Reviewerly/exHarmony huggingface dataset:
A Review Paper on Real Time Sign Language Detection for the Deaf and Dumb

Hand gesture is one of the styles used in sign language for non-verbal communication. It's most generally used by deaf &amp; dumb people who have hearing or speech problems to communicate among themselves or with normal people. Plathora sign language systems had been developed by numerous makers around the world but they're neither flexible nor cost-effective for the end druggies. Hence, it's a software which presents a system prototype that's suitable to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Dumb people are generally deprived of normal communication with other people in the society, also normal people find it hard to understand and communicate with them. These people have to depend on an interpreter or on some kind of visual communication. An interpreter won’t be always available and visual communication is substantially delicate to understand. As a normal person is ignorant of the grammar or meaning of numerous gestures that are part of a sign language, it's primarily limited to their families and/ or deaf and dumb community
Estimating Regression Coefficients using Bootstrap with application to Covid-19 Data

The linear regression model is often used by researchers and data analysts for predictive, descriptive, and inferential purposes.When working with empirical data, this model is based on a set of assumptions that are not always satisfied.In this situation, using more complicated regression algorithms that do not strictly rely on the same assumptions might be one answer.Nevertheless, transformations provide a simpler technique for improving the validity of model assumptions and allow the user to continue using the well-known model of linear regression.The main objective of this project is to provide a transformation for the linear model's response and predictor variables, as well as parameter estimation methods before the transformation and after the transformation.The bootstrap approach has been effectively used for many statistical estimates and inference issues, according to the paper.
Incorporating Constituent Syntax for Coreference Resolution

Syntax has been shown to benefit Coreference Resolution from incorporating long-range dependencies and structured information captured by syntax trees, either in traditional statistical machine learning based systems or recently proposed neural models. However, most leading systems use only dependency trees. We argue that constituent trees also encode important information, such as explicit span-boundary signals captured by nested multi-word phrases, extra linguistic labels and hierarchical structures useful for detecting anaphora. In this work, we propose a simple yet effective graph-based method to incorporate constituent syntactic structures. Moreover, we also explore to utilise higher-order neighbourhood information to encode rich structures in constituent trees. A novel message propagation mechanism is therefore proposed to enable information flow among elements in syntax trees. Experiments on the English and Chinese portions of OntoNotes 5.0 benchmark show that our proposed model either beats a strong baseline or achieves new state-of-the-art performance. (Code is available at https://github.com/Fantabulous-J/Coref-Constituent-Graph)
Adaptive Recommendation Method of IoT Apps and Ideological and Political Teaching Resources

In view of the current poor nature of online ideological and political teaching and the deficient impact of the executive teaching resource recommendation, this paper advances the examination on the versatile recommendation technique for online ideological and political teaching resources under the school enterprise cooperation mode, gathers and deals with the ideological and political teaching data in view of the teaching qualities of the school enterprise cooperation mode, and develops the assessment calculation of teaching data the board. At last, through tests, it is affirmed that the versatile recommendation technique for online ideological and political teaching resources under the school enterprise cooperation mode has high practicability and fully meets the research requirements.
Improvements on new quantum key agreement protocol with five-qubit Brown states

Recently, Cao et al. [Mod. Phys. Lett. A 34, 1950332 (2019)] presented a two-party quantum key agreement scheme using the five-qubit Brown states and the decoy protocol. They also extended their scheme to the multiparty case, and it was compared with existing works. The authors also introduced the security analysis that proves the security of their protocol against outsider and insider attacks. However, this work shows that the quantum Cao et al. [Mod. Phys. Lett. A 34, 1950332 (2019)] protocol fails to fulfill one of the most significant conditions of the quantum key agreement, which is fairness. The computed final key by the users does not have the same level of security, and the protection of users’ private data is not maintained equally. Finally, an improved version of Cao et al.’s protocol has been suggested.
A Hybrid Approach for Aspect-based Sentiment Analysis: A Case Study of Hotel Reviews

