Reviewerly/exHarmony huggingface dataset:
A new deep model based on part pooling for medical image classification

This paper proposes an effort to improve the discriminative ability of deep learning model for medical image classification. We formulate this problem as a fine-grained visual categorization task and introduce a deep neural network with part-level features which are trained by independent loss functions. The experiment is conduct on two open-source benchmark dataset. The accuracy and stability of the present model in classification prediction are tested via various metrics, such as accuracy, precision, recall, and Fl-score. Moreover, the learned feature is also visualized via dimensionality reduction technique. It is shown that the proposed network architecture is effective for improving model's performance for medical image classification, and the part-level feature efficiently enriches the granularity of feature increasing the discriminative ability.
Improving the Diagnosis of Breast Cancer by Combining Visual and Semantic Feature Descriptors

Mammography still remains the foremost effective procedure for early diagnosis of breast cancer. To compensate with the increasing number of readings that radiologists have to undertake, Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal and normal regions of interest in mammograms faster and more effectively than human readers. In this paper, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low- and high-level mammogram descriptors in a compact quaternionic form. Our proposed method has two major stages. Initially, a feature extraction process is proposed to extract low-level visual descriptors of Region of Suspicion (ROS), that utilizes two dimensional discrete transforms based on Angular Radial Transform (ART), Shapelets as well as textural representations based on the Haralick features. To further improve our method’s performance, the semantic information of the mammogram given by the radiologists is encoded in a 16-bit length word high-level feature vector. The features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using a trained radial basis function neural network. The proposed method is evaluated on regions taken from the DDSM database. The proposed feature set achieves a mean accuracy of 94.22% (±1.594) and AUC = 0.9578 that is 33% higher than using only visual descriptors, showing that semantic information can improve the diagnosis when combined with standard visual features.
Application of hybrid model of big data and BP network on strategy prediction

Abstract Big data refers to the data set that cannot be captured, managed and processed by conventional software tools within a certain period of time. It is a massive, high growth rate and diversified information asset that needs new processing mode to have stronger decision-making power, insight and process optimization ability. BP neural network can be applied to the prediction of models and the study of the relationship between different models. Therefore, the combination of big data technology and BP neural network can deal with large and complex nonlinear structural problems from a statistical point of view, and with high stability and accuracy. This paper collects a cloud computing revenue from 2015 to 2019, and analyzes the impact of strategy mode value by using the combination of big data and BP neural network. The conclusion is that the correlation coefficient is 0.99762, which proves that big data combined with BP neural network can make accurate and effective decision prediction.
“zones of flow (iii)”: a Paper and Light-based Immersive Water Environment

“zones of flow (iii)’’ is a photo-sensitive installation that investigates the fluid and changing connections between people, sea and land. It addresses the instantaneous but sometimes asynchronous connectivity between things as they move, are placed or displaced in their environments. The artwork brings origami structures, electronics and audiovisual media together to create an immersive cogitative environment. A video projection depicting water surfaces is mapped onto a 2.5m paper-boat. Underneath, arrays of light sensors receive waves of information and send signals to small paper-boats scattered across the floor. As the flotilla of small paper-boats is activated it becomes a changing water surface where non-linear patterns emerge over time.
Ontological Categories for Geo-Ontologies

Despite their recent development (see Chap. 6 ), geo-ontologiesGeo-ontologies still represent a complicated conundrum for most experts involved in their design. IT/computer scientists use ontologies for describing the meaning of dataData and their semanticsSemantics to make informationInformation resources built for humans also understandable for artificial agents (see Chap. 2 ). Geographers pursue conceptualizationsConceptualizations that describe the (geographical) domain of interest in a way that should be accessible, informative, and exhaustive for their final recipients (see Chaps. 2 and 6 ). In this context, philosophers/ontologists should offer conceptual solutions for carving nature at the joints and choosing how to categorize all the entities belonging to the geographical domain. This chapter aims to combine assumptionsAssumptions and requirements coming from these different areas of research, in order to show what categoriesCategories might complete the current domain of geo-ontologiesGeo-ontologies. The issue is approached by thinking of such a domain as a whole, composed of two different levelsLevels of categorizationCategorization. The first level concerns the IT componentsComponents shared among different ontologies. The second level deals with contentsContents, for which (philosophical and) geographical analysis can include categories that do not appear at the first level.
Book Cover and Content Similarity Retrieval Using Computer Vision and NLP Techniques

