Reviewerly/exHarmony huggingface dataset:
Hierarchical Topic Modelling for Knowledge Graphs

Recent years have demonstrated the rise of knowledge graphs as a powerful medium for storing data, showing their utility in academia and industry alike. This in turn has motivated substantial effort into modelling knowledge graphs in ways that reveal latent structures contained within them. In this paper, we propose a non-parametric hierarchical generative model for knowledge graphs that draws inspiration from probabilistic methods used in topic modelling. Our model discovers the latent probability distributions of a knowledge graph and organizes its elements in a tree of abstract topics. In doing so, it provides a hierarchical clustering of knowledge graph subjects as well as membership distributions of predicates and entities to topics. The main draw of such an approach is that it does not require any a priori assumptions about the structure of the tree other than its depth. In addition to presenting the generative model, we introduce an efficient Gibbs sampling scheme which leverages the Multinomial-Dirichlet conjugacy to integrate out latent variables, making the posterior inference process adaptable to large datasets. We quantitatively evaluate our model on three common datasets and show that it is comparable to existing hierarchical clustering techniques. Furthermore, we present a qualitative assessment of the induced hierarchy and topics.
Introduction

Visual question answering (VQA) is a challenging task that has received increasing attention from computer vision, natural language processing and all other AI communities. Given an image and a question in natural language format, reasoning over visual elements of the image and general knowledge are required to infer the correct answer, which may be presented in different formats. In this section, we first explain the motivation behind realizing VQA, i.e., the necessity of this new task and the benefits that the artificial intelligence (AI) field can derive from it. Subsequently, we categorize the VQA problem from different perspectives, including data type and task level. Finally, we present an overview and describe the structure of this book.
Comparison of Optimization Methods in Optimization of Two Bar Structure

In this paper, the analysis of the results obtained by optimizing the two bar structure is given. The minimum mass of the structure was chosen as an objective function. The constraint placed on the structure is the permissible stress in each bar. The optimization of the two bar structure was done with four methods: nonlinear programming, genetic algorithm, particle swarm optimization and ant colony optimization. The best results were obtained by particle swarm optimization.
Improving and Assessing the Prediction Capability of Machine Learning Algorithms for Breast Cancer Diagnosis

Currently, one of the most common forms of cancer is breast cancer. In 2020, breast cancer caused 2.3 million cases and approximately 685,000 deaths worldwide. Since breast cancer is the second leading cause of death among women, it is very important to detect whether a biopsy cell is benign or malignant at an early stage so that it is not fatal. However, the breast cancer diagnosis process is quite complex as it consists of several stages, such as collecting and analyzing multivariate samples. These time demanding procedures delay diagnosis and pose a risk for people. On the other hand, the rapid development of Machine Learning (ML) and its applications in healthcare are bringing a new perspective to process and analyze medical big data. In addition, ML techniques help medical experts by analyzing the data in a short time and reduce time pressure on decision making procedures. Taking those into consideration in this study, different ML algorithms are employed for predicting if a cell nucleus is benign or malignant using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. The ML algorithms utilized in this paper are: Support Vector Machines (SVM), Logistic Regression (LR) and Random Forest (RF). Dataset includes 32 attributes with 569 cases consisting of 357 benign and 212 malignant. To improve the accuracy of the results, hyperparameter tuning was done using Grid Search and results are compared. The simulation of algorithms is done by Python Programming language.
Spectral Features of Fe and Organic Carbon in Estimating Low and Moderate Concentration of Heavy Metals in Mangrove Sediments Across Different Regions and Habitat Types