This study presents a method of aspect-based sentiment analysis for customer reviews related to hotels. The considered hotel aspects are staff attentiveness, room cleanliness, value for money and convenience of location. The proposed method consists of two main components. The first component is used to assemble relevant sentences for each hotel aspect into relevant clusters of hotel aspects using BM25. We developed a corpus of keywords called the Keywords of Hotel Aspect (KoHA) Corpus, and the keywords of each aspect were used as queries to assemble relevant sentences of each hotel aspect into relevant clusters. Finally, customer review sentences in each cluster were classified into positive and negative classes using sentiment classifiers. Two algorithms, Support Vector Machines (SVM) with a linear and a RBF kernel, and Convolutional Neural Network (CNN) were applied to develop the sentiment classifier models. The model based on SVM with a linear kernel returned better results than other models with an AUC score of 0.87. Therefore, this model was chosen for the sentiment classification stage. The proposed method was evaluated using recall, precision and F1 with satisfactory results at 0.85, 0.87 and 0.86, respectively. Our proposed method provided an overview of customer feelings based on score, and also provided reasons why customers liked or disliked each aspect of the hotel. The best model from the proposed method was used to compare with a state-of-the-art model. The results show that our method increased recall, precision, and F1 scores by 2.44%, 2.50% and 1.84%, respectively.
THE EFFECTS OF CORRUPTION PERCEPTION ON POLITICAL PARTICIPATION: EVIDENCE FROM AFRICA

Scholars have previously examined the relationship between corruption and political participation with mixed results. However, some citizens are aware of corruption but choose to tolerate it. How does this tolerance for corruption influence citizens' political conduct? This research contributes to the literature by examining whether citizens' tolerance for corruption affects their participation in political activities, particularly on voter turnout and protest. The study uses regression analysis to evaluate the relationship between corruption tolerance and these two indicators of political participation. Using round 3 of the Afrobarometer data for 18 Sub-Saharan African countries, I hypothesize that individuals' tolerance of corruption decreases their willingness to vote in elections and participate in protests. The findings corroborate this expectation and demonstrate substantial robustness to various model specifications
DIFFERENCES IN TEENAGE GIRLS’ KNOWLEDGE AND ATTITUDE BEFORE AND AFTER BEING EXPOSED IN PUBLIC SERVICE ANNOUNCEMENT MEDIA OF "IRON SUPPLEMENTATION" (Study at SMP Negeri 10 Surabaya)

Introduction: Teenage girls are included in groups who are prone to anemia. Various attempts have been made to prevent anemia in teenage girls. Public service announcement of "Iron Supplementation" is one of the health promotion media related to the prevention of anemia. This research aims to determine the difference in the knowledge and attitudes of teenage girls before and after exposure to public service announcement media of "Iron Supplementation". Methods: This research is a quantitative study with pseudo-experimental methods. The knowledge and attitude variables are measured by the Paired T-test test and the Mann-Whitney test. The study subject amounted to 30 subjects for each group. Result: The results of the analysis show that there were differences in the students' knowledge (p = 0.00) before and after watching videos in both the "selfie" group and the "animation" group. There were differences in students' attitudes before and after watching the video on the "selfie" group (p = 0.00). However, there was no difference in the student's attitudes toward the "animation" group (p= 0.469). Conclusion: There was no difference in students' knowledge between selfie groups and animation groups after watching videos. There was a difference in attitudes between the "selfie" group and the "animation" group after watching videos. Public service announcement is needed in delivering health messages for changes in knowledge and attitudes. However, there is still a need for mentoring and direct support from teachers
An Analysis of People’s Emotional Change Toward Vaccines and Its Factors in the Corona Disaster

The developers of new vaccines against SARS-CoV-2 and governments have provided information on vaccine effectiveness and status on a daily basis to reassure people about vaccination against COVID-19. However, because the interest in vaccines and vaccination status varies by country and region, people do not always feel reassured. In this paper, we analyzed tweets posted on Twitter to elucidate the emotions people have toward COVID-19 vaccines and factors that cause such emotions to be expressed. We selected six countries for our analysis: Japan, the United States, the Great Britain, Canada, Australia, and India, and applied an emotion classification method using machine learning based on the eight types of emotions defined in Plutchik’s wheel of emotions. We also used a text analysis approach using dependency analysis and burst detection methods. The results of our emotion classification showed that fear was the most common emotion in Japan whereas anger and disgust were most common in the United States, Great Britain, Canada, and Australia; joy was most common in India. We also analyzed tweets during the period when a particular emotion was increased in the changes of the emotions represented as a time series based on the burst-detected dependency relations, and found several characteristics: many users posted vaccine-related news, one user would often post a large number of tweets with the same content, and the same event related to vaccines could arouse different emotions depending on the individual’s situation.
Philosophy in a Modern University: Problems of Teaching Methodology and Prospects in the Context of a New Education Model “2+2+2”

