Reviewerly/exHarmony huggingface dataset:
Logical Inference for Counting on Semi-structured Tables

Recently, the Natural Language Inference (NLI) task has been studied for semi-structured tables that do not have a strict format. Although neural approaches have achieved high performance in various types of NLI, including NLI between semi-structured tables and texts, they still have difficulty in performing a numerical type of inference, such as counting. To handle a numerical type of inference, we propose a logical inference system for reasoning between semi-structured tables and texts. We use logical representations as meaning representations for tables and texts and use model checking to handle a numerical type of inference between texts and tables. To evaluate the extent to which our system can perform inference with numerical comparatives, we make an evaluation protocol that focuses on numerical understanding between semi-structured tables and texts in English. We show that our system can more robustly perform inference between tables and texts that requires numerical understanding compared with current neural approaches.
A Survey on Deep Hashing Methods

Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries, which is a basic task in a range of fields, including computer vision and data mining. Hashing is one of the most widely used methods for its computational and storage efficiency. With the development of deep learning, deep hashing methods show more advantages than traditional methods. In this survey, we detailedly investigate current deep hashing algorithms including deep supervised hashing and deep unsupervised hashing. Specifically, we categorize deep supervised hashing methods into pairwise methods, ranking-based methods, pointwise methods as well as quantization according to how measuring the similarities of the learned hash codes. Moreover, deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods based on their semantic learning manners. We also introduce three related important topics including semi-supervised deep hashing, domain adaption deep hashing and multi-modal deep hashing. Meanwhile, we present some commonly used public datasets and the scheme to measure the performance of deep hashing algorithms. Finally, we discuss some potential research directions in conclusion.
Distinguishing Shapes of Breast Cancer Masses in Ultrasound Images by Using Logistic Regression Model

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of 88% and an Accuracy of almost 89%. We also came to the conclusion that the Fibroid mass is small and less white than the Fatty mass
Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory

This Special Issue on "Adaptive Signal Processing and Machine Learning Using Entropy and Information Theory" was birthed from observations of the recent trend in the literature [...].
The impact of digital leadership in the effectiveness of organizational crisis management by mediating strategic vigilance

The current research aims to verify the impact of digital leadership as an independent variable, in the effectiveness of crisis management as a response variable through centralizing strategic vigilance in the faculties of the University of Baghdad and its departments, and to investigate the level of interest of its leaders and subordinates in research variables, as well as coming up with recommendations that contribute to strengthening the practices of the three variables. In the university under study, and based on the researcher’s interest to diagnose the influence relationship between the variables, because of their importance in the university’s headquarters and its members on the one hand, and its reflection on society on the other hand, the descriptive exploratory approach was adopted in completing the chapters and sections of the current research, as the research community included university colleges Baghdad, consisting of (24) colleges, the researcher targeted a sample of (160) views that included(Dean, Associate Dean, Head of Department)By adopting the questionnaire that included (57) paragraphs, personal interviews, and field inspection to enhance the practical aspect, while the researcher used statistical programs
Semi parametric Estimators for Quantile Model via LASSO and SCAD with Missing Data

In this study, we made a comparison between LASSO &amp; SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya &amp; Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method
'The Role of Ambidextrous Leadership' Behaviors in Enhancing Organizational Energy': An Analytical Research General AL-Faris Company

The current research aims to identify the impact of ambidextrous leadership behaviors on organizational energy in Al-Faris Company. The descriptive analytical method was used as a research approach. Adept leadership includes two dimensions (open leadership behaviors and closed leadership behaviors), and organizational energy includes three dimensions (emotional energy, physical energy, and cognitive energy ). The research sample included all the administrative leaders (General Manager, Associate General manager, Department Manager, Division Official ) in AL-Faris Company / the Iraqi Ministry of Industry. The researcher distributed (74) valid questionnaires after retrieving (53) questionnaires, and the research relied on the statistical package (SPSS). The results indicated that there is a strong effect and a high correlation between the research variables. The most important dimensions of ambidextrous leadership behaviors are open leadership behaviors, which have a high impact on organizational energy. However, there is a weakness in the involvement of individuals in decision-making and the advertising side AL-Faris Company
The role of GRI standards in reporting the dimensions of sustainable development - An applied study in a number of local companies on the Iraq Stock Exchange