This paper proposes a computer vision and machine learning application for recognizing book covers and automatically retrieve similar book contents from the database. Recognizing the book covers relies on extracting the key points from the images of the book covers before they are used in a matching process. Moreover, the book description will automatically extract, retrieve, and generate a book representation using a Bag of Word (BOW) algorithm. The similarity function will use to measure the contents of the books. The experiments are conducted and evaluate the performance using the 30 real-image book covers. The output shows that the accuracy of book cover image recognition is 89.33% and from book description is 63.33%.
A Computational Tool to Study Vocal Participation of Women in UN-ITU Meetings

International organizations such as the United Nations drive policies that impact our everyday lives. Diverse representation of people and ideas in the decision making process of such bodies is critical to ensure that the policies work for everyone. One aspect of the representation is the partipants' expressed gender. In this work, we focus on analyzing meetings at the International Telecommunication Union (ITU). These meetings include a moderator who mediates the proceedings between delegates from across the world speaking in different languages. For the purpose of quantifying the participation of delegates, we propose a scalable, human-in-the-loop system to first identify the moderator's speech and estimate the speaking time with respect to gender for all the speakers. Our proposed system includes three main audio modules: speech activity detection, gender identification and moderator verification using a human-labelled speech probe. We then estimate percentage of speaking time controlled for the moderator's speech. We present detailed and multilingual performance evaluation of the component systems using state-of-the-art technologies for these tasks. Finally, we examine the vocal participation of female delegates in the 2018 ITU Plenipotentiary Conference spanning for 18 days and about 108 hours of audio recordings.
Narrating Images and Text through “NAYAN”

The Fourth Industrial Revolution is the fusion of artificial intelligence, blockchain technology, nanotechnology, robotics, drone technology, biotechnology, and a host of others. It will change the way humans will live, the communication patterns, how goods and services we produce and how we consume. These technological advancements should in some or the other be beneficial to those who are facing certain disabilities which will empower them to live a life without human assistance and to perform day-to-day tasks and activities with more ease. So, to help and assist people suffering from visual impairments or complete blindness with smoothly reading through books, articles, etc. we have developed an app called “ NAYAN ”.
E-portfolio as a tool of pedagogical management in the virtual information and educational environment of the university

One of the main tasks of the modern education system is not just to transfer to students as much knowledge as possible, but rather the ability to extract it, the ability to navigate the flow of up-to-date information. The quality and reliability of the future specialist’s knowledge, his ability to solve complex issues independently, and the success of his future professional activities depend on the organization of pedagogical management in the educational activities of students. The virtual educational environment is understood as an arbitrarily constructed structure of the information environment, consisting of a set of autonomous subjects which connections are nonlinear and probabilistic. The virtual educational environment implements students’ life strategies and values in digital social interaction. The purpose of the research is to determine the features of using the pedagogical technology of creating a portfolio in the electronic form as a tool of pedagogical management in the virtual information and educational environment of the University. The objectives of the research are to analyze the components of the electronic portfolio of students and compare them with the stages of implementation of the pedagogical technology for creating a students’ portfolio. The solution of the research tasks is based on the criterion of improving the professional literacy of the university students and evaluating their achievements using e-portfolio technology.
Guiding Deep Reinforcement Learning by Modelling Expert’s Planning Behavior

Deep Reinforcement Learning algorithm has shown its strength in solving complex robot control problems. Nevertheless, the existing challenge is that random and unnecessary exploration may lead to slow convergence, which causes difficulty in the training process. In this paper, we propose a novel framework of using a small amount of demonstration to guide the exploration. Unlike previous works, we model the demonstration as a function between direction preferences and goal-state distance using Neural Network. To evaluate our work, we designed several tasks in the robot simulation platform VREP. The results indicate that our algorithm can guide the exploration and hence improve the performance of Deep Deterministic Policy Gradient(DDPG).
Predicting University Development Based on Hybrid Cognitive Maps in Combination with Dendritic Networks of Neurons