The change in the status of philosophy in a modern university forces a critical review of the existing practices of its teaching and sets the task of finding modern and more effective ways of mastering philosophical thought. It is shown that their development should be preceded by a new formulation of the question of the mission of the modern university and the goals that society sets before it, in the form of requirements for its graduates from the main social actors – the state, business and civil society. The article highlights and discusses the main strategies for teaching philosophy in Russian universities, their advantages and disadvantages. It is shown that the most productive strategy that contributes to the emergence of a desire among future specialists to analyze social phenomena not only on the basis of personal life experience, today is a problem-oriented strategy, which initially involves a debatable discussion of philosophical problems. An analysis of the existing teaching practices shows that, despite the fundamental differences, all of them somehow face similar methodological difficulties, which many see as the way to overcome them in the search for a new "systematic". Without addressing questions about the fundamental possibility of its creation, the article substantiates the need to reach conventions on what a university course of philosophy should include in a mandatory plan for non-philosophical specialties, and to build its matrix structure on this basis. In search of a "new universality" and taking into account the expectations of modern students from philosophy, a practical approach should become the key in building the course program today. This approach makes it possible to outline the general architectonics of a university philosophy course aimed at developing critical thinking in the context of the transformation of higher education in accordance with the 2+2+2 model. The new logic of the organization of the bachelor's degree, which is based on the idea of delayed choice and conscious self-determination of students, will make it possible to solve the problem of developing critical thinking much more successfully. Setting the formation of critical thinking as a key goal of teaching philosophy can fulfill the role of the desired systematizing factor.
Promoting civic and voter education through the use of technological systems during the COVID-19 pandemic in Africa

A human rights perspective to this article intertwines the rights to information and political participation. It deals with the intersections between the provision of civic and voter education (CVE), and the opportunities and threats pertaining to the feasibility of finding digital solutions for enhanced voter participation in democratic electoral processes during the coronavirus disease (COVID-19) pandemic in Africa. Under normal circumstances and while conducted through physical contact sessions, CVE is aimed at providing citizens with communication, general and life skills to constructively participate in democratic electoral processes. The greater the attendance in CVE events, the greater the conviction that a significant number of participants have been enlightened and encouraged to fully participate. As a result, electoral democracy becomes enriched and consolidated. However, the impacts of the COVID-19 pandemic render the physical training and dissemination of this crucial CVE information cumbersome, principally when considering the strictly-restricted number of attendants at events, consequently placing in disarray the enhancement of voter participation. Electoral management bodies, which are inherently charged with the responsibility for promoting CVE, confront the challenges for securing meaningful voter participation. Reliance on technological systems in the promotion of a consolidated electoral democracy during the COVID-19 pandemic emerges as a measure of last resort. Cognisant of the numerous developmental challenges encountered by many African countries, the feasibility of digital solutions in this instance could be far-fetched. More so, the digital divide and its impacts militate the empowerment of poor voters in remote rural areas where access to technological infrastructure and equipment is distantly slim.
Variational Bayesian Approximation (VBA): A comparison between three optimization algorithms

In many Bayesian computations, first, we obtain the expression of the joint distribution of all the unknown variables given the observed data. In general, this expression is not separable in those variables. Thus, obtaining their marginals for each variable and computing the expectations are difficult and costly. This problem becomes even more difficult in high dimensional quandaries, which is an important issue in inverse problems. We may then try to propose a surrogate expression with which we can do approximate computations. Often a separable expression approximation can be useful enough. The Variational Bayesian Approximation (VBA) is a technique that approximates the joint distribution $p$ with an easier, for example separable, one $q$ by minimizing Kullback&amp;ndash;Leibler Divergence $KL(q|p)$. When $q$ is separable in all the variables, the approximation is also called Mean Field Approximation (MFA) and so $q$ is the product of the approximated marginals. A first standard and general algorithm is alternate optimization of $KL(q|p)$ with respect to $q_i$. A second general approach is its optimization in the Riemannian manifold. However, in this paper, for practical reasons, we consider the case where $p$ is in the exponential family and so is $q$. For this case, $KL(q|p)$ becomes a function of the parameters $\thetab$ of the exponential family. Then, we can use any other optimization algorithm to obtain those parameters. In this paper, we compare three optimization algorithms: standard alternate optimization, a gradient-based algorithm and a natural gradient algorithm and study their relative performances on three examples.
LEHAN: Link-Feature Enhanced Heterogeneous Graph Attention Network