The current research aims to shed light on the Global Reporting Initiative (GRI), which helps to report financial and non-financial information by economic units in general and listed on the Iraq Stock Exchange in particular. The research was based on a main premise that apply the criteria of the Global Reporting Initiative (GRI) would provide useful information to users to help them make appropriate decisions. To achieve the goal of the research, the descriptive analysis method was used, and quantitative analysis was used. At the level of the descriptive analysis method, a desk survey was conducted. As for the quantitative analysis, it relied on applied data through a questionnaire form (Questioners) as a research tool, and the Statistical Package for Social Sciences (spss) was used to analyze the research data and to reach the results. The research got a set of conclusions, the most important of which is that the accounting profession seeks to provide all information that is useful to users in the process of making appropriate decisions. Therefore the application of the Global Reporting Initiative would improve the quality of the information supplied to users as well as economic help units to Think about its strategy, manage risks, and provide a comprehensive picture of the performance of economic units Yeh. Based on the conclusions, a set of recommendations were presented, the most important of which was the need to prepare requirements and qualifications that help implement the Global Reporting Initiative by units to ensure the provision of information that is useful to users in the decision-making process. He urged economic units to submit reports through the application of the Global Reporting Initiative to improve the quality of information provided to users on the one hand and the possibility of improving the unit's performance on the other hand
Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning

Few-shot classification is a challenging problem due to the uncertainty caused by using few labelled samples. In the past few years, many methods have been proposed with the common aim of transferring knowledge acquired on a previously solved task, what is often achieved by using a pretrained feature extractor. Following this vein, in this paper we propose a novel transfer-based method which aims at processing the feature vectors so that they become closer to Gaussian-like distributions, resulting in increased accuracy. In the case of transductive few-shot learning where unlabelled test samples are available during training, we also introduce an optimal-transport inspired algorithm to boost even further the achieved performance. Using standardized vision benchmarks, we show the ability of the proposed methodology to achieve state-of-the-art accuracy with various datasets, backbone architectures and few-shot settings.
Teaching in Physical Education during Pandemic COVID-19: A Study of University Teachers

The COVID-19 pandemic is a health crisis deepening in the current world context, characterized by enormous globalization and scientific-technical development processes.The research aims to understand how university teachers of Physical Education had to reinvent the discipline and the pedagogical relationship based on bodily practices during the two years of the COVID-19 pandemic.The study is based on the interpretative-comprehensive paradigm, with a qualitative methodology.Twelve semi-structured interviews were conducted with university professors from two regional universities, located in the Maule region, Chile.The first category that emerges is Virtual Education, which is related to the challenges faced by teachers with distance learning.The second category refers to the pandemic's impact on the teaching staff's socio-emotional aspects, manifested through fatigue, exhaustion, and stress, affecting teaching practices due to the precarious work and the adverse conditions generated by the COVID-19 pandemic.In terms of conclusions, we can affirm that the COVID-19 pandemic caused profound changes in the teaching and learning processes in the area of Physical Education, with negative consequences for the moment, for the teaching staff as well as for the university students.
Development of MVMD-EO-LSTM Model for a Short-Term Photovoltaic Power Prediction

The accuracy and stability of short-term photovoltaic (PV) power prediction is crucial for power planning and dispatching in a grid system. For this reason, the multi-resolution variational modal decomposition (MVMD) method is proposed to achieve multi-scale input features mining for short-term PV power prediction. Here, the MVMD combined with Spearman extracts correlation features of the weather data. An equilibrium optimizer (EO) is integrated with MVMD to achieve optimal values of the long short-term memory (LSTM) parameters. Firstly, the correlation of input features is determined and selected by Spearman. The MVMD model is used to mine the high correlation features of solar radiation and conduct cross-correlation analysis to extract input feature components. Secondly, the similar weather days of the sample set are classified to ensure a good adaptability in different weather situations. Finally, the high correlation features are introduced into the photovoltaic power prediction model of EO optimized LSTM. Performance analysis using actual output power data from a PV plant shows that the proposed MVMD feature extraction method can effectively mine correlation features to achieve an optimized dataset under different seasons. Compared with the gray wolf and particle swarm optimization algorithms, the proposed model has a better optimization performance in a low discrimination of input feature decomposition components and low correlation with output power.
Dynamic Mixed Data Analysis and Visualization

