologies – especially the Internet and computers – can support human capital by ensuring that education reaches all levels of society equally and quickly. The use of IC technologies in these three sectors is the most effective way for Africa to achieve its sustainable development goals.
On the Higher-Bit Version of Approximate Inhomogeneous Short Integer Solution Problem

We explore a bitwise modification in Ajtai's one-way function. Our main contribution is to define the higher-bit approximate inhomogeneous short integer solution (ISIS) problem and prove its reduction to the ISIS problem. In this new instance, our main idea is to discard low-weighted bits to gain compactness.
Sentiment Analysis Framework Using Data Driven Approach

The Internet is a free and straightforward way to access an immense measure of crude content information that can be mined for sentiment analysis. For a long time, sentiment analysis has been used for market research, user opinion mining, recommendation systems, and analysis of people’s views on a topic. Many researchers have developed techniques for sentiment analysis, yet many complications remain. Selecting and understanding attribute patterns in a text dataset is essential to build a good model and know where this model can be used. Different text datasets have different relations between their attributes and classes. For example, let us take a dataset with totally random English texts labeled as positive or negative. We expect that extracted attributes for the positive or negative class are very heavy with general words that we consider positive or negative in everyday English use. However, if the dataset is created on a niche topic, such as an economic pandemic, we would probably see that positive and negative classes are heavy with words specific to these topics, or they may not be considered the classifier. However, we might want to give importance to those niche-specific attributes specifically. In this paper, we take five different datasets of different instance lengths. We go through some attribute selection techniques and use them under some classifiers to visualize a pattern, do sentence-level sentiment analysis, and finally extract patterns from the datasets to analyze them. There are few related works on these datasets, and our technique performs better than the existing works. This paper aims to present a method that can easily be fruitful for text mining and decent accuracy to any dataset.
Reconstruction of Chaotic Grayscale Image Encryption Based on Deep Learning

Chaotic encryption is widely used in the field of image encryption due to its initial value sensitivity, pseudo-randomness, and unpredictability of motion trajectories. In this paper, we propose to attack Lorenz chaotic encrypted system of grayscale images by the deep learning method via Residual Networks which contains skip connection and can automatically analyze the relationships of plaintext and ciphertext. After the training process to a series of input and output plaintext-ciphertext pairs, we can finally recover the image approximate to the plaintext image according to the ciphertext which is independent to the original plaintext-ciphertext pairs set. Numerical simulation verified that the result recovering from the chaotic encrypted system is very good.
Transformer with Prior Language Knowledge for Image Captioning

The Transformer architecture represents state-of-the-art in image captioning tasks. However, even the transformer uses positional encodings to encode sentences, its performance still not good enough in grammar. To improve the performance of image captioning, we present Prior Language Knowledge Transformer (PLKT)—a transformer-based model that can integrate learned a priori language knowledge for image captioning. In our proposal, when our model predicts the next word, it not only depends on the previously generated sequence but also relies on prior language knowledge. To obtain prior language knowledge, we embed a learnable memory vector inside the self-attention. Meanwhile, we use reinforcement learning to fine-tune the model in training. To prove the advancement and promising effectiveness of PLKT, we compare our approach with other recent image captioning methods in the experiments. Through objective results, our proposal increased the CIDEr score of the baseline by 0.6 points on the "Karpathy" test split when tested on COCO2014 dataset. In subjective results, our approach generated sentences is obviously better than baseline in grammar.