Graph Neural Networks (GNNs) have been studied extensively and have performed well in solving complex machine learning tasks in recent years. Many GNN-based approaches focused on representing homogeneous graphs with only a single type of nodes and links. However, many real-world networks are heterogeneous, involving various types of nodes and links. Existing GNN-based approaches for representing heterogeneous graphs only focused on node features and meta-paths, which often causes difficulties in reflecting link features to learn the graph representations. To overcome this limitation, we propose a Link-feature Enhanced Heterogeneous graph Attention Network (LEHAN) that focuses on the node and link features to represent heterogeneous graphs. LEHAN consists of the node attention block and the link attention block, where each block aggregates node features and link features by attention mechanism with meta-paths information. The extensive experimental evaluations show that LEHAN outperforms the state-of-the-art graph embedding algorithms in node classification and clustering on real-world heterogeneous graphs.
An Overview on Future Applications of Quantum Computing

Quantum computing, an emerging trend in computer engineering, originated from quantum physics, which deals with solving problems that were not solved by classical computing. It mainly stored and manipulated classical information in the form of quantum bits (basic unit of quantum information) or qubits. The advancement relies mainly on the particles that persist in two states (different transitions) at same time. The subatomic particles are basically information carriers that mainly consist of high energy particles such as electrons, photons, ions, etc. Quantum computing can be used to tackle various problems related to simulations and optimization. In a quantum computer, qubit or quantum-bit is used as a basic unit of memory. Qubits are created on physical systems and these systems follow the quantum superposition property. The qubits are linked together using quantum entanglement property. The series of qubits can be presented using different things. Quantum computers are exponentially faster than classical computers. Qubits have two quantum states similar to classical computers. The quantum state is represented as Dirac notation.
CCFont: Component-Based Chinese Font Generation Model Using Generative Adversarial Networks (GANs)

Font generation using deep learning has made considerable progress using image style transfer, but the automatic conversion/generation of Chinese characters still remains a difficult task owing to the complex character shape and large number of Chinese characters. Most known Chinese character generation models use the image conversion method of the Chinese character shape itself; however, it is difficult to reproduce complex Chinese characters. Recent methods have utilized character compositionality by separating up to three or four components to improve the quality of generated characters, but it is still difficult to generate high-quality results for complex Chinese characters with many components. In this study, we proposed the CCFont model (component-based Chinese font generation model using generative adversarial networks (GANs)) that automatically generates all Chinese characters using Chinese character components (up to 17 components). The CCFont model generates all Chinese characters in various styles using the components of Chinese characters based on conditional GAN. By acquiring local style information from the components, the information is more accurate and there is less information loss than when global information is obtained from the image of the entire character, reducing the failure of style conversion and improving quality to produce high-quality results. Additionally, the CCFont model generates high-quality results without any additional training (zero-shot font generation without any additional training) for the first-seen characters and styles. For example, the CCFont model, which was trained with only traditional Chinese (TC) characters, generates high-quality results for languages that can be divided into components, such as Korean and Thai, as well as simplified Chinese (SC) characters that are only seen during inference. CCFont can be adopted as a multi-lingual font-generation model that can be applied to all languages, which can be divided into components. To the best of our knowledge, the proposed method is the first to generate a zero-shot multilingual generation model using components. Qualitative and quantitative experiments were conducted to demonstrate the effectiveness of the proposed method.
Named Entity Recognition in XLNet Cyberspace Security Domain Based on Dictionary Embedding

With the increase of network security incidents, network security analysts need to analyze massive log information. The introduction of knowledge graph into the field of network security can facilitate analysis by security analysts. NER (Named Entity Recognition) is the upstream task of knowledge graph construction, and the quality of the NER model determines the quality of the knowledge graph to a certain extent. However, the general domain named entity recognition model cannot extract the entities in the network security domain very well. For this phenomenon, this paper proposes an XLNet-Feature-Att model, which uses XLNet in the embedding layer to embed words into the vector space, the encoding layer uses the improved BILSTM FB structure and the Attention layer, and the decoding layer uses the CRF model to achieve sequence labeling and binding. Finally, the experimental comparison is carried out on the data set in the field of network security, and the F1-score reaches the highest 92.28%.
Comparative Analysis of Machine Learning Techniques in Effort Estimation

In Software engineering effort estimation provides an important role for software development and managing project cost, quality, and time. Since last decades, software estimation has been receiving significant attention from researchers and substantial research has been performed using various techniques and algorithms of machine learning. This paper suggests different machine learning techniques such as Naïve Bayes, Random Forests Logistic Regression, stochastic gradient boosting, decision tree, and story point for estimation to assess prediction more efficiently. Nowadays uses of these approaches by software development industries for software estimation aim to tackle deficiencies of parametric and traditional estimation techniques, rise project. A comparative study of the mentioned techniques is presented and examined in this paper to critically evaluate the performance of these techniques.