One of the consequences of the big data revolution is that data are more heterogeneous than ever. A new challenge appears when mixed-type data sets evolve over time and we are interested in the comparison among individuals. In this work, we propose a new protocol that integrates robust distances and visualization techniques for dynamic mixed data. In particular, given a time t∈T={1,2,…,N}, we start by measuring the proximity of n individuals in heterogeneous data by means of a robustified version of Gower's metric (proposed by the authors in a previous work) yielding to a collection of distance matrices {D(t),∀t∈T}. To monitor the evolution of distances and outlier detection over time, we propose several graphical tools: First, we track the evolution of pairwise distances via line graphs; second, a dynamic box plot is obtained to identify individuals which showed minimum or maximum disparities; third, to visualize individuals that are systematically far from the others and detect potential outliers, we use the proximity plots, which are line graphs based on a proximity function computed on {D(t),∀t∈T}; fourth, the evolution of the inter-distances between individuals is analyzed via dynamic multiple multidimensional scaling maps. These visualization tools were implemented in the Shinny application in R, and the methodology is illustrated on a real data set related to COVID-19 healthcare, policy and restriction measures about the 2020-2021 COVID-19 pandemic across EU Member States.
Anti-hesitant Fuzzy Subalgebras, Ideals and Deductive Systems of Hilbert Algebras

The Hilbert algebra, one of several algebraic structures, was first described by Diego in 1966 [7] and has since been extensively studied by other mathematicians.Torra [18] was the first to suggest the idea of hesitant fuzzy sets (HFSs) in 2010, which is a generalization of the fuzzy sets defined by Zadeh [20] in 1965 as a function from a reference set to a power set of the unit interval.The significance of the ideas of hesitant fuzzy subalgebras, ideals, and filters in the study of the different logical algebras aroused our interest in applying these concepts to Hilbert algebras.In this paper, the concepts of HFSs to subalgebras (SAs), ideals (IDs), and deductive systems (DSs) of Hilbert algebras are introduced in terms of anti-types.We call them anti-hesitant fuzzy subalgebras (AHFSAs), anti-hesitant fuzzy ideals (AHFIDs), and anti-hesitant fuzzy deductive systems (AHFDSs).The relationships between AHFSAs, AHFIDs, and AHFDSs and their lower and strong level subsets are provided.As a result of the study, we found their generalization as follows: every AHFID of a Hilbert algebra Ω is an AHFSA and an AHFDS of Ω.We also study and find the conditions for the complement of an HFS to be an AHFSA, an AHFID, and an AHFDS.In addition, the relationships between the complements of AHFSAs, AHFIDs, and AHFDSs and their upper and strong level subsets are also provided.
Codes over Trees

In graph theory, a tree is one of the more popular families of graphs with a wide range of applications in computer science as well as many other related fields. While there are several distance measures over the set of all trees, we consider here the one which defines the so-called tree distance, defined by the minimum number of edit operations, of removing and adding edges, in order to change one tree into another. From a coding theoretic perspective, codes over the tree distance are used for the correction of edge erasures and errors. However, studying this distance measure is important for many other applications that use trees and properties on their locality and the number of neighbor trees. Under this paradigm, the largest size of code over trees with a prescribed minimum tree distance is investigated. Upper bounds on these codes as well as code constructions are presented. A significant part of our study is dedicated to the problem of calculating the size of the ball of trees of a given radius. These balls are not regular and thus we show that while the star tree has asymptotically the smallest size of the ball, the maximum is achieved for the path tree.
Attention Guided Feature Encoding for Scene Text Recognition