Soil or sediment spectral reflectance do not directly reflect information of spectrally featureless heavy metals. With mangrove sediment samples collected in five regions and four habitat types, this study aimed to explore the spectral features related to spectrally active constituents (Fe and organic carbon (OC)) in estimating low and moderate concentrations of four heavy metals (Cr, Ni, Cu and Pb). A total of 27 modeling strategies using XGBoost (extreme gradient boosting) model were compared for estimating each heavy metal: three types of spectral features (Fe, OC and heavy metal) * three spectral transformation methods (Log(1/R), first derivative (FD) and continuous wavelet transform (CWT)) * three variable selection methods (genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination (RFE)). The results showed that the combination of CWT and RFE was recommended for heavy metal estimation, and the ranking of model accuracy was Cu > Pb > Ni > Cr. The optimal XGBoost model was found using spectral features related to Fe for Cr ( (determination coefficient of independent validation) = 0.42, RMSE (root mean square error) = 0.32 and RPIQ (the ratio of performance to interquartile range) = 1.53) and Cu estimation ( = 0.87, RMSE = 0.19 and RPIQ = 2.20), spectral features related to OC for Ni estimation ( = 0.63, RMSE = 0.26 and RPIQ = 2.38) and spectral features related to Pb itself for Pb estimation ( = 0.82, RMSE = 0.23 and RPIQ = 2.59). Moreover, the XGBoost model transferability across different habitat types outperformed that across different regions and data ranges of Fe and OC. We conclude that the modeling strategy using spectral features related to spectrally active constituents has great potentials in quantifying low and moderate concentration of sediment heavy metals, and the samples with high Fe concentration (31.62-37.29 g kg -1 ) are recommended to improve estimation accuracy of low and moderate concentrations of heavy metals, which could provide sampling strategy development and early warning of heavy metal pollution in mangrove ecosystem.
Merging Global and Local Planners: Real-Time Replanning Algorithm of Redundant Robots Within a Task-Priority Framework

Task-priority inverse kinematics is a popular motion control algorithm which efficiently handles redundancy in robot manipulators. It has been recently extended in order to handle also set-based control objectives or inequality constraints. As any local motion planner it is prone to the occurrence of local minima. This work further extends set-based inverse kinematics by adding a motion planner in order to avoid such occurrence. Motion planners are usually computationally heavy especially in their eventual implementation with a task-priority architecture. To reduce this issue, the planner is implemented as a sampling-based algorithm which works in the reduced-dimensionality of the robot workspace applying Cartesian constraints only. The output trajectory is then checked against the inverse kinematics algorithm exploiting the redundancy and verifying the fulfillment of the joint-based task constraints. During the motion, inverse kinematics is then used also in real-time to ensure a reactive behavior to address, e.g., mismatch between the a-priori information and real-time perception acquisition. Also, the motion planner runs in background to adapt to changes in the environment or to accommodate incremental mapping. Comparison with alternative approaches are investigated and discussed. The most promising method is validated first in hundreds of numerical simulations to provide a solid statistical analysis and then experimentally with a Kinova Jaco2 7 DOFs manipulator equipped with an RGB-D sensor. Note to Practitioners—In this work we propose a motion planning algorithm that allows to effectively deploy robot manipulators in partially unstructured environments. It is structured in two layers: a Cartesian space global planner guarantees the feasibility of the trajectory with respect to potential obstacles in the environment, while a reactive local planner checks the feasibility at joint level. This architecture allows to define all the safety constraints both at Cartesian and joint space needed to operate in unstructured environments, while keeping the computation time lower with respect to standard approaches that define all the constraints in an offline global planner. The reduced computation time allows to effectively perform real-time replanning operations when needed, making the robotic system capable of adapting to a dynamic environment and suitable for sharing its workspace with human operators.
Physics-Based Optical Flow Estimation Under Varying Illumination Conditions

The physics-based optical flow (PBOF) model was derived from typical flow visulizations and it provided a more solid physical foundation for optical flow calculation. However, like many recent variational optical flow methods, it is not formulated to deal with the effect of illumination variation. In this paper, we propose a new efficient illumination-invariant optical flow estimation method, which improves the physics-based optical flow model. The proposed method introduces a multiplicative product of the velocity gradient and intensity from PBOF and an additive diffusion component to relax the brightness constancy assumption. The proposed method employs a linear transformation to reduce the influence of varying illumination on the data term. Then, a smoothness-sparsity regularization is used to constrain the minimization problem to have good edge-preserving for optical flow. Furthermore, the numerical implementation is given to solve the optimization problem. The accuracy of the developed method and the robustness against varying illumination are evaluated. The proposed method can provide motion estimation with acceptable accuracy for three optical flow datasets and cloud images of Jupiter’s great red spot, which have challenging illumination variations.
Unsupervised Detection of Dynamic Hand Gestures from Leap Motion Data

The effective and reliable detection and classification of dynamic hand gestures is a key element for building Natural User Interfaces, systems that allow the users to interact using free movements of their body instead of traditional mechanical tools. However, methods that temporally segment and classify dynamic gestures usually rely on a great amount of labeled data, including annotations regarding the class and the temporal segmentation of each gesture. In this paper, we propose an unsupervised approach to train a Transformer-based architecture that learns to detect dynamic hand gestures in a continuous temporal sequence. The input data is represented by the 3D position of the hand joints, along with their speed and acceleration, collected through a Leap Motion device. Experimental results show a promising accuracy on both the detection and the classification task and that only limited computational power is required, confirming that the proposed method can be applied in real-world applications.
Radial Basis Function Network Based Intelligent Scheme for Software Quality Prediction