The aim of the work is to substantiate and predict measures to ensure the increase in the values of the target indicators of the university's activity in the international institutional rating QS to the values necessary to increase the rating in subsequent years. Mathematical modeling of cognitive maps is used in conjunction with selective dendritic networks of neurons. The possibility of increasing the efficiency of using cognitive maps for predicting the development of a university using selective dendritic networks of neurons is shown. The proposed structure of hybrid cognitive maps allows you to naturally simplify the structure by removing non-working network connections of the global structure. The universal matrix description of the mathematical model of the network allows you to effectively form a computational algorithm and software for predicting the indicators of the development of the university. The results obtained allowed us to form a scenario plan for the necessary step-by-step increase in the values of target indicators, taking into account the hidden factors affecting them. The possibility of forming a cognitive map based on a multilayer dendritic network of direct propagation in the presence of unidirectional content links is shown.
You Can’t Learn What’s Not There: Self Supervised Learning and the Poverty of the Stimulus

Diathesis alternation describes the property of language that individual verbs can be used in different subcategorization frames. However, seemingly similar verbs such as drizzle and spray can behave differently in terms of the alternations they can participate in (drizzle/spray water on the plant; *drizzle/spray the plant with water). By hypothesis, primary linguistic data is not sufficient to learn which verbs alternate and which do not. We tested two state-of-the-art machine learning models trained by self supervision, and found little evidence that they could learn the correct pattern of acceptability judgement in the locative alternation. This is consistent with a poverty of stimulus argument that primary linguistic data does not provide sufficient information to learn aspects of linguistic knowledge. The finding has important consequences for machine learning models trained by self supervision, since they depend on the evidence present in the raw training input.
Creative Intervention for Acrophobia Sufferers through AIVE Concept

This research applies exposure to the visual appearance technology of virtual reality (VR). The motivation for this research is to generate a creative intervention by using regular smartphone devices and implementing them in VR using Google Cardboard as a medium visual display for exposure therapy at high altitudes. The VR application in this research is called acrophobia immersive virtual exposure (AIVE), which utilizes the Unity3D software to develop this treatment therapy application. The utilization of exposure therapy was carried out as a therapeutic medium for acrophobia sufferers. A commissioner was given to measure the usefulness of applications and devices in the VR environment created, and as many as 20 users had tested the VR device. The existing questionnaire was revised to develop a questionnaire for acrophobia sufferers, which was then used as an index measurement in the VR environment. The research is expected to be used to design a simulator and as a therapeutic medium using immersive VR devices in future studies.
CRU-Net: A Deep Learning Network for Semantic Segmentation of Pathological Tissue Slices

The study of cell nuclei is the starting point of modern medical pathology analysis and new drug development, and the semantic segmentation of pathological tissue slice images is a fundamental task of cell nucleus research[1]. This paper proposes a deep learning convolutional neural network for semantic segmentation of cell nuclei, where V-Net [6] is used as the basic framework for segmentation, and then the channel attention mechanism is added to its skip connections. The experiment is evaluated on the dataset of pathological tissue slice images, publicly released in the 2018 Kaggle Challenge data science bowl. The experimental results show that the improved deep learning convolutional neural network achieves excellent performance on the semantic segmentation task of pathological tissue slice images, and can be used as a tool for automatic segmentation of pathological tissue slice images.
Comparison between the NB and SVM Methods for Multiclass Arabic Sentiment Analysis

Sentiment analysis gives the ability of monitoring public opinions on different topics. It can be, for example, very helpful in business future plans. Despite the Arabic language being one of the most spoken languages, Arabic sentiment analysis researches face numerous challenges.The main objective of the present paper is to provide a study of Arabic sentiment analysis by comparing the Vector Support Machine (VSM) and Naïve Baye (NB) approaches as a function of dataset classes.
Removal of Rain Streaks in Air Using GAN

Rain streaks in the air can be harmful for the image quality, which makes photograph blur and unaesthetic. Pictures with rain streaks are also bad resource for target detection and recognition. Therefore, we promote a method to remove rain streaks based on generative adversarial networks. We find that the rain streaks exit mainly in high-frequency component of a picture. Therefore, our work focuses on the part contains the rain streaks and avoid affecting details and color information of the picture. We also introduced a quantization method to improve network to make it thinner. Based on experiment, rains in the picture are removed effectively. Images after processing are cleaner than the original ones.
Labeled k-partite Graph for Statement Annotation in the Web of Data