The real-life scene images exhibit a range of variations in text appearances, including complex shapes, variations in sizes, and fancy font properties. Consequently, text recognition from scene images remains a challenging problem in computer vision research. We present a scene text recognition methodology by designing a novel feature-enhanced convolutional recurrent neural network architecture. Our work addresses scene text recognition as well as sequence-to-sequence modeling, where a novel deep encoder–decoder network is proposed. The encoder in the proposed network is designed around a hierarchy of convolutional blocks enabled with spatial attention blocks, followed by bidirectional long short-term memory layers. In contrast to existing methods for scene text recognition, which incorporate temporal attention on the decoder side of the entire architecture, our convolutional architecture incorporates novel spatial attention design to guide feature extraction onto textual details in scene text images. The experiments and analysis demonstrate that our approach learns robust text-specific feature sequences for input images, as the convolution architecture designed for feature extraction is tuned to capture a broader spatial text context. With extensive experiments on ICDAR2013, ICDAR2015, IIIT5K and SVT datasets, the paper demonstrates an improvement over many important state-of-the-art methods.
BALANCING CYBERSECURITY AFTER THE PANDEMIC (Tips and Tricks)

Cyberattacks are expected to rise to every 11 seconds in 2021, according to a report by Cybersecurity Ventures. Also, due to a lot of work setups at homes, protection against malware and cyber-attacks may not be at its best. Viruses, data breaches, and denial of service attacks are just some of the leading cybersecurity threats. 2021 is the beginning of a new world after the pandemic, and now our devices are at significant risk of outside interference. To combat this great nemesis,
CYBERSECURITY AS A FISHING GAME

Before starting an introduction to our book’s subject, talking about how business leaders and ordinary users of modern ICT should respond to experienced and novice hackers’ devastating attacks, we would like to remind the history and evolution of communications development. It is well known that communication between people began when two persons, Adam and Eve, appeared. However, information transfer technologies appeared much later when the need arose for human communities to communicate and transfer information at a distance.
State of the Art: Image Hashing

Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and finding similar images from large-scale image content. The main challenge in image hashing techniques is robust feature extraction, which generates the same or similar hashes in images that are visually identical. In this article, we present a short review of the state-of-the-art traditional perceptual hashing and deep learning-based perceptual hashing methods, identifying the best approaches.
Curriculum-based Deep Reinforcement Learning for Quantum Control

Deep reinforcement learning has been recognized as an efficient technique to design optimal strategies for different complex systems without prior knowledge of the control landscape. To achieve a fast and precise control for quantum systems, we propose a novel deep reinforcement learning approach by constructing a curriculum consisting of a set of intermediate tasks defined by a fidelity threshold. Tasks among a curriculum can be statically determined using empirical knowledge or adaptively generated with the learning process. By transferring knowledge between two successive tasks and sequencing tasks according to their difficulties, the proposed curriculum-based deep reinforcement learning (CDRL) method enables the agent to focus on easy tasks in the early stage, then move onto difficult tasks, and eventually approaches the final task. Numerical simulations on closed quantum systems and open quantum systems demonstrate that the proposed method exhibits improved control performance for quantum systems and also provides an efficient way to identify optimal strategies with fewer control pulses.
The Role of Specialized Journalism in Processing Economic Crises in Sudan

There is a close relationship between information and economic decision. Hence the strategic role of the economic media, especially the specialized press on promoting the economy, keeping pace with comprehensive development, and dealing with crises. practicing criticism, evaluation, guidance, news, and spreading awareness and economic culture in society. The importance of the study and its goal is to know the programs, methods used by the specialized newspaper in dealing with crises, the extent and nature of press coverage of various economic issues. Therefore, the study adopted the descriptive and analytical approach relying on the content analysis tool to answer its questions and determine the role that specialized newspapers play in addressing economic crises in its subjects, applying on the Elaff Economic Newspaper. The study reached results proved that; the newspaper is characterized by comprehensive news by providing means of collecting various information, and can explain and interpret topics and present the facts related to the crisis. The newspaper covered most of the economic issues and fields that have a tangible impact on the economic conditions and people's livelihood. Confirming the newspaper's ability to answer users' questions and satisfy their desires by answering most of the news questions it provides regarding the content. The study resulted in many recommendations; the journalistic issues must be synchronized as they occur for the newspaper to be new and current. Paying attention on the unemployment and immigration issues, due to their importance and impact on the economy, serving the investor, the consumer also the business sector, and revealing local and international investment opportunities.