With the increasing interest in digital world, various software whether it is in e-commerce or in the entertainment industry, have gained much attention in recent time. It becomes important to access the quality of a software which is usually done with the expert supervision and results into more money and time. Neural network systems have been effectively incorporated by famous companies such as FlipKart, Snapdeal, Netflix, and others due to their capacity to expand the amount of quantifiable attributes for prediction. This paper presents a framework of a radial basis function network for software quality prediction and uses Thin-plate spline RBF as its activation function. This is a network with a single layer making it easier to be trained with effective prediction results. The paper presents the theory of its training using gradient descent based back propagation algorithm with the training data-set of 20 samples and MATLAB results are provided to support the theoretical claim. Furthermore, the proposed scheme has been validated for five unknown software samples which quality is required to be predicted and It is demonstrated that the predicted soft quality results by the proposed approach are very close to the actual software quality which shows the effectiveness of the proposed approach.
Other Useful Skills to Master

As you progress towards the end of the grand journey of productive and efficient data science that you took in this book, I would like to dedicate one complete chapter to the set of various disparate useful skills that a data scientist should strive to master to enhance their productivity. Unlike the previous chapters, where you examined similarly grouped skills (e.g., memory profilers or distributed computing tools), the tools and skills you’ll explore in this chapter may look somewhat disjointed from each other. It is true that they do not fall under one unifying class but taken as a whole, they truly aid any data scientist in performing their tasks with higher productivity. Unlike previous chapters, this chapter is not focused on one (or a small number of) Python tools/libraries. While I may be discussing a few useful Python libraries in some sections, elsewhere I may be discussing general technology features without any reference to a specific Python tool. In those sections, I may have general suggestions for what topics to learn and how to go about that.
Analysis on Advanced Encryption Standard with Different Image Steganography Algorithms: An Experimental Study

In this ever-changing world of technology, data security is of utmost importance. This research paper focuses on identifying the best combination of cryptography and steganography algorithms for securing data. The proposed approach developed a complete end-to-end system that encrypted text message using the Advanced Encryption Standard algorithm. The encrypted message was then embedded onto images using steganography techniques like Least Significant Bit, Discrete Cosine Transform and Discrete Wavelet Transform. The message was later decrypted and extracted. The performance of the algorithms was evaluated using various metrics. The best performing combination of algorithms for each metric was then identified.
Cryptocurrency Investing: Powerful Diversifier or Portfolio Kryptonite?

Explore the interactive landing page, here.Red dogs, stump tails and blue pups were just some of the creative names for the ultimately doomed currencies issued by poorly capitalized state-chartered banks during the wildcat banking era in U.S. monetary history from 1837 to 1863 – until Congress finally passed legislation that created a single centrally backed national U.S. currency.History rarely repeats itself, but it often rhymes – and 150 years later we are in an era with thousands of unregulated cryptocurrencies and digital tokens with a collective market cap over $1 trillion. These cryptocurrencies offer the promise of a frictionless, inclusive and decentralized network powered by blockchains and operated completely independently of central banks, which are increasingly seen as debasing fiat currencies by “printing money.”For institutional investors, cryptocurrencies also offer the allure of extraordinary and diversified returns in a market that is now of sufficient size and liquidity for meaningful institutional positions. Indeed, some market participants estimate that about 5% of total Bitcoin supply are now held by institutional investors via custodial intermediaries.To understand the investment implications of the evolving cryptocurrency landscape, we have drawn on the insights of more than 30 investment professionals across PGIM’s fixed income, equity and private alternatives managers – as well as leading economists, venture capitalists and crypto investors.
Transformer Model and Convolutional Neural Networks (CNNs) for Arabic to English Machine Translation