The Web of Data introduces issues in storing, representing, managing, and querying the massive amount of data with additional information i.e., meta-knowledge. These issues exist because when introducing meta-knowledge the graph size, the number of statements, and the query response time increase, and some redundancies are created. We propose a new approach called Labeled k-partite Graph data model (LKG) to overcome some of these issues. Our approach uses MSPARQL as a query language, an extension of SPARQL. Experiments were conducted using the SPARQL Performance Benchmark (SP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Bench) dataset (without meta-knowledge) and, the Bio-medical Knowledge Repository (BKR) dataset and Gov-track dataset (with meta-knowledge). For the former dataset, the LKG approach is compared with the RDF directed labeled graph, and for the latter, LKG is compared with the state-of-the-art approaches Singleton Property, RDF Reification, Named Graph and PaCE. The experiments analyzed the meta-knowledge representation in terms of number of edges, number of statements, storage space and redundancy creation. The results show that the LKG model performs better in terms of query response time, query length, and in terms of meta-knowledge representation, which gives an advantage over storage management, graph data management and gives also a faster information retrieval.
R tutorial: EDA in R

Now that we have discussed how we can explore our data by hand, we need to recognize that the size of modern datasets makes this exploration at best tedious and usually impossible. In this chapter, we look at how R helps us generate many of these exploratory analyses and how we can improve upon the base graphics that R provides us. In many cases, the code for the functions in R can be easily adapted to fit various scenarios. Some of these include adjusting calculation functions to account for missing data or including options to change plots to better present the data.
Chinese Text Classification Based on Adversarial Training

As the most basic and important application in the field of natural language processing, text classification has always been a hot research topic in the field of natural language processing. So far, a large number of excellent models have emerged, including SVM, TextCNN, TextRNN, DPCNN, Transformer, etc. Higher accuracy has always been the goal pursued by text classification algorithms. Therefore, this paper constructs a text classification algorithm based on adversarial training. By adding the adversarial disturbance to the word embedding layer, and then using the adversarial samples and the original samples to train the model together, the model's ability to correctly classify adversarial samples is improved, and the robustness of the model is improved, so as to improve the classification accuracy and improve the classification effect of the classification model, and it is proved on three benchmark data sets that this method can effectively improve the accuracy of the text classification model.
Portable Hybrid e-Learning Box for Agriculture to Support Farmer in Rural Areas: What2Grow Learning Box

This paper presents the integration of Android box and Kodi application to help people in rural areas learn agricultural content To be used for the benefit of farmers' cultivation. The content can be updated-when connected to the internet and can be used even without internet It also helps administrators to be aware of the usage of viewing content to help develop content suitable for users in each area.
Addressing the Constraints of Active Learning on the Edge

The design of machine learning methodology often does not take into account the limitations of edge computing. In particular, active learning approaches have not considered the constraints of the edge, such as separate data locations (labeled data is on the cloud whereas unlabeled data is on the edge), cold starting or low initial model performance, limited budget sizes due to bandwidth constraints, and computational constraints due to edge hardware. Active learning on the edge could help decide what data to cache on the edge and what data to prioritize for offloading, facilitating efficient use of memory and bandwidth resources. Active learning on the edge would also allow for a machine learning model to be trained using a minimal amount of data. In this work, we examine the constraints of performing active learning on the edge, propose an active learning method that seeks to address these constraints, and discuss advances needed at large to improve active learning on the edge.
Privacy-Preserving Cryptographic Model for Big Data Analytics

The raw data dissemination and publication are very important elements in applications like commercial, academic, medical, etc. Due to the rapid increase in large amount of open technical platforms like social networks, IOT devices, electronics gazettes, and mobile devices, the volume of data has grown over time. The very large data sets that are collected from these platforms have any size and structure. Such characteristics of data add more challenges in extracting results or applying further processing by analyzing and storing. To extract the meaningful knowledge or hidden pattern, big data analytics is used from this massive scale complex data. In both unstructured and structured data, the sensitivity does not be captured by the big data traditional model. Security of big data plays an important role due to the speed of ingesting and processing of data in big data analytics. A small change in data may lead to many complexities to user data. The privacy and security of big data analytics contradict with the extensive use of big data. There is a need of stronger encryption in order to secure user data. Thus, it is a time of need to improve security at several levels for privacy preservation of data. This chapter is concerned with security and privacy in big data. The aim is to encrypt data by developing multiple level of encrypting techniques. The private or personal data may be contained in big data which should not be leaked out. Knowledge mining in big data keeping the privacy measures is a major concern of data. A strong encryption model in form of multilevel is proposed for privacy preservation to the data in big data analytics. For wide applications, cryptography technique plays an important role in providing privacy to sensitive attributes in big data.