Arabic remains one of the richest languages in the world in terms of vocabulary with a large set of morphological features and relatively few resources compared to English. Given the challenge posed by the morphological richness of this language, Arabic Natural Language Processing (NLP) tasks like Named Entity Recognition (NER), Sentiment Analysis (SA), Question Answering (QA) and Machine Translation (MT) have proved to be very challenging to handle. Recently, the transformers based models, have proved to be very effective in terms of language understanding, and have obtained state-of-the-art results for many NLP tasks and in particular MT. In this paper, we proposed a novel Deep Learning architecture based on the Convolutional Neural Networks (CNNs) and the transformer model to further improve the results obtained on the Arabic-English Neural Machine Translation task. Moreover, a special preprocessing of the Arabic sentences based mainly on Farasa and AraBERT is carried out. Experiments on the UN Arabic-English datasets show that our approach outperforms the state-of-the-art Arabic MT systems.
Dynamic Video-Based Activity Recognition using 3D Convolutional Neural Network

Human activity recognition has become a topic of conversation in automated video surveillance and computer vision research in recent years. Surveillance systems with activity recognition algorithms are necessary to prevent treacherous activities from arising in particular. The research work introduces 3D Convolutional Neural Networks (3D CNN) with a spatial-temporal dynamic movement for activity recognition. The research provides a unique 3D CNN methodology for activity recognition by extracting kinematic motion information stored in consecutive neighboring frames and utilizing retrieves parameters from both the space and time dimensions. The experimental evaluation is performed using the benchmark KTH and Weizmann datasets, which describe the activities of persons in daily lives and work. There are numerous existing methods for activity recognition, therefore the proposed approach is compared to previously proposed methods and it is found that the proposed methodology vanquishes previously reported results with an accuracy of 97.52 % on the KTH dataset and 98.6 % on the Weizmann dataset.
Auditing and Testing AI – A Holistic Framework

This paper describes a framework that can be used to assess and analyze AI systems in terms of risk. The framework addresses the structure and components of AI systems at five layers and allows taking a holistic view of AI systems while focusing on specific aspects, such as discrimination or data.
Enhance Web-Components in Order to Increase Security and Maintainability

Today’s development of client-side web applications is based on one of the JavaScript-frameworks, such as Angular or React. The excessive dependencies that arise in the ecosystem from the Node-Package-Manager increase the security risk and the dependency of your own web application on third-party packages. Moreover, the frameworkless approach proposes a renaissance of classic web development, because it strives to avoid external dependencies as far as possible and to fall back on the standards. Whether the implementation achieves maintainability and security of frameworks is questionable. Therefore, it makes sense to research which core concepts of the frameworks meet the requirements for maintainability and security and how these are implemented. The novelty is that the concepts to be explored are moved to a standard in order to ensure the developer efficiency, security, performance and maintainability in the long term. This allows existing approaches to focus on other essential features.
Constitutional Reasoning There and Back Again: The Facebook Oversight Board as a Source of Transnational Constitutional Advice

The recently inaugurated Oversight Board is expected to increase transparency of content moderation on Facebook and Instagram. Rather than enforcing only corporate policies, however, the Board’s first decisions relied on ‘international standards of freedom of expression’. Using this development as a starting point, we ask how may freedom of expression restrict the dominance of private corporations over the digital sphere. In contrast to the literature that has explored this issue from a primarily court-centric perspective, we reconstruct the role of freedom of expression as a field of constitutional advice and theorize the role of the Oversight Board in this context. We argue that freedom of expression, in addition to being the subject matter of disputes before (supra)national courts and the Board, may be seen as both empowering and restricting community building and belonging in the digital sphere. While this facet of free speech is not new, it is likely to gain increased importance in the interaction between contemporary constitutional democracies and corporations that shape the digital. We place constitutional advice against this background and differentiate the uses it may have in the interaction between public and private power in the digital era.
Concurrency Traces

This chapter presents the basics of the theory of concurrency traces which are elements of equational partially commutative monoids of sequences. The theory of trace monoids has been developed and applied within diverse areas, such as combinatorics and, in particular, concurrency theory. Its origins are rooted in the theory of formal languages with its powerful repertoire of analysis tools and well developed structural properties. Trace theory attempts to transfer these ideas into the realm of concurrent system behaviours. A key novelty is the explicit treatment of independence between events within sequences of executed actions. This allows one to identify those sequences which differ only by the ordering of independent actions, resulting in a clear notion of equivalence between system executions. This approach has proved to be particularly suited for dealing with a wide range of distributed and concurrent systems. The restriction is that independence is introduced statically, at the level of actions rather than events, so it cannot be changed during the system execution. Applications of concurrency traces in the field of concurrent systems are motivated mainly by the fact that traces can represent partial orders capturing causality in system executions. This chapter presents the basic notions concerning concurrency traces and, in particular, makes explicit their relationship with causal partial orders.